bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deciphering progressive lesion areas in breast cancer spatial transcriptomics via TGR-NMF
Authors: Li Juntao , Xiang Shan , Wei Dongqing 
Published: January 2025

Abstract
Identifying spatial domains is critical for understanding breast cancer tissue heterogeneity and providing insights into tumor progression. However, dropout events introduces computational challenges and the lack of transparency in methods such as graph neural networks limits their interpretability. This study aimed to decipher disease progression-related spatial domains in breast cancer spatial transcriptomics by developing the three graph regularized non-negative matrix factorization (TGR-NMF). A unitization strategy was proposed to mitigate the impact of dropout events on the computational process, enabling utilization of the complete gene expression count data. By integrating one gene expression neighbor topology and two spatial position neighbor topologies, TGR-NMF was developed for constructing an interpretable low-dimensional representation of spatial transcriptomic data. The progressive lesion area that can reveal the progression of breast cancer was uncovered through heterogeneity analysis. Moreover, several related pathogenic genes and signal pathways on this area were identified by using gene enrichment and cell communication analysis.
A novel framework for phage-host prediction via logical probability theory and network sparsification
Authors: Wei Ankang , Zhan Huanghan , Xiao Zhen , Zhao Weizhong , Jiang Xingpeng 
Published: January 2025

Abstract
Bacterial resistance has emerged as one of the greatest threats to human health, and phages have shown tremendous potential in addressing the issue of drug-resistant bacteria by lysing host. The identification of phage–host interactions (PHI) is crucial for addressing bacterial infections. Some existing computational methods for predicting PHI are suboptimal in terms of prediction efficiency due to the limited types of available information. Despite the emergence of some supporting information, the generalizability of models using this information is limited by the small scale of the databases. Additionally, most existing models overlook the sparsity of association data, which severely impacts their predictive performance as well. In this study, we propose a dual-view sparse network model (DSPHI) to predict PHI, which leverages logical probability theory and network sparsification. Specifically, we first constructed similarity networks using the sequences of phages and hosts respectively, and then sparsified these networks, enabling the model to focus more on key information during the learning process, thereby improving prediction efficiency. Next, we utilize logical probability theory to compute high-order logical information between phages (hosts), which is known as mutual information. Subsequently, we connect this information in node form to the sparse phage (host) similarity network, resulting in a phage (host) heterogeneous network that better integrates the two information views, thereby reducing the complexity of model computation and enhancing information aggregation capabilities. The hidden features of phages and hosts are explored through graph learning algorithms. Experimental results demonstrate that mutual information is effective information in predicting PHI, and the sparsification procedure of similarity networks significantly improves the model’s predictive performance.
Predictive modelling of acute Promyelocytic leukaemia resistance to retinoic acid therapy
Authors: Sánchez-Villanueva José A , N’Guyen Lia , Poplineau Mathilde , Duprez Estelle , Remy Élisabeth , Thieffry Denis 
Published: January 2025

Abstract
Acute Promyelocytic Leukaemia (APL) arises from an aberrant chromosomal translocation involving the Retinoic Acid Receptor Alpha (RARA) gene, predominantly with the Promyelocytic Leukaemia (PML) or Promyelocytic Leukaemia Zinc Finger (PLZF) genes. The resulting oncoproteins block the haematopoietic differentiation program promoting aberrant proliferative promyelocytes. Retinoic Acid (RA) therapy is successful in most of the PML::RARA patients, while PLZF::RARA patients frequently become resistant and relapse. Recent studies pointed to various underlying molecular components, but their precise contributions remain to be deciphered. We developed a logical network model integrating signalling, transcriptional, and epigenetic regulatory mechanisms, which captures key features of the APL cell responses to RA depending on the genetic background. The explicit inclusion of the histone methyltransferase EZH2 allowed the assessment of its role in the resistance mechanism, distinguishing between its canonical and non-canonical activities. The model dynamics was thoroughly analysed using tools integrated in the public software suite maintained by the CoLoMoTo consortium (https://colomoto.github.io/). The model serves as a solid basis to assess the roles of novel regulatory mechanisms, as well as to explore novel therapeutical approaches in silico.
BioGSF: a graph-driven semantic feature integration framework for biomedical relation extraction
Authors: Yang Yang , Zheng Zixuan , Xu Yuyang , Wei Huifang , Yan Wenying 
Published: January 2025

Abstract
The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on pre-trained language models (PLMs) represents a common framework for end-to-end resolution of the biomedical relation extraction (RE) problem. Nevertheless, sequence-based PLMs, to a certain extent, fail to fully exploit the connections between semantics and the topological features formed by these connections. In this study, we presented a graph-driven framework named BioGSF for RE from the literature by integrating shortest dependency paths (SDP) with entity-pair graph through the employment of the graph neural network model. Initially, we leveraged dependency relationships to obtain the SDP between entities and incorporated this information into the entity-pair graph. Subsequently, the graph attention network was utilized to acquire the topological information of the entity-pair graph. Ultimately, the obtained topological information was combined with the semantic features of the contextual information for relation classification. Our method was evaluated on two distinct datasets, namely S4 and BioRED. The outcomes reveal that BioGSF not only attains the superior performance among previous models with a micro-F1 score of 96.68% (S4) and 96.03% (BioRED), but also demands the shortest running times. BioGSF emerges as an efficient framework for biomedical RE.
Bayesian gene set benchmark dose estimation for “omic” responses
Authors: Zilber Daniel , Messier Kyle P , House John , Parham Fred , Auerbach Scott S , Wheeler Matthew W 
Published: January 2025

Abstract

Motivation
Estimating a toxic reference point using tools like the benchmark dose (BMD) is a critical step in setting policy to regulate pollution and ensure safe environments. Toxicity can be measured for different endpoints, including changes in gene expression and histopathology for various tissues, and is typically explored one gene or tissue at a time in a univariate setting that ignores correlation. In this work, we develop a multivariate estimation procedure to estimate the BMD for specified gene sets. Our approach extends the foundational univariate approach by accounting for correlation in a statistically principled way.

Results
We illustrate the method using data from a 5-day rat study and Hallmark gene sets and compare to existing BMD results computed by the EPA for both gene sets and apical histopathology endpoints. In contrast to previous ad-hoc methods, our principled approach provides the needed extension to bring the foundational univariate method into the multivariate world of transcriptomics. In addition to use in a regulatory setting, our method can provide hypothesis generation when gene sets correspond to mechanistic pathways.

Availability and implementation
BS-BMD is implemented in R and C++ and available at https://github.com/NIEHS/BS-BMD.
FlowPacker: Protein side-chain packing with torsional flow matching
Authors: Lee Jin Sub , Kim Philip M 
Published: January 2025

Abstract

Motivation
Accurate prediction of protein side-chain conformations is necessary to understand protein folding, protein-protein interactions and facilitate de novo protein design.

Results
Here we apply torsional flow matching and equivariant graph attention to develop FlowPacker, a fast and performant model to predict protein side-chain conformations conditioned on the protein sequence and backbone. We show that FlowPacker outperforms previous state-of-the-art baselines across most metrics with improved runtime. We further show that FlowPacker can be used to inpaint missing side-chain coordinates and also for multimeric targets, and exhibits strong performance on a test set of antibody-antigen complexes.

Availability
Code is available at https://gitlab.com/mjslee0921/flowpacker.

Supplementary information
Supplementary data are available at Bioinformatics online.
Neurovascular phase coherence is altered in Alzheimer’s disease
Authors: Bjerkan Juliane , Meglič Bernard , Lancaster Gemma , Kobal Jan , McClintock Peter V E , Crawford Trevor J , Stefanovska Aneta 
Published: January 2025

Abstract
Alzheimer’s disease is the commonest form of dementia, but its cause still remains elusive. It is characterized by neurodegeneration, with amyloid-beta and tau aggregation. Recently, however, the roles of the vasculature and the neurovascular unit are being highlighted as important for disease progression. In particular, there is reduced microvascular density, and altered gene expression in vascular and glial cells. Structural changes naturally impact the functioning of the neurovascular unit, and the goal of the study was to quantify the corresponding changes in vivo, non-invasively. Our assessment is based on recordings of brain oxygenation, neuronal and cardiorespiratory activities, captured by functional near-infrared spectroscopy, electroencephalogram, electrocardiogram and respiration effort, respectively. Two groups were compared: an Alzheimer’s disease group (N = 19) and a control group (N = 20) of similar age. The time-series were analysed using methods that can capture multi-scale and time-varying oscillations such as the wavelet transform power and wavelet phase coherence. The Alzheimer’s disease group shows a significant decrease in the power of brain oxygenation oscillations compared to the control group. There is also a significant global reduction in the phase coherence between brain oxygenation time-series. The neurovascular phase coherence around 0.1 Hz is also significantly reduced in the Alzheimer’s disease group. In addition, the average respiration rate is increased in the Alzheimer’s disease group compared to the control group. We show that the phase coherence between vascular and neuronal activities is reduced in Alzheimer’s disease compared to the control group, indicating altered functioning of the neurovascular unit. The brain oxygenation dynamics reveals reduced power and coordination of oscillations, especially in frequency ranges that are associated with vasomotion. This could lead to reduced oxygen delivery to the brain, which could affect ATP production, and potentially reduce amyloid-beta clearance. These changes in neurovascular dynamics have potential for early diagnosis, as a marker of disease progression, and for evaluating the effect of interventions.
Amyloid-β and tau deposition in traumatic brain injury: a study of Vietnam War veterans
Authors: de Bruin Hannah , Groot Colin , Kamps Suzie , Vijverberg Everard G B , Steward Anna , Dehsarvi Amir , Pijnenburg Yolande A L , Ossenkoppele Rik , Franzmeier Nicolai 
Published: January 2025

Abstract
Traumatic brain injury is widely viewed as a risk factor for dementia, but the biological mechanisms underlying this association are still unclear. In previous studies, traumatic brain injury has been associated with the hallmark pathologies of Alzheimer’s disease, i.e. amyloid-β plaques and neurofibrillary tangles comprised of hyperphosphorylated tau. Depending on the type and location of trauma, traumatic brain injury can induce spatially heterogeneous brain lesions that may pre-dispose for the development of Alzheimer’s disease pathology in aging. Therefore, we hypothesized that a history of traumatic brain injury may be related to spatially heterogeneous amyloid-β and tau pathology patterns that deviate from the stereotypical temporo-parietal patterns in Alzheimer’s disease. To test this, we included 103 Vietnam War veterans of whom 65 had experienced traumatic brain injury (n = 40, 38.8% mild; n = 25, 24.3% moderate/severe). Most individuals had a history of 1 (n = 35, 53.8%) or 2 (n = 15, 23.1%) traumatic brain injury events. We included the group without a history of traumatic brain injury (n = 38, 36.9%) as controls. The majority was cognitively normal (n = 80, 77.7%), while a subset had mild cognitive impairment (n = 23, 22.3%). All participants underwent [18F]florbetapir/Amyvid amyloid-β PET and [18F]flortaucipir/Tauvid tau-PET 39.63 ± 18.39 years after their last traumatic brain injury event. We found no differences in global amyloid-β and tau-PET levels between groups, suggesting that a history of traumatic brain injury does not pre-dispose to accumulate amyloid-β or tau pathology in general. However, we found that traumatic brain injury was associated with altered spatial patterns of amyloid-β and tau, with relatively greater deposition in fronto-parietal brain regions. These regions are prone to damage in traumatic brain injury, while they are typically only affected in later stages of Alzheimer’s disease. Moreover, in our traumatic brain injury groups, the association between amyloid-β and tau was reduced in Alzheimer-typical temporal regions but increased in frontal regions that are commonly associated with traumatic brain injury. Altogether, while acknowledging the relatively small sample size and generally low levels of Alzheimer’s disease pathology in this sample, our findings suggest that traumatic brain injury induces spatial patterns of amyloid-β and tau that differ from patterns observed in typical Alzheimer’s disease. Furthermore, traumatic brain injury may be associated with a de-coupling of amyloid-β and tau in regions vulnerable in Alzheimer’s disease. These findings indicate that focal brain damage in early/mid-life may change neurodegenerative trajectories in late-life.
A photovoltaic-storage system configuration and operation optimization model based on Model Predictive Control
Authors: He Puyu , Zhang Yuhong , Jiao Jie , Ren Wenshi , Zhang Jiyuan , Long Zhuhan 
Published: January 2025

Abstract
The deployment of distributed photovoltaic technology is of paramount importance for developing a novel power system architecture wherein renewable energy constitutes the primary energy source. This paper investigates the construction and operation of a residential photovoltaic energy storage system in the context of the current step-peak-valley tariff system. Firstly, an introduction to the structure of the photovoltaic -energy storage system and the associated tariff system will be provided. Secondly, to minimize the investment and annual operational and maintenance costs of the photovoltaic -energy storage system, an optimal capacity allocation model for photovoltaic and storage is established, which serves as the foundation for the two-layer operation optimization model. And the installed capacity of photovoltaic and energy storage is derived from the capacity allocation model and utilized as the fundamental parameter in the operation optimization model. Furthermore, taking into account the impact of the step-peak-valley tariff on the user's long-term energy use strategy, a two-layer optimization operation algorithm for the photovoltaic -storage system based on model predictive control is proposed. The upper model is an annual optimization based on the step tariff, to maximize the annual comprehensive revenue. The lower model is a daily rolling optimization based on the peak-valley tariff, to minimize the daily operation cost. The operation schemes of the photovoltaic system and energy storage in the lower layer model utilize the upper layer optimization results as a reference point, correcting for any deviations in the system state due to uncertainty factors. Ultimately, the results of the arithmetic simulation demonstrate that the proposed models can delay the introduction of high-step tariffs and significantly enhance the overall benefit to residential users.
Study on the Assessment of Solar Energy Potential for Production of Electricity: A Case Study of Uzbekistan
Authors: Kodirov Dilshod , Tursunov Obid , Zhou Yuguang , Li Gang , Yu Qiang 
Published: January 2025

Abstract
Solar energy is a well-known form of renewable energy because of its direct conversion to heat and electricity, limited supply, ease of use, and potential for storage. Finding and selecting appropriate locations for the use of solar energy is essential given Uzbekistan's potential in this area, as well as the nation's need for this kind of energy. Hence, it is essential to create a thorough and comprehensive examination of the solar energy potential if Uzbekistan wants to use solar energy as its main renewable energy source for the production of electricity. The development of solar power generation, both nationally and regionally, would greatly benefit from the application of such an analysis technique. This study analyzes hourly, daily, monthly, and annual data on the effective use of solar energy in Uzbekistan. According to these investigations, the maximum power from solar photovoltaics was observed in July. In this case, the average value of fell in the range of 11-13 hours was 1.43 kWh/m2, and the value of the aggregate during the day was 7.15 kWh/m2. The results of the simulation experiment were compared with the generation of electricity from a 4.5 kW solar power plant. Consequently, the annual electricity generation was 1224.18 kWh. The forecast figure modeled by the PVsyst program was 1428.24 kWh. It was established that the difference between the results of the simulation experiment and the actual performance indicators was 14.5%. Based on satellite data, the lowest indicator of the intensity of direct solar radiation in the territory of the country was calculated for the Fergana Valley (Fergana, Andijan, and Namangan regions), which varied from 1500 kWh/m2 per year to 1900-2100 kWh/m2 per year in the north of the country (Republic of Karakalpakstan, Khorezm region, and north of the Navoi region).
Effect of Inter Turbine Spacing in Omnidirectional Wind for Savonius Cluster: A Computational Fluid Dynamics and Artificial Neural Network Approach
Authors: Malge Atul Umakant , Tomar Shivam Singh , Dewan Anupam 
Published: January 2025

Abstract
This research explores the potential of wind energy as a sustainable solution to the environmental challenges by focusing on the deployment of vertical axis wind turbines in confined spaces, such as urban rooftops, ship decks and gas stations etc. It aims to identify the optimal configuration for a cluster of three rotors by varying geometric parameters, assessing the omnidirectionality of the rotors, and using an artificial neural network as an optimization tool. The methodology involves two-dimensional unsteady Reynolds-averaged Navier-Stokes computational fluid dynamics simulations followed by building and training an artificial neural network. The results indicate that the best cluster configuration, achieving a power coefficient of 0.2517, consists of a horizontal distance of 0.08D, vertical distance of 0.1D, and wind direction of 232°. The accuracy of neural network is demonstrated by a mere 1.206% discrepancy with the computational analysis, thus highlighting its potential as a time-saving tool in the optimization process. Additionally, the average power coefficient across all wind directions was found to be 0.194, thus confirming the strong omnidirectional characteristics of cluster. The study also notes a significant speed-up region between the rotors, which creates a low-pressure area, enhancing the power coefficient of the downstream rotor and demonstrating the advantages of this pressure-based turbine configuration. This comprehensive study establishes the efficacy of neural network in optimizing wind turbine clusters, thus significantly expediting their design and development process.
Safety and Efficacy of 3 Alternative Regimens Against Relapsing Plasmodium vivax Malaria in Glucose 6-Phosphate Dehydrogenase–Deficient Patients in the Brazilian Amazon (ALTPRIM)
Authors: Barbosa Laila , Brito-Sousa José , Nascimento Cristiana , Pacheco Ana , Alexandre Márcia , Alencar Aline , Melo Marly , Omena Aretha , Souza Ingrid , Silva Emanuelle , Queiroz Michael , Siqueira Vitória , Rabelo Cristina , Baía-da-Silva Djane , Silva Débora , Rocha Yasmin , Barbosa Antônio , Castro Ramon , Almeida Anne , Brito Marcelo , Lopes Adriana , Balieiro Antônio , Costa Mônica , Amaral Thais , Valle Cristiane , Vieira Alexia , Gonzaga Jhon , Pereira Dhélio , Alecrim Maria , Monteiro Wuelton , Lacerda Marcus , Melo Gisely 
Published: January 2025

Abstract

Background
Daily primaquine-induced hemolysis is a common cause of complications during Plasmodium vivax malaria treatment in individuals with glucose 6-phosphate dehydrogenase deficiency (G6PDd). Alternative regimens balancing safety and efficacy are needed.

Methods
G6PDd participants with P. vivax malaria from 2 sites in Brazilian Amazon between 2018 and 2022 were randomly allocated to 3 arms that received chloroquine (CQ) from day 1 to day 3 plus (arm 1) a 7-day course of primaquine (PQ) (0.5 mg/kg/day), beginning at day 5; (arm 2) weekly PQ over 8 weeks (0.75 mg/kg/wk); or (arm 3) weekly CQ over 12 weeks (5 mg/kg/wk). A normal-G6PD participants group was also enrolled in parallel using CQ for 3 days plus PQ for 7 days. The primary focus was safety profile; secondary was the number of patients free from the first recurrence until day 180.

Results
Fifty-four G6PDd participants were enrolled. There were 2 participants in arm 1, but the arm was halted due to safety concerns. The weekly PQ group presented higher hemoglobin decreases in day 3 after first dose (Δhemoglobin = −1.61) than the weekly CQ group (Δhemoglobin = −0.99), but efficacy was superior over the 6-month follow-up.

Conclusions
Postponing the beginning of daily PQ to day 5, when less oxidative stress related to malaria itself would, in theory, decrease hemolytic effects of the drug in G6PDd patients, was not shown to be safe. Weekly CQ avoiding the first relapse did not stop further relapses. Weekly PQ, as already demonstrated in Southeast Asia, was equally safe and efficacious in patients from Latin America.
Clinical Trials Registration. NCT03529396.