bluuebunny/crossref_metadata_2025_split huggingface dataset:
Proteomic analysis of gut inLabeo rohitareveals ECM as Key Player in host’s Response toAeromonas hydrophilaInfection
Authors: Nissa Mehar Un , Pinto Nevil , Ghosh Biplab , Banerjee Anwesha , Singh Urvi , Goswami Mukunda , Srivastava Sanjeeva 
Published: February 2024

ABSTRACTIn the aquaculture sector, one of the challenges include disease outbreaks such as bacterial infections, particularly fromAeromonas hydrophila(Ah), impacting both wild and farmed fish. In this study, we conducted a proteomic analysis of the gut tissue inLabeo rohitafollowingAhinfection to elucidate the protein alterations and its implications for immune response. Our findings reveal significant dysregulation in extracellular matrix (ECM) associated proteins duringAhinfection, with increased abundance of elastin and Collagen alpha-3(VI) contributing to matrix rigidity. Pathway and enrichment analysis of differentially expressed proteins (DEPs) highlights the involvement of ECM-related pathways, including Focal adhesions, Integrin cell surface interactions, and actin cytoskeleton organization.Focal adhesions, crucial for connecting intracellular actin bundles to the ECM, play a pivotal role in immune response during infections. Increased abundance of integrin alpha 1, integrin beta 1, and Tetraspanin suggests their involvement in the host’s response toAhinfection. Proteins associated with actin cytoskeleton reorganization, such as myosin, tropomyosin, and phosphoglucomutase, exhibit increased abundance, influencing changes in cell behavior. Additionally, upregulated proteins like LTBP1 and Fibrillin-2 contribute to TGF-β signaling and focal adhesion, indicating their role in immune regulation.The study also identifies elevated levels of laminin, galectin 3, and tenascin-C, which interact with integrins and other ECM components, influencing immune cell migration and function. These proteins, along with decorin and lumican, act as immunomodulators, coordinating pro- and anti-inflammatory responses. ECM fragments released during pathogen invasion serve as “danger signals,” initiating pathogen clearance and tissue repair through Toll-like receptor signaling.IMPORTANCEThe study underscores the critical role of the extracellular matrix (ECM) and its associated proteins in the immune response of aquatic organisms during bacterial infections likeAeromonas hydrophila(Ah). Understanding the intricate interplay between ECM alterations and immune response pathways provides crucial insights for developing effective disease control strategies in aquaculture. By identifying key proteins and pathways involved in host defense mechanisms, this research lays the groundwork for targeted interventions to mitigate the impact of bacterial infections on fish health and aquaculture production.
Machine Learning Approaches in Label-Free Small Extracellular Vesicles Analysis with Surface-Enhanced Raman Scattering (SERS) for Cancer Diagnostics
Authors: Vang Der , Kelly Maria S. , Sheokand Manisha , Sharma Manju , Esfandiari Leyla , Dima Ruxandra I. , Strobbia Pietro 
Published: February 2024

AbstractEarly diagnosis remains of pivotal importance in reducing patient morbidity and mortality in cancer. To this end, liquid biopsy is emerging as a tool to perform broad cancer screenings. Small extracellular vesicles (sEVs), also called exosomes, found in bodily fluids can serve as important cancer biomarkers in these screenings. Our group has recently developed a label-free electrokinetic microchip to purify sEVs from blood. Herein, we demonstrate the feasibility to integrate this approach with surface-enhanced Raman scattering (SERS) analysis. SERS can be used to characterized extracted sEVs through their vibrational fingerprint that changes depending on the origin of sEVs. While these changes are not easily identified in spectra, they can be modeled with machine learning (ML) approaches. Common ML approaches in the field of spectral analysis use dimensionality reduction method that often function as a black box. To avoid this pitfall, we used Shapley additive explanations (SHAP) is a type of explainable AI (XAI) that bridges ML models and human comprehension by calculating the specific contribution of individual features to a model’s predictions, directly correlating model/decisions with the original data. Using these approaches we demonstrated a proof-of-concept model predictive of cancer from isolated sEVs, integrating the electrokinetic device and SERS. This work explores the use of explainable AI to perform diagnostic analysis on complex SERS data of clinical samples, while reporting interpretable biochemical information.
Molecular Architecture of Chitin and Chitosan-Dominated Cell Walls in Zygomycetous Fungal Pathogens by Solid-State NMR
Authors: Cheng Qinghui , Dickwella Widanage Malitha C. , Yarava Jayasubba Reddy , Latgé Jean-Paul , Wang Ping , Wang Tuo 
Published: February 2024

ABSTRACTZygomycetous fungal infections pose an emerging medical threat among individuals with compromised immunity and metabolic abnormalities. Our pathophysiological understanding of these infections, particularly the role of fungal cell walls in growth and immune response, remains limited. Here we conducted multidimensional solid-state NMR analysis to examine cell walls in five Mucorales species, including key mucormycosis causative agents likeRhizopusandMucorspecies. We show that the rigid core of the cell wall primarily comprises highly polymorphic chitin and chitosan, with minimal quantities of β-glucans linked to a specific chitin subtype. Chitosan emerges as a pivotal molecule preserving hydration and dynamics. Some proteins are entrapped within this semi-crystalline chitin/chitosan layer, stabilized by the sidechains of hydrophobic amino acid residues, and situated distantly from β-glucans. The mobile domain contains galactan- and mannan-based polysaccharides, along with polymeric α-fucoses. Treatment with the chitin synthase inhibitor nikkomycin removes the β-glucan-chitin/chitosan complex, leaving the other chitin and chitosan allomorphs untouched while simultaneously thickening and rigidifying the cell wall. These findings shed light on the organization of Mucorales cell walls and emphasize the necessity for a deeper understanding of the diverse families of chitin synthases and deacetylases as potential targets for novel antifungal therapies.
Providing insight into the mechanism of action of Cationic Lipidated Oligomers (CLOs) using metabolomics
Authors: Hussein Maytham , Mahboob Muhammad Bilal Hassan , Tait Jessica R. , Grace James L. , Montembault Véronique , Fontaine Laurent , Quinn John F. , Velkov Tony , Whittaker Michael R. , Landersdorfer Cornelia B. 
Published: February 2024

AbstractThe increasing resistance of clinically relevant microbes against current commercially available antimicrobials underpins the urgent need for alternative and novel treatment strategies. Cationic lipidated oligomers (CLOs) are innovative alternatives to antimicrobial peptides, and have reported antimicrobial potential. An understanding of their antimicrobial mechanism of action is required to rationally design future treatment strategies for CLOs, either in monotherapy or synergistic combinations. In the present study, metabolomics was used to investigate the potential metabolic pathways involved in the mechanisms of antibacterial activity of one CLO, C12-o-(BG-D)-10, which we have previously shown to be effective against methicillin-resistantStaphylococcus aureus(MRSA) ATCC 43300. The metabolomes of MRSA ATCC 43300 at 1, 3 and 6 h following treatment with C12-o-(BG-D)-10 (48 µg/mL i.e., 3x MIC) were compared to those of the untreated controls.Our findings reveal that the studied CLO, C12-o-(BG-D)-10, disorganized the bacterial membrane as the first step towards its antimicrobial effect, as evidenced by marked perturbations in the bacterial membrane lipids and peptidoglycan biosynthesis observed at early time points i.e., 1, and 3 h. Central carbon metabolism, and biosynthesis of DNA, RNA, and arginine were also vigorously perturbed, mainly at early time points. Moreover, bacterial cells were under osmotic and oxidative stress across all time points, evident by perturbations of trehalose biosynthesis and pentose phosphate shunt. Overall, this metabolomics study has, for the first time, revealed that the antimicrobial action of C12-o-(BG-D)-10 may potentially stem from the dysregulation of multiple metabolic pathways.ImportanceAntimicrobial resistance poses a significant challenge to healthcare systems worldwide. Novel anti-infective therapeutics are urgently needed to combat drug-resistant microorganisms. Cationic lipidated oligomers (CLOs) show promise as new antibacterial agents against Gram-positive pathogens likeStaphylococcus aureus(MRSA). Understanding their molecular mechanism(s) of antimicrobial action may help design synergistic CLO treatments along with monotherapy. Here, we describe the first metabolomics study to investigate the killing mechanism(s) of CLOs against MRSA. The results of our study indicate that the CLO, C12-o-(BG-D)-10, had a notable impact on the biosynthesis and organization of the bacterial cell envelope. C12-o-(BG-D)-10 also inhibits arginine, histidine, central carbon metabolism, and trehalose production, adding to its antibacterial characteristics. This work illuminates the unique mechanism of action of C12-o-(BG-D)-10 and opens an avenue to design innovative antibacterial oligomers/polymers for future clinical applications.
Habit learning shapes activity dynamics in the central nucleus of the amygdala
Authors: Amaya Kenneth A. , Carmichael J. Eric , Townsend Erica S. , Palmer Jensen A. , Stott Jeffrey J. , Smith Kyle S. 
Published: February 2024

AbstractAs animals perform instrumental tasks, they may develop a habit response with extended experience. Habits are automatic, inflexible, outcome value insensitive behaviors that are regulated by a network of brain regions including the central nucleus of the amygdala (CeA). Prior work has demonstrated that the CeA governs motivational pursuit and is necessary for habit formation. However, the behavioral features that CeA neurons encode in habit formation remain relatively unknown. To address this, we first used male and female Long-Evans rats to quantify CeA cFos expression after performance of a maze task. There, we found that animals with extended training show elevated cFos expression. Then, we implanted animals with drivable silicon probes to recordin-vivosingle unit electrophysiological activity from the CeA as animals developed habit responding on the maze. We observed significant activity during outcome consumption late in training while also observing elevated unit activity when animals consumed outcomes of larger magnitudes. Outcome related activity did not persist during probe tests following outcome devaluation, despite animals continuing to perform the task. Together, these data add to growing evidence that suggests that the CeA is involved in motivational processes that contribute to the development of habit formation.
Recurrent neural networks as neuro-computational models of human speech recognition
Authors: Brodbeck Christian , Hannagan Thomas , Magnuson James S. 
Published: February 2024

AbstractHuman speech recognition transforms a continuous acoustic signal into categorical linguistic units, by aggregating information that is distributed in time. It has been suggested that this kind of information processing may be understood through the computations of a Recurrent Neural Network (RNN) that receives input frame by frame, linearly in time, but builds an incremental representation of this input through a continually evolving internal state. While RNNs can simulate several keybehavioralobservations about human speech and language processing, it is unknown whether RNNs also develop computational dynamics that resemble humanneural speech processing. Here we show that the internal dynamics of long short-term memory (LSTM) RNNs, trained to recognize speech from auditory spectrograms, predict human neural population responses to the same stimuli, beyond predictions from auditory features. Variations in the RNN architecture motivated by cognitive principles further improve this predictive power. Moreover, different components of hierarchical RNNs predict separable components of brain responses to speech in an anatomically structured manner, suggesting that RNNs reproduce a hierarchy of speech recognition in the brain. Our results suggest that RNNs provide plausible computational models of the cortical processes supporting human speech recognition.
enmpa: An R package for ecological niche modeling using presence-absence data and generalized linear models
Authors: Arias-Giraldo Luis F. , Cobos Marlon E. 
Published: February 2024

AbstractHere, we present the new R package “enmpa,” which includes a range of tools for modeling ecological niches using presence-absence data via logistic generalized linear models. The package allows users to calibrate, select, project, and evaluate models using independent data. We have emphasized a comprehensive search for ideal predictor combinations, including linear, quadratic, and two-way interaction responses, to provide more detailed and robust model calibration processes. We demonstrate the use of the package with an example of a simulated pathogen and its niche. Since enmpa is designed specifically to work with presence-absence data, our tools are particularly useful for studies with data derived from a detection or non-detection sampling universe, such as pathogen testing results. enmpa can be downloaded from CRAN, and the source code is freely available on GitHub.
Estradiol elicits distinct firing patterns in arcuate nucleus kisspeptin neurons of females through altering ion channel conductances
Authors: Qiu Jian , Voliotis Margaritis , Bosch Martha A. , Li Xiao Feng , Zweifel Larry S. , Tsaneva-Atanasova Krasimira , O’Byrne Kevin T. , Rønnekleiv Oline K. , Kelly Martin J. 
Published: February 2024

AbstractHypothalamic kisspeptin (Kiss1) neurons are vital for pubertal development and reproduction. Arcuate nucleus Kiss1 (Kiss1ARH) neurons are responsible for the pulsatile release of Gonadotropin-releasing Hormone (GnRH). In females, the behavior of Kiss1ARHneurons, expressing Kiss1, Neurokinin B (NKB), and Dynorphin (Dyn), varies throughout the ovarian cycle. Studies indicate that 17β-estradiol (E2) reduces peptide expression but increasesVglut2mRNA and glutamate neurotransmission in these neurons, suggesting a shift from peptidergic to glutamatergic signaling. To investigate this shift, we combined transcriptomics, electrophysiology, and mathematical modeling. Our results demonstrate that E2 treatment upregulates the mRNA expression of voltage-activated calcium channels, elevating the whole-cell calcium current and that contribute to high-frequency burst firing. Additionally, E2 treatment decreased the mRNA levels of Canonical Transient Receptor Potential (TPRC) 5 and G protein-coupled K+(GIRK) channels. When TRPC5 channels in Kiss1ARHneurons were deleted using CRISPR, the slow excitatory postsynaptic potential (sEPSP) was eliminated. Our data enabled us to formulate a biophysically realistic mathematical model of the Kiss1ARHneuron, suggesting that E2 modifies ionic conductances in Kiss1ARHneurons, enabling the transition from high frequency synchronous firing through NKB-driven activation of TRPC5 channels to a short bursting mode facilitating glutamate release. In a low E2 milieu, synchronous firing of Kiss1ARHneurons drives pulsatile release of GnRH, while the transition to burst firing with high, preovulatory levels of E2 would facilitate the GnRH surge through its glutamatergic synaptic connection to preoptic Kiss1 neurons.
Multi-modal Phantom Experiments, mimicking Flow through the Mitral Heart Valve
Authors: Christierson Lea , Frieberg Petter , Lala Tania , Töger Johannes , Liuba Petru , Revstedt Johan , Isaksson Hanna , Hakacova Nina 
Published: February 2024

AbstractPurposeFluid-structure interaction (FSI) models are more commonly applied in medical research as computational power is increasing. However, understanding the accuracy of FSI models is crucial, especially in the context of heart valve disease in patient-specific models. Therefore, this study aimed to create a multi-modal benchmarking data set for FSI models, based on clinically important parameters, such as the pressure, velocity, and valve opening, with anin vitrophantom setup.MethodAnin vitrosetup was developed with a 3D-printed phantom mimicking the left heart, including a deforming mitral valve. A range of pulsatile flows was created with a computer-controlled motor-and-pump setup. Invasive catheter measurements, magnetic resonance imaging (MRI), and echocardiography (Echo) imaging were used to measure pressure and velocity in the domain. Furthermore, the valve opening was quantified based on cine MRI and Echo images.ResultThe experimental setup, with 0.5 % cycle-to-cycle variation, was successfully built and six different flow cases were investigated. Higher velocity through the mitral valve was observed for increased cardiac output. The pressure difference across the valve also followed this trend. The flow in the phantom was qualitatively assessed by the velocity profile in the ventricle and by streamlines obtained from 4D phase-contrast MRI.ConclusionA multi-modal set of validation data for FSI models has been created, based on parameters relevant for diagnosis of heart valve disease. All data is publicly available for future development of computational heart valve models.
London taxi drivers exploit neighbourhood boundaries for hierarchical route planning
Authors: Griesbauer Eva-Maria , Fernandez Velasco Pablo , Coutrot Antoine , Wiener Jan M. , Morley Jeremy G. , McNamee Daniel , Manley Ed , Spiers Hugo J. 
Published: February 2024

AbstractHumans show an impressive ability to plan over complex situations and environments. A classic approach to explaining such planning has been tree-search algorithms which search through alternative state sequences for the most efficient path through states. However, this approach fails when the number of states is large due to the time to compute all possible sequences. Hierarchical route planning has been proposed as an alternative, offering a computationally efficient mechanism in which the representation of the environment is segregated into clusters. Current evidence for hierarchical planning comes from experimentally created environments which have clearly defined boundaries and far fewer states than the real-world. To test for real-world hierarchical planning we exploited the capacity of London licensed taxi drivers to use their memory to construct a street by street plan across London, UK (>26,000 streets). The time to recall each successive street name was treated as the response time, with a rapid average of 1.8 seconds between each street. In support of hierarchical planning we find that the clustered structure of London’s regions impacts the response times, with minimal impact of the distance across the street network (as would be predicted by tree-search). We also find that changing direction during the plan (e.g. turning left or right) is associated with delayed response times. Thus, our results provide real-world evidence for how humans structure planning over a very large number of states, and give a measure of human expertise in planning.
Gut feeling: host and habitat as drivers of the microbiome in blackbuck (Antilope cervicapra)
Authors: Jana Ananya , Roy Shamik , Bagchi Sumanta , Isvaran Kavita , Karanth K Praveen 
Published: February 2024

AbstractThe gut microbiome can be shaped by both intrinsic host factors and extrinsic environmental factors. However, the relative importance of intrinsic and extrinsic factors in gut microbial composition has rarely been investigated, particularly for a single host across its natural range. Here, we characterise the gut microbiome of an endemic, endangered antelope, the blackbuck orAntilope cervicapra. We evaluated the influence of seven predictor variables, which were classified into intrinsic and extrinsic factors, on the gut microbiome. We determined which of these seven variables explains greater variation in the microbiome within (α-diversity) and between (β-diversity) the blackbuck host. We analysed the microbiome of 60 blackbuck hosts from ten different populations across India. We recorded 11800 unique OTUs across 30 known phyla and 2.9 million reads. We find an average of 2056 OTUs per individual, with firmicutes and bacteroidetes being the most dominant phyla. We find that nucleotide diversity (intrinsic), blackbuck population density (intrinsic), distance to human settlement (extrinsic), and anthropogenic land-use (extrinsic) explain within-host variation. In contrast, precipitation (extrinsic), nucleotide diversity, distance to human settlement, and anthropogenic land-use explain between-host variation. Overall, we also show that the genetic diversity of the host is more important than their environment for both within- and between-host variation in the microbiome, in blackbuck. Therefore, conservation efforts should be directed to not only preserve natural habitats but also increase the genetic pool of the blackbuck populations, which will positively impact their survival through diverse gut microbiomes.
High or Low Expectations: Expected intensity of action outcome is embedded in action kinetics
Authors: Buaron Batel , Reznik Daniel , Mukamel Roy 
Published: February 2024

AbstractGoal-directed actions are performed in order to attain certain sensory consequences in the world. However, expected attributes of these consequences can affect the kinetics of the action. In a set of three studies (n=120), we examined how expected attributes of stimulus outcome (intensity) shape the kinetics of the triggering action (applied force), even when the action and attribute are independent. We show that during action execution (button presses), the expected intensity of sensory outcome implicitly affects the applied force of the stimulus-producing action in an inverse fashion. Thus, participants applied more force when the expected intensity of the outcome was low (vs. high intensity outcome). In the absence of expectations or when actions were performed in response to the sensory event, no intensity-dependent force modulations were found. Thus, causality and expectations of stimulus intensity play an important role in shaping action kinetics. Finally, we examined the relationship between kinetics and perception and found no influence of applied force level on perceptual detection of low intensity (near-threshold) outcome stimuli, suggesting no causal link between the two. Taken together, our results demonstrate that action kinetics are implicitly embedded with high-level context such as the expectation of consequence intensity and the causal relationship with environmental cues.