bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-Coloured Sequential Resonance Energy Transfer for Simultaneous Ligand Binding at G Protein-Coupled Receptors
Authors: Valentini Alice , Dibnah Bethany , Ciba Marija , Ulven Trond , Hudson Brian D. , Rexen Ulven Elisabeth 
Published: August 2024

AbstractG protein coupled receptors (GPCRs) are the largest family of signalling proteins and highly successful drug targets. To date, most GPCR drugs interact with the binding pocket for the natural ligand, typically near the extracellular part of the transmembrane region. Recent advancements in structural biology have identified additional allosteric binding sites in other parts of these receptors. Allosteric sites provide several theoretical advantages, including the ability to modulate natural ligand function, and there is a need for better ways to study how ligands bind and interact with diVerent GPCR binding sites. We have developed an approach to study multiple ligands binding to the same receptor based on sequential resonance energy transfer between two fluorescent ligands bound simultaneously to a GPCR. We use this approach to gain insight into allosteric ligand interactions for a clinically relevant receptor. This method will provide important information to aid development of new GPCR drugs.
Neuropeptide oxytocin facilitates its own brain-to-periphery uptake by regulating blood flow dynamics and permeability
Authors: Rajamannar Preethi , Raz Oren , Levkowitz Gil 
Published: August 2024

AbstractThe hypothalamo-neurohypophyseal system is an important neuroendocrine brain-to-blood conduit through which the neurohormones oxytocin and arginine-vasopressin are released from the brain into the general circulation to affect peripheral physiological functions such as salt balance, metabolism and reproduction. However, the mechanism which executes fast and efficient neurohormone release to the periphery remains unsolved. We show, using live imaging in zebrafish, that a hyperosmotic physiological challenge elicits a local increase in neurohypophyseal blood flow velocities and a change in capillary diameter, which is dictated by the geometry of the hypophyseal vascular microcircuit. Genetic ablation of oxytocin neurons and inhibition of oxytocin receptor signaling attenuated changes in capillary blood flow and diameter. Optogenetic stimulation of oxytocin neurons resulted in an oxytocin receptor-dependent increase in blood flow velocities. Lastly, both osmotic challenge and oxytocin neuronal activation elicited a local rise in neurohypophyseal capillary permeability in an oxytocin signaling-dependent manner. Our study demonstrates that physiologically elicited changes in neurohypophyseal blood flow and permeability are regulated by oxytocin. We propose that oxytocin-dependent neuro-vascular coupling facilitates its efficient uptake into the blood circulation, suggesting a self-perpetuating mechanism of peripheral hormone transfer.
Releasing theoctoPus,an open-source digital tool to promote Integrated Pest Management
Authors: Bregaglio Simone , Rossi Eugenio , Ascari Lorenzo , Mongiano Gabriele , Cavallo Eleonora Del , Bajocco Sofia , Manici Luisa Maria , Pepe Antonio Gerardo , Bassi Chiara , Tiso Rocchina , Pietrangeli Fabio , Cattaneo Giovanna , Nigro Camilla , Gerardi Marco Secondo , Bussotti Simone , Sanchioni Angela , Tognetti Danilo , Sandra Mariangela , Lillo Irene De , Framarin Paolo , Ferdinando Sandra Di , Bugiani Riccardo 
Published: August 2024

AbstractMeeting the EU targets to halve chemical pesticide use by 2030 necessitates European farmers to adopt Integrated Pest Management principles as the standard. Decision support systems are valuable tools to meet this target and rely on individual disease models to identify conducive conditions to fungal infections. These models are often proprietary assets of digital startups and agrochemical companies, leading to a lack of transparency for farmers and a bias towards chemical solutions over sustainable practices. We presentoctoPus, the first free digital tool designed to support the control of primary infections of grapevine downy mildew, and we evaluate its performance and behavior on a wide set of environmental conditions and agricultural contexts. We implemented eight models from scientific articles (Rule310, Laore, EPI, IPI, DMcast, UCSC, Misfits, Magarey, the “tentacles”), and evaluated them across Italian grapevine areas from 2001 to 2020. Model outputs were integrated with phenology and susceptibility models (the “eyes”), which were calibrated using data from regional extension services’ bulletins. The simulated infections serve as predictors in a Random Forest algorithm (“brain”) that elaborates an overall risk level (very low to very high). The Llama large language model is used to generate user-supportive messages (the “mouth”).octoPusis released as an open-source software, which reads weather data, executes the models, and presents outputs in natural language and symbolic syntax. Our results showed reasonable accuracy in simulating grapevine phenology (RMSE = 9-10 days) and seasonal risk (RMSE ≈ 0.75). The infection models consistently identified a moisture and thermal north-south suitability gradient in Italy and accurately detected years with low or high downy mildew pressure. However, the models displayed significant differences in the number and dynamics of simulated infections, with two distinct patterns within the ensemble.By developing and releasing the first free and open-source tool to support the control of grapevine downy mildew, we address a critical gap in the availability and transparency of decision support systems for European farmers. Unlike proprietary models that often lack transparency and may favor agribusiness’ logic,octoPusprovides a comprehensive and accessible alternative that promotes Integrated Pest Management practices. We propose the adoption ofoctoPusby plant health authorities to identify areas for performance refinement and capabilities expansion.
CD131 Contributes to Ulcerative Colitis Pathogenesis by Promoting Macrophage Infiltration
Authors: Wu Zhiyuan , Liu Lindi , He Chenchen , Xiao Lin , Yun Duo , Chen Junliang , Liu Zhihao , Li Wenjun , Lv Qingjie , Tan Xiaodong 
Published: August 2024

AbstractBackgroundUlcerative colitis (UC) is a group of chronic inflammatory bowel disease (IBD) mainly affecting the colon. The exact etiology of ulcerative colitis remains elusive. CD131 is a receptor subunit mediating the effects of hematopoietic growth factors GM-CSF and IL-3, which regulate various inflammatory responses. The pleiotropic effects of the cytokines on intestinal inflammation suggest that additional factors influence their overall function, where the receptor may play a role.MethodsIn the present study, we investigated the role of CD131 in the pathogenesis of ulcerative colitis, with the use of murine colitis model established by administration of DSS in the drinking water.ResultsBy comparing the immune and inflammatory responses between wt and CD131-deficient mice, we found that CD131 contributed to DSS-induced murine colitis, which functioned in synergy with tissue- infiltrating macrophages. Besides, CD131 may have promoted the chemotaxis of macrophages and T cells into the colon through CCL4. In addition, we analyzed clinical data and pathology specimens from ulcerative colitis patients and found that CD131 was associated with the endoscopic and pathological severity of intestinal inflammation.ConclusionsThe present study provides a novel way to the understanding of the mechanisms of GM-CSF and IL-3 effects in the intestine, which will benefit the development of therapeutic approaches.
Boundary-Sensing Mechanism in Branched Microtubule Networks
Authors: Zaferani Meisam , Song Ryungeun , Wingreen Ned S. , Stone Howard A. , Petry Sabine 
Published: August 2024

AbstractThe self-organization of cytoskeletal networks in confined geometries requires sensing and responding to mechanical cues at nanometer to micron scales that allow for dynamic adaptation. Here, we show that the branching of microtubules (MTs) via branching MT nucleation combined with dynamic instability constitutes a boundary-sensing mechanism within confined spaces. Using a nanotechnology platform, we observe the self-organization of a branched MT network in a channel featuring a narrow junction and a closed end. Our observations reveal that branching MT nucleation occurs in the post-narrowing region only if that region exceeds a certain length before it terminates at the channel’s closed end. The length-dependent occurrence of branching MT nucleation arises from the dynamic instability of existing MTs when they interact with the channel’s closed end, combined with the specific timescale required for new MTs to nucleate at a point distant from the closed end, creating a mechanical feedback. Increasing the concentration of the base branching factor TPX2 accelerates nucleation kinetics and thus tunes the minimum length scale needed for occurrence of branching MT nucleation. As such, this feedback not only allows for adaptation to the local geometry, but also allows for tunable formation of MT networks in narrow (micron and submicron scale) channels. However, while a high concentration of TPX2 increases the kinetic rate of branching MT nucleation, it also stabilizes MTs at the channel’s closed end leading to MT growth and nucleation in the reversed direction, and thus hinders boundary sensing. After experimental characterization of boundary-sensing feedback, we propose a minimal model and execute numerical simulations. We investigate how this feedback, wherein growing MTs dynamically sense their physical environment and provide nucleation sites for new MTs, sets a length/time scale that steers the architecture of MT networks in confined spaces. This “search- and-branch” mechanism has implications for the formation of MT networks during neuronal morphogenesis, including axonal growth and the formation of highly branched dendritic networks, as well as for plant development and MT-driven guidance in fungi, and engineering nanotechnologies.
Hematophagy generates a convergent genomic signature in mosquitoes and sandflies
Authors: Devilliers Julien , Warren Ben , Rosato Ezio , Kyriacou Charalambos P. , Feuda Roberto 
Published: August 2024

AbstractBlood-feeding (hematophagy) is widespread across Diptera (true flies), yet the underlying molecular mechanisms remain poorly understood. Using phylogenomics, we show that four gene families associated with neuro-modulation, immune responses, embryonic development, and iron metabolism have undergone independent expansions within mosquitoes and sandflies. Our findings illuminate the underlying genetic basis for blood-feeding adaptations in these important disease vectors.
UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences
Authors: van der Zee Tim J. , Tecchio Paolo , Hahn Daniel , Raiteri Brent J. 
Published: August 2024

AbstractBackgroundBrightness-mode (B-mode) ultrasound is a valuable tool to non-invasively image skeletal muscle architectural changes during movement, but automatically estimating architectural features such as fascicle length remains a major challenge. Existing fascicle tracking algorithms either require time-consuming drift corrections or yield noisy estimates that require post-processing. We therefore aimed to develop an algorithm that tracks fascicles without drift and noise across a range of experimental conditions and image acquisition settings.MethodsWe applied a Kalman filter to combine fascicle length and fascicle angle estimates from existing and openly available UltraTrack and TimTrack algorithms into a hybrid algorithm called UltraTimTrack. We applied the hybrid algorithm to ultrasound image sequences collected from the human medial gastrocnemius of healthy individuals (N=8, 4 women), who performed cyclical submaximal plantar flexion contractions or remained at rest during passive ankle joint rotations at given frequencies and amplitudes whilst seated in a dynamometer chair. We quantified the algorithm’s tracking accuracy, noise, and drift as the respective mean, cycle-to-cycle, and accumulated between-contraction variability in fascicle length and fascicle angle. We expected UltraTimTrack’s estimates to be less noisy and to drift less across experimental conditions and image acquisition settings, compared with estimates from its parent algorithms.ResultsThe proposed algorithm had low-noise estimates like UltraTrack and was drift-free like TimTrack across the broad range of conditions we tested. Estimated fascicle length and fascicle angle deviations accumulated to 2.1 ± 1.3 mm (mean ± s.d.) and 0.8 ± 0.7 deg, respectively, over 120 cyclical contractions. Average cycle-to-cycle variability was 1.4 ± 0.4 mm and 0.6 ± 0.3 deg, respectively. In comparison, UltraTrack had similar cycle-to-cycle variability (1.1 ± 0.3 mm, 0.5 ± 0.1 deg) but greater cumulative deviation (67.0 ± 59.3 mm, 9.3 ± 8.6 deg), whereas TimTrack had similar cumulative deviation (1.9 ± 2.2 mm, 0.9 ± 1.0 deg) but greater variability (3.5 ± 1.0 mm, 1.4 ± 0.5 deg). UltraTimTrack was significantly less affected by experimental conditions and image acquisition settings than its parent algorithms. It also performed well on a previously published image sequence from the human tibialis anterior, yielding a smaller root-mean-square deviation from manual tracking (fascicle length: 2.7 mm, fascicle angle: 0.7 deg) than a recently proposed hybrid algorithm (fascicle length: 4.5 mm, fascicle angle: 0.8 deg) and a machine-learning (DL_Track) algorithm (fascicle length: 8.2 mm, fascicle angle: 4.8 deg).ConclusionWe developed a Kalman-filter-based method to improve fascicle tracking from B-mode ultrasound image sequences. The proposed algorithm provides low-noise, drift-free estimates of muscle architectural changes that may better inform muscle function interpretations.
Genome Restructuring around Innate Immune Genes in Monocytes in Alcohol-associated Hepatitis
Authors: Kim Adam , McMullen Megan R. , Bellar Annette , Streem David , Dasarathy Jaividhya , Welch Nicole , Dasarathy Srinivasan 
Published: August 2024

AbstractMany inflammatory genes in the immune system are clustered in the genome. The 3D genome architecture of these clustered genes likely plays a critical role in their regulation and alterations to this structure may contribute to diseases where inflammation is poorly controlled. Alcohol-associated hepatitis (AH) is a severe inflammatory disease that contributes significantly to morbidity in alcohol associated liver disease. Monocytes in AH are hyper-responsive to inflammatory stimuli and contribute significantly to inflammation. We performed high throughput chromatin conformation capture (Hi-C) technology on monocytes isolated from 4 AH patients and 4 healthy controls to better understand how genome structure is altered in AH. Most chromosomes from AH and healthy controls were significantly dissimilar from each other. Comparing AH to HC, many regions of the genome contained significant changes in contact frequency. While there were alterations throughout the genome, there were a number of hotspots containing a higher density of changes in structure. A few of these hotspots contained genes involved in innate immunity including the NK-gene receptor complex and the CXC-chemokines. Finally, we compare these results to scRNA-seq data from patients with AH challenged with LPS to predict how chromatin conformation impacts transcription of clustered immune genes. Together, these results reveal changes in the chromatin structure of monocytes from AH patients that perturb expression of highly clustered proinflammatory genes.
Tumor localization strategies of multi-cancer early detection tests: a quantitative assessment
Authors: Tyson Christopher , Li Kevin H. , Cao Xiting , O’Brien James M. , Fishman Elliot K. , O’Donnell Elizabeth K. , Duran Carlos , Parthasarathy Vijay , Rego Seema P. , Choudhry Omair A. , Beer Tomasz M. 
Published: August 2024

AbstractIntroductionBlood-based multi-cancer early detection (MCED) tests may expand the number of “screenable” cancers. Defining an optimal approach to diagnostic resolution for individuals with positive MCED test results is critical. Two prospective trials employed distinct diagnostic resolution approaches; one employed a molecular signal to predict tissue of origin (TOO) and the other used an imaging-based diagnostic strategy. Using mathematical modeling, we compared the diagnostic burden of each approach and characterized the risk of excess cancer incidence that may be attributable to radiation exposure associated with a false positive (FP) MCED test result and an imaging-based diagnostic strategy.MethodsA mathematical expression for diagnostic burden was derived using MCED test positive predictive value (PPV), molecular TOO localization accuracy, and the expected number of imaging procedures associated with each diagnostic outcome. Imaging and molecular TOO strategies were compared by estimating diagnostic burden across a wide range of MCED PPVs and TOO accuracies. Organ-specific radiation dose for diagnostic imaging was extracted from the literature and used as input to National Cancer Institute RADRat tool for estimating excess lifetime cancer risk due to radiation exposure.ResultsFor the molecular TOO diagnostic approach, an average of 2.1 procedures are required to reach diagnostic resolution for correctly-localized TPs, 4.4 procedures for incorrectly-localized TPs, and 4 procedures for FPs, vs. an average of 2.75 procedures for TPs and 2.4 for FPs with an imaging-based diagnostic strategy. Across the entire range of possible PPV and localization performance, a molecular TOO strategy resulted in a higher mean diagnostic burden: 3.6 procedures (SD 0.445) vs. 2.6 procedures (SD 0.1) for the imaging strategy. Predicted diagnostic burden was higher for molecular TOO in 95.5% of all possible PPV and TOO accuracy combinations; 79% or higher PPV would be required for a 90% accurate molecular TOO strategy to be less burdensome than imaging. The maximum rate of excess cancer incidence from radiation exposure for FP results from MCED screening between the ages of 50-84 was estimated at 64.6 per 100,000 (annual testing, 99% specificity), 48.5 per 100,000 (biennial testing, 98.5% specificity), and 64.6 per 100,000 (biennial testing, 98% specificity).ConclusionsThis analysis demonstrates that an imaging-based diagnostic strategy is more efficient than a molecular TOO-informed approach across 95.5% of all possible MCED PPV and TOO accuracy combinations. The use of an imaging-based approach for cancer localization can be efficient and low risk compared to a molecular-based approach.
Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases
Authors: DeGroat William , Abdelhalim Habiba , Peker Elizabeth , Sheth Neev , Narayanan Rishabh , Zeeshan Saman , Liang Bruce T. , Ahmed Zeeshan 
Published: August 2024

AbstractCardiovascular diseases (CVDs) are multifactorial diseases, requiring personalized assessment and treatment. The advancements in multi-omics technologies, namely RNA-seq and whole genome sequencing, have offered translational researchers a comprehensive view of the human genome; utilizing this data, we can reveal novel biomarkers and segment patient populations based on personalized risk factors. Limitations in these technologies in failing to capture disease complexity can be accounted for by using an integrated approach, characterizing variants alongside expression related to emerging phenotypes. Designed and implemented data analytics methodology is based on a nexus of orthodox bioinformatics, classical statistics, and multimodal artificial intelligence and machine learning techniques. Our approach has the potential to reveal the intricate mechanisms of CVD that can facilitate patient-specific disease risk and response profiling. We sourced transcriptomic expression and variants from CVD and control subjects. By integrating these multi-omics datasets with clinical demographics, we generated patient-specific profiles. Utilizing a robust feature selection approach, we reported a signature of 27 transcripts and variants efficient at predicting CVD. Here, differential expression analysis and minimum redundancy maximum relevance feature selection elucidated biomarkers explanatory of the disease phenotype. We used Combination Annotation Dependent Depletion and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVDs. Overall, we observed an XGBoost model hyperparameterized using Bayesian optimization perform the best (AUC 1.0). Using SHapley Additive exPlanations, we compiled risk assessments for patients capable of further contextualizing these predictions in a clinical setting. We discovered a 27-component signature explanatory of phenotypic differences in CVD patients and healthy controls using a feature selection approach prioritizing both biological relevance and efficiency in machine learning. Literature review revealed previous CVD associations in a majority of these diagnostic biomarkers. Classification models trained on this signature were able to predict CVD in patients with high accuracy. Here, we propose a framework generalizable to other diseases and disorders.
Diet driven differences in host tolerance are linked to shifts in global gene expression in a common avian host-pathogen system
Authors: Sauer Erin L. , Stacy Carson , Perrine Weston , Love Ashley C. , Lewis Jeffrey A. , DuRant Sarah E. 
Published: August 2024

AbstractAs humans alter the landscape, wildlife have become increasingly dependent on anthropogenic resources, altering interactions between individuals and subsequently disease transmission dynamics. Further, nutritional quantity and quality greatly impact an individual host’s immune capacity and ability to mitigate damage caused by infectious disease. Thus, understanding the impact of dietary nutrition on immune function is critical for predicting disease severity and transmission as human activity both facilitates the dispersal of pathogens and alters dietary options for wildlife. Here, we use transcriptomics to explore the previously unstudied molecular mechanisms underpinning diet-driven differences in pathogen tolerance using a widespread avian bacterial pathogen,Mycoplasma gallisepticum(MG). MG is an ideal model for understanding the dietary drivers of disease as the human supplementation that wild birds commonly rely on, bird feeders, are also an important source for MG transmission. Significant diet-driven differences in the expression of many genes encoding immune response and translational machinery proteins are seen both in the absence of MG and during the recovery period. Prior to infection, protein-fed birds are more transcriptionally primed for infection than lipid-fed birds which translates to greater tolerance in protein-fed birds during the recovery period. Given the significant importance of human supplemented food in wildlife disease systems, the molecular mechanisms by which interactions between diet and infection emerge provide insight into the ecological and immunological consequences of human behavior and wildlife disease.