bluuebunny/crossref_metadata_2025_split huggingface dataset:
The statistics of spectral shifts due to finite rank perturbations
Authors: Dietz Barbara , Schanz Holger , Smilansky Uzy , Weidenmüller Hans 
Published: November 2020

Abstract
This article is dedicated to the following class of problems. Start with an N × N Hermitian matrix randomly picked from a matrix ensemble—the reference matrix. Applying a rank-t perturbation to it, with t taking the values 1 ⩽ t ⩽ N, we study the difference between the spectra of the perturbed and the reference matrices as a function of t and its dependence on the underlying universality class of the random matrix ensemble. We consider both, the weaker kind of perturbation which either permutes or randomizes t
diagonal elements and a stronger perturbation randomizing successively t
rows and columns. In the first case we derive universal expressions in the scaled parameter τ = t/N for the expectation of the variance of the spectral shift functions, choosing as random-matrix ensembles Dyson’s three Gaussian ensembles. In the second case we find an additional dependence on the matrix size N.
Interactions of Helicoverpa punctigera (Lepidoptera: Noctuidae) Larvae and Adults With Four Native Host Plants Relative to Field Use Patterns
Authors: Jones Lachlan C , Rafter Michelle A , Walter Gimme H 
Published: November 2020

Abstract
Generalist insect herbivores may be recorded from a great variety of host plants. Under natural conditions, however, they are almost invariably associated with a few primary host species on which most of the juveniles develop. We experimentally investigated the interaction of the generalist moth Helicoverpa punctigera Wallengren (Lepidoptera: Noctuidae) with four of its native host plants, two designated primary hosts and two secondary hosts (based on field observations). We tested whether primary host plants support higher survival rates of larvae and whether they are more attractive to ovipositing moths and feeding larvae. We also evaluated whether relative attractiveness of host plants for oviposition matches larval survival rates on them—the preference-performance hypothesis. Moths laid significantly more eggs on two of the four host plant species, one of them a primary host, the other a secondary host. Larvae developed best when reared on the attractive secondary host, developed at intermediate levels on the two primary hosts, and performed worst on the less attractive secondary host. Relative attractiveness of the four host plants to caterpillars differed from that of the moths. Neither adult nor larval attraction to host plants fully supported the preference-performance hypothesis, but oviposition was better correlated with larval survival rates than was larval attraction. Our results suggest the relative frequency at which particular host species are used in the field may depend on factors not yet considered including the long-distance attractants used by moths and the relative distribution of host species.
Geographic Variation in Dominance of Spinosad Resistance in Colorado Potato Beetles (Coleoptera: Chrysomelidae)
Authors: Klein Coby , Baker Mitchell , Alyokhin Andrei , Mota-Sanchez David 
Published: November 2020

Abstract
Eastern New York State is frequently the site of Colorado potato beetle (Leptinotarsa decemlineata, Say) populations with the highest observed levels of insecticide resistance to a range of active ingredients. The dominance of a resistant phenotype will affect its rate of increase and the potential for management. On organic farms on Long Island, L. decemlineata evolved high levels of resistance to spinosad in a short period of time and that resistance has spread across the eastern part of the Island. Resistance has also emerged in other parts of the country as well. To clarify the level of dominance or recessiveness of spinosad resistance in different parts of the United States and how resistance differs in separate beetle populations, we sampled in 2010 beetle populations from Maine, Michigan, and Long Island. In addition, a highly resistant Long Island population was assessed in 2012. All populations were hybridized with a laboratory-susceptible strain to determine dominance. None of the populations sampled in 2010 were significantly different from additive resistance, but the Long Island population sampled in 2012 was not significantly different from fully recessive. Recessive inheritance of high-level resistance may help manage its increase.
Development of a Nostalgic Remembering Intervention
Authors: Fleury Julie , Komnenich Pauline , Coon David W. , Volk-Craft Barbara 
Published: November 2020

Background
Dyads receiving palliative care for advanced heart failure are at risk for the loss of feeling safe, experienced as a fractured sense of coherence, discontinuity in sense of self and relationships, and strained social connections and altered roles. However, few theory-based interventions have addressed feeling safe in this vulnerable population.

Purpose
The purpose of this article is to describe the development of the Nostalgic Remembering Intervention to strengthen feeling safe and promote adaptive physiological and psychological regulation in dyads receiving palliative care for heart failure.

Conclusions
Systematic intervention development is essential to understand what, for whom, why, and how an intervention works in producing outcomes. Program theory provided a systematic approach to the development of the Nostalgic Remembering Intervention, including conceptualization of the problem targeted by the intervention, specification of critical inputs and conditions that operationalize the intervention, and understanding the mediating processes leading to expected outcomes.

Clinical Implications
Creating a foundation for cardiovascular nursing research and practice requires continued, systematic development of theory-based interventions to best meet the needs of dyads receiving palliative care for heart failure. The development of the Nostalgic Remembering Intervention to strengthen feeling safe in dyads provides a novel and relevant approach.
accuEnhancer: Accurate enhancer prediction by integration of multiple cell type data with deep learning
Authors: Tung Yi-An , Yang Wen-Tse , Hsieh Tsung-Ting , Chang Yu-Chuan , Wu June-Tai , Oyang Yen-Jen , Chen Chien-Yu 
Published: November 2020

AbstractEnhancers are one class of the regulatory elements that have been shown to act as key components to assist promoters in modulating the gene expression in living cells. At present, the number of enhancers as well as their activities in different cell types are still largely unclear. Previous studies have shown that enhancer activities are associated with various functional data, such as histone modifications, sequence motifs, and chromatin accessibilities. In this study, we utilized DNase data to build a deep learning model for predicting the H3K27ac peaks as the active enhancers in a target cell type. We propose joint training of multiple cell types to boost the model performance in predicting the enhancer activities of an unstudied cell type. The results demonstrated that by incorporating more datasets across different cell types, the complex regulatory patterns could be captured by deep learning models and the prediction accuracy can be largely improved. The analyses conducted in this study demonstrated that the cell type-specific enhancer activity can be predicted by joint learning of multiple cell type data using only DNase data and the primitive sequences as the input features. This reveals the importance of cross-cell type learning, and the constructed model can be applied to investigate potential active enhancers of a novel cell type which does not have the H3K27ac modification data yet.AvailabilityThe accuEnhancer package can be freely accessed at:https://github.com/callsobing/accuEnhancer
Theory of temperature-dependent consumer-resource interactions
Authors: Synodinos Alexis D. , Haegeman Bart , Sentis Arnaud , Montoya José M. 
Published: November 2020

AbstractChanges in temperature affect consumer-resource interactions, which underpin the functioning of ecosystems. However, existing studies report contrasting predictions regarding the impacts of warming on biological rates and community dynamics. To improve prediction accuracy and comparability, we develop an approach that combines sensitivity analysis and aggregate parameters. The former determines which biological parameters impact the community most strongly. The use of aggregate parameters (i.e., maximal energetic efficiency,ρ, and interaction strength,κ), that combine multiple biological parameters, increases explanatory power and reduces the complexity of theoretical analyses. We illustrate the approach using empirically-derived thermal dependence curves of biological rates and applying it to consumer-resource biomass ratio and community stability. Based on our analyses, we generate four predictions: 1) resource growth rate regulates biomass distributions at mild temperatures, 2) interaction strength alone determines the thermal boundaries of the community, 3) warming destabilises dynamics at low and mild temperatures only, 4) interactions strength must decrease faster than maximal energetic efficiency for warming to stabilise dynamics. We argue for the potential benefits of directly working with the aggregate parameters to increase the accuracy of predictions on warming impacts on food webs and promote cross-system comparisons.
DIffusion-Prepared Phase Imaging (DIPPI): quantifying myelin in crossing fibres
Authors: Cottaar Michiel , Wu Wenchuan , Tendler Benjamin , Nagy Zoltan , Miller Karla , Jbabdi Saad 
Published: November 2020

AbstractPurposeMyelin has long been the target of neuroimaging research due to its importance in brain development, plasticity, and disease. However, most available techniques can only provide a voxel-averaged estimate of myelin content. In the human brain, white matter fibre pathways connecting different brain areas and carrying different functions often cross each other in the same voxel. A measure that can differentiate the degree of myelination of crossing fibres would provide a more specific marker of myelination.Theory & MethodsOne MRI signal property sensitive to myelin is the phase accumulation, which to date has also been limited to voxel-averaged myelin estimates. We use this sensitivity by measuring the phase accumulation of the signal remaining after diffusion weighting, which we call DIffusion-Prepared Phase Imaging (DIPPI). Including diffusion weighting before estimating the phase accumulation has two distinct advantages for estimating the degree of myelination: (1) it increases the relative contribution of intra-axonal water, whose phase is related linearly to the amount of myelin surrounding the axon (in particular the logg-ratio) and (2) it gives directional information, which can be used to distinguish between crossing fibres.ResultsUsing simulations and phantom data we argue that other sources of phase accumulation (i.e., movement-induced phase shift during the diffusion gradients, eddy currents, and other sources of susceptibility) can be either corrected for or are sufficiently small to still allow theg-ratio to be reliably estimated.ConclusionsThis new sequence is capable of providing ag-ratio estimate per fibre population crossing within a voxel.
Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy
Authors: Balluet Maël , Sizaire Florian , Habouz Youssef El , Walter Thomas , Pont Jérémy , Giroux Baptiste , Bouchareb Otmane , Tramier Marc , Pecreaux Jacques 
Published: November 2020

AbstractArtificial intelligence is nowadays used for cell detection and classification in optical microscopy, during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart, to make acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced dataset due to cost and time to prepare the samples and have the datasets annotated by experts. We propose here a real-time image processing, compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning for the sake of understanding the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher’s linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without any significant loss in accuracy, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4 % accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into 8 phases of the cell cycle using 12 feature-groups and operating a consumer market ARM-based embedded system. Interestingly, a simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimising these algorithms for smart microscopy.
The impact of exercise intensity on neurophysiological indices of food-related inhibitory control and cognitive control: A randomized crossover event-related potential (ERP) study
Authors: Bailey Bruce W. , Muir Alexandra M. , Bartholomew Ciera L. , Christensen William F. , Carbine Kaylie A. , Marsh Harrison , LaCouture Hunter , McCutcheon Chance , Larson Michael J. 
Published: November 2020

AbstractFood-related inhibitory control, the ability to withhold a dominant response towards highly palatable foods, influences dietary decisions. Food-related inhibitory control abilities may increase following a bout of aerobic exercise; however, the impact of exercise intensity on both food-related inhibitory control and broader cognitive control processes is currently unclear. We used a high-powered, within-subjects, crossover design to test how relative intensity of aerobic exercise influenced behavioral (response time, accuracy) and neural (N2 and P3 components of the scalp-recorded event-related potential [ERP]) measures of food-related inhibitory and cognitive control. Two hundred and thirteen participants completed three separate conditions separated by approximately one week in randomized order: two exercise conditions (35% [moderate] or 70% [vigorous] of VO2max) and seated rest. Directly following exercise or rest, participants completed a food-based go/no-go task and a flanker task while electroencephalogram data were recorded. Linear mixed models showed generally faster response times (RT) and improved accuracy following vigorous exercise compared to rest, but not moderate-intensity exercise; RTs and accuracy did not differ between moderate intensity exercise and rest conditions. N2 and P3 amplitudes were larger following vigorous exercise for the food-based go/no-go task compared to rest and moderate intensity exercise. There were no differences between exercise conditions for N2 amplitude during the flanker task; however, P3 amplitude was more positive following vigorous compared to rest, but not moderate exercise. Gender did not moderate exercise outcomes. Results suggest improved and more efficient food- related recruitment of later inhibitory control and cognitive control processes following vigorous exercise.
There is more than chitin synthase in insect resistance to benzoylureas: Molecular markers associated with teflubenzuron resistance inSpodoptera frugiperda
Authors: Bezerra do Nascimento Antonio Rogério , Corrêa Pavinato Vitor Antonio , Rodrigues Juliana Gonzales , Silva-Brandão Karina Lucas , Consoli Fernando Luis , Michel Andrew , Omoto Celso 
Published: November 2020

AbstractChitin synthesis inhibitors are successfully used in pest control and are an excellent option for use in integrated pest management programs due to their low non-target effects. Reports on field-evolved resistance of lepidopteran pests to chitin synthesis inhibitors and the selection of laboratory resistant strains to these products require a detailed investigation on the resistance mechanisms and on the identification of molecular markers to support the implementation of efficient monitoring and resistance management programs. Teflubenzuron is a chitin synthesis inhibitor highly effective in controlling lepidopteran pests, including nowadays the world widely distributed fall armyworm (FAW),Spodoptera frugiperda(J.E. Smith, 1797) (Lepidoptera: Noctuidae). We report the selection of a laboratory strain ofS. frugiperdaresistant to teflubenzuron, and its use for the characterization of the inheritance of resistance, evaluation of cross-resistance to other chitin-synthesis inhibitors and the identification of a set of single nucleotide polymorphisms (SNPs) for use as candidate molecular markers for monitoring the evolution of resistance ofS. frugiperdato teflubenzuron. The resistance of the selected strain ofS. frugiperdato teflubenzuron was characterized as polygenic, autosomal, and incompletely recessive. The resistance ratio observed was nearly 1,365-fold. Teflubenzuron-resistant strain showed some cross-resistance to lufenuron and novaluron but not to chlorfluazuron. We also detected a set of 72 SNPs that could support monitoring of the resistance frequency to teflubenzuron in field populations. Our data contribute to the understanding of the resistance mechanisms and the inheritance of polygenic resistance ofS. frugiperdato benzoylureas. We also contribute with candidate markers as tools for monitoring the emergence and spread of teflubenzuron resistance inS. frugiperda.
FASTAFS: file system virtualisation of random access compressed FASTA files
Authors: Hoogstrate Youri , Jenster Guido , van de Werken Harmen J. G. 
Published: November 2020

AbstractBackgroundThe FASTA file format used to store polymeric sequence data has become a bioinformatics file standard used for decades. The relatively large files require additional files beyond the scope of the original format, to identify sequences and provide random access. Currently, multiple compressors have been developed to archive FASTA files back and forth, but these lack direct access to targeted content or metadata of the archive. Moreover, these solutions are not directly backwards compatible to FASTA files, resulting in limited software integration.ResultsWe designed linux based a toolkit using Filesystem in Userspace (FUSE) that virtualises the content of DNA, RNA and protein FASTA archives into the filesystem. This guarantees in-sync virtualised metadata files and offers fast random-access decompression using Zstandard (zstd). The toolkit, FASTAFS, can track all system wide running instances, allows file integrity verification and can provide, instantly, scriptable access to sequence files and is easy to use and deploy.ConclusionsFASTAFS is a user-friendly and easy to deploy backwards compatible generic purpose solution to store and access compressed FASTA files, since it offers file system access to FASTA files as well as in-sync metadata files through file virtualisation. Using virtual filesystems as in-between layer offers the possibility to design format conversion without the need to rewrite code into different languages while preserving compatibility.Code Availabilityhttps://github.com/yhoogstrate/fastafs
A conserved superlocus regulates above- and belowground root initiation
Authors: Omary Moutasem , Gil-Yarom Naama , Yahav Chen , Steiner Evyatar , Efroni Idan 
Published: November 2020

AbstractDuring plant post-embryonic growth new meristems and associated stem cells form in different development contexts in order to respond to environmental cues. While underground lateral roots initiate from designated cells in the main root, an unknown mechanism allows cells to bypass the root/shoot identity trajectory and generate shoot-borne-roots. Using single-cell profiling of tomato (Solanum lycoperiscum)stems we isolated a rare transient cell population that serve as progenitors for shoot-borne-root meristems. Analysis of this population identified a transcription factor required for the formation of shoot-borne-roots which we namedSHOOT BORNE ROOTLESS (SBRL). Evolutionary analysis revealed thatSBRLfunction is deeply conserved in angiosperms and that it arose as part of an ancient duplicated superlocus, only lost in root-less plants, containing both shoot-borne and lateral root initiation regulators. We propose that the ability to activate a common transition state with context-specific regulators allows the remarkable developmental plasticity found in plants.One Sentence SummaryHighly conserved superlocus of LBD genes, acting within an early transition identity, regulates shoot-borne and lateral root formation.
The Palmitoyl Acyltransferase ZDHHC14 Controls Kv1-Family Potassium Channel Clustering at the Axon Initial Segment
Authors: Sanders Shaun S. , Hernandez Luiselys M. , Soh Heun , Karnam Santi , Walikonis Randall S. , Tzingounis Anastasios V. , Thomas Gareth M. 
Published: November 2020

AbstractThe palmitoyl acyltransferase (PAT) ZDHHC14 is highly expressed in the hippocampus and is the only PAT predicted to bind Type I PDZ domain-containing proteins. However, ZDHHC14’s neuronal roles are unknown. Here, we identify the PDZ domain-containing Membrane-associated Guanylate Kinase (MaGUK) PSD93 as a direct ZDHHC14 interactor and substrate. PSD93, but not other MaGUKs, localizes to the Axon Initial Segment (AIS). Using lentiviral-mediated shRNA knockdown in rat hippocampal neurons, we find that ZDHHC14 controls palmitoylation and AIS clustering of PSD93 and also of Kv1 potassium channels, which directly bind PSD93. Neurodevelopmental expression of ZDHHC14 mirrors that of PSD93 and Kv1 channels and, consistent with ZDHHC14’s importance for Kv1 channel clustering, loss of ZDHHC14 decreases outward currents and increases action potential firing in hippocampal neurons. To our knowledge, these findings identify the first neuronal roles and substrates for ZDHHC14 and reveal a previously unappreciated role for palmitoylation in control of neuronal excitability.Impact StatementZDHHC14 controls palmitoylation and axon initial segment targeting of PSD93 and Kv1-family potassium channels, events that are essential for normal neuronal excitability.
Longitudinal effects of antibiotics and fecal transplant on lemur gut microbiota structure, associations, and resistomes
Authors: Bornbusch Sally L. , Harris Rachel L. , Grebe Nicholas M. , Roche Kimberly , Dimac-Stohl Kristin , Drea Christine M. 
Published: November 2020

AbstractAntibiotics alter the diversity, structure, and dynamics of host-associated microbial consortia, including via development of antibiotic resistance; however, patterns of recovery from dysbiosis and methods to mitigate negative effects, remain poorly understood. We applied an ecological framework via long-term, integrated study of community structure, across scales, to improve understanding of host-microbe symbiosis during dysbiosis and recovery. We experimentally administered a broad-spectrum antibiotic alone or with subsequent fecal transfaunation to healthy, male ring-tailed lemurs (Lemur catta) and longitudinally tracked the diversity, composition, associations, and resistomes of their gut microbiota. Whereas microbial diversity recovered rapidly in lemurs, antibiotics caused long-term instability in community composition – effects that were attenuated by fecal transfaunation. Antibiotic resistance genes, which were universally present, including in treatment-naïve subjects, increased during and persisted after antibiotic treatment. Long-term, integrated study post antibiotic-induced dysbiosis revealed differential, metric-dependent evidence of recovery, beneficial effects of fecal transfaunation, and negative consequences to lemur resistomes.