Miracle-dz/UniFAR-FTUs huggingface dataset:
Differing total mRNA expression shapes the molecular and clinical phenotype of cancer

Cancers can vary greatly in their transcriptomes. In contrast to alterations in specific genes or pathways, the significance of differences in tumor cell total mRNA content is poorly understood. Studies using single-cell sequencing or model systems have suggested a role for total mRNA content in regulating cellular phenotypes. However, analytical challenges related to technical artifacts and cellular admixture have impeded examination of total mRNA expression at scale across cancers. To address this, we evaluated total mRNA expression using single cell sequencing, and developed a computational method for quantifying tumor-specific total mRNA expression (TmS) from bulk sequencing data. We systematically estimated TmS in 5,181 patients across 15 cancer types and observed close correlations with clinicopathologic characteristics and molecular features, where high TmS generally accompanies high-risk disease. At a pan-cancer level, high TmS is associated with increased risk of disease progression and death. Moreover, TmS captures tumor type-specific effects of somatic mutations, chromosomal instability, and hypoxia, as well as aspects of intratumor heterogeneity. Taken together, our results suggest that measuring total mRNA expression offers a broader perspective of tracking cancer transcriptomes, which has important clinical and biological implications.
Parallelized calculation of permutation tests

Motivation Permutation tests offer a straight forward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naive implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. Results Parallelization of the Green algorithm was found possible by nontrivial rearrangement of the structure of the algorithm. A speed-up – by orders of magnitude – is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g., the widely used asymptotic Mann-Whitney U-test. Availability In Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 license. Contact lukask@kth.se Supplementary information Supplementary data are available at Bioinformatics online.
Data on Vietnamese Students' Acceptance of Using VCTs for Distance Learning during the COVID-19 Pandemic

The outbreak of COVID-19 at the beginning of 2020 has heavily influenced education all around the world. In Vietnam, educational institutes were suspended, and distance learning was conducted to ensure students’ learning process, with distance learning occurring mainly via video conferencing tools (VTCs). The purpose of this paper is to provide data on Vietnamese students’ acceptance of using VCTs in distance learning during the COVID-19 pandemic through an extended technology acceptance model (TAM) and structural equation modeling (SEM) method. This study used the TAM of Venkatesh and Davis. The questionnaire was designed based on Venkatesh and Davis and Salloum et al.’s scale. An online survey with snowball sampling was selected in April. The final dataset consisted of 277 valid records. This data descriptor presented descriptive statistics (mean, standard deviation), internal consistency (Cronbach’s alpha), reliability and validity measures (composite reliability, average value extracted test), and factor loading of items of eight factors: output quality, computer playfulness, subjective norm, perceived usefulness, perceived ease of use, attitude towards to use, behavioral intention to use, and actual system to use. Results indicated that external factors such as subjective norm and computer playfulness had a significant impact on most TAM constructs. Furthermore, output quality was found to have a positive influence on students’ perceived usefulness and acceptance of VCTs in distance learning.
Establishing the foundational elements of a public procurement body of knowledge

Public procurement scholars have been striving to identify technical and behavioral competencies to drive toward professionalization. However, there is no vetted body of knowledge that practitioners and scholars can use to establish roles and responsibilities. This empirical study outlines a logical process to identify the foundational elements of the body of knowledge, specifically technical competencies, serving as the building blocks for advancement toward a recognized profession. Findings suggest that 87 job tasks can be classified under 6 job domains that contain many of the components and conceptual constructs for the field of public procurement. These domains can improve understanding of the knowledge, skills, and abilities necessary in public administration.
Three-dimensional Fib-Sem reconstruction of microtubule-organelle interaction in whole primary mouse beta cells

Microtubules play a major role in intracellular trafficking of cargo vesicles in endocrine cells and detailed knowledge of the microtubule network organization and its relation to other cell constituents is crucial for understanding primary cell function. However, their role in insulin transport and secretion is currently under debate. Here, we use Fib-Sem to image insulin secreting pancreatic islet beta cells in their entirety at an unprecedented resolution. We reconstruct all mitochondria, Golgi apparati, centrioles, insulin secretory granules and microtubules of seven beta cells, and generate a comprehensive spatial map of microtubule-organelle interactions. We find that microtubules form non-radial networks that are predominantly not connected to either centrioles or endomembranes. Moreover, microtubule number and length, but not microtubule density, vary with glucose stimulation. Furthermore, we show that insulin secretory granules are enriched near the plasma membrane where they associate with microtubules. In summary, we provide the first three-dimensional reconstructions of complete microtubule networks in primary mammalian cells together with evidence regarding their importance for insulin secretory granule positioning and thus supportive role in insulin secretion.
Improved Wound Closure Rates and Mechanical Properties Resembling Native Skin in Murine Diabetic Wounds Treated with a Tropoelastin and Collagen Wound Healing Device

Chronic wounds in patients suffering from type II diabetes mellitus (DMII) where wounds remain open with a complicated pathophysiology, healing, and recovery process is a public health concern. Normal wound healing plays a critical role in wound closure, restoration of mechanical properties, and the biochemical characteristics of the remodeled tissue. Biological scaffolds provide a tissue substitute to help facilitate wound healing by mimicking the extracellular matrix (ECM) of the dermis. In the current study an electrospun biomimetic scaffold, wound healing device (WHD), containing tropoelastin (TE) and collagen was synthesized to mimic the biochemical and mechanical characteristics of healthy human skin. The WHD was compared to a commercially available porcine small intestinal submucosa (SIS) matrix that has been used in both partial and full-thickness wounds, Oasis® Wound Matrix. Wound closure rates, histochemistry, qPCR, and mechanical testing of treated wound sites were evaluated. The WHD in a splinted, full-thickness, diabetic murine wound healing model demonstrated an enhanced rate of wound closure, decreased tissue inflammation, skin organ regeneration, and a stronger and more durable remodeled tissue that more closely mimics native unwounded skin compared to the control device.
Systematic discovery and functional interrogation of SARS-CoV-2 viral RNA-host protein interactions during infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a pandemic with growing global mortality. There is an urgent need to understand the molecular pathways required for host infection and anti-viral immunity. Using comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS), we identified 309 host proteins that bind the SARS-CoV-2 RNA during active infection. Integration of this data with viral ChIRP-MS data from three other positive-sense RNA viruses defined pan-viral and SARS-CoV-2-specific host interactions. Functional interrogation of these factors with a genome-wide CRISPR screen revealed that the vast majority of viral RNA-binding proteins protect the host from virus-induced cell death, and we identified known and novel anti-viral proteins that regulate SARS-CoV-2 pathogenicity. Finally, our RNA-centric approach demonstrated a physical connection between SARS-CoV-2 RNA and host mitochondria, which we validated with functional and electron microscopy data, providing new insights into a more general virus-specific protein logic for mitochondrial interactions. Altogether, these data provide a comprehensive catalogue of SARS-CoV-2 RNA-host protein interactions, which may inform future studies to understand the mechanisms of viral pathogenesis, as well as nominate host pathways that could be targeted for therapeutic benefit. Highlights · ChIRP-MS of SARS-CoV-2 RNA identifies a comprehensive viral RNA-host protein interaction network during infection across two species · Comparison to RNA-protein interaction networks with Zika virus, dengue virus, and rhinovirus identify SARS-CoV-2-specific and pan-viral RNA protein complexes and highlights distinct intracellular trafficking pathways · Intersection of ChIRP-MS and genome-wide CRISPR screens identify novel SARS-CoV-2-binding proteins with pro- and anti-viral function · Viral RNA-RNA and RNA-protein interactions reveal specific SARS-CoV-2-mediated mitochondrial dysfunction during infection
Look where you go: Characterizing eye movements toward optic flow

When we move through our environment, objects in the visual scene create optic flow patterns on the retina. Even though optic flow is ubiquitous in everyday life, it is not well understood how our eyes naturally respond to it. In small groups of human and non-human primates, optic flow triggers intuitive, uninstructed eye movements to the pattern’s focus of expansion (Knöll, Pillow & Huk, 2018). Here we investigate whether such intuitive oculomotor responses to optic flow are generalizable to a larger group of human observers, and how eye movements are affected by motion signal strength and task instructions. Observers (n = 43) viewed expanding or contracting optic flow constructed by a cloud of moving dots radiating from or converging toward a focus of expansion that could randomly shift. Results show that 84% of observers tracked the focus of expansion with their eyes without being explicitly instructed to track. Intuitive tracking was tuned to motion signal strength: saccades landed closer to the focus of expansion and smooth tracking was more accurate when dot contrast, motion coherence, and translational speed were high. Under explicit tracking instruction, the eyes aligned with the focus of expansion more closely than without instruction. Our results highlight the sensitivity of intuitive eye movements as indicators of visual motion processing in dynamic contexts.
Spermidine and spermine are the natural substrates of the Acinetobacter baumannii AmvA multidrug efflux pump

Multidrug efflux pumps are important drivers of antibiotic resistance in Acinetobacter baumannii and other pathogens, however their ‘natural’ roles beyond transport of clinical antimicrobials are poorly described. Polyamines are an ancient class of molecules with broad roles in all three kingdoms of life, and are the likely natural substrate of at least one efflux pump family. We have defined the transcriptome of A. baumannii following treatment with high levels of the polyamines putrescine, cadaverine, spermidine and spermine. These molecules influenced expression of multiple gene classes in A. baumannii including those associated with virulence, and the four polyamines induced distinct but overlapping transcriptional responses. Polyamine shock also induced expression of the MFS-family efflux pump gene amvA and its repressor gene amvR. Loss of amvA dramatically reduced tolerance to the long-chain triaamine spermidine, but caused only modest changes in resistance to known AmvA substrates such as acriflavine. We confirmed reduced accumulation of spermidine in amvA-deficient A. baumannii, and showed that its expression is induced by long-chain polyamines through its cognate regulator AmvR. Our findings suggest that the conserved A. baumannii efflux pump AmvA has evolved to export spermidine from the cell, but that its substrate recognition promiscuity also allows activity against clinically-important biocides and antibiotics. Importance AMR genes, including multidrug efflux pumps, evolved long before the ubiquitous use of antimicrobials in medicine and infection control. Multidrug efflux pumps often transport metabolites, signals and host-derived molecules in addition to antibiotics or biocides. Understanding the ancestral physiological roles of multidrug efflux pumps could help to inform the development of strategies to subvert their activity. In this study, we investigated the response of Acinetobacter baumannii to polyamines, a widespread, abundant class of amino acid-derived metabolites, which led us to identify long-chain polyamines as natural substrates of the disinfectant efflux pump AmvA. A second clinically-important efflux pump, AdeABC, also contributed to polyamine tolerance. Our results suggest that the disinfectant resistance capability that allows A. baumannii to survive in hospitals may have evolutionary origins in the transport of polyamine metabolites.
Effects of Malaise trap spacing on species richness and composition of terrestrial arthropod bulk samples

The Malaise trap has gained popularity for assessing diverse terrestrial arthropod communities because it collects large samples with modest effort. A number of factors that influence collection efficiency, placement being one of them. For instance, when designing larger biotic surveys using arrays of Malaise traps we need to know the optimal distance between individual traps that maximises observable species richness and community composition. We examined the influence of spacing between Malaise traps by metabarcoding samples from two field experiments at a site in Waterloo, Ontario, Canada. For one experiment, we used two trap pairs deployed at weekly increasing distance (3m increments from 3 to 27 m). The second experiment involved a total of 10 traps set up in a row at 3m distance intervals for three consecutive weeks. Results show that community similarity of samples decreases over distance between traps. The amount of species shared between trap pairs shows drops considerably at about 15m trap-to-trap distance. This change can be observed across all major taxonomic groups and for two different habitat types (grassland and forest). Large numbers of OTUs found only once within samples cause rather large dissimilarity between distance pairs even at close proximity. This could be caused by a large number of transient species from adjacent habitat which arrive at the trap through passive transport, as well as capture of rare taxa, which end up in different traps by chance.
Establishment of a new sex-determining allele driven by sexually antagonistic selection

Some species undergo frequent turnovers of sex-determining locus, rather than having stable diverged sex chromosomes. In such species, how often turnover occurs is a fundamental evolutionary question. We model the process with considering a linked locus under sexually antagonistic selection. The entire process of a turnover may be divided into two phases, which are referred to as the stochastic and deterministic phases. The stochastic phase is when a new sex-determining allele just arises and is still rare and random genetic drift plays an important role. In the deterministic phase, the new allele further increases in frequency by positive selection. The theoretical results currently available are for the deterministic phase, which demonstrated that a turnover of a newly arisen sex determining locus could benefit from selection at a linked locus under sexually antagonistic selection, by assuming that sexually antagonistic selection works in a form of balancing selection. In this work, we provide a comprehensive theoretical description of the entire process from the stochastic phase to the deterministic phase. In addition to balancing selection, we explore several other modes of selection on the linked locus. Our theory allows us make a quantitative argument on the rate of turnover and the effect of the mode of selection at the linked locus. We also performed simulations to explore the pattern of polymorphism around the new sex determining locus. We find that the pattern of polymorphism is informative to infer how selection worked through the turnover process.
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP and CAMEO experiments, and outperformed other state-of-the-art methods by at least 58.4% for the CASP 11&12 and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library. Availability The training and testing data, standalone package, and the online server for TripletRes are available at https://zhanglab.ccmb. med.umich. edu/TripletRes/. Author Summary Ab initio protein folding has been a major unsolved problem in computational biology for more than half a century. Recent community-wide Critical Assessment of Structure Prediction (CASP) experiments have witnessed exciting progress on ab initio structure prediction, which was mainly powered by the boosting of contact-map prediction as the latter can be used as constraints to guide ab initio folding simulations. In this work, we proposed a new open-source deep-learning architecture, TripletRes, built on the residual convolutional neural networks for high-accuracy contact prediction. The large-scale benchmark and blind test results demonstrate significant advancement of the proposed methods over other approaches in predicting medium- and long-range contact-maps that are critical for guiding protein folding simulations. Detailed data analyses showed that the major advantage of TripletRes lies in the unique protocol to fuse multiple evolutionary feature matrices which are directly extracted from whole-genome and metagenome databases and therefore minimize the information loss during the contact model training.
Structural basis for repurposing a 100-years-old drug suramin for treating COVID-19

The COVID-19 pandemic by non-stop infections of SARS-CoV-2 has continued to ravage many countries worldwide. Here we report the discovery of suramin, a 100-year-old drug, as a potent inhibitor of the SARS-CoV-2 RNA dependent RNA polymerase (RdRp) through blocking the binding of RNA to the enzyme. In biochemical assays, suramin and its derivatives are at least 20-fold more potent than remdesivir, the currently approved nucleotide drug for COVID-19. The 2.6 Å cryo-EM structure of the viral RdRp bound to suramin reveals two binding sites of suramin, with one site directly blocking the binding of the RNA template strand and the other site clash with the RNA primer strand near the RdRp catalytic active site, therefore inhibiting the viral RNA replication. Furthermore, suramin potently inhibits SARS-CoV-2 duplication in Vero E6 cells. These results provide a structural mechanism for the first non-nucleotide inhibitor of the SARS-CoV-2 RdRp and a rationale for repurposing suramin for treating COVID-19. One Sentence Summary Discovery and mechanism of suramin as potent SARS-CoV-2 RNA polymerase inhibitor and its repurposing for treating COVID-19.
Leading complementary schools as non-profit social entrepreneurship: Cases from Lithuania

The article explores the topic of leading complementary schools as models of non-profit social enterprises, reflecting the way the sector has developed in Lithuania. Complementary schools in Lithuania provide educational services often related to language and cultural education focusing on different groups such as minorities, immigrants and people with disabilities. They use client-focused activities to achieve social goals and maintain financial self-sufficiency. Therefore, the leadership of these organizations is of major importance. Group discussions were conducted with 32 heads of complementary schools to explore their perceptions of the entrepreneurship competencies they need to meet the challenges they face in establishing and sustaining their non-profit organizations. Viewing complementary schools as non-profit social enterprises and their leaders as social entrepreneurs contributes to the understanding of them as organizations and the wide range of competencies and practices needed to lead such schools effectively with implications for pre-service and in-post leadership development.