bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches
Authors: Kotula Hannah J. , Peralta Guadalupe , Frost Carol M. , Todd Jacqui H. , Tylianakis Jason M. 
Published: November 2020

AbstractBiological pest control (i.e. ‘biocontrol’) agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect impacts) remains a central challenge in biocontrol risk assessment. The analysis of ecological networks offers a promising approach to understanding the community-wide impacts of biocontrol agents (via direct and indirect interactions). Independently, species traits and phylogenies have been shown to successfully predict species interactions and network structure (alleviating the need to collect quantitative interaction data), but whether these approaches can be combined to predict indirect impacts of natural enemies remains untested. Whether predictions of interactions (i.e. direct effects) can be made equally well for generalists vs. specialists and across different habitat types is also untested for consumer-prey interactions, though previous work on mutualist networks suggests that interactions among generalists may be more difficult to predict. Here, we used two machine learning techniques (random forest and k-nearest neighbour; KNN) to test whether we could accurately predict empirically-observed quantitative host-parasitoid networks using trait, abundance, and phylogenetic information. Then, we tested whether the accuracy of machine-learning-predicted interactions depended on the generality of the interacting partners or on the source (habitat type) of the training data. Finally, we used these predicted networks to generate predictions of indirect effects via shared natural enemies (i.e. apparent competition), and tested these predictions against empirically observed indirect effects between hosts. We found that random-forest models predicted host-parasitoid pairwise interactions (which could be used to predict attack of non-target host species) more successfully than KNN, and this predictive ability depended on the generality of the interacting partners, but not the source (habitat type) of data used to train the models. Further, although our machine-learning informed methods could significantly predict indirect effects, the explanatory power for both direct and indirect interactions was reasonably low. Combining machine-learning and network approaches provides a starting point for reducing risk in biocontrol.
Auxin requirements for a meristematic state in roots depend on a dual brassinosteroid function
Authors: Ackerman-Lavert M. , Fridman Y. , Matosevich R , Khandal H , Friedlander L. , Vragović K. , Ben El R. , Horev G. , Efroni I , Savaldi-Goldstein S. 
Published: November 2020

SummaryThe organization of the root meristem is maintained by a complex interplay between plant hormones signaling pathways that both interpret and determine their accumulation and distribution. Brassinosteroids (BR) and auxin signaling pathways control the number of meristematic cells in the Arabidopsis root, via an interaction that appears to involve contradicting molecular outcomes, with BR promoting auxin signaling input but also repressing its output. However, whether this seemingly incoherent effect is significant for meristem function is unclear. Here, we established that a dual effect of BR on auxin, with BR simultaneously promoting auxin biosynthesis and repressing auxin transcriptional output, is essential for meristem maintenance. Blocking BR-induced auxin synthesis resulted in rapid BR-mediated meristem loss. Conversely, plants with reduced BR levels were resistant to loss of auxin biosynthesis and these meristems maintained their normal morphology despite a 10-fold decrease in auxin levels. In agreement, injured root meristems which rely solely on local auxin synthesis, regenerated when both auxin and BR synthesis were inhibited. Use of BIN2 as a tool to selectively inhibit BR signaling, revealed meristems with distinct phenotypes depending on the perturbed tissue; meristem reminiscent of BR-deficient mutants or of high BR exposure. This enabled mapping BR-auxin interactions to the outer epidermis and lateral root cap tissues, and demonstrated the essentiality of BR signaling in these tissues for meristem maintenance. BR activity in internal tissues however, proved necessary to control BR homeostasis. Together, we demonstrate a basis for inter-tissue coordination and how a critical ratio between these hormones determines the meristematic state.
COUNTEN – an AI-driven tool for rapid, and objective structural analyses of the Enteric Nervous System
Authors: Kobayashi Yuta , Bukowski Alicia , Das Subhamoy , Espenel Cedric , Gomez-Frittelli Julieta , Wagle Narayani , Bakshi Shriya , Saha Monalee , Kaltschmidt Julia A. , Venkataraman Archana , Kulkarni Subhash 
Published: November 2020

AbstractHealthy gastrointestinal functions require a healthy Enteric Nervous System (ENS). ENS health is often defined by the presence of normal ENS structure. However, we currently lack a comprehensive understanding of normal ENS structure as current methodologies of manual enumeration of neurons within tissue and ganglia can only parse limited tissue regions; and are prone to error, subjective bias, and peer-to-peer discordance. Thus, there is a need to craft objective methods and robust tools to capture and quantify enteric neurons over a large area of tissue and within multiple ganglia. Here, we report on the development of an AI-driven tool COUNTEN which parses HuC/D-immunolabeled adult murine myenteric ileal plexus tissues to enumerate and classify enteric neurons into ganglia in a rapid, robust, and objective manner. COUNTEN matches trained humans in identifying, enumerating and clustering myenteric neurons into ganglia but takes a fraction of the time, thus allowing for accurate and rapid analyses of a large tissue region. Using COUNTEN, we parsed thousands of myenteric neurons and clustered them in hundreds of myenteric ganglia to compute metrics that help define the normal structure of the adult murine ileal myenteric plexus. We have made COUNTEN freely and openly available to all researchers, to facilitate reproducible, robust, and objective measures of ENS structure across mouse models, experiments, and institutions.
Habituation and goal-directed arbitration mechanisms and failures under partial observability
Authors: Sánchez-Fibla Martí 
Published: November 2020

AbstractWe often need to make decisions under incomplete information (partial observability) and the brain manages to add the right minimal context to the decision-making. Partial observability may also be handled by other mechanisms than adding contextual experience / memory. We propose that parallel and sequential arbitration of Habituation (Model-Free, MF) and Goal-Directed (Model-Based, MB) behavior may be at play to deal with partial observability “on-the-fly”, and that MB may be of different types (going beyond the MF/MB dichotomy [4]). To illustrate this, we identify, describe and model with Reinforcement Learning (RL) a behavioral anomaly (an habituation failure) occurring during the so-called Hotel Elevators Rows (HER, for short) task: a prototypical partial observation situation that can be reduced to the well studied Two and One Sequence Choice Tasks. The following hypothesis are supported by RL simulation results: (1) a parallel (semi)model-based successor representation mechanism is operative while learning to habituate which detects model-based mismatches and serves as an habituation surveillance, (2) a retrospective inference is triggered to identify the source of the habituation failure (3) a model-free mechanism can trigger model-based mechanisms in states in which habituation failed. The “failures” in the title refer to: the habituation failures that need to be monitored and surveilled (1) and to the failures that we identified in prototypical state of the art Model-Based algorithms (like DynaQ) when facing partial observability. As other research on MF/MB arbitration shows, the identification of these new mechanisms could shine light into new treatments for addiction, compulsive behavior (like compulsive checking) and understand better accidents caused by habituation behaviors.
Direct Observation of the Mechanism of Antibiotic Resistance by Mix-and-Inject at the European XFEL
Authors: Pandey Suraj , Calvey George , Katz Andrea M. , Malla Tek Narsingh , Koua Faisal H. M. , Martin-Garcia Jose M. , Poudyal Ishwor , Yang Jay-How , Vakili Mohammad , Yefanov Oleksandr , Zielinski Kara A. , Bajt Saša , Awel Salah , Dörner Katerina , Frank Matthias , Gelisio Luca , Jernigan Rebecca , Kirkwood Henry , Kloos Marco , Koliyadu Jayanath , Mariani Valerio , Miller Mitchell D. , Mills Grant , Nelson Garrett , Olmos Jose L. , Sadri Alireza , Sato Tokushi , Tolstikova Alexandra , Xu Weijun , Ourmazd Abbas , Spence John H. C. , Schwander Peter , Barty Anton , Chapman Henry N. , Fromme Petra , Mancuso Adrian P. , Phillips George N. , Bean Richard , Pollack Lois , Schmidt Marius 
Published: November 2020

AbstractIn this study, we follow the diffusion and buildup of occupancy of the substrate ceftriaxone inM. tuberculosisβ-lactamase BlaC microcrystals by structural analysis of the enzyme substrate complex at single millisecond time resolution. We also show the binding and the reaction of an inhibitor, sulbactam, on a slower millisecond time scale. We use the ‘mix-and-inject’ technique to initiate these reactions by diffusion, and determine the resulting structures by serial crystallography using ultrafast, intense X-ray pulses from the European XFEL (EuXFEL) arriving at MHz repetition rates. Here, we show how to use the EuXFEL pulse structure to dramatically increase the size of the data set and thereby the quality and time resolution of “molecular movies” which unravel ligand binding and enzymatically catalyzed reactions. This shows the great potential for the EuXFEL as a tool for biomedically relevant research, particularly, as shown here, for investigating bacterial antibiotic resistance.One Sentence SummaryDirect observation of fast ligand binding in a biomedically relevant enzyme at near atomic resolution with MHz X-ray pulses at the European XFEL.
Learning epistatic polygenic phenotypes with Boolean interactions
Authors: Behr Merle , Kumbier Karl , Cordova-Palomera Aldo , Aguire Matthew , Ronen Omer , Ye Chengzhong , Ashley Euan , Butte Atul J. , Arnaout Rima , Brown Ben , Priest James , Yu Bin 
Published: November 2020

AbstractDetecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surroundingMC1Rand novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surroundingHLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.
Prophage elements function as reservoir for antibiotic resistance and virulence genes in nosocomial pathogens
Authors: Kondo Kohei , Kawano Mitsuoki , Sugai Motoyuki 
Published: November 2020

AbstractProphages are often involved in host survival strategies and contribute toward increasing the genetic diversity of the host genome. Prophages also drive horizontal propagation of various genes as vehicles. However, there are few retrospective studies contributing to the propagation of antimicrobial resistance (AMR) and virulence factor (VF) genes by prophage. In this study, we extracted complete genome sequences of seven pathogens, including ESKAPE bacteria andEscherichia colideposited in a public database, and examined the distribution of both AMR and VF genes in certain genomic regions of prophage, including prophage-like element. We found that the ratios of AMR and VF genes greatly varied among the seven species. More than 55% ofEnterobacter cloacaestrains had VF genes, but only 0.8% ofKlebsiella pneumoniaestrains had VF genes from prophages. The prophage types carrying AMR genes were detected in a broad range of hosts, whereas prophages containing VF genes were conserved in only one or two species, suggesting that distribution patterns of prophages were different between prophages encoding AMR or VF genes. We also found that the prophage containing class 1 integrase possessed a significantly higher number of AMR genes than prophages with no class 1 integrase. Moreover, AMR genes in the prophage were located near transposase and integrase. The results of this study reveal a comprehensive picture of AMR and VF genes present in prophage elements and provide new insights into the horizontal transfer of genes associated with antimicrobial resistance and pathogenicity.ImportanceAlthough we believe phages play an important role in horizontal gene transfer in exchanging genetic material, we do not know the distribution of the antimicrobial resistance and/or virulence genes in prophages. We collected different prophage elements from the complete genome sequence of seven species –Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, andEnterobacter cloacae, as well asEscherichia coli–, and characterized the distribution of antimicrobial resistance and virulence genes encoded in the prophage region. While virulence genes in prophage were found to be species-specific, antimicrobial resistance genes in prophages were highly conserved in various species. Integron structure was detected within prophage regions in almost all of the genera. Maximum of 11 antimicrobial resistance genes were found in a single prophage region, suggesting that prophages act as a reservoir for antimicrobial resistance genes. Our results highlight new insights on prophages as horizontal gene carriers.
HealthCall: Smartphone Enhancement of Brief Interventions to Improve Medication Adherence Among Patients in HIV care
Authors: Hasin Deborah , Aharonovich Efrat , Zingman Barry , Stohl Malka , Walsh Claire , Elliott Jennifer C. , Fink David , Knox Justin , Durant Sean , Menchaca Raquel , Sharma Anjali 
Published: November 2020

AbstractBackgroundHeavy drinking among People Living With HIV (PLWH) reduces antiretroviral adherence and worsens health outcomes. Lengthy interventions to reduce drinking and improve adherence are not feasible in most HIV primary care settings, and patients seldom follow referrals to outside treatment. Utilizing visual and video features of smartphone technology, we developed and tested HealthCall as an electronic (smartphone) means of increasing patient involvement in brief intervention to reduce drinking and improve medication adherence without making unfeasible demands on providers.MethodsAlcohol-dependent patients at a large urban HIV clinic were randomized to one of three groups: (1) Motivational Interviewing (MI) plus HealthCall (n=39), (2) NIAAA Clinician’s Guide (CG) plus HealthCall (n=38), or (3) CG-only (n=37). Baseline interventions targeting drinking reduction and medication adherence were ∼25 minutes, with brief (10-15 min) booster sessions at 30 and 60 days. HealthCall involved daily use of the smartphone for 3-5 min/day, covering drinking, medication adherence, and other aspects of the prior 24 hours. Our outcome, assessed at 30 and 60 days, and 3, 6 and 12 months, was ART adherence (using unannounced phone pill-count method; possible adherence scores: 0%-100%). Analysis: generalized linear mixed models with pre-planned contrasts.ResultsStudy retention was excellent (85%-94% across timepoints) and unrelated to treatment arm or patient characteristics. ART adherence was generally high throughout follow-up, with some decline by 12 months. Although both CG+HealthCall and MI+HealthCall evidenced benefits early in follow-up, by 6 months, ART adherence was 11% better among patients in CG+HealthCall than in CG-only (p=0.03) and 9% better than among patients in MI+HealthCall (p=0.07). Efficacy differed slightly by gender (p=.09).ConclusionHealthCall paired with CG resulted in better ART adherence than CG alone. MI+HealthCall’s early benefits diminished over time. Given the importance of ART adherence and drinking reduction among PLWH, and the low costs and time required for HealthCall, pairing HealthCall with brief interventions within HIV clinics merits widespread consideration.
Chromosome structure due to phospho-mimetic H2A modulates DDR through increased chromatin mobility
Authors: Fernandez Fabiola Garcia , Lemos Brenda , Khalil Yasmine , Batrin Renaud , Haber James E. , Fabre Emmanuelle 
Published: November 2020

AbstractIn budding yeast and mammals, double strand breaks trigger global chromatin mobility together with the rapid phosphorylation of the histone H2A over an extensive region of the chromatin. To assess the role of H2A phosphorylation in this response to DNA damage, we have constructed strains where H2A has been mutated to the phospho-mimetic H2A-S129E. We show that H2A-S129E mutant increases global motion of chromosomes even in the absence of DNA damage. The intrinsic chromatin mobility of H2A-S129E is not due to checkpoint activation, histone degradation or kinetochore anchoring. Rather, the increased intra-chromosomal distances observed in H2A-S129E mutant are consistent with chromatin structural changes. In this context, the Rad953BP1-dependent-checkpoint becomes dispensable. The increase in chromatin dynamics is favorable to NHEJ of a single double-strand break but is accompanied by a sharp decrease in inter-chromosomal translocation rates. We propose that changes in chromosomal conformation due to H2A phosphorylation are sufficient to modulate the DDR and maintain genome integrity.
QSM Reconstruction Challenge 2.0: Design and Report of Results
Authors: Bilgic Berkin , Langkammer Christian , Marques José P. , Meineke Jakob , Milovic Carlos , Schweser Ferdinand 
Published: November 2020

AbstractPurposeThe aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data.MethodsA two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realisticin silicohead phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground-truths available to mimic the clinical setting. At the second stage, ground-truths were provided for parameter optimization.Submissions were evaluated using eight numerical metrics and visual ratings.ResultsA total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep-learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and Total Variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art.ConclusionThe synthetic data provides a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithmsproduced the best results along all metrics. Future QSM challenges should asses if this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.
Integrative Ranking Of Enhancer Networks Facilitates The Discovery Of Epigenetic Markers In Cancer
Authors: Wang Qi , Wu Yonghe , Vorberg Tim , Eils Roland , Herrmann Carl 
Published: November 2020

AbstractRegulation of gene expression through multiple epigenetic components is a highly combinatorial process. Alterations in any of these layers, as is commonly found in cancer diseases, can lead to a cascade of downstream effects on tumor suppressor or oncogenes. Hence, deciphering the effects of epigenetic alterations on regulatory elements requires innovative computational approaches that can benefit from the huge amounts of epigenomic datasets that are available from multiple consortia, such as Roadmap or BluePrint. We developed a software tool named Irene (Integrative Ranking of Epigenetic Network of Enhancers), which performs quantitative analyses on differential epigenetic modifications through an integrated, network-based approach. The method takes into account the additive effect of alterations on multiple regulatory elements of a gene. Applying this tool to well-characterized test cases, it successfully found many known cancer genes from publicly available cancer epigenome datasets.