bluuebunny/crossref_metadata_2025_split huggingface dataset:
An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer’s disease
Authors: Lin Cui-Xiang , Li Hong-Dong , Deng Chao , Liu Weisheng , Erhardt Shannon , Wu Fang-Xiang , Zhao Xing-Ming , Guan Yuanfang , Wang Jun , Wang Daifeng , Hu Bin , Wang Jianxin 
Published: November 2021

Abstract
Alzheimer’s disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer’s brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer’s Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer’s brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53
Authors: Ben-Cohen Gil , Doffe Flora , Devir Michal , Leroy Bernard , Soussi Thierry , Rosenberg Shai 
Published: November 2021

Abstract
Correctly identifying the true driver mutations in a patient’s tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present TP53_PROF (prediction of functionality), a gene specific machine learning model to predict the functional consequences of every possible missense mutation in TP53, integrating human cell- and yeast-based functional assays scores along with computational scores. Variants were labeled for the training set using well-defined criteria of prevalence in four cancer genomics databases. The model’s predictions provided accuracy of 96.5%. They were validated experimentally, and were compared to population data, LFS datasets, ClinVar annotations and to TCGA survival data. Very high accuracy was shown through all methods of validation. TP53_PROF allows accurate classification of TP53 missense mutations applicable for clinical practice. Our gene specific approach integrated machine learning, highly reliable features and biological knowledge, to create an unprecedented, thoroughly validated and clinically oriented classification model. This approach currently addresses TP53 mutations and will be applied in the future to other important cancer genes.
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2
Authors: Su Xiaorui , Hu Lun , You Zhuhong , Hu Pengwei , Wang Lei , Zhao Bowei 
Published: November 2021

Abstract
The outbreak of COVID-19 caused by SARS-coronavirus (CoV)-2 has made millions of deaths since 2019. Although a variety of computational methods have been proposed to repurpose drugs for treating SARS-CoV-2 infections, it is still a challenging task for new viruses, as there are no verified virus-drug associations (VDAs) between them and existing drugs. To efficiently solve the cold-start problem posed by new viruses, a novel constrained multi-view nonnegative matrix factorization (CMNMF) model is designed by jointly utilizing multiple sources of biological information. With the CMNMF model, the similarities of drugs and viruses can be preserved from their own perspectives when they are projected onto a unified latent feature space. Based on the CMNMF model, we propose a deep learning method, namely VDA-DLCMNMF, for repurposing drugs against new viruses. VDA-DLCMNMF first initializes the node representations of drugs and viruses with their corresponding latent feature vectors to avoid a random initialization and then applies graph convolutional network to optimize their representations. Given an arbitrary drug, its probability of being associated with a new virus is computed according to their representations. To evaluate the performance of VDA-DLCMNMF, we have conducted a series of experiments on three VDA datasets created for SARS-CoV-2. Experimental results demonstrate that the promising prediction accuracy of VDA-DLCMNMF. Moreover, incorporating the CMNMF model into deep learning gains new insight into the drug repurposing for SARS-CoV-2, as the results of molecular docking experiments reveal that four antiviral drugs identified by VDA-DLCMNMF have the potential ability to treat SARS-CoV-2 infections.
The brain activation-based sexual image classifier (BASIC): a sensitive and specific fMRI activity pattern for sexual image processing
Authors: van ’t Hof Sophie R , Van Oudenhove Lukas , Janssen Erick , Klein Sanja , Reddan Marianne C , Kragel Philip A , Stark Rudolf , Wager Tor D 
Published: November 2021

Abstract
Previous studies suggest there is a complex relationship between sexual and general affective stimulus processing, which varies across individuals and situations. We examined whether sexual and general affective processing can be distinguished at the brain level. In addition, we explored to what degree possible distinctions are generalizable across individuals and different types of sexual stimuli, and whether they are limited to the engagement of lower-level processes, such as the detection of visual features. Data on sexual images, nonsexual positive and negative images, and neutral images from Wehrum et al. (2013) (N = 100) were reanalyzed using multivariate support vector machine models to create the brain activation-based sexual image classifier (BASIC) model. This model was tested for sensitivity, specificity, and generalizability in cross-validation (N = 100) and an independent test cohort (N = 18; Kragel et al. 2019). The BASIC model showed highly accurate performance (94–100%) in classifying sexual versus neutral or nonsexual affective images in both datasets with forced choice tests. Virtual lesions and tests of individual large-scale networks (e.g., visual or attention networks) show that individual networks are neither necessary nor sufficient to classify sexual versus nonsexual stimulus processing. Thus, responses to sexual images are distributed across brain systems.
A nonlinear hidden layer enables actor–critic agents to learn multiple paired association navigation
Authors: Kumar M Ganesh , Tan Cheston , Libedinsky Camilo , Yen Shih-Cheng , Tan Andrew Y Y 
Published: November 2021

Abstract
Navigation to multiple cued reward locations has been increasingly used to study rodent learning. Though deep reinforcement learning agents have been shown to be able to learn the task, they are not biologically plausible. Biologically plausible classic actor–critic agents have been shown to learn to navigate to single reward locations, but which biologically plausible agents are able to learn multiple cue–reward location tasks has remained unclear. In this computational study, we show versions of classic agents that learn to navigate to a single reward location, and adapt to reward location displacement, but are not able to learn multiple paired association navigation. The limitation is overcome by an agent in which place cell and cue information are first processed by a feedforward nonlinear hidden layer with synapses to the actor and critic subject to temporal difference error-modulated plasticity. Faster learning is obtained when the feedforward layer is replaced by a recurrent reservoir network.
Genome sequencing and analysis of two early-flowering cherry (Cerasus × kanzakura) varieties, ‘Kawazu-zakura’ and ‘Atami-zakura’
Authors: Shirasawa Kenta , Itai Akihiro , Isobe Sachiko 
Published: November 2021

Abstract
To gain genetic insights into the early-flowering phenotype of ornamental cherry, also known as sakura, we determined the genome sequences of two early-flowering cherry (Cerasus × kanzakura) varieties, ‘Kawazu-zakura’ and ‘Atami-zakura’. Because the two varieties are interspecific hybrids, likely derived from crosses between Cerasus campanulata (early-flowering species) and Cerasus speciosa, we employed the haplotype-resolved sequence assembly strategy. Genome sequence reads obtained from each variety by single-molecule real-time sequencing (SMRT) were split into two subsets, based on the genome sequence information of the two probable ancestors, and assembled to obtain haplotype-phased genome sequences. The resultant genome assembly of ‘Kawazu-zakura’ spanned 519.8 Mb with 1,544 contigs and an N50 value of 1,220.5 kb, while that of ‘Atami-zakura’ totalled 509.6 Mb with 2,180 contigs and an N50 value of 709.1 kb. A total of 72,702 and 69,528 potential protein-coding genes were predicted in the genome assemblies of ‘Kawazu-zakura’ and ‘Atami-zakura’, respectively. Gene clustering analysis identified 2,634 clusters uniquely presented in the C. campanulata haplotype sequences, which might contribute to its early-flowering phenotype. Genome sequences determined in this study provide fundamental information for elucidating the molecular and genetic mechanisms underlying the early-flowering phenotype of ornamental cherry tree varieties and their relatives.
Coulomb threshold rate-and-state model for fault reactivation: application to induced seismicity at Groningen
Authors: Heimisson Elías R , Smith Jonathan D , Avouac Jean-Philippe , Bourne Stephen J 
Published: November 2021

SUMMARY
A number of recent modelling studies of induced seismicity have used the 1994 rate-and-state friction model of Dieterich 1994 to account for the fact that earthquake nucleation is not instantaneous. Notably, the model assumes a population of seismic sources accelerating towards instability with a distribution of initial slip speeds such that they would produce earthquakes steadily in the absence of any perturbation to the system. This assumption may not be valid in typical intraplate settings where most examples of induced seismicity occur, since these regions have low stressing rates and initially low seismic activity. The goal of this paper is twofold. First, to derive a revised Coulomb rate-and-state model, which takes into account that seismic sources can be initially far from instability. Second, to apply and test this new model, called the Threshold rate-and-state model, on the induced seismicity of the Groningen gas field in the Netherlands. Stress changes are calculated based on a model of reservoir compaction since the onset of gas production. We next compare the seismicity predicted by our threshold model and Dieterich’s model with the observations. The two models yields comparable spatial distributions of earthquakes in good agreement with the observations. We find however that the Threshold model provides a better fit to the observed time-varying seismicity rate than Dieterich’s model, and reproduces better the onset, peak and decline of the observed seismicity rate. We compute the maximum magnitude expected for each model given the Gutenberg–Richter distribution and compare to the observations. We find that the Threshold model both shows better agreement with the observed maximum magnitude and provides result consistent with lack of observed seismicity prior to 1993. We carry out analysis of the model fit using a Chi-squared reduced statistics and find that the model fit is dramatically improved by smoothing the seismicity rate. We interpret this finding as possibly suggesting an influence of source interactions, or clustering, on a long timescale of about 3–5 yr.
Informing decisions with disparate stakeholders: cross-sector evaluation of cash transfers in Malawi
Authors: Ramponi Francesco , Nkhoma Dominic , Griffin Susan 
Published: November 2021

Abstract
The Social Cash Transfer Programme (SCTP) in Malawi is a cross-sectoral policy with impacts on health, education, nutrition, agriculture and welfare. Implementation of the SCTP requires collaboration across sectors and across national and international stakeholders. Economic evaluation can inform investment by indicating whether benefits exceed costs, but economic evaluations that provide an overall benefit–cost ratio typically assume a common agreed objective and agreed set of value judgements. In reality, the various stakeholders involved in the delivery of the SCTP may have different remits and objectives and may differ in how they value the impacts of the programme. We use the SCTP as a case study to illustrate a cross-sectoral analytical framework that accounts for these differences. The stakeholders that contribute to the SCTP include the Ministry of Gender, Ministry of Finance, Ministry of Economic Planning and Development and Global Fund. We estimate how the SCTP changes outcomes in education, health, net production and poverty, and distinguish outcomes in three groups: SCTP recipients; population in Malawi not eligible for the SCTP and population in other countries. After estimating the direct effects and opportunity costs from investing in the SCTP, we summarize the results according to different perspectives. The SCTP is estimated to provide benefits in excess of costs from the perspective of national stakeholders. From the perspective of an international donor interested in health outcomes, its health benefits do not outweigh the opportunity costs unless health improvement in SCTP recipients is valued at 18 times that of other potential spending beneficiaries or the donor values broader outcomes than health alone. This work illustrates the potential of a cross-sectoral economic evaluation to guide debate about stakeholder contributions to the SCTP, and the value judgements required to favour the SCTP above other policy options.
An epileptic encephalopathy associatedGABRG2missense mutation leads to pre- and postsynaptic defects in zebrafish
Authors: Zhou Jing , Liang Wenpeng , Wang Jie , Chen Juan , Liu Dong , Wang Xin , Wu Youjia , Zhang Qi , Shen Dingding 
Published: November 2021

AbstractMutations in the γ-aminobutyric acid type A (GABAA) receptor γ2 subunit gene, GABRG2, have been associated with a variety of epilepsy syndromes. A de novo mutation (c.T1027C, p.F343L) in GABRG2 was identified in a patient with early onset epileptic encephalopathy. Zebrafish overexpressing mutant human GABRG2 (F343L) subunits displayed spontaneous seizure activity and convulsive behaviors. In this study, we demonstrated that Tg (hGABRG2F343L) zebrafish displayed hyperactivity during light phase with normal circadian rhythm, as well as increased drug-induced locomotor activity. Real-time quantitative PCR, whole mount in situ hybridization and western blotting showed that Tg(hGABRG2F343L) zebrafish had altered expression of GABAA receptor subunits. Furthermore, investigation of synaptic protein expression and synapse ultrastructure uncovered a robust synaptic phenotype that is causally linked to GABRG2(F343L) mutation. Strikingly, Tg(hGABRG2F343L) zebrafish not only had postsynaptic defects, but also displayed an unanticipated deficit at the presynaptic level. Overall, our Tg(hGABRG2F343L) overexpression zebrafish model has expanded the GABAergic paradigm in epileptic encephalopathy from channelopathy to synaptopathy.
Novel microarchitecture of human endometrial glands: implications in endometrial regeneration and pathologies
Authors: Tempest Nicola , Hill Christopher J , Maclean Alison , Marston Kathleen , Powell Simon G , Al-Lamee Hannan , Hapangama Dharani K 
Published: November 2021

AbstractBACKGROUNDHuman endometrium remains a poorly understood tissue of the female reproductive tract. The superficial endometrial functionalis, the site of embryo implantation, is repeatedly shed with menstruation, and the stem cell-rich deeper basalis is postulated to be responsible for the regeneration of the functionalis. Two recent manuscripts have demonstrated the 3D architecture of endometrial glands. These manuscripts have challenged and replaced the prevailing concept that these glands end in blind pouches in the basalis layer that contain stem cells in crypts, as in the intestinal mucosa, providing a new paradigm for endometrial glandular anatomy. This necessitates re-evaluation of the available evidence on human endometrial regeneration in both health and disease in the context of this previously unknown endometrial glandular arrangement.OBJECTIVE AND RATIONALEThe aim of this review is to determine if the recently discovered glandular arrangement provides plausible explanations for previously unanswered questions related to human endometrial biology. Specifically, it will focus on re-appraising the theories related to endometrial regeneration, location of stem/progenitor cells and endometrial pathologies in the context of this recently unravelled endometrial glandular organization.SEARCH METHODSAn extensive literature search was conducted from inception to April 2021 using multiple databases, including PubMed/Web of Science/EMBASE/Scopus, to select studies using keywords applied to endometrial glandular anatomy and regeneration, and the references included in selected publications were also screened. All relevant publications were included.OUTCOMESThe human endometrial glands have a unique and complex architecture; branched basalis glands proceed in a horizontal course adjacent to the myometrium, as opposed to the non-branching, vertically coiled functionalis glands, which run parallel to each other as is observed in intestinal crypts. This complex network of mycelium-like, interconnected basalis glands is demonstrated to contain endometrial epithelial stem cells giving rise to single, non-branching functionalis glands. Several previous studies that have tried to confirm the existence of epithelial stem cells have used methodologies that prevent sampling of the stem cell-rich basalis. More recent findings have provided insight into the efficient regeneration of the human endometrium, which is preferentially evolved in humans and menstruating upper-order primates.WIDER IMPLICATIONSThe unique physiological organization of the human endometrial glandular element, its relevance to stem cell activity and scarless endometrial regeneration will inform reproductive biologists and clinicians to direct their future research to determine disease-specific alterations in glandular anatomy in a variety of endometrial pathological conditions.
Selective Orexin Receptor Antagonists as Novel Augmentation Treatments for Major Depressive Disorder: Evidence for Safety and Efficacy From a Phase 2B Study of Seltorexant
Authors: Jha Manish Kumar 
Published: November 2021

Abstract
There is a large unmet need for effective treatment of major depressive disorder (MDD), an often chronic/recurrent disorder that affects 1 in 5 adults during their lifetime in the United States. Clinicians and individuals with MDD often rely on augmentation approaches given the low rate of remission with the initial antidepressant treatment. Therefore, the report by Savitz and colleagues on the safety and efficacy of seltorexant is of great interest because it provides initial evidence for the antidepressant potential of drugs targeting orexin neurotransmission. Findings of this study suggest that seltorexant 20 mg is more effective than placebo, especially in individuals with moderate or insomnia symptoms at baseline. Given that insomnia is a common feature of depression, orexin 2 receptor antagonists may serve as important new treatment alternatives for people with MDD.
Respiratory antibacterial prescribing in primary care and the COVID-19 pandemic in England, winter season 2020–21
Authors: Andrews Amelia , Bou-Antoun Sabine , Guy Rebecca , Brown Colin S , Hopkins Susan , Gerver Sarah 
Published: November 2021

Abstract

Background
Antibacterial prescribing for respiratory tract infections (RTIs) accounts for almost half of all prescribing in primary care. Nearly a quarter of antibacterial prescribing in primary care is estimated to be inappropriate, the greatest being for RTIs. The COVID-19 pandemic has changed the provision of healthcare services and impacted the levels of antibacterials prescribed.

Objectives
To describe the changes in community antibacterial prescribing for RTIs in winter 2020–21 in England.

Methods
RTI antibacterial prescribing was measured in prescription items/1000 population for primary care from January 2014 and in DDDs/1000 population/day for the totality of RTI prescribing [combined with Accident & Emergency (A&E) in secondary care], from January 2016 to February 2021. Trends were assessed using negative binomial regression and seasonally adjusted interrupted time-series analysis.

Results
Antibacterials prescribed for RTIs reduced by a further 12.4% per season compared with pre-COVID (P &lt; 0.001). In winter 2020–21, RTI prescriptions almost halved compared with the previous winter in 2019–20 (P &lt; 0.001). The trend observed for total RTI prescribing (primary care with A&E) was similar to that observed in the community alone.

Conclusions
During COVID-19, RTI prescribing reduced in the community and the expected rise in winter was not seen in 2020–21. We found no evidence that RTI prescribing shifted from primary care to A&E in secondary care. The most likely explanation is a decrease in RTIs and presentations to primary care associated with national prevention measures for COVID-19.
Sex Specificity in Innate Immunity of Insect Larvae
Authors: Belousova Irina , Pavlushin Sergey , Subbotina Anna , Rudneva Natalya , Martemyanov Vyacheslav 
Published: November 2021

Abstract
The innate immunity of insects has been widely studied. Although the effect of sex on insect immunity has been extensively discussed, differences in immunity between the sexes of larvae insects remain largely unstudied. Studying larval sex differences in immunity may provide valuable information about the mechanisms underlying the insect immune system, which, in turn, can be valuable for the development and improvement of pest management. Here we compared the antibacterial activity in both the midgut tissue and cell-free hemolymph of Lymantria dispar L. (Lepidoptera: Erebidae) females and males at the larval stage without and after a challenge by entomopathogenic bacterium Bacillus thuringiensis Berliner. We also evaluated the sex-specific mortality of L. dispar induced by B. thuringiensis infection. We find that antibacterial activity in the midgut is activated by infection, but only in females. Thus, sex differences in immunity can have important effects even before sexual differentiation at adulthood.