bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic diversity and population history of eight Italian beef cattle breeds using measures of autozygosity
Authors: Fabbri Maria Chiara , Dadousis Christos , Tiezzi Francesco , Maltecca Christian , Lozada-Soto Emmanuel , Biffani Stefano , Bozzi Riccardo 
Published: February 2021

AbstractIn the present study, GeneSeek GGP-LDv4 33k single nucleotide polymorphism chip was used to detect runs of homozygosity (ROH) in eight Italian beef cattle breeds; six breeds with distribution limited to Tuscany (Calvana, Mucca Pisana, Pontremolese) or Sardinia (Sarda, Sardo Bruna and Sardo Modicana) and two cosmopolitan breeds (Charolais and Limousine). ROH detection analyses were used to estimate autozygosity and inbreeding and to identify genomic regions with high frequency of ROH, which might reflect selection signatures. Comparative analysis among breeds revealed differences in length and distribution of ROH and inbreeding levels. The Charolais, Limousine, Sarda, and Sardo Bruna breeds were found to have a high frequency of short ROH (30.000); Calvana and Mucca Pisana presented also runs longer than 16 Mbp. The highest level of average genomic inbreeding was observed in Tuscan breeds, around 0.3, while Sardinian and cosmopolitan breeds showed values around 0.2. The population structure and genetic distances were analyzed through principal component analysis and multidimensional scaling analysis, and resulted in a clear separation among the breeds, with clusters related to productive purposes and sample sizes. The frequency of ROH occurrence revealed eight breed-specific genomic regions where genes of potential selective and conservative interest are located (e.g.MYOG, Chitinases(BTA16),TIMELESS,APOF, Olfactory receptors,CACNG2(BTA5) and Collagens (BTA2)). In all breeds, we found the largest proportion of homozygous by descent segments to be those that represent inbreeding events that occurred around 32 generations ago; with Tuscan breeds also having a significant proportion of segments relating to more recent inbreeding.
Combined application of biochar with nitrogen fertilizer improves soil quality and reduces soil respiration whilst sustaining wheat grain yield in a semiarid environment
Authors: Yeboah Stephen , Jun Wu , Liqun Cai , Oteng-Darko Patricia , Renzhi Zhang 
Published: February 2021

AbstractThis study was conducted to investigate the effect of biochar, straw and N fertilizer on soil properties, soil respiration and grain yield of spring wheat (Triticum aestivumL.) in semi-arid Western Loess Plateau of northwestern China. The two carbon sources (straw and biochar) were applied alone or combined with nitrogen fertilizer (urea, 46% nitrogen [N]), whilst the soil without carbon is made up of nitrogen fertilizer applied at 0, 50 and 100 kg N/ha. The experiment was arranged in a randomized complete block design with three replicates and was conducted in 2014, 2015 and 2016 cropping season. Results showed that the greatest grain yields were found with 100 kg N ha−1fertilization rate under biochar, straw and soils without carbon, but the greatest effect occurred on the biochar amended soils. Biochar amendment produced the greatest grain yield at 1906 kg ha−1, followed by straw treated soils at 1643 kg ha−1, and soils without carbon the lowest at 1553 kg ha−1. This results is supported by the fact that, biochar amended soils (at 0–10 cm) increased soil organic C by 17.14% and 21.65% compared to straw treated soils and soils without carbon respectively. Seasonal soil respirations were between 19.05% and 23.67% lower in BN100compared with SN50and CN100. Soil respiration reduced with increasing N fertilization rates under all treatments, but the greatest effect occurred on biochar plots. Biochar amended soils decreased carbon emission by 26.80% and 9.54% compared to straw treated soils and soils without carbon amendment respectively. Increased grain yield and the decreased carbon emission in BN100translated into greater carbon emission efficiency (2.88 kg kg−1) which was significantly different compared with the other treatments. Combined application of biochar with 100 kg N ha−1in rainfed spring wheat was a suitable agricultural practice.
The non-benzodiazepine anxiolytic etifoxine limits the sensory-affective expression of pain following streptozotocin-induced diabetic neuropathy
Authors: Gazzo Géraldine , Ferrer Marlene Salgado , Poisbeau Pierrick 
Published: February 2021

AbstractMore than 450 million people worldwide suffer from diabetes, or 1 in 11 people. Chronic hyperglycemia degrades patients’ quality of life and the development of neuropathic pain contributes to the burden of this disease. In this study, we used the mouse model of streptozocin-induced diabetic type 1 neuropathy to assess the analgesic potential of etifoxine. Etifoxine is a prescribed anxiolytic that increases GABAAA receptor function through a direct positive allosteric modulation effect and, indirectly, by stimulating the production of endogenous GABAA receptor positive modulators such as allopregnanolone-type neurosteroids. We show that a curative or preventive treatment strongly and durably reduces mechanical hyperalgesia and anxiety in diabetic neuropathic mice. This analgesic and neuroprotective effect on painful symptoms and emotional comorbidities is promising and should now be clinically evaluated.
Regulation ofJacobaea vulgarisby varied cutting and restoration measures
Authors: Möhler Henrike , Diekötter Tim , Donath Tobias W. 
Published: February 2021

AbstractThe growth of the noxious grassland weedJacobaea vulgarisGaertn. in pastures is a threat to grazing animals. This is especially true when it dominates vegetation cover, which often occurs on non-intensively used pastures that are managed with the nature-conservation goal to maintain and promote biodiversity. Thus, we wanted to find management techniques to reduceJ. vulgariswithout harming the floral biodiversity on the pastures.We tested six different mechanical and cultural methods to reduce the presence and spread ofJ. vulgaris. Seven study sites in Northern Germany (Schleswig-Holstein) were treated with various measurements, including: milling and seeding, hay transfer, mowing regimes at different times and frequencies, and combinations of mowing and seeding.Our results show that cutting within the bloom of the plant at the end of June and again four weeks later, when the plant is in its second bloom was the most effective treatment in reducingJ. vulgarisabundance. This was the only treatment leading to a significant reduction in population growth rate and density without reducing surrounding plant species richness.The study reveals that management ofJ. vulgarisin non-intensively used pastures is possible, while preserving species-rich grasslands.
Flanker: a tool for comparative genomics of gene flanking regions
Authors: Matlock William , Lipworth Samuel , Constantinides Bede , Peto Timothy E.A. , Sarah Walker A. , Crook Derrick , Hopkins Susan , Shaw Liam P. , Stoesser Nicole 
Published: February 2021

AbstractAnalysing the flanking sequences surrounding genes of interest is often highly relevant to understanding the role of mobile genetic elements (MGEs) in horizontal gene transfer, particular for antimicrobial resistance genes. Here, we present Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of MGEs without prior knowledge of their structure. These clusters, known as ‘flank patterns’, are based on Mash distances, allowing for easy comparison of similarity across sequences. Additionally, Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence. We apply Flanker to two recent datasets describing plasmid-associated carriage of important carbapenemase genes (blaOXA-48 and blaKPC-2/3) and show that it successfully identifies distinct clusters of flank patterns, including both known and previously uncharacterised structural variants. For example, Flanker identified four Tn4401 profiles that could not be sufficiently characterised using TETyper or MobileElementFinder, demonstrating the utility of Flanker for flanking gene characterisation. Similarly, using a large (n=226) European isolate dataset, we confirm findings from a previous smaller study demonstrating association between Tn1999.2 andblaOXA-48upregulation and demonstrate 17 flank patterns (compared to the 5 previously identified). More generally the demonstration in this study that flank patterns are associated with to geographical regions and antibiotic susceptibility phenotypes suggests that they may be useful as epidemiological markers. Flanker is freely available under an MIT license athttps://github.com/wtmatlock/flanker.Data SummaryNCBI accession numbers for all sequencing data used in this study is provided in Supplementary Table 1. The analysis performed in this manuscript can be reproduced in a binder environment provided on the Flanker Github page (https://github.com/wtmatlock/flanker).
DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs
Authors: Lin Jiecong , Huang Lei , Chen Xingjian , Zhang Shixiong , Wong Ka-Chun 
Published: February 2021

AbstractMotivationThe cooperativity of transcription factors (TFs) is a widespread phenomenon in the gene regulation system. However, the interaction patterns between TF binding motifs remain elusive. The recent high-throughput assays, CAP-SELEX, have identified over 600 composite DNA sites (i.e. heterodimeric motifs) bound by cooperative TF pairs. However, there are over 25,000 inferentially effective heterodimeric TFs in human cell. It is not practically feasible to validate all heterodimeric motifs due to cost and labour. Therefore, it is highly demanding to develop a fast and accurate computational tool for heterodimeric motif synthesis.ResultsWe introduce DeepMotifSyn, a deep-learning-based tool for synthesizing heterodimeric motifs from monomeric motif pairs. Specifically, DeepMotifSyn is composed of heterodimeric motif generator and evaluator. The generator is a U-Net-based neural network that can synthesize heterodimeric motifs from aligned motif pairs. The evaluator is a machine-learning-based model that can score the generated heterodimeric motif candidates based on the motif sequence features. Systematic evaluations on CAP-SELEX data illustrates that DeepMotif-Syn significantly outperforms the current state-of-the-art predictors. In addition, DeepMotifSyn can synthesize multiple heterodimeric motifs with different orientation and spacing settings. Such a feature can address the shortcomings of previous models. We believe Deep-MotifSyn is a more practical and reliable model than current predictors on heterodimeric motif synthesis.Availability and implementationThe software is freely available athttps://github.com/JasonLinjc/deepMotifSyn.
Resurrection of endogenous retroviruses during aging reinforces senescence
Authors: Liu Xiaoqian , Liu Zunpeng , Wu Zeming , Ren Jie , Fan Yanling , Sun Liang , Cao Gang , Niu Yuyu , Zhang Baohu , Ji Qianzhao , Jiang Xiaoyu , Wang Cui , Wang Qiaoran , Ji Zhejun , Li Lanzhu , Esteban Concepcion Rodriguez , Yan Kaowen , Li Wei , Cai Yusheng , Wang Si , Zheng Aihua , Zhang Yong E. , Tan Shengjun , Cai Yingao , Song Moshi , Lu Falong , Tang Fuchou , Ji Weizhi , Zhou Qi , Belmonte Juan Carlos Izpisua , Zhang Weiqi , Qu Jing , Liu Guang-Hui 
Published: February 2021

SUMMARYWhether and how certain transposable elements with viral origins, such as endogenous retroviruses (ERVs) dormant in our genomes, can become awakened and contribute to the aging process are largely unknown. In human senescent cells, we found that HERVK (HML-2), the most recently integrated human ERVs, are unlocked to transcribe viral genes and produce retrovirus-like particles (RVLPs). These HERVK RVLPs constitute a transmissible message to elicit senescence phenotypes in young cells, which can be blocked by neutralizing antibodies. Activation of ERVs was also observed in organs of aged primates and mice, as well as in human tissues and serum from the elderly. Their repression alleviates cellular senescence and tissue degeneration and, to some extent, organismal aging. These findings indicate that the resurrection of ERVs is a hallmark and driving force of cellular senescence and tissue aging.In briefLiu and colleagues uncover the ways in which de-repression of human endogenous retrovirus triggers cellular senescence and tissue aging; the findings provide fresh insights into therapeutic strategies for alleviating aging.HighlightsDerepression of the endogenous retrovirus contributes to programmed agingUpregulation of HERVK triggers the innate immune response and cellular senescenceExtracellular HERVK retrovirus-like particles induce senescence in young cellsEndogenous retrovirus serves as a potential target to alleviate agingsGraphical abstract
BiSEK: a platform for a reliable differential expression analysis
Authors: Haas Roni , Light Dean , Festinger Yahav , Friedman Neta , Lamm Ayelet T. 
Published: February 2021

ABSTRACTDifferential Expression Analysis (DEA) of RNA-sequencing data is frequently performed for detecting key genes, affected across different conditions. Although DEA-workflows are well established, preceding reliability-testing of the input material, which is crucial for consistent and strong results, is challenging and less straightforward. Here we present Biological Sequence Expression Kit (BiSEK), a graphical user interface-based platform for DEA, dedicated to a reliable inquiry. BiSEK is based on a novel algorithm to track discrepancies between the data and the statistical model design. Moreover, BiSEK enables differential-expression analysis of groups of genes, to identify affected pathways, without relying on the significance of genes comprising them. Using BiSEK, we were able to improve previously conducted analysis, aimed to detect genes affected by FUBP1 depletion in chronic myeloid leukemia cells of mice bone-marrow. We found affected genes that are related to the regulation of apoptosis, supporting in-vivo experimental findings. We further tested the host response following SARS-CoV-2 infection. We identified a substantial interferon-I reaction and low expression levels of TLR3, an inducer of interferon-III (IFN-III) production, upon infection with SARS-CoV-2 compared to other respiratory viruses. This finding may explain the low IFN-III response upon SARS-CoV-2 infection. BiSEK is open-sourced, available as a web-interface.
Levetiracetam treatment normalizes levels of the presynaptic endocytosis machinery and restores non-amyloidogenic APP processing inAppknock-in mice
Authors: Rao Nalini R. , Savas Jeffrey N. 
Published: February 2021

SUMMARYIncreasing evidence indicates that toxic amyloid-beta (Aβ) peptides, produced by sequential proteolytic cleavage of the Amyloid Precursor Protein (APP), induce neuronal circuit hyperexcitability in the early stages of Alzheimer’s disease (AD). As a result, treatments that modulate this early excitatory/inhibitory imbalance could act as potential AD therapies. Levetiracetam, an atypical antiepileptic drug, has garnered recent interest, despite the mechanism(s) of action remaining elusive. In this study, we set out to identify the pathways and mechanisms primarily affected by levetiracetam in diseased brains of amyloid pathology. Using theAppknock-in mouse models and multiplexed TMT-quantitative mass spectrometry-based proteomic analysis to determine how levetiracetam affects the proteome, our findings demonstrate that levetiracetam treatment selectively normalizes levels of presynaptic endocytosis proteins and is capable of lowering Aβ42levels by altering APP processing. These novel findings demonstrate a mechanism of action for how levetiracetam lowers Aβ42production.
Conformist social learning leads to self-organised prevention against adverse bias in risky decision making
Authors: Toyokawa Wataru , Gaissmaier Wolfgang 
Published: February 2021

AbstractGiven the ubiquity of potentially adverse behavioural bias owing to myopic trial-and-error learning, it seems paradoxical that improvements in decision-making performance through conformist social learning, a process widely considered to be bias amplification, still prevail in animal collective behaviour. Here we show, through model analyses and large-scale interactive behavioural experiments with 585 human subjects, that conformist influence can indeed promote favourable risk taking in repeated experience-based decision making, even though many individuals are systematically biased towards adverse risk aversion. Although strong positive feedback conferred by copying the majority’s behaviour could result in unfavourable informational cascades, our differential equation model of collective behavioural dynamics identified a key role for increasing exploration by negative feedback arising when a weak minority influence undermines the inherent behavioural bias. This ‘collective behavioural rescue’, emerging through coordination of positive and negative feedback, highlights a benefit of collective learning in a broader range of environmental conditions than previously assumed and resolves the ostensible paradox of adaptive collective behavioural flexibility under conformist influences.
Parallel processing of natural images by overlapping retinal neuronal ensembles
Authors: Pérez-Ortega Jesús , Araya Joaquín , Ibaceta Cristobal , Herzog Rubén , Escobar María-José , Peña-Ortega Fernando , Carrillo-Reid Luis , Palacios Adrian G. 
Published: February 2021

AbstractEven though the retinal microcircuit organization has been described in detail at the single-cell level, little is known about how groups of retinal cells’ coordinated activity encode and process parallel information representing the spatial and temporal structure of changing environmental conditions. To describe the population dynamics of retinal neuronal ensembles, we used microelectrode array recordings that describe hundreds of retinal ganglion cells’ simultaneous activity in response to a short movie captured in the natural environment where our subject develops their visual behaviors. The vectorization of population activity allowed the identification of retinal neuronal ensembles that synchronize to specific segments of natural stimuli. These synchronous retinal neuronal ensembles were reliably activated by the same stimuli at different trials, indicating a robust population response of retinal microcircuits. The generation of asynchronous events required integrating a physiologically meaningful time window larger than 80 ms, demonstrating that retinal neuronal ensembles’ time integration filters non-structured visual information. Interestingly, individual neurons could be part of several ensembles indicating that parallel circuits could encode environmental conditions changes. We conclude that parallel neuronal ensembles could represent the functional unit of retinal computations and propose that the further study of retinal neuronal ensembles could reveal emergent properties of retinal circuits that individual cells’ activity cannot explain.
The Rise of West Nile Virus in Southern and Southeastern Europe: a spatial-temporal analysis investigating the combined effects of climate, land use and economic changes
Authors: Watts Matthew J. , Monteys Victor Sarto i , Mortyn P. Graham , Kotsila Panagiota 
Published: February 2021

AbstractWest Nile Virus (WNV) has recently emerged as a major public health concern in Europe, its recent expansion also coincided with some remarkable socio-economic and environmental changes, including an economic crisis and some of the warmest temperatures on record. Here we empirically investigate the drivers of this phenomenon at a European wide scale by constructing and analyzing a unique spatial-temporal data-set, that includes data on climate, land-use, the economy, and government spending on environmental related sectors. Drivers and risk factors of WNV were identified by building a conceptual framework, and relationships were tested using a Generalized Additive Model (GAM), which could capture complex non-linear relationships and also account for the spatial and temporal auto-correlation. Some of the key risk factors identified in our conceptual framework, such as a higher percentage of wetlands and arable land, climate factors (higher summer rainfall and higher summer temperatures) were positive predictors of WNV infections. Interestingly, winter temperatures of between 2 °C and 6 °C were among some of the strongest predictors of annual WNV infections, one possible explanation for this result is that successful overwintering of infected adult mosquitoes (likelyCulex pipiens) is key to the intensity of outbreaks for a given year. Furthermore, lower surface water extent over the summer is also associated with more more intense outbreaks, suggesting that drought, which is known to induce positive changes in WNV prevalence in mosquitoes is also contributing to the upward trend in WNV cases in affected regions. Our indicators representing the economic crisis were also strong predictors of WNV infections, suggesting there is an association between austerity and cuts to key sectors, which could have benefited vector species and the virus during this crucial period. These results, taken in the context of recent winter warming due to climate change, and more frequent droughts, may offer an explanation of why the virus has become so prevalent in Europe.
NanoTox: Development of a parsimoniousin silicomodel for toxicity assessment of metal-oxide nanoparticles using physicochemical features
Authors: AnanthaSubramanian Nilesh , Palaniappan Ashok 
Published: February 2021

AbstractMetal-oxide nanoparticles find widespread applications in mundane life today, and cost-effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress. Machine learning models use existing experimental data, and learn the relationship of various features to nanoparticle cytotoxicity to generate predictive models. In this work, we adopted a principled approach to this problem by formulating a feature space based on intrinsic and extrinsic physico-chemical properties, but exclusive of anyin vitrocharacteristics such as cell line, cell type, and assay method. A minimal set of features was developed by applying variance inflation analysis to the correlation structure of the feature space. Using a balanced dataset, a mapping was then obtained from the normalized feature space to the toxicity class using various hyperparameter-tuned machine learning models. Evaluation on an unseen test set yielded > 96% balanced accuracy for both the random forest model, and neural network with one hidden layer model. The obtained cytotoxicity models are parsimonious, with intelligible inputs, and include an applicability check. Interpretability investigations of the models yielded the key predictor variables of metal-oxide nanoparticle cytotoxicity. Our models could be applied on new, untested oxides, using a majority-voting ensemble classifier, NanoTox, that incorporates the neural network, random forest, support vector machine, and logistic regression models. NanoTox is the very first predictive nanotoxicology pipeline made freely available under the GNU General Public License (https://github.com/NanoTox).