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0.407124 | 496e06dca9ad4b608c45b734757cd16e | Isoflurane improves schizophrenia-related phenotypes induced by PV neuron’s GABA release inhibition in the DG. (A) AAV-CAG-DIO-EGFP-2A-Tettox was injected into the DG of PV-Cre mice to inhibit GABA release. (B) Representative traces of mice in the OFT. (C) Isoflurane treatment reversed the hyper-locomotion phenotype induced by PV neuron’s GABA release inhibition, revealed by OFT (n = 8, 10, and 8 mice in control, PV-Tet, and PV-ISO groups, respectively. One-way ANOVA, F(2, 23) = 18.24, p < 0.0001; post hoc test: Ctrl vs. PV-Tet, p < 0.0001; PV-Tet vs. PV-Tet, p = 0.002; Ctrl vs. PV-ISO, p = 0.079). (D) Isoflurane attenuated the pre-pulse inhibition deficit induced by PV positive neuron’s GABA release inhibition (n = 9, 10, and 10 mice in Ctrl, PV-Tet, and PV-ISO group, respectively). Two-way ANOVA, F(2, 72) = 2.873, p < 0.0001, post hoc test: Ctrl vs. PV-Tet, p = 0.01; PV-Tet vs. PV-ISO, p = 0.008; Ctrl vs. PV-ISO, p = 0.49). (E) The working memory deficit induced by PV neuron’s GABA release inhibition could be attenuated by isoflurane exposure (n = 9, 10, and 10 mice in Ctrl, PV-Tet, and PV-ISO group, respectively. One-way ANOVA, F(2, 19) = 5.683, p = 0.01; post hoc test: Ctrl vs. PV-Tet, p = 0.01; PV-Tet vs. PV-ISO, p = 0.01; Ctrl vs. PV-ISO, p = 0.32). (F) Isoflurane reversed HFS induced LTP deficit induced by PV neuron’s GABA release inhibition (n = 5 mice per group). (G) Quantitative analysis of data in (F). (Two-way ANOVA, F(2, 1155) = 713.5, p = 0.0002; post hoc test: Ctrl vs. PV-Tet, p = 0.0007; PV-Tet vs. PV-ISO, p < 0.0001; Ctrl vs. PV-ISO, p = 0.11). (H) Representative photomicrographs showing Ki67-positive cells in the DG. White arrowheads indicate target cells. Scale bar, 100 μm. (I) Quantification data of (H). Five mice in each group, and five sections were picked and counted in each mouse. One-way ANOVA, F(2, 24) = 23.59, p < 0.0001; post hoc test: Ctrl vs. PV-Tet, p < 0.0001; PV-Tet vs. PV-ISO, p < 0.0001; Ctrl vs. PV-ISO, p = 0.5034. Data are represented as mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001, ns, not significant. | PMC9687200 | biomedicines-10-02759-g006.jpg |
0.511614 | 10b72401d37b4f6b88ef0b980669e39b | Design of donor DNA/guide RNA (gRNA) hybrid duplex (DGybrid). DGybrid was formed by the hybridization of donor single-stranded oligodeoxynucleotide (ssODN) and 5′-40b-gRNA via annealing sequence (40 bases). For ssODN, the blue- and light-blue-colored sequences show homologous sequences to the target gene. The light-blue-colored sequence was also used for annealing with 5′-40b-gRNA. The yellow-colored bases show an introduced mutation. For 5′-40b-gRNA, the black- and red-colored sequences show the conventional gRNA sequence and extended sequence for annealing, respectively. | PMC9687273 | biomolecules-12-01621-g001.jpg |
0.432816 | 636105649f184b81a114bfe4fffc36b1 | Native-PAGE analysis to confirm DGybrid formation. Values above the lanes of 5′-40b-gRNA and ssODN indicate the relative molar amounts of the applied sample. In the two lanes at both ends, 1 kb Plus DNA Ladder was applied. The DGybrid samples were prepared in four different conditions: (1) TE buffer, (2) TE buffer + 10 mM NaCl, (3) TE buffer + 100 mM NaCl, and (4) ethanol precipitation of the sample prepared in condition (3). | PMC9687273 | biomolecules-12-01621-g002.jpg |
0.474531 | 333ee52e82994d66a9d5362df3013afa | Colony formation on the canavanine-containing medium after DGybrid introduction. Yeast cells were subject to electroporation of the nucleic acids; (A) DGybrid, (B) 5′-40b-gRNA only, (C) ssODN only, (D) gRNA and ssODN, (E) gRNA only, and (F) neither gRNA nor ssODN. | PMC9687273 | biomolecules-12-01621-g003.jpg |
0.535578 | 33cea7f8b9be4014a86762b7df891fd7 | Evaluation of the DGybrid-based genome-editing efficiency. The genome-editing efficiency was evaluated by counting the number of canavanine-resistant colonies. Error bars represent the SEM of three biological replicates starting from independent electroporation of nucleic acid solutions. Points represent each experimental data set. A two-tailed Student’s t-test was used to assess the statistical significance. | PMC9687273 | biomolecules-12-01621-g004.jpg |
0.484391 | 22f6256099e5448d8ca44c248f027618 | Evaluation of introduction efficiency of DNA/RNA hybrid into yeast cells. (A) Schematic of nucleic acids used to evaluate the introduction efficiency. (B) Density plots obtained from flow cytometry analysis. (a) The ratio of yeast cells that richly took up RNA (RNA-rich yeast cells), (b) The ratio of yeast cells that richly took up both RNA and ssODN (both RNA- and ssODN-rich yeast cells), and (c) The ratio of yeast cells that richly took up ssODN (ssODN-rich yeast cells). The data shown are representative of three independent experiments. (C) The introduction efficiency was evaluated using flow cytometry. Values indicate the ratio of RNA-, ssODN- or both RNA- and ssODN-rich yeast cells. Error bars represent the SEM of three biological replicates starting from independent electroporation of nucleic acid solutions. Points represent each experimental data set. A two-tailed Student’s t-test was used to assess the statistical significance. | PMC9687273 | biomolecules-12-01621-g005.jpg |
0.42374 | 703a61192ffe43e5a7629681c9930c81 | Metabolic routes for butyrate and propionate formation by representative bacterial genera and species from the human colon. Species shown in purple can utilise lactate to form butyrate; species shown in blue and green can, respectively, utilise lactate and succinate to produce proprionate. DHAP, dihydroxyacetonephosphate; PEP, phosphoenolpyruvate. Figure reprinted from Flint, Duncan et al., 2015 [23]. | PMC9688025 | biomolecules-12-01640-g001.jpg |
0.470969 | 26928cc812dc47b3a9d949d64ebbb00f | Arrangement of intestinal epithelial cells and intercellular junctions between epithelial cells. The apical junctions are composed of tight junctions and adherens junctions. Figure reprinted from Zhu, Sun and Du. 2018 [40]. | PMC9688025 | biomolecules-12-01640-g002.jpg |
0.414248 | 3d354a4aa3eb452b9ba14b22184eb4a9 | Host gene expression and microbial shifts across the spectrum of ileal IBD. Figure reprinted from Haberman, Tickle et al., 2014 [58]. | PMC9688025 | biomolecules-12-01640-g003.jpg |
0.50503 | 570763e660be49cf828a6668aeacb944 | Summary of supervised, semi-supervised, and unsupervised models of machine learning algorithms. | PMC9688370 | cancers-14-05595-g001.jpg |
0.431148 | e5f56f6c1c84455f8b619e667a047dcd | The architecture of neural network machine learning models for data processing and analysis, i.e., (a) ANN, (b) DNN, and (c) CNN methods. | PMC9688370 | cancers-14-05595-g002.jpg |
0.500852 | 6e277e684ece46fd9a94d1b45b4e4048 | Flow chart of the article selection, i.e., (a) review and (b) research articles used in the preparation of the present review article. | PMC9688370 | cancers-14-05595-g003.jpg |
0.39 | 87bdcc5c68624f63b32d80d17fa81e64 | Published articles count for the last 10 years for both review and journal articles. | PMC9688370 | cancers-14-05595-g004.jpg |
0.449841 | 4a0d117837c44c779c47620039aec025 | Application of deep neural network to classify segmentation in prostate cancer using TRUS image. Herein, input data: data collected from the medical examination of patients and healthy individuals. Data feed: sending structured and processed data to the machine learning model. Deep network: the architecture of machine learning model. Output: prediction for a different types of prostate cancer segmentation. | PMC9688370 | cancers-14-05595-g005.jpg |
0.420032 | 4f0444d56f2d42cf8a91e07052dd2202 | Usage of prostate-specific antigens (PSA) and other patient information to predict the chance of receiving a positive prostate biopsy. | PMC9688370 | cancers-14-05595-g006.jpg |
0.473785 | 018985a8b1514c67886308a5869c32f0 | Summary of AI direct application and assistance in PC treatment. | PMC9688370 | cancers-14-05595-g007.jpg |
0.43119 | ea8c7c92dcde44dc81ec9c43a535a7f1 | Treatment scheme. (a) Until March 2017, patients received two courses of induction chemotherapy (cisplatin and fluorouracil [FP]), followed by daily radiotherapy (RT) combined with weekly intra-arterial chemotherapy (IACT). (b) From April 2017, patients first received alternating chemoradiotherapy, which comprised two courses of chemotherapy (regimen FP or docetaxel, cisplatin, and fluorouracil [TPF]) and daily RT followed by concurrent RT with weekly IACT. | PMC9688766 | cancers-14-05529-g001.jpg |
0.506434 | f051cc4acf39457aa3faf5e046f627a2 | (a) External carotid arteriography obtained by administering contrast media via the external carotid arterial sheath (ECAS) on digital subtraction angiography (DSA). Arrow: tip of ECAS. (b) Superselective lingual arteriography via a steerable microcatheter through the ECAS on the DSA. Arrow: tip of ECAS; arrowhead: a steerable microcatheter. (c) Magnetic resonance image showing the injected contrast agent via the right lingual artery. (d) Arteriography of a branch from the ECA to the metastatic lymph nodes on the DSA. (e) Magnetic resonance image showing the injected contrast agent via the direct branch from the ECA. Scale bar. | PMC9688766 | cancers-14-05529-g002.jpg |
0.452576 | 46057f23cc45452dac76d6bd6252b7f7 | (A) Overall survival, (B) progression-free survival, and (C) local control rates analyzed by the Kaplan–Meier method. | PMC9688766 | cancers-14-05529-g003.jpg |
0.377899 | 95483d3e98e64b1e8f5bf28979715b1c | Scatter plot showing the tumor volume and total cumulative dose of CDDP administered with intra-arterial chemotherapy (IACT). Open circles are local control cases, closed circles are local recurrent cases within the perfusion area of IACT, and the closed triangle is a case of local recurrence outside the perfusion area of IACT. The tumor volume and total dose of CDDP were not correlated. | PMC9688766 | cancers-14-05529-g004.jpg |
0.435738 | 6df0e23593354454bbf8c37d51fc25a0 | Timescale for creation of lithium–pilocarpine model of status epilepticus. (B.W.: body weight). | PMC9688794 | cells-11-03560-g001.jpg |
0.475121 | 0a451eecd7eb4a7a90f1a6f38385f74c | CV staining showing cellular organization in the hippocampus, ATL, and neocortex of TLE rats. Arrows indicates morphological changes and disrupted organization of layers in the hippocampal samples of TLE rats compared to control rats (A,B). Arrows show altered morphological changes and neuronal organization in the ATL samples of TLE rats compared to control rats (C,D). No significant changes in the morphology and neuronal organization were observed in the neocortical samples of TLE rats compared to control rats (E,F). Scale bars: 20 μm. | PMC9688794 | cells-11-03560-g002.jpg |
0.447372 | 903c3bac35324801a4c6112d7fa7656a | Quantitative estimation of tryptophan–kynurenine pathway metabolites. (A) Concentration of tryptophan, (B) concentration of kynurenine, (C) concentration of kynurenic acid, (D) kynurenine–tryptophan ratio, (E) kynurenic acid–kynurenine ratio, (F) concentration of pyridoxal phosphate. CH, control hippocampus (n = 9); PH, pilocarpine hippocampus (n = 10); CATL, control ATL (n = 10); PATL, pilocarpine ATL (n = 10); CN, control neocortex (n = 8); PN, pilocarpine neocortex (n = 8). Data represented as mean ± SEM; * p < 0.05, Mann–Whitney U test. | PMC9688794 | cells-11-03560-g003.jpg |
0.491355 | cd4e8dd1330244d1b79b3f2fdd692920 | Spontaneous excitatory postsynaptic currents were suppressed after 30 min of perfusion with 10 μM kynurenic acid in the hippocampal and ATL samples of the TLE rats. (A) Representative traces in control condition and after perfusion with kynurenic acid from different groups. (B,C) Percentage reduction in frequency and amplitude of the spontaneous excitatory postsynaptic currents after 30 min perfusion with kynurenic acid. CH, control hippocampus; PH, pilocarpine hippocampus; CATL, control ATL; PATL, pilocarpine ATL; CN, control neocortex; PN, pilocarpine neocortex; KYNA, kynurenic acid. Control rat, n = 10; TLE rat, n = 10; Data represented as mean ± SEM; * p < 0.05, Mann–Whitney U test. | PMC9688794 | cells-11-03560-g004.jpg |
0.564781 | a1449a3a020245c0896997efbdc471bc | A positively oriented TrOFN Tr↔a,b,c,d. | PMC9689203 | entropy-24-01617-g001.jpg |
0.549141 | ac03e855872245d4be05ef406fc2349b | A negatively oriented TrOFN Tr↔a,b,c,d. | PMC9689203 | entropy-24-01617-g002.jpg |
0.451616 | 76c415e86a26466d9b1224e7a3770192 | Graphical representation of linguistic scale. Different types of linquistic values are marked by colour. | PMC9689203 | entropy-24-01617-g003.jpg |
0.45388 | fcfb752dfe89458598dc9cb96e0cf8dc | Global scores of 15 packages considered by Itex for OF-T, OF-H1, and OF-H2 scoring functions. | PMC9689203 | entropy-24-01617-g004.jpg |
0.410131 | 3bb3abc3fb0c4d55b05bae6ba66d8e04 | The systemic differences in offers evaluation for all offers from the negotiation space N2 and considered by Itex and OF-T, OF-H1, and OF-H2 scoring functions. | PMC9689203 | entropy-24-01617-g005.jpg |
0.434972 | 54f819e38b024a4f9a3a04efc962a5b0 | Global scores of 15 packages considered by Itex for OF-S, OF-T, OF-H1, and OF-H2 scoring functions. | PMC9689203 | entropy-24-01617-g006.jpg |
0.399844 | 8926c69d6530492499c364797a242a41 | CT images of the nasal cavity—(a) frontal section (b) transverse section. | PMC9689633 | diagnostics-12-02642-g001.jpg |
0.442947 | a994af16369d42fd91e4a54c8712667a | 3D model of the nasal cavity. | PMC9689633 | diagnostics-12-02642-g002.jpg |
0.445792 | a8784a8e5d7443aa8afde3c7186cf911 | Computational network of a 3D model. | PMC9689633 | diagnostics-12-02642-g003.jpg |
0.491 | 9b0309c4887d4c15b4a9a327091dafa8 | Model simulations of frontal sections 1–7 in the nasal cavity. | PMC9689633 | diagnostics-12-02642-g004.jpg |
0.498865 | 235d8aa646fb4685815138f3b5da7dbe | Evaluation of airflow (cm3/s) in frontal sections 2–7 in the nasal cavity. Distribution of airflow is color-coded: blue—the lowest; red—the highest. | PMC9689633 | diagnostics-12-02642-g005.jpg |
0.507362 | f2edfbe3f0fb4f359b1bbfe13d57c0a1 | Formation of small non-coding RNAs. miRNA genes are transcribed by RNA polymerase (Pol) II to generate pri-miRNA, which is then processed by DGCR8 and Drosha to form pre-miRNA inside the nucleus. It is further exported to cytoplasm by Exportin5 and Ran-GTP. Dicer and TRBP in cytoplasm further cleave and process pre-miRNA into mature, short double-stranded miRNA. One strand of the mature miRNA duplex binds with Argonaute protein to form RISC, while another strand is degraded. siRNA is derived from long dsRNA produced by transcription of sense and antisense strands by RNA Pol II and viral infection. Then, dsRNA is processed by Dicer into siRNA duplex, of which one strand binds with Argonaute protein to form the siRNA-induced silencing complexes, while another strand is degraded as well. As for piRNA, piRNA genes are transcribed by RNA Pol II to produce pri-piRNA through the primary procession pathway. pri-piRNA is exported into the cytoplasm and cleaved into pre-piRNA. Then, pre-piRNA is processed by Zuc and Hen1 to form mature piRNA. Mature piRNA in complex with PIWI proteins to work. Additionally, Aub and AGO3 coupled with mature piRNA cleave transcript-bearing sites complementary to the piRNA sequence, thus amplifying mature piRNA species through the “ping-pong” cycle. As for tsRNA, after it is transcribed by RNA Pol III, pre-tRNA undergoes processes and modifications to form mature tRNA. Ribonuclease cleavage in various region of pre-tRNA and mature tRNA generates different types of tsRNAs, including 3′U tRF, 5′tRF, 3′tRF, i-tRF, 5′tiRNA, and 3′tiRNA. Abbreviations: miRNA, microRNA; DGCR8, double-stranded RNA-binding protein; pri-miRNA, primary miRNA; pre-miRNA, precursor miRNA; TRBP, TAR RNA binding protein; RISC, RNA- induced silencing complex; siRNA, small interfering RNA; dsRNA, double-stranded RNA; piRNA, PIWI-interacting RNA; pri-piRNA, primary piRNA; pre-piRNA, precursor miRNA; AGO3, Argonaute 3; tsRNA, tRNA-derived small RNA. | PMC9690286 | genes-13-02072-g001.jpg |
0.441641 | ebb1bb3f0d0a49c89e8a6df6ca2fc217 | Tobacco consumption places. | PMC9690291 | ijerph-19-14772-g001.jpg |
0.378285 | 67b6380fc86b40f9b9554ce26a061158 | Places of exposure to tobacco smoke. | PMC9690291 | ijerph-19-14772-g002.jpg |
0.427469 | 567db079e6524aa5b648503b1c47368b | Frequency at which professors, other staff and students smoke at the entrance doors of university buildings. | PMC9690291 | ijerph-19-14772-g003.jpg |
0.358436 | 9d16958a4f2841cb9bc82a691824af37 | Sources of motivation to quit smoking. | PMC9690291 | ijerph-19-14772-g004.jpg |
0.45585 | 40cf9f1667e7493ca632f65ecf3082ee | Comparison of recognition index values (percentage) between control groups and groups treated with cariprazine in NORT. SCP—scopolamine * p ≤ 0.05 compared to saline + SCP, & p ≤ 0.05 compared to saline. | PMC9690696 | ijerph-19-14748-g001.jpg |
0.446436 | 2de7747027a54a44b195cd346f64b13e | Comparison of working memory index values (percentage) between control groups and groups treated with cariprazine in the T-maze task. SCP—scopolamine * p ≤ 0.05 compared to saline + SCP. | PMC9690696 | ijerph-19-14748-g002.jpg |
0.467815 | 11e2a9b95633451e9529db77d1f30247 | Comparison of spontaneous alternation values (percentage) between control groups and groups treated with cariprazine in the Y-maze task. SCP—scopolamine * p ≤ 0.05 compared to saline + SCP. | PMC9690696 | ijerph-19-14748-g003.jpg |
0.423484 | afcc50cdc18642fc9ac0f70d795c972a | The architecture of the dietary recommender system for ELSA-Brasil study participants. | PMC9690822 | ijerph-19-14934-g001.jpg |
0.469908 | e9f4f0d537e54a75914a24cafbf8a40f | ROC curve, a possible way to compare the efficiency of two systems by comparing the size of the area under the curve, where a larger area indicates better performance. The default plot of the ROC curve plots the true positive rate (TPR) against the false positive rate (FPR). | PMC9690822 | ijerph-19-14934-g002.jpg |
0.505856 | 7e0a3740cffb45f788e62650002d86cf | Comparison of precision–recall curves for item- and user-based recommender methods; precision represents correctly recommended items divided by total recommended items; recall represents correctly recommended items divided by total useful recommendations. | PMC9690822 | ijerph-19-14934-g003.jpg |
0.42332 | 5a10f87f4456478eb046bb7c54d55a6d | Musculoskeletal human arm model. Skeletal representation of the arm with the bones of humerus, ulna, radius, wrist and hand on the left. Shoulder and elbow joints are in blue. Model predictive control is applied to skeletal system to obtain reference trajectories. These joint trajectories are used as supervised signals in trajectory mimicking problem definition of deep reinforcement learning to obtain time-dependent muscle activities. Shoulder and elbow joints are controlled by 14 extensor and flexor antagonistic muscles on the right; TRILong, TRIMed, TRILat, BICLong, BICShort, BRA, ANC, BRD, ECRL, ECRB, ECU, FCR, FCU, PL. Simulation and visualization of the musculoskeletal system are done with OpenSim 4.0 (Seth et al. 2018) | PMC9691497 | 422_2022_940_Fig1_HTML.jpg |
0.430649 | de52563146244142be9c73ece9d5a3b3 | Muscle-tendon unit. The MTU is comprised of muscle and tendon unit with a length described by (\documentclass[12pt]{minimal}
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\begin{document}$$l_{MTU}$$\end{document}lMTU). In muscle unit, there exists a contractile element (CE) and passive elastic element (PEE), together with a length of (\documentclass[12pt]{minimal}
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\begin{document}$$l_{CE}$$\end{document}lCE). Tendon is connected to muscle unit as series elastic element (SEE). The pennation angle and state variable of contraction dynamics are \documentclass[12pt]{minimal}
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\begin{document}$$\theta $$\end{document}θ and s, respectively | PMC9691497 | 422_2022_940_Fig2_HTML.jpg |
0.436887 | 8226b2dbfd674682bbeab87dc0012da6 | Schematic of the optimization and learning framework for musculoskeletal control problems. A Flow of equations for the MPC in skeletal systems. We start the solution of the MPC by writing the Euler-Lagrangian Dynamics (A3) of the skeletal system without muscles. Solving Euler-Lagrangian equation, one can the obtain the skeletal dynamics without muscle control (A4) that constraints the solution of the MPC (A2). This system dynamics then incorporated in the objective function of MPC along with user defined equality and inequality constraints (A2). B Solution of the MPC yields the joint trajectories and velocities with necessary torque activities that provides the supervised signal to reward function of the Deep RL. C A variant of policy gradient methods, PPO is written as a minimization of target angles provided by MPC and observed angles from musculoskeletal system. D The movement of the hand gradually converges to optimal hand trajectory after 200 batches. E An actor-critic architecture of a deep neural network is used to integrate the solution of PPO. Whereas the critic network evaluates the solution by assigning a value to each decision, actor network controls 14 antagonistic muscles within a closed-loop architecture. F. Both actor and critic networks receive the states of the muscles as input and actor network activates the muscles to generate the desired movement in musculoskeletal system | PMC9691497 | 422_2022_940_Fig3_HTML.jpg |
0.442381 | 33f7e0beadbe412eb933bdb2fe8d26b9 | Eight equidistant centre-out reaching experiment. A The schematic representation of the human musculoskeletal arm, movement in the sagittal direction. B Human data is taken and adapted from Shadmehr et al. (2005) “Copyright 2005 Society for Neuroscience”. D Simulation results of eight reaching experiments, trained neural network is executed eight times to analyse the variability in the model. Each reaching target is indicated with different colours. D With same colour code as in C, however only the end points of the hand trajectory are given on top of the musculoskeletal schema. E Normalized velocity profile of tipping point of the hand in human data, taken from Shadmehr et al. (2005) “Copyright 2005 Society for Neuroscience”. F Simulation results of the velocity profile of the tipping point of the hand. The mean and variance of the simulation results show strong correlation with experimental results | PMC9691497 | 422_2022_940_Fig4_HTML.jpg |
0.398161 | e75ca5e7440d4a95a772e9ded6432969 | Learning curve of precise timing control experiment. Two experimental setting is provided, blue line with 0.4 seconds ending time, as well as magenta line with 0.7 seconds ending time. The scale of error is given in log-scale with respect to batch numbers. For each experiment, we performed 10 training and provided the mean (dark lines) and confidence interval (light shading). In both settings, learning curved converged to a stable solution where the acceptance rate is below 0.1 | PMC9691497 | 422_2022_940_Fig5_HTML.jpg |
0.424944 | c077d0b7247e4b30b25f1b6420732d8b | Precise timing control with 0.4 and 0.7s target time to reach the goal positions. A. The results are given as average of ten execution of the trained neural network for both experiments. The elbow target is indicated with a purple dashed line while targeted time is given with a green dashed line for both experimental settings. For each experiment, the difference between the desired trajectory and simulation results is given in the inner figures, the range of difference is negligible. Both optimal shoulder and elbow trajectories are also provided with grey lines to compare the results visually. B. The error of the difference between the desired and actual trajectories in elbow and shoulder joint is summed and averaged for all execution of the trained neural network. Due to higher speed at the beginning of the experiment in 0.4s target time, the shoulder joint shows higher variance of movement which is visible in the total error | PMC9691497 | 422_2022_940_Fig6_HTML.jpg |
0.426727 | de70dc174fbc488fb6d6cee22a2c6f4e | Weight lifting experiment with 1, 2, 5, 10 and 20 kilos. The results of two different parameter settings of the reward function in Deep RL. A Displacement of the hand with respect to initial position. Colour codes indicate different weights. The goal position is indicated with a green square. The musculoskeletal arm manages to lift 1 and 2 kilos only. Each experiment is repeated 5 times and each of them provided. B Similar to A but the parameters of the reward function prioritize the elbow trajectory. 1, 2 and 5 kilos are lifted; however, there exists overshooting in the experiment with 2 kilos. C Trajectories of the elbow and shoulder joints with identical parameters of the reward function \documentclass[12pt]{minimal}
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\begin{document}$$w_{q,e} = {\dot{w}}_{q,e} = \ddot{w}_{q,e} = 1; w_{q,s} = {\dot{w}}_{q,s} = \ddot{w}_{q,s} = 1$$\end{document}wq,e=w˙q,e=w¨q,e=1;wq,s=w˙q,s=w¨q,s=1. D Similar to C except elbow joint has higher coefficients in the reward function, \documentclass[12pt]{minimal}
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\begin{document}$$w_{q,e} = {\dot{w}}_{q,e} = \ddot{w}_{q,e} = 1; w_{q,s} = {\dot{w}}_{q,s} = \ddot{w}_{q,s} = 0.2$$\end{document}wq,e=w˙q,e=w¨q,e=1;wq,s=w˙q,s=w¨q,s=0.2. The elbow joint control shows increased performance | PMC9691497 | 422_2022_940_Fig7_HTML.jpg |
0.455891 | 8071c3a351fd4614a68510b4d0c39ab9 | Muscle load with respect to weights. The recruitment of the flexor and extensor muscles is given in blue and red, respectively. The flexor muscle recruitment is approximately linear to the weight load up to 10 kilos; then it saturates around \documentclass[12pt]{minimal}
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\begin{document}$$\%80$$\end{document}%80. Extensor muscles are recruited for low weights; then it gradually diminishes at around 20 kilos | PMC9691497 | 422_2022_940_Fig8_HTML.jpg |
0.452963 | 795a47f4e32c4b4a83913186d2513b17 | Obstacle avoidance task. A The schematic representation of the musculoskeletal arm movement. Only 4 snapshots of the movement are given for the sake of visual clarity. The obstacle is given in grey block. B The simulation results of the hand displacement in case of obstacle avoidance in blue. Red trajectories represents the same task without an obstacle. Each task is executed 10 times | PMC9691497 | 422_2022_940_Fig9_HTML.jpg |
0.433995 | 55cdcacf98164a11be63013d43e5d2bc | Flowcharts of patient selection. The detailed selection process of stage IV NSCLC patients from 2004 to 2015 for incidence-rate analysis (top) and the detailed selection process of stage IV NSCLC patients from 2010 to 2015 for presentations and survival outcomes analysis (bottom). NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer. | PMC9691661 | fonc-12-894780-g001.jpg |
0.372828 | 4647fd7e82c2415fb02221e84722a7d9 | Line charts of the incidence of stage IV NSCLC patients from 2004 to 2015. The incidence-rate analyses of stage IV NSCLC patients in the entire cohort (A). The incidence-rate analyses of stage IV NSCLC patients aged ≤45 years in the entire cohort (B); and the incidence-rate analyses of stage IV NSCLC patients aged ≤45 years in the stage IV NSCLC cohort. (C) NSCLC, non-small cell lung cancer. | PMC9691661 | fonc-12-894780-g002.jpg |
0.425749 | a50562403fa84b55b00d4e13db0686d4 | Survival comparisons between stage IV NSCLC patients aged ≤45 years and stage IV NSCLC patients aged >45 years. Kaplan−Meier survival curve comparison before PSM (A). Kaplan−Meier survival curve comparison after PSM (B). Competing risk analyses before PSM (C) and competing risk analyses after PSM (D). NSCLC, non-small cell lung cancer; PSM, propensity score matching. | PMC9691661 | fonc-12-894780-g003.jpg |
0.43815 | a583b97d2ff741f5af1cbfa1fa83bcf7 | Sampling sites and the division of 1 ha sampling plot. | PMC9691764 | fpls-13-1014643-g001.jpg |
0.496808 | 209431e9ff2b4c44b6ab8b5373351bde | Correlation coefficients of soil properties. * and ** indicate a significant correlation at the 0.05 and 0.01, respectively. | PMC9691764 | fpls-13-1014643-g002.jpg |
0.439412 | 210d2ea92b67466a9c590082d4f1ca5b | Linear fitting of SPIs based on the soil MDS and soil TDS. | PMC9691764 | fpls-13-1014643-g003.jpg |
0.387676 | 6bfe87ca61d74b92a98cb94f4eafa832 | Forest plots of the observed and the predicted values of species diversity indices calculated by MLR. (A) Shannon–Wiener index; (B) Simpson index; (C) Pielou index. | PMC9691764 | fpls-13-1014643-g004.jpg |
0.581829 | 891b96ba68b0431f828258e6060bbc3b | Reverse cumulative distribution of residuals of MLR and RF models. (A–C) represent Shannon–Wiener, Simpson and Pielou diversity indices, respectively. (D) is the results of RMSE and MRE of the two prediction models. | PMC9691764 | fpls-13-1014643-g005.jpg |
0.396426 | c2dbfe38ed3e458e8692dfc551587002 | Contributions of the soil MDS indicator to the variability of species diversity measured using RF model. | PMC9691764 | fpls-13-1014643-g006.jpg |
0.444485 | 4c063ffa21974c53881b5a9ef937c0d3 | Spatial variations in species diversity interpolated using the original (left) and RF-predicted values (right), respectively. | PMC9691764 | fpls-13-1014643-g007.jpg |
0.417664 | 955aa368d97d47ecba97fa3cb55e03f4 | TEM cross section of the InGaAs (8 nm)/Al2O3 (100 nm)/Ge structure. | PMC9692264 | micromachines-13-01806-g001.jpg |
0.461568 | f35619f2d46c4b45a826dfaf4f535168 | (a) Cross section views of devices and (b) key fabrication process of gate-last fabrication scheme of InGaAs-OI nMOSFET and Ge pMOSFET with dual-gate oxide technique. | PMC9692264 | micromachines-13-01806-g002.jpg |
0.455455 | f6e4e7f27dad4cc59b2ba5fcf1d03df6 | (a) Current characteristics of NiGe/Ge junctions with OPO treatment and control sample, (b) contact resistance and film resistance of NiGe films with OPO treatment and control sample. | PMC9692264 | micromachines-13-01806-g003.jpg |
0.406618 | 72b76a75931b4dbd93f9b0e3b72e2093 | Experiment of Ge MOSCAPs with different capping oxide thickness from 1 nm to 4 nm. (a) Fabrication process, (b) capacitance equivalent thicknesses of different MOSCAPs. | PMC9692264 | micromachines-13-01806-g004.jpg |
0.430747 | 96cff6e618184078a480f9928af5926c | Electron characteristics of Ge pMOSFET and InGaAs-OI nMOSFET with width/length = 50 μm/50 μm under gate-last fabrication process (a) Id-Vg curves of Ge pMOSFET, (b) Id-Vg curves of InGaAs-OI nMOSFET, (c) Id-Vd curves of both devices. | PMC9692264 | micromachines-13-01806-g005.jpg |
0.435378 | 91900ca688d44d28b47d26d8b5cb996e | (a) Cross section views of devices and (b) Key fabrication process of gate-first fabrication scheme of InGaAs-OI nMOSFET and Ge pMOSFET with dual-gate oxide technique. | PMC9692264 | micromachines-13-01806-g006.jpg |
0.464364 | aedcdbe05e37484e8e325c7d4ea6004a | Electron characteristics of Ge pMOSFET and InGaAs-OI nMOSFET with width/length = 50 μm/100 μm under gate-first fabrication process (a) Id-Vg curves of Ge pMOSFET, (b) Id-Vg curves of InGaAs-OI nMOSFET, (c) Id-Vd curves of both devices. | PMC9692264 | micromachines-13-01806-g007.jpg |
0.443949 | 1b9025e0e7754ce0a42767a067cdb8e9 | Electron mobility comparison of InGaAs-OI nMOSFET with two-stage gate oxide and one-stage gate oxide (width/length = 50 μm/100 μm). | PMC9692264 | micromachines-13-01806-g008.jpg |
0.458911 | 9d5328ebf9c34c37ad165e56d160d77b | The flowchart of the systems biology method and systematic drug discovery design. The construction of candidate GWGEN, real GWGEN, core GWGEN and core signaling pathways for investigating carcinogenic mechanisms to identify the biomarkers as drug targets of MIBC and ABC, and systematic drug discovery and design of potential drug combinations as multiple-molecule drugs to target the corresponding multiple drug targets for the treatment of MIBC and ABC. | PMC9692470 | ijms-23-13869-g001.jpg |
0.40258 | 682e0af6740d4e97a554fce255eb3923 | The common and specific core signaling pathways and their downstream cellular dysfunctions between MIBC and ABC. The figure shows the genetic and epigenetic carcinogenic mechanisms of MIBC and ABC. The orange background contains the specific core signaling pathways of MIBC. The green background contains the overlapping core signaling pathways between MIBC and ABC (i.e., common core signaling pathways). The blue background contains the specific core signaling pathways of ABC. The gene symbols in red or green font denote the selected significant biomarkers as drug targets. | PMC9692470 | ijms-23-13869-g002.jpg |
0.429362 | f3ba77e22c1e4de5bd7387d0f27dd930 | The flowchart of systematic drug design and discovery of MIBC and ABC. The drug-target interaction databases contain drug-target interaction data to construct the drug-target feature vector. After data preprocessing, the data is divided into training data and testing data to train the DNN-based DTI model. The feature vectors of biomarkers and drugs from drug-target interaction databases are used for the well-trained DNN-based DTI model to predict candidate drugs for the identified biomarkers (drug targets) of MIBC and ABC. The candidate drugs are then filtered by the drug design specifications to obtain potential drug combinations as multiple-molecule drugs for the treatment of MIBC and ABC. | PMC9692470 | ijms-23-13869-g003.jpg |
0.411044 | 2e42506533b241f3a9df47af49d9f7f0 | Image of 3 × 3 mm optical coherence tomography angiography (OCTA) performed by Deep-Range Imaging (DRI)-Triton SS-OCT device. (A) Retinal superficial capillary plexus (SCP). (B) Retinal deep capillary plexus (DCP). (C) Outer retina. (D) Choriocapillaris plexus (CC). (E) OCT profile (in orange the area in which SCP vessel density (VD) is studied). (F) Vessel density at the SCP as the percentage of pixels occupied by blood flow in the central area and in the 4 quadrants. (G) Fundus photography showing the examined OCTA area as a square in the central area. | PMC9692971 | jcm-11-06725-g001.jpg |
0.457301 | 6f0e178dcfec4804adce85809653ede9 | Example of foveal avascular zone (FAZ) area and diameters measured manually in both superficial (SCP) and deep capillary retinal plexuses (DCP). (A) FAZ area of the SCP. (B) FAZ diameter of the SCP. (C) FAZ area of the DCP. (D) FAZ diameter of the DCP. | PMC9692971 | jcm-11-06725-g002.jpg |
0.395053 | bfa754be4d4a4e86a9d1c4332ab5a969 | Macular status prior to surgery and type of surgery. Abbreviations: PPV, pars plana vitrectomy; SB, scleral buckling. | PMC9692971 | jcm-11-06725-g003.jpg |
0.473867 | f025f59cd954484f890ed85dac8c2e36 | Anatomical finding in patients with macula-on RDD vs. their fellow eyes. (A–C) Macula-on RDD. (D–F) Fellow healthy eye. (A,D) represent superficial capillary plexus (SCP); (B,E) represent deep capillary plexus (DCP); (C,F) represent VD in the SCP. | PMC9692971 | jcm-11-06725-g004.jpg |
0.501471 | 207d04c6cfe146ec89c99462bb197811 | Anatomical finding in patients with macula-off RDD vs. their fellow eyes. (A–C) Macula-on RDD. (D–F) Contralateral healthy eye. (A,D) represent superficial capillary plexus (SCP); (B,E) represents deep capillary plexus; and (C,F) represents vessel density in the SCP. | PMC9692971 | jcm-11-06725-g005.jpg |
0.436731 | 3f5e6d8c50f143c0b5bef5c8d2083d94 | Flowchart of children living with HIV enrolled in the present study. | PMC9693172 | viruses-14-02350-g001.jpg |
0.477392 | 5a89d402e3ed4bc1bd25dbd235cc28fb | Kaplan–Meier survival curves indicating the effect of cART initiation on time to virologic suppression of HIV RNA (N = 37). In total 13 infants initiated cART at up to 6 months of life, and 24 after 6 months. There were 4 children who never achieved undetectable VL after starting cART. | PMC9693172 | viruses-14-02350-g002.jpg |
0.429713 | 0b9ae5e8ec7f472ba3d36782ba4fd850 | Virologic and immunologic endpoints by time of treatment initiation. (A) HIV RNA VL at 6 months of age (N = 33); (B) HIV DNA ddPCR at 6 months of age (N = 32); (C) HIV RNA VL at 6–11 years of age (N = 37); (D) HIV DNA ddPCR at 6–11 years of age (N = 35). | PMC9693172 | viruses-14-02350-g003.jpg |
0.506806 | c6dbeeb5ee9a422b98578941599c21d3 | HIV RNA viral load kinetics according to response to therapy status. (A) Complete responders (N = 15) children with an undetectable VL at study entry with HIV RNA viral loads undetectable more than 75% of the time since cART initiation in early childhood. The dashed red line depicts the child’s age at cART initiation. (B) Partial Responders (N = 8), children with an undetectable VL at study entry with undetectable HIV RNA VL between 50–75% of the time since cART initiation in early childhood. (C) Non-responders (N = 14), children with detectable HIV RNA VL at study entry and/or HIV RNA VL detectable up to 50% of the time since cART initiation in early childhood. | PMC9693172 | viruses-14-02350-g004.jpg |
0.445193 | 733ed5ce62f241998a6088c1fa4e8669 | Viral reservoirs during childhood (6–11 years of age) measured by HIV DNA ddPCR. (A) HIV DNA ddPCR according to HIV-1 Western blot results; (B) HIV DNA ddPCR results according to response to cART; One child classified as a responder based on HIV RNA did not have ddPCR available, and 1 classified by HIV RNA as a non-responder did not have ddPCR. | PMC9693172 | viruses-14-02350-g005.jpg |
0.415347 | 9197a61e81414e07b8ebbd35b3b5ff51 | Structure–activity relationship between polyphenols and α-amylase and α-glucosidase enzymes. | PMC9693262 | life-12-01692-g001.jpg |
0.455637 | e57f83167d1045f9b054e00d4f6d08ef | The time curve of the cleavage of substrate S01 (purple curve) and substrate S03 (red curve). The data were averaged from three independent experiments. Sequences of substrates S01 and S03 are presented in Table 1. | PMC9693425 | ijms-23-13889-g001.jpg |
0.426479 | 874c326622344dc5bfb79533dfbe879c | (a) The time curve of cleavage of substrate S01. The data were averaged from three independent experiments. The degree (%) of cleavage and error can be found in Table 1. (b) Illustration of the complex between Cas9-sgRNA and a DNA substrate. The cleavage site is boldfaced, and sequences forming the complementary duplex are highlighted in green and yellow. REC—recognition lobe of Cas9, NUC—nuclease lobe, which comprises the conserved RuvC and HNH domains. | PMC9693425 | ijms-23-13889-g002.jpg |
0.429707 | dbe5074a512a4263b47efa9d2d1d0695 | The time curve of the cleavage of substrates (a) S03, (b) S04, and (c) S05. The data were averaged from three independent experiments. The degree (%) of cleavage and error can be found in Table 1. (d) The sequences of DNA substrates (S03, S04, and S05). The cleavage site is boldfaced, and the sequences forming a complementary duplex are highlighted in green and yellow. | PMC9693425 | ijms-23-13889-g003.jpg |
0.432169 | 264db141e67545ecb7634aafb6f61abe | PLS-DA score plots for the visualisation of clustering of AMI versus healthy samples based on (A) lipidomics and (B) metabolomics analysis, and the corresponding model performance measures across the number of components generated from (C) metabolomics and (D) lipidomics analysis. | PMC9693522 | metabolites-12-01080-g001.jpg |
0.478302 | 3c1787d1798e46cababa8e259811d52c | Distribution of lipid classes of annotated significant lipidomic features. | PMC9693522 | metabolites-12-01080-g002.jpg |
0.437299 | f8353577b30f4435b4126c178876f24d | Visualisation of correlations amongst omics datasets via (A) sample scatterplot displaying the first component in each omics block (upper diagonal) and Pearson correlation between each component (lower diagonal), (B) correlation circle plot representing feature contributions from each omics block, and (C) circos plot showing the correlations (r > 0.33) between omics features as indicated by the red (positive correlation) and green (negative correlation) links. | PMC9693522 | metabolites-12-01080-g003.jpg |
0.432751 | 089a8b8f0a794eaf8cb9e14b8ab47b9b | Relevance network plots of significant omics features from the discrimination of AMI vs. healthy patients, displaying (A) key cluster amongst all four omics with strong correlations between features (r ≥ 0.67; p-value < 0.05), and (B) tri-omics (glycomics + metallomics + lipidomics) correlations (r ≥ 0.5; p-value < 0.05). The color key indicates the correlation coefficient values annotated by the connection lines between variables. Red colored connection lines denote positive correlations, while green colored connection lines denote negative correlations between variables. The intensity of the colors is scaled according to the magnitude of the correlation coefficient values. | PMC9693522 | metabolites-12-01080-g004.jpg |
0.421526 | 6f93300902ea41bc9c54d40a82db9d11 | Relevance networks of significant omics features across various bi-omics combinations (all r ≥ 0.4; p-value < 0.05). The color key indicates the correlation coefficient values annotated by the connection lines between variables. Red colored connection lines denote positive correlations, while green colored connection lines denote negative correlations between variables. The intensity of the colors is scaled according to the magnitude of the correlation coefficient values. | PMC9693522 | metabolites-12-01080-g005.jpg |
0.361261 | 2d97fcf6f20d487481b5fec9b8c7e80f | Classification performance of glycomics, metallomics, metabolomics and lipidomics datasets as shown from (A) block-PLS-DA analysis and comparison of individual blocks with a consensus multi-omics model, and (B) hierarchical cluster analysis of the multi-omics dataset. | PMC9693522 | metabolites-12-01080-g006.jpg |
0.428317 | ac7b49473a144faa8c48e3c1d8e011e1 |
Orthologous groups between the E. formosa females and the male and female E. suzannae transcriptomes.
The above figure shows an OrthoVenn2 diagram of the orthologous groups between the E. formosa females and the male and female E. suzannae (e-value = 1 × 10−5) [60]. TransDecoder, using a minimum amino acid length of 50, was run on the E. formosa assemblies to obtain the coding sequences. The resulting peptide sequence output (27,161 sequences) was tested against the predicted proteins from the male and female E. suzannae transcriptomes. The top Venn diagram depicts the number of orthologous protein clusters shared between the three transcriptomes. The middle bar graph depicts the total number of orthologous clusters present for each transcriptome. Lastly, the bottom graph shows (left to right) the number of clusters that were shared by all three transcriptomes, by any two transcriptomes, or were unique to one of the three assemblies. | PMC9693781 | gigabyte-2022-68-g001.jpg |
0.408634 | 77de799a72e049f8b84e935ec65a2f3c | Increased glucose level can lead to end-stage renal disease. Hyperglycemia leads to oxidative stress which in turn activates elevated levels of lipid peroxidation, protein oxidation, and nucleic acid peroxidation that leads toward glomerular injury, then interstitial fibrosis to microvascular dysfunction and finally inflammation which further downregulate eGFR proteinuria and finally end-stage renal disease. | PMC9694611 | medicina-58-01604-g001.jpg |
0.488265 | 243428132198462e9c2f7d039a104388 | Age, weight, BMI, systolic and diastolic blood pressure of all the individuals. There is a significant difference between control and diseased group, an elevated level of BMI and weight can be observed between control vs. diseased group. Where p ≤ 0.05, * (less significance) represents the significant difference between groups. | PMC9694611 | medicina-58-01604-g002.jpg |
0.412363 | 93c093e285f54162af98b510dca9f33f | Hbg, RBCs, WBCs prothrombin, platelets, HCT. Of all the individuals there is a significant difference between control and diseased group between all the groups except platelets. Where p ≤ 0.05. *, **, *** (less, moderate, highly significant) represents the significant difference between groups. | PMC9694611 | medicina-58-01604-g003.jpg |
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