bluuebunny/crossref_metadata_2025 huggingface dataset:
Ever-increasing viral diversity associated with the red imported fire ant Solenopsis invicta (Formicidae: Hymenoptera)
Authors: Xavier César A.D. , Allen Margaret L. , Whitfield Anna E.
Published: August 2020

Background

Advances in sequencing and analysis tools have facilitated discovery of many new viruses from invertebrates, including ants. Solenopsis invicta is an invasive ant that has quickly spread around world causing significant ecological and economic impacts. Its virome has begun to be characterized pertaining to potential use of viruses as natural enemies. Although the S. invicta virome is best characterized among ants, most studies have been performed in its native range, with little information from invaded areas.

Methods

Using a metatranscriptome approach, we further characterized viruses associated with S. invicta, in two introduced areas, U.S and Taiwan. The data set used here was obtained from different stages (larvae, pupa, and adults) of S. invicta life cycle. Publicly available RNA sequences from GenBank’s Sequence Read Archive were downloaded and de novo assembled using CLC Genomics Workbench 20.0.1. Contigs were compared against the non-redundant protein sequences and those showing similarity to viral sequences were further analyzed.

Results

We characterized five putative new viruses associated with S. invicta transcriptomes. Sequence comparisons revealed extensive divergence across ORFs and genomic regions with most of them sharing less than 40% amino acid identity with those closest homologous sequences previously characterized. The first negative-sense single-stranded RNA viruses included in the orders Bunyavirales and Mononegavirales are reported. In addition, two positive single-strand viruses and one single strand DNA virus were also characterized. While the presence of a putative tenuivirus associated with S. invicta was previously suggested to be a contamination, here we characterized and present strong evidence that Solenopsis invicta virus 14 (SINV-14) is a tenui-like virus that has a long-term association with the ant. Furthermore, based on virus abundance compared to housekeeping genes, phylogenetic relationships, and completeness of viral coding sequences, our results suggest that four of five viruses reported, those being SINV-14, SINV-15, SINV-16 and SINV-17, replicate in the ant S. invicta.

Conclusions

The present study expands our knowledge about viral diversity associated with S. invicta in introduced areas with potential to be used as biological control agents, which will require further biological characterization.
PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies
Authors: Vantini Michele , Mannerström Henrik , Rautio Sini , Ahlfors Helena , Stockinger Brigitta , Lähdesmäki Harri
Published: August 2020

We propose PairGP, a non-stationary Gaussian process method to compare gene expression timeseries across several conditions that can account for paired longitudinal study designs and can identify groups of conditions that have different gene expression dynamics. We demonstrate the method on both simulated data and previously unpublished RNA-seq time-series with five conditions. The results show the advantage of modeling the pairing effect to better identify groups of conditions with different dynamics. The implementations is available athttps://github.com/michelevantini/PairGP
FAM19A5l affects mustard oil-induced peripheral nociception in zebrafish
Authors: Jeong Inyoung , Yun Seongsik , Shahapal Anu , Cho Eun Bee , Hwang Sun Wook , Seong Jae Young , Park Hae-Chul
Published: August 2020

Family with sequence similarity 19 (chemokine (C-C motif)-like), member A5 (FAM19A5) is a chemokine-like secretory protein recently identified to be involved in the regulation of osteoclast formation, post-injury neointima formation, and depression. Here, we identifiedFAM19A5l, an orthologous zebrafish gene that originated from a common ancestralFAM19A5gene.FAM19A5lwas expressed in trigeminal and dorsal root ganglion neurons as well as distinct neuronal subsets of the central nervous system of zebrafish. Interestingly,FAM19A5l+trigeminal neurons were nociceptors that co-localized with TRPA1b and TRPV1, and responded to mustard-oil treatment. Behavioral analysis revealed that the nociceptive response to mustard oil decreased inFAM19A5l-knockout zebrafish larvae. In addition,TRPA1bandNGFamRNA levels were down- and up-regulated inFAM19A5l-knockout and - overexpressing transgenic zebrafish, respectively. Together, our data suggested that FAM19A5l played a role in nociceptive responses to mustard oil by regulatingTRPA1bandNGFaexpression in zebrafish.
Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships
Authors: Huber Florian , Ridder Lars , Verhoeven Stefan , Spaaks Jurriaan H. , Diblen Faruk , Rogers Simon , van der Hooft Justin J.J.
Published: August 2020

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm -- Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.
Metamers of Bayesian computation
Authors: Sohn Hansem , Jazayeri Mehrdad
Published: August 2020

There are two sharply debated views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative model-free hypothesis posits that decisions are optimized through trial and error without explicit internal models for priors and cost functions. To distinguish between these possibilities, we introduce a novel paradigm that probes sensitivity of humans to transitions between prior-cost pairs that demand the same optimal policy (metamers) but distinct internal models. We demonstrate the utility of our approach in two experiments that were classically explained by model-based Bayesian theory. Our approach validates the model-based strategy in an interval timing task but not in a visuomotor rotation task. More generally, our work provides a domain-general approach for testing the circumstances under which humans implement model-based Bayesian computations.
Connectome-mediated prediction of future tau-PET burden in Alzheimer’s disease
Authors: Damasceno Pablo F. , Joie Renaud La , Shcherbinin Sergey , Southekal Sudeepti , Kotari Vikas , Higgins Ixavier A. , Collins Emily C. , Rabinovici Gil D. , Mintun Mark A , Raj Ashish
Published: August 2020

Alzheimer’s Disease (AD) tau pathology originates in the brainstem and subsequently spreads to the entorhinal cortex, hippocampus and finally to temporal, parietal and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to trans-neuronal spread of misfolded tau protein along the projection pathways of affected neurons. The aggregation of tau is being increasingly recognized as a trustworthy biomarker preceding the appearance of Alzheimer’s disease (AD) symptoms. One major goals of disease modifying therapies has been to stop or slow down the tau aggregation process. In order to evaluate drug efficacy, it would be desirable to have an accurate model predictive of a patient’s future tau burden, against which the tau measurements from drug-receiving cohorts could be compared. Here we report the development of such a model, evaluated in a cohort of 88 subjects clinically diagnosed as Mild Cognitively Impaired (MCI = 60) or Alzheimer’s disease (AD = 28) and tracked over a period of 18 months. Our approach combined data-driven and model-based methodologies, with the goal of predicting changes in tau within suitably specified target regions. We show that traditional statistical methods, allied to a network diffusion model for tau propagation in the brain, provide a remarkable prediction of the magnitude of incremental tau deposited in particular cortical areas of the brain over this period (MCI: R2= 0.65±0.16; AD: R2= 0.71±0.11) from baseline data. Our work has the potential to greatly strengthen the repertoire of analysis tools used in AD clinical trials, opening the door to future interventional trials with far fewer sample sizes than currently required.
Individual heterogeneity and its importance for metapopulation dynamics
Authors: Masier Stefano , Dahirel Maxime , Mortier Frederik , Bonte Dries
Published: August 2020

Landscape connectedness shapes the exchange of individuals among patches, and hence metapopulation connectivity and dynamics. Connectedness, and its resulting effects on connectivity are therefore rightfully central in conservation biology. However, besides determining demographic fluxes of individuals between patches, connectedness also generates phenotypic sorting and thus impacts local and regional eco-evolutionary dynamics. Despite the central role of connectedness, its effects on individual phenotypic heterogeneity and spatial organization are so far neglected in theory and applications.

Through experimental metapopulations ofTetranychus urticae(two-spotted spider mite) with three levels of landscape connectedness and by regularly removing phenotypic structure in a subset of these populations, we tested how regional and local population dynamics are determined both by network connectedness and phenotypic spatial organization.

We find that the self-organizing phenotypic spatial structure increases local equilibrium population sizes and variability. It in contrast dampens the effects of imposed connectedness differences on population sizes and is therefore anticipated to improve metapopulation persistence. Contrary to theoretical expectations, the most locally connected patches within the network showed an overall reduced local population size, possibly originating from a faster depletion of resources from immigrants or transiting individuals.

This experiment shows how metapopulation dynamics can significantly deviate from theoretical expectations due to individual heterogeneity. This potential rescue effect stemming from phenotypical self-organization in space is a key point to consider for conservation actions, especially based on translocations.
Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation
Authors: Song Seungmoon , Kidziński Łukasz , Peng Xue Bin , Ong Carmichael , Hicks Jennifer , Levine Sergey , Atkeson Christopher G. , Delp Scott L.
Published: August 2020

Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Despite advances in neuroscience techniques, it is still difficult to measure and interpret the activity of the millions of neurons involved in motor control. Thus, researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This “Learn to Move” competition, which we have run annually since 2017 at the NeurIPS conference, has attracted over 1300 teams from around the world. Top teams adapted state-of-art deep reinforcement learning techniques to produce complex motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.
Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues
Authors: DeVilbiss Andrew W. , Zhao Zhiyu , Martin-Sandoval Misty S. , Ubellacker Jessalyn M. , Tasdogan Alpaslan , Agathocleous Michalis , Mathews Thomas P. , Morrison Sean J.
Published: August 2020

Little is known about the metabolic regulation of rare cell populations because most metabolites are hard to detect in small numbers of cells. We previously described a method for metabolomic profiling of flow cytometrically-isolated hematopoietic stem cells (HSCs) that detects approximately 60 metabolites in 10,000 cells (Agathocleous et al., 2017). Here we describe a new method involving hydrophilic liquid interaction chromatography (HILIC) and high-sensitivity orbitrap mass spectrometry that detected approximately 160 metabolites in 10,000 HSCs, including many more glycolytic and lipid intermediates. We improved chromatographic separation, increased mass resolution, minimized ion suppression, extracted with acetonitrile, and eliminated sample drying. Most metabolites did not significantly change during cell preparation and sorting. We used this method to profile HSCs and circulating melanoma cells. HSCs exhibited increased glycerophospholipid metabolites relative to unfractionated bone marrow cells and altered purine biosynthesis after methotrexate treatment in vivo. Circulating melanoma cells were depleted for purine intermediates relative to subcutaneous tumors, suggesting they decrease purine synthesis during metastasis. These methods facilitate the routine metabolomic analysis of rare cell populations from tissues.

Impact statement

We developed a method for metabolomic analysis of small numbers of flow cytometrically isolated cells from rare cell populations such as hematopoietic stem cells and circulating cancer cells.
The relative coding strength of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks
Authors: Xu Yaoda , Vaziri-Pashkam Maryam
Published: August 2020

Any given visual object input is characterized by multiple visual features, such as identity, position and size. Despite the usefulness of identity and nonidentity features in vision and their joint coding throughout the primate ventral visual processing pathway, they have so far been studied relatively independently. Here we document the relative coding strength of object identity and nonidentity features in a brain region and how this may change across the human ventral visual pathway. We examined a total of four nonidentity features, including two Euclidean features (position and size) and two non-Euclidean features (image statistics and spatial frequency content of an image). Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with identity outweighed the non-Euclidean features, but not the Euclidean ones, in higher levels of visual processing. A similar analysis was performed in 14 convolutional neural networks (CNNs) pretrained to perform object categorization with varying architecture, depth, and with/without recurrent processing. While the relative coding strength of object identity and nonidentity features in lower CNN layers matched well with that in early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Similar results were obtained regardless of whether a CNN was trained with real-world or stylized object images that emphasized shape representation. Together, by measuring the relative coding strength of object identity and nonidentity features, our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.

SIGNIFICANCE STATEMENT

This study documented the relative coding strength of object identity compared to four types of nonidentity features along the human ventral visual processing pathway and compared brain responses with those of 14 CNNs pretrained to perform object categorization. Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with the coding strength of the different nonidentity features differed at higher levels of visual processing. While feature coding in lower CNN layers matched well with that of early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.
Geographic potential of the world’s largest hornet,Vespa mandariniaSmith (Hymenoptera: Vespidae), worldwide and particularly in North America
Authors: Nuñez-Penichet Claudia , Osorio-Olvera Luis , Gonzalez Victor H. , Cobos Marlon E. , Jiménez Laura , DeRaad Devon A. , Alkishe Abdelghafar , Contreras-Díaz Rusby G. , Nava-Bolaños Angela , Utsumi Kaera , Ashraf Uzma , Adeboje Adeola , Peterson A. Townsend , Soberón Jorge
Published: August 2020

The Asian giant hornet (AGH,Vespa mandarinia) is the world’s largest hornet, occurring naturally in the Indomalayan region, where it is a voracious predator of pollinating insects including honey bees. In September 2019, a nest of Asian giant hornets was detected outside of Vancouver, British Columbia and in May 2020 an individual was detected nearby in Washington state, indicating that the AGH successfully overwintered in North America. Because hornets tend to spread rapidly and become pests, reliable estimates of the potential invasive range ofV. mandariniain North America are needed to assess likely human and economic impacts, and to guide future eradication attempts. Here, we assess climatic suitability for AGH in North America, and suggest that, without control, this species could establish populations across the Pacific Northwest and much of eastern North America. Predicted suitable areas for AGH in North America overlap broadly with areas where honey production is highest, as well as with species-rich areas for native bumble bees and stingless bees of the genusMeliponain Mexico, highlighting the economic and environmental necessity of controlling this nascent invasion.
Estimation of parental effects using polygenic scores
Authors: Balbona Jared , Kim Yongkang , Keller Matthew C.
Published: August 2020

Offspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents’ nontransmitted alleles, one can estimate the effect of “genetic nurture”— a type of passive gene-environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to a) account for the influence of assortative mating at both equilibrium and disequilibrium (after a single generation of assortment), and b) include measured parental phenotypes, allowing for the estimation of the total variation due to additive genetic effects and their covariance with the familial environment. By utilizing path analysis techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.
Two pathways drive meiotic chromosome axis assembly inSaccharomyces cerevisiae
Authors: Heldrich Jonna , Milano Carolyn R. , Markowitz Tovah E. , Ur Sarah N. , Vale-Silva Luis A. , Corbett Kevin D. , Hochwagen Andreas
Published: August 2020

Successful meiotic recombination, and thus fertility, depends on conserved axis proteins that organize chromosomes into arrays of anchored chromatin loops and provide a protected environment for DNA exchange. Here, we show that the stereotypic chromosomal distribution of axis proteins inS. cerevisiaeis the additive result of two independent pathways: a cohesin-dependent pathway, which was previously identified and mediates focal enrichment of axis proteins at gene ends, and a parallel cohesin-independent pathway that recruits axis proteins to broad genomic islands with high gene density. These islands exhibit elevated markers of crossover recombination as well as increased nucleosome density, which we show is a direct consequence of the underlying DNA sequence. A predicted PHD domain in the center of the axis factor Hop1 specifically mediates cohesin-independent axis recruitment. Intriguingly, other chromosome organizers, including cohesin, condensin, and topoisomerases, are differentially depleted from the same regions even in non-meiotic cells, indicating that these DNA sequence-defined chromatin islands exert a general influence on the patterning of chromosome structure.
Elicitation of potent neutralizing antibody responses by designed protein nanoparticle vaccines for SARS-CoV-2
Authors: Walls Alexandra C. , Fiala Brooke , Schäfer Alexandra , Wrenn Samuel , Pham Minh N. , Murphy Michael , Tse Longping V. , Shehata Laila , O'Connor Megan A. , Chen Chengbo , Navarro Mary Jane , Miranda Marcos C. , Pettie Deleah , Ravichandran Rashmi , Kraft John C. , Ogohara Cassandra , Palser Anne , Chalk Sara , Lee E-Chiang , Kepl Elizabeth , Chow Cameron M. , Sydeman Claire , Hodge Edgar A. , Brown Brieann , Fuller Jim T. , Dinnon Kenneth H. , Gralinski Lisa E. , Leist Sarah R. , Gully Kendra L. , Lewis Thomas B. , Guttman Miklos , Chu Helen Y. , Lee Kelly K. , Fuller Deborah H. , Baric Ralph S. , Kellam Paul , Carter Lauren , Pepper Marion , Sheahan Timothy P. , Veesler David , King Neil P.
Published: August 2020

A safe, effective, and scalable vaccine is urgently needed to halt the ongoing SARS-CoV-2 pandemic. Here, we describe the structure-based design of self-assembling protein nanoparticle immunogens that elicit potent and protective antibody responses against SARS-CoV-2 in mice. The nanoparticle vaccines display 60 copies of the SARS-CoV-2 spike (S) glycoprotein receptor-binding domain (RBD) in a highly immunogenic array and induce neutralizing antibody titers roughly ten-fold higher than the prefusion-stabilized S ectodomain trimer despite a more than five-fold lower dose. Antibodies elicited by the nanoparticle immunogens target multiple distinct epitopes on the RBD, suggesting that they may not be easily susceptible to escape mutations, and exhibit a significantly lower binding:neutralizing ratio than convalescent human sera, which may minimize the risk of vaccine-associated enhanced respiratory disease. The high yield and stability of the protein components and assembled nanoparticles, especially compared to the SARS-CoV-2 prefusion-stabilized S trimer, suggest that manufacture of the nanoparticle vaccines will be highly scalable. These results highlight the utility of robust antigen display platforms for inducing potent neutralizing antibody responses and have launched cGMP manufacturing efforts to advance the lead RBD nanoparticle vaccine into the clinic.
Phylogeography and population genetic structure of red muntjacs: Evidence of enigmatic Himalayan red muntjac from India
Authors: Singh Bhim , Kumar Ajit , Uniyal Virendra Prasad , Gupta Sandeep Kumar
Published: August 2020

We investigated the phylogeographic pattern of red muntjac across its distribution range, intending to address the presence of distinct lineages from Northwest India. The Complete mitogenome analysis revealed that India holds three mitochondrial lineages of red muntjac, whereas four were identified from its entire distribution range: Himalayan red muntjac (M. (m.) aureus), Northern red muntjac (M. vaginalis), Srilankan and Western Ghat India (M. malabaricus) and Southern red muntjac (M. muntjak) from Sundaland. The newly identified Himalayan red muntjac found in the Northwestern part of India, which was previously described based on their morphological differences. Estimates of the divergence dating indicate that the Northwest and Northern lineage split during the late Pleistocene approximately 0.83 Myr (CI95%:0.53 to 1.26), which is the younger lineages, whereasM. malabaricusis the most primitive lineage among all the red muntjac. Microsatellite results also supported the mitochondrial data and evident the presence of three distinct genetic clusters within India. The pronounced climate fluctuation during the Quaternary period was considered as a critical factor influencing the current spatial distribution of forest-dwelling species that restricted themselves in northwest areas. Based on molecular data, this study provided evidence of a new lineage within the red muntjac group from India that required to be managed as an evolutionary significant unit (ESUs). It highlighted a need for the taxonomic revision of Himalayan red muntjac (M. (m.) aureus) and also suggested its conservation status under IUCN Red List.
DNN-DTIs: improved drug-target interactions prediction using XGBoost feature selection and deep neural network
Authors: Chen Cheng , Shi Han , Han Yu , Jiang Zhiwen , Cui Xuefeng , Yu Bin
Published: August 2020

Research, analysis, and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad, composition, transition and distribution, Moreau-Broto autocorrelation, and structure feature. Then, the drug compounds are encoded using substructure fingerprint. Next, we utilize XGBoost to determine nonredundant and important feature subset, then the optimized and balanced sample vectors could be obtained through SMOTE. Finally, a DTIs predictor, DNN-DTIs, is developed based on deep neural network (DNN) via layer-by-layer learning. Experimental results indicate that DNN-DTIs achieves outstanding performance than other predictors with the ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's dataset. Therefore, DNN-DTIs's accurate prediction performance on Network1 and Network2 make it logical choice for contributing to the study of DTIs, especially, the drug repositioning and new usage of old drugs.
Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG
Authors: Shamsi Foroogh , Haddad Ali , Najafizadeh Laleh
Published: August 2020

Objective

Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.

Approach

The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification.

Main results

Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% about only 500 ms after stimulus presentation.

Significance

Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms).This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.
Deep and superficial layers of the primary somatosensory cortex are critical for whisker-based texture discrimination in mice
Authors: Park Jung M , Hong Y Kate , Rodgers Chris C , Dahan Jacob B , Harano Nina , Schmidt Ewoud RE , Bruno Randy M
Published: August 2020

The neocortex, comprised of multiple distinct layers, processes sensory input from the periphery, makes decisions, and executes actions. Despite extensive investigation of cortical anatomy and physiology, the contributions of different cortical layers to sensory guided behaviors remain unknown. Here, we developed a two-alternative forced choice (2AFC) paradigm in which head-fixed mice use a single whisker to either discriminate textures of parametrically varied roughness or detect the same textured surfaces. Lesioning the barrel cortex revealed that 2AFC texture discrimination, but not detection, was cortex-dependent. Paralyzing the whisker pad had little effect on performance, demonstrating that passive can rival active perception and cortical dependence is not movement-related. Transgenic Cre lines were used to target inhibitory opsins to excitatory cortical neurons of specific layers for selective perturbations. Both deep and superficial layers were critical for texture discrimination. We conclude that even basic cortical computations require coordinated transformation of sensory information across layers.

Abstract Figure
Src-dependent NM2A tyrosine-phosphorylation regulates actomyosin dynamics
Authors: Brito Cláudia , Mesquita Francisco S. , Osório Daniel S. , Pereira Joana Maria , Billington Neil , Sellers James R. , Carvalho Ana X. , Cabanes Didier , Sousa Sandra
Published: August 2020

Non-muscle myosin 2A (NM2A) is a key cytoskeletal enzyme that along with actin assembles into actomyosin filaments inside cells. NM2A is fundamental in cellular processes requiring force generation such as cell adhesion, motility and cell division, and plays important functions in different stages of development and during the progression of viral and bacterial infections. We previously identified a novel tyrosine phosphorylation on residue 158 (pTyr158) in the motor domain of NM2A. This phosphorylation is dependent on Src kinase and is promoted byListeria monocytogenesinfection of epithelial cells, however its role is unknown. Here we show that Listeriolysin O (LLO), the pore-forming toxin (PFT) secreted byL. monocytogenes, is sufficient to trigger NM2A pTyr158by activating Src, an upstream regulator of actomyosin remodeling. We further address the role of NM2A pTyr158on the organization and dynamics of the actomyosin cytoskeleton and find that, by controlling the activation of the NM2A, the status of the pTyr158alters cytoskeletal organization, dynamics of focal adhesions and cell motility.In vitro, we observe that non-phosphorylatable and phospho-mimetic versions of NM2A at Tyr158display motor and ATPase activities similar to the wild-type NM2A, which indicates that the phenotype of these mutants in cells is independent of their ability to translocate actin filaments. Importantly, we find the regulation of this phosphorylation site to be of physiological relevance inCaenorhabditis elegans, in particular in response to intoxication by a PFT and to heat shock. We conclude that the control of the phosphorylation status at NM2A Tyr158is a conserved trait that contributes to the regulation of actomyosin dynamics and the ability of cells to respond to bacterial infection. We propose Src-dependent NM2A pTyr158as a novel layer of regulation of the actomyosin cytoskeleton.
Multimodal Label-free Monitoring of Adipogenic Stem Cell Differentiation using Endogenous Optical Biomarkers
Authors: Mehta Nishir , Shaik Shahensha , Prasad Alisha , Chaichi Ardalan , Sahu Sushant P. , Hasan Syed Mohammad Abid , Donnarumma Fabrizio , Murray Kermit K. , Devireddy Ram , Gartia Manas Ranjan
Published: August 2020

Stem cell-based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective therapy requires precise non-destructive evaluation of the purity of stem cells with high sensitivity (< 0.001% of the number of cells). Here, we report a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping (SAM)-based machine learning analysis to distinguish differentiating human adipose derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enabled identification of differentiated cells at single cell resolution. The label-free HSI method was compared with the standard methods such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. Further, we performed Raman microscopy and multiphoton-based metabolic imaging to provide complimentary information for the functional imaging of the hASCs. Finally, the HSI method was validated using matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label-free identification of stem cell differentiation with high spatial and chemical resolution. This could provide a powerful tool to assess the safety and efficacy of stem cell-based regenerative therapies.
The Cell Wall Integrity MAPK pathway controls actin cytoskeleton assembly during fungal somatic cell fusion
Authors: Serrano Antonio , Hammadeh Hamzeh H. , Schwarz Natalie , Brandt Ulrike , Fleißner André
Published: August 2020

Somatic cell fusion is widely studied in the filamentous fungusNeurospora crassa. The interaction of genetically identical germlings is mediated by a signaling mechanism in which the cells take turns in signal-sending and receiving. The switch between these physiological states is represented by the alternating membrane recruitment of the SO protein and the MAPK MAK-2. This dialog-like behavior is observed until the cells establish physical contact, when the cell-wall-integrity MAK-1 is recruited to the contact area to control the final steps of the cell fusion process. This work revealed, for the first-time, an additional MAK-1-function during the tropic growth phase. Specific inhibition of MAK-1 during tropic-growth resulted in disassembly of the actin-aster, and mislocalization of SO and MAK-2. Similar defects were observed after the inhibition of the Rho-GTPase RAC-1, suggesting a functional link between them, being MAK-1 upstream of RAC-1. In contrast, after inhibition of MAK-2, the actin-aster stayed intact, however, its subcellular localization became instable within the cell-membrane. Together these observations led to a new working model, in which MAK-1 promotes the formation and stability of the actin-aster, while MAK-2 controls its positionning and cell growth directionality.

Summary statement

The CWI MAPK MAK-1 pathway controls actin cytoskeleton assembly at the cell tips through activation of the Rho-GTPase RAC-1 exclusively on somatic cell fusion.
DeepPSC (protein structure camera): computer vision-based protein backbone structure reconstruction from alpha carbon trace as a case study
Authors: Zhang Xing , Luo Junwen , Cai Yi , Zhu Wei , Yang Xiaofeng , Cai Hongmin , Lin Zhanglin
Published: August 2020

Deep learning has been increasingly used in protein tertiary structure prediction, a major goal in life science. However, all the algorithms developed so far mostly use protein sequences as input, whereas the vast amount of protein tertiary structure information available in the Protein Data Bank (PDB) database remains largely unused, because of the inherent complexity of 3D data computation. In this study, we propose Protein Structure Camera (PSC) as an approach to convert protein structures into images. As a case study, we developed a deep learning method incorporating PSC (DeepPSC) to reconstruct protein backbone structures from alpha carbon traces. DeepPSC outperformed all the methods currently available for this task. This PSC approach provides a useful tool for protein structure representation, and for the application of deep learning in protein structure prediction and protein engineering.
Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ)
Authors: Balbuena Juan A. , Montlleó Clara , Llopis-Belenguer Cristina , Blasco-Costa Isabel , Sarabeev Volodimir L. , Morand Serge
Published: August 2020

1. Most species in ecological communities are rare whereas only a few are common. This distributional paradox has intrigued ecologists for decades but the interpretation of species abundance distributions remains elusive.

2. We present Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ) as an R package. FuzzyQ shifts the focus from the prevailing species-categorization approach to develop a quantitative framework that seeks to place each species along a rare-commonness gradient. Given a community surveyed over a number of sites, quadrats, or any other convenient sampling unit, FuzzyQ uses a fuzzy clustering algorithm that estimates a probability for each species to be common or rare based on abundance-occupancy information. Such as probability can be interpreted as a commonness index ranging from 0 to 1. FuzzyQ also provides community-level metrics about the coherence of the allocation of species into the common and rare clusters that are informative of the nature of the community under study.

3. The functionality of FuzzyQ is shown with two real datasets. We demonstrate how FuzzyQ can effectively be used to monitor and model spatio-temporal changes in species commonness, and assess the impact of species introductions on ecological communities. We also show that the approach works satisfactorily with a wide range of communities varying in species richness, dispersion and abundance currencies.

4. FuzzyQ produces ecological indicators easy to measure and interpret that can give both clear, actionable insights into the nature of ecological communities and provides a powerful way to monitor environmental change on ecosystems. Comparison among communities is greatly facilitated by the fact that the method is relatively independent of the number of sites or sampling units considered. Thus, we consider FuzzyQ as a potentially valuable analytical tool in community ecology and conservation biology.
Sigflow: an automated and comprehensive pipeline for cancer genome mutational signature analysis
Authors: Wang Shixiang , Tao Ziyu , Wu Tao , Liu Xue-Song
Published: August 2020

Mutational signatures are recurring DNA alteration patterns caused by distinct mutational events during the evolution of cancer. In recent years, several bioinformatics tools are available for mutational signature analysis. However, most of them focus on specific type of mutation, or have limited scope of application. A pipeline tool for comprehensive mutational signature analysis is still lacking. Here we present Sigflow pipeline, which provides an one-stop solution forde novosignature extraction, reference signature fitting, signature stability analysis, sample clustering based on signature exposure in different types of genome DNA alterations including single base substitution (SBS), doublet base substitution (DBS), small insertion and deletion (INDEL), and copy number alteration. A Docker image is constructed to solve the complex and time-consuming installation issues, and this enables reproducible research by version control of all dependent tools along with their environments. The Sigflow pipeline can be applied to both human and mouse genomes.

Availability and implementation

Sigflow is an open source software under academic free license (AFL) v3.0 and it is freely available athttps://github.com/ShixiangWang/sigminer.workfloworhttps://hub.docker.com/r/shixiangwang/sigflow.

Contact

liuxs@shanghaitech.edu.cn
Detection and genetic analysis of infectious spleen and kidney necrosis virus (ISKNV) in ornamental fish from non-clinical cases: First report from India
Authors: Pattanayak Sabyasachi , Paul Anirban , Sahoo Pramoda Kumar
Published: August 2020

Infectious spleen and kidney necrosis virus (ISKNV), a type species of the genusMegalocytivirus, poses a threat to ornamental fish trade as most cases show nonspecific symptoms, thus making timely diagnosis challenging. Apparently health molly (Poecilia sphenops) and angelfish (Pterophyllum scalare) collected from two distinct geographic localities of India were screened for four genera underIridoviridae,Megalocytivirusi.e, ISKNV, turbot reddish body iridovirus (TRBIV) and red seabream iridovirus (RSIV); ranaviruses and Singapore grouper iridovirus; and Lymphocystivirus through molecular approach. In total seven numbers out of 35 samples (20%), ISKNV genome fragments were detected. A PCR assay using major capsid protein (MCP) gene was standardised to detect and differentiate infections within theMegalocytivirusgenus, even without aid of sequencing. This forms the first report of ISKNV infection in ornamental fish from India. Moreover, the ISKNV infection was confirmed by PCR and sequence analysis of MCP and ATPase genes. The sequence of these genes showed that Indian isolate being 99-100% similar to the complete genome or reference strain of ISKNV. Phylogenetic reconstruction demonstrated the present strain belonging to ISKNV genotype I. Furthermore, structural stability of the MCP revealed this strain was more stable than ISKNV genotype II, RSIV and TRBIV at 25°C and pH 7.0. Thus, strong pan-India surveillance is recommended to reduce trade risk.
Genetic diversity and population structure analyses in the Alpine plum (Prunus brigantinaVill.) confirm its affiliation to the Armeniaca section
Authors: Shuo Liu , Stephane Decroocq , Elodie Harte , David Tricon , Aurelie Chague , Gulnara Balakishiyeva , Tatiana Kostritsyna , Timur Turdiev , Marion Fisher-Le Saux , Sylvie Dallot , Tatiana Giraud , Veronique Decroocq
Published: August 2020

In-depth characterization of the genetic diversity and population structure of wild relatives of crops is of paramount importance for genetic improvement and biodiversity conservation, and is particularly crucial when the wild relatives of crops are endangered. In this study, we therefore sampled the Alpine plum (Briançon apricot)Prunus brigantinaVill. across its natural distribution in the French Alps, where its populations are severely fragmented and its population size strongly impacted by humans. We analysed 71 wildP. brigantinasamples with 34 nuclear markers and studied their genetic diversity and population structure, with the aim to informin situconservation measures and build a core collection for long-term ex-situ conservation. We also examined the genetic relationships ofP. brigantinawith other species in the Prunophora subgenus, encompassing the Prunus (Eurasian plums), Prunocerasus (North-American plums) and Armeniaca (apricots) sections, to check its current taxonomy. We detected a moderate genetic diversity inP. brigantinaand a Bayesian model-based clustering approach revealed the existence of three genetically differentiated clusters, endemic to three geographical regions in the Alps, which will be important forin situconservation measures. Based on genetic diversity and population structure analyses, a subset of 36 accessions were selected forex-situconservation in a core collection that encompasses the whole detectedP. brigantinaallelic diversity. Using a dataset of cultivated apricots and wild cherry plums (P. cerasifera) genotyped with the same markers, we detected gene flow neither with EuropeanP. armeniacacultivars nor with diploid plums. In contrast with previous studies, dendrograms and networks placedP. brigantinacloser to Armeniaca species than to Prunus species. Our results thus confirm the classification ofP. brigantinawithin the Armeniaca section; it also illustrates the importance of the sampling size and design in phylogenetic studies.