bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning
Authors: Hossain Md. Shahadat , Pathan A. Q. M. Sala Uddin , Islam Md. Nur , Tonmoy Mahafujul Islam Quadery , Rakib Mahmudul Islam , Munim Md. Adnan , Saha Otun , Fariha Atqiya , Al Reza Hasan , Roy Maitreyee , Bahadur Newaz Mohammed , Rahaman Md. Mizanur 
Published: May 2021

AbstractGenomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identify 3334545 mutations (14.01 mutations per isolate), suggesting a high mutation rate. Strains from India showed the highest no. of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. Besides the most prominently occurring mutations (D416G, F106F, P314L, and UTR:C241T), we identify L93L, A222V, A199A, V30L, and A220V mutations which are in the top 10 most frequent mutations. Multi-nucleotide mutations GGG>AAC, CC>TT, TG>CA, and AT>TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C>T, A>G, and A>T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T>C, G>A, and G>T mutations, respectively. T>G\A, C>G\A, and A>T\C are not anticipated in the future. Since SARS-CoV-2 is evolving continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide.
DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins
Authors: Misiura Mikita , Shroff Raghav , Thyer Ross , Kolomeisky Anatoly B. 
Published: May 2021

AbstractPrediction of side chain conformations of amino acids in proteins (also termed ‘packing’) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this work we evaluate the potential of Deep Neural Networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr and Trp being up to 50% smaller.
CZON-cutter: a CRISPR-Cas9 system with multiplexed organelle imaging in a simple unicellular alga
Authors: Tanaka Naoto , Mogi Yuko , Fujiwara Takayuki , Yabe Kannosuke , Toyama Yukiho , Higashiyama Tetsuya , Yoshida Yamato 
Published: May 2021

AbstractThe simple cellular structure of the unicellular alga Cyanidioschyzon merolae consists of one nucleus, one mitochondrion, one chloroplast, and one peroxisome per cell and offers unique advantages to investigate mechanisms of organellar proliferation and the cell cycle. Here, we describe an engineered clustered, regularly interspaced, short palindromic repeats (CRISPR)-associated protein 9 (Cas9) system, CZON-cutter, for simultaneous genome editing and organellar visualization. We engineered a C. merolae strain expressing a nuclear-localized Cas9-Venus nuclease to target editing at a locus defined by a single-guide RNA (sgRNA). We then successfully edited the algal genome and visualized the mitochondrion and peroxisome in transformants by fluorescent protein reporters with different excitation wavelengths. Fluorescent protein labeling of organelles in living transformants allows validation of phenotypes associated with organellar proliferation and the cell cycle, even when the edited gene is essential. Combined with the exceptional biological features of C. merolae, CZON-cutter will be instrumental for investigating cellular and organellar division in a high-throughput manner.SummaryAn engineered CRISPR-Cas9 system, named CZON-cutter, for simultaneous genome editing and fluorescent protein labeling of organelles in Cyanidioschyzon merolae can be used to validate intracellular function of a particular gene, even if it is essential.
Monitoring of SARS-CoV-2 B.1.1.7 variant early-phase spreading in South-Moravian Region in the Czech Republic and evaluation of its pathogenicity
Authors: Diabelko Daniel , Dvorackova Milada , Heroldova Monika Dvorakova , Forte Giancarlo , Cundrle Ivan , Ruzicka Filip , Vrbsky Jan 
Published: May 2021

AbstractSARS-CoV-2 emerged in Wuhan, China, in December 2019. Starting in January 2020, over a period of several months, the initial virus (Wuhan-Hu-1/2019; Wu et al. 2020) diverged in a descendant strain carrying D614G amino acid mutation in spike protein. By summer 2020 this novel coronavirus (nCoV) became the most dominant form of the virus circulating worldwide and raised serious international concern. Currently (April 2021), there are 3598 subsequent PANGO branched lineages recognized that carry numerous mutations. To date, the most emerging lineages of SARS-CoV-2 worldwide include B.1.1.7 lineage with a frequency of 48% followed by several dozens of others with frequencies 7.5% or less, such as B.1.351, B.1.1.28, B.1.2, B.1.1.519, P.1, R.1, etc. (www.nextrain.org, Centers for Disease Control and Prevention; CDC 2020 www.cdc.gov).In this study, we monitored the spreading of B.1.1.7 lineage from the early phase of its appearance until it became predominant in the South-Moravian region of the Czech Republic. We measured significantly associated clinical marker (Ct; cycle threshold) correlating with viral load in B.1.1.7 lineage. Interestingly, we found that the spreading of B.1.1.7 strain was associated with a shift in patients average age, as compared to the former predominant lineage. Finally, we calculated the impact of the B.1.1.7 lineage on hospitalization and case fatality of the patients on the intensive care unit in the central South-Moravian faculty hospital.
Visualizing the Role of Lipid Dynamics during Infrared Neural Stimulation with Hyperspectral Stimulated Raman Scattering Microscopy
Authors: Adams Wilson R , Gautam Rekha , Locke Andrea , Borrachero-Conejo Ana I. , Dollinger Bryan , Throckmorton Graham A. , Duvall Craig , Jansen E Duco , Mahadevan-Jansen Anita 
Published: May 2021

AbstractInfrared neural stimulation, or INS, is a method of using pulsed infrared light to yield label-free neural stimulation with broad experimental and translational utility. Despite its robust demonstration, the mechanistic and biophysical underpinnings of INS have been the subject of debate for more than a decade. The role of lipid membrane thermodynamics appears to play an important role in how fast IR-mediated heating nonspecifically drives action potential generation. Direct observation of lipid membrane dynamics during INS remains to be shown in a live neural model system. To directly test the involvement of lipid dynamics in INS, we used hyperspectral stimulated Raman scattering (hsSRS) microscopy to study biochemical signatures of high-speed vibrational dynamics underlying INS in a live neural cell culture model. Findings suggest that lipid bilayer structural changes are occurring during INSin vitroin NG108-15 neuroglioma cells. Lipid-specific signatures of cell SRS spectra were found to vary with stimulation energy and radiant exposure. Spectroscopic observations were verified against high-speed ratiometric fluorescence imaging of a conventional lipophilic membrane structure reporter, di-4-ANNEPS. Overall, the presented data supports the hypothesis that INS causes changes in the lipid membrane of neural cells by changing lipid membrane packing order – which coincides with likelihood of cell stimulation. Furthermore, this work highlights the potential of hsSRS as a method to study biophysical and biochemical dynamics safely in live cells.
Distinct movement patterns generate stages of spider web-building
Authors: Corver Abel , Wilkerson Nicholas , Miller Jeremiah , Gordus Andrew 
Published: May 2021

AbstractThe geometric complexity and stereotypy of spider webs have long generated interest in their algorithmic origin. Like other examples of animal architecture, web construction is the result of several assembly phases that are driven by distinct behavioral stages coordinated to build a successful structure. Manual observations have revealed a range of sensory cues and movement patterns used during web construction, but methods to systematically quantify the dynamics of these sensorimotor patterns are lacking. Here, we apply an analytical pipeline to quantify web-making behavior of the orb-weaverUloborus diversus. Position tracking revealed stereotyped stages of construction that could occur in typical or atypical progressions across individuals. Using an unsupervised clustering approach, we identified general and stage-specific leg movements. A Hierarchical Hidden Markov Model revealed that stages of web-building are characterized by stereotyped sequences of actions largely shared across individuals, regardless of whether these stages progress in a typical or atypical fashion. Web stages could be predicted based on action-sequences alone, revealing that web-stages are a physical manifestation of underlying behavioral phases.HighlightsSpider centroid trajectories indicate stereotyped progression of web-building stages.Unsupervised movement clustering reveals a shared set of movements which correspond to previously defined behaviors that define web-making across individuals.Stages of web-building use both stage-specific and non-specific behaviors.Stereotyped and distinct action sequences are predictive of stages of web-building.
BaySiCle: A Bayesian Inference joint kNN method for imputation of single-cell RNA-sequencing data making use of local effect
Authors: Singh Abhishek Narain 
Published: May 2021

AbstractThere is a marked technical variability and a high amount of missing observations in the single-cell data that we obtain from experiments. Apart from that clearly each of the batch of experiments have a batch effect on every cell in the batch. This batch effect can be taken into advantage for dealing with imputation, given that all the cells in a given batch belong to the same tissue. Here we introduce ‘BaySiCle’, a novel Bayesian inference based method combined with k-nearest neighbors algorithm for the imputation of missing data in scRNA-seq counts. The priors are found out based on expression value across cells for all the single cells of the same batch. We demonstrate using sample scRNA-seq datasets and simulated expression data that BaySiCle allows robust imputation of missing values generating realistic transcript distributions that match single molecule fluorescence in situ hybridization measurements. By using priors as obtained by the dataset structures in the not just the experimental set-up batch, but also the same group of cells, BaySiCle improves accuracy of imputation to be that much closer to its similar alternatives.Availability and implementationThe Python Jupyter notebook ‘BaySiCle’ is published on GitHub GitHub - abinarain/BaySiCel: Single Cell Data Imputation using Bayesian statistics and kNN
Hydrodynamics of gliding penguin flipper suggests the adjustment of sweepback with swimming speeds
Authors: Maeda Masateru , Harada Natsuki , Tanaka Hiroto 
Published: May 2021

AbstractHydrodynamic performance of a gliding penguin flipper (wing) considering the backward sweep was estimated with computational fluid dynamics (CFD) simulation. A flipper of a gentoo penguin (Pygoscelis papua) was 3D scanned, smoothed, and a numerical fluid mesh was generated. For accurate yet resource-saving computation, an embedded large-eddy simulation (ELES) methods was employed, where the flow near the flipper was solved with large-eddy simulation (LES) and flow far away from the flipper was solved with Reynolds-averaged Navier-Stokes (RANS). The relative flow speed was fixed at 2 m s−1, close to the typical foraging speed for the penguin species. The sweep angle was set to be 0°, 30°, and 60°, while the angle of attack was varied between −40° and 40°, both are within the realistic ranges in the wing kinematics measurement of penguins in an aquarium. It was revealed that a higher sweep angle reduces the lift slope, but the lift coefficient is unchanged at a high angle of attack. Drag coefficient was reduced across the angles of attack with increasing the sweep angles. The drag polars suggest the sweep angle may be adjusted with the change in swimming speed and anhedral (negative dihedral) angle to minimise drag while maintaining the vertical force balance to counteract the positive buoyancy. This will effectively expand the swimming envelope of the gliding penguin, similar to a flying counterpart such as swift.
Effect of salinity and water dilution on environmental DNA degradation in freshwater environments
Authors: Saito Tatsuya , Doi Hideyuki 
Published: May 2021

ABSTRACTEnvironmental DNA (eDNA) analysis methods have been developed to detect the distribution and abundance/biomass of organisms in various environments. eDNA generally degrades quickly, thus the study of eDNA degradation is critical for eDNA evaluation. However, there have only been a few studies of eDNA degradation experiments in which the salt concentration and water dilution were controlled. In this study, the effects of degradation were experimentally evaluated by controlling the salinity and water dilution of pond water. An experiment was conducted to evaluate the effects of salinity and dilution on eDNA detection with fragmental eDNA and free cell-derived eDNA using pond water, diluted pond water, and saline pond water. We quantified the eDNA copies of free cells, fragmental DNA, and the eDNA from Cyprinus carpio. In both the diluted and saline pond water, we found that the degradation rate of eDNA was much slower than that in pond water. Furthermore, the DNA concentration did not exponentially decrease in both the saline purified water and purified water samples. For the lower degradation rate in salt water, we interpreted that salts may affect DNA degradation factors such as microbe compositions and activities. The effect of salinity and dilution on eDNA detection provides fundamental information about the degradation process of eDNA, which is essential to understand the behavior of eDNA in natural environments.
Self-Supervised Pretraining for Transferable Quantitative Phase Image Cell Segmentation
Authors: Vicar Tomas , Chmelik Jiri , Jakubicek Roman , Chmelikova Larisa , Gumulec Jaromir , Balvan Jan , Provaznik Ivo , Kolar Radim 
Published: May 2021

AbstractIn this paper, U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is used in the post-processing step. Additionally, we proposed a self-supervised pretraining technique using nonlabelled data, which is trained to reconstruct multiple image distortions and improved the segmentation performance by from 0.67 to 0.70 of Object-wise Intersection over Union. Moreover, we publish a new dataset of manually labelled images suitable for this task together with the unlabelled data for self-supervised pretraining.Graphical AbstractHighlightsFour strategies for instance cell segmentation with U-Net were compared.Specialised post-processing pipelines with tunable/optimizable parameters were designed for each segmentation strategy.Transferability to different cell types by optimisation of post-processing parameters was tested.The proposed self-supervised pretraining method improved both segmentation performance and transferability to different cell types.A new manually labelled quantitative phase imaging dataset for cell segmentation with unlabelled data for self-supervised pretraining was created.
Three classes of epigenomic regulators converge to hyperactivate the essential maternal gene deadhead within a heterochromatin mini-domain
Authors: Torres-Campana Daniela , Horard Béatrice , Denaud Sandrine , Benoit Gérard , Loppin Benjamin , Orsi Guillermo A. 
Published: May 2021

ABSTRACTThe formation of a diploid zygote is a highly complex cellular process that is entirely controlled by maternal gene products stored in the egg cytoplasm. This highly specialized transcriptional program is tightly controlled at the chromatin level in the female germline. As an extreme case in point, the massive and specific ovarian expression of the essential thioredoxin Deadhead (DHD) is critically regulated in Drosophila by the histone demethylase Lid and its partner, the histone deacetylase complex scaffold Sin3A, via yet unknown mechanisms. Here, we identified the Brahma chromatin remodeler sub-unit Snr1 and the insulator component Mod(mdg4) as essential for dhd expression and investigated how these epigenomic effectors act with Lid and Sin3A to hyperactivate dhd. Using Cut&Run chromatin profiling with a dedicated data analysis procedure, we found that dhd is intriguingly embedded in an H3K27me3/H3K9me3-enriched mini-domain flanked by DNA regulatory elements, including a dhd promoter-proximal element essential for its expression. Surprisingly, Lid, Sin3A, Snr1 and Mod(mdg4) impact H3K27me3 and this regulatory element in distinct manners. However, we show that these effectors activate dhd independently of H3K27me3/H3K9me3, and that these marks are not required to repress dhd. Together, our study demonstrates an atypical and critical role for chromatin regulators Lid, Sin3A, Snr1 and Mod(mdg4) to trigger tissue-specific hyperactivation within a unique heterochromatin mini-domain.AUTHOR SUMMARYGene expression is tightly regulated by conserved protein complexes that act at the chromatin level to allow or restrict transcription. Such epigenetic control of gene activity defines the identity of different cell types during development, as well as their response to environmental cues. Yet, how multiple chromatin factors converge to achieve precise gene regulation remains difficult to address, partly due to the lack of biological situations where these intricate relationships can be studied. In this paper, we have addressed this issue by dissecting the regulation of deadhead, an essential gene specifically and massively expressed in the Drosophila germline. Unexpectedly, we found that its hyperactivation occurs despite deadhead being embedded in an apparently unfavorable chromatin mini-domain, notably featuring repressive histone modifications. We further demonstrate that four chromatin effectors, Lid, Sin3A, Snr1 and Mod(mdg4), have distinct, atypical and essential roles to ensure deadhead expression within this chromatin environment. Together, our findings put into perspective our understanding on these regulatory factors by illustrating how they can exert a biologically essential function via non-canonical mechanisms.
Knockdown of a novel ATPase domain of capsid protein inhibits genome packaging in potato leaf roll virus
Authors: Kumar Jitesh , Kumar Ravi Ranjan , Das Dilip Kumar , Mohanty Auroshikha , Rajani Kumari , Kumari Namaste , Kumar Vinod , Kumar Sunil , Ranjan Tushar 
Published: May 2021

AbstractPotato leaf roll virus (PLRV) uses powerful molecular machines to package its genome into a viral capsid employing ATP as fuel. Although, recent bioinformatics and structural studies have revealed detailed mechanism of DNA packaging, little is known about the mechanochemistry of genome packaging in small plant viruses such as PLRV. We have identified a novel P-loop-containing ATPase domain with two Walker A-like motifs, two arginine fingers, and two sensor motifs distributed throughout the polypeptide chain of PLRV capsid protein (CP). The composition and arrangement of the ATP binding and hydrolysis domain of PLRV CP is unique and rarely reported. The discovery of the system sheds new light on the mechanism of viral genome packaging, regulation of viral assembly process, and evolution of plant viruses. Here, we used the RNAi approach to suppress CP gene expression, which in turn prevented PLRV genome packaging and assembly in Solanum tuberosum cv. Khufri Ashoka. Potato plants agroinfiltrated with siRNA constructs against the ATPase domain of CP exhibited no rolling symptoms upon PLRV infection, indicating that the silencing of CP gene expression is an efficient method for generating PLRV-resistant potato plants. Moreover, our findings provide a robust approach to generate PLRV-resistant potato plants, which can be further extended to other species. Finally, we propose a new mechanism of genome packaging and assembly in plant viruses.
Signatures of genetic variation in human microRNAs point to processes of positive selection related to population-specific disease risks
Authors: Villegas-Mirón Pablo , Gallego Alicia , Bertranpetit Jaume , Laayouni Hafid , Espinosa-Parrilla Yolanda 
Published: May 2021

AbstractThe occurrence of natural variation in human microRNAs has been the focus of numerous studies during the last twenty years. Most of them have been dedicated to study the role of specific mutations in diseases, like cancer, while a minor fraction seek to analyse the diversity profiles of microRNAs in the genomes of human populations. In the present study we analyse the latest human microRNA annotations in the light of the most updated catalog of genetic variation provided by the 1000 Genomes Project. We show by means of thein silicoanalysis of noncoding variation of microRNAs that the level of evolutionary constraint of these sequences is governed by the interplay of different factors, like their evolutionary age or the genomic location where they emerged. The role of mutations in the shaping of microRNA-driven regulatory interactions is emphasized with the acknowledgement that, while the whole microRNA sequence is highly conserved, the seed region shows a pattern of higher genetic diversity that appears to be caused by the dramatic frequency shifts of a fraction of human microRNAs. We highlight the participation of these microRNAs in population-specific processes by identifying that not only the seed, but also the loop, are particularly differentiated regions among human populations. The quantitative computational comparison of signatures of population differentiation showed that candidate microRNAs with the largest differences are enriched in variants implicated in gene expression levels (eQTLs), selective sweeps and pathological processes. We explore the implication of these evolutionary-driven microRNAs and their SNPs in human diseases, such as different types of cancer, and discuss their role in population-specific disease risk.