bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accept-rejectdecision-making revealed via a quantitative and ethological study ofC. elegansforaging
Authors: Haley Jessica A. , Chen Tianyi , Aoi Mikio , Chalasani Sreekanth H. 
Published: September 2024

AbstractDecision-making is a ubiquitous component of animal behavior that is often studied in the context of foraging. Foragers make a series of decisions while locating food (food search), choosing between food types (diet or patch choice), and allocating time spent within patches of food (patch-leaving). Here, we introduce a framework for investigating foraging decisions using detailed analysis of individual behavior and quantitative modeling in the nematodeCaenorhabditis elegans. We demonstrate thatC. elegansmakeaccept-rejectpatch choice decisions upon encounter with food. Specifically, we show that when foraging amongst small, dispersed, and dilute patches of bacteria,C. elegansinitiallyrejectseveral bacterial patches, opting to prioritize exploration of the environment, before switching to a more exploitatory foraging strategy during subsequent encounters. Observed across a range of bacterial patch densities, sizes, and distributions, we use a quantitative model to show that this decision toexploreorexploitis guided by available sensory information, internal satiety signals, and learned environmental statistics related to the bacterial density of recently encountered andexploitedpatches. We behaviorally validated model predictions on animals that had been food-deprived, animals foraging in environments with multiple patch densities, and null mutants with defective sensory modalities. Broadly, we present a framework to study ecologically relevant foraging decisions that could guide future investigations into the cellular and molecular mechanisms underlying decision-making.
Chromosome-level Genome Assembly and Annotation ofPetunia hybrida
Authors: Saei Ali , Hunter Donald , Hilario Elena , David Charles , Ireland Hilary , Esfandiari Azadeh , King Ian , Grierson Ella , Wang Lei , Boase Murray , Kramer Matthew , Shakya Shankar , Bowman Megan , Barbey Christopher R. , Chagné David 
Published: September 2024

AbstractPetunia hybridais the world’s most popular garden plant and is regarded as a supermodel for studying the biology associated with the Asterid clade, the largest of the two major groups of flowering plants. Unlike other Solanaceae, petunia has a base chromosome number of seven, not 12. This along with recombination suppression has previously hindered efforts to assemble its genome to chromosome level. Here we achieve a chromosome-level assembly forP. hybridausing a combination of short-read and long-read sequencing, optical mapping (Bionano) and Hi-C technologies. The resulting assembly spans 1253.6 Mb with a BUSCO score of 99.8%. A total of 35,089 genes were predicted and of those 29,655 were functionally annotated. Syntenic regions between petunia, tomato and pepper were identified, highlighting rearrangements that have occurred since their divergence indicating that the 12 chromosomes of Solanaceae did not originate from whole genome duplication of an ancestral species with seven chromosomes like petunia. This chromosome-level assembly will significantly enhance trait mapping efficiency in petunia and serve as a valuable resource for functional genomic studies in this key plant model.
A Novel Technique for Fluorescence Lifetime Tomography
Authors: Nizam Navid Ibtehaj , Pandey Vikas , Erbas Ismail , Smith Jason T. , Intes Xavier 
Published: September 2024

AbstractFluorescence lifetime has emerged as a unique imaging modality for quantitatively assessingin vivothe molecular environment of diseased tissues. Although fluorescence lifetime microscopy (in 2D) is a mature field, 3D imaging in deep tissues remains elusive and challenging owing to scattering. Herein, we report on a deep neural network (coined AUTO-FLI) that performs both 3D intensity and quantitative lifetime reconstructions in deep tissues. The proposed Deep Learning (DL)-based approach involves anin silicoscheme to generate fluorescence lifetime data accurately. The developed DL model is validated bothin silicoand on experimental phantoms. Overall, AUTO-FLI provides accurate 3D quantitative estimates of both intensity and lifetime distributions in highly scattering media, demonstrating its unique potential for fluorescence lifetime-based molecular imaging at the mesoscopic and macroscopic scale.
The effect of intrauterine growth restriction on the developing pancreatic immune system
Authors: Golden Thea N. , Garifallou James P. , Conine Colin C. , Simmons Rebecca A. 
Published: September 2024

ABSTRACTImmune cells in the pancreas are known to participate in organ development. However, the resident pancreatic immune system has yet to be fully defined. Immune cells also play a role in pathology and are implicated in diseases such as diabetes induced by intrauterine growth restriction (IUGR). We hypothesized that the resident immune system is established during neonatal development and disrupted by IUGR. Using single cell RNAseq and flow cytometry we identified many immune cell populations in the near-term fetus (at embryologic day 22) and neonatal (postnatal day 1, 7, &14) islets, non-endocrine pancreas, and the spleen in the rat. Using flow cytometry, we observed the resident immune system is established during neonatal development in the pancreas and spleen. We identified 9 distinct immune populations in the pancreatic islets and 8 distinct immune populations in the spleen by single cell RNAseq. There were no sex-specific differences in the relative proportion of immune cells in the pancreas or spleen. Finally, we tested if IUGR disrupted the neonatal immune system using bilateral uterine artery ligation. We found significant changes to the percentage of CD11B+ HIS48- and CD8+ T cells in the islets and non-endocrine pancreas and in the spleen. IUGR-induced alterations were influenced by the tissue environment and the sex of the offspring. Future research to define the role of these immune cells in pancreatic development may identify disrupted pathways that contribute to the development of diabetes following IUGR.
PoreVision: A Program for Enhancing Efficiency and Accuracy in SEM Pore Analyses
Authors: Olevsky Levi M. , Jacques Mason , Hixon Katherine R. 
Published: September 2024

AbstractPorous bulk materials are frequently utilized as cell scaffolds in tissue engineering. Previous studies have highlighted the significance of scaffold pore size and pore orientation in influencing cell migration and differentiation. Moreover, there exists a considerable body of research focused on optimizing pore characteristics to enhance scaffold performance. However, current methods for numerical pore characterization typically involve expensive machines or manual size measurements using image manipulation software. In this project, our objective is to develop a user-friendly, versatile, and freely accessible software tool using Python scripting. This tool aims to streamline and objectify pore characterization, thereby accelerating research efforts and providing a standardized framework for researchers working with porous materials. By facilitating quantitative analysis of pore size, shape, and orientation, our software tool will contribute to a more comprehensive understanding of scaffold properties and their impact on cellular behavior. Ultimately, we aim to aid researchers in the field of tissue engineering with a user-friendly tool that enhances the reproducibility and reliability of pore characterization analyses.
Artificial intelligence and omics-based autoantibody profiling highlights autoimmunity targeting ligand-receptor interaction in dementia
Authors: Matsuda Kazuki M , Umeda-Kameyama Yumi , Iwadoh Kazuhiro , Miyawaki Masashi , Yakabe Mitsutaka , Ishii Masaki , Ogawa Sumito , Akishita Masahiro , Sato Shinichi , Yoshizaki Ayumi 
Published: September 2024

AbstractDementia is a neurodegenerative syndrome marked by the accumulation of disease-specific proteins and immune dysregulation, including autoimmune mechanisms involving autoantibodies. Current diagnostic methods are often invasive, time-consuming, or costly. This study explores the use of proteome-wide autoantibody screening (PWAS) for noninvasive dementia diagnosis by analyzing serum samples from Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and age-matched cognitively normal individuals (CNIs). Serum samples from 35 subjects were analyzed utilizing our original wet protein arrays that covers approximately 90% of human transcriptome, revealing elevated gross autoantibody levels in AD and DLB patients compared to CNIs. A total of 229 autoantibodies were differentially elevated in AD and/or DLB, effectively distinguishing between patient groups. Machine learning models showed high accuracy in classifying AD, DLB, and CNIs. Gene ontology analysis highlighted autoantibodies targeting neuroactive ligands/receptors in AD and lipid metabolism proteins in DLB. Notably, autoantibodies targeting neuropeptide B (NPB) and adhesion G protein-coupled receptor F5 (ADGRF5) showed significant correlations with clinical traits including Mini Mental State Examination scores, suggesting a role in dementia pathogenesis. The study demonstrates the potential of PWAS and AI integration as a noninvasive diagnostic tool for dementia, uncovering biomarkers that could enhance understanding of disease mechanisms. Limitations include demographic differences, small sample size, and lack of external validation. Future research should involve longitudinal observation in larger, diverse cohorts and functional studies to clarify autoantibodies’ roles in dementia pathogenesis and their diagnostic and therapeutic potential.
Genetic Risk for Alcohol Use Disorder in Relation to Individual Symptom Criteria: Do Polygenic Indices Provide Unique Information for Understanding Severity and Heterogeneity?
Authors: Kim Yongguk , Lane Sean P. , Miller Alex P. , Wilhelmsen Kirk C. , Gizer Ian R. 
Published: September 2024

AbstractAlcohol Use Disorder (AUD) is a heterogenous category with many unique configurations of symptoms. Previous investigations of AUD heterogeneity using molecular genetics methods studied the association between genetic liability and individual AUD symptoms at the latent level or focusing on a small number of genetic variants. Notably, these studies did not investigate potential severity differences between symptoms in their genetic analyses. Therefore, the current study aimed to examine the genetic risk for individual AUD symptom criteria by using a polygenic risk score (PRS) approach to assess the relative severity of each AUD symptom and test for associates with AUD symptoms above and beyond a unidimensional AUD construct. An AUD PRS was created using summary statistics obtained from published genome-wide association studies (GWAS), and Multiple Indicators Multiple Causes (MIMIC) models were employed to examine the effect of the PRS on overall AUD severity as well as on individual symptoms after accounting for this overall effect. The phenotypic severity of AUD symptoms was highly correlated with the genetic severity of AUD symptoms (r= 0.78). Results of MIMIC models indicated that the AUD PRS significantly predicted the AUD factor. Regression paths testing the unique, direct effects of the PRS on individual AUD symptoms, independent of the latent AUD factor, were not significant. These results imply that PRSs derived from GWAS of AUD influence symptom expression through a single genetic factor that is highly correlated with the relative severity of individual symptoms when measured at the phenotypic level. Item-level GWAS of AUD symptoms are needed to further parse heterogeneous symptom expression and allow for more nuanced tests of these conclusions.
How floods may affect the spatial spread of respiratory pathogens: the case of Emilia-Romagna, Italy in May 2023
Authors: Ascione Claudio , Valdano Eugenio 
Published: September 2024

AbstractThe negative impact of floods on public health has been increasing, as climate change makes these events more frequent and intense. Floods are known to cause direct injury and favor the spread of many waterborne and vector-borne pathogens. Their effect on the circulation of respiratory pathogens, like influenza and SARS-CoV-2, is, however, still unclear. In this study, we quantify this effect through the analysis of large-scale behavioral data coupled to mathematical models of epidemic spread. We focus on the devastating floods occurred in Italy in 2023 and measure how they impacted human contact patterns within and between communities. We find a substantial increase in contacts occurring 3 weeks after the floods, both among residents of the affected areas and between them and those living in distant, unaffected areas of Italy. Then, through mathematical simulations, we determine that these disrupted contact patterns can carry a circulating pathogen to previously unaffected geographic areas, as well as increasing infection counts across the country. Our findings may help set up protocols to use large-scale human contact data to contain epidemic outbreaks before, during and in the aftermath of floods.
AI-enabled CT-guided end-to-end quantification of total cardiac activity in 18FDG cardiac PET/CT for detection of cardiac sarcoidosis
Authors: Miller Robert JH , Shanbhag Aakash , Marcinkiewicz Anna M , Struble Helen , Fujito Hidesato , Kransdorf Evan , Kavanagh Paul , Liang Joanna X. , Builoff Valerie , Dey Damini , Berman Daniel S , Slomka Piotr J 
Published: September 2024

ABSTRACTPurpose[18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) plays a central role in diagnosing and managing cardiac sarcoidosis. We propose a fully automated pipeline for quantification of [18F]FDG PET activity using deep learning (DL) segmentation of cardiac chambers on computed tomography (CT) attenuation maps and evaluate several quantitative approaches based on this framework.MethodsWe included consecutive patients undergoing [18F]FDG PET/CT for suspected cardiac sarcoidosis. DL segmented left atrium, left ventricular(LV), right atrium, right ventricle, aorta, LV myocardium, and lungs from CT attenuation scans. CT-defined anatomical regions were applied to [18F]FDG PET images automatically to target to background ratio (TBR), volume of inflammation (VOI) and cardiometabolic activity (CMA) using full sized and shrunk segmentations.ResultsA total of 69 patients were included, with mean age of 56.1 ± 13.4 and cardiac sarcoidosis present in 29 (42%). CMA had the highest prediction performance (area under the receiver operating characteristic curve [AUC] 0.919, 95% confidence interval [CI] 0.858 – 0.980) followed by VOI (AUC 0.903, 95% CI 0.834 – 0.971), TBR (AUC 0.891, 95% CI 0.819 – 0.964), and maximum standardized uptake value (AUC 0.812, 95% CI 0.701 – 0.923). Abnormal CMA (≥1) had a sensitivity of 100% and specificity 65% for cardiac sarcoidosis. Lung quantification was able to identify patients with pulmonary abnormalities.ConclusionWe demonstrate that fully automated volumetric quantification of [18F]FDG PET for cardiac sarcoidosis based on CT attenuation map-derived volumetry is feasible, rapid, and has high prediction performance. This approach provides objective measurements of cardiac inflammation with consistent definition of myocardium and background region.
Elevated blood glucose levels are associated with the progression of brain hypometabolism, and HDL-C andAPOE4add to this association
Authors: Lathika Rajendrakumar Aravind , Ukraintseva Svetlana , Bagley Olivia , Duan Matt , Yashin Anatoliy I. , Arbeev Konstantin G. ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: September 2024

AbstractBackgroundBrain glucose hypometabolism has consistently been found in neurodegenerative disorders, including Alzheimer’s disease (AD). High blood glucose and HDL cholesterol (HDL-C) levels have also been linked to neurodegeneration and AD. However, there is limited understanding of the relationships between dementia-related risk factors in the brain and blood.MethodsA linear mixed model was used to examine the relationship between blood glucose and HDL-C levels and the progression of brain hypometabolism, adjusting forAPOE4and other clinical covariates. The hypometabolic convergence index (HCI) was measured by fluorodeoxyglucose-18 (FDG) positron emission tomography (PET) in participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Data visualizations were generated to understand the joint effects of plasma glucose, HDL-C, andAPOE4on HCI.ResultsThere were 336 individuals (781 observations), of whom 22.62% had AD. The majority were male (63.98%) and of white race, and 48.51% were carriers ofAPOE4. Over time, high blood glucose level was associated with the progression of brain glucose hypometabolism (β=0.33, 95% CI: 0.02, 0.64, p<0.05). A high plasma HDL-C level (β=1.22, 95% CI: 0.09, 2.35, p<0.05), more study visits (β=1.67, 95% CI: 1.37, 1.98, p<0.001), and being anAPOE4allele carrier (β=1.29, 95% CI: 0.15, 2.42, p<0.05) were also significant predictors of brain hypometabolism progression.APOE4carrier status and number of visits account for the largest proportion of the variance from the fixed effects model. Random effects due to participant characteristics and fixed effects together accounted for 95.2% of the model variance. Subgroup analysis revealed that these effects were observed only in those without AD.ConclusionHigh plasma glucose levels facilitated the progression of brain hypometabolism. The effect was more prominent in theAPOE4double-carriers with elevated HDL-C. Elevated blood glucose may reflect systemic insulin resistance, which could impair brain glucose uptake, resulting in brain hypometabolism. Controlling blood glucose and HDL-C levels inAPOE4carriers may improve brain metabolism, potentially delaying the onset of dementia.
“This is a quiz” Premise Input: A Key to Unlocking Higher Diagnostic Accuracy in Large Language Models
Authors: Asari Yusuke , Kurokawa Ryo , Sonoda Yuki , Hagiwara Akifumi , Kamohara Jun , Fukushima Takahiro , Gonoi Wataru , Abe Osamu 
Published: September 2024

AbstractPurposeLarge language models (LLMs) are neural network models trained on vast amounts of textual data, showing promising performance in various fields. In radiology, studies have demonstrated the strong performance of LLMs in diagnostic imaging quiz cases. However, the inherent differences of prior probabilities of a final diagnosis between clinical and quiz cases pose challenges for LLMs, as LLMs had not been informed about the quiz nature in previous literature, while human physicians can optimize the diagnosis, consciously or unconsciously, depending on the situation. The present study aimed to test the hypothesis that notifying LLMs about the quiz nature might improve diagnostic accuracy.MethodsOne-hundred-and-fifty consecutive cases from the “Case of the Week” radiological diagnostic quiz case series on the American Journal of Neuroradiology website were analyzed. GPT-4o and Claude 3.5 Sonnet were used to generate top three differential diagnoses based on the textual clinical history and figure legends. The prompts included or excluded information about the quiz nature for both models. Two radiologists evaluated the accuracy of the diagnoses. McNemar’s test assessed differences in correct response rates.ResultsInforming the quiz nature improved the diagnostic performance of both models. Specifically, Claude 3.5 Sonnet’s primary diagnosis and GPT-4o’s top 3 differential diagnoses significantly improved when the quiz nature was informed.ConclusionInforming the quiz nature of cases significantly enhances LLMs’ diagnostic performances. This insight into LLMs’ capabilities could inform future research and applications, highlighting the importance of context in optimizing LLM-based diagnostics.
Mean daily temperatures can predict the thermal limits of malaria transmission better than rate summation
Authors: Shocket Marta S. , Bernhardt Joey R. , Miazgowicz Kerri L. , Orakzai Alyzeh , Savage Van M. , Hall Richard J. , Ryan Sadie J. , Murdock Courtney C. 
Published: September 2024

AbstractTemperature shapes the distribution, seasonality, and magnitude of mosquito-borne disease outbreaks. Mechanistic models predicting transmission often use mosquito and pathogen thermal responses from constant temperature experiments. However, mosquitoes live in fluctuating environments. Rate summation (nonlinear averaging) is a common approach to infer performance in fluctuating environments, but its accuracy is rarely validated. We measured three mosquito traits that impact transmission (bite rate, survival, fecundity) in a malaria mosquito (Anopheles stephensi) across temperature gradients with three diurnal temperature ranges (0, 9 and 12°C). We compared thermal suitability models with temperature-trait relationships observed under constant temperatures, fluctuating temperatures, and those predicted by rate summation. We mapped results acrossAn. stephenesi’s native Asian and invasive African ranges. We found: 1) daily temperature fluctuation significantly altered trait thermal responses; 2) rate summation partially captured decreases in performance near thermal optima, but also incorrectly predicted increases near thermal limits; and 3) while thermal suitability characterized across constant temperatures did not perfectly capture suitability in fluctuating environments, it was more accurate for estimating and mapping thermal limits than predictions from rate summation. Our study provides insight into methods for predicting mosquito-borne disease risk and emphasizes the need to improve understanding of organismal performance under fluctuating conditions.
Sex-Specific Effects of Early Life Unpredictability on Hippocampal and Amygdala Responses to Novelty in Adolescents
Authors: Davis Elysia Poggi , Leonard Bianca T. , Jirsaraie Robert J. , Keator David B. , Small Steven L. , Sandman Curt A. , Risbrough Victoria B , Stern Hal S. , Glynn Laura M. , Yassa Michael A. , Baram Tallie Z. , Rasmussen Jerod M. 
Published: September 2024

AbstractBackgroundUnpredictable childhood experiences are an understudied form of early life adversity that impacts neurodevelopment in a sex-specific manner. The neurobiological processes by which exposure to early-life unpredictability impacts development and vulnerability to psychopathology remain poorly understood. The present study investigates the sex-specific consequences of early-life unpredictability on the limbic network, focusing on the hippocampus and the amygdala.MethodsParticipants included 150 youth (54% female). Early life unpredictability was assessed using the Questionnaire of Unpredictability in Childhood (QUIC). Participants engaged in a task-fMRI scan between the ages of 8 and 17 (223 total observations) measuring BOLD responses to novel and familiar scenes.ResultsExposure to early-life unpredictability associated with BOLD contrast (novel vs. familiar) in a sex-specific manner. For males, but not females, higher QUIC scores were associated with lower BOLD activation in response to novel vs. familiar stimuli in the hippocampal head and amygdala. Secondary psychophysiological interaction (PPI) analyses revealed complementary sex-specific associations between QUIC and condition-specific functional connectivity between the right and left amygdala, as well as between the right amygdala and hippocampus bilaterally.ConclusionExposure to unpredictability in early life has persistent implications for the functional operations of limbic circuits. Importantly, consistent with emerging experimental animal and human studies, the consequences of early life unpredictability differ for males and females. Further, impacts of early-life unpredictability were independent of other risk factors including lower household income and negative life events, indicating distinct consequences of early-life unpredictability over and above more commonly studied types of early life adversity.