bluuebunny/crossref_metadata_2025_split huggingface dataset:
Age-Related Decline in Blood-Brain Barrier Function is More Pronounced in Males than Females in Parietal and Temporal Regions
Authors: Shao Xingfeng , Shou Qinyang , Felix Kimberly , Ojogho Brandon , Jiang Xuejuan , Gold Brian T. , Herting Megan M , Goldwaser Eric L , Kochunov Peter , Hong L. Elliot , Pappas Ioannis , Braskie Meredith , Kim Hosung , Cen Steven , Jann Kay , Wang Danny JJ 
Published: January 2024

AbstractThe blood-brain barrier (BBB) plays a pivotal role in protecting the central nervous system (CNS), shielding it from potential harmful entities. A natural decline of BBB function with aging has been reported in both animal and human studies, which may contribute to cognitive decline and neurodegenerative disorders. Limited data also suggest that being female may be associated with protective effects on BBB function. Here we investigated age and sex-dependent trajectories of perfusion and BBB water exchange rate (kw) across the lifespan in 186 cognitively normal participants spanning the ages of 8 to 92 years old, using a non-invasive diffusion prepared pseudo-continuous arterial spin labeling (DP-pCASL) MRI technique. We found that the pattern of BBB kw decline with aging varies across brain regions. Moreover, results from our DP-pCASL technique revealed a remarkable decline in BBB kw beginning in the early 60s, which was more pronounced in males. In addition, we observed sex differences in parietal and temporal regions. Our findings provide in vivo results demonstrating sex differences in the decline of BBB function with aging, which may serve as a foundation for future investigations into perfusion and BBB function in neurodegenerative and other brain disorders.Significance statementThe blood-brain barrier (BBB) serves as a critical protection mechanism for the CNS. A natural decline of BBB function with aging has been reported in both animal and human studies, with possible differences in BBB function by sex. Using our MRI technique, DP-pCASL that measures water exchange rate (kw) without contrast in 186 participants from diverse race and age groups, we identified age and sex-specific patterns in BBB kw especially in parietal and temporal regions. We observed of a decline in kw beginning in the early 60s, especially in males. Our study unveils the dynamic spatiotemporal pattern of kw differences with age and sex, which serve as a foundation for understanding aberrations of BBB function in neurodegenerative and other brain disorders.
Indicators to assess interannual variability in marine connectivity processes: a semi-theoretical approach
Authors: Clavel-Henry Morane , Bahamon Nixon , Aguzzi Jacopo , Navarro Joan , López Miguel , Company Joan B. 
Published: January 2024

AbstractOceanographic connectivity in an effective network of protected areas is crucial for restoring and stabilising marine populations. However, temporal variability of connectivity is rarely considered as a criterion in designing and evaluating marine conservation planning. In this study, indicators were defined to characterise occurrence, strength and frequency of the temporal variability in connectivity in a northwestern Mediterranean Sea area. Indicators were tested on semi-theoretically-estimated connections provided by the runs of a passive particle transport model in a climatological year and in three years between 2006 - 2020, showing large deviation from the climatological year. The indicators compared the temporal variability in connectivity of four zones, highlighting differences in connectivity due to their locations and the mesoscale hydrodynamics, and identifying areas that require further investigation. The three indicators also showed that the temporal variability in connectivity was influenced by the duration and depth of particle transport, although no consistent pattern was observed in the indicator variations of the compared zones. Provided that specific objectives will be given when parameterising transport models (i.e., selection of focus species and time period), indicators of temporal variability in connectivity have potential to support, and correctly implement, spatial conservation planning, prioritise the protection of marine resources, and measure the effectiveness of Marine Protected Areas, in line with a long-term vision of ocean management.
CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation
Authors: Aslam Saba , Rasool Abdur , Wu Hongyan , Li Xiaoli 
Published: January 2024

AbstractContinual learning, the ability of a model to learn over time without forgetting previous knowledge and, therefore, be adaptive to new data, is paramount in dynamic fields such as disease outbreak prediction. Deep neural networks, i.e., LSTM, are prone to error due to catastrophic forgetting. This study introduces a novel CEL model forcontinual learning by leveraging domain adaptation viaElastic Weight Consolidation (EWC). This model aims to mitigate the catastrophic forgetting phenomenon in a domain incremental setting. The Fisher Information Matrix (FIM) is constructed with EWC to develop a regularization term that penalizes changes to important parameters, namely, the important previous knowledge. CEL’s performance is evaluated on three distinct diseases, Influenza, Mpox, and Measles, with different metrics. The high R-squared values during evaluation and reevaluation outperform the other state-of-the-art models in several contexts, indicating that CEL adapts to incremental data well. CEL’s robustness and reliability are underscored by its minimal 65% forgetting rate and 18% higher memory stability compared to existing benchmark studies. This study highlights CEL’s versatility in disease outbreak prediction, addressing evolving data with temporal patterns. It offers a valuable model for proactive disease control with accurate, timely predictions.
Unveiling the role of Tdark genes in genetic diseases and phenotypes through bioinformatics-based functional enrichment and network analyses
Authors: Kafita Doris , Dzobo Kevin , Nkhoma Panji , Sinkala Musalula 
Published: January 2024

AbstractIn recent years, there has been growing interest in understanding the role of dark genes in genetic diseases and phenotypes. Despite their lack of functional characterisation, dark genes account for a significant portion of the human genome and are believed to play a role in regulating gene expression and cellular processes. We investigated the role of dark genes in genetic diseases and phenotypes by conducting integrative network analyses and functional enrichment studies across multiple large-scale molecular datasets. Our investigation revealed a predominant association of both dark and light genes with psoriasis. Furthermore, we found that the transcription factors UBTF and NFE2L2 are potential regulators of both dark and light genes associated with tuberculosis. In contrast, the transcription factors SUZ12 and TP63 are potential regulators of both dark and light genes associated with interstitial cystitis. Further network analysis of dark genes, includingCALHM6, HCP5, PRRG4, DDX60LandRASA2,revealed a notably high weighted degree of association with genetic diseases and phenotypes. Moreover, our analysis revealed that numerous genetic diseases and phenotypes, including psoriasis, pick disease, tuberculosis, ulcerative colitis, interstitial cystitis, and Crohn’s disease, exhibited shared gene linkages. Additionally, we conducted a protein-protein interaction analysis to reveal 16 dark genes that encode hub proteins, includingR3HDM2, RPUSD4, FASTKD5, andMRPL15,that could play a role in many genetic diseases and phenotypes, and are widely expressed across body tissues. Our findings contribute to the understanding of the genetic basis of diseases and provide potential therapeutic targets for future research. Identifying dysregulated dark genes in disease states can lead to new strategies for prevention, diagnosis, and treatment, thus advancing our understanding of disease mechanisms.
Pulsed low-dose-rate radiation (PLDR) reduces the tumor-promoting responses induced by conventional chemoradiation in pancreatic cancer-associated fibroblasts
Authors: Franco-Barraza Janusz , Luong Tiffany , Wong Jessica K. , Raghavan Kristopher , Handorf Elizabeth , Vendramini-Costa Débora B. , Francescone Ralph , Gardiner Jaye C. , Meyer Joshua E. , Cukierman Edna 
Published: January 2024

AbstractPancreatic cancer is becoming increasingly deadly, with treatment options limited due to, among others, the complex tumor microenvironment (TME). This short communications study investigates pulsed low-dose-rate radiation (PLDR) as a potential alternative to conventional radiotherapy for pancreatic cancer neoadjuvant treatment.Our ex vivo research demonstrates that PLDR, in combination with chemotherapy, promotes a shift from tumor-promoting to tumor-suppressing properties in a key component of the pancreatic cancer microenvironment we called CAFu (cancer-associated fibroblasts and selfgenerated extracellular matrix functional units). This beneficial effect translates to reduced desmoplasia (fibrous tumor expansion) and suggests PLDR’s potential to improve total neoadjuvant therapy effectiveness.To comprehensively assess this functional shift, we developed the HOST-Factor, a single score integrating multiple biomarkers. This tool provides a more accurate picture of CAFu function compared to individual biomarkers and could be valuable for guiding and monitoring future therapeutic strategies.Our findings support the ongoingNCT04452357clinical trial testing PLDR safety and TME normalization potential in pancreatic cancer patients. The HOST-Factor will be used in samples collected from this trial to validate its potential as a key tool for personalized medicine in this aggressive disease.
Vaping and smoking cue reactivity in young adult electronic cigarette users who have never smoked combustible cigarettes: A functional neuroimaging study
Authors: Liu Jiaying , Shi Zhenhao , Fabbricatore Jessica L. , McMains Joshua T. , Worsdale Allison , Jones Erin C. , Wang Yidi , Sweet Lawrence H. 
Published: January 2024

ABSTRACTIntroductionThe rapid growth in the use of electronic cigarettes (e-cigarettes) among young adults who have never smoked combustible cigarettes is concerning, as it raises the potential for chronic vaping and nicotine addiction. A key characteristic of drug addiction is the elevated neural response to conditioned drug-related cues (i.e., cue reactivity). Generalized reactivity to both vaping and smoking cues may signify an increased risk for smoking initiation in non- smoking vapers. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate brain responses to vaping and smoking cues in young adult never-smoking vapers.MethodsSixty-six young adult never-smoking vapers underwent functional MRI while viewing visual cues pertaining to vaping, smoking, and nicotine-unrelated unconditioned reward (i.e., food). A priori region-of-interest analysis combined with exploratory whole-brain analysis was performed to characterize neural reactivity to vaping and smoking cues in comparison to food cues.ResultsThe medial prefrontal cortex and the posterior cingulate cortex, regions that play a key role in drug cue reactivity, showed significantly increased neural response to vaping cues compared to food cues. The posterior cingulate cortex additionally showed increased neural responses to smoking cues compared to food cues.ConclusionsDespite never having smoked combustible cigarettes, young adult vapers exhibited heightened neural susceptibility to both vaping and smoking cues within brain systems associated with cue reactivity. The findings shed light on the mechanisms underlying nicotine addiction and smoking initiation risk in this critical population and may contribute to the development of science-based interventions and regulatory measures in the future.IMPLICATIONSThe escalating vaping prevalence among US never-smoking young adults is alarming, due to its potential ramifications for nicotine addiction development. Nicotine addiction is characterized by elevated neural response to conditioned nicotine-related cues. Using functional neuroimaging, we showed that young adult non-smoking vapers exhibited heightened neural susceptibility to both vaping and smoking cues within brain systems previously associated with cue reactivity. Such cross-reactivity to both types of nicotine cues may serve as the mechanism underlying nicotine addiction and smoking initiation risk in this population. Our findings may contribute to the development of science-based interventions and regulatory measures addressing the vaping epidemic.
PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications
Authors: Feng Jiarui , Province Michael , Li Guangfu , Payne Philip R.O. , Chen Yixin , Li Fuhai 
Published: January 2024

AbstractRecently, large-scale scRNA-seq datasets have been generated to understand the complex and poorly understood signaling mechanisms within microenvironment of Alzheimer’s Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. Though a set of targets have been identified, however, it remains a challenging to infer the core intra- and inter-multi-cell signaling communication networks using the scRNA-seq data, considering the complex and highly interactive background signaling network. Herein, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and signaling communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy, which divides the complex signaling networks into signaling paths, and then score and rank them using a novel graph transformer architecture to infer the intra- and inter-cell signaling communications. We evaluated PathFinder using scRNA-seq data of APOE4-genotype specific AD mice models and identified novel APOE4 altered intra- and inter-cell interaction networks among neurons, astrocytes, and microglia. PathFinder is a general signaling network inference model and can be applied to other omics data-driven signaling network inference.
Nonlinear relationship between platelet count and 30-day in-hospital mortality in ICU stroke patients: a multicenter retrospective cohort study
Authors: Wang Lan-xiang , Liu Ren-li , Zhou Pan , Hu Hao-fei , Deng Zhe 
Published: January 2024

AbstractBackgroundEvidence of the relationship between platelet count and 30-day in-hospital mortality in ICU stroke patients is still scarce. Therefore, the purpose of this study was to explore the relationship between platelet count and 30-day in-hospital mortality among ICU stroke patients.MethodsWe conducted a multicenter retrospective cohort study using data from 8029 ICU stroke patients in the US eICU-CRD v2.0 database from 2014 to 2015. Utilizing binary logistic regression, smooth curve fitting, and subgroup analyses, we examined the link between platelet count and 30-day in-hospital mortality.ResultsThe 30-day in-hospital mortality prevalence was 14.02%, and the mean platelet count of 223×109/L. Adjusting for covariates, our findings revealed an inverse association between platelet count and 30-day in-hospital mortality (OR = 0.975, 95% CI: 0.966, 0.984). Subgroup analyses supported the robustness of these results. Moreover, a nonlinear relationship was observed between platelet count and 30-day in-hospital mortality, with the inflection point at 163×109/L. On the left side of the inflection point, the effect size (OR) was 0.92 (0.89, 0.95), while on the right side, the relationship was not statistically significant.ConclusionThis study establishes an independent negative association between platelet count and 30-day in-hospital mortality in ICU stroke patients. Furthermore, a nonlinear relationship with a saturation effect was identified, suggesting that maintaining the platelet count around 163×109/L can reduce 30-day in-hospital mortality in these patients.
Pixel walking along the boreal forest–Arctic tundra ecotone: Large scale ground-truthing of satellite-derived greenness (NDVI)
Authors: Wong Russell E. , Berner Logan T. , Sullivan Patrick F. , Potter Christopher S. , Dial Roman J. 
Published: January 2024

ABSTRACTSatellite remote sensing of climate-driven changes in terrestrial ecosystems continues to improve, yet interpreting and rigorously validating these changes requires extensive ground-truthed data. Satellite measurements of vegetation indices, such as the Normalized Difference Vegetation Index (NDVI, or vegetation greenness), indicate widespread vegetation change in the Arctic that is associated with rapid warming. Plot-based studies have indicated greater vegetation greenness generally corresponds to greater plant biomass and deciduous shrub cover. However, the spatial scale of traditional plot-based sampling is much smaller than the resolution of most satellite imagery and thus does not fully describe how plant characteristics such as structure and taxonomic composition relate to satellite measurements of greenness. To improve interpretation of Landsat measurements of vegetation greenness in the Arctic, we developed and implemented a method that links satellite measurements with ground-based vegetation classifications. Here we describe data collected across the central Brooks Range of Alaska by field sampling hundreds of Landsat pixels per day, with a field campaign total of 23,213 pixels (30 m). Our example dataset shows that vegetation with the greatest Landsat greenness was taller than 1m, woody, and deciduous; vegetation with lower greenness tended to be shorter, evergreen, or non-woody. We also show that understory vegetation influences Landsat greenness. Our methods advance efforts to inform satellite data with ground-based vegetation observations using field samples at spatial scales more closely matched to the resolution of remotely sensed imagery.
Kalium channelrhodopsins effectively inhibit neurons in the small model animals
Authors: Ott Stanislav , Xu Sangyu , Lee Nicole , Hong Ivan Hee Kean , Anns Jonathan , Suresh Danesha Devini , Zhang Zhiyi , Zhang Xianyuan , Harion Raihanah , Ye Weiying , Chandramouli Vaishnavi , Jesuthasan Suresh , Saheki Yasunori , Claridge-Chang Adam 
Published: January 2024

AbstractThe analysis of neural circuits has been revolutionized by optogenetic methods. Light-gated chloride-conducting anion channelrhodopsins (ACRs)—recently emerged as powerful neuron inhibitors. For cells or sub-neuronal compartments with high intracellular chloride concentrations, however, a chloride conductance can have instead an activating effect. The recently discovered light-gated, potassium-conducting, kalium channelrhodopsins (KCRs) might serve as an alternative in these situations, with potentially broad application. As yet, KCRs have not been shown to confer potent inhibitory effects in the small genetically tractable animals. Here, we evaluated the utility of KCRs to suppress behavior and inhibit neural activity inDrosophila,C. elegans, and zebrafish. In direct comparisons with ACR1, a KCR1 variant with enhanced plasma-membrane trafficking displayed comparable potency, but with improved properties that include reduced toxicity and superior efficacy in putative high-chloride cells. This comparative analysis of behavioral inhibition between chloride- and potassium-selective silencing tools establishes KCRs as next-generation optogenetic inhibitors forin vivocircuit analysis in behaving animals.
Reformer: Deep learning model for characterizing protein-RNA interactions from sequence at single-base resolution
Authors: Shen Xilin , Li Xiangchun 
Published: January 2024

AbstractProtein-RNA interactions play an essential role in the regulation of transcription, translation, and metabolism of cellular RNA. Here, we develop Reformer, a deep learning model that predicts protein-RNA binding affinity purely from sequence. We developed Reformer with 155 RNA binding protein (RBP) targets from 3 cell lines. Reformer achieved high prediction accuracy at single-base resolution when tasking with inferring protein- and cell-type-specific binding affinity. We conducted electrophoretic mobility shift assays to validate high-impact RNA regulation mutations predicted by Reformer. In addition, Reformer learned to capture protein binding motifs that cannot be discovered by eCLIP-seq experiments. Furthermore, we demonstrated that motif signatures related to RNA processing functions are encoded within Reformer. In conclusion, Reformer will facilitate interpretation of the regulation mechanisms underlying RNA processing.
The probability of a unique gene occurrence at the tips of a phylogenetic tree in the absence of horizontal gene transfer (the last-one-out)
Authors: Bremer Nico , Martin William F. , Steel Mike 
Published: January 2024

AbstractGene loss is an important process in gene and genome evolution. If a gene is present at the root of a rooted binary phylogenetic tree and can be lost in one descendant lineage, it can be lost in other descendant lineages as well, and potentially can be lost in all of them, leading to extinction of the gene on the tree. In that case, just before the gene goes extinct in the rooted phylogeny, there will be one lineage that still retains the gene for some period of time, representing a ‘last-one-out’ distribution. If there are many (hundreds) of leaves in one clade of a phylogenetic tree, yet only one leaf possesses the gene, it will look like the result of a recent gene acquisition, even though the distribution at the tips was generated by loss. Here we derive the probability of observing last-one-out distributions under a Markovian loss model and a given gene loss rateµ. We find that the probability of observing such cases can be calculated mathematically, and can be surprisingly high, depending upon the tree and the rate of gene loss. Examples from real data show that gene loss can readily account for the observed frequency of last-one-out gene distribution patterns that might otherwise be attributed to lateral gene transfer.