bluuebunny/crossref_metadata_2025_split huggingface dataset:
<em>Young Elizabeth</em><em>: Princess. Prisoner. Queen</em><em>. </em>By Nicola Tallis. London: Michael O&rsquo;Mara Books, 2024. ISBN 978-1-78929-519-1. xxxiv + 398 pp. &pound;25.
Authors: Strong Johanna 
Published: January 2025

Young Elizabeth: Princess. Prisoner. Queen. By Nicola Tallis. London: Michael O’Mara Books, 2024. ISBN 978-1-78929-519-1. xxxiv + 398 pp. £25.
Controlling Factors of Hydraulic Fractures Based on a Post-Fracturing Cored Well in Glutenite Reservoirs: A Case Study from Mahu Depression, Junggar Basin
Authors: Li Xiangyang , Ji Hancheng , Wang Haibo , Li Fengxia , Huang Zhiwen , Chen Liang 
Published: January 2025

Summary
A test site was established for hydraulic fracturing testing, and a horizontal well was drilled for coring to study hydraulic fracture (HF) in the Baikouquan Formation (Fm) on the northern slope of Mahu Depression, Junggar Basin, western China. A total of 345 fractures were obtained over 323.19-m cores, and HFs were identified by integrating the core, imaging logs, and computed tomography (CT) scans. Shear and tensional fractures, both forms of HFs, were found in the glutenite reservoir, with shear fractures being nearly twice as common as tensional fractures. This result contradicts the hypothesis of HF propagation in the glutenite reservoir during petroleum exploration, which suggests that the HF is prone to bypass the gravels, mainly forming tensional fractures. The condition of building an HF network was evaluated based on the present in-situ stress, rock brittleness, and natural fracture. Fracturing treatments are examined to understand the roots of the varying distribution of HFs. The fracture geometry was further studied based on post-fracturing cores. The HFs consist of a principal fracture subperpendicular to the wellbore with a considerable width and branch fractures that intersect the wellbore obliquely at a smaller angle with a lower width. Controlling factors for the propagation of HFs were investigated from both geological and fracturing treatment perspectives. The findings are as follows: (1) HFs prefer to penetrate gravels in the glutenite reservoir when the gravel is large in size, poorly sorted, and high in content; (2) two parameters control the fracture density and mode of fracture propagation, which are the minimum distance between the cored well and the fractured well’s cluster of perforation (SC) and distance density (DS); higher fracture densities and more shear fractures can occur when SC is lower and DS is higher.
Temperature’s Impact on High-Overmature Shale’s Mechanical Properties: Atomic Force Microscopy Study
Authors: Zhou Tingqiang , Yan Haifeng , Zhou Xiaoyi , Zhang Jinxing , Liao Yuhui , Tang Xin , Chen Xiangru 
Published: January 2025

Summary
In the field of geological engineering, heat treatment techniques are crucial for optimizing the mechanical properties of rocks, particularly in soft formations and those with special geological conditions. These techniques significantly enhance the pore volume (PV) and optimize the overall performance of rocks. In the study, atomic force microscopy (AFM) and fluid injection experiments were used to analyze the PV, surface properties, and micromechanical behaviors of Niutitang shale under different temperature treatments. The results showed that the PV of shale significantly improved under temperature treatment at 400°C, especially the volume of pores 2–20 nm in size, and the projection area of a pore also expanded compared with that under ambient temperature. In addition, the geometric instability of the pore cross section is enhanced, and the reduced modulus reaches its minimum in the 400°C treated shale. The study reveals the threshold effect of temperature on the microstructure of shale provides a scientific basis for temperature control in the heat treatment process and offers theoretical guidance for optimizing the performance of rocks in engineering applications.
WITHDRAWN: Robust Biofilm-Forming Ionizing Radiation Resistant Bacillus subtilis isolated from Ramsar-Iran, Emphasis on Molecular Genetic profiling Analysis
Authors: ali dhuha saeed , Author) reza vazifehmand(Former Corresponding , Rukayadi Yaya , Radu Son , Mirpour Mirsasan , sekawi zamberi , Author) Nor-Khaizura Mahmud Ab Rashid(New Corresponding 
Published: January 2025

Abstract

The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
DOLPHIN Advances Cell Representation Beyond Gene-Level by Integrating Exon-Level Quantification and Junction Reads with Deep Neural Networks
Authors: Song Kailu , Zheng Yumin , Zhao Bowen , Eidelman David H. , Ding Jun 
Published: January 2025

Abstract

The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets.
The Proficiency and Variability of Mathematical Ability in Populations with Autism Spectrum Disorder: A Meta-analysis
Authors: Mou Yi , Li Jiaxi , Ke Zijun , Li Xueyan , Zhang Bo , Liao Yini 
Published: January 2025

Abstract

The fundamental characteristics of mathematical ability in individuals with Autism Spectrum Disorder (ASD), specifically proficiency level and variability, remain inadequately understood. This meta-analysis reveals that individuals with ASD exhibit significantly lower math scores (Hedge’s g = -0.181/-0.592) and greater variability (natural logarithm of variability ratio, lnVR = 0.179/0.272) compared to the general population, as represented by norms of standardized math tests (M = 100, SD = 15; 3,051 participants) and typically developing (TD) control groups (2,351 participants). Group discrepancies in proficiency and variability were moderated by intelligence, age, or their interactions. The moderate math-intelligence relationship in the population with ASD provides a theoretical framework for studying their math abilities. Additionally, the discrepancy in math proficiency between the ASD and TD groups increases over the past four decades. These findings underscore the need for sustained, individualized mathematical education for individuals with ASD, and the importance of investigating the developmental trajectories of mathematical skills in ASD.
Probabilistic Greedy Algorithm Solver Using Magnetic Tunneling Junctions for Traveling Salesman Problem
Authors: Kämpfe Thomas , Zhang Ran , Li Xiaohan , Wan Cai-Hua , Hoffmann Raik , Hindenberg Meike , Xu Yingqian , Liu Shiqiang , Kong Dehao , Xiong Shilong , He Shikun , Vardar Alptekin , Dai Qiang , Gong Junlu , Sun Yihui , Zheng Zejie , Yu Guoqiang , Han Xiufeng 
Published: January 2025

Abstract

Combinatorial optimization problems are foundational challenges in fields such as artificial intelligence, logistics, and network design. Traditional algorithms, including greedy methods and dynamic programming, often struggle to balance computational efficiency and solution quality, particularly as problem complexity scales. To overcome these limitations, we propose a novel and efficient probabilistic optimization framework that integrates true random number generators (TRNGs) based on spin-transfer torque magnetic tunneling junctions (STT-MTJs). The inherent stochastic switching behavior of STT-MTJs enables dynamic configurability of random number distributions, which we leverage to introduce controlled randomness into a probabilistic greedy algorithm. By tuning a temperature parameter, our algorithm seamlessly transitions between deterministic and stochastic strategies, effectively balancing exploration and exploitation. Furthermore, we apply this framework to the traveling salesman problem (TSP), showcasing its ability to consistently produce high-quality solutions across diverse problem scales. Our algorithm demonstrates superior performance in both solution quality and convergence speed compared to classical approaches, such as simulated annealing and genetic algorithms. Specifically, in larger TSP instances involving up to 70 cities, it retains its performance advantage, achieving near-optimal solutions with fewer iterations and reduced computational costs. This work highlights the potential of integrating MTJ-based TRNGs into optimization algorithms, paving the way for future applications in probabilistic computing and hardware-accelerated optimization.
Enhancing the Prediction of Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Routine Full-Breast Mammograms
Authors: Zhang Daqu , Dihge Looket , Bendahl Pär-Ola , Arvidsson Ida , Dustler Magnus , Ellbrant Julia , Gulis Kim , Hjärtström Malin , Ohlsson Mattias , Rejmer Cornelia , Schmidt David , Zackrisson Sophia , Edén Patrik , Rydén Lisa 
Published: January 2025

Abstract

Background: With a trend toward de-escalation of axillary surgery in breast cancer, prediction models incorporating imaging modalities can help reassess the need for surgical axillary staging. Although mammography is routinely performed for breast cancer imaging, its potential in nodal staging remains underutilized. This study aims to employ advancements in deep learning (DL) to comprehensively evaluate the potential of routine mammograms for predicting lymph node metastasis (LNM) in preoperative clinical settings.
Methods: This retrospective study included 1,265 cN0 T1-T2 breast cancer patients, comprising 368 node-positive and 897 node-negative cases, diagnosed from 2009-2017 at three Swedish institutions. Patients diagnosed in 2017 were assigned to the independent test set (n=123, site 2) and the external test set (n=103, site 3), while the remaining patients (n=1,039, site 1 and 2) were used for model development and double cross-validation. A neck module, in conjunction with a ResNet backbone pretrained on unlabeled mammograms, was developed to extract global information from full-breast or region-of-interest (ROI) mammograms by predicting five cancer outcomes. Clinicopathological characteristics were combined with the learned mammogram features to predict LNM collaboratively. The models were evaluated using area under the receiver operating characteristic (ROC) curve (AUC), calibration, and decision curve analysis.
Results: Compared to models using only clinical variables, incorporating full-breast mammograms with preoperative clinical variables improved the ROC AUC from 0.690 ± 0.063 (SD) to 0.774 ± 0.057 in the independent test set and from 0.584 ± 0.068 to 0.637 ± 0.063 in the external test set. The combined model showed good calibration and, at sensitivity ≥ 90%, achieved a better net benefit, and a higher sentinel lymph node biopsy reduction rate of 41.7% in the independent test set. Full-breast mammograms showed comparable ability to tumor ROIs in predicting LNM.
Conclusion: Our findings underscore that routine mammograms, particularly full-breast images, can enhance preoperative nodal status prediction. They may substitute key postoperative predictors such as pathological tumor size and multifocality, aiding patient stratification before surgery. Interestingly, the added predictive value of mammography was consistent across all sites, whereas the overall performance varied over time periods and sites, likely due to advancements in equipment and procedures.
Prognostic gene expression and microRNA profiling signatures and genetic alterations in primary testicular diffuse large B-cell lymphoma
Authors: Young Ken , Shi Wen-Yu , Xu-Monette Zijun , Jia Youchao , Tzankov Alexandar , Go Heounjeong , Li Ling , Ponzoni Maurilio , Wang Yafei , Zhai Qiongli , Perry Anamarija , Wang Shi , Wang Xiaoxiao , Chiu April , Xu Mina , Visco Carlo , Dybkaer Karen , Withers Henry , Long Mark , Yuan Alyssa , Miao Yi , Li Jianyong , Macias Everardo , Shuai Wen , Wang Bangchen , Bhagat Govind , Zu Youli , Pan Zenggang , Choi William , Montes-Moreno Santiago , Chen Weina , Krieken J. Han van , Møller Michael , Zhan Fenghuang , Parsons Ben , Zhang Shanxiang , Hsi Eric , Sohani Aliyah , Abramson Jeremy , Ferreri Andrés , Xu Bing , Li Yong 
Published: January 2025

Abstract

Primary testicular diffuse large B-cell lymphoma (PT-DLBCL) is a rare and aggressive lymphoma with molecular heterogeneity not well characterize. In this study, we performed next-generation sequencing analysis for a large number of DNA and RNA samples from patients with PT-DLBCL. DNA sequencing analysis identified ≥ 3 chromosomes with copy number variations (CNVs) and microsatellite instability as prognostic biomarkers, rather than MYD88 mutations and genetic subtypes. Remarkably, targeted RNA-seq analysis in 195 patients revealed that TP53 mutations with a ≥ 40% variant allele frequency had significantly adverse prognostic impact, and that a 150-gene expression signature subdivided PT-DLBCL into two distinct clusters, termed as testicular lymphoma tumor (TLT) and microenvironment (ME) subtypes. The TLT subtype featured upregulation of genes involved in B-cell receptor signaling, cell cycle, DNA damage and repair, higher frequencies of CNVs and MYD88 mutations, elder ages, larger tumor sizes, and significantly poorer survival. Genomic microRNA profiling analysis identified significantly differentially expressed microRNAs between 113 PT-DLBCL and 180 systemic DLBCL patients, and further subdivided the PT-DLBCL cohort by microRNA signatures. The subcohort with upregulation of 16 microRNAs associated with PT-DLBCL and testicular tissue expression had significantly better survival. This study revealed characteristic genetic, gene expression, and microRNA profiles and heterogeneity in PT-DLBCL.
Assessment of Nutritional Status and Related Factors Among Patients Undergoing Maintenance Hemodialysis at Hargeisa Group Hospital, Somaliland
Authors: Nuh abdulkadir Mohamed , Ahmed Abdeta Mukhtar , Ali Hamse Arab , Abdi Hamse Adan 
Published: January 2025

Abstract

Background: Chronic kidney disease (CKD) is a long-term condition characterized by the gradual loss of kidney function over time with a reduction in the glomerular filtration rate. More than 800 million people are affected by chronic kidney diseases worldwide. Its prevalence is notably pronounced among older populations and those with inadequately managed diabetes mellitus and hypertension. Nutritional status among patients with chronic kidney disease (CKD) in East African countries, including Somaliland, has emerged as a significant concern requiring comprehensive attention. Several studies conducted across this region have indicated varying degrees of malnutrition among CKD patients.
Objective: This study aimed to assess nutritional status and related factors among patients with chronic kidney disease on haemodialysis at Hargeisa Group Hospital, Hargeisa, to generate evidence for practice.
Methods: An institution-based cross-sectional study was conducted for a period of four weeks from 30 June to 27 July 20222 at Hargeisa Group Hospital, Department of Haemodialysis.
Results: A multivariate linear regression model adjusted for different background characteristics, duration of chronic kidney disease, scheduled physical activity before becoming sick, history of anorexia and hemoglobin level was significantly associated with nutritional status (P < 0.05). For a unit increase in the duration of chronic kidney disease, the SGA-DMS score increased by 0.061 (ß = 0.061, P < 0.001). Similarly, a unit increase in the duration of anorexia (SGA-DMS) score increased by 4.746 (ß = 4.746, P < 0.001), indicating malnutrition. Conversely, having any scheduled physical activity before becoming sick decreased the SGA-DMS score by -4.604 (ß = -4.604, P =0.042). Conversely, a unit increase in the haemoglobin level decreased (SGA-DMS) the score by -1.463 (ß = -1.463, P =0.014).
Conclusion: Malnutrition is notably prevalent among hemodialysis patients at Hargeisa Group Hospital, particularly among those subjected to extended dialysis and those with a history of anorexia.
Tail risk market spillovers of oil to agricultural commodities: A time-frequency quantile approach
Authors: Olayinka Hammed Abiola , Khan Naveed , Adesina Oluwaseun A. , Yaya OlaOluwa S. 
Published: January 2025

Abstract

The interplay between commodities and oil prices provides a trade-off for investors and consumers. However, the increasing interconnectedness between these two series helps investors to design efficient investment strategies. Therefore, in this paper, the risk spillovers from oil market prices to that of agricultural commodities, namely, corn, wheat, soybeans, sugar, cotton, cocoa, coffee, lean hogs, and live cattle for the period spanning from May 1987 to December 2023 was investigated. For the analysis, we employ a quantile frequency connectedness approach, our findings reveal that under all market conditions (normal, bearish, and bullish) corn, wheat, and soybean are the return spillovers’ net transmitters, but sugar is a consistent net shocks receiver across all frequencies. Furthermore, agricultural commodities receive oil shocks from WTI during normal market conditions. At lower market conditions, the connectedness between commodities and oil is weaker than that of extreme market conditions (higher quantiles). Moreover, the network plots show that short-run connectedness dominates the long-run connectedness at price returns extremes. Similarly, our finding reports significant implications for policymakers, portfolio managers, and investors to diversify their investments in different market conditions.
JEL codes: C22 F21 G11 G13 G32
Bioinformatics Identification of Lactate-Associated Genes in Hepatocellular Carcinoma: G6PD’s Role in Immune Modulation
Authors: Qu Hao-ran , Wang Chao-qun , Sun Su-juan , Zhang Wen-wen , Du Xuan-shuang , Shuozhuo Yi-ao , Wang Xi-cheng , Pan Qin , Luo Feng-ling , Wu Hong-yan , Zhang Xiao-lian , Liu Min 
Published: January 2025

Abstract

Background
Hepatocellular carcinoma (HCC) is a major global health issue, with poor prognosis often associated with dysregulated metabolic pathways, especially lactate metabolism. This study explored the prognostic significance of lactate-associated genes in HCC and their potential as therapeutic targets.
Methods
We analyzed RNA-seq and clinical data from 374 patients with HCC from The Cancer Genome Atlas (TCGA) database. Using Cox regression, LASSO analysis, and Kaplan-Meier survival curves, we identified key lactate-associated genes associated with patient outcomes. Functional validations, including Western blot, flow cytometry, and molecular docking studies, were performed to confirm the biological impact of these genes.
Results
G6PD, IK, and CALML5 were identified as significant prognostic markers for HCC. A prognostic model was developed that effectively stratified patients into risk groups, which correlated with survival. G6PD’s role in immune modulation and its potential as a drug target were validated through biochemical assays and computational analyses. Functional assays in HepG2 cells confirmed that alterations in G6PD expression affect T cell activity, with knockdown enhancing IFN-γ production and overexpression inhibiting it, demonstrating G6PD’s role in immune evasion.
Conclusions
This study establishes lactate metabolism genes, particularly G6PD, as key prognostic markers in HCC. The validation of G6PD’s immunomodulatory effects further supports its potential as a therapeutic target for strategies aimed at enhancing immune surveillance and treatment outcomes in HCC.
Identification and Quantification of Primary Pollutants Impacting Aquaculture in Mine Surface Plant Areas
Authors: Kashale Chimanga , Jere Ezekiel Bob , Chembe Christopher 
Published: January 2025

Abstract

The aim of this study was to investigate and identify the primary pollutants impacting aquaculture in mine surface plant areas of Kalumbila District. Water samples were collected from four sites near mining operations to analyze key pollutants, including iron, copper, and cobalt, along with water quality parameters such as pH, dissolved oxygen (DO), temperature, and conductivity. Results revealed elevated cobalt levels (up to 60 mg/L at Site 1, far exceeding the recommended 0.01 mg/L), significant copper contamination, and slightly acidic conditions, particularly near the contamination source. Dissolved oxygen levels were lowest at Site 1 (4.5 ± 0.6 mg/L), indicating potential stress on aquatic life. The study demonstrated that proximity to mining activities exacerbates pollution levels, with pollutant concentrations decreasing at sites further away due to natural attenuation. The toxic effects of heavy metals, low DO, and high conductivity on aquatic ecosystems were assessed, highlighting severe risks such as gill damage, bioaccumulation, and osmotic stress, particularly at Site 1. These findings align with related studies on the ecological impacts of mining and emphasize the urgent need for targeted mitigation strategies. Recommendations include implementing advanced pollution control technologies, enhancing monitoring systems, and promoting sustainable mining practices. The study underscores the importance of balancing industrial activities with environmental preservation to safeguard aquatic ecosystems and local livelihoods.