bluuebunny/crossref_metadata_2025_split huggingface dataset:
A complex of BRCA2 and PP2A-B56 is required for DNA repair by homologous recombination
Authors: Ambjoern Sara Marie , Duxin Julien P. , Hertz Emil PT , Nasa Isha , Duro Joana , Kruse Thomas , Mendez Blanca Lopez , Rymarczyk Beata , Cressey Lauren E. , van Overeem Hansen Thomas , Kettenbach Arminja , Oestergaard Vibe H. , Lisby Michael , Nilsson Jakob 
Published: April 2021

AbstractMutations in the tumour suppressor gene BRCA2 are associated with predisposition to breast and ovarian cancers. BRCA2 has a central role in maintaining genome integrity by facilitating the repair of toxic DNA double-strand breaks (DSBs) by homologous recombination (HR). BRCA2 acts by promoting RAD51 nucleoprotein filament formation on resected single-stranded DNA, but how BRCA2 activity is regulated during HR is not fully understood. Here, we delineate a pathway where ATM and ATR kinases phosphorylate a highly conserved region in BRCA2 in response to DSBs. These phosphorylations stimulate the binding of the protein phosphatase PP2A-B56 to BRCA2 through a conserved binding motif. We show that the phosphorylation-dependent formation of the BRCA2-PP2A-B56 complex is required for efficient RAD51 loading to sites of DNA damage and HR-mediated DNA repair. Moreover, we find that several cancer-associated mutations in BRCA2 deregulate the BRCA2-PP2A-B56 interaction and sensitize cells to PARP inhibition. Collectively, our work uncovers PP2A-B56 as a positive regulator of BRCA2 function in HR with clinical implications for BRCA2 and PP2A-B56 mutated cancers.
A compendium of predicted growths and derived symbiotic relationships between 803 gut microbes in 13 different diets
Authors: Singh Rohan , Dutta Anirban , Bose Tungadri , Mande Sharmila S. 
Published: April 2021

AbstractGut health is intimately linked to dietary habits and the microbial community (microbiota) that flourishes within. The delicate dependency of the latter on nutritional availability is also strongly influenced by symbiotic relationships (such as, parasitic or mutualistic) between the resident microbes, often affecting their growth rate and ability to produce key metabolites. Since, cultivating the entire repertoire of gut microbes is an infeasible task, metabolic models (genome-based metabolic reconstructions) could be employed to predict their growth patterns and interactions. Here, we have used 803 gut microbial metabolic models from the Virtual Metabolic Human repository, and subsequently optimized and simulated them to grow on 13 dietary compositions. The presented pairwise interaction data (https://osf.io/ay8bq/) and the associated bacterial growth rates are expected to be useful for (a) deducing microbial association patterns, (b) diet-based inference of personalised gut profiles, and (c) as a steppingstone for studying multi-species metabolic interactions.
Polypharmacological Re-programming of Tumor-associated Macrophages Restores Antitumor Immunity
Authors: Nishida-Aoki Nao , Gujral Taranjit S. 
Published: April 2021

AbstractTumor-associated macrophages (TAMs) are an important component of the tumor microenvironment (TME), and they promote tumor progression, metastasis, and resistance to therapies. However, although TAMs represent a promising target for therapeutic intervention, the complexity of TME has made the study of TAMs challenging. Here, we established a physiologically-relevantin vitroTAM polarization system that recapitulates TAM pro-tumoral activities. We used this system for phenotypic kinase inhibitor screening and identified a multi-targeted compound BMS-794833 as the most potent inhibitor of TAM polarization. BMS-794833 decreased pro-tumoral properties of TAMs and suppressed tumor growth in mouse triple-negative breast cancer models. The effect of BMS-794833 was not dependent on its primary targets (MET and VEGFR2) but on its effect on multiple signaling pathways, including focal adhesion kinases, SRC family kinases, STAT3, and p38 MAP kinases. Our study underlines the efficacy of polypharmacological strategies in re-programming complex signaling cascades activated during TAM polarization.
PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning
Authors: Mahin Kazi Ferdous , Robiuddin Md. , Islam Mujahidul , Ashraf Shayed , Yeasmin Farjana , Shatabda Swakkhar 
Published: April 2021

AbstractCancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related treatments are coming into light. In this paper, we propose PanClassif, a method that requires a very few and effective genes to detect cancer from RNA-seq data and is able to provide performance gain in several wide range machine learning classifiers. We have taken 22 types of cancer samples from The Cancer Genome Atlas (TCGA) having 8287 cancer samples and 680 normal samples. Firstly, PanClassif usesk-Nearest Neighbor (k-NN) smoothing to smooth the samples to handle noise in the data. Then effective genes are selected by Anova based test. For balancing the train data, PanClassif applies an oversampling method, SMOTE. We have performed comprehensive experiments on the datasets using several classification algorithms. Experimental results shows that PanClassif out-perform existing state-of-the-art methods available and shows consistent performance for two single cell RNA-seq datasets taken from Gene Expression Omnibus (GEO). PanClassif improves performances of a wide variety of classifiers for both binary cancer prediction and multi-class cancer classification. PanClassif is available as a python package (https://pypi.org/project/panclassif/). All the source code and materials of PanClassif are available athttps://github.com/Zwei-inc/panclassif.
Subjective confidence reflects representation of Bayesian probability in cortex
Authors: Geurts Laura S. , Cooke James R. H. , van Bergen Ruben S. , Jehee Janneke F. M. 
Published: April 2021

AbstractWhat gives rise to the human sense of confidence? Here, we tested the Bayesian hypothesis that confidence is based on a probability distribution represented in neural population activity. We implemented several computational models of confidence, and tested their predictions using psychophysics and fMRI. Using a generative model-based fMRI decoding approach, we extracted probability distributions from neural population activity in human visual cortex. We found that subjective confidence tracks the shape of the decoded distribution. That is, when sensory evidence was more precise, as indicated by the decoded distribution, observers reported higher levels of confidence. We furthermore found that neural activity in the insula, anterior cingulate, and prefrontal cortex was linked to both the shape of the decoded distribution and reported confidence, in ways consistent with the Bayesian model. Altogether, our findings support recent statistical theories of confidence and suggest that probabilistic information guides the computation of one’s sense of confidence.
First record of a mermithid nematode (Nematoda: Mermithidae) parasitizing winged females of gall-forming aphids (Hemiptera: Aphididae: Eriosomatinae)
Authors: Tong Xin , Kanzaki Natsumi , Akimoto Shin-ichi 
Published: April 2021

AbstractJuvenile mermithid nematodes were found to parasitize winged females (sexuparae) ofErisoma auratumandTetraneura radicicola. The morphological characteristics of mermithid nematodes are briefly described. The 18S rDNA and 28S rDNA extracted from one nematode were sequenced and used to construct a Bayesian phylogenetic tree, on which the host ranges of mermithid nematodes were represented. Our study indicated that mermithid parasitism of sexuparae led to fewer and smaller sexual female embryos. This is the first record of a mermithid in relation to eriosomatine aphids and the fourth record with respect to Aphididae.
Ultrastructural insight into SARS-CoV-2 attachment, entry and budding in human airway epithelium
Authors: Pinto Andreia L , Rai Ranjit K , Brown Jonathan C , Griffin Paul , Edgar James R , Shah Anand , Singanayagam Aran , Hogg Claire , Barclay Wendy S , Futter Clare E , Burgoyne Thomas 
Published: April 2021

AbstractUltrastructural studies of SARS-CoV-2 infected cells are crucial to better understand the mechanisms of viral entry and budding within host cells. Many studies are limited by the lack of access to appropriate cellular models. As the airway epithelium is the primary site of infection it is essential to study SARS-CoV-2 infection of these cells. Here, we examined human airway epithelium, grown as highly differentiated air-liquid interface cultures and infected with three different isolates of SARS-CoV-2 including the B.1.1.7 variant (Variant of Concern 202012/01) by transmission electron microscopy and tomography. For all isolates, the virus infected ciliated but not goblet epithelial cells. Two key SARS-CoV-2 entry molecules, ACE2 and TMPRSS2, were found to be localised to the plasma membrane including microvilli but excluded from cilia. Consistent with these observations, extracellular virions were frequently seen associated with microvilli and the apical plasma membrane but rarely with ciliary membranes. Profiles indicative of viral fusion at the apical plasma membrane demonstrate that the plasma membrane is one site of entry where direct fusion releasing the nucleoprotein-encapsidated genome occurs. Intact intracellular virions were found within ciliated cells in compartments with a single membrane bearing S glycoprotein. Profiles strongly suggesting viral budding from the membrane was observed in these compartments and this may explain how virions gain their S glycoprotein containing envelope.
Head model personalization: A framework for morphing lifespan brain images and brains with substantial anatomical changes
Authors: Li Xiaogai 
Published: April 2021

AbstractFinite element (FE) head models have emerged as a powerful tool in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Personalized head models are needed to account for geometric variations among subjects for more reliable predictions. However, the generation of subject-specific head models with conforming hexahedral elements suitable for studying the biomechanics of TBIs remains a significant challenge, which has been a bottleneck hindering personalized simulations. This study presents a framework capable of generating lifespan brain models and pathological brains with substantial anatomical changes, morphed from a previously developed baseline model. The framework combines hierarchical multiple feature and multimodality imaging registrations with mesh grouping, which is shown to be efficient with a heterogeneous dataset of seven brains, including a newborn, 1-year-old (1Y), 2Y, 6Y, adult, 92Y, and a hydrocephalus brain. The personalized models of the seven subjects show competitive registration accuracy, demonstrating the potential of the framework for generating personalized models for almost any brains with substantial anatomical changes. The family of head injury models generated in this study opens vast opportunities for studying age-dependent and groupwise brain injury mechanisms. The framework is equally applicable for personalizing head models in other fields, e.g., in tDCS, TMS, TUS, as an efficient approach for generating subject-specific head models than from scratch.
Nej1XLFinteracts with Sae2CTIPto inhibit Dna2 mediated resection at DNA double strand break
Authors: Mojumdar Aditya , Adam Nancy , Cobb Jennifer A. 
Published: April 2021

AbstractThe two major pathways of DNA double strand break (DSB) repair, non-homologous end-joining (NHEJ) and homologous recombination (HR), are highly conserved from yeast to mammals. The regulation of 5’ DNA resection controls repair pathway choice and influences repair outcomes. Nej1 was first identified as a canonical NHEJ factor involved in stimulating the ligation of broken DNA ends and more recently, it was shown to be important for DNA end-bridging and inhibiting 5’ resection mediated by Dna2-Sgs1. Nej1 interacts with Sae2 and this impacts DSB repair in three ways. First, Nej1 inhibits MRX-Sae2 interactions and Sae2 localization to a DSB. Second, Nej1 inhibits Sae2-dependent recruitment of Dna2 in the absence of Sgs1. Third,NEJ1andSAE2showed an epistatic relationship for DNA end-bridging, an event that restrains the broken ends and reduces the frequency of genomic deletions from developing at the DSB. Deletion ofNEJ1suppressed the synthetic lethality ofsae2Δsgs1Δ and was dependent on the nuclease activity of Dna2. These Nej1 functions promote end-joining DSB repair, but could also be relevant for controlling resection initiation during HR repair.HighlightsNej1 physically interacts with Sae2 and inhibits end-resection at a DSB.Nej1 inhibits Sae2 interactions with the MRX complex.Nej1 inhibits Sae2-dependent recruitment of Dna2 to a DSB.NEJ1 and SAE2 are epistatic for DNA end-bridging.Deletion ofNEJ1suppresses the synthetic lethality ofsae2Δsgs1Δ, which is dependent on Dna2 nuclease activity.
BioNERO: an all-in-one R/Bioconductor package for comprehensive and easy biological network reconstruction
Authors: Almeida-Silva Fabricio , Venancio Thiago M. 
Published: April 2021

ABSTRACTSummaryCurrently, standard network analysis workflows rely on many different packages, often requiring users to have a solid statistics and programming background. Here, we presentBioNERO,an R package that aims to integrate all aspects of network analysis workflows, including expression data preprocessing, gene coexpression and regulatory network inference, functional analyses, and intra and interspecies network comparisons. The state-of-the-art methods implemented inBioNEROensure that users can perform all analyses with a single package in a simple pipeline, without needing to learn a myriad of package-specific syntaxes.BioNEROoffers a user-friendly framework that can be easily incorporated in systems biology pipelines.Availability and implementationThe package is available at Bioconductor (http://bioconductor.org/packages/BioNERO).
Nonclassical monocytes are prone to migrate into tumor in diffuse large B-cell lymphoma
Authors: Gallou Simon Le , Lhomme Faustine , Irish Jonathan M. , Mingam Anna , Pangault Celine , Monvoisin Celine , Ferrant Juliette , Azzaoui Imane , Rossille Delphine , Bouabdallah Krimo , Damaj Gandhi , Cartron Guillaume , Godmer Pascal , Gouill Steven Le , Casasnovas René-Olivier , Molina Thierry Jo , Houot Roch , Lamy Thierry , Tarte Karin , Fest Thierry , Roussel Mikael 
Published: April 2021

AbstractAbsolute count of circulating monocytes has been proposed as an independent prognostic factor in diffuse large B-cell lymphoma (DLBCL). However, monocyte nomenclature includes various subsets with pro-, anti-inflammatory, or suppressive functions, and their clinical relevance in DLBCL has been poorly explored. Herein, we broadly assessed circulating monocyte heterogeneity in 91 DLBCL patients. Classical- (cMO, CD14posCD16neg) and intermediate- (iMO, CD14posCD16pos) monocytes accumulated in DLBCL peripheral blood and exhibited an inflammatory phenotype. On the opposite, nonclassical monocytes (ncMO, CD14lowCD16pos) were decreased in peripheral blood. Tumor-conditioned monocytes presented similarities with ncMO phenotype from DLBCL and were prone to migrate in response to CCL3, CCL5, and CXCL12, and presented similarities with DLBCL-infiltrated myeloid cells, as defined by mass cytometry. Finally, we demonstrated the adverse value of an accumulation of nonclassical monocytes in 2 independent cohorts of DLBCL.Key pointsNonclassical monocytes are prone to migrate to DLBCL tumorHigh count of circulating nonclassical monocytes is an independent adverse event in DLBCL
Deep RNN with Pseudo Loss Objective for Forecasting Stop-over Decisions of Wild Migratory Birds
Authors: Owoeye Kehinde 
Published: April 2021

AbstractForecasting stop-over decisions and mapping the stop-over sites of wild migratory birds is fast becoming important in light of recent developments affecting global health. Migratory wild birds stop at sites with access to food resources so they can rest before continuing with their journey. Unfortunately, these sites offer opportunities for these birds to spread pathogens and viruses by interacting with the ecosystem. While previous work has focused on predicting stop-over sites using historical information, we emphasize that this is not useful for any planning efforts by health authorities and instead offer a new perspective by proposing an approach that can forecast the duration of stop-over. In this work, first we cast this problem as a classification task and show how pseudo labels and losses in a Bi-directional recurrent neural network can help improve performance given the presence of significantly underrepresented class. We use dataset of Turkey vulture (avian pox vector) movement over several years for the forecasting task where we compare our approach with a variety of baselines and show that it outperforms them. We also use this dataset and the White Fronted Geese (avian flu vector) movement dataset to understand the nature of the habitats used for stop-over using a publicly available model pre-trained on more than half a million land cover images. By knowing the preferred stop-over habitats and the time spent in and between stop-overs using our model, we can help relevant authorities come up with efficient intervention measures.
Remotely sensed carotenoid dynamics predict photosynthetic phenology in conifer and deciduous forests
Authors: Wong Christopher Y.S. , Mercado Lina M. , Arain M. Altaf , Ensminger Ingo 
Published: April 2021

AbstractCrucially, the phenology of photosynthesis conveys the length of the growing season. Assessing the timing of photosynthetic phenology is key for terrestrial ecosystem models for constraining annual carbon uptake. However, model representation of photosynthetic phenology remains a major limitation. Recent advances in remote sensing allow detecting changes of foliar pigment composition that regulate photosynthetic activity. We used foliar pigments changes as proxies for light-use-efficiency (LUE) to model gross primary productivity (GPP) from remote sensing data. We evaluated the performance of LUE-models with GPP from eddy covariance and against MODerate Resolution Imaging Spectroradiometer (MODIS) GPP, a conventional LUE model, and a process-based dynamic global vegetation model at an evergreen needleleaf and a deciduous broadleaf forest. Overall, the LUE-models using foliar pigment information best captured the start and end of season, demonstrating that using regulatory carotenoids and photosynthetic efficiency in LUE models can improve remote monitoring of the phenology of forest vegetation.
Total factor productivity growth and its decomposition in the Chinese construction industry since China's accession to the World Trade Organization
Authors: Wang Yuhe , Ye Gui , Zheng Chenli , Zhang Shilian 
Published: April 2021

Purpose
Since China's accession of the World Trade Organization (WTO), its construction industry has attained unprecedented growth. However, for the sources of this enormous growth, a controversy regarding the total factor productivity growth (TFPG) still remains in production practice and extant studies. In view of this, the purpose of this paper is to measure TFPG and to explore its sources in the industry post-WTO accession.

Design/methodology/approach
This study presents an innovative source analysis of TFPG. Stochastic frontier approach is adopted to measure TFPG and to explore its sources by decomposing TFPG into technical progress (TP), technical efficiency change (TEC), allocative efficiency change (AEC) and scale efficiency change (SEC). Although China joined WTO in 2001, to provide an effective baseline, the study period is from 2000 to 2017.

Findings
The empirical results reveal that TFPG presented an overall downward evolutionary trend, but it still maintained a high growth post-WTO accession. From the perspective of decomposition, TP was the main source of TFPG. Furthermore, as a neglected source, interaction effects among TP, TEC, AEC and SEC have been demonstrated to have a significant influence on the cumulative TFPG.

Practical implications
To make the results be reliable, the authors discuss the empirical findings mainly by revealing the reasons behind the evolutions of TFPG and its sources. Based on these revealed reasons, government and policy makers can further refine and summarize some more detailed and targeted policy implications to improve TFPG.

Originality/value
By providing many empirical evidences to solve the aforesaid TFPG controversy, this paper, therefore, enriches the body of knowledge on growth theories, especially at the level of industrial economics.
Measurement and determinants of total factor productivity: evidence from Indian banking industry
Authors: Bhuyan Biswabhusan , Patra Subhamitra , Bhuian Ranjan Kumar 
Published: April 2021

PurposeThe purpose of this study is to measure the level of total factor productivity of the Indian banking sector and to identify both the bank-specific and macroeconomic determinants of the total factor productivity after the global subprime mortgage crisis.Design/methodology/approachThe research sample consists of 61 commercial banks including 21 public sector banks, 18 private sector banks and 22 foreign banks. The annual data is collected from the website of Reserve Bank of India from 2008 to 2019. The authors employed the non-parametric DEA approach to estimate Malmquist total factor productivity index for each bank as well as across different ownership groups. The panel data estimation technique was used to identify the determinants of total factor productivity.FindingsThe results suggested that an increase in the technological shift raised the bank's productivity above the optimal frontier. Among the bank-specific determinants, the bank size and bank diversifications are significantly declining productivity, whereas credit-deposit ratio and return on asset significantly increasing productivity. Among the macro-specific determinants, inflation, growth rate and fiscal deficit ratio negatively affect productivity, whereas capital formation to the GVA ratio boosts the level of productivity.Research limitations/implicationsThe authors have used intermediate method to select the inputs and outputs as per the suitability to the context. However, the disaggregate level such as state and district level analysis can be done using production and value-added approaches to explore the regional variations of the banking performance. Furthermore, the parametric methods such as stochastic frontier analysis can be used to examine banking performance, which the authors left for the future research.Practical implicationsThis study suggested that banks should increase the economies of scale of their total assets and focus on the interest-earning activity. The banks need to proactively operate the business policy by following the changing path of inflation. The banks need to reduce their rate of fiscal-deficit to the GVA with the purpose to boost their level of productivity.Originality/valueThe study provides an important implication for bankers and policymakers in terms of heightening the banking performance during the period of dynamic economic events.