bluuebunny/crossref_metadata_2025_split huggingface dataset:
Small DNA elements that act as both insulators and silencers in plants
Authors: Jores Tobias , Mueth Nicholas A. , Tonnies Jackson , Char Si Nian , Liu Bo , Grillo-Alvarado Valentina , Abbitt Shane , Anand Ajith , Deschamps Stéphane , Diehn Scott , Gordon-Kamm Bill , Jiao Shuping , Munkvold Kathy , Snowgren Heather , Sardesai Nagesh , Fields Stanley , Yang Bing , Cuperus Josh T. , Queitsch Christine 
Published: September 2024

AbstractInsulators arecis-regulatory elements that separate transcriptional units, whereas silencers are elements that repress transcription regardless of their position. In plants, these elements remain largely uncharacterized. Here, we use the massively parallel reporter assay Plant STARR-seq with short fragments of eight large insulators to identify more than 100 fragments that block enhancer activity. The short fragments can be combined to generate more powerful insulators that abolish the capacity of the strong viral 35S enhancer to activate the 35S minimal promoter. Unexpectedly, when tested upstream of weak enhancers, these fragments act as silencers and repress transcription. Thus, these elements are capable of both insulating or repressing transcription dependent upon regulatory context. We validate our findings in stable transgenicArabidopsis, maize, and rice plants. The short elements identified here should be useful building blocks for plant biotechnology efforts.
Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt
Authors: Ramon Aubin , Ni Mingyang , Predeina Olga , Gaffey Rebecca , Kunz Patrick , Onuoha Shimobi , Sormanni Pietro 
Published: September 2024

1AbstractIn-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalisability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported. Here, we present a comprehensive study exploring various approaches for protein fitness prediction from limited data, leveraging pre-trained embeddings, repeated stratified nested cross-validation, and ensemble learning to ensure an unbiased assessment of the performances. We applied our framework to introduce NanoMelt, a predictor of nanobody thermostability trained with a dataset of 640 measurements of apparent melting temperature, obtained by integrating data from the literature with 129 new measurements from this study. We find that an ensemble model stacking multiple regression using diverse sequence embeddings achieves state-of-the-art accuracy in predicting nanobody thermostability. We further demonstrate NanoMelt’s potential to streamline nanobody development by guiding the selection of highly stable nanobodies. We make the curated dataset of nanobody thermostability freely available and NanoMelt accessible as a downloadable software and webserver.2Significance StatementRapidly predicting protein biophysical traits with accuracy is a key goal in protein engineering, yet efforts to develop reliable predictors are often hindered by limited and disparate experimental measurements. We introduce a framework to predict biophysical traits using few training data, leveraging diverse machine learning approaches via a semi-supervised framework combined with ensemble learning. We applied this framework to develop NanoMelt, a tool to predict nanobody thermostability trained on a new dataset of apparent melting temperatures. Nanobodies are increasingly important in research and therapeutics due to their ease of production and small size, which allows deeper tissue penetration and seamless combination into multi-specific compounds. NanoMelt outperforms available methods for protein thermostability prediction and can streamline nanobody development by guiding the design and selection of highly stable nanobodies during discovery and optimization campaigns.
Enhancing Biomarker-Based Oncology Trial Matching Using Large Language Models
Authors: Khoury Nour Al , Shaik Maqsood , Wurmus Ricardo , Akalin Altuna 
Published: September 2024

AbstractClinical trials are an essential component of drug development for new cancer treatments, yet the information required to determine a patient’s eligibility for enrollment is scattered in large amounts of unstructured text. Genomic biomarkers are especially important in precision medicine and targeted therapies, making them essential for matching patients to appropriate trials. Large language models (LLMs) offer a promising solution for extracting this information from clinical trial data, aiding both physicians and patients in identifying suitable matches. In this study, we explore various LLM strategies for extracting genetic biomarkers from oncology trials to improve patient enrollment rates. Our results show that open-source language models, when applied out-of-the-box, effectively capture complex logical expressions and structure genomic biomarkers in disjunctive normal form, outperforming closed-source models such as GPT-4 and GPT-3.5-Turbo. Furthermore, fine-tuning these open-source models with additional data significantly enhances their performance.
M2 Macrophage Exosomes Reverse Cardiac Functional Decline in Mice with Diet-Induced Myocardial Infarction by Suppressing Type 1 Interferon Signaling in Myeloid Cells
Authors: Ng Martin , Gao Alex S. , Phu Tuan Anh , Vu Ngan K. , Raffai Robert L. 
Published: September 2024

AbstractEffective treatment strategies to alleviate heart failure that develops as a consequence of myocardial infarction (MI) remain an unmet need in cardiovascular medicine. In this study, we uncovered that exosomes produced by human THP-1 macrophages cultured with the cytokine IL-4 (THP1-IL4-exo), reverse cardiac functional decline in mice that develop MI as a consequence of diet-induced occlusive coronary atherosclerosis. Therapeutic benefits of THP1-IL4-exo stem from their ability to reprogram circulating Ly-6Chimonocytes into an M2-like phenotype and suppress Type 1 Interferon signaling in myeloid cells within the bone marrow, the circulation, and cardiac tissue. Collectively, these benefits suppress myelopoiesis, myeloid cell recruitment to cardiac tissue, and preserve populations of resident cardiac macrophages that together mitigate cardiac inflammation, adverse ventricular remodeling, and heart failure. Our findings introduce THP1-IL4-exo, one form of M2-macrophage exosomes, as novel therapeutics to preserve cardiac function subsequent to MI.
Increased Inflammation as well as Decreased Endoplasmic Reticulum Stress and Translation Differentiate Pancreatic Islets of Pre-symptomatic Stage 1 Type 1 Diabetes and Non-diabetic Cases
Authors: Swensen Adam C. , Piehowski Paul D. , Chen Jing , Chan X’avia Y. , Kelly Shane S. , Petyuk Vladislav A. , Moore Ronald J. , Nasif Lith , Butterworth Elizabeth A. , Atkinson Mark A. , Kulkarni Rohit N. , Campbell-Thompson Martha , Mathews Clayton E. , Qian Wei-Jun 
Published: September 2024

AbstractAims/hypothesisProgression to type 1 diabetes (T1D) is associated with genetic factors, the presence of autoantibodies, and a decline in β cell insulin secretion in response to glucose. Very little is known regarding the molecular changes that occur in human insulin-secreting β-cells prior to the onset of T1D. Herein, we applied an unbiased proteomics approach to identify changes in proteins and potential mechanisms of islet dysfunction in islet autoantibody-positive organ donors with pre-symptomatic stage 1 T1D (HbA1c ≤ 6). We aimed to identify pathways in islets that are indicative of β-cell dysfunction.MethodsMultiple islet sections were collected through laser microdissection of frozen pancreatic tissues of organ donors positive for islet autoantibodies (AAb+, n=5), compared to age/sex-matched nondiabetic controls (ND, n=5) obtained from the Network for Pancreatic Organ donors with Diabetes (nPOD). Islet sections were subjected to mass spectrometry-based proteomics and analyzed with label-free quantification followed by pathway and functional annotations.ResultsAnalyses resulted in ∼4,500 proteins identified with low false discovery rate (FDR) <1%, with 2,165 proteins reliably quantified in every islet sample. We observed large inter-donor variations that presented a challenge for statistical analysis of proteome changes between donor groups. We therefore focused on the three multiple AAb+ cases (mAAb+) with high genetic risk and their three matched controls for a final statistical analysis. Approximately 10% of the proteins (n=202) were significantly different between mAAb+ cases versus ND. The significant alterations clustered around major functions for upregulation in the immune response and glycolysis, and downregulation in endoplasmic reticulum (ER) stress response as well as protein translation and synthesis. The observed proteome changes were further supported by several independent published datasets, including proteomics dataset fromin vitroproinflammatory cytokine-treated human islets and single cell RNA-seq data sets from AAb+ cases.Conclusion/interpretationIn-situ human islet proteome alterations at the stage 1 of AAb+ T1D centered around several major functional categories, including an expected increase in immune response genes (elevated antigen presentation / HLA), with decreases in protein synthesis and ER stress response, as well as compensatory metabolic response. The dataset serves as a proteomics resource for future studies on β cell changes during T1D progression and pathogenesis.
A GeneralizedAddermechanism for Cell Size Homeostasis: Implications for Stochastic Dynamics of Clonal Proliferation
Authors: Nieto César , Vargas-García César Augusto , Singh Abhyudai 
Published: September 2024

AbstractMeasurements of cell size dynamics have revealed phenomeno-logical principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is theadder, where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of theadderconsidering that cell size follows anyarbitrary non-exponential growth law. Our results show that the main requirement to obtain anadderregardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of non-exponential cell size dynamics (withadder-basedcell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time reaching a non-zero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines.
Molecular computation at equilibrium via programmable entropy
Authors: Wang Boya , Chalk Cameron , Doty David , Soloveichik David 
Published: September 2024

AbstractSynthetic molecular information processing is typically designed through programming kinetic pathways, so that molecules bind, unbind, or incur conformational changes in some desired order. In DNA nanotechnology, this paradigm is exemplified by strand displacement cascades that control signal transmission through toehold sequestering, and algorithmic tile assembly where correct computation emerges from enforcing ordered growth. In contrast, thermodynamic programming focuses solely on the desired end-state rather than the path, often allowing simpler reasoning and requiring fewer parameters, as well as avoiding energetically-favored, yet undesired, error states that often frustrate kinetic approaches. Here we demonstrate a thermodynamics-first paradigm based on the Thermodynamic Binding Networks (TBN) model, where the system’s equilibrium is determined by two simple factors: maximizing the number of complementary domain-level bonds while favoring configurations with more separate molecular complexes. We first validate the model by quantitatively measuring the free energy benefit of configurations with more separate complexes but identical binding, confirming the entropic driving force central to the TBN abstraction. We then construct signal propagation circuits including fan-in and fan-out, seeded-assembly systems that perform Boolean logic computation, and systems for synthesis of concatemers of size quadratic in that of the substrates (by computing their least common multiple). Realizing these TBN constructions required addressing experimental challenges including ensuring geometric feasibility of desired complexes through systematic domain ordering and compensating for multi-arm junction penalties. Our work may enable new ways to engineer complex molecular behaviors and help inform the understanding of the computational power of kinetics versus thermodynamics for molecular systems.
ECSfinder: Optimized prediction of evolutionarily conserved RNA secondary structures from genome sequences
Authors: Gaonac’h-Lovejoy Vanda , Mattick John S. , Sauvageau Martin , Smith Martin A. 
Published: September 2024

AbstractAccurate prediction of RNA secondary structures is essential for understanding the evolutionary conservation and functional roles of long noncoding RNAs (lncRNAs) across diverse species. In this study, we benchmarked two leading tools for predicting evolutionarily conserved RNA secondary structures (ECSs)—SISSIzandR-scape— using two distinct experimental frameworks: one focusing on well-characterized mitochondrial RNA structures and the other on experimentally validated Rfam structures embedded within simulated genome alignments. While both tools performed comparably overall, each displayed subtle preferences in detecting ECSs. To address these limitations, we evaluated two interpretable machine learning approaches that integrate the strengths of both methods. By balancing thermodynamic stability features fromRNALalifoldandSISSIzwith robust covariation metrics fromR-scape, a random forest classifier significantly outperformed both conventional tools. This classifier was implemented inECSfinder, a new tool that provides a robust, interpretable solution for genome-wide identification of conserved RNA structures, offering valuable insights into lncRNA function and evolutionary conservation.ECSfinderis designed for large-scale comparative genomics applications and promises to facilitate the discovery of novel functional RNA elements.
LiverMap pipeline for 3D imaging of human liver reveals volumetric spatial dysregulation of cirrhotic vasculobiliary architecture
Authors: Fabyan Wesley B. , Fortin Chelsea L. , Kenerson Heidi L. , Simmonds Susana P. , Liu Jonathan T. C. , Yeh Matthew M. , Carr Rotonya M. , Yeung Raymond S. W. , Stevens Kelly R. 
Published: September 2024

AbstractThe liver contains an intricate microstructure that is critical for liver function. Architectural disruption of this spatial structure is pathologic. Unfortunately, 2D histopathology – the gold standard for pathological understanding of many liver diseases – can misrepresent or leave gaps in our understanding of complex 3D structural features. Here, we utilized immunostaining, tissue clearing, microscopy, and computational software to create 3D multilobular reconstructions of both non-fibrotic and cirrhotic human liver tissue. We found that spatial architecture in human cirrhotic liver samples with varying etiologies had sinusoid zonation dysregulation, reduction in glutamine synthetase-expressing pericentral hepatocytes, regression of central vein networks, disruption of hepatic arterial networks, and fragmentation of biliary networks, which together suggest a pro-portalization/decentralization phenotype in cirrhotic tissue. Further implementation of 3D pathological analyses may provide a deeper understanding of cirrhotic pathobiology and inspire novel treatments for liver disease.
Plant responses to wheat curl mites and wheat streak mosaic virus (WSMV): first evidence of virus tolerance in wheat
Authors: Pingault Lise , Albrecht Tessa , Broders Kirk , Rushton Jennifer , Louis Joe , Nachappa Punya , Nalam Vamsi 
Published: September 2024

AbstractWheat curl mites (WCM) are arthropod pests that pose significant threats to wheat crops by causing direct damage by feeding, and transmitting viruses such as Wheat Streak Mosaic Virus (WSMV), Triticum Mosaic Virus (TriMV), and High Plains Wheat Mosaic Virus (HPWMoV), leading to substantial losses in wheat, barley, oats, and rye. Over three years of field screening, we found that the cultivar Hatcher consistently produced higher yields under high WSMV disease pressure, outperforming Mace and TAM112, which carry theWsm1gene and a QTL for curl mite resistance, respectively, indicating tolerance. To investigate the mechanisms underlying the tolerance phenotype in Hatcher, we compared its response to WCM and WSMV infection with a susceptible genotype, CO15D173R. Transcriptomic analysis revealed a nuanced interplay between plant defense and growth in Hatcher, with upregulation of genes related to jasmonic acid (JA), salicylic acid (SA), and abscisic acid (ABA) pathways, indicating a coordinated defense response. The activation of lignin biosynthesis genes points to a potential role of cell wall strengthening in deterring WCM and WSMV. Additionally, the regulation of genes involved in growth-related hormonal pathways such as gibberellic acid (GA), and brassinosteroids (BR) highlights Hatcher’s ability to maintain growth disease pressure. Our findings provide insight into the intricate network of phytohormones, growth-defense trade-offs, and cell wall modifications contributing to Hatcher’s tolerance to WCM and WSMV. This knowledge can inform the development of tolerant wheat varieties and enhance integrated pest management strategies, ultimately safeguarding wheat production.
Hypoxia-mediated fine-tuning of the TLR7/9-triggered human PDC-derived IFN-α response is mediated by combined cellular and soluble IFN-regulators
Authors: Lenkewitz Alexander , Ben Brahim Ibtissem , Herrmann Felix , Islam Noor-A-Kasida , Ticha Olga , Bekeredjian-Ding Isabelle 
Published: September 2024

AbstractHypoxia occurs in settings where stringent control of inflammation is mandatory to avoid tissue damage. Here, we analyzed how hypoxia-induced mediators alter recognition of pathogen-derived molecular patterns (PAMPs). The most relevant finding was that hypoxia selectively targets IFN-α induced upon stimulation with nucleic acid-sensing Toll-like receptors (TLR7/8 and -9) in PBMC. Notably, IFN-α secretion was reduced, but IFN-α producing capacity preserved. Corroborating these findings IFN-dependent cytokines IP-10 and IL-12 p70 were reduced under hypoxia, while other cytokines such as TNF remained unaffected. A role for hypoxia-inducible factors (HIF) was confirmed with prolyl hydroxylase (PHD) inhibitors CoCl2and DMOG. Plasmacytoid dendritic cells (PDC) were identified as the major source of IFN-α and experiments in B cell/PDC and monocyte/PDC cocultures indicated that T cells are not required and both lymphoid and myeloid cells equally support inhibition of IFN-α. Moreover, supernatants from unstimulated PBMC generated under hypoxic conditions were not suppressive but further experiments indicated that hypoxia-specific release of IFN-α regulators lactate and TGF-β can contribute to the IFN-α blockage and might synergize with PGE2. However, each one of these factors alone is insufficient to block IFN-α secretion. These findings suggest that control of the PDC response occurs via combined soluble factors and cell contact-dependent mechanisms, thereby enabling a fine-tuning of the anti-microbial immune response in hypoxia.
Generative AI for Qualitative Analysis in a Maternal Health Study: Coding In-depth Interviews using Large Language Models (LLMs)
Authors: Qiao Shan , Fang Xingyu , Garrett Camryn , Zhang Ran , Li Xiaoming , Kang Yuhao 
Published: September 2024

Study ObjectivesIn-depth interviews are one of the most widely used approaches for qualitative studies in public health. The coding of transcripts is a critical step for information extraction and preliminary analysis. However, manual coding is often labor-intensive and time-consuming. The emergence of generative artificial intelligence (GenAI), supported by Large Language Models (LLMs), presents new opportunities to understand human languages, which may significantly facilitate the coding process. This study aims to build a computational coding framework that uses GenAI to automatically detect and extract themes from in-depth interview transcripts.MethodsWe conducted an experiment using transcripts of in-depth interviews with maternity care providers in South Carolina. We leveraged ChatGPT to perform two tasks automatically: (1) deductive coding, which involves applying a predefined set of codes to dialogues; and (2) inductive coding, which can generate codes from dialogues without any preconceptions or assumptions. We fine-tuned ChatGPT to understand the content of the interview transcripts, enabling it to detect and summarize codes. We then evaluated the performance of the proposed approach by comparing the codes generated by ChatGPT with those generated manually by human coders, involving human-in-the-loop evaluation.ResultsThe results demonstrated the potential of GenAI in detecting and summarizing codes from in-depth interview transcripts. ChatGPT could be utilized for both deductive and inductive coding processes. The overall accuracy of GenAI is higher than 80% and the codes it generated showed high positive associations with those generated manually. More impressively, GenAI reduced the time required for coding by 81%, demonstrating its efficiency compared to traditional methods.DiscussionGenAI models like ChatGPT show high generalizability, scalability and efficiency in handling large datasets, and are proficient in multi-level semantic structure identification. They demonstrate promising results in qualitative coding, making it a valuable tool for supporting people in public health research. However, challenges such as inaccuracy, systematic biases, and privacy concerns must be addressed when using them in practice. GenAI-based coding results should be handled with caution and reviewed by human coders to ensure accuracy and reliability.