Escherichia coli bacteriophage vB_EcoP_PR_Kaz2018, isolated from a water sample. vB_EcoP_PR_Kaz2018 is a linear double-stranded DNA T7-like podophage with a genome of 39,704 bp containing 45 predicted open reading frames (ORFs).Here, we describe the complete genome sequence of the  Escherichia coli bacteriophage vB_EcoP_PR_Kaz2018, isolated from a water sample. vB_EcoP_PR_Kaz2018 is a linear double-stranded DNA T7-like podophage with a genome of 39,704 bp containing 45 predicted open reading frames (ORFs).Here, we describe the complete genome sequence of the  Escherichia coli bacteriophage vB_EcoP_PR_Kaz2018 was isolated from a water sample and is capable of infecting encapsulated Escherichia coli expressing the K1 capsular antigen, a major causative agent of neonatal septicemia, sepsis, and meningitis . The DNA library was prepared using the Nextera XT DNA sample preparation kit (Illumina). Whole-genome sequencing was performed with an Illumina MiSeq sequencing platform. Low-quality reads were filtered and adapters trimmed with Trimmomatic (Enterobacter phage K1F (GenBank accession number AM084414), with 100% query coverage and 95.28% identity; Escherichia phage LM33_P1 (LT594300), with 86% query coverage and 95.22% identity; Escherichia phage PE3-1 (KJ748011), with 85% query coverage and 95.13% identity; and Escherichia phage YZ1 (MG845865), with 84% query coverage and 94.73% identity.The genomic DNA was extracted from the phage lysate of the mmomatic  from themmomatic . As a remmomatic . The avemmomatic , 6. The mmomatic , 8, nameE. coli.The complete genome of phage vB_EcoP_PR_Kaz2018 is a linear double-stranded DNA (dsDNA) of 39,704 bp, with 178-bp-long terminal repeats. The GC content of the genome was 49.8%. Gene prediction was done using GeneMark and PHAST . A totalEscherichia phage vB_EcoP_PR_Kaz2018 was deposited in GenBank under the accession number MN510331. Raw sequence reads are available under BioProject accession number PRJNA574554.The complete genome sequence of the


Although the de novo transcriptome assembly of non-model organisms has been on the rise recently and new tools are frequently developing, there is still a knowledge gap about which assembly software should be used to build a comprehensive de novo assembly.In recent years, massively parallel complementary DNA sequencing (RNA sequencing [RNA-Seq]) has emerged as a fast, cost-effective, and robust technology to study entire transcriptomes in various manners. In particular, for non-model organisms and in the absence of an appropriate reference genome, RNA-Seq is used to reconstruct the transcriptome de novo assembly tools are applied to 9 RNA-Seq data sets spanning different kingdoms of life. Overall, we built >200 single assemblies and evaluated their performance on a combination of 20 biological-based and reference-free metrics. Our study is accompanied by a comprehensive and extensible Electronic Supplement that summarizes all data sets, assembly execution instructions, and evaluation results. Trinity, SPAdes, and Trans-ABySS, followed by Bridger and SOAPdenovo-Trans, generally outperformed the other tools compared. Moreover, we observed species-specific differences in the performance of each assembler. No tool delivered the best results for all data sets.Here, we present a large-scale comparative study in which 10 de novo transcriptome assembly.We recommend a careful choice and normalization of evaluation metrics to select the best assembling results as a critical step in the reconstruction of a comprehensive  Even though a reference genome is available, it is still recommended to complement a gene expression study by a de novo transcriptome assembly to identify transcripts that have been missed by the genome assembly process or are just not appropriately annotated ) has established itself as a powerful technique to understand versatile molecular mechanisms and to address various biological questions . In partnnotated .At first glance, the transcriptome assembly process seems similar to genome assembly, but actually, there are fundamental differences and various challenges. On the one hand, some transcripts might have a shallow expression level, while others are highly expressed ,4,6. Espde novo transcriptome assembly of non-model organisms has been on the rise recently, and new tools are frequently developed. Now there is a knowledge gap: which assembly software and parameter settings should be used to construct a good assembly? In addition, there is no consensus about which metrics should be used to evaluate the quality of multiple de novo transcriptome assemblies.The de novo transcriptome assembly  ) are staupported .IDBA-Tran (v1.1.1), a novel assembly tool that claims to be more robust regarding uneven expression levels in RNA-Seq data  64-bit). Of course, how easily a tool can be installed and executed depends heavily on the machine used, the server setup, and how familiar the user is with the programing language the tool is based on. Nevertheless, it should be the goal of each publicly available piece of software to be as user-friendly as possible. Therefore, we collected our experiences during the installation and execution of each assembler to share our observations Table .Electronic Supplement publicly available at www.rna.uni-jena.de/supplements/assembly [doi.org/10.17605/OSF.IO/5ZDX4 [BUSCO and TransRate as well as other results are also archived in the GigaScience GigaDB respository [A comprehensive assembly  accompanIO/5ZDX4 . AdditioS1Supplementary Table : Data sets and preprocessing.S2Supplementary Table : Assembly tools.S3Supplementary Files : Executed assembly commands.S4Supplementary Figures : HISAT2 re-mapping rate.S5Supplementary Figures : rnaQUAST statistics.S6Supplementary Tables : TransRate.S7Supplementary Figures : ExN50.S8Supplementary Figures : BUSCO.S9Supplementary Tables : DETONATE.S10Supplementary Tables : Selected main metrics.S11Supplementary Figures : Runtime and memory consumption.S12Supplementary Figures : -normalized scores per data set and metric.GIGA-D-18-00307_Original_Submission.pdfClick here for additional data file.GIGA-D-18-00307_Revision_1.pdfClick here for additional data file.GIGA-D-18-00307_Revision_2.pdfClick here for additional data file.GIGA-D-18-00307_Revision_3.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_2.pdfClick here for additional data file.Reviewer_1_Report_Original_Submission -- Andrey D. Prjibelski, M.Sc.9/26/2018 ReviewedClick here for additional data file.Reviewer_1_Report_Revision_1 -- Andrey D. Prjibelski, M.Sc.2/7/2019 ReviewedClick here for additional data file.Reviewer_2_Report_Original_Submission -- Brian Haas9/27/2018 ReviewedClick here for additional data file.Reviewer_2_Report_Revision_1 -- Brian Haas1/27/2019 ReviewedClick here for additional data file.Homo sapiens; KC: k-mer compression; MK: multiple k-mer; MS: metric score; nt: nucleotides; OMS: overall metric score; RNA-Seq: RNA sequencing.BUSCO: benchmarked universal single-copy orthologs; Chr1: chromosome 1; EBOV: Ebola virus; HSA: The authors declare that they have no competing interests.This work has been funded by the German Research Foundation (DFG) projects Collaborative Research Center/Transregio 124—“Pathogenic fungi and their human host: Networks of Interaction,” subproject B5; DFG SPP-1596—“Ecology and species barriers in emerging viral diseases”; and CRC 1076 “AquaDiva”, subproject A06.M.M. conceived the research idea. M.H. designed the project, performed calculations and analysis, interpreted the data, and wrote the main manuscript. M.M. contributed in discussions and in proofreading the final manuscript. This work is part of the doctoral thesis of M.H. All authors read and approved the final manuscript.


The gene LsWRKY (Lsat_1_v5_gn_6_12161) incorrectly did not appear in


Helicobacter pylori causes gastric cancer in 1–2% of cases but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori–colonized individuals experience no detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing–driven ENteric Immunity SImulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary, and partial differential equations.In silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells.The outputs were analyzed using 2 sequential stages: the first used a partial rank correlation coefficient regression–based and the second a metamodel-based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak, and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.The hybrid model of  The outputs from the first stage (152 × 20 runs) and second stage (115 × 20 runs) were combined to provide the training data to build a spatiotemporal metamodel. For the second-stage analyses, we utilized a metamodeling-based approach. Metamodels are surrogate models that can be used as a substitute for the simulation model , cov[M], ..., cov[M]T, that contains spatial covariance between the k design points and a given prediction point kxk covariance matrix of the vector of simulation errors associated with the vector of point estimates k is the kx 1 vector of ones and Similar as in Ankenman et al. , shown in et al. , the besequation :(4)\docn et al.  is givenTo implement the metamodeling approach as described above, the unknown model parameters are estimated through maximizing the log-likelihood function. The underlying standard assumption is that .g., see  and 45]\document.g., see  is used To determine the effect of input variables on the output, we employed the variance decomposition method. These methods involve the decomposition of the variance of the output as a sum of the variance produced by each input parameter .Second-stage analysis, and the Sobol’ indices were computed as described in  were shown to have positive influence on the resident macrophage cells, whereas the T-cell type transition parameters [p_iTregtoTh17 = (0.3–0.4) and p_Th17toiTreg = (0.1–0.2)] showed a negative impact on the resident macrophages. Similarly, we performed the PRCC analysis for all the cell populations under consideration during the infection (not shown).Global sensitivity analysis) were selected to be varied for the second-stage design. All the selected inputs are shown in The significant parameters (marked in blue bars) obtained from the SA of the output from the first-stage design of experiments , using the mlegp package in R  obtained. The diagnostic plots denote the black circles, which are the cross-validated prediction. Cross-validation is in the sense that for predictions made at design point x, all observations at design point x are removed from the training set. The lower panel represents the residual plots for the cell populations: (a) H. pylori, (b) resident macrophages, (c) monocyte-derived macrophages in the LP, and (d) tolerogenic DCs in the GLN compartment.ABM: agent-based model; DC: dendritic cell; ENISI MSM: Enteric Immunity Simulator multiscale modeling; GLN: gastric lymph node; GP: Gaussian process; HPC: high-performance computing; IFN-γ: interferon γ; IL: interleukin; iTreg: induced regulatory T; KO: knockout: LP: lamina propria; LSODA: Livermore Solver for Ordinary Differential Equations; ODE: ordinary differential equation; PDE: partial differential equation; PPARγ: peroxisome proliferator-activated receptor γ; PRCC: partial rank correlation coefficient; RRID: research identification initiative ID; SA: sensitivity analysis; SRCC: Spearman rank correlation coefficient; STAT3: signal transducer and activator of transcription 3; TGF-β: transforming growth factor β; Th: T helper; Treg: regulatory T; WT: wild type.The authors declare that they have no competing interests.www.nimml.org). The funding body had no role in the design of the study, data collection, analysis, interpretation of data, and writing of the manuscript.This work was supported by the Defense Threat Reduction Agency (DTRA) grant HDTRA1–18-1–0008 to J.B.R. and R.H. and funds from the Nutritional Immunology and Molecular Medicine Laboratory (H. pylori experimental data. J.B.R., V.A., and R.H. supervised the project. J.B.R. and R.H. edited the manuscript. J.B.R., A.L., N.T.J., S.H., V.A., X.C., and R.H. participated in discussions on the model and results. All authors provided critical feedback on the project.M.V., R.H., and J.B.R. formulated the model, implemented, performed the simulations, analyzed model-generated outputs, made the figures, and wrote the manuscript. M.V., A.L., J.B.R., R.H., and S.H. formulated the model. S.H., A.L., and V.A. implemented the code architecture and benchmarked the parallel version of the hybrid model. X.C. and M.V. wrote the codes for global sensitivity analysis and generated the design matrices. N.T.J. generated macrophage and giz062_GIGA-D-18-00435_Original_SubmissionClick here for additional data file.giz062_GIGA-D-18-00435_Revision_1Click here for additional data file.giz062_GIGA-D-18-00435_Revision_2Click here for additional data file.giz062_GIGA-D-18-00435_Revision_3Click here for additional data file.giz062_GIGA-D-18-00435_Revision_4Click here for additional data file.giz062_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz062_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giz062_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giz062_Response_to_Reviewer_Comments_Revision_3Click here for additional data file.giz062_Reviewer_1_Report_Original_SubmissionChang Gong -- 12/9/2018 ReviewedClick here for additional data file.giz062_Reviewer_1_Report_Revision_1Chang Gong -- 1/28/2019 ReviewedClick here for additional data file.giz062_Reviewer_2_Report_Original_SubmissionElsje Pienaar -- 12/17/2018 ReviewedClick here for additional data file.giz062_Reviewer_2_Report_Revision_1Elsje Pienaar -- 1/31/2019 ReviewedClick here for additional data file.giz062_Reviewer_2_Report_Revision_2Elsje Pienaar -- 2/18/2019 ReviewedClick here for additional data file.giz062_Reviewer_3_Report_Revision_2Paul Macklin, Ph.D. -- 2/25/2019 ReviewedClick here for additional data file.giz062_Reviewer_3_Report_Revision_3Paul Macklin, Ph.D. -- 3/21/2019 ReviewedClick here for additional data file.giz062_Reviewer_3_Report_Revision_4Paul Macklin, Ph.D. -- 4/7/2019 ReviewedClick here for additional data file.giz062_Supplement_FilesClick here for additional data file.


Diabetes is a significant health concern with more than 30 million Americans living with diabetes. Onset of diabetes increases the risk for various complications, including kidney disease, myocardial infractions, heart failure, stroke, retinopathy, and liver disease. In this paper, we study and predict the onset of these complications using a network-based approach by identifying fast and slow progressors. That is, given a patient’s diagnosis of diabetes, we predict the likelihood of developing one or more of the possible complications, and which patients will develop complications quickly. This combination of "if a complication will be developed” with ”how fast it will be developed” can aid the physician in developing better diabetes management program for a given patient. Diabetes is a significant public health concern in the United States. According to the Center for Disease Control (CDC), in 2015 it was estimated that 30.3 million people have diabetes, with 23.1 million cases diagnosed and 7.2 million undiagnosed . 90 to 9To achieve the objective of predictability of onset of complications, we first represent a patient’s disease history as a network based on what happens in the second year after a diabetes diagnosis. Genetic determinants and other independent accelerating factors of the complications of diabetes  clearly The proposed network developed in this study will not only provide a useful modeling construct but also a mechanism for visualizing disease complications. The use of networks to understand disease progression has been studied before, such as in Alzheimer’s  and hearWe use a large data set comprising of Type 2 Diabetes patients in Indiana, collected over 20 years obtained through the Regenstrief Institute. This data includes both diagnosis codes taken from the International Statistical Classification of Diseases and Related Health Problems, Ninth Revision and Tenth Revision,  and clinical laboratory test results. Researchers have had success using ICD codes to predict future disease states . We creaPredicting diabetic complications is incredibly challenging due to the inequality of healthcare consumption and the speed at which patients receive diagnoses. In our work, we posit that by establishing appropriate thresholds and choosing balanced populations, we can ensure that even patients who infrequently visit their physician can still benefit from our models.The Regenstrief Institute created one of the earliest electronic medical record systems in 1972 to support research and continues to handle the research use of the INPC (Indiana Network for Patient Care) database . With thIn a collaboration of Indiana Biosciences Research Institute, Regenstrief Institute, and industrial partners, a primary data set of type 2 diabetes mellitus (T2DM) patients was created. Using inclusion criteria of one T2D diagnosis code OR a laboratory glycated hemoglobin HbA1C) test results ≥ 6.5% OR at least one Medi-Span-defined anti-diabetes medication where the patients were ≥ 18 years of age on date of first inclusion criteria. Using this criteria, a primary T2DM cohort of 805,867 individuals was identified from INPC over 20 years (1995-2015). The demographics, diagnosis codes, medical procedures, prescriptions, and results from clinical laboratory tests were extracted for these individuals. This exC test reTo clean this T2DM data set, the extracted INPC data placed on a secure Amazon Web Services (AWS) server. This large T2DM dataset across 20 years was multi-modal and there were many missing parameters across the records, as well as inconsistency in the measurements identified by error codes or per-patient longitudinal analysis or out of range values. In addition, we had to take into account the correction of features that were reported for quality control (QC) checks. To that end, we implemented a comprehensive a data cleaning framework to normalize the features, remove bad or missing values, and have consistent units of measure was done using PySpark. The feature values were normalized and extreme values were identified and filtered on minimum and maximum values ever measured for a parameter. Additionally, if any values were +/- 2 standard deviations from the median, they were filtered. Also, we looked for more than two distribution patterns in the data where potentially two different units of measure were applied to the same variable, which could indicate a problem with poor previous data integration. After this extensive effort to clean all the issues from this "real-world" captured data set from INPC, an "analysis-ready" data set was created for the modeling. An overview of the size of the different data tables is given in Table Type 2 diabetes mellitus - ICD9/ICD10 codes 249, 250, 357.2, 362.[01-07], 366.41, E10, E11Kidney disease - ICD9/ICD10 codes 584, 586, 585, 403, 404, 581, 583, 588, N18, N17, N19, I12, I13, N04, N05, N08, N25, 593Liver disease - ICD9/ICD10 codes 571, 572, 573, K76, K75Heart failure - defined as ICD9/ICD10 codes 428, I50Myocardial infarction - ICD9/ICD10 codes 410, 412, I21Stroke - ICD9/ICD10 codes 435, G45, 430, 431, I60, I61, 432, I62, 436, 433, 434Retinopathy - ICD9/ICD10 codes 362, H35We use the following to categorize primary T2DM diagnoses and complications: We further sample to create the following data about patients: patient diagnosis, which contains all the diagnoses codes (ICD-9/ICD-10) received by a patient, demographics, which contains age, gender, and race/ethnicity information, and clinical variables, which contains metabolic measurements taken while at the doctor’s office. Header files for the diagnosis table is given in Table We detail the network construction in Algorithm 1, and network pruning in Algorithm 2. We retain a listing of the edges and nodes that represent the fast paths to diabetic complications, along with the nodes that result in the largest information gain.There are three primary data sources that we use to build our models: patient demographic data, which remains constant throughout the duration of the study and is represented by nodes at the beginning of the network at time zero; patient diagnosis, which contains all the diagnoses that occur over the course of a patient’s visit with a doctor or healthcare provider; and clinical variables, which contain all the available measurements and laboratories tests available in the patient’s health records as contained in INPC.We tested the following clinical variables and grouped them into quartiles, which were included in the clinical variables file: non high-density lipoprotein cholesterol (Non-HDL C), low-density lipoprotien (LDL) high-density lipoprotein (HDL) ratio, thyroid-stimulating hormone (TSH), fibrosis-4 (Fib 4) index, total cholesterol, low-density lipoprotein cholesterol (LDL C), high-density lipoprotein cholesterol (HDL C), cholesterol ratio, total bilirubin, basophil platlet count (PC), monocyte count, aspartate transaminase to platelet ratio index (APRI), neutrophil count, albumin, alkaline phosphatase (ALP), aspartate transaminase (AST) alanine transaminase (ALT) ratio. eosinophil PC, protein, HbA1C, ALT, estimated glomerular filtration rate (eGFR), AST, lymphocyte PC, calcium, red blood PC, platelet count, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), glucose, blood urea nitrogen (BUN), chloride, creatinine, and carbon dioxide (CO2).Additionally included in the clinical variables file were the following variables, pre-processed into normal and abnormal statuses: weight classification, HDL C, high serum creatinine, high urine glucose, hyperglycemia, hypertension, hypertriglyceridemia, impaired fasting glycemia (IFG), impaired glucose tolerance (IGT), LDL C, and triglycerides. Finally, we also quartile the age of the patients so that we have large groups to test on. Then every piece of information in a patient history is linked all other nodes, thus creating a heterogeneous network. An example of the network is given in Fig. After building the network, we prune it by discarding any edges that do not contain statistically significant differences between the fast and slow progressors as defined by using a two-proportion Z test score.To determine if a patient is a slow or fast progressor, the nodes and edges of the sub-network that match the patient’s medical history are traversed and their individual probability of developing a complication is computed. We assume that the node and edge weights, corresponding to the percentages of patients who suffer from that complication that are contained by that node or edge, are equally likely and statistically independent. These weights are multiplied together to get the probability of being a fast progressor. To decrease noise, we experimentally concluded that the weights, or percent likelihood of developing the specific complication of diabetes, corresponding to the top 12 most significant edges and nodes are used as determined by the two-proportion Z-test. In other words, for each individual patient, we only used the most significant parts of their individual network to predict whether or not that patient was a fast or slow progressor. The average AUC values from each of these experiments is shown in Table w0,...,wn correspond to the n most significant edge and node weights as determined by the two-proportion Z-test, where n≤12. Remove wh from the computation, which corresponds to the lowest probability of developing the complication. Let The method to compute the probability that an individual will be a fast or slow progressor is: Let Only information in patient history that occurred in the second year following a Type 2 diabetes diagnosis is considered. Healthy patients survive longer than sickly ones, so if we extend our analysis for too long after a diabetes diagnosis, the data will become biased towards healthy patients. Patients tend to move and change doctors, and analyzing what occurs in the second year after the diagnosis will ensure that many patients are still in the system. We can see in Fig. Diagnoses are truncated to the first three digits of the ICD-9 or ICD-10 code to remove the disease subtypes and only focus on the primary diagnoses.All nodes that are not shared by at least one percent of the population are removed.All patients that have received less than five diagnoses or more than twice the median amount of diagnoses are removed. This assists with biases introduced by individuals having an excessive medical history or too few observations.The cleaned dataset is sampled to ensure that our fast and slow progressors have the same number of patients.The significance on the edges is computed and any edges that do not test for a two-proportion z-test with 95 percent confidence are removed.Fast progressors are defined as patients who develop a complication of diabetes faster than 75 percent of the population. All patients from our dataset who develop the complication before being diagnosed with diabetes, or up to one year afterwards are removed.Slow progressors are defined as patients who develop a complication of diabetes slower than 75 percent of the population. Everyone retained in our network is eventually diagnosed with the complication which assists in making sure the datasets are balanced and with limited bias.Every node and every edge is given a Z-score, which corresponds to the likelihood of a significant difference between fast and slow progressors. Every node and edge will be given the percent likelihood that a patient who has the condition given in the node, or combination of conditions as represented by an edge, will be a fast or slow progressor.We only consider new diagnoses that occur after a diabetes diagnosis. We do not consider diagnoses or lab values that occurred before the type 2 diabetes diagnosis. Incorporating past values might be included in future work. Our test set contained 20 percent of our patients. The percent likelihood of their complication development was computed against the patient network generated from the 80 percent training set. We queried the large network for nodes and edges corresponding to an individual patient’s disease history. Because all the edges that failed to show a significant difference between the fast and slow progressors were pruned, the sub-network might be disconnected. The top five conditions that lead to each complication by percentage of fast progressors and Z-score are given in Table The results for these predictions of fast progressors for onset of these various diabetic complications are shown in Table Diabetic complications are often correlated with one another, which might reflect the generalized damage that the body has taken from a micro and macrovascular perspective . Others Many of the top confidence nodes are shared between different complications. Correlations between the fast and slow progressors are given in Table In our future work, we would like to examine the false positives and identify what causes them to not develop complications immediately, even though their diagnosis history and lab results identify them as fast progressors. This will inform health management strategies – lifestyle, behavioral or environmental factors – in addition to the medication to manage diabetes. We believe this analysis should help enable recommendations for diabetic patients to limit development of complications.Given a patient’s disease history and lab results, we can predict their likelihood of developing complications from diabetes. We also show what disease diagnoses or lab results (from our heterogeneous network or graph) are most likely to lead to specific diabetic complications. We reaffirm that diabetes is a complicated disease. It continues to be important for diabetic patients to manage their disease and be aware of the complications. The diagnoses graphs can help illuminate health problems faced by many patients and what might be the best course of disease management. Not managing complications, especially for fast progressors, can cause rapid development of uncontrolled diabetes, from which it is hard to recover. Moreover, disease diagnoses graphs can also be a useful tool for physicians to understand the effects of co-morbid conditions, and personalize a wellness and disease management plan. This can lead to an improvement in both individual and population health outcomes.Below is a list of data columns included in the clinical variables file: STUDYID, AGE, DAYS_VIS_INDEX, GENDER, INDEX_AGE, angiotensin converting enzyme (ace), acetaminophen, acetone, act, albumin, albumin_creatinine_ratio, albumin_globulin_ratio, alcohol_pc, aldolase, aldosterone, alp, alp_bone_isoenzyme, alpha_1_antitrypsin, alpha_1_globulin, alpha_2_globulin, alpha_tocopherol, alt, ammonia, amylase, anion_gap, aorta_sinuses_diam, aortic_root_diam, aov_peak_pressure, aov_peak_velocity, apri, arterial_diastolic_bp, ast, ast_alt_ratio, antithrombin iii (atiii), band count (cnt), band_pc, bard_score, base_excess, basophil_count, basophil_pc, beta2_microglobulin, beta_globulin, beta_hydroxybutyrate, bicarbonate, blast_count, blast_pc, body mass index (bmi), body_surface_area, bun, bun_cr, bun_post_dialysis, bun_pre_dialysis, complement 3 (c3), complement 4 (c4), c_peptide, calciferol, calcium, calcium_albumin, carboxyhemoglobin, cyclic citrullinated peptide (ccp), cluster of differentiation (cd) 2_t_cells, cd3_t_cells, cd4_cd8_ratio, cd4_helper_t, cd4_t_cells, cd8_supprs_t_cells, cd8_t_cells, carcinoembryonic antigen (cea), cell_count, chloride, cholecalciferol, cholesterol_ratio, creatine kinase (ck)_bb), ck_index, ck_mb, ck_mb_tot, ck_mm, ck_total, chronic kidney disease (ckd)_stage, co2, colony_count, conjugated_bilirubin, cortisol, creatinine, creatinine_ck, creatinine_clear, c-reactive protein (crp), central venous pressure (cvp), d_dimer, (dehydroepiandrosterone) dhea_s, diabetic_nephropathy_status, diabetic_status, diastolic_bp, diastolic_bp_standing, direct_bilirubin, epstein-barr (ebv)_antibody, eGFR, eosinophil_count, eosinophil_pc, esr, estradiol_unconjugated, estrogen, factor_viii_activity, fasting_glucose, forced expiratory flow (fef)25_75, ferritin, fib_4_index, fibrinogen, fraction of inspired oxygen (fio2), folate, free_lambda, fructosamine, follicle-stimulationg hormone (fsh), gamma-glutamyl transpeptidase (ggt), globulin, glucose, glucose_gtt_1h, glucose_gtt_1hr_ob, glucose_gtt_2h, glucose_gtt_3h, glucose_gtt_pp, hba1c, hdl_c, hdl_c_status, hdl_ cholesterol (chol), hdl_ldl, height, hepatitis (hepb)_ab, hemoglobin (hgb), hemoglobin a2 (hgb_a2), high_serum_creatinine_status, high_urine_glucose_status, histamine, homeostatic model assessment of beta cell function (homa_b), homeostatic model assessment of insulin resistance (homa_ir), homocysteine, hyperglycemia_status, hypertension_status, hypertriglyceridemia_status, ifg_status, immunoglobulin a (iga), immunoglobulin e (ige), insulin-like growth factor 1 (igf_1), immunoglobulin g (igg), immunoglobulin m (igm), igt_status, immature_granulocytes_pc, indirect_bilirubin, insulin, iron, interventricular septum (ivs)_thickness, left atrium (la)_diameter, lactate, lactate_dehydrogenase, lactic acid dehydrogenase (ldh)_1, ldh_2, ldh_3, ldh_4, ldh_5, ldl_c, ldl_c_status, ldl_hdl_ratio, lh, lipase, lipoprotein (lpa), left ventricle (lv)_mass, lv_stroke_volume, lv_systolic_volume, left ventricular outflow tract (lvot)_peak_gradient, lvot_peak_velocity, left ventricular posterior wall (lvpw)_thickness_diastolic, lymphocyte_atypical, lymphocyte_count, lymphocyte_pc, lymphocyte_reactive, lymphocyte_variant, lymphotycte cerebrospinal fluid (csf), macrophage_pc, map, mch, mcv, mean_arterial_pressure, mean_glucose_bld_ghb_test, mesothelial_cells_pc, metamyelocytes_count, metamyelocytes_pc, methemoglobin, methemoglobin_pc, mixed_mono_count, mixed_mono_pc, monocyte_count, monocyte_csf_pc, monocyte_pc, myelocyte_count, myelocyte_pc, nafld_fibrosis_score, neutrophil_count, neutrophil_pc, non_hdl_c, nucleated red blood cells (nrbc)_count, nrbc_pc, nrbc_white blood cell (wbc), N-terminal pro b-type natriuretic peptide (nt_probnp), nucleated_cell_count, oxygen (o2), oxyhemoglobin_pc, p_wave_offset, p_wave_onset, partial pressure of carbon dioxide (pco2), ph, phosphorus, platelet_count, partial pressure of oxygen (po2), poly_count, poly_pc, potassium, pr_interval, pre_diabetic_status, progesterone_17_OH, promyelocytes_count, prostate_free, prostrate_total, protein, pulse, qt_corrected, quantitative insulin-sensitivity check index (quicki), red blood cell distribution width (rdw), red_blood_cell_count_csf, red_blood_pc, renal_exocrine pancreatic insufficiency (epi)_cells, respiratory_rate, selenium, serum_osmolality, smudge_cell_count, sodium, systolic_bp, systolic_bp_standing, triiodothyronine (t3)_free, t3_total, thyroxine (t4)_free, t4_total, t_wave_axis, t_wave_offset, temperature, testosterone_free, testosterone_total, total iron binding capacity (tibc), total_bilirubin, total_cholesterol, triglyceride_hdl_ratio, triglycerides, triglycerides_status, troponin, troponin_2h, tsh, urine albumin-to-creatinine ratio (uacr), unconjugated_billirubin, uric_acid, urine_albumin, urine_ascorbate, urine_bacteria, urine_billirubin, urine_cast, urine_chloride, urine_cortisol_free, urine_creatinine, urine_creatinine_24, urine_crystals, urine_epithelial_cells, urine_gamma_globulin, urine_glucose, urine_granular_cast, urine_hgb, urine_hyaline_cast, urine_ketones, urine_microalbumin, urine_microalbumin_24, urine_microalbumin_creatinine_ratio, urine_microalbumin_creatinine_ratio_24, urine_potassium, urine_protein, urine_protein_24, urine_protein_creatinine_ratio, urine_red blood cells (rbc), urine_specific gravity (sp_grav), urine_squaous_epithelial (epi)_cells, urine_trans_epi_cells, urine_urea_nitrogen, urine_urobilinogen, urine_waxy_cast, vitamin (vit)_a, vit_b1, vit_b12, vit_d2, vit_25-hydroxyvitamin d2(d2_25_oh), very low-density lipoprotein (vldl), vldl_c, waist_circumference, wbc_count, wbc_count_csf, weight, weight_classification, zinc, CARDIOVASCULAR, NEPHROPATHY, LIVER, OUTCOME


Scientific Reports 10.1038/s41598-019-52700-w, published online 12 November 2019Correction to: In this Article, there is a typographical error in the Data availability section, where:https://gin.gnode.org/USZ_NCH/Scalp_EEG_HFO.”“The EEG data and HFO markings are freely available at should read:https://gin.g-node.org/USZ_NCH/Scalp_EEG_HFO.”“The EEG data and HFO markings are freely available at