bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimation of incidence from aggregated current status data without differential mortality
Authors: Brinks Ralph 
Published: May 2021

AbstractWe use a historical data about breathlessness in British coal miners to compare two methods centered around a differential equation for deriving the age-specific incidence from aggregated current status data with age information, i.e. age-specific prevalence data. Special focus is put on estimating confidence bounds. For this, we derive a maximum likelihood (ML) estimator for estimating the age-specific incidence from the prevalence data and confidence bounds are calculated based on classical ML theory. Second, we construct a Markov-Chain-Monte-Carlo (MCMC) algorithm to estimate confidence bounds, which implements a weighted version of the differential equation into the prior of the MCMC algorithm. The confidence bounds for both methods are compared and it turns out that the MCMC estimates approach the ML estimates if the prior gives strong weight to the differential equation.
GlyNet: A Multi-Task Neural Network for Predicting Protein-Glycan Interactions
Authors: Carpenter Eric J. , Seth Shaurya , Yue Noel , Greiner Russell , Derda Ratmir 
Published: May 2021

AbstractAdvances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet’s output of continuous values provides more detailed results than classification models. Such continuous outputs are easily converted by a following classifier, and in this form GlyNet outperforms reported classifiers. GlyNet is the first multi-output regression model for protein-glycan interactions and will serve as an important benchmark, facilitating development of quantitative computational glycobiology.
Distinct behavior of the little finger during the vertical translation of an unsteady thumb platform while grasping
Authors: Rajakumar Banuvathy , Varadhan SKM 
Published: May 2021

AbstractObject stabilization while grasping is a common topic of research in motor control and robotics. Forces produced by the peripheral fingers (index and little) play a crucial role in sustaining the rotational equilibrium of a handheld object. In this study, we examined the contribution of the peripheral fingers towards object stabilization when the rotational equilibrium is disturbed. For this purpose, the thumb was placed over an unsteady platform and vertically translated. The task was to trace a trapezoid or an inverted trapezoid pattern by moving the thumb platform in the vertical direction. The thumb displacement data served as visual feedback to trace the pattern displayed. Participants were instructed to maintain the handle in static equilibrium at all times. We observed that the change in the normal force of the little finger due to the downward translation of the thumb was significantly greater than the change in the normal force of the index finger due to the upward translation. We speculate that morphological correlations (between thumb and little finger) during the displacement of the thumb might be a reason for such large increases in the little finger forces.
Beyond category-supervision: instance-level contrastive learning models predict human visual system responses to objects
Authors: Konkle Talia , Alvarez George A. 
Published: May 2021

ABSTRACTAnterior regions of the ventral visual stream have substantial information about object categories, prompting theories that category-level forces are critical for shaping visual representation. The strong correspondence between category-supervised deep neural networks and ventral stream representation supports this view, but does not provide a viable learning model, as these deepnets rely upon millions of labeled examples. Here we present a fully self-supervised model which instead learns to represent individual images, where views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find category information implicitly emerges in the feature space, and critically that these models achieve parity with category-supervised models in predicting the hierarchical structure of brain responses across the human ventral visual stream. These results provide computational support for learning instance-level representation as a viable goal of the ventral stream, offering an alternative to the category-based framework that has been dominant in visual cognitive neuroscience.
Mutational hotspot in the SARS-CoV-2 Spike protein N-terminal domain conferring immune escape potential
Authors: Kubik Slawomir , Arrigo Nils , Bonet Jaume , Xu Zhenyu 
Published: May 2021

ABSTRACTGlobal efforts are being taken to monitor the evolution of SARS-CoV-2, aiming at early identification of mutations with the potential of increasing viral infectivity or virulence. We report a striking increase in the frequency of recruitment of diverse substitutions at a critical residue (W152), positioned in the N-terminal domain (NTD) of the Spike protein, observed repeatedly across independent phylogenetic and geographical contexts. We investigate the impact these mutations might have on the evasion of neutralizing antibodies. Finally, we uncover that NTD is a region exhibiting particularly high frequency of mutation recruitments, suggesting an evolutionary path on which the virus maintains optimal efficiency of ACE2 binding combined with the flexibility facilitating the immune escape.
Digital PCR to quantify ChAdOx1 nCoV-19 copies in blood and tissues
Authors: Badbaran Anita , Mailer Reiner , Dahlke Christine , Woens Jannis , Fathi Anahita , Mellinghoff Sibylle C. , Renné Thomas , Addo Marylyn M. , Riecken Kristoffer , Fehse Boris 
Published: May 2021

AbstractVaccination with the adenoviral-vector based Astra Zeneca ChAdOx1 nCov-19 vaccine is efficient and safe. However, in rare cases vaccinated individuals developed life-threatening thrombotic complications, including thrombosis in cerebral sinus and splanchnic veins. Monitoring of the applied vector in vivo represents an important precondition to study the molecular mechanisms underlying vaccine-driven adverse effects now referred to as vaccine-induced immune thrombotic thrombocytopenia (VITT). We previously have shown that digital PCR is an excellent tool to quantify transgene copies in vivo. Here we present a highly sensitive digital PCR for in-situ quantification of ChAdOx1 nCoV-19 copies. Using this method, we quantified vector copies in human serum 24, 72 and 168 hours post vaccination, and in a variety of murine tissues in an experimental vaccination model 30 minutes post injection. We describe a method for high-sensitivity quantitative detection of ChAdOx1 nCoV-19 with possible implications to elucidate the mechanisms of severe ChAdOx1 nCov-19 vaccine complications.
Sustainability of agricultural systems of indigenous people in Hidalgo, Mexico
Authors: Leyva Doris , de la Torre Mayra , Coronado Yaxk’in 
Published: May 2021

AbstractAgricultural sustainability depends on complex relationships between environmental, economic and social aspects, in particular with the small farm holders from indigenous communities. This work was centered in two municipalities of Hidalgo State in Mexico, Ixmiquilpan (mainly irrigated systems) and El Cardonal (rainfed systems). Our objective was to understand the relationships between the small farm-holders and their agricultural systems. We evaluated the sustainability of their agricultural systems and did some recommendations. We applied the Framework for the Evaluation of Management Systems using Indicators (MESMIS, Spanish acronym), thirty-one indicators were identified, and the quantitative indexes were established to assess sustainability. The results showed that adaptability was a critical factor for irrigated and rainfed systems, the main problem identified was youth migration. Additionally, the access to water and economic resources, as well as environmental resources management, are imperious needs to increase the yield of agriculture crops. Therefore, a holistic approach taking into account the organization of small producers and synergy between indigenous knowledge and modern technologies, are required for the territorial development of the communities.
Denoising-based Image Compression for Connectomics
Authors: Minnen David , Januszewski Michał , Blakely Tim , Shapson-Coe Alexander , Schalek Richard L. , Ballé Johannes , Lichtman Jeff W. , Jain Viren 
Published: May 2021

AbstractConnectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of 3d reconstruction and synaptic detection accuracy.
Non-coding mutations reveal cancer driver cistromes in luminal breast cancer
Authors: Ghamrasni Samah El , Quevedo Rene , Hawley James , Mazrooei Parisa , Hanna Youstina , Cirlan Iulia , Zhu Helen , Bruce Jeff , Oldfield Leslie E. , Yang S. Y. Cindy , Guilhamon Paul , Reimand Jüri , Cescon Dave , Done Susan J. , Lupien Mathieu , Pugh Trevor J 
Published: May 2021

AbstractWhole genome sequencing of primary breast tumors enabled the identification of cancer driver genes 1,2 and non-coding cancer driver plexuses from somatic mutations 3–6. However, differentiating driver and passenger events among non-coding genetic variants remains a challenge to understand the etiology of cancer and inform delivery of personalized cancer medicine. Herein, we reveal an enrichment of non-coding mutations in cis-regulatory elements that cover a subset of transcription factors linked to tumor progression in luminal breast cancers. Using a cohort of 26 primary luminal ER+PR+ breast tumors, we compiled a catalogue of ∼100,000 unique cis-regulatory elements from ATAC-seq data. Integrating this catalogue with somatic mutations from 350 publicly available breast tumor whole genomes, we identified four recurrently mutated individual cis-regulatory elements. By then partitioning the non-coding genome into cistromes, defined as the sum of binding sites for a transcription factor, we uncovered cancer driver cistromes for ten transcription factors in luminal breast cancer, namely CTCF, ELF1, ESR1, FOSL2, FOXA1, FOXM1 GATA3, JUND, TFAP2A, and TFAP2C in luminal breast cancer. Nine of these ten transcription factors were shown to be essential for growth in breast cancer, with four exclusive to the luminal subtype. Collectively, we present a strategy to find cancer driver cistromes relying on quantifying the enrichment of non-coding mutations over cis-regulatory elements concatenated into a functional unit drawn from an accessible chromatin catalogue derived from primary cancer tissues.
Magnetic Resonance Spectroscopy Frequency and Phase Correction Using Convolutional Neural Networks
Authors: Ma David J. , Le Hortense A-M. , Ye Yuming , Laine Andrew F. , Lieberman Jeffery A. , Rothman Douglas L. , Small Scott A. , Guo Jia 
Published: May 2021

AbstractWe introduce DeepSPEC, a novel convolutional neural network (CNN) -based approach for frequency-and-phase correction (FPC) of MRS spectra to achieve fast and accurate FPC of single-voxel PRESS MRS and MEGA-PRESS data. In DeepSPEC, two neural networks, including one for frequency correction and one for phase correction were trained and validated using published simulated and in vivo PRESS and MEGA-PRESS MRS dataset with wide-range artificial frequency and phase offsets applied. DeepSPEC was subsequently tested and compared to the current deep learning solution - a “vanilla” neural network approach using multilayer perceptrons (MLP). Furthermore, random noise was added to the original simulated dataset to further investigate the model performance with noise at varied signal-to-noise (SNR) levels (i.e., 6 dB, 3 dB, and 1.5 dB). The testing showed that DeepSPEC is more robust to noise compared to the MLP-based approach due to having a smaller absolute error in both frequency and phase offset prediction. The DeepSPEC framework was capable of correcting frequency offset with 0.01±0.01 Hz and phase offset with 0.12±0.09° absolute errors on average for unseen simulated data at a high SNR (12 dB) and correcting frequency offset with 0.01±0.02 Hz and phase offset within -0.07±0.44° absolute errors on average at very low SNR (1.5 dB). Furthermore, additional frequency and phase offsets (i.e., small, moderate, large) were applied to the in vivo dataset, and DeepSPEC demonstrated better performance for FPC when compared to the MLP-based approach. Results also show DeepSPEC has superior performance than the model-based SR implementation (mSR) in FPC by having higher accuracy in a wider range of additional offsets. These results represent a proof of concept for the use of CNNs for preprocessing MRS data and demonstrate that DeepSPEC accurately predicts frequency and phase offsets at varying noise levels with state-of-the-art performance.
Cryptic speciation in freshwaters: are speciation in lentic species shaped by paleohydrological events?
Authors: Sil Maitreya , Roy Abhishikta , Palden Tenzin , Karanth K. Praveen , Aravind N. A. 
Published: May 2021

AbstractThe Indian subcontinent is extremely diverse in terms of its flora and fauna. However, there is a severe underestimation of the biotic diversity in invertebrate groups as well as a lack of understanding of the processes generating diversity in these groups. Here we aimed to jointly estimate the cryptic diversity of two freshwater snail species complexes – Pila globosa and Pila virens, and uncover the processes behind the speciation in these groups. We employed phylogenetic, morphometric, population genetic and niche modelling technique to address these questions. We found out that both species complexes consist of several genetically and ecologically distinct putative species. The speciation was primarily driven by allopatric isolation into different river basins. A combination of paleoclimatic and paleohydrological changes during the Miocene have shaped the speciation events. Local climatic adaptation also could have contributed towards some of the speciation events. The study sheds light on the complex interaction between the biology of the species and the environment that shape the diversification patterns in a group.
Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study
Authors: Chandran Varun Arunachalam , Pliatsikas Christos , Neufeld Janina , O’Connell Garret , Haffey Anthony , DeLuca Vincent , Chakrabarti Bhismadev 
Published: May 2021

AbstractAutism Spectrum Disorders (ASD) are a set of neurodevelopmental conditions characterised by difficulties in social interaction and communication as well as stereotyped and restricted patterns of interest. Autistic traits exist in a continuum across the general population, whilst the extreme end of this distribution is diagnosed as clinical ASD. While many studies have investigated brain structure in autism using a case-control design, few have used a dimensional approach. To add to this growing body of literature, we investigated the structural brain correlates of autistic traits in a mixed sample of adults (N=91) with and without a clinical diagnosis of autism. We examined regional brain volumes (using voxel-based morphometry and surface-based morphometry) and white matter microstructure properties (using Diffusion Tensor Imaging). Our findings show widespread grey matter differences, including in the social brain regions, and some evidence for white matter microstructure differences related to higher autistic traits. These grey matter and white matter microstructure findings from our study are consistent with previous reports and support the brain structural differences in ASD. These findings provide further support for shared aetiology for autistic traits across the diagnostic divide.
A Reference-free Approach for Cell Type Classification with scRNA-seq
Authors: Sun Qi , Peng Yifan , Liu Jinze 
Published: May 2021

AbstractThe single-cell RNA sequencing (scRNA-seq) has become a revolutionary technology to detect and characterize distinct cell populations under different biological conditions. Unlike bulk RNA-seq, the expression of genes from scRNA-seq is highly sparse due to limited sequencing depth per cell. This is worsened by tossing away a significant portion of reads that cannot be mapped during gene quantification. To overcome data sparsity and fully utilize original sequences, we propose scSimClassify, a reference-free and alignment-free approach to classify cell types withk-mer level features derived from raw reads in a scRNA-seq experiment. The major contribution of scSimClassify is the simhash method compressingk-mers with similar abundance profiles into groups. The compressedk-mer groups (CKGs) serve as the aggregatedk-mer level features for cell type classification. We evaluate the performance of CKG features for predicting cell types in four scRNA-seq datasets comparing four state-of-the-art classification methods as well as two scRNA-seq specific algorithms. Our experiments demonstrate that the CKG features lend themselves to better performance than traditional gene expression features in scRNA-seq classification accuracy in the majority of cases. Because CKG features can be efficiently derived from raw reads without a resource-intensive alignment process, scSimClassify offers an efficient alternative to help scientists rapidly classify cell types without relying on reference sequences. The current version of scSimClassify is implemented in python and can be found athttps://github.com/digi2002/scSimClassify.
Kite-shaped molecules block SARS-CoV-2 cell entry at a post-attachment step
Authors: Chan Shiu-Wan , Shafi Talha , Ford Robert C. 
Published: May 2021

ABSTRACTAnti-viral small molecules are currently lacking for treating coronavirus infection. The long development timescales for such drugs are a major problem, but could be shortened by repurposing existing drugs. We therefore screened a small library of FDA-approved compounds for potential severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antivirals using a pseudovirus system that allows a sensitive read-out of infectivity. A group of structurally-related compounds, showing moderate inhibitory activity with IC50values in the 1-5µM range, were identified. Further studies demonstrated that these ‘kite-shaped’ molecules were surprisingly specific for SARS-CoV and SARS-CoV-2 and that they acted early in the entry steps of the viral infectious cycle, but did not affect virus attachment to the cells. Moreover the compounds were able to prevent infection in both kidney- and lung-derived human cell lines. The structural homology of the hits allowed the production of a well-defined pharmacophore that was found to be highly accurate in predicting the anti-viral activity of the compounds in the screen. We discuss the prospects of repurposing these existing drugs for treating current and future coronavirus outbreaks.
The role of memory and perspective shifts in systematic biases during object location estimation
Authors: Segen Vladislava , Colombo Giorgio , Avraamides Marios , Slattery Timothy , Wiener Jan M. 
Published: May 2021

AbstractOur previous research highlighted a systematic bias in a spatial memory task, with participants correctly detecting object movements in the same direction as the perspective shift, whilst misjudging the direction of object movements if those were in the opposite direction to the perspective shift. The aim of the current study was to investigate if the introduction of perspective shifts results in systematic biases in object location estimations. To do so, we asked participants to encode the position of an object in a virtual room and to then estimate the object’s position following a perspective shift. In addition, by manipulating memory load (perception and memory condition) we investigated if the bias in object position estimates results from systematic distortions introduced in spatial memory. Overall, our results show that participants make systematic errors in estimating object positions in the same direction as the perspective shift. This bias was present in both the memory and the perception condition. We propose that the systematic bias in the same direction as the perspective shift is driven by difficulties in understanding the perspective shifts that may lead participants to use an egocentric representation of object positions as an anchor when estimating the object location following a perspective shift, thereby giving rise to a systematic shift in errors in the same direction as the perspective shift.
Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment
Authors: Fu Yilei , Mahmoud Medhat , Muraliraman Viginesh Vaibhav , Sedlazeck Fritz J. , Treangen Todd J. 
Published: May 2021

AbstractBackgroundLong-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hotspots reduces read alignment accuracy and impedes structural variant detection.FindingsWe tested our hypothesis by implementing a read mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via e.g. minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long read mapper (NGMLR). In support of our hypothesis, we show Vulcan improves the alignments for Oxford Nanopore Technology (ONT) long-reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read mapping methods alone.ConclusionsVulcan is the first long-read mapping framework that combines two distinct gap penalty modes, resulting in improved structural variant recall and precision. Vulcan is open-source and available under the MIT License athttps://gitlab.com/treangenlab/vulcan