bluuebunny/crossref_metadata_2025_split huggingface dataset:
The cost of behavioral flexibility: reversal learning driven by a spiking neural network
Authors: Ghazinouri Behnam , Cheng Sen 
Published: May 2024

AbstractTo survive in a changing world, animals often need to suppress an obsolete behavior and acquire a new one. This process is known as reversal learning (RL). The neural mechanisms underlying RL in spatial navigation have received limited attention and it remains unclear what neural mechanisms maintain behavioral flexibility. We extended an existing closed-loop simulator of spatial navigation and learning, based on spiking neural networks [8]. The activity of place cells and boundary cells were fed as inputs to action selection neurons, which drove the movement of the agent. When the agent reached the goal, behavior was reinforced with spike-timing-dependent plasticity (STDP) coupled with an eligibility trace which marks synaptic connections for future reward-based updates. The modeled RL task had an ABA design, where the goal was switched between two locations A and B every 10 trials. Agents using symmetric STDP excel initially on finding target A, but fail to find target B after the goal switch, persevering on target A. Using asymmetric STDP, using many small place fields, and injecting short noise pulses to action selection neurons were effective in driving spatial exploration in the absence of rewards, which ultimately led to finding target B. However, this flexibility came at the price of slower learning and lower performance. Our work shows three examples of neural mechanisms that achieve flexibility at the behavioral level, each with different characteristic costs.
Stress-induced brain extracellular vesicles ameliorate anxiety behaviour
Authors: Mizohata Yusuke , Yoshioka Yusuke , Koga Minori , Toda Hiroyuki , Ohta Hiroyuki , Kobayashi Yasunobu , Ochiya Takahiro , Morimoto Yuji 
Published: May 2024

AbstractExtracellular vesicles (EVs), nano particles secreted by all types of cells, serve as a communication network, carrying information through the bloodstream to distant cells1,2. Notably, brain cells secrete EVs that play a crucial role in regulating neurological functions3-5. Meanwhile the brain detects acute stress and activates mechanisms to enhance stress resistance and maintain homeostasis6,7. However, the specific contribution of brain-derived extracellular vesicles (BDEVs) in modulating the stress response remains elusive. Here we found that administration of the acute stress-induced BDEVs to mice reduced anxiety-related behaviours, and this reduction was also induced by the administration of only three microRNAs (miRNAs) (miR-199a-3p, miR-99b-3p and miR-140-5p) included in the acute stress-induced BDEVs. Furthermore, we showed that miR-199a-3p contributes to the mechanism of the anxiolytic effect through the suppression ofMecp2in neurons. These findings elucidate the role of BDEVs in modulating mental activity under acute stress and provide insights into the underlying molecular mechanisms. Our results open up new avenues for therapeutic strategies in the treatment of anxiety disorders using EVs or miRNAs.
Effects of Concussion on the Relative Contributions of Sensorimotor Memories during Adaptation to Unpredictable Spring-Like Loads
Authors: Lantagne Devon D , Mrotek Leigh A , Ahamed Sheikh I , Beardsley Scott A , Grove Andrew J , Leigh David H , Smith Carolyn S , Thomas Danny G , Scheidt Robert A 
Published: May 2024

AbstractWe examined the extent to which concussion impacts how implicit sensorimotor memories are used to compensate for changes in hand-held loads during goal-directed reaching. Recently concussed individuals performed computerized cognition tests and a robotic test of sensorimotor adaptation on three occasions: as soon as possible after injury; after clearance to return to activity; three months after injury. Non-concussed individuals (controls) were tested at inter-session intervals mimicking concussed group intervals. During robotic testing, subjects grasped the handle of a horizontal planar robot while reaching repeatedly to a target. The robot exerted spring-like forces that changed unpredictably between trials; this allowed us to estimate contributions of implicit sensorimotor memories to trial-by-trial performance by fitting a computational model to the time series of reach errors and robot forces. Symptom severity varied considerably within the concussed group at the first session. Computerized cognition tests revealed longer reaction times in the concussed group relative to control group in Session 1 only. Concussed subjects likewise had slower reaction time in the reaching task during the first but not later sessions. Computational modeling found abnormally high values of effective limb compliance in concussed individuals relative to the control group in the first session only, but did not find group differences in how sensorimotor memories contribute to reach adaptation in any session. Analysis of control group models identified a practice effect affecting the memory coefficients that may have masked initial effects of concussion on how implicit memories contribute to sensorimotor adaptation. Although a practice effect and a heterogeneous concussed cohort preclude strong conclusions, our findings suggest procedural improvements that may decrease the robotic test’s sensitivity to practice effects and increase its sensitivity to concussion-related changes in how implicit memories contribute to sensorimotor adaptation to unpredictable hand-held loads during reaching.
Impact of microchannel width on axons for brain-on-chip applications
Authors: Vulić Katarina , Amos Giulia , Ruff Tobias , Kasm Revan , Ihle Stephan J. , Küchler Jöel , Vörös János , Weaver Sean 
Published: May 2024

AbstractTechnologies for axon guidance forin vitrodisease models and bottom up investigations are increasingly being used in neuroscience research. One of the most prevalent patterning methods is using polydimethylsiloxane (PDMS) microstructures due to compatibility with microscopy and electrophysiology which enables systematic tracking of axon development with precision and efficiency. Previous investigations of these guidance platforms have noted axons tend to follow edges and avoid sharp turns; however, the specific impact of spatial constraints remains only partially explored. We investigated the influence of microchannel width beyond a constriction point, as well as the number of available microchannels, on axon growth dynamics. Further, by manipulating the size of micron/submicron-sized PDMS tunnels we investigated the space restriction that prevents growth cone penetration showing that restrictions smaller than 350nm were sufficient to exclude axons. This research offers insights into the interplay of spatial constraints, axon development, and neural behavior. The findings are important for designingin vitroplatforms andin vivoneural interfaces for both fundamental neuroscience and translational applications in rapidly evolving neural implant technologies.
Feature selective adaptation of numerosity perception
Authors: Caponi Camilla , Castaldi Elisa , Grasso Paolo Antonino , Arrighi Roberto 
Published: May 2024

ABSTRACTPerceptual adaptation has been used to infer the existence of numerosity detectors, which allow humans to quickly estimate the number of objects in a scene. While adaptation was originally thought to affect numerosity perception regardless of the low-level features of the items, a recent study demonstrated that adaptation is more pronounced when the adapting and adapted (test) stimuli share the same color, compared to when they were colored differently. In this study we explored whether such adaptation reduction depends on a novelty effect induced by changes in stimulus features or whether this effect is observed only when implying an identity change of the stimuli. To this aim, we performed six experiments in which numerosity adaptation was investigated in conditions in which adapting and adapted stimuli were either matched or differed for several low-level (color, luminance, shape, and motion) or high-level (letters’ identity, face emotions) features. Numerosity adaptation was consistently observed across all conditions, but it was reduced when adaptor and test differed in color, luminance and shape. However, when stimuli differed in their motion profile, a very salient perceptual change that does not imply a change in items’ identity, adaptation selectivity vanished. Moreover, adaptation selectivity was not observed when items’ identity was changed by spatial rotations of the same stimulus (letters) or when stimuli were matched for the global configuration (face outline) but differed for the arrangement of local features (mouth, nose, eyes). Interestingly, image dissimilarity between test and adaptor, as quantified by Gabor filters simulating a simplified model of the primary visual cortex, nicely predicted the strength of numerosity adaptation across all conditions. Overall, changes in stimulus identity defined by low-level features, rather than novelty in general, determined the strength of the adaptation effects, provided that the changes were readily noticeable. Our findings suggest that numerosity mechanisms may be able to operate on segregated and categorized visual items in addition to the total quantity of the set, with part of the aftereffects induced by numerosity adaptation occurring after feature-binding.
Ecological drivers of CRISPR immune systems
Authors: Xiao Wei , Weissman JL , Johnson Philip L.F. 
Published: May 2024

1AbstractCRISPR-Cas is the only known adaptive immune system of prokaryotes. It is a powerful defense system against mobile genetic elements such as bacteriophages. While CRISPR-Cas systems can be found throughout the prokaryotic tree of life, they are distributed unevenly across taxa and environments. Since adaptive immunity is more useful in environments where pathogens persist or reoccur, the density and/or diversity of the host/pathogen community may drive the uneven distribution of CRISPR system. We directly tested hypotheses connecting CRISPR incidence with prokaryotic density/diversity by analyzing 16S rRNA and metagenomic data from publicly available environmental sequencing projects. In terms of density, we found that CRISPR systems are significantly favored in lower abundance (less dense) taxa and disfavored in higher abundance taxa, at least in marine environments. When we extended this work to compare taxonomic diversity between samples, we found CRISPR system incidence strongly correlated with diversity in human oral environments. Together, these observations confirm that, at least in certain types of environments, the prokaryotic ecological context indeed plays a key role in selecting for CRISPR immunity.2ImportanceMicrobes must constantly defend themselves against viral pathogens, and a large proportion of prokaryotes do so using the highly effective CRISPR-Cas adaptive immune system. However, many prokaryotes do not. We investigated the ecological factors behind this uneven distribution of CRISPR-Cas immune systems in natural microbial populations. We found strong patterns linking CRISPR-Cas systems to prokaryotic density within ocean environments and to prokaryotic diversity within human oral environments. Our study validates previous within-lab experimental results that suggested these factors might be important and confirms that local environment and ecological context interact to select for CRISPR immunity.
Alternative splicing across theC. elegansnervous system
Authors: Weinreb Alexis , Varol Erdem , Barrett Alec , McWhirter Rebecca M. , Taylor Seth R. , Courtney Isabel , Basavaraju Manasa , Poff Abigail , Tipps John A. , Collings Becca ,   The CeNGEN Consortium, Krishnaswamy Smita , Miller David M. , Hammarlund Marc 
Published: May 2024

AbstractAlternative splicing is a key mechanism that shapes neuronal transcriptomes, helping to define neuronal identity and modulate function. Here, we present an atlas of alternative splicing across the nervous system ofCaenorhabditis elegans. Our analysis identifies novel alternative splicing in key neuronal genes such asunc-40/DCC andsax-3/ROBO. Globally, we delineate patterns of differential alternative splicing in almost 2,000 genes, and estimate that a quarter of neuronal genes undergo differential splicing. We introduce a web interface for examination of splicing patterns across neuron types. We explore the relationship between neuron type and splicing patterns, and between splicing patterns and differential gene expression. We identify RNA features that correlate with differential alternative splicing, and describe the enrichment of microexons. Finally, we compute a splicing regulatory network that can be used to generate hypotheses on the regulation and targets of alternative splicing in neurons.
Flawed machine-learning confounds coding sequence annotation
Authors: Champion DJ , Chen Ting-Hsuan , Thomson Susan , Black Michael A. , Gardner Paul P. 
Published: May 2024

AbstractBackgroundDetecting protein coding genes in genomic sequences is a significant challenge for understanding genome functionality, yet the reliability of bioinformatic tools for this task remains largely unverified. This is despite some of these tools having been available for several decades, and being widely used for genome and transcriptome annotation.ResultsWe perform an assessment of nucleotide sequence and alignment-basedde novoprotein-coding detection tools. The controls we use exclude any previous training dataset and include coding exons as a positive set and length-matched intergenic and shuffled sequences as negative sets.Our work demonstrates that several widely used tools are neither accurate nor computationally efficient for the protein-coding sequence detection problem. In fact, just three of nine tools significantly outperformed a naive scoring scheme. Furthermore, we note a high discrepancy between self-reported accuracies and the accuracy achieved in our study. Our results show that the extra dimension from conserved and variable nucleotides in alignments have a significant advantage over single sequence approaches.ConclusionsThese results highlight significant limitations in existing protein-coding annotation tools that are widely used for lncRNA annotation. This shows a need for more robust and efficient approaches to training and assessing the performance of tools for identifying protein-coding sequences. Our study paves the way for future advancements in comparative genomic approaches and we hope will popularise more robust approaches to genome and transcriptome annotation.
Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device
Authors: Jung Taesung , Zeng Nanyu , Fabbri Jason D. , Eichler Guy , Li Zhe , Willeke Konstantin , Wingel Katie E. , Dubey Agrita , Huq Rizwan , Sharma Mohit , Hu Yaoxing , Ramakrishnan Girish , Tien Kevin , Mantovani Paolo , Parihar Abhinav , Yin Heyu , Oswalt Denise , Misdorp Alexander , Uguz Ilke , Shinn Tori , Rodriguez Gabrielle J. , Nealley Cate , Gonzales Ian , Roukes Michael , Knecht Jeffrey , Yoshor Daniel , Canoll Peter , Spinazzi Eleonora , Carloni Luca P. , Pesaran Bijan , Patel Saumil , Youngerman Brett , Cotton R. James , Tolias Andreas , Shepard Kenneth L. 
Published: May 2024

AbstractMinimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-μm-thick, mechanically flexible micro-electrocorticography (μECoG) BCI, integrating 256×256 electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording and 16,384 stimulation channels, from which we can simultaneously record up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.
Deep-tissue spatial omics: imaging whole-embryo transcriptomics and subcellular structures at high spatial resolution
Authors: Gandin Valentina , Kim Jun , Yang Liang-Zhong , Lian Yumin , Kawase Takashi , Hu Amy , Rokicki Konrad , Fleishman Greg , Tillberg Paul , Castrejon Alejandro Aguilera , Stringer Carsen , Preibisch Stephan , Liu Zhe J. 
Published: May 2024

SummaryThe inherent limitations of fluorescence microscopy, notably the restricted number of color channels, have long constrained comprehensive spatial analysis in biological specimens. Here, we introduce cycleHCR technology that leverages multicycle DNA barcoding and Hybridization Chain Reaction (HCR) to surpass the conventional color barrier. cycleHCR facilitates high-specificity, single-shot imaging per target for RNA and protein species within thick specimens, mitigating the molecular crowding issues encountered with other imaging-based spatial omics techniques. We demonstrate whole-mount transcriptomics imaging of 254 genes within an E6.5∼7.0 mouse embryo, achieving precise three-dimensional gene expression and cell fate mapping across a specimen depth of ∼ 310 µm. Utilizing expansion microscopy alongside protein cycleHCR, we unveil the complex network of 10 subcellular structures in primary mouse embryonic fibroblasts. Furthermore, in mouse hippocampal slice, we image 8 protein targets and profile the transcriptome of 120 genes, uncovering complex gene expression gradients and cell-type specific nuclear structural variances. cycleHCR provides a unifying framework for multiplex RNA and protein imaging, offering a quantitative solution for elucidating spatial regulations in deep tissue contexts for research and potentially diagnostic applications.
Global meta-analysis reveals urban-associated behavioral differences among wild populations
Authors: Burkhard Tracy T. , Dochtermann Ned A. , Charmantier Anne 
Published: May 2024

AbstractUrbanization is causing fundamental changes to natural environments, effecting rapid and substantial adaptive phenotypic change in wild populations. While a large body of work has investigated how urbanization may shapeinterspecificvariation in behavioral traits, such as via urban environmental filtering, no study has yet quantitatively assessed global patterns of urban-associatedintraspecificbehavioral variation. Here, we conducted a phylogenetic meta-analysis to assess urban-associated behavioral differences in wild populations of birds, mammals, amphibians, reptiles, and insects. We focused on four commonly measured behaviors (boldness, aggression, activity, and exploration) and extracted paired urban-nonurban effect size estimates for phenotypic means and variances (k= 278), behavioral repeatability (k= 13), and behavioral correlations (k= 14) from 80 studies. We found clear evidence that urban populations exhibit heightened average boldness, aggression, exploration, and activity compared to nonurban conspecifics, a result that was robust among species, geographic region, and ecological niche. Further, our results suggested that “generalist” species have the strongest behavioral responses. Conversely, we did not find strong evidence linking urbanization to changes in phenotypic variation, behavioral repeatability, or behavioral correlations. Our results summarize data from a rapidly evolving field in urban ecology and demonstrate geographically and taxonomically widespread differences in behavior between urban and nonurban populations, suggesting parallel natural selection across urban species and populations.
Employee learning and skilling in AI embedded organizations – predictors and outcomes
Authors: Kumar Shubham , Mittal Shashank 
Published: May 2024

Purpose
In this research article we aim to answer two research questions: - With ever increasing AI based integrations in organizations, it is imperative to understand how employees learn and adapt to these newer technologies? What is the impact of this learning and adaptation on AI-based employee productivity?

Design/methodology/approach
A survey-based research design has been conducted among the employees working in technology companies. Questionnaires were administered by the researchers physically using snowball based purposive sampling to reach the participants who are working in the AI driven technology companies. The data was analyzed using Structural Equation Modelling and numerous studies have previously used this approach for model fit and testing.

Findings
it was found that employee learning has significant positive mediational impact on the relationship between AI trust, knowledge sharing and AI skills and AI-employee collaboration. SEM results indicate that data fits the proposed model well.

Practical implications
findings indicate that as the trust in AI develops, employees are more interested in learning and increasing their understanding of AI driven work and increase their AI skills such as Machine learning and data analytics. To increase AI-employee collaborations and upskilling: organizations will need to foster AI training opportunities; make employees work on projects having AI based sociotechnical systems; create environment of knowledge sharing among employees on AI themes.

Originality/value
The use of Artificial Intelligence (AI) is ubiquitous, and more and more organizations are adopting AI in their operations which lead to massive scale changes in the functioning of organizations. These large-scale massive transformations are posing challenges and opportunities at the same time. At the same time, it created immense challenges for employees’ adjustment to the new learning and skilling requirements posed by AI (Sengupta et al., 2022). It is therefore expected that employees embrace the associated new technologies of AI such as big data analytics, Machine learning and Robotics.
Does employee engagement mediate the nexus of job resource and employee turnover intentions?
Authors: Otoo Frank Nana Kweku 
Published: May 2024

PurposeEngaged employees assure organizational competitiveness and sustainability. The purpose of this study is to explore the relationship between job resources and employee turnover intentions, with employee engagement as a mediating variable.Design/methodology/approachData were collected from 934 employees of eight wholly-owned pharmaceutical industries. The proposed model and hypotheses were evaluated using structural equation modeling. Construct reliability and validity was established through confirmatory factor analysis.FindingsData supported the hypothesized relationship. The results show that job autonomy and employee engagement were significantly associated. Supervisory support and employee engagement were significantly associated. However, performance feedback and employee engagement were nonsignificantly associated. Employee engagement had a significant influence on employee turnover intentions. The results further show that employee engagement mediates the association between job resources and employee turnover intentions.Research limitations/implicationsThe generalizability of the findings will be constrained due to the research’s pharmaceutical industry focus and cross-sectional data.Practical implicationsThe study’s findings will serve as valuable pointers for stakeholders and decision-makers in the pharmacuetical industry to develop a proactive and well-articulated employee engagement intervention to ensure organizational effectiveness, innovativeness and competitiveness.Originality/valueBy empirically demonstrating that employee engagement mediates the nexus of job resources and employee turnover intentions, the study adds to the corpus of literature.