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Wrapper verification | Many Internet information-management applications (e.g., information integration systems) require a library of wrappers, specialized information extraction procedures that translate a source's native format into a structured representation suitable for further application-specific processing. Maintaining wrappers is tedious and error-prone, because the formatting regularities on which wrappers rely change frequently on the decentralized and dynamic Internet. The wrapper verification problem is to determine whether a wrapper is operating correctly. Standard regression testing approaches are inappropriate, because both the formatting regularities on which wrappers rely and the source's underlying content may change. We introduce RAPTURE, a fully-implemented, domain-independent wrapper verification algorithm. RAPTURE computes a probabilistic similarity measure between a wrapper's expected and observed output, where similarity is defined in terms of simple numeric features (e.g., the length, or the fraction of punctuation characters) of the extracted strings. Experiments with numerous actual Internet sources demostrate that RAPTURE performs substantially better than standard regression testing. |
INS/GPS Integration Architectures | An inertial navigation system (INS) exhibits relatively low noise from second to second, but tends to drift over time. Typical aircraft inertial navigation errors grow at rates between 1 and 10 nmi/h (1.8 to 18 km/h) of operation. In contrast, Global Positioning System (GPS) errors are relatively noisy from second to second, but exhibit no long-term drift. Using both of these systems is superior to using either alone. Integrating the information from each sensor results in a navigation system that operates like a drift-free INS. There are further benefits to be gained depending on the level at which the information is combined. This presentation will focus on integration architectures, including “loosely coupled,” “tightly coupled,” and “deeply integrated” configurations. (Deep integration is trademarked by Draper Laboratory.) The advantages and disadvantages of each level of integration will be listed. Examples of current and future systems will be cited. 1.0 INTRODUCTION INS/GPS integration is not a new concept [Refs. 1, 2, 3, 4]. Measurements of noninertial quantities have long been incorporated into inertial navigation systems to limit error growth. Examples shown in Figure 1 are barometric “altitude” measurements, Doppler ground speed measurements, Doppler measurements to communications satellites, and range measurements to Omega stations. |
Motif-Based Hyponym Relation Extraction from Wikipedia Hyperlinks | Discovering hyponym relations among domain-specific terms is a fundamental task in taxonomy learning and knowledge acquisition. However, the great diversity of various domain corpora and the lack of labeled training sets make this task very challenging for conventional methods that are based on text content. The hyperlink structure of Wikipedia article pages was found to contain recurring network motifs in this study, indicating the probability of a hyperlink being a hyponym hyperlink. Hence, a novel hyponym relation extraction approach based on the network motifs of Wikipedia hyperlinks was proposed. This approach automatically constructs motif-based features from the hyperlink structure of a domain; every hyperlink is mapped to a 13-dimensional feature vector based on the 13 types of three-node motifs. The approach extracts structural information from Wikipedia and heuristically creates a labeled training set. Classification models were determined from the training sets for hyponym relation extraction. Two experiments were conducted to validate our approach based on seven domain-specific datasets obtained from Wikipedia. The first experiment, which utilized manually labeled data, verified the effectiveness of the motif-based features. The second experiment, which utilized an automatically labeled training set of different domains, showed that the proposed approach performs better than the approach based on lexico-syntactic patterns and achieves comparable result to the approach based on textual features. Experimental results show the practicability and fairly good domain scalability of the proposed approach. |
Early detection of promoted campaigns on social media | Social media expose millions of users every day to information campaigns - some emerging organically from grassroots activity, others sustained by advertising or other coordinated efforts. These campaigns contribute to the shaping of collective opinions. While most information campaigns are benign, some may be deployed for nefarious purposes, including terrorist propaganda, political astroturf, and financial market manipulation. It is therefore important to be able to detect whether a meme is being artificially promoted at the very moment it becomes wildly popular. This problem has important social implications and poses numerous technical challenges. As a first step, here we focus on discriminating between trending memes that are either organic or promoted by means of advertisement. The classification is not trivial: ads cause bursts of attention that can be easily mistaken for those of organic trends. We designed a machine learning framework to classify memes that have been labeled as trending on Twitter. After trending, we can rely on a large volume of activity data. Early detection, occurring immediately at trending time, is a more challenging problem due to the minimal volume of activity data that is available prior to trending. Our supervised learning framework exploits hundreds of time-varying features to capture changing network and diffusion patterns, content and sentiment information, timing signals, and user meta-data. We explore different methods for encoding feature time series. Using millions of tweets containing trending hashtags, we achieve 75% AUC score for early detection, increasing to above 95% after trending. We evaluate the robustness of the algorithms by introducing random temporal shifts on the trend time series. Feature selection analysis reveals that content cues provide consistently useful signals; user features are more informative for early detection, while network and timing features are more helpful once more data is available. |
Pastoral care and gays against the background of same-sex relationships in the Umwelt of the New Testament | The focus of the article is to show how the hegemony of heteronormativity compromises attempts at gay-friendly pastoral care and counselling with sexual minorities. Ecclesial resolutions with regard to same-sex relationships are based on Biblical propositions, theologies of heterosexual marriage, and often also on social stereotypes. This article investigates the textual evidence on same-sex intimacy in antiquity in order to demonstrate that views on sexuality and marriage are not fixed, but change over time. It also traces the formation of the theology of heterosexual marriage in the institutionalized Christian religion. Same-sex intimacy during the period from the end of the Roman Republic and the beginning of the Roman Imperial period is discussed, as well as during early Christianity up to and until marriage was sacramentalized. As a consequence of this historical legacy, churches have largely condemned same-sex relationships and have alienated sexual minorities from the faith community. The article contends that the hegemony of heteronormativity is based on an essentialist view on sexuality, as well as a positivist ethical reading of the texts of the New Testament and the contemporary world. It illustrates that the ecclesia itself has not yet been transformed by the gospel message of inclusive love. |
Attribute Discovery via Predictable Discriminative Binary Codes | We present images with binary codes in a way that balances discrimination and learnability of the codes. In our method, each image claims its own code in a way that maintains discrimination while being predictable from visual data. Category memberships are usually good proxies for visual similarity but should not be enforced as a hard constraint. Our method learns codes that maximize separability of categories unless there is strong visual evidence against it. Simple linear SVMs can achieve state-of-the-art results with our short codes. In fact, our method produces state-of-the-art results on Caltech256 with only 128dimensional bit vectors and outperforms state of the art by using longer codes. We also evaluate our method on ImageNet and show that our method outperforms state-of-the-art binary code methods on this large scale dataset. Lastly, our codes can discover a discriminative set of attributes. |
Bridging High-Level Synthesis to RTL Technology Libraries | The output of high-level synthesis typically consists of a netlist of generic RTL components and a state sequencing table. While module generators and logic synthesis tools can be used to map RTL components into standard cells or layout geometries, they cannot provide technology mapping into the data book libraries of functional RTL cells used commonly throughout the industrial design community. In this paper, we introduce an approach to implementing generic RTL components with technology-specific RTL library cells. This approach addresses the criticism of designers who feel that high-level synthesis tools should be used in conjunction with existing RTL data books. We describe how GENUS, a library of generic RTL components, is organized for use in high-level synthesis and how DTAS, a functional synthesis system, is used to map GENUS components into RTL library cells. |
Look-ahead before you leap: end-to-end active recognition by forecasting the effect of motion | Visual recognition systems mounted on autonomous moving agents face the challenge of unconstrained data, but simultaneously have the opportunity to improve their performance by moving to acquire new views of test data. In this work, we first show how a recurrent neural network-based system may be trained to perform end-to-end learning of motion policies suited for the “active recognition” setting. Further, we hypothesize that active vision requires an agent to have the capacity to reason about the effects of its motions on its view of the world. To verify this hypothesis, we attempt to induce this capacity in our active recognition pipeline, by simultaneously learning to forecast the effects of the agent’s motions on its internal representation of its cumulative knowledge obtained from all past views. Results across two challenging datasets confirm both that our end-toend system successfully learns meaningful policies for active recognition, and that “learning to look ahead” further boosts recognition performance. |
FUNCTION AND SURFACE APPROXIMATION BASED ON ENHANCED KERNEL REGRESSION FOR SMALL SAMPLE SETS | The function approximation problem is to find the appropriate relationship between a dependent and independent variable(s). Function approximation algorithms generally require sufficient samples to approximate a function. Insufficient samples may cause any function approximation algorithm to result in unsatisfactory predictions. To solve this problem, a function approximation algorithm called Weighted Kernel Regression (WKR), which is based on Nadaraya-Watson kernel regression (NWKR), is proposed. In the proposed framework, the original NWKR algorithm is enhanced by expressing the observed samples in a square kernel matrix. The WKR is trained to estimate the weight for the testing phase. The weight is estimated iteratively and governed by the error function to find a good approximation model. Four experiments are conducted to show the capability of the WKR. The results show that the proposed WKR model is effective in cases where the target function is non-linear and the given training sample is small. The performance of the WKR is also compared with other existing function approximation algorithms, such as artificial neural networks (ANN). |
Neural activity associated with metaphor comprehension: spatial analysis | Though neuropsychological data indicate that the right hemisphere (RH) plays a major role in metaphor processing, other studies suggest that, at least during some phases of this processing, a RH advantage may not exist. The present study explores, through a temporally agile neural signal--the event-related potentials (ERPs)--, and through source-localization algorithms applied to ERP recordings, whether the crucial phase of metaphor comprehension presents or not a RH advantage. Participants (n=24) were submitted to a S1-S2 experimental paradigm. S1 consisted of visually presented metaphoric sentences (e.g., "Green lung of the city"), followed by S2, which consisted of words that could (i.e., "Park") or could not (i.e., "Semaphore") be defined by S1. ERPs elicited by S2 were analyzed using temporal principal component analysis (tPCA) and source-localization algorithms. These analyses revealed that metaphorically related S2 words showed significantly higher N400 amplitudes than non-related S2 words. Source-localization algorithms showed differential activity between the two S2 conditions in the right middle/superior temporal areas. These results support the existence of an important RH contribution to (at least) one phase of metaphor processing and, furthermore, implicate the temporal cortex with respect to that contribution. |
Deoxyribonucleic acid methylation and gene expression of PPARGC1A in human muscle is influenced by high-fat overfeeding in a birth-weight-dependent manner. | CONTEXT
Low birth weight (LBW) and unhealthy diets are risk factors of metabolic disease including type 2 diabetes (T2D). Genetic, nongenetic, and epigenetic data propose a role of the key metabolic regulator peroxisome proliferator-activated receptor gamma, coactivator 1alpha (PPARGC1A) in the development of T2D.
OBJECTIVE
Our objective was to investigate gene expression and DNA methylation of PPARGC1A and coregulated oxidative phosphorylation (OXPHOS) genes in LBW and normal birth weight (NBW) subjects during control and high-fat diets. DESIGN, SUBJECTS, AND MAIN OUTCOME MEASURES: Twenty young healthy men with LBW and 26 matched NBW controls were studied after 5 d high-fat overfeeding (+50% calories) and after a control diet in a randomized manner. Hyperinsulinemic-euglycemic clamps were performed and skeletal muscle biopsies excised. DNA methylation and gene expression were measured using bisulfite sequencing and quantitative real-time PCR, respectively.
RESULTS
When challenged with high-fat overfeeding, LBW subjects developed peripheral insulin resistance and reduced PPARGC1A and OXPHOS (P < 0.05) gene expression. PPARGC1A methylation was significantly higher in LBW subjects (P = 0.0002) during the control diet. However, PPARGC1A methylation increased in only NBW subjects after overfeeding in a reversible manner. DNA methylation of PPARGC1A did not correlate with mRNA expression.
CONCLUSIONS
LBW subjects developed peripheral insulin resistance and decreased gene expression of PPARGC1A and OXPHOS genes when challenged with fat overfeeding. The extent to which our finding of a constitutively increased DNA methylation in the PPARGC1A promoter in LBW subjects may contribute needs to be determined. We provide the first experimental support in humans that DNA methylation induced by overfeeding is reversible. |
Current Multistage Drug Delivery Systems Based on the Tumor Microenvironment | The development of traditional tumor-targeted drug delivery systems based on EPR effect and receptor-mediated endocytosis is very challenging probably because of the biological complexity of tumors as well as the limitations in the design of the functional nano-sized delivery systems. Recently, multistage drug delivery systems (Ms-DDS) triggered by various specific tumor microenvironment stimuli have emerged for tumor therapy and imaging. In response to the differences in the physiological blood circulation, tumor microenvironment, and intracellular environment, Ms-DDS can change their physicochemical properties (such as size, hydrophobicity, or zeta potential) to achieve deeper tumor penetration, enhanced cellular uptake, timely drug release, as well as effective endosomal escape. Based on these mechanisms, Ms-DDS could deliver maximum quantity of drugs to the therapeutic targets including tumor tissues, cells, and subcellular organelles and eventually exhibit the highest therapeutic efficacy. In this review, we expatiate on various responsive modes triggered by the tumor microenvironment stimuli, introduce recent advances in multistage nanoparticle systems, especially the multi-stimuli responsive delivery systems, and discuss their functions, effects, and prospects. |
Mode of reproduction and interbreeding capabilities of two host races of reniform nematode, Rotylenchulus reniformis* | |
Finding expert users in community question answering | Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise, and therefore new matching questions may be missed and not receive a proper answer. We focus on finding experts for a newly posted question. We investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model. |
Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy. | Insights from appraisal theories of emotion are used to integrate elements of theories on collective action. Three experiments with disadvantaged groups systematically manipulated procedural fairness (Study 1), emotional social support (Study 2), and instrumental social support (Study 3) to examine their effects on collective action tendencies through group-based anger and group efficacy. Results of structural equation modeling showed that procedural fairness and emotional social support affected the group-based anger pathway (reflecting emotion-focused coping), whereas instrumental social support affected the group efficacy pathway (reflecting problem-focused coping), constituting 2 distinct pathways to collective action tendencies. Analyses of the means suggest that collective action tendencies become stronger the more fellow group members "put their money where their mouth is." The authors discuss how their dual pathway model integrates and extends elements of current approaches to collective action. |
DESIGN OF A HUMAN-LIKE RANGE OF MOTION HIP JOINT FOR HUMANOID ROBOTS | For a humanoid robot to have the versatility of humans, it needs to have similar motion capabilities. This paper presents the design of the hip joint of the Tactical Hazardous Operations Robot (THOR), which was created to perform disaster response duties in human-structured environments. The lower body of THOR was designed to have a similar range of motion to the average human. To accommodate the large range of motion requirements of the hip, it was divided into a parallel-actuated universal joint and a linkage-driven pin joint. The yaw and roll degrees of freedom are driven cooperatively by a pair of parallel series elastic linear actuators to provide high joint torques and low leg inertia. In yaw, the left hip can produce a peak of 115.02 [Nm] of torque with a range of motion of -20° to 45°. In roll, it can produce a peak of 174.72 [Nm] of torque with a range of motion of -30° to 45°. The pitch degree of freedom uses a Hoeken’s linkage mechanism to produce 100 [Nm] of torque with a range of motion of -120° to 30°. |
Online Adaptive Hidden Markov Model for Multi-Tracker Fusion | In this paper, we propose a novel method for visual object tracking called HMMTxD. The method fuses observations from complementary out-of-the box trackers and a detector by utilizing a hidden Markov model whose latent states correspond to a binary vector expressing the failure of individual trackers. The Markov model is trained in an unsupervised way, relying on an online learned detector to provide a source of tracker-independent information for a modified BaumWelch algorithm that updates the model w.r.t. the partially annotated data. We show the effectiveness of the proposed method on combination of two and three tracking algorithms. The performance of HMMTxD is evaluated on two standard benchmarks (CVPR2013 and VOT) and on a rich collection of 77 publicly available sequences. The HMMTxD outperforms the state-of-the-art, often significantly, on all datasets in almost all criteria. |
Juniper: A Tree+Table Approach to Multivariate Graph Visualization | Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree-table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc. |
A literature survey on Facial Expression Recognition using Global Features | Facial Expression Recognition (FER) is a rapidly growing and ever green research field in the area of Computer Vision, Artificial Intelligent and Automation. There are many application which uses Facial Expression to evaluate human nature, feelings, judgment, opinion. Recognizing Human Facial Expression is not a simple task because of some circumstances due to illumination, facial occlusions, face color/shape etc. In these paper, we present some method/techniques such as Principal Component Analysis (PCA), Linear Discriminate Analysis (LDA), Gabor Filter/Energy, Line Edge Mapping (LEM), Neural Network, Independent Component Analysis (ICA) which will directly or/and indirectly used to recognize human expression in |
Marketing Theory : Overview of Ontology , Epistemology , and Axiology Aspects | This article discusses about the marketing theory from the perspective of philosophy of science. The discussion focused on the aspects of the ontology, epistemology, and axiology. From the aspect of the ontology, it seems that the essence of marketing is a useful science and mutually beneficial for both the marketer and the stakeholders, or in other words that marketing knowledge is useful knowledge for the benefit of humankind that can be realized through the exchange process. Side of the ontology covers what the substance of marketing knowledge, the substance of truth and reality that is inherent with marketing. Meanwhile, aspects of epistemology cover a variety of approaches, methods, sources, structure and validation or marketing truth. Finally, axiology fields are related to ethics in marketing and marketing research. Marketing ethics and ethics in marketing research is a crucial matter, including in this case is trust. |
Be Appropriate and Funny: Automatic Entity Morph Encoding | Internet users are keen on creating different kinds of morphs to avoid censorship, express strong sentiment or humor. For example, in Chinese social media, users often use the entity morph “方便面 (Instant Noodles)” to refer to “周永康 (Zhou Yongkang)” because it shares one character “康 (Kang)” with the well-known brand of instant noodles “康师傅 (Master Kang)”. We developed a wide variety of novel approaches to automatically encode proper and interesting morphs, which can effectively pass decoding tests 1. |
A service-oriented middleware for building context-aware services | The advancement of wireless networks and mobile computing necessitates more advanced applications and services to be built with context-awareness enabled and adaptability to their changing contexts. Today, building context-aware services is a complex task due to the lack of an adequate infrastructure support in pervasive computing environments. In this article, we propose a ServiceOriented Context-Aware Middleware (SOCAM) architecture for the building and rapid prototyping of context-aware services. It provides efficient support for acquiring, discovering, interpreting and accessing various contexts to build context-aware services. We also propose a formal context model based on ontology using Web Ontology Language to address issues including semantic representation, context reasoning, context classification and dependency. We describe our context model and the middleware architecture, and present a performance study for our prototype in a smart home environment. q 2004 Elsevier Ltd. All rights reserved. |
Clockwork Convnets for Video Semantic Segmentation | Recent years have seen tremendous progress in still-image segmentation; however the naı̈ve application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: 1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and 2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of “clockwork” convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video. The accuracy and efficiency of clockwork convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video datasets. |
The Architecture of a System for the Indexing of Images by Content | This paper presents the architecture of an image database system which provides a platform for the accommodation of various algorithms for interactive and automatic indexing, storage, and retrieval of medical images by content. The system maintains a dynamic hierarchy of image classes. The class hierarchy is used to narrow down the search to images of the same modality, anatomical characteristics, etc. Each image is classified into an image class based on information supplied by the user or obtained from the image itself. An important feature of the system is its ability to support multiple image indexing by content methods, in the form of description types. During system installation, one or more description types are selected for each image class based on the inherent characteristics of the class. Then, for each description type, a content description of each image in the class is generated and inserted in the description database. In the case of a query, the system generates one or more descriptions of the query image, automatically or interactively, and searches the logical database for similar descriptions. The images whose descriptions match those of the query image are retrieved for browsing by the user. This system may also be used as a platform for evaluating image content description methods, since new methods can be added to it easily. |
KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks. | Accurately predicting protein-ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compare this approach to other machine-learning and scoring methods using several diverse data sets. The results for the standard PDBbind (v.2016) core test-set are state-of-the-art with a Pearson's correlation coefficient of 0.82 and a RMSE of 1.27 in pK units between experimental and predicted affinity, but accuracy is still very sensitive to the specific protein used. KDEEP is made available via PlayMolecule.org for users to test easily their own protein-ligand complexes, with each prediction taking a fraction of a second. We believe that the speed, performance, and ease of use of KDEEP makes it already an attractive scoring function for modern computational chemistry pipelines. |
Optical Sensors and Methods for Underwater 3D Reconstruction | This paper presents a survey on optical sensors and methods for 3D reconstruction in underwater environments. The techniques to obtain range data have been listed and explained, together with the different sensor hardware that makes them possible. The literature has been reviewed, and a classification has been proposed for the existing solutions. New developments, commercial solutions and previous reviews in this topic have also been gathered and considered. |
Water quality index and fractal dimension analysis of water parameters | Statistical analysis of water quality parameters were analyzed at Harike Lake on the confluence of Beas and Sutlej rivers of Punjab (India). Mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation, regression lines, correlation coefficient, Hurst exponent, fractal dimension and predictability index were estimated for each water parameter. Monthly variation of water quality index using month-wise and parameter-wise value of quality rating and actual value present in water sample was calculated and compared with World Health Organization/ Environmental Protection Agency standard value of these parameters. It was observed that Brownian time series behavior exists of potential of hydrogen with total dissolved solids, hardness, alkalinity, sulfate, chloride and conductance parameters; biochemical oxygen demand with total dissolved solids, hardness, alkalinity, sulfate, chloride, conductance and calcium parameters; dissolved oxygen with total dissolved solids, hardness, alkalinity, sulfate, chloride, conductance and calcium parameters; ferrous with total dissolved solids, hardness, alkalinity, sulfate, conductance and calcium parameters; chromium with total dissolved solids, hardness, alkalinity, sulfate, chloride, conductance and zinc parameters; zinc with total dissolved solids, hardness, sulfate, chloride, conductance and calcium parameters; fluoride with total dissolved solids, hardness, alkalinity, sulfate, chloride and conductance parameters; nitrate with total dissolved solids, sulfate and conductance parameters; nitrite with potential of hydrogen, total dissolved solids, hardness, alkalinity, sulfate, chloride, conductance and calcium parameters. Also, using water quality index, it was observed that water of the lake was severely contaminated and became unfit for drinking and industrial use. |
Culture-Based Creativity in the Regional Strategy of Development : Is Russia in Game ? | In the knowledge-based society, economic growth depends on the implementation of new ideas. Creative people, creative industries, and creative economies are considered as the crucial drivers of the economic prosperity and change management. This chapter analyzes regional specificity of Russia in creation and support of creativity within social and economic development, using the Global Entrepreneurship Monitor, Impact Report, and the G20 Entrepreneurship Barometer. Using data from Inglehardt’s World Values Survey, the analysis of cultural assignments in the decision-making in Russia will continue compared to diverse European practices. It will be a valuable basis for further exploration of collision between global economic systems, demands for creativity and innovation, internal Russian institutional and societal resources for support/rejection of innovation, and culturally indoctrinated behavioral patterns of young researchers and intellectual entrepreneurs, articulated as drivers of the new economy. CONCEPTUAL FRAMEWORK Culture is one of the most discussable concepts in extant academic literature. It exists on three levels of analysis: meta, mezzo, and micro (with implications to organization, corporate and other forms of culture). There are several dimensions of culture that could be found within the academic milieu: culture and cultural differences in management, social psychology, and linguistic anthropology. On the meta-level of analysis since the 1950s, we can see the changes in perception of culture away from the objectivist framing the concept as a way life, including traits, values, and behavioral patterns (Herbig & Dunphy, 1998). The new wave of reassessment of culture came into being during the 1980s called the “cultural turn” (Jameson, 1998). It helped to develop a more flexible definition of Oxana Karnaukhova Southern Federal University, Russia |
A new six-port junction based on substrate integrated waveguide technology | A six-port junction based on the substrate integrated waveguide (SIW) technology is proposed and presented. In this design of such a junction, the SIW is first converted to an equivalent rectangular waveguide, then regular rectangular waveguide design techniques are used. In this structure, an SIW power divider and SIW hybrid 3-dB coupler are designed as fundamental building blocks. A six-port junction circuit operating at 24 GHz is fabricated and measured. Good agreement between simulated and measured results is found for the proposed six-port junction. |
Category Specific Post Popularity Prediction | Social media have become dominant in everyday life during the last few years where users share their thoughts and experiences about their enjoyable events in posts. Most of these posts are related to different categories related to: activities, such as dancing, landscapes, such as beach, people, such as a selfie, and animals such as pets. While some of these posts become popular and get more attention, others are completely ignored. In order to address the desire of users to create popular posts, several researches have studied post popularity prediction. Existing works focus on predicting the popularity without considering the category type of the post. In this paper we propose category specific post popularity prediction using visual and textual content for action, scene, people and animal categories. In this way we aim to answer the question What makes a post belonging to a specific action, scene, people or animal category popular? To answer to this question we perform several experiments on a collection of 65K posts crawled from Instagram. |
Endodontic applications of cone-beam volumetric tomography. | The ability to assess an area of interest in 3 dimensions might benefit both novice and experienced clinicians alike. High-resolution limited cone-beam volumetric tomography (CBVT) has been designed for dental applications. As opposed to sliced-image data of conventional computed tomography (CT) imaging, CBVT captures a cylindrical volume of data in one acquisition and thus offers distinct advantages over conventional medical CT. These advantages include increased accuracy, higher resolution, scan-time reduction, and dose reduction. Specific endodontic applications of CBVT are being identified as the technology becomes more prevalent. CBVT has great potential to become a valuable tool in the modern endodontic practice. The objectives of this article are to briefly review cone-beam technology and its advantages over medical CT and conventional radiography, to illustrate current and future clinical applications of cone-beam technology in endodontic practice, and to discuss medicolegal considerations pertaining to the acquisition and interpretation of 3-dimensional data. |
Emotion detection in suicide notes | 0957-4174/$ see front matter 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.eswa.2013.05.050 ⇑ Corresponding author at: LT3 Language and Translation Technology Team, University College Ghent, Groot-Brittanniëlaan 45, 9000 Ghent, Belgium. Tel.: +32 9 224 97 53. E-mail addresses: [email protected] (B. Desmet), veronique.hoste@ hogent.be (V. Hoste). Bart Desmet a,b,⇑, Véronique Hoste a,c |
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates | Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data. The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously. Based on the pictorial structure of Kinect's skeletal data, an effective tree-structure based traversal framework is also proposed. In order to deal with the noise in the skeletal data, a new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell. Moreover, we introduce a novel multi-modal feature fusion strategy within the LSTM unit in this paper. The comprehensive experimental results on seven challenging benchmark datasets for human action recognition demonstrate the effectiveness of the proposed method. |
Differential effects of HIV viral load and CD4 count on proliferation of naive and memory CD4 and CD8 T lymphocytes. | We previously showed that HIV infection leads to expansion of a rapidly proliferating pool (s(1)) of CD4 and CD8 T lymphocytes. In the current study, we used in vivo labeling with bromodeoxyuridine to characterize the kinetics of naive, memory, and activated (HLA-DR(+)/CD38(+)) subpopulations of CD4 and CD8 T lymphocytes, and to examine the relationship between kinetic parameters and baseline CD4 counts, HIV viral load, potential markers of microbial translocation, and cytokine levels. Activated cells showed the highest proliferation rates, followed by effector and central memory cells, with naive cells showing the lowest rates, for both CD4 and CD8 T cells. HIV viral load correlated with s(1) of CD4 and CD8 effector memory cells, as well as CD8 naive cells, whereas CD4 cell counts correlated inversely with naive CD4 s(1). Endotoxin levels showed a weak negative association with CD4 but not CD8 s(1). INF-γ and TNF-α were associated with s(1) for CD4 and CD8 cells, respectively. Thus, HIV is the primary driving force behind the activation and proliferation of most subsets of both CD4 and CD8 T lymphocytes, whereas naive CD4 cell proliferation likely represents a homeostatic response. Microbial translocation does not appear to play an important role in this proliferation. |
A technical and economic assessment of computer vision for industrial inspection and robotic assembly | The use of computer vision to detect, measure, and perhaps guide the assembly of man-made components is potentially a very significant research and development area. The efficacy of these techniques for any given application depends on both technical and economic considerations. This paper will explore both these considerations using appropriate generic examples. It is our goal to first present a concise discussion of the present state of many technical and economic factors and then extrapolate these factors into the future for the purpose of guiding further investigations. |
Lyapunov based model reference adaptive control for aerial manipulation | This paper presents a control scheme to achieve dynamic stability in an aerial vehicle with dual multi-degree of freedom manipulators using a lyapunov based model reference adaptive control. Our test flight results indicate that we can accurately model and control our aerial vehicle when both moving the manipulators and interacting with target objects. Using the Lyapunov stability theory, the controller is proven to be stable. The simulation results showed how the MRAC is capable of stabilizing the oscillations produced from the unstable PI-D attitude control loop. Finally a high level control system based on a switching automaton is proposed in order to ensure the safety of the aerial manipulation missions. |
A Scalable Priority Queue Architecture for High Speed Network Processing | Priority queues are essential for implementing various types of service disciplines on network devices. However, state-of-the-art priority queues can barely catch up with the OC192 line speed and the size of active priorities is limited to a few hundred KB which is far from the worst-case requirement. Our hybrid design stores most priorities sorted in the FIFO queue and they are used for dequeue only. Since the dequeue operation always takes the highest priorities from the head of the FIFO queue, it can be efficiently handled using caching in the SRAM and wide word accesses to the DRAM. Meanwhile, incoming priorities are stored temporarily in a small input heap. Once the input heap is full, it becomes creation heap and the priorities are then quickly dequeued from the creation heap and transformed into a sorted array to be stored in the FIFO queues. When these operations are in progress, previously emptied creation heap is connected to the input and continues to get incoming priorities. Swapping between input and creation heap sustains continuous operation of the system. Our results show that this hybrid design can meet the requirements of OC-3072 line speed and provide enough capacity queuing a large number of priorities in the worst case. Also, the required DRAM bandwidth and SRAM size (which is precious) are reasonable. Keywords-Network Processor; Priority Queue; FIFO Queue. |
Body-weight-supported treadmill rehabilitation after stroke. | BACKGROUND
Locomotor training, including the use of body-weight support in treadmill stepping, is a physical therapy intervention used to improve recovery of the ability to walk after stroke. The effectiveness and appropriate timing of this intervention have not been established.
METHODS
We stratified 408 participants who had had a stroke 2 months earlier according to the extent of walking impairment--moderate (able to walk 0.4 to <0.8 m per second) or severe (able to walk <0.4 m per second)--and randomly assigned them to one of three training groups. One group received training on a treadmill with the use of body-weight support 2 months after the stroke had occurred (early locomotor training), the second group received this training 6 months after the stroke had occurred (late locomotor training), and the third group participated in an exercise program at home managed by a physical therapist 2 months after the stroke (home-exercise program). Each intervention included 36 sessions of 90 minutes each for 12 to 16 weeks. The primary outcome was the proportion of participants in each group who had an improvement in functional walking ability 1 year after the stroke.
RESULTS
At 1 year, 52.0% of all participants had increased functional walking ability. No significant differences in improvement were found between early locomotor training and home exercise (adjusted odds ratio for the primary outcome, 0.83; 95% confidence interval [CI], 0.50 to 1.39) or between late locomotor training and home exercise (adjusted odds ratio, 1.19; 95% CI, 0.72 to 1.99). All groups had similar improvements in walking speed, motor recovery, balance, functional status, and quality of life. Neither the delay in initiating the late locomotor training nor the severity of the initial impairment affected the outcome at 1 year. Ten related serious adverse events were reported (occurring in 2.2% of participants undergoing early locomotor training, 3.5% of those undergoing late locomotor training, and 1.6% of those engaging in home exercise). As compared with the home-exercise group, each of the groups receiving locomotor training had a higher frequency of dizziness or faintness during treatment (P=0.008). Among patients with severe walking impairment, multiple falls were more common in the group receiving early locomotor training than in the other two groups (P=0.02).
CONCLUSIONS
Locomotor training, including the use of body-weight support in stepping on a treadmill, was not shown to be superior to progressive exercise at home managed by a physical therapist. (Funded by the National Institute of Neurological Disorders and Stroke and the National Center for Medical Rehabilitation Research; LEAPS ClinicalTrials.gov number, NCT00243919.). |
THE EFFECT OF INFORMATION ON PRODUCT QUALITY : EVIDENCE FROM RESTAURANT HYGIENE GRADE CARDS | This study examines the effect of an increase in product quality information to consumers on rms’ choices of product quality. In 1998 Los Angeles County introduced hygiene quality grade cards to be displayed in restaurant windows. We show that the grade cards cause (i) restaurant health inspection scores to increase, (ii) consumer demand to become sensitive to changes in restaurants’ hygiene quality, and (iii) the number of foodborne illness hospitalizations to decrease. We also provide evidence that this improvement in health outcomes is not fully explained by consumers substituting from poor hygiene restaurants to good hygiene restaurants. These results imply that the grade cards cause restaurants to make hygiene quality improvements. |
3D Object Pose Refinement in Range Images | Estimating the pose of objects from range data is a problem of considerable practical importance for many vision applications. This paper presents an approach for accurate and efficient 3D pose estimation from 2.5D range images. Initialized with an approximate pose estimate, the proposed approach refines it so that it accurately accounts for an acquired range image. This is achieved by using a hypothesize-and-test scheme that combines Particle Swarm Optimization (PSO) and graphicsbased rendering to minimize a cost function of object pose that quantifies the misalignment between the acquired and a hypothesized, rendered range image. Extensive experimental results demonstrate the superior performance of the approach compared to the Iterative Closest Point (ICP) algorithm that is commonly used for pose refinement. |
Gender and use of substance abuse treatment services. | Women are more likely than men to face multiple barriers to accessing substance abuse treatment and are less likely to seek treatment. Women also tend to seek care in mental health or primary care settings rather than in specialized treatment programs, which may contribute to poorer treatment outcomes. When gender differences in treatment outcomes are reported, however, women tend to fare better than men. Limited research suggests that gender-specific treatment is no more effective than mixed-gender treatment, though certain women may only seek treatment in women-only programs. Future health services research should consider or develop methods for (1) improving care for women who seek help in primary care or mental health settings, (2) increasing the referral of women to specialized addiction treatment, (3) identifying subgroups of women and men who would benefit from gender-specific interventions, and (4) addressing gender-specific risk factors for reduced treatment initiation, continuation, and treatment outcomes. |
How to write a rave review. | The Thomson Reuters (ISI) Web of Knowledge service lists data for nearly 7400 science journals. How many journals do you read on a regular basis? How many topics can you keep up with? If your answer to either question reached double digits, you are well beyond most scientists. The truth is that the number of papers published each year is so large that no one can keep up with the pace of science. If you want to gather even basic knowledge of a subject that is not within your immediate sphere of activity, you must rely on some sort of summary document that can bring you “up to speed” on the current state of a discipline. This is the perfect role for a review article. Scientific review articles are critical analyses of available information about a particular topic. Unlike research articles, review articles do not present new data. Their purpose is to assess and put into perspective what is already known. Unlike research articles written on narrowly defined topics for a specialized audience of peers, review articles often examine broader topics for a more general audience. For example, a review of the extracellular matrix might be published in a journal whose readers are surgeons, rather than research specialists and pathologists with a greater knowledge of the topic, or it might be read by specialists who need to keep up with developments in related subspecialties. Many reviews, however, are written on narrow topics. For example, a review of mass spectrometry principles would be quite general, whereas a review of mass spectrometry in the clinical laboratory would be more specific and a review of the ionization effect in mass spectrometry would be even more specific. There are 3 main types of review articles. The most common, which we discuss in detail in this paper, is the traditional narrative or “scholarly” review, in which the author evaluates and synthesizes what is already known about a topic. The problem with many narrative reviews is that they are vague and even eccentric in the collection, selection, and interpretation of the information they discuss. Often only a selected group of studies is considered (“cherry picking”), and the selection is quite likely biased. Because clinical review articles are often used by clinicians as guides for making decisions, many journals publish a second type, the systematic review. These reviews use explicit and rigorous methods to identify, critically evaluate, and synthesize all relevant studies in order to present a concise summary of the best available evidence regarding a sharply defined clinical question (1–3 ). A review protocol defining the design and methods for a systematic review—including how studies or trials will be identified and the inclusion and exclusion criteria—is written before the review begins. A systematic plan is established to see that all relevant studies or trials (or at least as many as possible) are identified and included in any analyses that are done. A systematic review does not necessarily contain a statistical synthesis of the results from the studies included. The reviewer might find that the designs of the studies identified differ too greatly to permit the studies to be combined, for example, or that the results available for each study cannot be combined because of differences in the way outcomes were assessed. In such cases, the reviewer may simply report—as in a high-quality scholarly review—a wellreasoned but nonstatistical assessment of what might be drawn from the cumulative review. The third type of review article is the metaanalysis, which is a systematic review that uses a specific methodological and statistical technique for combining quantitative data from several independent studies. Established standards already exist for conducting and writing a systematic review or metaanalysis (4, 5 ). Scientific review articles are usually solicited by journal editors when they feel that some aspect of their journal’s discipline has reached a point at which the research and findings in disparate studies need to be critically evaluated by someone competent to do a comprehensive assessment of the work, to separate the wheat from the chaff, to synthesize the ideas and findings the work comprises, and to bring an overall perspective to the field or topic. That person is usually a well-known and well-respected scientist in the field, although not necessarily an “elder statesman.” Depending on the topic, the discipline, and the circumstances, journal editors might invite a review from a promising younger researcher when they expect the younger researcher to be more likely to consider the invitation a valuable career opportunity, to be more 1 Department of Surgery, University of California, San Francisco, San Francisco, CA; 2 Department of Pathology, University of Michigan Health System, Ann Arbor, MI. * Address correspondence to this author at: Department of Surgery, University of California, San Francisco, 1600 Divisidero St., Room C-322, San Francisco, CA 94143-1674. E-mail [email protected]. Received December 20, 2010; accepted December 28, 2010. Previously published online at DOI: 10.1373/clinchem.2010.160622 Clinical Chemistry 57:3 388–391 (2011) Clinical Chemistry Guide to Scientific Writing |
ITEM RESPONSE THEORY | Item Response Theory is based on the application of related mathematical models to testing data. Because it is generally regarded as superior to classical test theory, it is the preferred method for developing scales, especially when optimal decisions are demanded, as in so-called high-stakes tests. The term item is generic: covering all kinds of informative item. They might be multiple choice questions that have incorrect and correct responses, but are also commonly statements on questionnaires that allow respondents to indicate level of agreement (a rating or Likert scale), or patient symptoms scored as present/absent, or diagnostic information in complex systems. IRT is based on the idea that the probability of a correct/keyed response to an item is a mathematical function of person and item parameters. The person parameter is construed as (usually) a single latent trait or dimension. Examples include general intelligence or the strength of an attitude. |
Hyponatremia as predictor of worse outcome in real world patients admitted with acute heart failure. | BACKGROUND
Our aim was to determine if hyponatremia, defined as serum sodium level < 135 mmol/L, is a predictor of worse outcome in a cohort of real-world patients with heartfailure (HF).
METHODS
We used data of the National registry of HF (RICA) from Spain, an ongoing multicenter, prospective cohort study. The patients were assigned to two groups regarding sodium levels. Primary end-point was first all-cause readmission, or death by any cause. Secondary end-points were the number of days hospitalized, and the presence of complications.
RESULTS
We identified 973 patients, 147 (15.11%) with hyponatremia. The median age of patients enrolled was 77.25 ± 8.79 years-old, the global comorbidity measured by Charlson comorbidity index (CCI) was upper 3 points and preserved ejection fraction was present in67.1% of them. Clinical complications during admission were significantly higher in the patients with hyponatremia (35.41%, p < 0.001) and this remained as significant predictor after logistic regression adjustment (OR 1.08, p < 0.01). Also mortality and readmissions were more frequent in patients with hyponatremia (20.69% and 22.41%, respectively) but after Cox regression adjustment hyponatremia in our cohort was not associated with increase in 90-day all-cause mortality and readmissions, and only CCI remained significant for primaryend-point (HR 1.08, p < 0.001).
CONCLUSIONS
Hyponatremia is an independent predictor of complications during hospitalization in our real-world cohort, but was not associated with 90 days mortality or readmissions. Global comorbidity, however, played an important role, and could influence the mortality and readmissions of our patients. |
Prediction of Employee Turnover in Organizations using Machine Learning Algorithms A case for Extreme Gradient Boosting | Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate. This is the key challenge that is the focus of this paper, and one that has not been addressed historically. The novel contribution of this paper is to explore the application of Extreme Gradient Boosting (XGBoost) technique which is more robust because of its regularization formulation. Data from the HRIS of a global retailer is used to compare XGBoost against six historically used supervised classifiers and demonstrate its significantly higher accuracy for predicting employee turnover. Keywords—turnover prediction; machine learning; extreme gradient boosting; supervised classification; regularization |
Neurobiology of cocaine addiction: implications for new pharmacotherapy. | The development of pharmacotherapies for cocaine addiction has been disappointingly slow. However, new neurobiological knowledge of how the brain is changed by chronic pharmacological insult with cocaine is revealing novel targets for drug development. Certain drugs currently being tested in clinical trials tap into the underlying cocaine-induced neuroplasticity, including drugs promoting GABA or inhibiting glutamate transmission. Armed with rationales derived from a neurobiological perspective that cocaine addiction is a pharmacologically induced disease of neuroplasticity in brain circuits mediating normal reward learning, one can expect novel pharmacotherapies to emerge that directly target the biological pathology of addiction. |
Kinematics of a new class of smart actuators for soft robots based on generalized pneumatic artificial muscles | The growing interest in robots that interact safely with humans and surroundings have prompted the need for soft structural embodiments including soft actuators. This paper explores a class of soft actuators inspired in design and construction by Pneumatic Artificial Muscles (PAMs) or McKibben Actuators. These bio-inspired actuators consist of fluid-filled elastomeric enclosures that are reinforced with fibers along a specified orientation and are in general referred to as Fiber-Reinforced Elastomeric Enclosures (FREEs). Several recent efforts have mapped the fiber configurations to instantaneous deformation, forces, and moments generated by these actuators upon pressurization with fluid. However most of the actuators, when deployed undergo large deformations and large overall motions thus necessitating the study of their large-deformation kinematics. This paper analyzes the large deformation kinematics of FREEs. A concept called configuration memory effect is proposed to explain the smart nature of these actuators. This behavior is tested with experiments and finite element modeling for a small sample of actuators. The paper also describes different possibilities and design implications of the large deformation behavior of FREEs in successful creation of soft robots. |
An Investigation of the Interrelationships between Motivation, Engagement, and Complex Problem Solving in Game-based Learning | Digital game-based learning, especially massively multiplayer online games, has been touted for its potential to promote student motivation and complex problem-solving competency development. However, current evidence is limited to anecdotal studies. The purpose of this empirical investigation is to examine the complex interplay between learners’ motivation, engagement, and complex problem-solving outcomes during game-based learning. A theoretical model is offered that explicates the dynamic interrelationships among learners’ problem representation, motivation (i.e., interest, competence, autonomy, relatedness, self-determination, and selfefficacy), and engagement. Findings of this study suggest that learners’ motivation determine their engagement during gameplay, which in turn determines their development of complex problem-solving competencies. Findings also suggest that learner’s motivation, engagement, and problem-solving performance are greatly impacted by the nature and the design of game tasks. The implications of this study are discussed in detail for designing effective game-based learning environments to facilitate learner engagement and complex problemsolving competencies. |
PRIVACY-PRESERVING PUBLIC AUDITING FOR DATA STORAGE SECURITY IN CLOUD COMPUTING | Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize the public key based homomorphic authenticator and uniquely integrate it with random mask technique to achieve a privacy-preserving public auditing system for cloud data storage security while keeping all above requirements in mind. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient. |
How words can and cannot be learned by observation. | Three experiments explored how words are learned from hearing them across contexts. Adults watched 40-s videotaped vignettes of parents uttering target words (in sentences) to their infants. Videos were muted except for a beep or nonsense word inserted where each "mystery word" was uttered. Participants were to identify the word. Exp. 1 demonstrated that most (90%) of these natural learning instances are quite uninformative, whereas a small minority (7%) are highly informative, as indexed by participants' identification accuracy. Preschoolers showed similar information sensitivity in a shorter experimental version. Two further experiments explored how cross-situational information helps, by manipulating the serial ordering of highly informative vignettes in five contexts. Response patterns revealed a learning procedure in which only a single meaning is hypothesized and retained across learning instances, unless disconfirmed. Neither alternative hypothesized meanings nor details of past learning situations were retained. These findings challenge current models of cross-situational learning which assert that multiple meaning hypotheses are stored and cross-tabulated via statistical procedures. Learners appear to use a one-trial "fast-mapping" procedure, even under conditions of referential uncertainty. |
The Institutionalization of Institutional Theory | Excerpt] Our primary aims in this effort are twofold: to clarify the independent theoretical contributions of institutional theory to analyses of organizations, and to develop this theoretical perspective further in order to enhance its use in empirical research. There is also a more general, more ambitious objective here, and that is to build a bridge between two distinct models of social actor that underlie most organizational analyses, which we refer to as a rational actor model and an institutional model. The former is premised on the assumption that individuals are constantly engaged in calculations of the costs and benefits of different action choices, and that behavior reflects such utility-maximizing calculations. In the latter model, by contrast, 'oversocialized' individuals are assumed to accept and follow social norms unquestioningly, without any real reflection or behavioral resistance based on their own particular, personal interests. We suggest that these two general models should be treated not as oppositional but rather as representing two ends of a continuum of decisionmaking processes and behaviors. Thus, a key problem for theory and research is to specify the conditions under which behavior is more likely to resemble one end of this continuum or the other. In short, what is needed are theories of when rationality is likely to be more or less bounded. A developed conception of institutionalization processes provides a useful point of departure for exploring this issue. |
Combinational Method for Face Recognition: Wavelet, PCA and ANN | This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, the Neural Network (MLP) is explored to achieve a robust decision in presence of wide facial variations. The computational load of the proposed method is greatly reduced as comparing with the original PCA based method on the Yale and ORL face databases. Moreover, the accuracy of the proposed method is improved. |
Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic | Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech Recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs. |
A Multiscalar Drought Index Sensitive to Global Warming : The Standardized Precipitation Evapotranspiration Index | The authors propose a new climatic drought index: the standardized precipitation evapotranspiration index (SPEI). The SPEI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character with the capacity to include the effects of temperature variability on drought assessment. The procedure to calculate the index is detailed and involves a climatic water balance, the accumulation of deficit/surplus at different time scales, and adjustment to a log-logistic probability distribution. Mathematically, the SPEI is similar to the standardized precipitation index (SPI), but it includes the role of temperature. Because the SPEI is based on a water balance, it can be compared to the self-calibrated Palmer drought severity index (sc-PDSI). Time series of the three indices were compared for a set of observatories with different climate characteristics, located in different parts of the world. Under global warming conditions, only the sc-PDSI and SPEI identified an increase in drought severity associated with higher water demand as a result of evapotranspiration. Relative to the sc-PDSI, the SPEI has the advantage of being multiscalar, which is crucial for drought analysis and monitoring. |
Defining immunological dysfunction in sepsis: A requisite tool for precision medicine. | OBJECTIVES
Immunological dysregulation is now recognised as a major pathogenic event in sepsis. Stimulation of immune response and immuno-modulation are emerging approaches for the treatment of this disease. Defining the underlying immunological alterations in sepsis is important for the design of future therapies with immuno-modulatory drugs.
METHODS
Clinical studies evaluating the immunological response in adult patients with Sepsis and published in PubMed were reviewed to identify features of immunological dysfunction. For this study we used key words related with innate and adaptive immunity.
RESULTS
Ten major features of immunological dysfunction (FID) were identified involving quantitative and qualitative alterations of [antigen presentation](FID1), [T and B lymphocytes] (FID2), [natural killer cells] (FID3), [relative increase in T regulatory cells] (FID4), [increased expression of PD-1 and PD-ligand1](FID5), [low levels of immunoglobulins](FID6), [low circulating counts of neutrophils and/or increased immature forms in non survivors](FID7), [hyper-cytokinemia] (FID8), [complement consumption] (FID9), [defective bacterial killing by neutrophil extracellular traps](FID10).
CONCLUSIONS
This review article identified ten major features associated with immunosuppression and immunological dysregulation in sepsis. Assessment of these features could help in utilizing precision medicine for the treatment of sepsis with immuno-modulatory drugs. |
Practical filtering for efficient ray-traced directional occlusion | Ambient occlusion and directional (spherical harmonic) occlusion have become a staple of production rendering because they capture many visually important qualities of global illumination while being reusable across multiple artistic lighting iterations. However, ray-traced solutions for hemispherical occlusion require many rays per shading point (typically 256-1024) due to the full hemispherical angular domain. Moreover, each ray can be expensive in scenes with moderate to high geometric complexity. However, many nearby rays sample similar areas, and the final occlusion result is often low frequency. We give a frequency analysis of shadow light fields using distant illumination with a general BRDF and normal mapping, allowing us to share ray information even among complex receivers. We also present a new rotationally-invariant filter that easily handles samples spread over a large angular domain. Our method can deliver 4x speed up for scenes that are computationally bound by ray tracing costs. |
The Multi Centre Canadian Acellular Dermal Matrix Trial (MCCAT): study protocol for a randomized controlled trial in implant-based breast reconstruction | BACKGROUND
The two-stage tissue expander/implant (TE/I) reconstruction is currently the gold standard method of implant-based immediate breast reconstruction in North America. Recently, however, there have been numerous case series describing the use of one-stage direct to implant reconstruction with the aid of acellular dermal matrix (ADM). In order to rigorously investigate the novel application of ADM in one-stage implant reconstruction, we are currently conducting a multicentre randomized controlled trial (RCT) designed to evaluate the impact on patient satisfaction and quality of life (QOL) compared to the two-stage TE/I technique.
METHODS/DESIGNS
The MCCAT study is a multicenter Canadian ADM trial designed as a two-arm parallel superiority trial that will compare ADM-facilitated one-stage implant reconstruction compared to two-stage TE/I reconstruction following skin-sparing mastectomy (SSM) or nipple-sparing mastectomy (NSM) at 2 weeks, 6 months, and 12 months. The source population will be members of the mastectomy cohort with stage T0 to TII disease, proficient in English, over the age of 18 years, and planning to undergo SSM or NSM with immediate implant breast reconstruction. Stratified randomization will maintain a balanced distribution of important prognostic factors (study site and unilateral versus bilateral procedures). The primary outcome is patient satisfaction and QOL as measured by the validated and procedure-specific BREAST-Q. Secondary outcomes include short- and long-term complications, long-term aesthetic outcomes using five standardized photographs graded by three independent blinded observers, and a cost effectiveness analysis.
DISCUSSION
There is tremendous interest in using ADM in implant breast reconstruction, particularly in the setting of one-stage direct to implant reconstruction where it was previously not possible without the intermediary use of a temporary tissue expander (TE). This unique advantage has led many patients and surgeons alike to believe that one-stage ADM-assisted implant reconstruction should be the procedure of choice and should be offered to patients as the first-line treatment. We argue that it is crucial that this technique be scientifically evaluated in terms of patient selection, surgical technique, complications, aesthetic outcomes, cost-effectiveness, and most importantly patient-reported outcomes before it is promoted as the new gold standard in implant-based breast reconstruction.
TRIAL REGISTRATION
ClinicalTrials.gov: NCT00956384. |
CRISPR/Cas9-mediated correction of human genetic disease | The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) protein 9 system (CRISPR/Cas9) provides a powerful tool for targeted genetic editing. Directed by programmable sequence-specific RNAs, this system introduces cleavage and double-stranded breaks at target sites precisely. Compared to previously developed targeted nucleases, the CRISPR/Cas9 system demonstrates several promising advantages, including simplicity, high specificity, and efficiency. Several broad genome-editing studies with the CRISPR/Cas9 system in different species in vivo and ex vivo have indicated its strong potential, raising hopes for therapeutic genome editing in clinical settings. Taking advantage of non-homologous end-joining (NHEJ) and homology directed repair (HDR)-mediated DNA repair, several studies have recently reported the use of CRISPR/Cas9 to successfully correct disease-causing alleles ranging from single base mutations to large insertions. In this review, we summarize and discuss recent preclinical studies involving the CRISPR/Cas9-mediated correction of human genetic diseases. |
Effect of postural insoles on static and functional balance in children
with cerebral palsy: A randomized controlled study | BACKGROUND
Improved gait efficiency is one of the goals of therapy for children with cerebral palsy (CP). Postural insoles can allow more efficient gait by improving biomechanical alignment.
OBJECTIVE
The aim of the present study was to determine the effect of the combination of postural insoles and ankle-foot orthoses on static and functional balance in children with CP.
METHOD
A randomized, controlled, double-blind, clinical trial. After meeting legal requirements and the eligibility criteria, 20 children between four and 12 years of age were randomly allocated either to the control group (CG) (n=10) or the experimental group (EG) (n=10). The CG used placebo insoles and the EG used postural insoles. The Berg Balance Scale, Timed Up-and-Go Test, Six-Minute Walk Test, and Gross Motor Function Measure-88 were used to assess balance as well as the determination of oscillations from the center of pressure in the anteroposterior and mediolateral directions with eyes open and closed. Three evaluations were carried out: 1) immediately following placement of the insoles; 2) after three months of insole use; and 3) one month after suspending insole use.
RESULTS
The EG achieved significantly better results in comparison to the CG on the Timed Up-and-Go Test as well as body sway in the anteroposterior and mediolateral directions.
CONCLUSION
Postural insoles led to an improvement in static balance among children with cerebral palsy, as demonstrated by the reduction in body sway in the anteroposterior and mediolateral directions. Postural insole use also led to a better performance on the Timed Up-and-Go Test. |
Towards Knowledge-Driven Annotation | While the Web of data is attracting increasing interest and rapidly growing in size, the major support of information on the surface Web are still multimedia documents. Semantic annotation of texts is one of the main processes that are intended to facilitate meaning-based information exchange between computational agents. However, such annotation faces several challenges such as the heterogeneity of natural language expressions, the heterogeneity of documents structure and context dependencies. While a broad range of annotation approaches rely mainly or partly on the target textual context to disambiguate the extracted entities, in this paper we present an approach that relies mainly on formalized-knowledge expressed in RDF datasets to categorize and disambiguate noun phrases. In the proposed method, we represent the reference knowledge bases as co-occurrence matrices and the disambiguation problem as a 0-1 Integer Linear Programming (ILP) problem. The proposed approach is unsupervised and can be ported to any RDF knowledge base. The system implementing this approach, called KODA, shows very promising results w.r.t. state-of-the-art annotation tools in cross-domain experimentations. |
Deep Learning for Infrared Thermal Image Based Machine Health Monitoring | The condition of a machine can automatically be identified by creating and classifying features that summarize characteristics of measured signals. Currently, experts, in their respective fields, devise these features based on their knowledge. Hence, the performance and usefulness depends on the expert's knowledge of the underlying physics or statistics. Furthermore, if new and additional conditions should be detectable, experts have to implement new feature extraction methods. To mitigate the drawbacks of feature engineering, a method from the subfield of feature learning, i.e., deep learning (DL), more specifically convolutional neural networks (NNs), is researched in this paper. The objective of this paper is to investigate if and how DL can be applied to infrared thermal (IRT) video to automatically determine the condition of the machine. By applying this method on IRT data in two use cases, i.e., machine-fault detection and oil-level prediction, we show that the proposed system is able to detect many conditions in rotating machinery very accurately (i.e., 95 and 91.67% accuracy for the respective use cases), without requiring any detailed knowledge about the underlying physics, and thus having the potential to significantly simplify condition monitoring using complex sensor data. Furthermore, we show that by using the trained NNs, important regions in the IRT images can be identified related to specific conditions, which can potentially lead to new physical insights. |
Evaluation of a resilience intervention to enhance coping strategies and protective factors and decrease symptomatology. | OBJECTIVE
In this pilot study, the authors examined the effectiveness of a 4-week resilience intervention to enhance resilience, coping strategies, and protective factors, as well as decrease symptomatology during a period of increased academic stress.
PARTICIPANTS AND METHODS
College students were randomly assigned to experimental (n = 30) and wait-list control (n = 27) groups. The experimental group received a psychoeducational intervention in 4 two-hour weekly sessions. Measures of resilience, coping strategies, protective factors, and symptomatology were administered pre- and postintervention to both groups.
RESULTS
Analyses indicated that the experimental group had significantly higher resilience scores, more effective coping strategies (i.e., higher problem solving, lower avoidant), higher scores on protective factors (i.e., positive affect, self-esteem, self-leadership), and lower scores on symptomatology (i.e., depressive symptoms, negative affect, perceived stress) postintervention than did the wait-list control group.
CONCLUSIONS
These findings indicate that this resilience program may be useful as a stress-management and stress-prevention intervention for college students. |
What does your selfie say about you? | Selfies refer to self-portraits taken by oneself using a digital camera or a smartphone. They become increasingly popular in social media. However, little is known about how selfies reflect their owners’ personality traits and how people judge others’ personality from selfies. In this study, we examined the association between selfies and personality by measuring participants’ Big Five personality and coding their selfies posted on a social networking site. We found specific cues in selfies related to agreeableness, conscientiousness, neuroticism, and openness. We also examined zero-acquaintance personality judgment and found that observers had moderate to strong agreement in their ratings of Big Five personality based on selfies. However, they could only accurately predict selfie owners’ degree of openness. This study is the first to reveal personality-related cues in selfies and provide a picture-coding scheme that can be used to analyze selfies. We discussed the difference between personality expression in selfies and other types of photos, and its possible relationship with impression management of social media users. 2015 Elsevier Ltd. All rights reserved. |
Effect of dependent errors in the assessment of diagnostic or screening test accuracy when the reference standard is imperfect. | When no gold standard is available to evaluate a diagnostic or screening test, as is often the case, an imperfect reference standard test must be used instead. Furthermore, the errors of the test and its reference standard may not be independent. Some authors have opined that positively dependent errors will lead to overestimation of test performance. Although positive dependence does increase agreement between the test and the reference standard, it is not clear if test accuracy will necessarily be overestimated in this situation, and the case of negatively associated test errors is even less clear. To examine this issue in more detail, we derive the apparent sensitivity, specificity, and overall accuracy of a test relative to an imperfect reference standard and the bias in these parameters. We demonstrate that either positive or negative bias can occur if the reference standard is imperfect. The type and magnitude of bias depend on several components: the disease prevalence, the true test sensitivity and specificity, the covariance between the false-negative test errors among the true disease cases, and the covariance between the false-positive test errors among the true noncases. If, for example, sensitivity and specificity are 0.8 for both the test and reference standard and the errors have a moderate positive dependence, test sensitivity is then underestimated at low prevalence but overestimated at high prevalence, while the opposite occurs for specificity. We illustrate these ideas through general numerical calculations and an empirical example of screening for breast cancer with magnetic resonance imaging and mammography. |
Long-term athletic development- part 1: a pathway for all youth. | The concept of developing talent and athleticism in youth is the goal of many coaches and sports systems. Consequently, an increasing number of sporting organizations have adopted long-term athletic development models in an attempt to provide a structured approach to the training of youth. It is clear that maximizing sporting talent is an important goal of long-term athletic development models. However, ensuring that youth of all ages and abilities are provided with a strategic plan for the development of their health and physical fitness is also important to maximize physical activity participation rates, reduce the risk of sport- and activity-related injury, and to ensure long-term health and well-being. Critical reviews of independent models of long-term athletic development are already present within the literature; however, to the best of our knowledge, a comprehensive examination and review of the most prominent models does not exist. Additionally, considerations of modern day issues that may impact on the success of any long-term athletic development model are lacking, as are proposed solutions to address such issues. Therefore, within this 2-part commentary, Part 1 provides a critical review of existing models of practice for long-term athletic development and introduces a composite youth development model that includes the integration of talent, psychosocial and physical development across maturation. Part 2 identifies limiting factors that may restrict the success of such models and offers potential solutions. |
A Time-sensitive Networking (TSN) Simulation Model Based on OMNET++ | Industrial and automation control systems require that data be delivered in a highly predictable manner in terms of time. Time-sensitive Networking (TSN), an extension of the Ethernet, is a set of protocols developed and maintained by the IEEE 802.1 Task Group; the protocols deal with time synchronization, traffic scheduling, and network configuration, etc. TSN yields promising solutions for real-time and deterministic networks. Here, we develop a TSN simulation model based on OMNET++; we model a TSN-enabled switch that schedules traffic using gate control lists (GCLs). Simulation verified that the model guaranteed deterministic end-to-end latency. |
Security Issues in the Internet of Things ( IoT ) : A Comprehensive Study | Wireless communication networks are highly prone to security threats. The major applications of wireless communication networks are in military, business, healthcare, retail, and transportations. These systems use wired, cellular, or adhoc networks. Wireless sensor networks, actuator networks, and vehicular networks have received a great attention in society and industry. In recent years, the Internet of Things (IoT) has received considerable research attention. The IoT is considered as future of the internet. In future, IoT will play a vital role and will change our living styles, standards, as well as business models. The usage of IoT in different applications is expected to rise rapidly in the coming years. The IoT allows billions of devices, peoples, and services to connect with others and exchange information. Due to the increased usage of IoT devices, the IoT networks are prone to various security attacks. The deployment of efficient security and privacy protocols in IoT networks is extremely needed to ensure confidentiality, authentication, access control, and integrity, among others. In this paper, an extensive comprehensive study on security and privacy issues in IoT networks is provided. Keywords—Internet of Things (IoT); security issues in IoT; security; privacy |
Lonically Conductive Polymers | In 1834 Michael Faraday reported that when lead fluoride (PbF2) was heated red hot, it conducted an electric current and so did the metallic vessel it was heated in. This was a startling observation, since most simple salts are electronic insulators. The high conductivity that Faraday observed is now known to be due to ionic conductivity, and not electronic conductivity. At elevated temperatures (500-700°C) the fluoride anion possesses high ionic conductivity and can easily be transported through the lead fluoride lattice. This was the first report of a high ionic conductivity solid electrolyte. Until the mid-1970s all research on solid ionic conductors had centered on inorganic compounds (primarily ceramics, such as the various phases of alumina, stabilized zirconia, etc.). With the discovery of new ionic conducting polymers by Wright,21,22 Armand,2,3 and others, and the numerous advantages that ionic conducting polymers have over ceramics in device fabrication and operating temperature range, it is not surprising that fast ionic conduction in polymers is currently an area of great interest. This interest is a result of a desire both to understand the ionic conduction mechanism in polymers and to use these polymers in applications such as high-energy-density batteries, electrochronic displays, specific-ion sensors, and other electrochemical devices that capitalize on the unique electronic, ionic, and mechanical properties of ionic conducting polymers. |
Sentiment Analysis by Capsules | In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis. For a given problem, one capsule is built for each sentiment category e.g., ‘positive’ and ‘negative’. Each capsule has an attribute, a state, and three modules: representation module, probability module, and reconstruction module. The attribute of a capsule is the assigned sentiment category. Given an instance encoded in hidden vectors by a typical RNN, the representation module builds capsule representation by the attention mechanism. Based on capsule representation, the probability module computes the capsule’s state probability. A capsule’s state is active if its state probability is the largest among all capsules for the given instance, and inactive otherwise. On two benchmark datasets (i.e., Movie Review and Stanford Sentiment Treebank) and one proprietary dataset (i.e., Hospital Feedback), we show that RNN-Capsule achieves state-of-the-art performance on sentiment classification. More importantly, without using any linguistic knowledge, RNN-Capsule is capable of outputting words with sentiment tendencies reflecting capsules’ attributes. The words well reflect the domain specificity of the dataset. ACM Reference Format: Yequan Wang1 Aixin Sun2 Jialong Han3 Ying Liu4 Xiaoyan Zhu1. 2018. Sentiment Analysis by Capsules. InWWW 2018: The 2018 Web Conference, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3178876.3186015 |
Guided inpainting and filtering for Kinect depth maps | Depth maps captured by Kinect-like cameras are lack of depth data in some areas and suffer from heavy noise. These defects have negative impacts on practical applications. In order to enhance the depth maps, this paper proposes a new inpainting algorithm that extends the original fast marching method (FMM) to reconstruct unknown regions. The extended FMM incorporates an aligned color image as the guidance for inpainting. An edge-preserving guided filter is further applied for noise reduction. To validate our algorithm and compare it with other existing methods, we perform experiments on both the Kinect data and the Middlebury dataset which, respectively, provide qualitative and quantitative results. The results show that our method is efficient and superior to others. |
The impact of personality traits on users' information-seeking behavior | Although personality traits may influence information-seeking behavior, little is known about this topic. This study explored the impact of the Big Five personality traits on human online information seeking. For this purpose, it examined changes in eye-movement behavior in a sample of 75 participants (36 male and 39 female; age: 22–39 years; experience conducting online searches: 5–12 years) across three types of information-seeking tasks – factual, exploratory, and interpretive. The International Personality Item Pool Representation of the NEO PI-R TM (IPIP-NEO) was used to assess the participants’ personality profile. Hierarchical cluster analysis was used to categorize participants based on their personality traits. A three cluster solution was found (cluster one consists of participants who scored high in conscientiousness; cluster two consists of participants who scored high in agreeableness; and cluster three consists of participants who scored high in extraversion). Results revealed that individuals high in conscientiousness performed fastest in most information-seeking tasks, followed by those high in agreeableness and extraversion. This study has important practical implications for intelligent human – computer interfaces, personalization, and related applications. © 2016 Elsevier Ltd. All rights reserved. |
Science and technology of fast ion conductors | Fundamentals.- Computer Modeling of Superionics.- Materials Systems.- Crystalline Anionic Fast Ion Conduction.- Amorphous Fast Ion Conductors.- Multiphase and Polycrystalline Fast Ion Conductors.- Characterization Techniques.- Spectroscopic Investigations of Glasses.- X-ray and Neutron Scattering Studies of Superionics.- Electrochemical Measurement Techniques.- Chemical and Electronic Stability.- Phase Stability of Crystalline Fast Ion Conductors.- Degradation of Ceramics in Alkali-Metal Environments.- Mixed Ionic-Electronic Conduction in Fast Ion Conductors and Certain Semiconductors.- Applications.- Novel Solid State Galvanic Cell Gas Sensors.- Remarks on Application of Fast Ion Conductors.- Some Future Trends in Solid State Ionics.- Contributed Papers.- Fast Ion Conduction in the Gd2 (ZrxTi1-x)2 O7 Pyrochlore System.- Plasma Sprayed Zirconia Electrolytes.- Free Lithium Ion Conduction in Lithium Borate Glasses Doped with Li2SO4.- Effects of Halide Substitutions in Potassium Haloborates.- Evidence of Boron-Oxygen Network Modifications in Alkali Borate Glasses.- Characterization of Alkali-Oxide Electrolyte Glass in Thin Films.- Glass Solid Electrolyte Thin Films.- Electronic Structure, Bonding, and Lithium Migration Effects Involving the Surface of the Mixed Conductor ss-LiAl.- Defects with Variable Charges: Influence on Chemical Diffusion and on the Evaluation of Electrochemical Experiments.- Attendees and Lecturers. |
Peripartum hysterectomy in Taiwan. | OBJECTIVE
To investigate the incidence and associated risk factors for peripartum hysterectomy in singleton pregnancies.
METHODS
A retrospective cohort study of all women with singleton pregnancies admitted for delivery in 2002 taken from the National Healthcare Insurance database. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for maternal and hospital characteristics using logistic regression.
RESULTS
There were 287 peripartum hysterectomies in 214 237 singleton pregnancies (0.13%). Cesarean delivery, vaginal birth after cesarean (VBAC), and repeat cesarean delivery had higher hysterectomy rates than vaginal delivery, with adjusted ORs of 12.13 (95% CI 8.30-17.74), 5.12 (95% CI 1.19-21.92), and 3.84 (95% CI 2.52-5.86), respectively. Pregnancies complicated with placenta previa, gestational diabetes mellitus (GDM), and premature labor were associated with significantly increased risks for peripartum hysterectomy (P<0.05).
CONCLUSION
Risk factors for peripartum hysterectomy included cesarean delivery, VBAC, repeat cesarean, placenta previa, GDM, and premature labor. VBAC and repeat cesarean had a similar risk. |
Progressive disease after ABMT for Hodgkin’s disease | A major limitation of ABMT for relapsed/refractory Hodgkin’s disease is disease recurrence post-transplantation. We retrospectively reviewed 68 patients undergoing ABMT from January 1987 to June l993. All received a uniform preparatory regimen (CBV). The median patient age was 30; 75% received prior radiation therapy and all patients received prior chemotherapy. Thirty-one percent presented at the time of transplantation with tumor masses larger than 10 cm. Sixty-two percent received autologous marrow alone and 38% PBPC with or without autologous bone marrow. Overall and progression-free survival are 43 and 36% at 5 years. Median follow-up for survivors is 59 months. Multivariate analysis revealed that tumor bulk was the most powerful poor prognostic factor for both survival and progression-free survival. Those transplanted with non-bulky tumors had an overall survival and progression-free survival of 52 and 44%, respectively, compared to those transplanted with bulky tumors who had an overall survival and progression-free survival of 22 and 16% (P = 0.03 and P = 0.04, respectively). Twenty-seven patients have relapsed. Four relapsed more than 2 years after ABMT. Four of the 27 patients who have relapsed remain alive, two without evidence of disease. The time after transplant to relapse was prognostically important, with no patients who relapsed within 6 months of ABMT still being alive, compared with 25% of patients who relapsed 7 or more months after ABMT who are still alive. We conclude that salvage therapy for relapse after ABMT is appropriate, as some patients may achieve prolonged survival. The time from transplant to relapse is an important survival predictor. |
Analyzing Hypersensitive AI: Instability in Corporate-Scale Machine Learning | Predictive geometric models deliver excellent results for many Machine Learning use cases. Despite their undoubted performance, neural predictive algorithms can show unexpected degrees of instability and variance, particularly when applied to large datasets. We present an approach to measure changes in geometric models with respect to both output consistency and topological stability. Considering the example of a recommender system using word2vec, we analyze the influence of single data points, approximation methods and parameter settings. Our findings can help to stabilize models where needed and to detect differences in informational value of data points on a large scale. |
Student nurses' experiences of community-based practice placement learning: a qualitative exploration. | United Kingdom (UK) health policy has adopted an increasing community and primary care focus over recent years (Department of Health, 1997; Department of Health, 1999. Making a Difference: Strengthening the Nursing, Midwifery and Health Visitor Contribution to Health and Health Care. Department of Health, London; Department of Health, 2004. The NHS Knowledge and Skills Framework (NHS KSF). Department of Health, London). Nursing practice, education and workforce planning are called upon to adapt accordingly (Department of Health, 2004. The NHS Knowledge and Skills Framework (NHS KSF). Department of Health, London; Kenyon, V., Smith, E., Hefty, L., Bell, M., Martaus, T., 1990. Clinical competencies for community health nursing. Public Health Nursing 7(1), 33-39; United Kingdom Central Council for Nursing, Midwifery and Health Visiting, 1986. Project 2000: A New Preparation for Practice. UKCC, London). Such changes have major implications for pre-registration nursing education, including its practice placement element. From an educational perspective, the need for increased community nursing capacity must be balanced with adequate support for student nurses' learning needs during community-based placements. This qualitative study explored six second year student nurses' experiences of 12 week community-based practice placements and the extent to which these placements were seen to meet their perceived learning needs. The data came from contemporaneous reflective diaries, completed by participants to reflect their 'lived experience' during their practice placements (Landeen, J., Byrne, Brown, B., 1995. Exploring the lived experiences of psychiatric nursing students through self-reflective journals. Journal of Advanced Nursing 21(5), 878-885; Kok, J., Chabeli, M.M., 2002. Reflective journal writing: how it promotes reflective thinking in clinical nursing education: a students' perspective. Curationis 25(3), 35-42; Löfmark, A., Wikblad, K., 2001. Facilitating and obstructing factors for development of learning in clinical practice: a student perspective. Issues and innovations in Nursing Education. Journal of Advanced Nursing 34(1), 43-50; Priest, H., 2004. Phenomenology. Nurse Researcher 11(4), 4-6; Stockhausen, L., 2005. Learning to become a nurse: student nurses' reflections on their clinical experiences. Australian Journal of Nursing 22(3), 8-14). The data were analysed using content analysis techniques, exploring their contextual meaning through the development of emergent themes (Neuendorf, K.A., 2002. The Content Analysis Guidebook. Sage Publications, London). The identified themes related to elements of students' basic skill acquisition, the development of their working relationships with mentors, patients and others, the learning opportunities offered by community practice placements and the effects that such placements had on their confidence to practice. These themes are discussed with regard to the published literature, to arrive at conclusions and implications for future nursing education, practice and research. |
Cataract surgical coverage and outcome in the Tibet Autonomous Region of China. | BACKGROUND
A recently published, population based survey of the Tibet Autonomous Region (TAR) of China reported on low vision, blindness, and blinding conditions. This paper presents detailed findings from that survey regarding cataract, including prevalence, cataract surgical coverage, surgical outcome, and barriers to use of services.
METHODS
The Tibet Eye Care Assessment (TECA) was a prevalence survey of people from randomly selected households from three of the seven provinces of the TAR (Lhoka, Nakchu, and Lingzhr), representing its three main environmental regions. The survey, conducted in 1999 and 2000, assessed visual acuity, cause of vision loss, and eye care services.
RESULTS
Among the 15,900 people enumerated, 12,644 were examined (79.6%). Cataract prevalence was 5.2% and 13.8%, for the total population, and those over age 50, respectively. Cataract surgical coverage (vision <6/60) for people age 50 and older (85-90% of cataract blind) was 56% overall, 70% for men and 47% for women. The most common barriers to use of cataract surgical services were distance and cost. In the 216 eyes with cataract surgery, 60% were aphakic and 40% were pseudophakic. Pseudophakic surgery left 19% of eyes blind (<6/60) and an additional 20% of eyes with poor vision (6/24-6/60). Aphakic surgery left 24% of eyes blind and an additional 21% of eyes with poor vision. Even though more women remained blind than men, 28% versus 18% respectively, the different was not statistically significant (p = 0.25).
CONCLUSIONS
Cataract surgical coverage was remarkably high despite the difficulty of providing services to such an isolated and sparse population. Cataract surgical outcome was poor for both aphakic and pseudophakic surgery. Two main priorities are improving cataract surgical quality and cataract surgical coverage, particularly for women. |
38 GHz and 60 GHz angle-dependent propagation for cellular & peer-to-peer wireless communications | As the cost of massively broadband® semiconductors continue to be driven down at millimeter wave (mm-wave) frequencies, there is great potential to use LMDS spectrum (in the 28-38 GHz bands) and the 60 GHz band for cellular/mobile and peer-to-peer wireless networks. This work presents urban cellular and peer-to-peer RF wideband channel measurements using a broadband sliding correlator channel sounder and steerable antennas at carrier frequencies of 38 GHz and 60 GHz, and presents measurements showing the propagation time delay spread and path loss as a function of separation distance and antenna pointing angles for many types of real-world environments. The data presented here show that at 38 GHz, unobstructed Line of Site (LOS) channels obey free space propagation path loss while non-LOS (NLOS) channels have large multipath delay spreads and can exploit many different pointing angles to provide propagation links. At 60 GHz, there is notably more path loss, smaller delay spreads, and fewer unique antenna angles for creating a link. For both 38 GHz and 60 GHz, we demonstrate empirical relationships between the RMS delay spread and antenna pointing angles, and observe that excess path loss (above free space) has an inverse relationship with transmitter-to-receiver separation distance. |
Six degree-of-freedom haptic rendering using voxel sampling | A simple, fast, and approximate voxel-based approach to 6-DOF haptic rendering is presented. It can reliably sustain a 1000 Hz haptic refresh rate without resorting to asynchronous physics and haptic rendering loops. It enables the manipulation of a modestly complex rigid object within an arbitrarily complex environment of static rigid objects. It renders a short-range force field surrounding the static objects, which repels the manipulated object and strives to maintain a voxel-scale minimum separation distance that is known to preclude exact surface interpenetration. Force discontinuities arising from the use of a simple penalty force model are mitigated by a dynamic simulation based on virtual coupling. A generalization of octree improves voxel memory efficiency. In a preliminary implementation, a commercially available 6-DOF haptic prototype device is driven at a constant 1000 Hz haptic refresh rate from one dedicated haptic processor, with a separate processor for graphics. This system yields stable and convincing force feedback for a wide range of user controlled motion inside a large, complex virtual environment, with very few surface interpenetration events. This level of performance appears suited to applications such as certain maintenance and assembly task simulations that can tolerate voxel-scale minimum separation distances. |
Color image segmentation: advances and prospects | Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in di!erent color spaces. Therefore, we "rst discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc.; then review some major color representation methods and their advantages/disadvantages; "nally summarize the color image segmentation techniques using di!erent color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics-based method are investigated as well. 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. |
Discrete quadratic curvature energies | Efficient computation of curvature-based energies is important for practical implementations of geometric modeling and physical simulation applications. Building on a simple geometric observation, we provide a version of a curvature-based energy expressed in terms of the Laplace operator acting on the embedding of the surface. The corresponding energy--being quadratic in positions--gives rise to a constant Hessian in the context of isometric deformations. The resulting isometric bending model is shown to significantly speed up common cloth solvers, and when applied to geometric modeling situations built onWillmore flow to provide runtimes which are close to interactive rates. |
Traffic sign classification using hough transform and SVM | In this paper, classification of traffic signs in Turkey with the help of their some features such as color and shape is explained. In the algorithm that is generated in MATLAB, firstly trafic signs are distinguished from the other objects of the image and then filtered by their colors. After filtering, edge detection is processed and then Hough Transform and SVM are used for shape classification. |
Comparison of the intrusion effects on the maxillary incisors between implant anchorage and J-hook headgear. | INTRODUCTION
Recently, miniscrews have been used to provide anchorage during orthodontic treatment, especially for incisor intrusion. Miniscrews during incisor intrusion are commonly used in implant orthodontics. Traditionally, effective incisor intrusion has been accomplished with J-hook headgear. In this study, we compared the effect of incisor intrusion, force vector, and amount of root resorption between implant orthodontics and J-hook headgear.
METHODS
Lateral cephalometric radiographs from 8 patients in the implant group and 10 patients in the J-hook headgear group were analyzed for incisor retraction. The estimated force vector was analyzed in the horizontal and vertical directions in both groups. Root resorption was also measured on periapical radiographs.
RESULTS
In the implant group, significant reductions in overjet, overbite, maxillary incisor to palatal plane, and maxillary incisor to upper lip were observed after intrusion of the incisors. In the J-hook headgear group, significant reductions in overjet, overbite, maxillary incisor to upper lip, and maxillary incisor to SN plane were observed after intrusion of the incisors. There were significantly greater reductions in overbite, maxillary incisor to palatal plane, and maxillary incisor to upper lip in the implant group than in the J-hook headgear group. Estimated force analysis resulted in significantly more force in the vertical direction and less in the horizontal direction in the implant group. Furthermore, significantly less root resorption was observed in the implant group compared with the J-hook headgear group.
CONCLUSIONS
The maxillary incisors were effectively intruded by using miniscrews as orthodontic anchorage without patient cooperation. The amount of root resorption was not affected by activating the ligature wire from the miniscrew during incisor intrusion. |
Robust Multi-pose Facial Expression Recognition | Previous research on facial expression recognition mainly focuses on near frontal face images, while in realistic interactive scenarios, the interested subjects may appear in arbitrary non-frontal poses. In this paper, we propose a framework to recognize six prototypical facial expressions, namely, anger, disgust, fear, joy, sadness and surprise, in an arbitrary head pose. We build a multi-pose training set by rendering 3D face scans from the BU-4DFE dynamic facial expression database [17] at 49 different viewpoints. We extract Local Binary Pattern (LBP) descriptors and further utilize multiple instance learning to mitigate the influence of inaccurate alignment in this challenging task. Experimental results demonstrate the power and validate the effectiveness of the proposed multi-pose facial expression recognition framework. |
Fast and Accurate Prediction of Sentence Specificity | Recent studies have demonstrated that specificity is an important characterization of texts potentially beneficial for a range of applications such as multi-document news summarization and analysis of science journalism. The feasibility of automatically predicting sentence specificity from a rich set of features has also been confirmed in prior work. In this paper we present a practical system for predicting sentence specificity which exploits only features that require minimum processing and is trained in a semi-supervised manner. Our system outperforms the state-of-the-art method for predicting sentence specificity and does not require part of speech tagging or syntactic parsing as the prior methods did. With the tool that we developed — SPECITELLER — we study the role of specificity in sentence simplification. We show that specificity is a useful indicator for finding sentences that need to be simplified and a useful objective for simplification, descriptive of the differences between original and simplified sentences. |
Semantic embedding space for zero-shot action recognition | The number of categories for action recognition is growing rapidly. It is thus becoming increasingly hard to collect sufficient training data to learn conventional models for each category. This issue may be ameliorated by the increasingly popular “zero-shot learning” (ZSL) paradigm. In this framework a mapping is constructed between visual features and a human interpretable semantic description of each category, allowing categories to be recognised in the absence of any training data. Existing ZSL studies focus primarily on image data, and attribute-based semantic representations. In this paper, we address zero-shot recognition in contemporary video action recognition tasks, using semantic word vector space as the common space to embed videos and category labels. This is more challenging because the mapping between the semantic space and space-time features of videos containing complex actions is more complex and harder to learn. We demonstrate that a simple self-training and data augmentation strategy can significantly improve the efficacy of this mapping. Experiments on human action datasets including HMDB51 and UCF101 demonstrate that our approach achieves the state-of-the-art zero-shot action recognition performance. |
Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation | Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. However, those approaches incur high costs in the making of dictionaries and/or features, which hinder generalization of tasks. Examples of these approaches include an approach that uses a polarity dictionary that cannot handle unknown or newly invented words and another approach that uses a complex model with 13 types of feature templates. We propose a novel high performance sentiment classification method with stacked denoising auto-encoders that uses distributed word representation instead of building dictionaries or utilizing engineering features. The results of experiments conducted indicate that our model achieves state-of-the-art performance in Japanese sentiment classification tasks. |
Building health promoting work settings: identifying the relationship between work characteristics and occupational stress in Australia. | Occupational stress is a serious threat to the health of individual workers, their families and the community at large. The settings approach to health promotion offers valuable opportunities for developing comprehensive strategies to prevent and reduce job stress. However, there is evidence that many workplace health promotion programs adopt traditional, lifestyle-oriented strategies when dealing with occupational stress, and ignore the impact that the setting itself has on the health of employees. The aim of the present study was to address two of the barriers to adopting the settings approach; namely the lack of information on how psycho-social work characteristics can influence health, and not having the confidence or knowledge to identify or address organizational-level issues. A comprehensive occupational stress audit involving qualitative and quantitative research methods was undertaken in a small- to medium-sized public sector organization in Australia. The results revealed that the work characteristics 'social support' and 'job control' accounted for large proportions of explained variance in job satisfaction and psychological health. In addition to these generic variables, several job-specific stressors were found to be predictive of the strain experienced by employees. When coupled with the results of other studies, these findings suggest that work characteristics (particularly control and support) offer valuable avenues for creating work settings that can protect and enhance employee health. The implications of the methods used to develop and complete the stress audit are also discussed. |
The Sound of Pixels | We introduce PixelPlayer, a system that, by leveraging large amounts of unlabeled videos, learns to locate image regions which produce sounds and separate the input sounds into a set of components that represents the sound from each pixel. Our approach capitalizes on the natural synchronization of the visual and audio modalities to learn models that jointly parse sounds and images, without requiring additional manual supervision. Experimental results on a newly collected MUSIC dataset show that our proposed Mix-and-Separate framework outperforms several baselines on source separation. Qualitative results suggest our model learns to ground sounds in vision, enabling applications such as independently adjusting the volume of sound sources. |
On Unifying Deep Generative Models | Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques. |
The preparatory set: a novel approach to understanding stress, trauma, and the bodymind therapies | Basic to all motile life is a differential approach/avoid response to perceived features of environment. The stages of response are initial reflexive noticing and orienting to the stimulus, preparation, and execution of response. Preparation involves a coordination of many aspects of the organism: muscle tone, posture, breathing, autonomic functions, motivational/emotional state, attentional orientation, and expectations. The organism organizes itself in relation to the challenge. We propose to call this the "preparatory set" (PS). We suggest that the concept of the PS can offer a more nuanced and flexible perspective on the stress response than do current theories. We also hypothesize that the mechanisms of body-mind therapeutic and educational systems (BTES) can be understood through the PS framework. We suggest that the BTES, including meditative movement, meditation, somatic education, and the body-oriented psychotherapies, are approaches that use interventions on the PS to remedy stress and trauma. We discuss how the PS can be adaptive or maladaptive, how BTES interventions may restore adaptive PS, and how these concepts offer a broader and more flexible view of the phenomena of stress and trauma. We offer supportive evidence for our hypotheses, and suggest directions for future research. We believe that the PS framework will point to ways of improving the management of stress and trauma, and that it will suggest directions of research into the mechanisms of action of BTES. |
Randomized, double-blind, placebo-controlled, proof-of-concept study of the cortical spreading depression inhibiting agent tonabersat in migraine prophylaxis. | Tonabersat is a novel putative migraine prophylactic agent with an unique stereospecific binding site in the brain. Tonabersat has been shown, in animal models, to inhibit experimentally induced cortical spreading depression, the likely underlying mechanism for migraine aura, and cerebrovascular responses to trigeminal nerve stimulation. The aim was to study the potential for tonabersat as a migraine preventive. A randomized, double-blind, placebo-controlled, multicentre, parallel group study recruited patients with migraine with and without aura experiencing between two and six migraine attacks per month. After a 1-month baseline they received tonabersat 20 mg daily for 2 weeks and 40 mg daily for a further 10 weeks. The primary end-point was the change in mean number of migraine headache days between the third month and the baseline period in the intention-to-treat population comparing the placebo (n = 65) and tonabersat (n = 58) groups. At the primary end-point there was a 1.0-day (95% confidence interval -0.33, 2.39; P = 0.14) difference in reduction in migraine days between tonabersat and placebo. There were 10 secondary efficacy end-points, of which two were statistically significant. In month 3 of treatment, the responder rate, defined as a 50% reduction in migraine attacks, was 62% for tonabersat and 45% for placebo (P < 0.05), and the rescue medication use was reduced in the tonabersat group compared with placebo by 1.8 days (P = 0.02). Placebo responses were particularly high for all end-points. At least one treatment-emergent adverse event was reported in the tonabersat group in 61% of patients compared with 51% in the placebo group; none was worrisome. Placebo responses were unexpectedly high in this trial, complicating straightforward interpretation of the study results. The good tolerability and promising efficacy results support further exploration of higher doses of tonabersat in larger controlled trials. |
Convex Color Image Segmentation with Optimal Transport Distances | This work is about the use of regularized optimal-transport distances for convex, histogram-based image segmentation. In the considered framework, fixed exemplar histograms define a prior on the statistical features of the two regions in competition. In this paper, we investigate the use of various transport-based cost functions as discrepancy measures and rely on a primaldual algorithm to solve the obtained convex optimization problem. |
Bio-medical ( EMG ) Signal Analysis and Feature Extraction Using Wavelet Transform | In this paper, the multi-channel electromyogram acquisition system is being developed using programmable system on chip (PSOC) microcontroller to obtain the surface of EMG signal. The two pairs of single-channel surface electrodes are utilized to measure the EMG signal obtained from forearm muscles. Then different levels of Wavelet family are used to analyze the EMG signal. Later features in terms of root mean square, logarithm of root mean square, centroid of frequency, as well as standard deviation were used to extract the EMG signal. The proposed method of feature extraction for extracting EMG signal states that root means square feature extraction method gives better performance as compared to the other features. In the near future, this method can be used to control a mechanical arm as well as robotic arm in field of real-time processing. |
Radiotherapy in implant-based immediate breast reconstruction: risk factors, surgical outcomes, and patient-reported outcome measures in a large Swedish multicenter cohort | The purpose of this large cohort study was to analyze the effects of prior and postoperative radiotherapy (RT) on surgical outcomes and patient-reported outcome measures (PROMs) in implant-based immediate breast reconstruction (IBR). All breast cancer patients (n = 725, of whom 29 had bilateral IBR) operated with implant-based IBR at four Stockholm hospitals from 2007 to 2011 were included. The median follow-up was 43 months. Three groups were compared: no RT (n = 386), prior RT (n = 64), and postoperative RT (n = 304). Outcomes were IBR failure (implant loss with or without secondary autologous reconstruction), unplanned reoperations, and PROMs, as measured by the BreastQ® questionnaire. IBR failure occurred in 22/386 (6 %) of non-irradiated cases, 16/64 (25 %) after prior and 45/304 (15 %) after postoperative RT (p < 0.001). Failure risk was higher after prior than postoperative RT (HR 9.28 vs. 3.08). Further risk factors were high BMI, less surgeon reconstructive experience, and postoperative infection, while the use of permanent implants lowered the risk of IBR failure. The estimated 5 years IBR failure rate was 10.4 % for non-irradiated, 28.2 % for previously and 25.2 % for postoperatively irradiated patients (p < 0.001). At least one unplanned reoperation occurred in 169/384 of non-irradiated (44 %), 42/64 (66 %) of previously, and 180/303 (59 %) of postoperatively irradiated breasts (p < 0.001). Further contributing factors were the use of one-stage expander and permanent implants, less surgeon reconstructive experience, and smoking. RT significantly impaired scores on all scales of the BreastQ®. However, a clear majority of women in all groups would choose IBR again. Implant-based IBR remains a feasible option for women undergoing mastectomy as patient satisfaction levels are high. After prior RT, however, autologous alternatives should be considered. |
Fascial components of the myofascial pain syndrome. | Myofascial pain syndrome (MPS) is described as the muscle, sensory, motor, and autonomic nervous system symptoms caused by stimulation of myofascial trigger points (MTP). The participation of fascia in this syndrome has often been neglected. Several manual and physical approaches have been proposed to improve myofascial function after traumatic injuries, but the processes that induce pathological modifications of myofascial tissue after trauma remain unclear. Alterations in collagen fiber composition, in fibroblasts or in extracellular matrix composition have been postulated. We summarize here recent developments in the biology of fascia, and in particular, its associated hyaluronan (HA)-rich matrix that address the issue of MPS. |
Effects of Si phase refinement on the plasma electrolytic oxidation of eutectic Al-Si alloy | Abstract The plasma electrolytic oxidation (PEO) process of high-silicon aluminum alloys is hard to be started and the properties of the PEO coating is also not satisfactory, which is caused by the existence of coarse eutectic or primary silicon (Si) phase in the substrate. In this study, the Si phase in the eutectic Al-Si alloy was firstly controlled to a much smaller size by 0.1 wt % strontium (Sr) modification treatment. Subsequently, PEO was carried out on the Sr-modified Al-Si alloy. It was shown that as the eutectic Si phase in the substrate getting much smaller and more homogeneous, PEO was much easier to be started. The PEO coating was thicker and more uniform at the initial discharge stage compared with that on the un-modified eutectic Al-Si alloy. The influence of Si refinement was less pronounced after certain time of spark discharge oxidation. After 30 min treatment, the PEO coating was composed of γ-Al 2 O 3 , α-Al 2 O 3 , mullite and some amorphous phase. The PEO coating on Sr-modified Al-Si alloy showed a more compacted structure and better anticorrosion property compared with that on the un-modified eutectic Al-Si alloy. |
Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care. | BACKGROUND
Comparative effectiveness research, medical product safety evaluation, and quality measurement will require the ability to use electronic health data held by multiple organizations. There is no consensus about whether to create regional or national combined (eg, "all payer") databases for these purposes, or distributed data networks that leave most Protected Health Information and proprietary data in the possession of the original data holders.
OBJECTIVES
Demonstrate functions of a distributed research network that supports research needs and also address data holders concerns about participation. Key design functions included strong local control of data uses and a centralized web-based querying interface.
RESEARCH DESIGN
We implemented a pilot distributed research network and evaluated the design considerations, utility for research, and the acceptability to data holders of methods for menu-driven querying. We developed and tested a central, web-based interface with supporting network software. Specific functions assessed include query formation and distribution, query execution and review, and aggregation of results.
RESULTS
This pilot successfully evaluated temporal trends in medication use and diagnoses at 5 separate sites, demonstrating some of the possibilities of using a distributed research network. The pilot demonstrated the potential utility of the design, which addressed the major concerns of both users and data holders. No serious obstacles were identified that would prevent development of a fully functional, scalable network.
CONCLUSIONS
Distributed networks are capable of addressing nearly all anticipated uses of routinely collected electronic healthcare data. Distributed networks would obviate the need for centralized databases, thus avoiding numerous obstacles. |
Fully Textile, PEDOT:PSS Based Electrodes for Wearable ECG Monitoring Systems | Goal: To evaluate a novel kind of textile electrodes based on woven fabrics treated with PEDOT:PSS, through an easy fabrication process, testing these electrodes for biopotential recordings. Methods: Fabrication is based on raw fabric soaking in PEDOT:PSS using a second dopant, squeezing and annealing. The electrodes have been tested on human volunteers, in terms of both skin contact impedance and quality of the ECG signals recorded at rest and during physical activity (power spectral density, baseline wandering, QRS detectability, and broadband noise). Results: The electrodes are able to operate in both wet and dry conditions. Dry electrodes are more prone to noise artifacts, especially during physical exercise and mainly due to the unstable contact between the electrode and the skin. Wet (saline) electrodes present a stable and reproducible behavior, which is comparable or better than that of traditional disposable gelled Ag/AgCl electrodes. Conclusion: The achieved results reveal the capability of this kind of electrodes to work without the electrolyte, providing a valuable interface with the skin, due to mixed electronic and ionic conductivity of PEDOT:PSS. These electrodes can be effectively used for acquiring ECG signals. Significance: Textile electrodes based on PEDOT:PSS represent an important milestone in wearable monitoring, as they present an easy and reproducible fabrication process, very good performance in wet and dry (at rest) conditions and a superior level of comfort with respect to textile electrodes proposed so far. This paves the way to their integration into smart garments. |
Serum adipocyte fatty acid-binding protein levels in patients with critical illness are associated with insulin resistance and predict mortality | INTRODUCTION
Hyperglycemia and insulin resistance are commonplace in critical illness, especially in patients with sepsis. Recently, several hormones secreted by adipose tissue have been determined to be involved in overall insulin sensitivity in metabolic syndrome-related conditions, including adipocyte fatty-acid binding protein (A-FABP). However, little is known about their roles in critical illness. On the other hand, there is evidence that several adipose tissue gene expressions change in critically ill patients.
METHODS
A total of 120 patients (72 with sepsis, 48 without sepsis) were studied prospectively on admission to a medical ICU and compared with 45 healthy volunteers as controls. Various laboratory parameters and metabolic and inflammatory profiles were assessed within 48 hours after admission. Clinical data were collected from medical records.
RESULTS
Compared with healthy controls, serum A-FABP concentrations were higher in all critically ill patients, and there was a trend of higher A-FABP in patients with sepsis. In multivariate correlation analysis in all critically ill patients, the serum A-FABP concentrations were independently related to serum creatinine, fasting plasma glucose, total cholesterol, TNF-alpha, albumin, and the Acute Physiology and Chronic Health Evaluation II scores. In survival analysis, higher A-FABP levels (> 40 ng/ml) were associated with an unfavorable overall survival outcome, especially in sepsis patients.
CONCLUSIONS
Critically ill patients have higher serum A-FABP concentrations. Moreover, A-FABP may potentially serve as a prognostic biomarker in critically ill patients with sepsis. |
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