An integration of enhanced social force and crowd control models for high-density crowd simulation

Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population

Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk

HIV Protein Sequence Hotspots for Crosstalk with Host Hub Proteins

HIV proteins target host hub proteins for transient binding interactions. The presence of viral proteins in the infected cell results in out-competition of host proteins in their interaction with hub proteins, drastically affecting cell physiology. Functional genomics and interactome datasets can be used to quantify the sequence hotspots on the HIV proteome mediating interactions with host hub proteins. In this study, we used the HIV and human interactome databases to identify HIV targeted host hub proteins and their host binding partners (H2). We developed a high throughput computational procedure utilizing motif discovery algorithms on sets of protein sequences, including sequences of HIV and H2 proteins. We identified as HIV sequence hotspots those linear motifs that are highly conserved on HIV sequences and at the same time have a statistically enriched presence on the sequences of H2 proteins. The HIV protein motifs discovered in this study are expressed by subsets of H2 host proteins potentially outcompeted by HIV proteins. A large subset of these motifs is involved in cleavage, nuclear localization, phosphorylation, and transcription factor binding events. Many such motifs are clustered on an HIV sequence in the form of hotspots. The sequential positions of these hotspots are consistent with the curated literature on phenotype altering residue mutations, as well as with existing binding site data. The hotspot map produced in this study is the first global portrayal of HIV motifs involved in altering the host protein network at highly connected hub nodes

mtDNA Variation and Analysis Using MITOMAP and MITOMASTER

International audienceThe MITOMAP database of human mitochondrial DNA (mtDNA) information has been an important compilation of mtDNA variation for researchers, clinicians and genetic counselors for the past twenty-five years. The MITOMAP protocol shows how users may look up human mitochondrial gene loci, search for public mitochondrial sequences, and browse or search for reported general population nucleotide variants as well as those reported in clinical disease. Within MITOMAP is the powerful sequence analysis tool for human mitochondrial DNA, MITOMASTER. The MITOMASTER protocol gives step-by-step instructions showing how to submit sequences to identify nucleotide variants relative to the rCRS, to determine the haplogroup, and to view species conservation. User-supplied sequences, GenBank identifiers and single nucleotide variants may be analyzed.</p

A comparison of survival analysis methods for cancer gene expression RNA-Sequencing data

Identifying genetic biomarkers of patient survival remains a major goal of large-scale cancer profiling studies. Using gene expression data to predict the outcome of a patient's tumor makes biomarker discovery a compelling tool for improving patient care. As genomic technologies expand, multiple data types may serve as informative biomarkers, and bioinformatic strategies have evolved around these different applications. For categorical variables such as a gene's mutation status, biomarker identification to predict survival time is straightforward. However, for continuous variables like gene expression, the available methods generate highly-variable results, and studies on best practices are lacking. We investigated the performance of eight methods that deal specifically with continuous data. K-means, Cox regression, concordance index, D-index, 25th–75th percentile split, median-split, distribution-based splitting, and KaplanScan were applied to four RNA-sequencing (RNA-seq) datasets from the Cancer Genome Atlas. The reliability of the eight methods was assessed by splitting each dataset into two groups and comparing the overlap of the results. Gene sets that had been identified from the literature for a specific tumor type served as positive controls to assess the accuracy of each biomarker using receiver operating characteristic (ROC) curves. Artificial RNA-Seq data were generated to test the robustness of these methods under fixed levels of gene expression noise. Our results show that methods based on dichotomizing tend to have consistently poor performance while C-index, D-index, and k-means perform well in most settings. Overall, the Cox regression method had the strongest performance based on tests of accuracy, reliability, and robustness

Nucleic acid recognition and antiviral activity of 1,4-substituted terphenyl compounds mimicking all faces of the HIV-1 Rev protein positively-charged α-helix

Small synthetic molecules mimicking the three-dimensional structure of α-helices may find applications as inhibitors of therapeutically relevant protein-protein and protein-nucleic acid interactions. However, the design and use of multi-facial helix mimetics remains in its infancy. Here we describe the synthesis and application of novel bilaterally substituted p-terphenyl compounds containing positively-charged aminoalkyl groups in relative 1,4 positions across the aromatic scaffold. These compounds were specifically designed to mimic all faces of the arginine-rich α-helix of the HIV-1 protein Rev, which forms deeply embedded RNA complexes and plays key roles in the virus replication cycle. Two of these molecules recognized the Rev site in the viral RNA and inhibited the formation of the RRE-Rev ribonucleoprotein complex, a currently unexploited target in HIV chemotherapy. Cellular assays revealed that the most active compounds blocked HIV-1 replication with little toxicity, and likely exerted this effect through a multi-target mechanism involving inhibition of viral LTR promoter-dependent transcription and Rev function. Further development of this scaffold may open new avenues for targeting nucleic acids and may complement current HIV therapies, none of which involve inhibitors interfering with the gene regulation processes of the virus.This project was supported by Ministerio de Economía y Competitividad of Spain (Grants BFU2012–30770 and BFU2015–65103-R to J.G.; CTQ2013-43310 and CTQ2017-84249-P to S.F. and FIS PI16CIII/0034 to J.A.; and FPU15/01485 predoctoral fellowship to D.M.S.), Generalitat Valenciana of Spain (FPA/2015/014 and APOTIP/2016/A007 to J.G. and PROMETEOII/2014/073 to S.F.), the Spanish AIDS Research Network (RD16CIII/0002/0001-ISCIII–FEDER to J.A.), Universidad Católica de Valencia (2017-114-001 and 2018-114-001 to J.G.), and European AIDS Vaccine Initiative 2020 (ID 681137 to J.A.). The authors thank Ainhoa Sánchez for carrying out initial fluorescence anisotropy experiments, Ángel Cantero-Camacho for designing and testing the primers used to amplify LTRc, and Jerónimo Bravo and Antonio Pineda for facilitating access to ITC equipment. Plasmid pLTR(HTLV)-luc (pGL4.20-U3R) was kindly donated by Thomas Kress.S

Molecular Diagnostic Outcomes from 700 Cases: What Can We Learn from a Retrospective Analysis of Clinical Exome Sequencing?

Clinical exome sequencing (CES) aids in the diagnosis of rare genetic disorders. Herein, we report the molecular diagnostic yield and spectrum of genetic alterations contributing to disease in 700 pediatric cases analyzed at the Children's Hospital of Philadelphia. The overall diagnostic yield was 23%, with three cases having more than one molecular diagnosis and 2.6% having secondary/additional findings. A candidate gene finding was reported in another 8.4% of cases. The clinical indications with the highest diagnostic yield were neurodevelopmental disorders (including seizures), whereas immune- and oncology-related indications were negatively associated with molecular diagnosis. The rapid expansion of knowledge regarding the genome's role in human disease necessitates reanalysis of CES samples. To capture these new discoveries, a subset of cases (n = 240) underwent reanalysis, with an increase in diagnostic yield. We describe our experience reporting CES results in a pediatric setting, including reporting of secondary findings, reporting newly discovered genetic conditions, and revisiting negative test results. Finally, we highlight the challenges associated with implementing critical updates to the CES workflow. Although these updates are necessary, they demand an investment of time and resources from the laboratory. In summary, these data demonstrate the clinical utility of exome sequencing and reanalysis, while highlighting the critical considerations for continuous improvement of a CES test in a clinical laboratory