bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heritable endogenization of an RNA virus in a mammalian species
Authors: Iida Atsuo , Tarigan Ronald , Shimoda Hiroshi , Endo Kanako , Furuta Masaya , Takemae Hitoshi , Hayasaka Daisuke , Ichiyanagi Kenji , Maeda Ken , Hondo Eiichi 
Published: January 2020

AbstractViruses are considered one of the driving forces for genome rearrangements via infection and endogenization into the host genome (Kazazian, 2004; Maksakova et al., 2006). In 2010, Horie and colleagues demonstrated that a non-retroviral RNA virus, Borna disease virus (BDV), could integrate into the genome in cultured somatic cells (Horie et al., 2010). However, germline transmission of viral-derived sequences using animal models is yet to be experimentally demonstrated. In this study, we reported a case of heritable endogenization using the encephalomyocarditis virus (EMCV) and laboratory mice. The EMCV is a small non-enveloped single-strand RNA virus without its own reverse transcriptase activity (Carocci et al., 2012). Here, we demonstrated that the EMCV genomic RNA was reverse transcribed into DNA fragments in the murine testes. The DNA sequence originated from the RNA genome of EMCV was also detected in the liver and earlobes of the offspring generated from the EMCV-infected father. This suggests that the exogenous sequence derived from the EMCV is transmitted into the host germline and inherited across subsequent generations. This first experimental demonstration of viral endogenization proposes reconsideration about the impact of viruses as a driving force for genome modification.
Reinforcement meta-learning optimizes visuomotor learning
Authors: Sugiyama Taisei , Schweighofer Nicolas , Izawa Jun 
Published: January 2020

AbstractReinforcement learning enables the brain to learn optimal action selection, such as go or not go, by forming state-action and action-outcome associations. Does this mechanism also optimize the brain’s willingness to learn, such as learn or not learn? Learning to learn by rewards, i.e., reinforcement meta-learning, is a crucial mechanism for machines to develop flexibility in learning, which is also considered in the brain without empirical examinations. Here, we show that humans learn to learn or not learn to maximize rewards in visuomotor learning tasks. We also show that this regulation of learning is not a motivational bias but is a result of an instrumental, active process, which takes into account the learning-outcome structure. Our results thus demonstrate the existence of reinforcement meta-learning in the human brain. Because motor learning is a process of minimizing sensory errors, our findings uncover an essential mechanism of interaction between reward and error.
Cocaine Triggers Glial-Mediated Synaptogenesis
Authors: Wang Junshi , Li King-Lun , Shukla Avani , Beroun Ania , Ishikawa Masago , Huang Xiaojie , Wang Yao , Wang Yao Q. , Bastola Noah D. , Huang Hugh H. , Kramer Lily E. , Chao Terry , Huang Yanhua H. , Sesack Susan R. , Nestler Eric J. , Schlüter Oliver M. , Dong Yan 
Published: January 2020

AbstractSynaptogenesis is essential in forming new neurocircuits during development, and this is mediated in part by astrocyte-released thrombospondins (TSPs) and activation of their neuronal receptor, α2δ-1. Here, we show that this developmental synaptogenic mechanism is utilized during cocaine experience to induce spinogenesis and the generation of AMPA receptor-silent glutamatergic synapses in the adult nucleus accumbens (NAc). Specifically, cocaine administration activates NAc astrocytes, and preventing this activation blocks cocaine-induced generation of silent synapses. Furthermore, knockout of TSP2, or pharmacological inhibition or viral-mediated knockdown of α2δ-1, prevents cocaine-induced generation of silent synapses. Moreover, disrupting TSP2-α2δ-1-mediated spinogenesis and silent synapse generation in the NAc occludes cue-induced cocaine seeking after withdrawal from cocaine self-administration and cue-induced reinstatement of cocaine seeking after drug extinction. These results establish that silent synapses are generated by an astrocyte-mediated synaptogenic mechanism in response to cocaine experience and embed critical cue-associated memory traces that promote cocaine relapse.
Genetic diversity evaluation among fifty sugarcane accessions from the Zimbabwe Sugar Industry using DNA-SSR markers and qualitative traits
Authors: Mugehu E , Mpofu L.T. , Manjeru P 
Published: January 2020

AbstractDue to the increasingly diverse needs of the Zimbabwe Sugar Industry, the need for developing improved varieties of sugarcane cannot be overemphasized. Since the establishment of experiment station in 1966, the Zimbabwe Sugar Industry (ZSI) has always been dependent on importation of crosses from other countries for development and release. However, the ZSI is now planning on developing its own crossing facilities. The ZSI’s gene bank has up to 746 imported varieties which can be used as potential parents in a breeding program. With the impending establishment of Zimbabwe’s own crossing facilities, morphological and molecular characterization of the available germplasm is essential to identify parental lines which can be used during crossing. The main objective of this study was to determine the genetic and phenotypic diversity of fifty ZSAES gene bank sugarcane varieties (accessions) and assess how the existing diversity can be utilized in a breeding program in parental selection and variety identification. Fifty sugarcane accessions were chosen based on country of origin and availability of seed and evaluated for qualitative morphological traits followed by molecular evaluation using DNA-SSR markers in a PCR based protocol. Fifty ‘one eyed’ setts were planted in the greenhouse and transplanted into labeled asbestos pots with ZSAES media. Fresh leaf samples were collected from the plants for DNA extraction using a modified CTAB method. Field trials for morphological assessment were laid out in a completely randomized design in a 1.5 × 4m plot. Agglomerative, hierarchical UPGMA clustering based on morphological traits with SAS®(9.3) categorized the fifty accessions into two main groups. The morphological traits managed to classify the accessions according to pedigree data, source of origin and similarities in traits like internode waxiness and adherence of leaf sheath. Contrastingly, molecular UPGMA cluster analysis by DarWin®(6.0) grouped the accessions into three main clusters. The three clusters were aligned according to time of release of varieties with the latter varieties in cluster III and the older ones in cluster I. The groups also showed clustering according to similarities in pedigree and source of origin. The molecular and morphological phylogenetic clusters suggest horizontal gene transfer through parallel and convergent evolution. Similarity trends observed also highlight similarities in breeding goals in different parts of the world. The information obtained from the study justifies the need for more importation of varieties from across the world to broaden the genetic base of the germplasm pool in the ZSI’s breeding program. Information from morphological data can be used for identification of released varieties in the ZSI using unique descriptors. More varieties should be analyzed and agronomic characterization should also be done to complement the results of this study and be incorporated into a database.
Deep learning for population size history inference: design, comparison and combination with approximate Bayesian computation
Authors: Sanchez Théophile , Cury Jean , Charpiat Guillaume , Jay Flora 
Published: January 2020

AbstractFor the past decades, simulation-based likelihood-free inference methods have enabled researchers to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong opportunity to tackle tasks that current methods hardly solve. However, high data dimensionality forces most methods to summarize large genomic datasets into a relatively small number of handcrafted features (summary statistics). Here we propose an alternative to summary statistics, based on the automatic extraction of relevant information using deep learning techniques. Specifically, we design artificial neural networks (ANNs) that take as input single nucleotide polymorphic sites (SNPs) found in individuals sampled from a single population and infer the past effective population size history. First, we provide guidelines to construct artificial neural networks that comply with the intrinsic properties of SNP data such as invariance to permutation of haplotypes, long scale interactions between SNPs and variable genomic length. Thanks to a Bayesian hyperparameter optimization procedure, we evaluate the performance of multiple networks and compare them to well established methods like Approximate Bayesian Computation (ABC). Even without the expert knowledge of summary statistics, our approach compares fairly well to an ABC based on handcrafted features. Furthermore we show that combining deep learning and ABC can improve performance while taking advantage of both frameworks. Finally, we apply our approach to reconstruct the effective population size history of cattle breed populations.
Loss of Tet2 affects proliferation and drug sensitivity through altered dynamics of cell-state transitions
Authors: Morinishi Leanna , Kochanowski Karl , Levine Ross L. , Wu Lani F. , Altschuler Steven J. 
Published: January 2020

SummaryA persistent puzzle in cancer biology is how mutations, which neither alter canonical growth signaling pathways nor directly interfere with drug mechanism, can still recur and persist in tumors. One notable example is the loss-of-function mutation of the DNA demethylase Tet2 in acute myeloid leukemias (AMLs) that frequently persists from diagnosis through remission and relapse (Rothenberg-Thurleyet al., 2018; Corces-Zimmermanet al., 2014; Nibourelet al., 2010), but whose fitness advantage in the setting of anti-leukemic chemotherapy is unclear. Here we use paired isogenic human AML cell lines to show that Tet2 loss-of-function alters the dynamics of transitions between differentiated and stem-like states. Mathematical modeling and experimental validation reveal that these altered cell-state dynamics can benefit the cell population by slowing population decay during drug treatment and lowering the number of survivor cells needed to re-establish the initial population. These studies shed light on the functional and phenotypic effects of a Tet2 loss-of-function in AML, illustrate how a single gene mutation can alter a cells’ phenotypic plasticity, and open up new avenues in the development of strategies to combat AML relapse.
Ethanol tolerance inEscherichia coliDH5-Alpha developed by serial exposure to sublethal doses is conferred to wild strains by horizontal gene transfer
Authors: Borana Ronak , Bari Shreya 
Published: January 2020

AbstractMicroorganisms evolve novel mechanisms and pathways to mitigate various stresses. These are developed by the accumulation of beneficial mutations over many generations. Such adaptations are often transferred from mutant microorganisms that develop it intrinsically to wild ones through horizontal gene transfer (HGT). It allows the latter to acquire favourable traits without having to employ resources to natively evolve. We ascertained this inEscherichia coliby first developing tolerance to ethanol, a potent disinfectant, by laboratory evolution and then transmitting it to the wild strain by HGT. Naturally, wild typeE. colicannot survive beyond 35% v/v ethanol in LB media. By serially increasing the concentration of ethanol by 5% v/v and selecting the surviving colonies, we were able to impose an artificial selection pressure. This in vitro microevolution increased the ethanol tolerance in our mutant to 75% v/v. To test if this tolerance could be transferred to the wild strain through HGT, we meticulously exposed the ancestral wildE. colito the newly tolerant mutants to facilitate the exchange of genetic material. After our exposure, the unadapted wild typeE. coliacquired tolerance up to 55% v/v through transformation and 45% v/v through transduction. The baseline tolerance of 35% v/v remained unchanged after conjugation. While our results are still preliminary, they provide interesting insights into the role horizontal gene transfer in developing resistance to bacteriocidal stressors.
High Content Phenotypic Profiling in Oesophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery
Authors: Hughes Rebecca E , Elliott Richard J R , Munro Alison F , Makda Ashraff , O’Neill J Robert , Hupp Ted , Carragher Neil O 
Published: January 2020

AbstractOesophageal adenocarcinoma (OAC) is a highly heterogeneous disease, dominated by large-scale genomic rearrangements and copy number alterations. Such characteristics have hampered conventional target-directed drug discovery and personalized medicine strategies contributing to poor outcomes for patients diagnosed with OAC. We describe the development and application of a phenotypic-led OAC drug discovery platform incorporating image-based, high-content cell profiling and associated image-informatics tools to classify drug mechanism-of-action (MoA). We applied a high-content Cell Painting assay to profile the phenotypic response of 19,555 compounds across a panel of six OAC cell lines representing the genetic heterogeneity of disease, a pre-neoplastic Barrett’s oesophagus line and a non-transformed squamous oesophageal line. We built an automated phenotypic screening and high-content image analysis pipeline to identify compounds that selectively modified the phenotype of OAC cell lines. We further trained a machine-learning model to predict the MoA of OAC selective compounds using phenotypic fingerprints from a library of reference compounds.We identified a number of phenotypic clusters enriched with similar pharmacological classes e.g. Methotrexate and three other antimetabolites which are highly selective for OAC cell lines. We further identify a small number of hits from our diverse chemical library which show potent and selective activity for OAC cell lines and which do not cluster with the reference library of known MoA, indicating they may be selectively targeting novel oesophageal cancer biology. Our results demonstrate that our OAC phenotypic screening platform can identify existing pharmacological classes and novel compounds with selective activity for OAC cell phenotypes.
Innate immune gene expression inAcropora palmatais consistent despite variance in yearly disease events
Authors: Young Benjamin , Serrano Xaymara M. , Rosales Stephanie , Miller Margaret W. , Williams Dana , Traylor-Knowles Nikki 
Published: January 2020

AbstractCoral disease outbreaks are expected to increase in prevalence, frequency and severity due to climate change and other anthropogenic stressors. This is especially worrying for the Caribbean branchingAcropora palmatawhich has already seen an 80% decrease in its coral cover, with this primarily due to disease. Despite the importance of this species, there has yet to be a characterization of its transcriptomic response to disease exposure. In this study we provide the first transcriptomic analysis of 12A. palmatagenotypes, and their symbiont Symbiodiniaceae, exposed to disease in 2016 and 2017. Year was the primary driver of sample variance forA. palmataand the Symbiodiniaceae. Lower expression of ribosomal genes in the coral, and higher expression of transmembrane ion transport genes in the Symbiodiniaceae indicate that the increased virulence in 2017 may have been due to a dysbiosis between the coral and Symbiodiniaceae. We also identified a conserved suite of innate immune genes responding to the disease challenge that was activated in both years. This included genes from the Toll-like receptor and lectin pathways, and antimicrobial peptides. Co-expression analysis identified a module positively correlated to disease exposure rich in innate immune genes, with D-amino acid oxidase, a gene implicated in phagocytosis and microbiome homeostasis, as the hub gene. The role of D-amino acid oxidase in coral immunity has not been characterized but holds potential as an important enzyme for responding to disease. Our results indicate thatA. palmatamounts a similar immune response to disease exposure as other coral species previously studied, but with unique features that may be critical to the survival of this keystone Caribbean species.
Optimal spectral templates for triggered feedback experiments
Authors: Kulkarni Anand S , Troyer Todd W 
Published: January 2020

AbstractIn the field of songbird neuroscience, researchers have used playback of aversive noise bursts to drive changes in song behavior for specific syllables within a bird’s song. Typically, a short (~5-10 msec) slice of the syllable is selected for targeting and the average spectrum of the slice is used as a template. Sounds that are sufficiently close to the template are considered a match. If other syllables have portions that are spectrally similar to the target, false positive errors will weaken the operant contingency. We present a gradient descent method for template optimization that increases the separation in distance between target and distractors slices, greatly improving targeting accuracy. Applied to songs from five adult Bengalese finches, the fractional reduction in errors for sub-syllabic slices was 51.54±22.92%. At the level of song syllables, we use an error metric that controls for the vastly greater number of distractors vs. target syllables. Setting 5% average error (misses + false positives) as a minimal performance criterion, the number of targetable syllables increased from 3 to 16 out of 61 syllables. At 10% error, targetable syllables increased from 11 to 26. By using simple and robust linear discriminant methods, the algorithm reaches near asymptotic performance when using 10 songs as training data, and the error increases by <2.3% on average when using only a single song for training. Targeting is temporally precise, with average jitter of 3.33 msec for the 16 accurately targeted syllables. Because the algorithm is concerned only with the problem of template selection, it can be used as a simple and robust front end for existing hardware and software implementations for triggered feedback.
Compressive Big Data Analytics: An Ensemble Meta-Algorithm for High-dimensional Multisource Datasets
Authors: Marino Simeone , Zhao Yi , Zhou Nina , Zhou Yiwang , Toga Arthur , Zhao Lu , Jian Yingsi , Yang Yichen , Chen Yehu , Wu Qiucheng , Wild Jessica , Cummings Brandon , Dinov Ivo D. 
Published: January 2020

AbstractHealth advances are contingent on continuous development of new methods and approaches to foster data driven discovery in the biomedical and clinical health sciences. Open-science offers hope for tackling some of the challenges associated with Big Data and team-based scientific discovery. Domain-independent reproducibility, area-specific replicability, curation, analysis, organization, management and sharing of health-related digital objects are critical components.This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA). Applied to high-dimensional data, CBDA identifies salient features and key biomarkers for reliable and reproducible forecasting of binary or multinomial outcomes. The method relies on iterative subsampling, combines function optimization and statistical inference, and generates ensemble predictions of observed univariate outcomes. In this manuscript, we extend the CBDA technique by (1) efficiently handling extremely large datasets, (2) generalizing the internal and external validation steps, (3) expanding the set of base-learners for joint ensemble prediction, (4) introduce an automated selection of CBDA specifications, and (5) provide mechanisms to assess CBDA convergence, evaluate the prediction accuracy, and measure result consistency.We validated the CBDA 2.0 technique using synthetic datasets as well as a population-wide census-like study, which grounds the mathematical models and the computational algorithm into translational health research settings. Specifically, we empirically validated the CBDA technique on a large-scale clinical study (UK Biobank), which includes imaging, cognitive, and clinical assessment data. The UK Biobank archive presents several difficult challenges related to the aggregation, harmonization, modeling, and interrogation of the information. These problems are related to the complex longitudinal structure, feature heterogeneity, multicollinearity, incongruency, and missingness, as well as violations of classical parametric assumptions that require novel health analytical approaches.Our results showcase the scalability, efficiency and potential of CBDA tocompresscomplex data into structural information leading to derived knowledge and translational action. The results of the real case-study suggest new and exciting avenues of research in the context of identifying, tracking, and treating mental health and aging-related disorders. Following open-science principles, we share the entire end-to-end protocol, source-code, and results. This facilitates independent validation, result reproducibility, and team-based collaborative discovery.
CAM emerges in a leaf metabolic model under water-saving constraints in different environments
Authors: Töpfer Nadine , Braam Thomas , Shameer Sanu , Ratcliffe R. George , Sweetlove Lee J. 
Published: January 2020

AbstractCrassulacean Acid Metabolism (CAM) evolved in arid environments as a water-saving alternative to C3photosynthesis. There is great interest in engineering more drought-resistant crop species by introducing CAM into C3plants. However, one of the open questions is whether full CAM or alternative water-saving flux modes would be more productive in the environments typically experienced by C3crops. To study the effect of temperature and relative humidity on plant metabolism we coupled a time-resolved diel model of leaf metabolism to an environment-dependent gas-exchange model. This model allowed us to study the emergence of CAM or CAM-like behaviour as a result of a trade-off between leaf productivity and water-saving. We show that vacuolar storage capacity in the leaf is a major determinant of the extent of CAM and shapes the occurrence of phase II and IV of the CAM cycle. Moreover, the model allows us to study alternative flux routes and we identify mitochondrial isocitrate dehydrogenase (ICDH) and an isocitrate-citrate-proline-2OG cycle as a potential contributor to initial carbon fixation at night. Simulations across a wide range of environmental parameters show that the water-saving potential of CAM strongly depends on the environment and that the additional water-saving effect of carbon fixation by ICDH can reach up to 4% for the conditions tested.