bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tissue-specific responses of the central carbon metabolism in tomato fruit to low oxygen stress
Authors: Li Xindan , Terzoudis Konstantinos , Hertog Maarten L.A.T.M. , Nicolaï Bart M. 
Published: November 2024

AbstractTomato (Solanum lycopersicumL.) is an important model plant whose fleshy fruit consists of well-differentiated tissues. Recently it was shown that these tissues develop hypoxia during fruit development and ripening. Therefore, we employed a combination of metabolomics and isotopic labeling to investigate the central carbon metabolic response of tomato fruit tissues (columella, septa and mesocarp) to low O2stress. The concentration and13C-label enrichment of intermediates from the central carbon metabolism were analyzed using gas chromatography–mass spectrometry. The results showed an increase in glycolytic activity and the initiation of fermentation in response to low O2conditions. In addition, the up-regulation of the GABA shunt and accumulation of amino acids, alanine and glycine, were observed under low O2conditions. Notably, tissue specificity was observed at the metabolite level, with concentrations of most metabolites being highest in columella tissue. In addition, there were tissue-specific differences in the central carbon metabolism with the columella exhibiting the highest metabolic activity and sensitivity to the changes in O2concentration, followed by septa and mesocarp tissues. Our results are consistent with common plant responses and adaptive mechanisms to low O2stress, while unravelling some tissue-specific differences, increasing our understanding of the intact fruit response to low O2stress.
DiCARN-DNase: Enhancing Cell-to-Cell Hi-C Resolution Using Dilated Cascading ResNet with Self-Attention and DNase-seq Chromatin Accessibility Data
Authors: Olowofila Samuel , Oluwadare Oluwatosin 
Published: November 2024

AbstractThe spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains the leading method for unraveling 3D genome structures, but the limited availability of high-resolution Hi-C data poses significant challenges for comprehensive analysis. Deep learning models have been developed to predict high-resolution Hi-C data from low-resolution counterparts. Early CNN-based models improved resolution but struggled with issues like blurring and capturing fine details. In contrast, GAN-based methods encountered difficulties in maintaining diversity and generalization. Additionally, most existing algorithms perform poorly in cross-cell line generalization, where a model trained on one cell type is used to enhance high-resolution data in another cell type. In this work, we propose DiCARN (Dilated Cascading Residual Network) to overcome these challenges and improve Hi-C data resolution. DiCARN leverages dilated convolutions and cascading residuals to capture a broader context while preserving fine-grained genomic interactions. Additionally, we incorporate DNase-seq data into our model, providing a robust framework that demonstrates superior generalizability across cell lines in high-resolution Hi-C data reconstruction. DiCARN is publicly available athttps://github.com/OluwadareLab/DiCARN
Transcutaneous Vagus Nerve Stimulation Reduces Pain and OA Progression in Mouse Models of Post-Traumatic Osteoarthritis
Authors: Yadav Shivmurat , Morris Lynsie , Conner Taylor , Lumry Jessica , South Sanique M , Prinz Emmaline , Izda Vladislav , Dyson Gabby , Barrett Montana , Stavrakis Stavros , Jeffries Matlock A , Griffin Timothy M , Humphrey Mary Beth 
Published: November 2024

AbstractCurrently, there are no disease-modifying osteoarthritis (OA) drugs (DMOAD) to prevent OA progression and there are limitations on pain relieving therapeutics. Vagus nerve stimulation (VNS) delivered by an implantable device is FDA-approved for refractory epilepsy and severe depression. Here, we investigated the efficacy of transcutaneous VNS (tVNS) for preventing OA progression and providing pain relief in two mouse models of post-traumatic OA (PTOA): the surgical destabilized medial meniscus (DMM) and the non-surgical forced tibial compression anterior cruciate ligament rupture (ACLR). Here, we show that 2 weeks of tVNS significantly reduced histological OA scores in male and female mice after ACLR compared to sham stimulation. In female, but not male, mice, tVNS reduced hyperalgesia and mechanical allodynia. In the slower DMM model, 8 weeks of tVNS improved weight bearing in male and female mice, but only female mice had improved hyperalgesia. Male mice had lower OA histological scores. Serum proinflammatory cytokines were significantly reduced by tVNS in both models but differed by gender and model. Overall, these results provide strong pre-clinical evidence that tVNS reduces OA progression, improves pain, and suppresses pro-inflammatory cytokines, making it a promising DMOAD.
CytoSpatio: Learning cell type spatial relationships using multirange, multitype point process models
Authors: Chen Haoran , Murphy Robert F. 
Published: November 2024

SummaryRecent advances in multiplexed fluorescence imaging have provided new opportunities for deciphering the complex spatial relationships among various cell types across diverse tissues. We introduce CytoSpatio, open-source software that constructs generative, multirange, and multitype point process models that capture interactions among multiple cell types at various distances simultaneously. On analyzing five cell types across five tissues, our software showed consistent spatial relationships within the same tissue type, with certain cell types like proliferating T cells consistently clustering across tissue types. It also revealed that the attraction-repulsion relationships between cell types like B cells and CD4-positive T cells vary with tissue type. CytoSpatio can also generate synthetic tissue structures that preserve the spatial relationships seen in training images, a capability not provided by previous descriptive, motif-based approaches. This potentially allows spatially realistic simulations of how cell relationships affect tissue biochemistry.
The effect of deferring feedback on rule-based and information-integration category learning
Authors: Edmunds C. E. R. , Carpenter Kathryn , Wills Andy J. , Milton Fraser 
Published: November 2024

AbstractPrevious work has shown that deferring feedback significantly impairs two-dimensional information-integration category learning, often thought to recruit an implicit learning system, but leaves intact unidimensional rule-based learning, commonly assumed to engage an explicit system (Smith et al., 2014). These results were taken to support the influential COmpetition between Verbal and Implicit Systems (COVIS) dual-process theory. This conclusion has subsequently been challenged by the finding that this dissociation disappears when the number of relevant dimensions is matched between tasks (Le Pelley et al., 2019). However, as well as replacing a unidimensional rule-based task with a two-dimensional conjunction task, Le Pelley et al. also changed the stimuli that were used in their study making it unclear which of these alterations was driving the difference in results. The current paper directly examined how both category structure and stimulus type influence the deferred feedback effect. We replicated both the original sets of results but found that deferred feedback also impaired information-integration learning to a greater extent than a conjunction task when Smith et al.’s original stimuli were used. This result suggests that the effect of deferred feedback on category learning is more complicated than has previously been documented and highlights the critical role the choice of stimuli has in determining whether the effect is obtained.
An Integrated Germline and Somatic Genomic Model Improves Risk Prediction for Coronary Artery Disease
Authors: Yang Xiong , Kim Min Seo , Zhu Xinyu , Uddin Md Mesbah , Nakao Tetsushi , Jemma Cho So Mi , Koyama Satoshi , Xu Tingfeng , Reeskamp Laurens F. , Zhang Rufan , Liu Zhaoqi , Yunga A , de Vries Paul S. , Vasan Ramachandran S. , Boerwinkle Eric , Morrison Alanna C. , Psaty Bruce M. , Tracy Russell P. , Heckbert Susan R. , Cho Michael H. , Yun Jeong H , Palmer Nicholette D. , Bowden Donald W. , Murabito Joanne M. , Levy Daniel , Heard-Costa Nancy L. , O’Connor George T. , Becker Lewis C. , Kral Brian G. , Yanek Lisa R. , Raffield Laura M. , Hidalgo Bertha , Rotter Jerome I. , Rich Stephen S. , Taylor Kent D. , Post Wendy S. , Kooperberg Charles , Reiner Alexander P. , Mitchell Braxton D. , Kardia Sharon L.R. , Smith Jennifer A. , Peyser Patricia A. , Bielak Lawrence F. , Yon Dong Keon , Won Hong-Hee , Arnett Donna K. , Smith Albert V. , Gabriel Stacey B. , Ellinor Patrick T. ,   NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Natarajan Pradeep , Wang Minxian , Fahed Akl C. 
Published: November 2024

AbstractMultiple germline and somatic genomic factors are associated with risk of coronary artery disease (CAD), but there is no single measure of risk that integrates all information from a DNA sample, limiting clinical use of genomic information. To address this gap, we developed an integrated genomic model (IGM), analogous to a clinical risk calculator that combines various clinical risk factors into a unified risk estimate. The IGM includes six genetic drivers for CAD, including germline factors (familial hypercholesterolemia [FH] variants, CAD polygenic risk score [PRS], proteome PRS, metabolome PRS) and somatic factors (clonal hematopoiesis of indeterminate potential [CHIP], and leukocyte telomere length [LTL]). We evaluated the IGM on CAD risk prediction in the UK Biobank (N=391,536), and validated it in the Trans-Omics for Precision Medicine (TOPMed) program (N=34,177). The 10-year CAD risk based on the IGM profile ranged from 1.1% to 15.5% in the UK Biobank and from 3.8% to 33.0% in TOPMed, with a more pronounced gradient in males than females. IGM captured the cumulative effect of multiple genetic drivers, identifying individuals at high risk for CAD despite lacking obvious high risk genetic factors, or individuals at low risk for CAD despite having known genetic risk variants such as FH and CHIP. The IGM had the highest performance in younger individuals (C-statistic 0.805 [95% CI, 0.699-0.913] for age ≤ 45 years). In middle age, IGM augmented the performance of the Pooled Cohort Equations (PCE), a clinical risk calculator for CAD. Adding IGM to PCE resulted in a continuous net reclassification index of 33.45% (95% CI, 32.11%-34.76%). We present the first model that integrates all currently available information from a single “DNA biopsy” to translate complex genetic information into a single risk estimate.
Evaluation of T Cell Receptor Construction Methods from scRNA-Seq Data
Authors: Tian Ruonan , Yu Zhejian , Xue Ziwei , Wu Jiaxin , Wu Lize , Cai Shuo , Gao Bing , He Bing , Zhao Yu , Yao Jianhua , Lu Linrong , Liu Wanlu 
Published: November 2024

ABSTRACTT cell receptors (TCRs) serve pivotal roles in the adaptive immune system by enabling recognition and response to pathogens and irregular cells. Various methods exist for TCR construction from single-cell RNA sequencing (scRNA-seq) datasets, each with its unique characteristics regarding accuracy, sensitivity, adaptability, usability, time, and memory consumption. Yet, a comprehensive understanding of their relative strengths and weaknesses for different applications remains elusive. In our research, we implemented a benchmark analysis utilizing experimental single-cell immune profiling datasets encompassing paired scRNA-seq as input and scTCR-seq datasets as ground truth reference from human and mouse. Additionally, we introduced a novel simulator, YASIM-scTCR (Yet Another Simulator for single-cell TCR), capable of generating scTCR-seq reads containing a diverse array of TCR- derived sequences under different sequencing depths and read lengths. Our results consistently showed that TRUST4 outperformed others across multiple datasets, while MiXCR and DeRR also demonstrated considerable accuracy. We also discovered that the sequencing depth inherently imposes a critical constraint on successful TCR construction from scRNA-seq data. In summary, we present a benchmark study to aid researchers in choosing the most appropriate methods for reconstructing TCR from scRNA-seq data.
Density-based optimization for unbiased, reproducible clustering applied to single molecule localization microscopy
Authors: Hammer Joseph L. , Devanny Alexander J. , Kaufman Laura J. 
Published: November 2024

AbstractSingle molecule localization microscopy (SMLM) has provided insight into the spatial organization of molecules at length scales below the diffraction limit of visible light. In SMLM data, density-based clustering approaches have proven to be valuable tools for probing the nanoscale structure of biological molecules, although little guidance is available for evaluating the accuracy of these results, which are often strongly dependent on user-input parameters. Here, we develop an efficient implementation of density-based cluster validation (DBCV) that can quantitatively evaluate clustering performance in SMLM-sized datasets without ground truth knowledge. We demonstrate that maximizing DBCV scores accurately identifies ground truth clustering in noisy, simulated datasets. By coupling DBCV score maximization with Bayesian optimization, we outline an optimization method, DBOpt, that selects unbiased input parameters for density-based clustering algorithms. We demonstrate that optimal input parameters can be selected for popular algorithms (DBSCAN, HDBSCAN, OPTICS) with minimal user input. Lastly, we show that DBOpt reports accurate feature sizes in 2D and 3D experimental datasets. Taken together, we propose an analysis pipeline that can be applied to a diverse array of experimental data that will improve the integrity and quality of cluster analyses in the broader scientific community.
Targeted Alpha Therapy with [225Ac]Ac-Macropa-Isatuximab for CD38-positive Hematological Malignancies
Authors: Alvarez Natalia Herrero , Bauer David , Michel Alexa L. , Carter Lukas M. , Lewis Jason S. 
Published: November 2024

ABSTRACTBackgroundHematological malignancies include diverse cancers related to immune system cells and blood-forming tissue. Treatment resistance and relapse are common despite the introduction of novel therapeutics. Following the development of the PET probe [89Zr]Zr-DFO-isatuximab, targeting the overexpressed CD38 receptor, we developed [225Ac]Ac-Macropa-isatuximab for targeted alpha therapy in murine models of human multiple myeloma (MM) and lymphoma.MethodsIn vitro studies were performed using the CD38 expressing MM1.S (human MM) and Daudi (human lymphoma). Radiopharmaceutical therapy studies were performed in female NSG mice, and the disseminated disease models were established by intravenous injection of luciferase-transfected cells. Mice were randomized (n = 10 mice per cohort) and received a high activity (5.5 kBq) or low activity (2.7 kBq) of [225Ac]Ac-Macropa-Isatuximab in single or multiple cycles. Controls receiving untargeted [225Ac]Ac-Macropa-IgG or saline were included. Tumor burden was measured by bioluminescence imaging (BLI). Weight loss greater than 20% or leg paralysis were used as endpoints.Results[225Ac]Ac-Macropa-Isatuximab was obtained with high radiochemical yield and purity (>95%). It displayed high immunoreactivity and excellent stability in human serum over 10 days. The therapeutic effect of [225Ac]Ac-mcp-Isatuximab was significant for all the treatment cohorts compared to the saline control after administration of the first cycle. Low activities were better tolerated in general, resulting in extended median survivals. The most successful regimen in the MM model was the dual cycle of low activity, with a median survival of 60 days (P < 0.001). In the lymphoma model, the single high activity of 5.5 kBq and four low activity of 2.7 kBq resulted in similar median survivals of 143 days (P < 0.0001) and 147 days (P < 0.0001). These regimens resulted in complete responses with no detectable cancer cells in some cases. Remarkably, no significant kidney damage was observed in any of the models.Conclusion[225Ac]Ac-Macropa-Isatuximab selectively targets CD38, reducing tumor growth and, in some cases, tumor eradication, especially when applied in multiple cycles. Importantly, it has not been associated with significant toxicities at levels of administered activity required for therapeutic benefit, indicating its promise as a therapeutic option.
Phase Separation to Resolve Growth-Related Circuit Failures
Authors: Zhang Rong , Yang Wangfei , Zhang Rixin , Rijal Sadikshya , Youssef Abdelrahaman , Zheng Wenwei , Tian Xiao-Jun 
Published: November 2024

AbstractFluctuations in host cell growth poses a significant challenge to synthetic gene circuits, often disrupting circuit function. Existing solutions typically rely on circuit redesign with alternative topologies or additional control elements, yet a broadly applicable approach remains elusive. Here, we introduce a new strategy based on liquid-liquid phase separation (LLPS) to stabilize circuit performance. By engineering a self-activating circuit with transcription factors (TF) fused to an intrinsically disordered region (IDR), we enable the formation of TF condensates at the promoter region, maintaining local TF concentration despite growth-mediated dilution. This condensate formation preserves bistable memory in the self-activating circuit, demonstrating that phase separation can robustly counteract growth fluctuations, offering a novel design principle for resilient synthetic circuits.
Hypoxia exposure fine-tunes mitochondrial function in sea turtle cells
Authors: Arango B. Gabriela , Ensminger David C. , Xing Dianna , Godard-Codding Celine A. , Vázquez-Medina José Pablo 
Published: November 2024

ABSTRACTSea turtles experience extreme fluctuations in oxygen availability derived from extended breath-hold diving, yet the cellular adjustments that underlie hypoxia tolerance in these animals remain poorly understood. Here, we used metabolite profiling, extracellular flux assays, and microscopic analyses of the mitochondrial reticulum to study how primary cells derived from sea turtles and lizards cope with extended hypoxia exposure. Cells from both species proliferate in primary culture, stain positive for fibroblast markers, are metabolically active, and stabilize HIF1-α when exposed to chemical or environmental hypoxia. In contrast to lizard cells, sea turtle cells exhibit a faster and more robust response to 1 or 24-hour hypoxia exposure (0.1% O2), upregulating antioxidant pathways and optimizing oxygen use rather than relying on glycolytic metabolism. Similarly, the mitochondrial reticulum is maintained without apparent fragmentation during hypoxia exposure in sea turtle cells. Consistent with these observations, mitochondrial function readily recovers in sea turtle but not lizard cells upon reoxygenation. These findings show that sea turtle cells undergo intrinsic metabolic adjustments to cope with extreme oxygen fluctuations, aligning with the remarkable hypoxic tolerance exhibited by these animals, which can endure up to 7 hours of breath-holding underwater.
Effects of ultrasound-guided high ankle block combined with general anesthesia on postoperative cognitive function in fragile elderly patients based on the analysis of scale and EEG monitor
Authors: Xia Ziwei , Ma Guangkuo , Xue Huanjia , Wang Fangjun , Wang Liwei , Wang Kai 
Published: November 2024

AbstractIntroductionUltrasound-guided high ankle block can provide prolonged analgesia for foot and ankle surgeries while preserving ankle motor function. Given that fragile elderly patients are prone to cognitive impairment after general anesthesia, this study intends to implement high ankle block for fragile elderly patients undergoing foot and ankle fracture surgery, and use intraoperative electroencephalogram (EEG) analysis combined with postoperative scales to investigate its effect on patients’ postoperative cognitive function.Methods and analysisThis randomized controlled trial will be conducted in Xuzhou Central Hospital. A total of 126 elderly patients scheduled to undergo unilateral ankle fracture (uni/bi-/triple ankle) incision and reduction internal fixation (ORIF) surgery will be randomly assigned to either the HAB group or the GB group in a 1:1 ratio. Patients will receive ultrasound-guided high ankle block and general anesthesia (HAB group) or general anesthesia alone (GB group). Multimodal analgesia will include intraoperative flurbiprofen ester and postoperative patients will be given appropriate medications according to the postoperative pain management “three-step ladder”. The primary outcome indicator was the incidence of dNCR at 7th day postoperation. The secondary outcome indicators were the incidence of dNCR at 1st and 30th days postoperation; alpha relative power, alpha power, and burst inhibition ratio in each brain region at 30 min after induction of anesthesia; MoCA scores, patient stress response indexes: ACTH, NE, Ang-II, β-EP, Glu, and Cor levels; patients’ VAS pain scores; patients’ satisfaction scores; first time getting out of bed postoperatively; and time of discharge from the hospital. Safety outcomes were dizziness, headache, vomiting, urinary retention, bradycardia, tachycardia, hypotension, and hypertension.Ethics and disseminationEthics approval was obtained from the Ethics Committee of the Xuzhou Central Hospital. All patients will provide written informed consent. The results of this study will be published in a peer-reviewed journal.Trial registration numberChinese Clinical Trial Registry (ChiCTR2400050927)
A Decade of Progress in Artificial Intelligence for Fundus Image-Based Diabetic Retinopathy Screening (2014–2024): A Bibliometric Analysis
Authors: Huang Yuting , Qi Yunfei , Liu Chi , Jing Fengshi , Li Changjin , Wang Minghao , Zhu Congcong , Gui Peng , Ge Zongyuan , Han Xiaotong 
Published: November 2024

AbstractBackground/AimsDiabetic retinopathy (DR) screening using artificial intelligence (AI) has evolved significantly over the past decade. This study aimed to analyze research trends, developments, and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis.MethodsThe study analyzed 1,172 publications from the Web of Science Core Collection database using CiteSpace and Microsoft Excel. The analysis included publication trends, citation patterns, institutional collaborations, and keyword emergence analysis.ResultsPublications showed consistent growth from 2014-2022, with a peak in 2021. India (26%), China (20.05%), and USA (9.98%) dominated research output. IEEE ACCESS was the leading publication venue with 44 articles. Research evolved from traditional image processing to deep learning approaches, with recent emphasis on multimodal AI models. The analysis identified three distinct phases: CNN-based systems (2014-2020), Vision Transformers and innovative learning paradigms (2020-2022), and large foundation models (2022-2024).ConclusionThe field shows mature development in traditional AI approaches while transitioning toward multimodal learning technologies. Future directions indicate increased focus on telemedicine integration, innovative AI algorithms, and real-world implementation.What is already known on this topicAI-based DR screening has been developing since the 1960s, with significant acceleration after 2014 due to deep learning advances.Traditional manual analysis of fundus images is time-consuming and error-prone.What this study addsComprehensive mapping of research evolution in AI-based DR screening over the past decade.Identification of research concentration in specific geographical areas and emerging trends in multimodal AI approaches.How this study might affect research, practice or policyHighlights the need for increased international collaboration and technology sharing.Suggests focus areas for future research, including multimodal learning and real-world implementation.Provides direction for healthcare organizations and researchers in adopting and developing AI-based DR screening technologies.