Search

The soybean crop, Glycine max (L.) Merr., is consumed by humans, Homo sapiens, worldwide. While the respective bodies of literature and -omics data for each of these organisms are extensive, comparatively few studies investigate the molecular biological processes occurring between the two. We are interested in elucidating the network of protein-protein interactions (PPIs) involved in human-soybean allergies. To this end, we leverage state-of-the-art sequence-based PPI predictors amenable to predicting the enormous comprehensive interactome between human and soybean. A network-based analytical approach is proposed, leveraging similar interaction profiles to identify candidate allergens and proteins involved in the allergy response. Interestingly, the predicted interactome can be explored from two complementary perspectives: which soybean proteins are predicted to interact with specific human proteins and which human proteins are predicted to interact with specific soybean proteins. A total of eight proteins (six specific to the human proteome and two to the soy proteome) have been identified and supported by the literature to be involved in human health, specifically related to immunological and neurological pathways. This study, beyond generating the most comprehensive human-soybean interactome to date, elucidated a soybean seed interactome and identified several proteins putatively consequential to human health.

Web scraping, defined as the automated extraction of information online, is an increasingly important means of producing data in the social sciences. We contribute to emerging social science literature on computational methods by elaborating on web scraping as a means of automated access to information. We begin by situating the practice of web scraping in context, providing an overview of how it works and how it compares to other methods in the social sciences. Next, we assess the benefits and challenges of scraping as a technique of information production. In terms of benefits, we highlight how scraping can help researchers answer new questions, supersede limits in official data, overcome access hurdles, and reinvigorate the values of sharing, openness, and trust in the social sciences. In terms of challenges, we discuss three: technical, legal, and ethical. By adopting “algorithmic thinking in the public interest” as a way of navigating these hurdles, researchers can improve the state of access to information on the Internet while also contributing to scholarly discussions about the legality and ethics of web scraping. Example software accompanying this article are available within the supplementary materials.

Our understanding of the prevalence of mental health disorders (MHDs) in society is in the midst of a paradigm shift: where MHDs were once considered rare within a population, studies through the last decade have converged to the conclusion that they are, in fact, near universal. Consequently, the demand for mental health treatment has resulted in the training of Primary-Care Physicians (PCPs) to identify, diagnose, and treat common MHDs. As generalists, PCPs require specialised point-of-care clinical resources to educate their patients and provide them with evidence-based treatment plans; UpToDate is one such resource. As a database of synthesized peer-reviewed medical information, written and approved by physician-experts from their review of contemporary peer-reviewed literature, this resource is considered a gold standard. Here, we examine an MHD-specific investigative case study on Generalized Anxiety Disorder where the synthesized UpToDate medical information was found to be in conflict with the original studies. In this era of unrelenting bombardment of digital data, the responsibility of assessing the truth of the information falls to the consumer. While a reliance on reputable information-sharing platforms facilitates both the access and assessment of truth, we discuss the risks of unintended errors, their propagation, and the potential impact at the point-of-care.

Humans have sought to cheat death for as long as we have been cognizant of our mortality. History’s early explorers of the frontier of immortality include alchemists in the pursuit of an elixir of life and emperors who, ironically, hastened their own death from the consumption of mercurial concoctions. Scientifically grounded approaches to the extension of the human lifespan emerged in the 20th century and were based on hormonal rejuvenation, calorie restriction, and most recently, the consumption of supplements with purported anti-aging effects. A combination of three “longevity drugs” has recently been championed by Dr. David Sinclair, co-discoverer of the lifespan-regulating sirtuin enzymes, and author of the epigenetics-focused Information Theory of Aging (ITA). In this work, we investigate the evidence behind Sinclair’s ITA, highlight concerns related to his regimen, and reflect on the possibility that we are at a nexus in time preceding a dramatic increase in human healthspans. Promisingly, if the ITA holds true, individuals will be uniquely empowered to “hack” their own immortality.

Audio measurements are fundamental to daily life and have been used to perform a variety of tasks including the classification of human sounds (e.g. talking or coughing) and environmental acoustic monitoring. Audio visualization methods have been introduced to represent both the time- and frequency-domain information of a recording. In this paper we introduce Chaos Game Representation (CGR) to investigate possible reoccurring local and global patterns within audio measurements to supplement current audio visualization methods and for possible use in the training and evaluation of learning algorithms. A major challenge of the application of CGR within audio-space is quantization. Here, we leverage the non-uniform $\mu$ -law ( $\mu=255$ ) quantization as the basis for the first quantized audio CGR representation. We propose a 256-nodal arrangement of the quantized states from an audio measurement for playing the Chaos Game to generate visualizations that capture both local and global sequential time-series information. CGRs were generated for 287,756 individual audio measurements (pure sinusoids, linear & quadratic chirps, and ambient audio measurements from DCASE2018). A typology of visually observable patterns is discussed describing the relationship between the time-series audio signal and their resulting CGR visualizations. These images may be leveraged for training image-based classifiers for audio classification tasks.

MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predicting which miRNA will target which gene. Application of these predictors to large numbers of unique miRNA–gene pairs has resulted in datasets comprising tens of millions of scored interactions; the largest among these is mirDIP. We here demonstrate that miRNA target prediction can be significantly improved ($$p < 0.001$$p<0.001) through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction. The RP method, aptly named RPmirDIP, augments the original mirDIP prediction scores by leveraging local thresholds from the two complimentary views available to each miRNA–gene pair, rather than apply a traditional global decision threshold. Application of this novel RPmirDIP predictor promises to help identify new, unexpected miRNA–gene interactions. A dataset of RPmirDIP-scored interactions are made available to the scientific community at cu-bic.ca/RPmirDIP and https://doi.org/10.5683/SP2/LD8JKJ.

Summary Synthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS) was developed to design synthetic binding proteins (SBPs) that bind pre-determined targets while minimizing off-target interactions. InSiPS is a genetic algorithm that refines a pool of random sequences over hundreds of generations of mutation and selection to produce SBPs with pre-specified binding characteristics. As a proof of concept, we design SBPs against three yeast proteins and demonstrate binding and functional inhibition of two of three targets in vivo. Peptide SPOT arrays confirm binding sites, and a permutation array demonstrates target specificity. Our foundational approach will support the field of de novo design of small binding polypeptide motifs and has robust applicability while offering potential advantages over the limited number of techniques currently available., Graphical Abstract, Highlights • InSiPS engineers synthetic binding proteins (SBPs) using primary protein sequence • SBPs are designed to a bind a target protein and avoid “off-target” interactions • Binding and functional inhibition of two of three target proteins in yeast is demonstrated • Our new approach offers advantages over alternative tools that rely on 3D models, Biological Sciences; Bioinformatics; Protein Family Determination

All protein-protein interaction (PPI) predictors require the determination of an operational decision threshold when differentiating positive PPIs from negatives. Historically, a single global threshold, typically optimized via cross-validation testing, is applied to all protein pairs. However, we here use data visualization techniques to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles. The recent development of high throughput PPI predictors has enabled the comprehensive scoring of all possible protein-protein pairs. This, in turn, has given rise to context, enabling us now to evaluate a PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective (RP), which estimates a localized threshold on a per-protein basis using several rank order metrics. By considering a putative PPI from the perspective of each of the proteins within the pair, RP rescores the predicted PPI and applies a cascaded Random Forest classifier leading to improvements in recall and precision. We here validate RP using two state-of-the-art PPI predictors, the Protein-protein Interaction Prediction Engine and the Scoring PRotein INTeractions methods, over five organisms: Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus. Results demonstrate the application of a post hoc RP rescoring layer significantly improves classification (p

The need for larger-scale and increasingly complex protein-protein interaction (PPI) prediction tasks demands that state-of-the-art predictors be highly efficient and adapted to inter- and cross-species predictions. Furthermore, the ability to generate comprehensive interactomes has enabled the appraisal of each PPI in the context of all predictions leading to further improvements in classification performance in the face of extreme class imbalance using the Reciprocal Perspective (RP) framework. We here describe the PIPE4 algorithm. Adaptation of the PIPE3/MP-PIPE sequence preprocessing step led to upwards of 50x speedup and the new Similarity Weighted Score appropriately normalizes for window frequency when applied to any inter- and cross-species prediction schemas. Comprehensive interactomes for three prediction schemas are generated: (1) cross-species predictions, where Arabidopsis thaliana is used as a proxy to predict the comprehensive Glycine max interactome, (2) inter-species predictions between Homo sapiens-HIV1, and (3) a combined schema involving both cross- and inter-species predictions, where both Arabidopsis thaliana and Caenorhabditis elegans are used as proxy species to predict the interactome between Glycine max (the soybean legume) and Heterodera glycines (the soybean cyst nematode). Comparing PIPE4 with the state-of-the-art resulted in improved performance, indicative that it should be the method of choice for complex PPI prediction schemas.

Ensuring the resiliency of critical infrastructures is essential in modern society, but much of the deployed infrastructure has yet to fully leverage modern technical developments. This pap...

Rank order data are pervasive in science and in our daily lived experience. With the advent of high performance computing and the commensurate increase in available data, the opportunity to capture the overall distribution of values by means of nonparametric curve fitting enables the identification of exceptional points in large datasets. With a rank order structure, these distributions may exhibit a “knee” delineating a threshold between exceptional points and those of the baseline. Given an accurate characterization of the distribution of prediction scores, including careful identification of the knee, we have previously shown that predictive performance can be significantly improved by leveraging this “context”. This paper examines the nonparametric characterization of such distributions. Locally weighted regression (LOESS) is a widely used nonparametric approach to curve fitting. Here, we revisit the assumptions behind the selection of kernel functions for nonparametric curve fitting of biological and biomedical data exhibiting rare or exceptional instances. We propose a new linear asymmetric kernel function and compare it to the commonly used tricube kernel used in LOESS. We evaluate its ability to fit rank order data in the domain of protein-protein interaction prediction. The proposed linear kernel significantly improved predictive performance $(p ) of two state-of-the-art predictors and promises to be widely applicable in related machine learning pipelines and nonparametric regression tasks.

Google Street View and the emergence of self-driving vehicles afford an unprecedented capacity to observe our planet. Fused with dramatic advances in artificial intelligence, the capability to extract patterns and meaning from those data streams heralds an era of insights into the physical world. In order to draw appropriate inferences about and between environments, the systematic selection of these data is necessary to create representative and unbiased samples. To this end, we introduce the Systematic Street View Sampler (S3) framework, enabling researchers to produce their own user-defined datasets of Street View imagery. We describe the algorithm and express its asymptotic complexity in relation to a new limiting computational resource (Google API Call Count). Using the Amazon Mechanical Turk distributed annotation environment, we demonstrate the utility of S3 in generating high quality representative datasets useful for machine vision applications. The S3 algorithm is open-source and available at github.com/CU-BIC/S3 along with the high quality dataset representing power infrastructure in rural regions of southern Ontario, Canada.

Many populated valleys in the western United States experience increased concentrations of particulate matter with diameter of less than 2.5 μm (PM2.5) during winter stagnation conditions. Further study into the chemical components composing wintertime PM2.5 and how the composition and level of wintertime PM2.5 are related to meteorological conditions can lead to a better understanding of the causes of high PM2.5 and aid in development and application of emission controls. The results can also aid in short-term air-pollution forecasting and implementation of periodic emission controls such as burning bans. This study examines relationships between PM2.5 concentrations and wintertime atmospheric stability (defined by heat deficit) during snow-covered and snow-free conditions from 2000 to 2013 for five western U.S. urbanizations: Salt Lake City, Utah; Reno, Nevada; Boise, Idaho; Missoula, Montana; and Spokane, Washington. Radiosonde data were used where available to calculate daily heat deficit, which was compared with PM2.5 concentration for days with snow cover and days with no snow cover. Chemically speciated PM2.5 data were compared for snow-cover and snow-free days to see whether the chemical abundances varied by day category. Wintertime PM2.5 levels were highly correlated with heat deficit for all cities except Spokane, where the airport sounding does not represent the urban valley. For a given static stability, snow-cover days experienced higher PM2.5 levels than did snow-free days, mainly because of enhanced ammonium nitrate concentrations. Normalizing average PM2.5 to the heat deficit reduced year-to-year PM2.5 variability, resulting in stronger downward trends, mostly because of reduced carbonaceous aerosol concentrations. The study was limited to western U.S. cities, but similar results are expected for other urban areas in mountainous terrain with cold, snowy winters.

The progressive elucidation of positive protein-protein interactions (PPIs) as wet-lab techniques continue to improve in both throughput and precision has increased the number and quality of known PPIs across the spectrum of life. Creating high quality datasets of positive PPIs is critical for training PPI prediction algorithms and for assessing the performance of PPI detection efforts. We present the Positome, a web service to acquire sets of positive PPIs based on user-defined criteria pertaining to data provenance including interaction type, throughput level, and detection method selection in addition to filtration by multiple lines of evidence (i.e. PPIs reported by independent research groups). The Positome provides a tunable interface to obtain a specified subset of interacting PPIs from the BioGRlD database. Both intra- and inter-species PPIs are supported. Using a number of model organisms, we demonstrate the trade-off between data quality and quantity, and the benefit of higher data quality on PPI prediction precision and recall. A web interface and REST web service are available at http://bioinf.sce.carleton.ca/POSITOME/.

The production of anti-Zika virus (ZIKV) therapeutics has become increasingly important as the propagation of the devastating virus continues largely unchecked. Notably, a causal relationship between ZIKV infection and neurodevelopmental abnormalities has been widely reported, yet a specific mechanism underlying impaired neurological development has not been identified. Here, we report on the design of several synthetic competitive inhibitory peptides against key pathogenic ZIKV proteins through the prediction of protein-protein interactions (PPIs). Often, PPIs between host and viral proteins are crucial for infection and pathogenesis, making them attractive targets for therapeutics. Using two complementary sequence-based PPI prediction tools, we first produced a comprehensive map of predicted human-ZIKV PPIs (involving 209 human protein candidates). We then designed several peptides intended to disrupt the corresponding host-pathogen interactions thereby acting as anti-ZIKV therapeutics. The data generated in this study constitute a foundational resource to aid in the multi-disciplinary effort to combat ZIKV infection, including the design of additional synthetic proteins.

Episodic memory is a complex memory system which allows recall and mental re-experience of previous episodes from one's own life. Real-life episodic memories are about events in their spatiotemporal context and are typically visuospatial, rather than verbal. Yet often, tests of episodic memory use verbal material to be recalled (word lists, stories). The Real-World What-Where-When memory test requires participants to hide a total of 16 different objects in 16 different locations over two temporal occasions, 2 h apart. Another two hours later, they are then asked to recall which objects (What) they had hidden in which locations (Where) and on which of the two occasions (When). In addition to counting the number of correctly recalled complete what-where-when combinations, this task can also be used to test real-world spatial memory and object memory. This task is sensitive to normal cognitive aging, and correlates well with performance on other episodic memory tasks, while at the same time providing more ecological validity and being cheap and easy to run.