bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors influencing the COVID-19 daily deaths peak across European countries
Authors: Jabłońska Katarzyna , Aballéa Samuel , Toumi Mondher 
Published: November 2020

AbstractOBJECTIVESThe purpose of this study was to determine predictors of the height of COVID-19 daily deaths peak and time to the peak, in order to explain their variability across European countries.STUDY DESIGNFor 34 European countries, publicly available data were collected on daily numbers of COVID-19 deaths, population size, healthcare capacity, government restrictions and their timing, tourism and change in mobility during the pandemic.METHODSUnivariate and multivariate generalised linear models using different selection algorithms (forward, backward, stepwise and genetic algorithm) were analysed with height of COVID-19 daily deaths peak and time to the peak as dependent variables.RESULTSThe proportion of the population living in urban areas, mobility at the day of first reported death and number of infections when borders were closed were assessed as significant predictors of the height of COVID-19 daily deaths peak. Testing the model with variety of selection algorithms provided consistent results. Total hospital bed capacity, population size, number of foreign travellers and day of border closure, were found as significant predictors of time to COVID-19 daily deaths peak.CONCLUSIONSOur analysis demonstrated that countries with higher proportions of the population living in urban areas, with lower reduction in mobility at the beginning of the pandemic, and countries which closed borders having more infected people experienced higher peak of COVID-19 deaths. Greater bed capacity, bigger population size and later border closure could result in delaying time to reach the deaths peak, whereas a high number of foreign travellers could accelerate it.
Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus
Authors: Chiroma Haruna , Ezugwu Absalom E. , Jauro Fatsuma , Al-Garadi Mohammed A. , Abdullahi Idris N. , Shuib Liyana 
Published: November 2020

AbstractBackground and ObjectiveThe COVID-19 pandemic has caused severe mortality across the globe with the USA as the current epicenter, although the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight the COVID-19 pandemic from a different perspective. To the best of the authors’ knowledge, no comprehensive survey with bibliometric analysis has been conducted on the adoption of machine learning for fighting COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine-learning-based technologies to fight the COVID-19 pandemic from a different perspective, including an extensive systematic literature review and a bibliometric analysis.MethodsA literature survey methodology is applied to retrieve data from academic databases, and a bibliometric technique is subsequently employed to analyze the accessed records. Moreover, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis, and analysis are presented. The convolutional neural network (CNN) is found mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest computed tomography (CT) scan images. Similarly, a bibliometric analysis of machine-learning-based COVID-19-related publications in Scopus and Web of Science citation indexes is performed. Finally, a new perspective is proposed to solve the challenges identified as directions for future research. We believe that the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators.ResultsThe findings in this study reveal that machine-learning-based COVID-19 diagnostic tools received the most considerable attention from researchers. Specifically, the analyses of the results show that energy and resources are more dispensed toward COVID-19 automated diagnostic tools, while COVID-19 drugs and vaccine development remain grossly underexploited. Moreover, the machine-learning-based algorithm predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images.ConclusionsThe challenges hindering practical work on the application of machine-learning-based technologies to fight COVID-19 and a new perspective to solve the identified problems are presented in this study. We believe that the presented survey with bibliometric analysis can help researchers determine areas that need further development and identify potential collaborators at author, country, and institutional levels to advance research in the focused area of machine learning application for disease control.
A quick and cost-effective method for DNA-free total RNA isolation using magnetic silica beads
Authors: Das Aniruddha , Das Debojyoti , Das Arundhati , Panda Amaresh C. 
Published: November 2020

ABSTRACTCurrent RNA purification methods widely use silica-based columns that allow quick isolation of high-quality and good quantities of RNA. However, the major limitations include high cost, the requirement of different kits for small RNA isolation, genomic DNA contamination, and not being flexible. Here, we used the in-house RNA isolation reagent (RIR) for cell lysis, followed by RNA precipitation using isopropanol. RNA isolated using the in-house RIR resulted in a similar quantity and quality compared to the commercial TRIzol. Furthermore, the commercial RNA isolation kits with silica-based columns recommend genomic DNA digestion during or after RNA purification, adding time and cost to RNA purification. Here, we developed an optimized in-house protocol for isolating high-quality RNA free of genomic DNA contamination using magnetic silica beads without needing DNase digestion. Additionally, our method purifies total RNA along with the small RNA fraction, including miRNAs, which usually require a separate kit for extraction. Additionally, the RNA prepared with our method was equally suitable for mRNA and miRNA expression analysis using RT-qPCR. Together, the in-house method of RNA isolation using the magnetic silica beads has exhibited comparable or better total RNA extraction compared to commercial kits at a fraction of the cost and across various cells and tissues.
Loss ofCnot6limpairs inosine RNA modifications in mouse oocytes
Authors: Brachova Pavla , Alvarez Nehemiah S. , Christenson Lane K. 
Published: November 2020

AbstractMammalian oocytes must degrade maternal transcripts through a process called translational mRNA decay, in which maternal mRNA undergoes translational activation, followed by deadenylation and mRNA decay. Once a transcript is translationally activated, it becomes deadenylated by the CCR4-NOT complex. Knockout ofCnot6l, a deadenylase within the CCR4-NOT complex, results in mRNA decay defects during MI entry. Knockout ofBtg4, an adaptor protein of the CCR4-NOT complex, results in mRNA decay defects following fertilization. Therefore, mechanisms controlling mRNA turnover have significant impacts on oocyte competence and early embryonic development. Post-transcriptional inosine RNA modifications can impact mRNA stability, possibly through a translation mechanism. Here, we assessed inosine RNA modifications in oocytes fromCnot6l-/-andBtg4-/-mice, which display stabilization of mRNA and over-translation of the stabilized transcripts. If inosine modifications have a role in modulating RNA stability, we hypothesize that in these mutant backgrounds, we would observe changes or a disruption in inosine mRNA modifications. To test this, we used a computational approach to identify inosine RNA modifications in total and polysomal RNA-seq data during meiotic maturation (GV, MI, and MII stages). We observed pronounced depletion of inosine mRNA modifications in oocytes fromCnot6l-/-, but not inBtg4-/-mice. Additionally, analysis of ribosome-associated RNA revealed clearance of inosine modified mRNA. These observations suggest a novel mechanism of mRNA clearance during oocyte maturation, in which inosine-containing transcripts decay in an independent, but parallel mechanism to CCR4-NOT deadenylation.
SARS-CoV-2 Infects Human Engineered Heart Tissues and Models COVID-19 Myocarditis
Authors: Bailey Adam L. , Dmytrenko Oleksandr , Greenberg Lina , Bredemeyer Andrea L. , Ma Pan , Liu Jing , Penna Vinay , Lai Lulu , Winkler Emma S. , Sviben Sanja , Brooks Erin , Nair Ajith P. , Heck Kent A. , Rali Aniket S. , Simpson Leo , Saririan Mehrdad , Hobohm Dan , Stump W. Tom , Fitzpatrick James A. , Xie Xuping , Shi Pei-Yong , Hinson J. Travis , Gi Weng-Tein , Schmidt Constanze , Leuschner Florian , Lin Chieh-Yu , Diamond Michael S. , Greenberg Michael J. , Lavine Kory J. 
Published: November 2020

AbstractEpidemiological studies of the COVID-19 pandemic have revealed evidence of cardiac involvement and documented that myocardial injury and myocarditis are predictors of poor outcomes. Nonetheless, little is understood regarding SARS-CoV-2 tropism within the heart and whether cardiac complications result directly from myocardial infection. Here, we develop a human engineered heart tissue model and demonstrate that SARS-CoV-2 selectively infects cardiomyocytes. Viral infection is dependent on expression of angiotensin-I converting enzyme 2 (ACE2) and endosomal cysteine proteases, suggesting an endosomal mechanism of cell entry. After infection with SARS-CoV-2, engineered tissues display typical features of myocarditis, including cardiomyocyte cell death, impaired cardiac contractility, and innate immune cell activation. Consistent with these findings, autopsy tissue obtained from individuals with COVID-19 myocarditis demonstrated cardiomyocyte infection, cell death, and macrophage-predominate immune cell infiltrate. These findings establish human cardiomyocyte tropism for SARS-CoV-2 and provide an experimental platform for interrogating and mitigating cardiac complications of COVID-19.
Innervation modulates the functional connectivity between pancreatic endocrine cells
Authors: Yang Yu Hsuan Carol , Briant Linford J.B. , Raab Christopher , Mullapudi Sri Teja , Maischein Hans-Martin , Kawakami Koichi , Stainier Didier Y.R. 
Published: November 2020

AbstractDirect modulation of pancreatic endocrine cell activity by autonomic innervation has been debated. To resolve this question, we established anin vivoimaging model which also allows chronic and acute neuromodulation. Starting at a stage when zebrafish islet architecture is reminiscent of that in adult rodents, we imaged calcium dynamics simultaneously in multiple islet cell types. We first find that activity coupling between beta cells increases upon glucose exposure. Surprisingly, glucose exposure also increases alpha-alpha, alpha-beta and beta-delta coordination. We further show that both chronic and acute loss of nerve activity diminish activity coupling, as observed upon gap junction depletion. Notably, chronic loss of innervation severely disrupts delta cell activity, suggesting that delta cells receive innervation which coordinates its output. Overall, these data show that innervation plays a vital role in the establishment and maintenance of homotypic and heterotypic cellular connectivity in pancreatic islets, a process critical for islet function.
Condensin extrudes DNA loops in steps up to hundreds of base pairs that are generated by ATP binding events
Authors: Ryu Je-Kyung , Rah Sang-Hyun , Janissen Richard , Kerssemakers Jacob W. J. , Bonato Andrea , Michieletto Davide , Dekker Cees 
Published: November 2020

AbstractThe condensin SMC protein complex organizes chromosomal structure by extruding loops of DNA. Its ATP-dependent motor mechanism remains unclear but likely involves steps associated with large conformational changes within the ~50 nm protein complex. Here, using high-resolution magnetic tweezers, we resolve single steps in the loop extrusion process by individual yeast condensins. The measured median step sizes range between 20-40 nm at forces of 1.0-0.2 pN, respectively, comparable with the holocomplex size. These large steps show that, strikingly, condensin typically reels in DNA in very sizeable amounts with ~200 bp on average per single extrusion step at low force, and occasionally even much larger, exceeding 500 bp per step. Using Molecular Dynamics simulations, we demonstrate that this is due to the structural flexibility of the DNA polymer at these low forces. Using ATP-binding-impaired and ATP-hydrolysis-deficient mutants, we find that ATP binding is the primary step-generating stage underlying DNA loop extrusion. We discuss our findings in terms of a scrunching model where a stepwise DNA loop extrusion is generated by an ATP-binding-induced engagement of the hinge and the globular domain of the SMC complex.
A biologically oriented algorithm for spatial sound segregation
Authors: Chou Kenny F , Best Virginia , Colburn H Steven , Sen Kamal 
Published: November 2020

AbstractListening in an acoustically cluttered scene remains a difficult task for both machines and hearing-impaired listeners. Normal-hearing listeners accomplish this task with relative ease by segregating the scene into its constituent sound sources, then selecting and attending to a target source. An assistive listening device that mimics the biological mechanisms underlying this behavior may provide an effective solution for those with difficulty listening in acoustically cluttered environments (e.g., a cocktail party). Here, we present a binaural sound segregation algorithm based on a hierarchical network model of the auditory system. In the algorithm, binaural sound inputs first drive populations of neurons tuned to specific spatial locations and frequencies. Lateral inhibition then sharpens the spatial response of the neurons. Finally, the spiking response of neurons in the output layer are then reconstructed into audible waveforms via a novel reconstruction method. We evaluate the performance of the algorithm with psychoacoustic measures of normal-hearing listeners. This two-microphone algorithm is shown to provide listeners with perceptual benefit similar to that of a 16-microphone acoustic beamformer in a difficult listening task. Unlike deep-learning approaches, the proposed algorithm is biologically interpretable and does not need to be trained on large datasets. This study presents a biologically based algorithm for sound source segregation as well as a method to reconstruct highly intelligible audio signals from spiking models.Author SummaryAnimal and humans can navigate complex auditory environments with relative ease, attending to certain sounds while suppressing others. Normally, various sounds originate from various spatial locations. This paper presents an algorithmic model to perform sound segregation based on how animals make use of this spatial information at various stages of the auditory pathway. We showed that the performance of this two-microphone algorithm provides as much benefit to normal-hearing listeners a multi-microphone algorithm. Unlike mathematical and machine-learning approaches, our model is fully interpretable and does not require training with large datasets. Such an approach may benefit the design of machine hearing algorithms. To interpret the spike-trains generated in the model, we designed a method to recover sounds from model spikes with high intelligibility. This method can be applied to spiking neural networks for audio-related applications, or to interpret each node within a spiking model of the auditory cortex.
Repurposing chloramphenicol acetyltransferase for a robust and efficient designer ester biosynthesis platform
Authors: Seo Hyeongmin , Lee Jong-Won , Giannone Richard J. , Dunlap Noah J. , Trinh Cong T. 
Published: November 2020

AbstractRobust and efficient enzymes are essential modules for metabolic engineering and synthetic biology strategies across biological systems to engineer whole-cell biocatalysts. By condensing an acyl-CoA and an alcohol, alcohol acyltransferases (AATs) can serve as an interchangeable metabolic module for microbial biosynthesis of a diverse class of ester molecules with broad applications as flavors, fragrances, solvents, and drop-in biofuels. However, the current lack of robust and efficient AATs significantly limits their compatibility with heterologous precursor pathways and microbial hosts. Through bioprospecting and rational protein engineering, we identified and repurposed chloramphenicol acetyltransferases (CATs) from mesophilic prokaryotes to function as robust and efficient AATs compatible with at least 21 alcohol and 8 acyl-CoA substrates for microbial biosynthesis of linear, branched, saturated, unsaturated and/or aromatic esters. By plugging the best engineered CAT (CATec3 Y20F) into the gram-negative mesophilic bacteriumEscherichia coli, we demonstrated that the recombinant strain could effectively convert various alcohols into desirable esters, for instance, achieving a titer of 13.9 g/L isoamyl acetate with 95% conversion by fed-batch fermentation. The recombinantE. coliwas also capable of simulating the ester profile of roses with high conversion (> 97%) and titer (> 1 g/L) from fermentable sugars at 37°C. Likewise, a recombinant gram-positive, cellulolytic, thermophilic bacteriumClostridium thermocellumharboring CATec3 Y20F could produce many of these esters from recalcitrant cellulosic biomass at elevated temperatures (>50°C) due to the engineered enzyme’s remarkable thermostability. Overall, the engineered CATs can serve as a robust and efficient platform for designer ester biosynthesis from renewable and sustainable feedstocks.
N6-methyladenosine modification of HIV-1 RNA evades RIG-I-mediated sensing to suppresses type-I interferon induction in monocytic cells
Authors: Chen Shuliang , Kumar Sameer , Tirumuru Nagaraja , Welch Jennifer L. , Hu Lulu , He Chuan , Stapleton Jack T. , Wu Li 
Published: November 2020

AbstractN6-methyladenosine (m6A) is a prevalent RNA modification that plays a key role in regulating eukaryotic cellular mRNA functions. RNA m6A modification is regulated by two groups of cellular proteins, writers and erasers that add or remove m6A, respectively. HIV-1 RNA contains m6A modifications that modulate viral infection and gene expression in cells. However, it remains unclear whether m6A modifications of HIV-1 RNA modulate innate immune responses in cells or HIV-1-infected individuals. Here we show that m6A modification of HIV-1 RNA suppresses the expression of antiviral cytokine type-I interferon (IFN-I) in human monocytic cells. Transfection of differentiated monocytic cells with HIV-1 RNA fragments containing a single m6A-modification significantly reduced IFN-I mRNA expression relative to their unmodified RNA counterparts. We generated HIV-1 with altered RNA m6A levels by manipulating the expression of the m6A erasers or pharmacological inhibition of m6A addition in virus-producing cells. RNA transfection and viral infection of differentiated monocytic cells demonstrated that HIV-1 RNA with decreased m6A levels enhanced IFN-I expression, whereas HIV-1 RNA with increased m6A modifications had opposite effects. Our mechanistic studies revealed that m6A of HIV-1 RNA escaped the RIG-I-mediated RNA sensing and activation of the transcription factors IRF3 and IRF7 that drive IFN-I gene expression. Moreover, RNA of peripheral blood mononuclear cells from HIV-1 viremic patients showed increased m6A levels that correlated with increasedIFN-ImRNA expression compared to levels from HIV-1-suppressed patients on antiretroviral therapy. Together, our results suggest that RNA m6A modifications regulate viral replication and antiviral innate immune responses in HIV-1-infected individuals.Author SummaryHIV-1 is known as a weak inducer of antiviral cytokines including IFN-I, but it is unclear how HIV-1 evades innate immunity. Different types of RNA modifications including m6A within the HIV-1 genome modulate viral replication; however, the role of m6A modifications of HIV-1 RNA in regulating innate immune responses remains elusive. In this study, we found that HIV-1 RNA modified with m6A suppresses the expression of IFN-I in differentiated monocytic cells by avoiding innate immune detection of viral RNA mediated by RIG-I, an RNA sensor in host cells. We also observed significantly increased RNA m6A modifications of peripheral blood mononuclear cells from HIV-1 viremic patients compared to virally suppressed patients on combined antiretroviral therapy, suggesting a functional link between m6A modifications and antiretroviral treatment. Investigating the functions of m6A modifications of HIV-1 RNA in regulating innate immune sensing and IFN-I induction in monocytic cells can help understand the mechanisms of HIV-1 persistence.
Towards a Better Understanding of Reverse-Complement Equivariance for Deep Learning Models in Regulatory Genomics
Authors: Zhou Hannah , Shrikumar Avanti , Kundaje Anshul 
Published: November 2020

AbstractPredictive models mapping double-stranded DNA to signals of regulatory activity should, in principle, produce analogous (or “equivariant”) predictions whether the forward strand or its reverse complement (RC) is supplied as input. Unfortunately, standard neural networks can produce highly divergent predictions across strands, even when the training set is augmented with RC sequences. Two strategies have emerged to enforce equivariance: conjoined/“siamese” architectures, and RC parameter sharing or RCPS. However, the connections between the two remain unclear, comparisons to strong baselines are lacking, and neither has been adapted to base-resolution signal profile prediction. Here we extend conjoined & RCPS models to base-resolution signal prediction, and introduce a strong baseline: a standard model (trained with RC data augmentation) that is made conjoined only after training, which we call “post-hoc” conjoined. Through benchmarks on diverse tasks, we find post-hoc conjoined consistently performs best or second-best, surpassed only occasionally by RCPS, and never underperforms conjoined-during-training. We propose an overfitting-based hypothesis for the latter finding, and study it empirically. Despite its theoretical appeal, RCPS shows mediocre performance on several tasks, even though (as we prove) it can represent any solution learned by conjoined models. Our results suggest users interested in RC equivariance should default to post-hoc conjoined as a reliable baseline before exploring RCPS. Finally, we present a unified description of conjoined & RCPS architectures, revealing a broader class of models that gradually interpolate between RCPS and conjoined while maintaining equivariance.