Using concepts from art theory, a proposed computational model enables computers to predict an observer's positive or negative response to an abstract painting. It can also generate abstract paintings that elicit ...
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Using concepts from art theory, a proposed computational model enables computers to predict an observer's positive or negative response to an abstract painting. It can also generate abstract paintings that elicit an intended emotional response.
The binding affinities (IC50) reported for diverse structural and chemical classes of human beta-secretase 1 (BACE-1) inhibitors in literature were modeled using multiple in silico ligand based modeling approaches and...
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The binding affinities (IC50) reported for diverse structural and chemical classes of human beta-secretase 1 (BACE-1) inhibitors in literature were modeled using multiple in silico ligand based modeling approaches and statistical techniques. The descriptor space encompasses simple binary molecular fingerprint, one- and two-dimensional constitutional, physicochemical, and topological descriptors, and sophisticated three-dimensional molecular fields that require appropriate structural alignments of varied chemical scaffolds in one universal chemical space. The affinities were modeled using qualitative classification or quantitative regression schemes involving linear, nonlinear, and deep neural network (DNN) machine-learning methods used in the scientific literature for quantitative-structure activity relationships (QSAR). In a departure from tradition, similar to 20% of the chemically diverse data set (205 compounds) was used to train the model with the remaining similar to 80% of the structural and chemical analogs used as part of an external validation (1273 compounds) and prospective test (69 compounds) sets respectively to ascertain the model performance. The machine -learning methods investigated herein performed well in both the qualitative classification (similar to 70% accuracy) and quantitative IC50 predictions (RMSE similar to 1 log). The success of the 2D descriptor based machine learning approach when compared against the 3D field based technique pursued for hBACE-1 inhibitors provides a strong impetus for systematically applying such methods during the lead identification and optimization efforts for other protein families as well.
This paper develops a computational model and applies it to investigate the performance of a rechargeable battery that is comprised of a molten-sodium anode, a NASICON sodium-ion-conducting separator membrane, and a i...
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This paper develops a computational model and applies it to investigate the performance of a rechargeable battery that is comprised of a molten-sodium anode, a NASICON sodium-ion-conducting separator membrane, and a iodine-based cathode. The cathode compartment is comprised of a porous-carbon felt and an aqueous catholyte that supports an iodine redox process. The battery chemistry can be represented globally as 2Na(+) + 2I(-) reversible arrow 2Na + I-2. The ion transport is represented in terms of a Nernst-Planck formulation that includes four mobile species within the catholyte, Na+, I-, I-3(-) and I-2. The charge transfer chemistry is modeled using a Butler-Volmer formulation. The model considers solubility limits and the potential precipitation of NaI and I-2. Parameter studies investigate the influences of charge and discharge rates, total elemental iodine loading, and effective conductivity of the carbon-felt cathode. (C) 2016 Elsevier Ltd. All rights reserved.
Introduction: There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the ...
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Introduction: There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. Areas covered: This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. Expert opinion: Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.
Replicating oncolytic viruses represent a promising treatment approach against cancer, specifically targeting the tumor cells. Significant progress has been made through experimental and clinical studies. Besides thes...
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Replicating oncolytic viruses represent a promising treatment approach against cancer, specifically targeting the tumor cells. Significant progress has been made through experimental and clinical studies. Besides these approaches, however, mathematical models can be useful when analyzing the dynamics of virus spread through tumors, because the interactions between a growing tumor and a replicating virus are complex and nonlinear, making them difficult to understand by experimentation alone. Mathematical models have provided significant biological insight into the field of virus dynamics, and similar approaches can be adopted to study oncolytic viruses. The review discusses this approach and highlights some of the challenges that need to be overcome in order to build mathematical and computation models that are clinically predictive. WIREs Syst Biol Med 2016, 8:242-252. doi: 10.1002/wsbm.1332 For further resources related to this article, please visit the .
Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been...
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Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been rigorously evaluated. Furthermore, despite these recommendations, there is a wide variation in the nutritional strategies employed amongst providers, particularly regarding the inclusion of parenteral lipids for protein-free caloric support. Since randomized clinical trials during acute metabolic decompensation are difficult and potentially dangerous, mathematical modeling of metabolism can serve as a surrogate for the preclinical evaluation of nutritional interventions aimed at restoring nitrogen balance during acute decompensation in AA IEM. Avalidated computational model of human macronutrient metabolism was adapted to predict nitrogen balance in response to various nutritional interventions in a simulated patient with a urea cycle disorder (UCD) during acute metabolic decompensation due to dietary non-adherence or infection. The nutritional interventions were constructed from published recommendations as well as clinical anecdotes. Overall, dextrose alone (DEX) was predicted to be better at restoring nitrogen balance and limiting nitrogen excretion during dietary non-adherence and infection scenarios, suggesting that the published recommended nutritional strategy involving dextrose and parenteral lipids (ISO) may be suboptimal. The implications for patients with AA IEM are that the medical course during acute metabolic decompensation may be influenced by the choice of protein-free caloric support. These results are also applicable to intensive care patients undergoing catabolism (postoperative phase or sepsis), where parenteral nutritional support aimed at restoring nitrogen balance may be more tailored regarding metabolic fuel selection.
Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. computational models are increasingly used as a tool to identify potential m...
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Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating-omits and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity. (C) 2015 Elsevier Ltd. All rights reserved.
computational modeling of organizational process is an important problem in understanding the organizational operation during insurgent event. The key of modeling organizational dynamics during event process is to des...
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computational modeling of organizational process is an important problem in understanding the organizational operation during insurgent event. The key of modeling organizational dynamics during event process is to describe the uncertainty of individual behaviors. Towards this end, in this paper, the dynamic organizational evolution during an insurgent event is analyzed, and the individual behaviors are modeled as task-oriented self-organization process. A real insurgent event is used to analyze the organizational process, and the organizational structure, task workflow and information flow during the process are studied. Experimental results demonstrate that the proposed model is consistent with the empirical research. The method provides a way to understand the dynamic organizational evolution process and exhibits potential application in strategy development.
Background: Treatment of bifurcation lesion disease is complex with limited studies that describe the influence of lesion anatomy on clinical outcomes. Hypothesis: computational simulations can be used to understand t...
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ISBN:
(纸本)9781728101149
Background: Treatment of bifurcation lesion disease is complex with limited studies that describe the influence of lesion anatomy on clinical outcomes. Hypothesis: computational simulations can be used to understand the interplay between morphological characteristics of lesion and clinical diagnostic metrics. Methods: Geometric modifications along the bifurcation in a patient-derived left coronary artery were made to incorporate unique combination of anatomic features: curvature, length and occlusion severity. The resulting geometries were used to perform CFD simulations using physiological flow parameters. Three diagnostic metrics, resting gradient, instantaneous wave free ratio (iFR) and diastolic-systolic velocity ratio (DSVR), were computed from the simulations. Results: We report occlusion severity to be an independent predictor for lower resting gradient and iFR values, whereas lesion length and curvature did not yield dramatic changes in iFR and resting gradient. Our results suggest that DSVR is more sensitive to nuanced flow disturbances;however, it may be complex to derive direct correspondence to disease severity relative to resting gradient and iFR. Conclusion: Spatial lesion characteristics can be used to determine diseased bifurcation cases that may lead to interventional complications.
The objective of this study is to use individualized heart computer models in evaluating efficacy of myocardial infarct (MI) mass determined by two different MRI techniques in predicting patient risk for post-MI ventr...
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ISBN:
(纸本)9783030009342;9783030009335
The objective of this study is to use individualized heart computer models in evaluating efficacy of myocardial infarct (MI) mass determined by two different MRI techniques in predicting patient risk for post-MI ventricular tachycardia (VT). 27 patients with MI underwent late gadolinium-enhanced MRI using inversion-recovery fast gradient echo (IR-FGRE) and multi-contrast late enhancement (MCLE) prior to implantable cardioverter defibrillators (ICD) implantation and were followed up for 6-46 months. The myocardium, MI core (IC), and border zone (BZ) were segmented from the images using previously validated techniques. The segmented structures were then reconstructed as a high-resolution label map in 3D. Individualized image-based computational models were built separately for each imaging technique;simulations of propensity to VT were conducted with each model. The imaging methods were evaluated for sensitivity and specificity by comparing simulated inducibility of VT to clinical outcome (appropriate ICD therapy) in patients. Twelve patients had at least one appropriate ICD therapy for VT at follow-up. For both MCLE and IR-FGRE, the outcomes of the simulations of VT were significantly different between the groups with and without ICD therapy. Between the IR-FGRE and MCLE, the virtual models built using the latter may have yielded higher sensitivity and specificity in predicting appropriate ICD therapy.
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