Intra- and post-pandemic impact of the COVID-19 outbreak on Stanford Health Care.

Stanford Health Care, which provides about 7% of overall healthcare to approximately 9 million people in the San Francisco Bay Area, has undergone significant changes due to the opening of a second hospital in late 2019 and, more importantly, the COVID-19 pandemic. We examine the impact of these events on anatomic pathology (AP) cases, aiming to enhance operational efficiency in response to evolving healthcare demands. We extracted historical census, admission, lab tests, operation, and AP data since 2015. An approximately 45% increase in the volume of laboratory tests (P < 0.0001) and a 17% increase in AP cases (P < 0.0001) occurred post-pandemic. These increases were associated with progressively increasing (P < 0.0001) hospital census. Census increase stemmed from higher admission through the emergency department (ED), and longer lengths of stay mostly for transfer patients, likely due to the greater capability of the new ED and changes in regional and local practice patterns post-pandemic. Higher census led to overcapacity, which has an inverted U relationship that peaked at 103% capacity for AP cases and 114% capacity for laboratory tests. Overcapacity led to a lower capability to perform clinical activities, particularly those related to surgical procedures. We conclude by suggesting parameters for optimal operations in the post-pandemic era.

Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends.

Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning". Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Characterizing interactions in E-cadherin assemblages.

Cadherin intermolecular interactions are critical for cell-cell adhesion and play essential roles in tissue formation and the maintenance of tissue structures. In this study, we focus on E-cadherin, a classical cadherin that connects epithelial cells, to understand how they interact in cis and trans conformations when attached to the same cell or opposing cells. We employ coevolutionary sequence analysis and molecular dynamics simulations to confirm previously known interaction sites as well as to identify new interaction sites. The sequence coevolutionary results yield a surprising result indicating that there are no strongly favored intermolecular interaction sites, which is unusual and suggests that many interaction sites may be possible, with none being strongly preferred over others. By using molecular dynamics, we test the persistence of these interactions and how they facilitate adhesion. We build several types of cadherin assemblages, with different numbers and combinations of cis and trans interfaces to understand how these conformations act to facilitate adhesion. Our results suggest that, in addition to the established interaction sites on the EC1 and EC2 domains, an additional plausible cis interface at the EC3-EC5 domain exists. Furthermore, we identify specific mutations at cis/trans binding sites that impair adhesion within E-cadherin assemblages.

Sp3 is essential for normal lung morphogenesis and cell cycle progression during mouse embryonic development.

Members of the Sp family of transcription factors regulate gene expression via binding GC boxes within promoter regions. Unlike Sp1, which stimulates transcription, the closely related Sp3 can either repress or activate gene expression and is required for perinatal survival in mice. Here, we use RNA-seq and cellular phenotyping to show how Sp3 regulates murine fetal cell differentiation and proliferation. Homozygous Sp3-/- mice were smaller than wild-type and Sp+/- littermates, died soon after birth and had abnormal lung morphogenesis. RNA-seq of Sp3-/- fetal lung mesenchymal cells identified alterations in extracellular matrix production, developmental signaling pathways and myofibroblast/lipofibroblast differentiation. The lungs of Sp3-/- mice contained multiple structural defects, with abnormal endothelial cell morphology, lack of elastic fiber formation, and accumulation of lipid droplets within mesenchymal lipofibroblasts. Sp3-/- cells and mice also displayed cell cycle arrest, with accumulation in G0/G1 and reduced expression of numerous cell cycle regulators including Ccne1. These data detail the global impact of Sp3 on in vivo mouse gene expression and development.

ISCB Student Council Symposium 2021, a virtual global venue: challenges and lessons learned.

Since 2004, the ISCB Student Council has been organizing different symposia worldwide, gathering together the community of young computational biologists. Due to the coronavirus disease 2019 (COVID-19) pandemic situation, the world scientific community was forced to cancel in-person meetings for almost two years, imposing the adoption of virtual formats instead. After the successful editions of our continental symposia in 2020 in the USA, Latin America, and Europe, we organized our flagship global event, the Student Council Symposium (SCS) 2021, trying to apply all previous lessons learned and to exploit the advantages that virtuality has to offer.

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches. Objectives: The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions. Materials and Methods: In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF). Results: Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs. Conclusions: Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.

Characterizing domain-specific open educational resources by linking ISCB Communities of Special Interest to Wikipedia.

MOTIVATION: Wikipedia is one of the most important channels for the public communication of science and is frequently accessed as an educational resource in computational biology. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further improvement in quality, especially when compared to related scientific fields such as genetics and medicine. Facilitating involvement of members from ISCB Communities of Special Interest (COSIs) would improve a vital open education resource in computational biology, additionally allowing COSIs to provide a quality educational resource highly specific to their subfield. RESULTS: We generate a list of around 1500 English Wikipedia articles relating to computational biology and describe the development of a binary COSI-Article matrix, linking COSIs to relevant articles and thereby defining domain-specific open educational resources. Our analysis of the COSI-Article matrix data provides a quantitative assessment of computational biology representation on Wikipedia against other fields and at a COSI-specific level. Furthermore, we conducted similarity analysis and subsequent clustering of COSI-Article data to provide insight into potential relationships between COSIs. Finally, based on our analysis, we suggest courses of action to improve the quality of computational biology representation on Wikipedia.

Simulated Drug Efflux for the AbgT Family of Membrane Transporters.

Drug extrusion through molecular efflux pumps is an important mechanism for the survival of many pathogenic bacteria by removing drugs, providing multidrug resistance (MDR). Understanding molecular mechanisms for drug extrusion in multidrug efflux pumps is important for the development of new antiresistance drugs. The AbgT family of transporters involved in the folic acid biosynthesis pathway represents one such important efflux pump system. In addition to the transport of the folic acid precursor p-amino benzoic acid (PABA), members of this family are involved in the efflux of several sulfa drugs, conferring drug resistance to the bacteria. With the availability of structures for two members of this family (YdaH and MtrF), we investigate molecular pathways for transport of PABA and a sulfa drug (sulfamethazine) particularly for the YdaH transporter using steered molecular dynamics. Our analyses reveal the probable ligand migration pathways through the transporter, which also identifies key residues along the transport pathway. In addition, simulations using both PABA and sulfamethazine show how the protein is able to transport ligands of different shapes and sizes out of the pathogen. Our observations confirm previously reported functional residues for transport along the pathways by which YdaH transporters achieve antibiotic resistance to shuttle drugs out of the cells.

Xyloglucan Xylosyltransferase 1 Displays Promiscuity Toward Donor Substrates During in Vitro Reactions.

Glycosyltransferases (GTs) are a large family of enzymes that add sugars to a broad range of acceptor substrates, including polysaccharides, proteins and lipids, by utilizing a wide variety of donor substrates in the form of activated sugars. Individual GTs have generally been considered to exhibit a high level of substrate specificity, but this has not been thoroughly investigated across the extremely large set of GTs. Here we investigate xyloglucan xylosyltransferase 1 (XXT1), a GT involved in the synthesis of the plant cell wall polysaccharide, xyloglucan. Xyloglucan has a glucan backbone, with initial side chain substitutions exclusively composed of xylose from uridine diphosphate (UDP)-xylose. While this conserved substitution pattern suggests a high substrate specificity for XXT1, our in vitro kinetic studies elucidate a more complex set of behavior. Kinetic studies demonstrate comparable kcat values for reactions with UDP-xylose and UDP-glucose, while reactions with UDP-arabinose and UDP-galactose are over 10-fold slower. Using kcat/KM as a measure of efficiency, UDP-xylose is 8-fold more efficient as a substrate than the next best alternative, UDP-glucose. To the best of our knowledge, we are the first to demonstrate that not all plant XXTs are highly substrate specific and some do show significant promiscuity in their in vitro reactions. Kinetic parameters alone likely do not explain the high substrate selectivity in planta, suggesting that there are additional control mechanisms operating during polysaccharide biosynthesis. Improved understanding of substrate specificity of the GTs will aid in protein engineering, development of diagnostic tools, and understanding of biological systems.

Highlights of the 3rd ISCB Africa Student Council Symposium 2019 in Ghana.

The article elaborates on the program highlights of the 3rd African Student Council Symposium 2019. The one-day symposium was held in Kwame Nkrumah University of Science and Technology (KNUST), Ghana, on 11 November 2019 during the 6th joint international bioinformatics conference of the ISCB and ASBCB. It consisted of three sessions that included keynote talks by Prof Christine Orengo and Dr. Amel Ghouila, and seven selected student speaker talks from different areas of bioinformatics. The students benefited from networking and learning about ongoing research work by their peers hailing from different countries of the African region. The symposium proved to be pivotal to strengthen connections in the African bioinformatics student community.

Structure and function of LCI1: a plasma membrane CO2 channel in the Chlamydomonas CO2 concentrating mechanism.

Microalgae and cyanobacteria contribute roughly half of the global photosynthetic carbon assimilation. Faced with limited access to CO2 in aquatic environments, which can vary daily or hourly, these microorganisms have evolved use of an efficient CO2 concentrating mechanism (CCM) to accumulate high internal concentrations of inorganic carbon (Ci ) to maintain photosynthetic performance. For eukaryotic algae, a combination of molecular, genetic and physiological studies using the model organism Chlamydomonas reinhardtii, have revealed the function and molecular characteristics of many CCM components, including active Ci uptake systems. Fundamental to eukaryotic Ci uptake systems are Ci transporters/channels located in membranes of various cell compartments, which together facilitate the movement of Ci from the environment into the chloroplast, where primary CO2 assimilation occurs. Two putative plasma membrane Ci transporters, HLA3 and LCI1, are reportedly involved in active Ci uptake. Based on previous studies, HLA3 clearly plays a meaningful role in HCO3- transport, but the function of LCI1 has not yet been thoroughly investigated so remains somewhat obscure. Here we report a crystal structure of the full-length LCI1 membrane protein to reveal LCI1 structural characteristics, as well as in vivo physiological studies in an LCI1 loss-of-function mutant to reveal the Ci species preference for LCI1. Together, these new studies demonstrate LCI1 plays an important role in active CO2 uptake and that LCI1 likely functions as a plasma membrane CO2 channel, possibly a gated channel.

Global network of computational biology communities: ISCB's Regional Student Groups breaking barriers.

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: "Nurture the new generation of computational biologists".

Nurturing tomorrow's leaders: The ISCB Student Council Symposia in 2018.

The Student Council of the International Society for Computational Biology (ISCB-SC) is a student-focused organization for researchers from all early career levels of training (undergraduates, masters, PhDs and postdocs) that organizes bioinformatics and computational biology activities across the globe. Among its activities, the ISCB-SC organizes several symposia in different continents, many times, with the help of the Regional Student Groups (RSGs) that are based on each region. In this editorial we highlight various key moments and learned lessons from the 14th Student Council Symposium (SCS, Chicago, USA), the 5th European Student Council Symposium (ESCS, Athens, Greece) and the 3rd Latin American Student Council Symposium (LA-SCS, Viña del Mar, Chile).

Reflections on a journey: a retrospective of the ISCB Student Council symposium series.

This article describes the motivation, origin and evolution of the student symposia series organised by the ISCB Student Council. The meeting series started thirteen years ago in Madrid and has spread to four continents. The article concludes with the highlights of the most recent edition of annual Student Council Symposium held in conjunction with the 25th Conference on Intelligent Systems for Molecular Biology and the 16th European Conference on Computational Biology, in Prague, in July 2017.

Highlights of the second ISCB Student Council Symposium in Africa, 2017.

Student Council Symposiums (SCSs) have been found to be very useful for students and young researchers. This is especially true given that the events are held directly before large international conferences, giving attendees a chance to gain exposure and have a warm up to the social nuances involved in attending such a meeting. This was the second SCS held in Africa in conjunction with the International Society for Computational Biology (ISCB) and the African Society for Bioinformatics and Computational Biology's (ASBCB) biennial meeting. This symposium was organised by students within the society inside Africa and was held on the 10 th of October 2017 in Entebbe, Uganda.

Prediction of Phenotypic Effects of Variants Observed in LOC_Os04g36720 of FRO1 Gene in Rice (Oryza sativa L.).

In rice, ferric-chelate reductase-1 (FRO1) (LOC_Os04g36720) gene was present on chromosome number 4 and its beginning and ending coordinates where coding sequence lies are 22182599 and 22186943, respectively. It plays a vital role in metal homeostasis and iron transportation in plants. Based on the alignment results, location of single-nucleotide variants is located in open reading frame and their effects of variants were predicted using SIFT sequence tool. The non-synonymous variants at position 342 and 436 lies in helical and coil parts of the protein, respectively, as predicted by Psi-pred server. PSI-Blast which resulted in significant hits and the most similar protein sequence (Accession ID: NP_001052896) with available sequence features displayed 100 % identity with query cover of 99 %. Results suggest the non-synonymous variant at position 436 (Accession ID: TBGI204002) lies in FAD-binding domain and nsSNV at position 342 (Accession ID: TBGI203998) lies in periphery of NADP. The SNPs were also analyzed for the deleterious effect by PANTHER subPSEC scores and I-mutant score, and it was postulated that SNPs would be hampering on biological as well as molecular function of FRO 1 gene of rice. A cutoff of -3 corresponds to a 50 % probability that a score is deleterious. From this, the probability that a given variant will cause a deleterious effect on protein function is estimated by P deleterious, such that a subPSEC score of -4 corresponds to a P deleterious of 0.79. Hence, to study the phenotypic consequences of variant TBGI204002, we performed comparative molecular docking studies of native modeled protein and protein with induced mutation as receptors and FAD as ligand to be utilized for binding. The docking process was performed by AutoDock 4.2 software with Lamarckian Genetic algorithm as computational algorithm. Results suggest binding energies are higher in case of mutation-induced protein which suggests presence of variant TBGI204002 enhances binding of FAD ligand at FAD-binding domain site. In case of TBGI203998, similar comparative docking procedure was performed with FAD as binding ligand, which suggests presence of variant does not impact FAD binding at the domain site. We revealed impact of SNPs on the protein structure and its function using sequence-based tools.

ISCB-Student Council Narratives: Strategical development of the ISCB-Regional Student Groups in 2016.

Regional Student Groups are groups established and managed by the ISCB-Student Council in different regions of the world. The article highlights some of the initiatives and management lessons from our 'top-performing' Spotlight Regional Student Groups (RSGs), RSG-Argentina and RSG-UK, for the current year (2016). In addition, it details some of the operational hurdles faced by RSGs and possible solutions.

Workshops: a great way to enhance and supplement a degree.

As part of the International Society for Computational Biology Student Council (ISCB-SC), Regional Student Groups (RSGs) have helped organise workshops in the emerging fields of bioinformatics and computational biology. Workshops are a great way for students to gain hands-on experience and rapidly acquire knowledge in advanced research topics where curriculum-based education is yet to be developed. RSG workshops have improved dissemination of knowledge of the latest bioinformatics techniques and resources among student communities and young scientists, especially in developing nations. This article highlights some of the benefits and challenges encountered while running RSG workshops. Examples cover a variety of subjects, including introductory bioinformatics and advanced bioinformatics, as well as soft skills such as networking, career development, and socializing. The collective experience condensed in this article is a useful starting point for students wishing to organise their own tailor-made workshops.