bluuebunny/crossref_metadata_2025_split huggingface dataset:
Attention-mediated genetic influences on psychotic symptomatology in adolescence
Authors: Chang Sarah E. , Hughes Dylan E. , Zhu Jinhan , Hyat Mahnoor , Salone Sullivan D. , Goodman Zachary T. , Roffman Joshua L. , Karcher Nicole R. , Hernandez Leanna M. , Forsyth Jennifer K. , Bearden Carrie E. 
Published: December 2024

ABSTRACT
Attention problems are among the earliest precursors of schizophrenia (SCZ). Here we examine relationships between multi-trait polygenic scores (PGS), psychotic spectrum symptoms, and attention-related phenotypes in an adolescent cohort (ABCD; n=11,855, mean baseline age: 9.93). Across three biennial visits, greater attentional variability and altered functional connectivity were associated with severity of psychotic-like experiences (PLEs). In European ancestry youth, neuropsychiatric and cognitive PGS were associated with greater PLE severity and greater attentional variability; notably, the effect of multi-trait PGS on PLEs weakened over time. Attentional variability partially mediated relationships between multi-trait PGS and PLEs, explaining 4-16% of these associations. Lastly, multi-trait PGS parsed by developmental co-expression patterns were significantly associated with greater PLE severity, though effect sizes were larger for genome-wide PGS. Findings suggest that broad neurodevelopmental liability is implicated in pathophysiology of psychotic spectrum symptomatology in adolescence, and attentional variability may act as an intermediate between risk variants and symptom expression.
TCMM: A Unified Database for Traditional Chinese Medicine Modernization and Therapeutic Innovations
Authors: Ren Zhixiang , Ren Yiming , Li Zeting , Xu Huan 
Published: December 2024

ABSTRACTMining the potential of traditional Chinese medicine (TCM) in treating modern diseases requires a profound understanding of its action mechanism and a comprehensive knowledge system that seamlessly bridges modern medical insights with traditional theories. However, existing databases for modernizing TCM are plagued by varying degrees of information loss, which impede the multidimensional dissection of pharmacological effects. To address this challenge, we introduce traditional Chinese medicine modernization (TCMM), the currently largest modernized TCM database that integrates pioneering intelligent pipelines. By aligning high-quality TCM and Western medicine data, TCMM boasts the most extensive TCM modernization knowledge, including 20 types of modernized TCM concepts such as prescription, ingredient, target and 46 biological relations among them, totaling 3,447,023 records. We demonstrate the efficacy and reliability of TCMM with two features, prescription generation and knowledge discovery, the outcomes show consistency with biological experimental results. A publicly available web interface is athttps://www.tcmm.net.cn/.
Mapping echocardiogram reports to a structured ontology: a task for statistical machine learning or large language models?
Authors: Subramaniam Suganya , Rizvi Sara , Ramesh Ramya , Sehgal Vibhor , Gurusamy Brinda , Arif Hikamtullah , Tran Jeffrey , Thamman Ritu , Anyanwu Emeka , Mastouri Ronald , Mackensen G. Burkhard , Arnaout Rima 
Published: December 2024

AbstractBackgroundBig data has the potential to revolutionize echocardiography by enabling novel research and rigorous, scalable quality improvement. Text reports are a critical part of such analyses, and ontology is a key strategy for promoting interoperability of heterogeneous data through consistent tagging. Currently, echocardiogram reports include both structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can help and includes both non-deep learning and deep-learning (e.g., large language model, or LLM) based techniques. Challenges to date in using echo text with LLMs include small corpus size, domain-specific language, and high need for accuracy and clinical meaning in model results.MethodsWe tested whether we could map echocardiography text to a structured, three-level hierarchical ontology using NLP. We used two methods: statistical machine learning (EchoMap) and one-shot inference using the Generative Pre-trained Transformer (GPT) large language model. We tested against eight datasets from 24 different institutions and compared both methods against clinician-scored ground truth.ResultsDespite all adhering to clinical guidelines, there were notable differences by institution in what information was included in data dictionaries for structured reporting. EchoMap performed best in mapping test set sentences to the ontology, with validation accuracy of 98% for the first level of the ontology, 93% for the first and second level, and 79% for the first, second, and third levels. EchoMap retained good performance across external test datasets and displayed the ability to extrapolate to examples not initially included in training. EchoMap’s accuracy was comparable to one-shot GPT at the first level of the ontology and outperformed GPT at second and third levels.ConclusionsWe show that statistical machine learning can achieve good performance on text mapping tasks and may be especially useful for small, specialized text datasets. Furthermore, this work highlights the utility of a high-resolution, standardized cardiac ontology to harmonize reports across institutions.
Development and Validation of Prediction Models for Sentinel Lymph Node Status Indicating Postmastectomy Radiotherapy in Breast Cancer: a Population-Based Study of 18 185 Women
Authors: Svensson Miriam , Bendahl Pär-Ola , Alkner Sara , Hansson Emma , Rydén Lisa , Dihge Looket 
Published: December 2024

AbstractBackgroundPostmastectomy radiotherapy (PMRT) impairs the outcome of immediate breast reconstruction (IBR) in patients with breast cancer, and the sentinel lymph node (SLN) status is crucial in evaluating the need for PMRT. This study aimed to develop models to preoperatively predict the risk for SLN metastasis indicating the need for PMRT.MethodsWomen diagnosed with clinically node-negative (cN0) T1-T2 breast cancer from January 2014 to December 2017 were identified within the Swedish National Quality Register for Breast Cancer. Nomograms for nodal prediction based on preoperatively accessible patient and tumor characteristics were developed using adaptive LASSO logistic regression. The prediction of ≥1 and >2 SLN macrometastases (macro-SLNMs) adheres to the current guidelines on use of PMRT and reflects the exclusion criteria in ongoing clinical trials aiming to de-escalate locoregional radiotherapy in patients with 1-2 macro-SLNMs, respectively. Predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and calibration plots.ResultsOverall, 18 185 women were grouped into training (n=13 656) and validation (n= 4529) cohorts. The well-calibrated nomograms predicting ≥1 and >2 macro-SLNMs displayed AUCs of 0.708 and 0.740, respectively, upon validation. By using the nomogram for ≥1 macro-SLNMs, the risk could be updated from the pre-test population prevalence 13% to the post-test range 2%-75%.ConclusionNomograms based on routine patient and tumor characteristics could be used for prediction of SLN status that would indicate PMRT need and assist the decision-making on IBR for patients with cN0 breast cancer.
Beyond IQ: Executive function deficits and their relation to functional, clinical, and neuroimaging outcomes in 3q29 deletion syndrome
Authors: Pollak Rebecca M , Sefik Esra , Aberizk Katrina , Duan Kuaikuai , Espana Roberto , Guest Ryan M , Goldman-Yassen Adam E , Goines Katrina , Novacek Derek M , Saulnier Celine A , Klaiman Cheryl , Pulver Stormi , Cubells Joseph F , Burrell T Lindsey , Shultz Sarah , Walker Elaine F , Murphy Melissa M , Mulle Jennifer G 
Published: December 2024

AbstractBackground3q29 deletion syndrome (3q29del) is a rare (∼1:30,000) genomic disorder associated with a wide array of neurodevelopmental and psychiatric phenotypes. Prior work by our team identified clinically significant executive function deficits in 47% of individuals with 3q29del; however, the nuances of executive function in this population have not been described.MethodsWe used the Behavior Rating Inventory of Executive Function (BRIEF) to perform the first in-depth assessment of real-world executive functioning in a cohort of 32 individuals with 3q29del (62.50% male, mean age=14.50±8.26 years). High-resolution structural magnetic resonance imaging was performed on a subset of participants (n=24).ResultsWe found global deficits in executive function; individuals with 3q29del scored significantly higher than the population mean on the BRIEF Global Executive Composite (GEC) and all subscales. 81.25% of study subjects (n=26) scored in the clinical range on at least one BRIEF subscale. BRIEF GEC T scores were significantly higher among 3q29del participants with a diagnosis of ADHD, and BRIEF GEC T scores were significantly associated with schizophrenia spectrum symptoms as measured by the SIPS. The BRIEF-2 ADHD Form accurately (sensitivity=86.70%) classified individuals with 3q29del based on ADHD diagnosis status, highlighting its potential use as a screener for ADHD in this population. BRIEF GEC T scores were significantly correlated with cerebellar white matter and subregional cerebellar cortex volumes.ConclusionsTogether, these data expand our understanding of the phenotypic spectrum of 3q29del and identify executive function as a core feature linked to both psychiatric and neuroanatomical features of the syndrome.
An AI-Cyborg System for Adaptive Intelligent Modulation of Organoid Maturation
Authors: Liu Ren , Ren Zhaolin , Zhang Xinhe , Li Qiang , Wang Wenbo , Lin Zuwan , Lee Richard T. , Ding Jie , Li Na , Liu Jia 
Published: December 2024

AbstractRecent advancements in flexible bioelectronics have enabled continuous, long-term stable interrogation and intervention of biological systems. However, effectively utilizing the interrogated data to modulate biological systems to achieve specific biomedical and biological goals remains a challenge. In this study, we introduce an AI-driven bioelectronics system that integrates tissue-like, flexible bioelectronics with cyber learning algorithms to create a long-term, real-time bidirectionalbioelectronic interface withoptimizedadaptive intelligentmodulation (BIO-AIM). When integrated with biological systems as an AI-cyborg system, BIO-AIM continuously adapts and optimizes stimulation parameters based on stable cell state mapping, allowing for real-time, closed-loop feedback through tissue-embedded flexible electrode arrays. Applied to human pluripotent stem cell-derived cardiac organoids, BIO-AIM identifies optimized stimulation conditions that accelerate functional maturation. The effectiveness of this approach is validated through enhanced extracellular spike waveforms, increased conduction velocity, and improved sarcomere organization, outperforming both fixed and no stimulation conditions.
Convolutional-LSTM Approach for Temporal Catch Hotspots (CATCH): An AI-Driven Model for Spatiotemporal Forecasting of Fisheries Catch Probability Densities
Authors: Agmata Altair , Guðmundsson Svanur 
Published: December 2024

AbstractEfficient fisheries management is crucial for sustaining both marine ecosystems and the economies that heavily depend on them, such as Iceland. Current fishing practices involve decisions informed by a combination of personal experience, current data on environmental and oceanographic conditions, reports from other captains, and target species within the constraints of the fishing quota. However, the intricate spatiotemporal dynamics of fish behaviour make it difficult to predict fish stock distributions. Despite technological breakthroughs in fishing vessel data collection, much of the decision-making still relies heavily on subjective judgment, highlighting the need for more robust, data-driven predictive methods. This paper presents CATCH, a convolutional long short-term memory neural network model that forecasts fish stock probability densities over time and space in Icelandic waters. The framework represents the first utilization of large-scale Icelandic fishing fleet data integrating multidimensional inputs like depth, bottom temperature, and catch data to produce accurate, multivariate forecasts. The model demonstrates high accuracy, low error metrics, and strong structural similarity to observed data, generalizing well across key species such as Atlantic cod, haddock, saithe, golden redfish, and Greenland halibut. Its promising results suggest deep learning models have the potential to optimize fisheries operations, enhance sustainability, and support data-driven decision-making.
A rapid approach for linear epitope vaccine profiling reveals unexpected epitope tag immunogenicity
Authors: Browne-Cole Kirsten , Hanning Kyrin R. , Beijerling Kevin , Rousseau Meghan , Loh Jacelyn , Kelton William 
Published: December 2024

AbstractAntibody epitope profiling is essential for assessing the robustness of vaccine-induced immune responses, particularly while in development. Despite advancements in computational tools, high throughput experimental epitope validation remains an important step. Here, we describe a readily accessible method for rapid linear epitope profiling using phage-displayed oligo pools in combination with Nanopore deep sequencing. We applied this approach to TeeVax3, a Group A Streptococcus vaccine candidate, to investigate the antibody response generated in a pre-clinical rabbit model and assess antigen immunogenicity. Surprisingly, we found a strong bias in antibody binding response towards the N-terminal epitope tag used for purification. These tags are widely reported to have low immunogenicity and are frequently left uncleaved in pre-clinical studies. We further confirmed that the observed immune response against the epitope tag dominated even the conformational binding response and, using synthetic peptides, narrowed the epitope down to a set of 10 residues inclusive of the Histidine residues. Our findings highlight the importance of epitope-tag removal in pre-clinical studies and demonstrate the utility of rapid nanopore sequencing for early-stage vaccine evaluation.
Oligomerization of PRMT1 Regulated RNA-Binding Protein Cascade Promotes Pancreatic Ductal Adenocarcinoma
Authors: Ru Yanxia , Zhou Xinyi , Wang Xijiao , Sun Wenxin , He Yaohui , Hu Guosheng , Li Wenjuan , Hu Die , Jiang Meizhi , Su Zhiming , Niu Fengfeng , Chen Gang , Zeng Jinzhang , Sui Sen-Fang , Liu Wen , Li Yaowang , Chen Siming 
Published: December 2024

AbstractPRMT1 is the predominant type I protein arginine methyltransferase responsible for generating monomethylarginine and asymmetric dimethylarginine (MMA and ADMA) in various protein substrates. It regulates numerous biological processes, including RNA metabolism, mRNA splicing, DNA damage repair, and chromatin dynamics. The crystal structure of rat PRMT1 has been previously reported as a homodimer; however, higher-order oligomeric species of human PRMT1 have been observed in vivo, and their structural basis remains elusive. In this study, we present the cryo-EM structure of human PRMT1 in its oligomeric form, revealing novel interfaces crucial for PRMT1 self-assembly and normal function. PRMT1 shows a strong preference for intrinsically disordered RGG/RG motifs, which are commonly found in RNA-binding proteins (RBPs), highlighting its critical role in regulating RNA metabolism. In vitro studies indicate that disrupting PRMT1 oligomerization impairs its binding affinity for RGG motif substrates, thereby reducing arginine methylation levels on these substrates. This finding emphasizes the essential role of the oligomeric state of PRMT1 in its function with RGG motif-containing RBPs. In vivo, the loss of PRMT1 oligomerization leads to decreased global ADMA levels in pancreatic ductal adenocarcinoma (PDAC) cells and inhibits PDAC tumor growth. Collectively, we propose that PRMT1 oligomerization, rather than mere dimerization, is sufficient for PRMT1-driven PDAC tumor growth, offering novel insights into the potential therapeutic targeting of PRMT1 oligomeric forms in PDAC.Graphical abstract
Forecasting Dengue across Brazil with LSTM Neural Networks and SHAP-Driven Lagged Climate and Spatial Effects
Authors: Chen Xiang , Moraga Paula 
Published: December 2024

AbstractBackgroundDengue fever is a mosquito-borne viral disease that poses significant health risks and socioeconomic challenges in Brazil, necessitating accurate forecasting across its 27 federal states. With the country’s diverse climate and geographical spread, effective dengue prediction requires models that can account for both climate variations and spatial dynamics. This study addresses these needs by using Long Short-Term Memory (LSTM) neural networks enhanced with SHapley Additive exPlanations (SHAP) integrating optimal lagged climate variables and spatial influence from neighboring states.MethodAn LSTM-based model was developed to forecast dengue cases across Brazil’s 27 federal states, incorporating a comprehensive set of climate and spatial variables. SHAP was used to identify and select the most important lagged climate predictors. Additionally, lagged dengue cases from neighboring states were included to capture spatial dependencies. Model performance was evaluated using MAE, MAPE, and CRPS, with comparisons to baseline models.ResultsThe LSTM-Climate-Spatial model consistently demonstrated superior performance, effectively integrating temporal, climatic, and spatial information to capture the complex dynamics of dengue transmission. SHAP-enhanced variable selection improved accuracy by focusing on key drivers such as temperature, precipitation and humidity. The inclusion of spatial effects further strengthened forecasts in highly connected states showcasing the model’s adaptability and robustness.ConclusionThis study presents a scalable and robust framework for dengue forecasting across Brazil, effectively integrating temporal, climatic, and spatial information into an LSTM-based model. By integrating diverse data sources, the framework captures key transmission drivers, demonstrating the potential of LSTM neural networks for robust predictions. These findings provide valuable insights to enhance public health strategies and outbreak preparedness in Brazil.
Evaporation and pathogenesis of levitated bacteria-laden surrogate respiratory fluid droplets: At different relative humidity and evaporation stages
Authors: Agharkar Amey Nitin , Hajra Dipasree , Dewangan Kush Kumar , Roy Durbar , Chakravortty Dipshikha , Basu Saptarshi 
Published: December 2024

AbstractHypothesisAerosols are the principal cause of airborne infections and respiratory diseases. Droplets ejected from the host can evaporate and form a precipitate in the air (aerosol mode), or evaporate for some time, and fall on the ground (mixed mode) or directly fall on the ground and evaporate as sessile mode. Different evaporation modes, stages of evaporation and the relative humidity (RH) conditions affect the survival and infectivity of the bacteria in the precipitate.ExperimentsWe have investigated three droplet diameter reduction ratio-based stages of evaporation of a bacteria-laden levitated droplet at two different RH settings and evaporation modes (aerosol and mixed) mimicking real-life scenarios. The low RH condition mimics evaporation in arid regions. e.g., Delhi and the high RH conditions imitate cold areas like London. The study analyses the mass transport, micro-characterizes the samples, and investigates the survival and infectivity of bacteria in the sample.FindingsThe bacteria survive more in the high RH condition than in the low RH condition for all diameter reduction ratio-based stages and modes of evaporation. For the aerosol mode, at a fixed RH condition, the evaporation time plays a vital role as the bacteria in early-stage partially dried samples are more viable than the full precipitate. The evaporation rate, and the generation of reactive oxygen species (ROS) cause a remarkable difference in the viability and infectivity of the bacterial samples. Therefore, our findings report that the evaporation history of an infected droplet is an indispensable factor in determining bacterial viability and subsequent infectivity.
What are you talking about? Representation of the Topic of Speech in the Human Brain
Authors: Boncz Adam , Tóth Brigitta , Winkler István 
Published: December 2024

AbstractA sentence outside context provides very restricted information to the listener. Real understanding requires one to be able to place it within the sequence of sentences forming a coherent “story” and just as importantly, within one’s general knowledge of the “topic”. Here we demonstrate the existence of functional brain networks robustly sensitive to the topic of ca. 6-minute-long coherent newspaper articles. Main network hubs are located in parietal and temporal brain regions. These networks changed very slowly during the course of the article, consistent with their involvement in representing the topic rather than the story aspect of the context. Because even the meaning of words can depend on the topic (see e.g., the word “shuttle” in transportation and weaving) the topic-sensitive network must also be intimately linked with the mental lexicon underlying language comprehension.Significance StatementWhen we listen to someone talking, our brain continuously integrates the speech stream with our general knowledge associated with the topic. While this is a central process of comprehension, it is still unclear how the brain maintains the relevant representations. Here we demonstrate the existence of a cortical network, which is sensitive to the topic of the speech just heard. This network may form the neural substrate for “topic representation” in the brain. Understanding how such representations are maintained and accessed would let us reveal the computations enabling rapid linguistic integration, a key process in human communication.
Examining Saudi Arabia’s housing tenure and price trends: a comparative study with global context
Authors: Dano Umar Lawal 
Published: December 2024

Purpose
This paper aims to analyze and compare housing tenure and model housing price index (HPI) in Saudi Arabia with selected Organization for Economic Cooperation Development (OECD) countries.

Design/methodology/approach
The research uses quantitative data from the Saudi 2022 Statistical Census and OECD sources. Analytical methods include polynomial regression modeling for housing price trends and analysis of variance (ANOVA) to explore the relationship between housing variables, alongside descriptive and inferential statistics.

Findings
The polynomial regression analysis reveals distinct HPI trends across the studied countries, indicating stability and growth. Countries like Australia, France and the US are projected to see substantial HPI increases by 2026, reaching values around 175, signaling strong market recovery and growth. Greece’s trajectory is marked by fluctuations, recovering modestly post-2020, while Saudi Arabia’s market shows stability with a slight increase forecasted to 92.8 by 2026. The ANOVA analysis for Saudi Arabia highlights significant regional differences in housing tenure, with economic conditions and housing types significantly impacting tenure patterns.

Originality/value
This study fills a void in research by offering a comparative analysis of housing tenure and HPI, shedding light on how economic and demographic factors influence housing trends. The findings are crucial for policymakers to develop targeted strategies that address affordability and stability, catering to diverse demographic needs.