bluuebunny/crossref_metadata_2025_split huggingface dataset:
Random Saturation Mutagenesis to Generate Highly Diverse Libraries for Directed Evolution
Authors: Weitz David , Zhang Xinge , Chen Anqi , Chen Liyin , Thakur Rohan , Ellis Perry , Xiao Yi , Milčić Miloš , Milcic Karla 
Published: January 2025

Abstract

Directed evolution improves the function of enzymes by diversifying the wild-type to generate variants and searching for the desired functions. When prior knowledge about the wild-type is limited, random mutagenesis is employed to introduce mutations at arbitrary positions. However, conventional methods exhibit significant bias in the distribution of amino acid substitutions, resulting in inaccessible chemical properties, limiting the chemical diversity, and hindering discovery of substantially improved variants. To overcome such limitations, we develop Random Saturation Mutagenesis (RSM) that uniformly introduces all amino acid substitutions across the gene at controlled rates by using overlapping site-mutagenesis oligo pools. Sequencing results illustrate the high quality and diversity of RSM libraries as compared to conventional libraries. We demonstrate the utility of RSM by improving thermotolerance of LipaseA. With only one round of mutagenesis, we identify two variants with over 40°C improvement in thermotolerance, demonstrating the potential of RSM to transform and accelerate directed evolution.
SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integration
Authors: Chen Huajun , Ding Keyan , Yu Jing , Huang Junjie , Yang Yuchen , Zhang Qiang 
Published: January 2025

Abstract

Scientific research increasingly relies on specialized computational tools, yet effectively utilizing these tools demands substantial domain expertise. While Large Language Models (LLMs) show promise in tool automation, they struggle to seamlessly integrate and orchestrate multiple tools for complex scientific workflows. Here, we present SciToolAgent, an LLM-powered agent that automates hundreds of scientific tools across biology, chemistry, and materials science. At its core, SciToolAgent leverages a scientific tool knowledge graph that enables intelligent tool selection and execution through graph-based retrieval-augmented generation. The agent also incorporates a comprehensive safety-checking module to ensure responsible and ethical tool usage. Extensive evaluations on a curated benchmark demonstrate that SciToolAgent significantly outperforms existing approaches. Case studies in protein engineering, chemical reactivity prediction, chemical synthesis, and metal-organic framework screening further demonstrate SciToolAgent's capability to automate complex scientific workflows, making advanced research tools accessible to both experts and non-experts.
Tropical deforestation causes considerable heat-related mortality
Authors: Reddington Carly , Smith Callum , Butt Edward , Baker Jessica , Oliveira Beatriz , Yamba Edmund , Spracklen Dominick 
Published: January 2025

Abstract

Tropical deforestation causes local climate warming and is a potential risk to human health. Previous studies have shown tropical deforestation increases heat stress and reduces safe outdoor working hours, but the impact of deforestation on heat-related mortality has not been quantified. Here we make the first pan-tropical assessment of the human mortality resulting from the local climate warming caused by tropical deforestation. Analysis of satellite data shows tropical deforestation during 2001 to 2020 exposed 345 million people to local climate warming with population-weighted daytime land surface warming of 0.27°C. We estimate this warming results in 28,000 additional deaths per year. Heat-related mortality rates were greatest in Southeast Asia with 9 deaths for every 100,000 people living in deforested areas, followed by Tropical Africa (5 deaths per 100,000 people) and Tropical Central and South America (3 deaths per 100,000 people). In regions that experienced forest loss, the local climate warming from deforestation is responsible for over one third of total climate related mortality highlighting the human health risk of local warming due to tropical deforestation.
Barriers in the control of Schistosomiasis with Mass distribution of Praziquantel in Bauchi State, Nigeria - A Phenomenological study
Authors: Charles Adeyemo Sunday , Oluwafemi Are-Daniel Obehi , Yetunde Adeleke Folasade , Maleka Abdulwaris Salisu , Joseph Akintade Odunlami , Raphael Ajayi Ayodele , Muhammad Abdulsalam Hussaini , Rabiu Nafi'u , Yunusa Usman 
Published: January 2025

Abstract

Schistosomiasis is a major public health problem in sub-Saharan Africa and major public health concern. There has been reportage of over 700 million of population at risk arising from an estimated affected population of at least 230 million worldwide. Estimates indicate that approximately 29 million individuals in Nigeria are infected with S. haematobium, while an alarming 101.3 million people are considered at risk. Despite the World Health Organization's recommendations for the widespread distribution of Praziquantel and the efforts of the Bauchi government to ensure access to clean water and health education in accordance with these guidelines, the prevalence of the issue continues to be alarmingly high. This study aims to explore the factors contributing to the failure of widespread distribution of Praziquantel to reduce the prevalence of schistosomiasis in Bauchi State, with a specific focus on the Dass local government area. The research is conducted in the Dass Local Government Area (LGA) of Bauchi State, Nigeria, which is positioned in the north-eastern region of the country. In-depth interviews were carried out with 20 stakeholders at the local government level. Coding was done using qualitative analysis software, ATLAS TI software for the data management and analysis data were coded into themes based on the research objectives. The inadequate involvement of government and NGOs and Community-related factors including fear of side effects and stigmatization where among factors highlighted as major barriers. This research underscores the various obstacles to successful schistosomiasis management in Dass Local Government via mass drug administration (MDA). Tackling issues associated with drug availability, the well-being of healthcare personnel, community education, and logistical challenges necessitates a collaborative effort among multiple stakeholders.
Predicting High Confidence ctDNA Somatic Variants with Ensemble Machine Learning Models
Authors: Maruzani Rugare , Fowler Anna , Brierley Liam , Jorgensen Andrea 
Published: January 2025

Abstract

Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA as a cancer biomarker depends on the ability to accurately detect somatic variants associated with cancer. Accurate somatic variant detection in circulating cell free DNA (cfDNA) NGS data requires filtering strategies to remove germline variants, and NGS artifacts. Rule-based variant filtering methods either remove a substantial number of true positive ctDNA variants along with false variant calls or retain an implausibly large number of total variants. Machine Learning (ML) enables identification of complex patterns which may improve ability to distinguish between real somatic ctDNA variants and false positive calls. We built two Random Forest (RF) models for predicting high confidence somatic ctDNA variants in low and high depth cfDNA NGS data. Low depth models were fitted and evaluated on whole exome sequencing (WES) cfDNA data at depths of approximately 10X while the high depth data was sequenced at approximately 500X. Both models utilise a set of 15 features from variants detected by bcftools, FreeBayes, LoFreq and Mutect2. High confidence ground truth sets were obtained from matched tissue biopsy samples. We benchmarked our models against rule-based filtering with a set of hard, medium, and soft thresholds. Precision-recall curves showed the high depth model outperformed rule-based filtering at all thresholds in validation data (PR-AUC 0.71). Partial dependence plots showed membership in the COSMIC database, absence from the dbSNP common variants database, and increasing read depth increased mean probability of high confidence somatic variant prediction in both models. Our results demonstrate the utility of supervised ML models for filtering variants in cfDNA data.
A Low-Cost and Scalable Landslide Monitoring and Early Warning System for Mountainous Regions Using Deep Learning-based Computer Vision
Authors: Chen Jie , Song Longfei , Pu Yuanyuan , Apel Derek 
Published: January 2025

Abstract

Landslides pose severe risks to lives, property, and infrastructure, particularly in mountainous regions where monitoring is hindered by harsh environmental conditions and complex terrains. This study proposes a scalable, low-cost landslide monitoring and early warning system that integrates deep learning and computer vision technologies. Using a single optical camera, time-lapse images of slopes were captured and processed through advanced alignment, masking, and histogram equalization to maintain accuracy under adverse conditions. The system combines static monitoring via image differencing with dynamic monitoring using a RAFT-based optical flow model to detect surface deformations and real-time displacements. Field tests conducted on Washington Makahdiot Cliff demonstrated the system’s capability to identify landslide risks across three deformation phases. Static analysis detected significant grayscale changes (> 100) linked to actual slope deformations, while dynamic monitoring achieved a displacement detection accuracy of 94.6% for movements up to 25 cm. A novel early warning algorithm, based on thresholds for abnormal pixel changes, successfully classified risk levels, with landslide risk coefficients exceeding 0.4 during landslide events and remaining below 0.1 otherwise. The system proved resilient to environmental challenges, such as fog and strong winds, and demonstrated a positive correlation between landslide risk coefficients and rainfall, underscoring the importance of integrating real-time environmental data. This method provides an effective, robust, and cost-efficient solution for landslide monitoring and mitigation in complex terrains.
UNICORN: Towards Universal Cellular Expression Prediction with an Explainable Multi-Task Learning Framework
Authors: Zhao Hongyu , Liu Tianyu , Huang Tinglin , Lin Yingxin , Ying Rex 
Published: January 2025

Abstract

Sequence-to-function analysis is a challenging task in human genetics, especially in predicting cell-type-specific multi-omic phenotypes from biological sequences such as individualized gene expression. Here, we present UNICORN, a new method with improved prediction performances than the existing methods. UNICORN takes the embeddings from biological sequences as well as external knowledge from pre-trained foundation models as inputs and optimizes the predictor with carefully-designed loss functions. We demonstrate that UNICORN outperforms the existing methods in both gene expression prediction and multi-omic phenotype prediction at the cellular level and the cell-type level, and it can also generate uncertainty scores of the predictions. Moreover, UNICORN is able to link personalized gene expression profiles with corresponding genome information. Finally, we show that UNICORN is capable of characterizing complex biological systems for different disease states or perturbations. Overall, embeddings from foundation models can facilitate the understanding of the role of biological sequences in the prediction task, and incorporating multi-omic information can enhance prediction performances.
Experimental and numerical study on tribological behavior and machinability in titanium indirect cryogenic machining with minimum quantity lubrication
Authors: Kim Doyoung , Ha Ju-Hyung , Ha Min Gi , Kim Dongmin 
Published: January 2025

Abstract

Cryogenic machining involves spraying cryogenic coolants to reduce the cutting tool temperature. In this study, cryogenic machining was applied to a titanium alloy with minimum quantity lubrication (MQL), and the tribological and machining performance were evaluated. As side-down milling was performed, the effects of cryogenic cooling and MQL were experimentally and numerically investigated with long machining distances (40, 000 mm), and the cutting force, tool wear, and tool temperature were analyzed. Compared to the wet condition, under the cryoMQL condition, which represents the simultaneous application of cryogenic cooling and MQL, the cutting force and flank wear length decreased by up to 17.7% and 46.4%, respectively. The cryogenically cooled and lubricated cutting tool enhanced the tribological performance, slowing tool wear. The reduced surface friction of the tool and tool wear decreased the frictional force and changed the trend of the cutting force according to the machining distance. The cryoMQL milling was simulated using DEFORM software. In the numerical study, a decrease in the tool temperature, which affects the reduction in cutting force and tool wear, was observed under cryoMQL conditions. The maximum tool temperature was reduced by 46.5% compared with that under wet conditions.
Assuring Correctness, Testing, and Verification of Communicating Stream X-Machine (CSXM)-Based Compiler by Integrating Machine Learning Models
Authors: SANUSI BASHIR A. , OGUNSHILE EMMANUEL K. , AYDIN MEHMET 
Published: January 2025

Abstract

Traditional compilers face challenges in detecting and correcting complex errors due to their reliance on static rule-based methods. This research introduces an innovative hybrid approach that integrates Machine Learning (ML) models with a Communicating Stream X-Machine (CSXM)-based compiler to improve error detection and possible suggestion. The CSXM formal method is combined with advanced ML techniques, including Random Forest (RF), Convolutional Neural Network (CNN), and an optimized CNN-LSTM model, leveraging temporal dependencies for improved performance. The ML-enhanced compiler was trained and evaluated on the DeepFix and Generated datasets using metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the CNN-LSTM outperformed RF and CNN across both datasets, achieving an accuracy of 89.72% on DeepFix and 90.00% on the Generated dataset, with minimal performance variance. This research demonstrates the potential of AI-driven compilers, providing a foundation for future exploration of intelligent, adaptive, and efficient compilation processes, and setting a benchmark for hybrid approaches in software engineering.
Kidney pathological findings of MYH9-related disease: a cross-sectional nationwide survey in Japan
Authors: Nakatani Ryo , Miura Kenichiro , Shirai Yoko , Ohtsuka Yasufumi , Ohwada Yoko , Konomoto Takao , Morohashi Tamaki , Tsugawa Koji , Taneda Sekiko , Honda Kazuho , Kunishima Shinji , Ishikura Kenji , Hattori Motoshi 
Published: January 2025

Abstract

Background
MYH9-related disease (MYH9-RD) is characterized by macrothrombocytopenia, hearing loss, and progressive kidney dysfunction. However, kidney biopsy is seldom performed because of low platelet counts. Although a review based on case reports described pathological findings of MYH9-RD included focal segmental glomerulosclerosis (FSGS) and glomerular basement membrane (GBM) abnormalities similar to those in Alport syndrome, no comprehensive studies of MYH9-RD have been performed.
Methods
We conducted a nationwide survey of MYH9-RD patients and collected kidney pathological samples, and immunofluorescence and electron microscopy images. All pathological samples were examined by multiple pediatric nephrologists and pathologists.
Results
Nine kidney biopsy samples from eight patients were included. Mesangial matrix expansion was observed in all nine samples (100%), and two samples (22%) had FSGS. Segmental foot process effacement was observed in all nine samples (100%), accompanied by irregularly aggregated podocyte dense material in seven samples (78%). Immunofluorescence analysis revealed three samples (33%) had immunoglobulin and/or complement deposition. Electron dense deposits (EDD) in the mesangium and GBM were observed in five samples (56%), including three samples with positive immunofluorescence. Electron microscopy also demonstrated endothelial cell swelling in seven samples (78%) and GBM abnormalities in five samples (55%), including thinning, thickening and splitting of the lamina densa.
Conclusions
Mesangial matrix expansion and segmental foot process effacement were commonly observed in MYH9-RD patients. In addition, various pathological findings including immunoglobulin and complement deposition with EDD and GBM abnormalities were also observed. These results may have important implications for the disease mechanism and treatment of MYH9-RD.
Fusion-Based Hybrid Model for SMS Spam Detection Integrating Local, Sequential, and Contextual Features
Authors: Al-Kaabi Hussein , Al-Ibraheemi Fuqdan , Jasim Ali Kadhim , AL-Rekabi Mohammed , parchami jaber 
Published: January 2025

Abstract

In the era of pervasive digital communication, SMS spam poses significant threats, including financial fraud and phishing attacks, necessitating robust detection mechanisms. This paper introduces a novel hybrid model for SMS spam detection, leveraging advanced deep-learning techniques to capture diverse textual features comprehensively. The model integrates Convolutional Neural Networks (CNN) for local feature extraction, Bidirectional Long Short-Term Memory (Bi-LSTM) networks for sequential dependencies, and Bidirectional Encoder Representations from Transformers (BERT) for contextual embeddings. A parallel architecture combines these components to achieve a holistic representation of SMS content. Fused feature vectors undergo attention-based selection to enhance computational efficiency while preserving critical information. Evaluated on the UCI SMS Spam Collection dataset using a 10-fold cross-validation strategy, the proposed model achieves a remarkable accuracy of 99.68%, outperforming state-of-the-art techniques. This work addresses the limitations of traditional and hybrid methods, offering a highly reliable and adaptable solution to the evolving challenges of SMS spam detection. Future directions include real-time adaptability, multimodal integration, and resource-efficient deployment.
Analyzing the Risk Factors for Hypoglycemia in Elderly Patients with Type 2 Diabetes using Constrainable Neural Additive Models
Authors: Si Hui , Bai Wei , Wang Difei , Kang Xin , Sun Longfeng , Tian Hui , Yan Shuangtong , Miao Xinyu , Hou Xin 
Published: January 2025

Abstract

Background
To identify factors that increase the risk of hypoglycemia in elderly Chinese patients diagnosed with type 2 diabetes.
Methods
Elderly patients with type 2 diabetes (n = 2,603, aged ≥ 60 years) were recruited from 150 hospitals across 21 provinces in China and underwent physical examinations, auxiliary tests, biochemical measurements, and a thorough review of their pertinent medical history in an outpatient setting.
Results
Among the included patients, 393 (15.1%) experienced hypoglycemia. Patients who were diagnosed before the age of 60 years exhibited a greater incidence of hypoglycemia than those diagnosed at or after the age of 60 years (18.2% vs. 11.1%, respectively). The incidence of hypoglycemia was significantly greater in patients using insulin and/or secretagogues than in those not using insulin and/or secretagogues (18.2% vs. 7.4%, respectively). A greater risk of hypoglycemia was also observed in patients with a family history of diabetes compared with those without a family history of diabetes (21.9% vs. 12.7%, respectively), as well as in patients with ≥ 1 chronic diabetes complications compared with those without complications (17.6% vs. 10.8%, respectively). The incidence of hypoglycemia in elderly type 2 diabetic patients increased with age and disease duration.
Conclusion
Risk factors for hypoglycemia in elderly Chinese patients with type 2 diabetes include an age at diagnosis younger than 60 years, prolonged disease duration, a family history of diabetes, chronic diabetic complications, and the use of secretagogues or insulin.
Volatility Dynamics and Futures Pricing: A GARCH-Based Analysis of CAC40 Markets
Authors: Houssam BOUGHABI 
Published: January 2025

Abstract

The futures market is wavering with uncertainty during an hesitating interest rate monetary policy, France had sorted out from a hard Euro debt crisis between 2016 and 2019, which had left interest rate in chaos, we have experienced an econometrics model using the software technology, by calibrating our model with the futures prices of CAC40.
The futures market proves to be stable in the beginning of calibration, indeed our model had respectively shown that the futures market could set a smart alarm for the monetary policy in France. The market of CAC40 index is volatile under those circumstances which finds more information in spinning a beautiful benchmark for political manoeuvring using interest rate as a way of adjusting France economics to the monetary scheme. We have estimated volatility using the Garch variant which proves empirically to be more suitable, we had found persistence and non vulnerability to past shocks.
PhosF3C: A Feature Fusion Architecture with Fine-Tuned Protein Language Model and Conformer for prediction of general phosphorylation site
Authors: Liu Yuhuan , Zhong Haitian , Zhai Jixiu , Wang Xueying , LU Tianchi 
Published: January 2025

Abstract

Protein phosphorylation, a key post-translational modification (PTM), provides essential insight into protein properties, making its prediction highly significant. Using the emerging capabilities of large language models (LLMs), we apply LoRA fine-tuning to ESM2, a powerful protein large language model, to efficiently extract features with minimal computational resources, optimizing task-specific text alignment. Additionally, we integrate the conformer architecture with the Feature Coupling Unit (FCU) to enhance local and global feature exchange, further improving prediction accuracy. Our model achieves state-of-the-art (SOTA) performance, obtaining AUC scores of 79.5%, 76.3%, and 71.4% at the S, T, and Y sites of the general data sets. Based on the powerful feature extraction capabilities of LLMs, we conduct a series of analyses on protein representations, including studies on their structure, sequence, and various chemical properties (such as Hydrophobicity (GRAVY), Surface Charge, and Isoelectric Point). We propose a test method called Linear Regression Tomography (LRT) which is a top-down method using representation to explore the model's feature extraction capabilities, offering a pathway to improved interpretability. Our resources, including data and code, are publicly accessible at \url{https://github.com/SkywalkerLuke/PhosF3C}