A graph theoretical approach to experimental prioritization in genome-scale investigations.

The goal of systems biology is to gain a network level understanding of how gene interactions influence biological states, and ultimately inform upon human disease. Given the scale and scope of systems biology studies, resource constraints often limit researchers when validating genome-wide phenomena and potentially lead to an incomplete understanding of the underlying mechanisms. Further, prioritization strategies are often biased towards known entities (e.g. previously studied genes/proteins with commercially available reagents), and other technical issues that limit experimental breadth. Here, heterogeneous biological information is modeled as an association graph to which a high-performance minimum dominating set solver is applied to maximize coverage across the graph, and thus increase the breadth of experimentation. First, we tested our model on retrieval of existing gene functional annotations and demonstrated that minimum dominating set returns more diverse terms when compared to other computational methods. Next, we utilized our heterogenous network and minimum dominating set solver to assist in the process of identifying understudied genes to be interrogated by the International Mouse Phenotyping Consortium. Using an unbiased algorithmic strategy, poorly studied genes are prioritized from the remaining thousands of genes yet to be characterized. This method is tunable and extensible with the potential to incorporate additional user-defined prioritizing information. The minimum dominating set approach can be applied to any biological network in order to identify a tractable subset of features to test experimentally or to assist in prioritizing candidate genes associated with human disease.

Establishment, Persistence, and Reactivation of Latent HIV-1 Infection in Renal Epithelial Cells.

HIV-1 persistence in different cell types presents the main obstacle to an HIV-1 cure. We have previously shown that the renal epithelium is a site of HIV-1 infection and that the kidney represents a separate viral compartment from blood. Whether renal cells can harbor latent virus that can be reactivated upon treatment with latency reversing agents (LRAs) is unknown. To address this question, we developed an in vitro HIV-1 latency model in renal tubule epithelial (RTE) cells using a dual color HIV-1 reporter virus, R7/E-/GFP/EF1a-mCherry (R7GEmC), and evaluated the effect of LRAs, both as single agents and in combination, on viral reactivation. Our data show that HIV-1 can establish latency in RTE cells early postinfection. While the pool of latently infected cells expanded overtime, the percentage of productively infected cells declined. Following LRA treatment only a small fraction of latently infected cells, both T cells and RTE cells, could be reactivated, and the drug combinations more effective in reactivating HIV transcription in RTE cells differed from those more active in T cells. Our study demonstrates that HIV can establish latency in RTE cells and that current LRAs are only marginally effective in inducing HIV-1 reactivation. This suggests that further study of LRA dynamics in non-T cells may be warranted to assess the suitability of LRAs as a sterilizing cure strategy. IMPORTANCE Anti-retroviral therapy (ART) has dramatically reduced HIV-related morbidity and mortality. Despite this success, a number of challenges remain, including the long-term persistence of multiple, clinically latent viral reservoirs capable of reactivation in the absence of ART. As efforts proceed toward HIV eradication or functional cure, further understanding of the dynamics of HIV-1 replication, establishment of latency and mechanisms of reactivation in reservoirs harboring the virus throughout the body is necessary. HIV-1 can infect renal epithelial cells and the expression of viral genes in those cells contributes to the development of HIV associated nephropathy (HIVAN) in untreated individuals. The significance of our work is in developing the first model of HIV-1 latency in renal epithelial cells. This model enhances our understanding of HIV-1 latency and persistence in the kidney and can be used to screen candidate latency reversing agents.

Exploring links between pathogen avoidance motivation, COVID-19 case counts, and immune function.

OBJECTIVES: The selection pressures exerted by pathogens have played important roles in shaping the biology and behavior of animals, including humans. Immune systems recognize and respond to cues of infection or damage by coordinating cellular, humoral, and metabolic shifts that promote recovery. Moreover, animals also possess a repertoire of behavioral tools to help combat the threat of pathogens, often referred to as the behavioral immune system. Recently, researchers have begun to examine how cognitive, affective, and behavioral disease avoidance mechanisms interact with the biological immune system. METHODS: The present study explored relationships among individual differences in behavioral immune system activity (e.g., pathogen disgust), shifts in SARS-CoV-2 infection risk (i.e., 7-day case averages), and immune function in a community cohort from McLennan County, Texas, USA (n = 387). RESULTS: Levels of disease concern were not consistently associated with immune markers. However, serum levels of IFN-γ, TNF-α, IL-2, and IL-8, as well as serum killing ability of Escherichia coli, each varied with case counts. Additional analyses found that case counts also predicted changes in stress physiology, but not subjective measures of distress. However, follow-up mediation models did not provide evidence that relationships between case counts and immunological outcomes were mediated through levels of stress. CONCLUSIONS: The present project provides initial evidence that markers of immune function may be sensitive to changes in infection risk during the COVID-19 pandemic. This adds to the growing body of research finding relationships among behavioral and biological pathogen management mechanisms.

Waco COVID Survey: A Community-Based SARS-CoV-2 Serological Surveillance Study in Central Texas.

In early-2020, the epidemiology of the SARS-CoV-2 virus was still in discovery and initial reports about the role of asymptomatic individuals were developing. The Waco COVID Survey was implemented in mid-2020 with targeted serological surveillance to assess relationships among risk factors and asymptomatic transmission in McLennan County, Texas, USA. Because large-scale random sampling of the population was not feasible, a targeted and repeated sampling of specific clustered groups of asymptomatic individuals was employed. This included four waves (initial intake [n = 495], two follow-ups separated by a month [n = 348; n = 287], and a final follow-up one year later [n = 313]) of sampling participants in different risk categories: (a) healthcare workers (e.g., physicians, nurses, etc.) and first responders, (b) essential service employees (e.g., convenience and grocery stores, restaurants focused on delivery and carry-out), (c) employees whose businesses began reopening on May 1 (e.g., dine-in restaurants, churches, etc.) including church attendees, and (d) individuals that practiced intensive isolation. The survey collected information on demographics, compliance with public health recommendations, satisfaction with government responses, health history, attitudes regarding the SARS-CoV-2 virus and COVID-19 disease, health behaviors, personality, stress, and general affect. Results illustrate pandemic fatigue over time, the influence of political leniency on opinions and behaviors, the importance of face coverings in preventing infection, and the positive impact of vaccination in the community. This project remains one of the largest longitudinal SARS-CoV-2 antibody seroprevalence surveys in the US, and details for successful implementation and community involvement are discussed.

Protein classification using modified n-grams and skip-grams.

Motivation: Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). Results: A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. Availability and implementation: m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. Contact: erich_baker@baylor.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Cisplatin-resistant triple-negative breast cancer subtypes: multiple mechanisms of resistance.

BACKGROUND: Understanding mechanisms underlying specific chemotherapeutic responses in subtypes of cancer may improve identification of treatment strategies most likely to benefit particular patients. For example, triple-negative breast cancer (TNBC) patients have variable response to the chemotherapeutic agent cisplatin. Understanding the basis of treatment response in cancer subtypes will lead to more informed decisions about selection of treatment strategies. METHODS: In this study we used an integrative functional genomics approach to investigate the molecular mechanisms underlying known cisplatin-response differences among subtypes of TNBC. To identify changes in gene expression that could explain mechanisms of resistance, we examined 102 evolutionarily conserved cisplatin-associated genes, evaluating their differential expression in the cisplatin-sensitive, basal-like 1 (BL1) and basal-like 2 (BL2) subtypes, and the two cisplatin-resistant, luminal androgen receptor (LAR) and mesenchymal (M) subtypes of TNBC. RESULTS: We found 20 genes that were differentially expressed in at least one subtype. Fifteen of the 20 genes are associated with cell death and are distributed among all TNBC subtypes. The less cisplatin-responsive LAR and M TNBC subtypes show different regulation of 13 genes compared to the more sensitive BL1 and BL2 subtypes. These 13 genes identify a variety of cisplatin-resistance mechanisms including increased transport and detoxification of cisplatin, and mis-regulation of the epithelial to mesenchymal transition. CONCLUSIONS: We identified gene signatures in resistant TNBC subtypes indicative of mechanisms of cisplatin. Our results indicate that response to cisplatin in TNBC has a complex foundation based on impact of treatment on distinct cellular pathways. We find that examination of expression data in the context of heterogeneous data such as drug-gene interactions leads to a better understanding of mechanisms at work in cancer therapy response.

GeneWeaver: data driven alignment of cross-species genomics in biology and disease.

The GeneWeaver data and analytics website (www.geneweaver.org) is a publically available resource for storing, curating and analyzing sets of genes from heterogeneous data sources. The system enables discovery of relationships among genes, variants, traits, drugs, environments, anatomical structures and diseases implicitly found through gene set intersections. Since the previous review in the 2012 Nucleic Acids Research Database issue, GeneWeaver's underlying analytics platform has been enhanced, its number and variety of publically available gene set data sources has been increased, and its advanced search mechanisms have been expanded. In addition, its interface has been redesigned to take advantage of flexible web services, programmatic data access, and a refined data model for handling gene network data in addition to its original emphasis on gene set data. By enumerating the common and distinct biological molecules associated with all subsets of curated or user submitted groups of gene sets and gene networks, GeneWeaver empowers users with the ability to construct data driven descriptions of shared and unique biological processes, diseases and traits within and across species.

Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification.

BACKGROUND: The Monkey Alcohol Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well-documented nonhuman primate (NHP) alcohol self-administration studies. This macaque model has demonstrated categorical drinking norms reflective of human drinking populations, resulting in consumption pattern classifications of very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD) individuals. Here, we expand on previous findings that suggest ethanol drinking patterns during initial drinking to intoxication can reliably predict future drinking category assignment. METHODS: The classification strategy uses a machine-learning approach to examine an extensive set of daily drinking attributes during 90 sessions of induction across 7 cohorts of 5 to 8 monkeys for a total of 50 animals. A Random Forest classifier is employed to accurately predict categorical drinking after 12 months of self-administration. RESULTS: Predictive outcome accuracy is approximately 78% when classes are aggregated into 2 groups, "LD and BD" and "HD and VHD." A subsequent 2-step classification model distinguishes individual LD and BD categories with 90% accuracy and between HD and VHD categories with 95% accuracy. Average 4-category classification accuracy is 74%, and provides putative distinguishing behavioral characteristics between groupings. CONCLUSIONS: We demonstrate that data derived from the induction phase of this ethanol self-administration protocol have significant predictive power for future ethanol consumption patterns. Importantly, numerous predictive factors are longitudinal, measuring the change of drinking patterns through 3 stages of induction. Factors during induction that predict future heavy drinkers include being younger at the time of first intoxication and developing a shorter latency to first ethanol drink. Overall, this analysis identifies predictive characteristics in future very heavy drinkers that optimize intoxication, such as having increasingly fewer bouts with more drinks. This analysis also identifies characteristic avoidance of intoxicating topographies in future low drinkers, such as increasing number of bouts and waiting longer before the first ethanol drink.

Increased levels of the acetaldehyde-derived DNA adduct N 2-ethyldeoxyguanosine in oral mucosa DNA from Rhesus monkeys exposed to alcohol.

Alcohol is a human carcinogen. A causal link has been established between alcohol drinking and cancers of the upper aerodigestive tract, colon, liver and breast. Despite this established association, the underlying mechanisms of alcohol-induced carcinogenesis remain unclear. Various mechanisms may come into play depending on the type of cancer; however, convincing evidence supports the concept that ethanol's major metabolite acetaldehyde may play a major role. Acetaldehyde can react with DNA forming adducts which can serve as biomarkers of carcinogen exposure and potentially of cancer risk. The major DNA adduct formed from this reaction is N (2)-ethylidenedeoxyguanosine, which can be quantified as its reduced form N (2)-ethyl-dG by LC-ESI-MS/MS. To investigate the potential use of N (2)-ethyl-dG as a biomarker of alcohol-induced DNA damage, we quantified this adduct in DNA from the oral, oesophageal and mammary gland tissues from rhesus monkeys exposed to alcohol drinking over their lifetimes and compared it to controls. N (2)-Ethyl-dG levels were significantly higher in the oral mucosa DNA of the exposed animals. Levels of the DNA adduct measured in the oesophageal mucosa of exposed animals were not significantly different from controls. A correlation between the levels measured in the oral and oesophageal DNA, however, was observed, suggesting a common source of formation of the DNA adducts. N (2) -Ethyl-dG was measured in mammary gland DNA from a small cohort of female animals, but no difference was observed between exposed animals and controls. These results support the hypothesis that acetaldehyde induces DNA damage in the oral mucosa of alcohol-exposed animals and that it may play role in the alcohol-induced carcinogenic process. The decrease of N (2)-ethyl-dG levels in exposed tissues further removed from the mouth also suggests a role of alcohol metabolism in the oral cavity, which may be considered separately from ethanol liver metabolism in the investigation of ethanol-related cancer risk.

PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

BACKGROUND: Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. RESULTS: We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. CONCLUSION: PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data.

A persistent challenge lies in the interpretation of consensus and discord from functional genomics experimentation. Harmonizing and analyzing this data will enable investigators to discover relations of many genes to many diseases, and from many phenotypes and experimental paradigms to many diseases through their genomic substrates. The GeneWeaver.org system provides a platform for cross-species integration and interrogation of heterogeneous curated and experimentally derived functional genomics data. GeneWeaver enables researchers to store, share, analyze, and compare results of their own genome-wide functional genomics experiments in an environment containing rich companion data obtained from major curated repositories, including the Mouse Genome Database and other model organism databases, along with derived data from highly specialized resources, publications, and user submissions. The data, largely consisting of gene sets and putative biological networks, are mapped onto one another through gene identifiers and homology across species. A versatile suite of interactive tools enables investigators to perform a variety of set analysis operations to find consilience among these often noisy experimental results. Fast algorithms enable real-time analysis of large queries. Specific applications include prioritizing candidate genes for quantitative trait loci, identifying biologically valid mouse models and phenotypic assays for human disease, finding the common biological substrates of related diseases, classifying experiments and the biological concepts they represent from empirical data, and applying patterns of genomic evidence to implicate novel genes in disease. These results illustrate an alternative to strict emphasis on replicability, whereby researchers classify experimental results to identify the conditions that lead to their similarity.

On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types.

BACKGROUND: Integrating and analyzing heterogeneous genome-scale data is a huge algorithmic challenge for modern systems biology. Bipartite graphs can be useful for representing relationships across pairs of disparate data types, with the interpretation of these relationships accomplished through an enumeration of maximal bicliques. Most previously-known techniques are generally ill-suited to this foundational task, because they are relatively inefficient and without effective scaling. In this paper, a powerful new algorithm is described that produces all maximal bicliques in a bipartite graph. Unlike most previous approaches, the new method neither places undue restrictions on its input nor inflates the problem size. Efficiency is achieved through an innovative exploitation of bipartite graph structure, and through computational reductions that rapidly eliminate non-maximal candidates from the search space. An iterative selection of vertices for consideration based on non-decreasing common neighborhood sizes boosts efficiency and leads to more balanced recursion trees. RESULTS: The new technique is implemented and compared to previously published approaches from graph theory and data mining. Formal time and space bounds are derived. Experiments are performed on both random graphs and graphs constructed from functional genomics data. It is shown that the new method substantially outperforms the best previous alternatives. CONCLUSIONS: The new method is streamlined, efficient, and particularly well-suited to the study of huge and diverse biological data. A robust implementation has been incorporated into GeneWeaver, an online tool for integrating and analyzing functional genomics experiments, available at http://geneweaver.org. The enormous increase in scalability it provides empowers users to study complex and previously unassailable gene-set associations between genes and their biological functions in a hierarchical fashion and on a genome-wide scale. This practical computational resource is adaptable to almost any applications environment in which bipartite graphs can be used to model relationships between pairs of heterogeneous entities.

Chronic alcohol self-administration in monkeys shows long-term quantity/frequency categorical stability.

BACKGROUND: The current criteria for alcohol use disorders (AUDs) do not include consumption (quantity/frequency) measures of alcohol intake, in part due to the difficulty of these measures in humans. Animal models of ethanol (EtOH) self-administration have been fundamental in advancing our understanding of the neurobiological basis of AUD and can address quantity/frequency measures with accurate measurements over prolonged periods of time. The nonhuman primate model of voluntary oral alcohol self-administration has documented both binge drinking and drinking to dependence and can be used to test the stability of consumption measures over time. METHODS: Here, an extensive set of alcohol intakes (g/kg/d) was analyzed from a large multi-cohort population of Rhesus (Macaca mulatta) monkeys (n = 31). Daily EtOH intake was uniformly distributed over chronic (12 months) access for all animals. RESULTS: Underlying this distribution of intakes were subpopulations of monkeys that exhibited distinctive clustering of drinking patterns, allowing us to categorically define very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD). These categories were stable across the 12 months assessed by the protocol, but exhibited fluctuations when examined at shorter intervals. CONCLUSIONS: The establishment of persistent drinking categories based on quantity/frequency suggests that consumption variables can be used to track long-term changes in behavioral, molecular, or physiochemical mechanisms related to our understanding of diagnosis, prevention, intervention, and treatment efficacies.

Monkey alcohol tissue research resource: banking tissues for alcohol research.

BACKGROUND: An estimated 18 million adults in the United States meet the clinical criteria for diagnosis of alcohol abuse or alcoholism, a disorder ranked as the third leading cause of preventable death. In addition to brain pathology, heavy alcohol consumption is comorbid with damage to major organs including heart, lungs, liver, pancreas, and kidneys. Much of what is known about risk for and consequences of heavy consumption derive from rodent or retrospective human studies. The neurobiological effects of chronic intake in rodent studies may not easily translate to humans due to key differences in brain structure and organization between species, including a lack of higher-order cognitive functions, and differences in underlying prefrontal cortical neural structures that characterize the primate brain. Further, rodents do not voluntarily consume large quantities of ethanol (EtOH) and they metabolize it more rapidly than primates. METHODS: The basis of the Monkey Alcohol Tissue Research Resource (MATRR) is that nonhuman primates, specifically monkeys, show a range of drinking excessive amounts of alcohol (>3.0 g/kg or a 12 drink equivalent per day) over long periods of time (12 to 30 months) with concomitant pathological changes in endocrine, hepatic, and central nervous system (CNS) processes. The patterns and range of alcohol intake that monkeys voluntarily consume parallel what is observed in humans with alcohol use disorders and the longitudinal experimental design spans stages of drinking from the EtOH-naïve state to early exposure through chronic abuse. Age- and sex-matched control animals self-administer an isocaloric solution under identical operant procedures. RESULTS: The MATRR is a unique postmortem tissue bank that provides CNS and peripheral tissues, and associated bioinformatics from monkeys that self-administer EtOH using a standardized experimental paradigm to the broader alcohol research community. CONCLUSIONS: This resource provides a translational platform from which we can better understand the disease processes associated with alcoholism.

Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings.

BACKGROUND: Leukocoria is defined as a white reflection and its manifestation is symptomatic of several ocular pathologies, including retinoblastoma (Rb). Early detection of recurrent leukocoria is critical for improved patient outcomes and can be accomplished via the examination of recreational photography. To date, there exists a paucity of methods to automate leukocoria detection within such a dataset. METHODS: This research explores a novel classification scheme that uses fuzzy logic theory to combine a number of classifiers that are experts in performing multichannel detection of leukocoria from recreational photography. The proposed scheme extracts features aided by the discrete cosine transform and the Karhunen-Loeve transformation. RESULTS: The soft fusion of classifiers is significantly better than other methods of combining classifiers with p = 1.12 × 10-5. The proposed methodology performs at a 92% accuracy rate, with an 89% true positive rate, and an 11% false positive rate. Furthermore, the results produced by our methodology exhibit the lowest average variance. CONCLUSIONS: The proposed methodology overcomes non-ideal conditions of image acquisition, presenting a competent approach for the detection of leukocoria. Results suggest that recreational photography can be used in combination with the fusion of individual experts in multichannel classification and preprocessing tools such as the discrete cosine transform and the Karhunen-Loeve transformation.

GeneWeaver: a web-based system for integrative functional genomics.

High-throughput genome technologies have produced a wealth of data on the association of genes and gene products to biological functions. Investigators have discovered value in combining their experimental results with published genome-wide association studies, quantitative trait locus, microarray, RNA-sequencing and mutant phenotyping studies to identify gene-function associations across diverse experiments, species, conditions, behaviors or biological processes. These experimental results are typically derived from disparate data repositories, publication supplements or reconstructions from primary data stores. This leaves bench biologists with the complex and unscalable task of integrating data by identifying and gathering relevant studies, reanalyzing primary data, unifying gene identifiers and applying ad hoc computational analysis to the integrated set. The freely available GeneWeaver (http://www.GeneWeaver.org) powered by the Ontological Discovery Environment is a curated repository of genomic experimental results with an accompanying tool set for dynamic integration of these data sets, enabling users to interactively address questions about sets of biological functions and their relations to sets of genes. Thus, large numbers of independently published genomic results can be organized into new conceptual frameworks driven by the underlying, inferred biological relationships rather than a pre-existing semantic framework. An empirical 'ontology' is discovered from the aggregate of experimental knowledge around user-defined areas of biological inquiry.

KNeXT: a NetworkX-based topologically relevant KEGG parser.

Automating the recreation of gene and mixed gene-compound networks from Kyoto Encyclopedia of Genes and Genomes (KEGG) Markup Language (KGML) files is challenging because the data structure does not preserve the independent or loosely connected neighborhoods in which they were originally derived, referred to here as its topological environment. Identical accession numbers may overlap, causing neighborhoods to artificially collapse based on duplicated identifiers. This causes current parsers to create misleading or erroneous graphical representations when mixed gene networks are converted to gene-only networks. To overcome these challenges we created a python-based KEGG NetworkX Topological (KNeXT) parser that allows users to accurately recapitulate genetic networks and mixed networks from KGML map data. The software, archived as a python package index (PyPI) file to ensure broad application, is designed to ingest KGML files through built-in APIs and dynamically create high-fidelity topological representations. The utilization of NetworkX's framework to generate tab-separated files additionally ensures that KNeXT results may be imported into other graph frameworks and maintain programmatic access to the original x-y axis positions to each node in the KEGG pathway. KNeXT is a well-described Python 3 package that allows users to rapidly download and aggregate specific KGML files and recreate KEGG pathways based on a range of user-defined settings. KNeXT is platform-independent, distinctive, and it is not written on top of other Python parsers. Furthermore, KNeXT enables users to parse entire local folders or single files through command line scripts and convert the output into NCBI or UniProt IDs. KNeXT provides an ability for researchers to generate pathway visualizations while persevering the original context of a KEGG pathway. Source code is freely available at https://github.com/everest-castaneda/knext.

A Systematic Approach to Evidence-Based Design for Measurable Health and Wellness Outcomes: Curating and Translating Evidence to Practice.

OBJECTIVES: Evaluating evidence from peer-review literature for use in evidence-based design is often challenging for the design disciplines, requiring access to the peer-reviewed literature, expertise in evaluating methods and findings, and translating the results into actionable design and operational recommendations. PURPOSE: The purpose of this methods paper is to elucidate the process for systematic evaluation of research to translate evidence into practical application to improve design for occupant health and wellness. BACKGROUND: Researchers have found strong connections in environmental design influence on health and wellness that have proven to be substantiative in the effort to improve health and well-being. Design has the capacity to encourage healthy choices and decisions within the built environment. Translation of evidence into applied design solutions may improve public health. METHODS: A protocol is presented that culminates in the translation of evidence into design recommendations focused on improving occupant health. The protocol includes preparation for the literature search and review, search strategy, study selection, data analysis, and development of the literature review. RESULTS: After evaluation of the evidence is completed, there were several positive findings in the example that stakeholders could utilize to improve the health of building occupants with programs and design to support nutrition, physical activity, and circadian entrainment. CONCLUSIONS: There are a variety of software tools and processes to utilize in the curation of evidence to improve the built environment with relevant design recommendations and operational considerations affecting the personal, social, and economic health of our society.

Aon: a service to augment Alliance Genome Resource data with additional species.

OBJECTIVE: Cross-species comparative genomics requires access to accurate homology data across the entire range of annotated genes. The Alliance of Genome Resources (AGR) provides an open-source and comprehensive database of homology data calculated using a wide array of algorithms at differing stringencies to elucidate orthologous relationships. However, the current AGR application program interface (API) is limited to five homology endpoints for nine species. While AGR provides a robust resource for several canonical species, its utility can be greatly enhanced by increased filtering and data processing options and incorporating additional species. RESULTS: Here, we describe a novel API tool, AON, that expands access to the AGR orthology resource by creating a data structure that supports 50 additional endpoints. More importantly, it provides users with a framework for adding bespoke endpoints, custom species, and additional orthology data. We demonstrate AON's functionality by incorporating the service into the GeneWeaver ecosystem for supporting cross-species data analysis.

Long-term drinking stability in the open-access self-administration monkey model.

The Non-Human Primate (NHP) model for the study of Alcohol Use Disorders (AUD) as developed in our laboratories is critical to our understanding of the pathophysiology of voluntary, chronic, ethanol consumption. Previous work in this model established categories of ethanol consumption that parallel reported categories of human consumption across a spectrum spanning low drinking, binge drinking, heavy drinking, and very heavy drinking, albeit at generally higher daily intakes across categories than documented in people. Original categories assigned to ethanol consumption patterns were established using a limited cohort of rhesus macaques. This study revisits the validity of categorical drinking using an additional 28 monkeys. In addition to finding categorical representations consistent with the original 2014 report, our findings demonstrate that drinking categories remain stable across the observed 12 months of nearly consistent access to ethanol (22 h/day), termed "open access". Animals occupying the two ends of the spectrum, "low" and "very heavy" drinkers, exhibit the largest stability. The findings also indicate a slight escalatory drift over time, with very heavy drinking animals experiencing fatigue near the end of open access.