bluuebunny/crossref_metadata_2025_split huggingface dataset:
Statistical relationships across epigenomes using large-scale hierarchical clustering
Authors: Kim Anastasiia , Lubbers Nicholas , Steadman Christina R. , Sanbonmatsu Karissa Y. 
Published: October 2024

AbstractEnvironmental toxins and pathogens can influence epigenetic modifications on chromosomes, thereby leaving trace evidence of exposures. However, the avalanche of epigenomic data is difficult to parse for biological interpretation given non-linear complex patterns and relationships. This attractive challenge in epigenomic data lends itself to machine learning for discerning infectivity and susceptibility. In this study, we explore over 3,000 epigenomes of uninfected individuals and provide a comprehensive characterization of the relationships among epigenetic modifiers, their modifiers, and specific immune cell types across all chromosomes using hierarchical clustering.
A hybrid computational model of cancer spheroid growth with ribose-induced collagen stiffening
Authors: Botticelli Margherita , Metzcar John , Phillips Thomas , Cox Susan , Keshavanarayana Pradeep , Spill Fabian 
Published: October 2024

AbstractMetastasis, the leading cause of death in cancer patients, arises when cancer cells disseminate from a primary solid tumour to distant organs. Growth and invasion of the solid tumour often involve collective cell migration, which is profoundly influenced by cell-cell interactions and the extracellular matrix (ECM). The ECM’s biochemical composition and mechanical properties, such as stiffness, regulate cancer cell behaviour and migration dynamics. Mathematical modelling serves as a pivotal tool for studying and predicting these complex dynamics, with hybrid discrete-continuous models offering a powerful approach by combining agent-based representations of cells with continuum descriptions of the surrounding microenvironment. In this study, we investigate the impact of ECM stiffness, modulated via ribose-induced collagen cross-linking, on cancer spheroid growth and invasion. We employed a hybrid discrete-continuous model implemented in PhysiCell to simulate spheroid dynamics, successfully replicating three-dimensionalin vitroexperiments. The model incorporates detailed representations of cell-cell and cell-ECM interactions, ECM remodelling, and cell proliferation. Our simulations align with experimental observations of two breast cancer cell lines, non-invasive MCF7 and invasive HCC1954, under varying ECM stiffness conditions. The results demonstrate that increased ECM stiffness due to ribose-induced cross-linking inhibits spheroid invasion in invasive cells, whereas non-invasive cells remain largely unaffected. Furthermore, our simulations show that higher ECM degradation by the cells not only enables spheroid growth and invasion but also facilitates the formation of multicellular protrusions. Conversely, increasing the maximum speed that cells can reach due to cell-ECM interactions enhances spheroid growth while promoting single-cell invasion. This hybrid modelling approach enhances our understanding of the interplay between cancer cell migration, proliferation, and ECM mechanical properties, paving the way for future studies incorporating additional ECM characteristics and microenvironmental conditions.
Data of the Insect Biome Atlas: a metabarcoding survey of the terrestrial arthropods of Sweden and Madagascar
Authors: Miraldo A , Sundh J , Iwaszkiewicz-Eggebrecht E , Buczek M , Goodsell R , Johansson H , Fisher BL , Raharinjanahary D , Rajoelison ET , Ranaivo C , Randrianandrasana C , Rafanomezantsoa J-J , Manoharan L , Granqvist E , van Dijk LJA , Alberg L , Åhlén D , Aspebo M , Åström S , Bellviken A , Bergman P-E , Björklund S , Björkman MP , Deng J , Desborough L , Dolff E , Eliasson A , Elmquist H , Emanuelsson H , Erixon R , Fahlen L , Frogner C , Fürst P , Grabs A , Grudd H , Guasconi D , Gunnarsson M , Häggqvist S , Hed A , Hörnström E , Johansson H , Jönsson A , Kanerot S , Karlsson A , Karlsson D , Klinth M , Kraft T , Lahti R , Larsson M , Lernefalk H , Lestander Y , Lindholm L-T , Lindholm M , Ljung U , Ljung K , Lundberg J , Lundin E , Malmenius M , Marquina D , Martinelli J , Mertz L , Nilsson J , Patchett A , Persson N , Persson J , Prus-Frankowska M , Regazzoni E , Rosander K-G , Rydgård M , Sandblom C , Skord J , Stålhandske T , Svensson F , Szpryngiel S , Tajani K , Tyboni M , Ugarph C , Vestermark L , Vilhelmsson J , Wahlgren N , Wass A , Wetterstrand P , Łukasik P , Tack AJM , Andersson AF , Roslin T , Ronquist F 
Published: October 2024

AbstractWe present the data from the Insect Biome Atlas project (IBA), characterizing the terrestrial arthropod faunas of Sweden and Madagascar. Over 12 months, weekly Malaise trap samples were collected at 203 locations within 100 sites in Sweden and at 50 locations within 33 sites in Madagascar; this was complemented by soil and litter samples from each site. The field samples comprise 4,749 Malaise trap, 192 soil and 192 litter samples from Sweden and 2,566 Malaise trap and 190 litter samples from Madagascar. Samples were processed using mild lysis or homogenization, followed by DNA metabarcoding of COI (418 bp). The data comprise 698,378 non-chimeric sequence variants from Sweden and 687,866 from Madagascar, representing 33,989 (33,046 Arthropoda) and 77,599 (77,380 Arthropoda) operational taxonomic units, respectively. These are the most comprehensive data presented on these faunas so far, allowing unique analyses of the size, composition, spatial turnover and seasonal dynamics of the sampled communities. They also provide an invaluable baseline against which to gauge future changes.
Benchmarking of Low Coverage Sequencing Workflows for Precision Genotyping in Eggplant
Authors: Baraja-Fonseca Virginia , Arrones Andrea , Vilanova Santiago , Plazas Mariola , Prohens Jaime , Bombarely Aureliano , Gramazio Pietro 
Published: October 2024

AbstractLow-coverage whole-genome sequencing (lcWGS) presents a cost-effective solution for genotyping, particularly in applications requiring high marker density and reduced costs. In this study, we evaluated lcWGS for eggplant genotyping using eight founder accessions from the first eggplant MAGIC population (MEGGIC), testing various sequencing coverages and minimum depth of coverage (DP) thresholds with two SNP callers, Freebayes and GATK. Reference SNP panels were used to estimate the percentage of common biallelic SNPs (i.e, true positives, TP) relative to the low coverage datasets (accuracy) and the SNP panels themselves (sensitivity), along with the percentage of TP with the same genotype across the two datasets (genotypic concordance). Sequencing coverages as low as 1X and 2X achieved high accuracy but lacked sufficient sensitivity and genotypic concordance. However, 3X sequencing reached approximately 10% less sensitivity than 5X while maintaining genotypic concordance above 90% at any DP threshold. Freebayes outperformed GATK in terms of sensitivity and genotypic concordance. Therefore, we used this software to conduct a pilot test with some MEGGIC lines from the fifth generation of selfing (S5), comparing their datasets with a gold standard (GS). Sequencing coverages as low as 1X identified a substantial number of TP, with 3X significantly increasing the yield, particularly at moderate DP thresholds. Additionally, at least 30% of the TP were consistently genotyped in all lines when using coverages greater than 2X, regardless of the DP threshold applied. This study highlights the importance of using a GS to reduce false positives and demonstrates that lcWGS, with proper filtering, is a valuable alternative to high-coverage sequencing for eggplant genotyping, with potential applications to other crops.
Multimodal Freezing of Gait Detection: Analyzing the Benefits of Physiological Data
Authors: Yang Po-Kai , Filtjens Benjamin , Ginis Pieter , Goris Maaike , Nieuwboer Alice , Gilat Moran , Slaets Peter , Vanrumste Bart 
Published: October 2024

AbstractFreezing of gait (FOG) is a debilitating symptom of Parkinson’s disease (PD), characterized by an absence or reduction in forward movement of the legs despite the intention to walk. Detecting FOG during free-living conditions presents significant challenges, particularly when using only inertial measurement unit (IMU) data, as it must be distinguished from voluntary stopping events that also feature reduced forward movement. Influences from stress and anxiety, measurable through galvanic skin response (GSR) and electrocardiogram (ECG), may assist in distinguishing FOG from normal gait and stopping. However, no study has investigated the fusion of IMU, GSR, and ECG for FOG detection. Therefore, this study introduced two methods: a twostep approach that first identified reduced forward movement segments using a Transformer-based model with IMU data, followed by an XGBoost model classifying these segments as FOG or stopping using IMU, GSR, and ECG features; and an end-to-end approach employing a multi-stage temporal convolutional network to directly classify FOG and stopping segments from IMU, GSR, and ECG data. Results showed that the two-step approach with all data modalities achieved an average F1 score of 0.728 and F1@50 of 0.725, while the end-to-end approach scored 0.771 and 0.759, respectively. However, no significant difference was found compared to using only IMU data in both approaches (p-values: 0.466 to 0.887). In conclusion, adding physiological data does not provide a statistically significant benefit in distinguishing between FOG and stopping.
Longitudinal impact of different treatment sequences of second-generation antipsychotics on metabolic outcomes: a study using targeted maximum likelihood estimation
Authors: Feng Yaning , Wong Kenneth Chi-Yin , Leung Perry Bok-Man , Lee Benedict Ka-Wa , Sham Pak-Chung , Lui Simon Sai-Yu , So Hon-Cheong 
Published: October 2024

AbstractBackgroundSecond-generation antipsychotics (SGAs) cause metabolic side-effects. However, patients’ metabolic profiles were influenced by many time-invariant and time-varying confounders. Real-world evidence on the long-term, dynamic effects of SGAs (e.g. different treatment sequences) is limited. We employed advanced causal inference methods to evaluate metabolic impact of SGAs in a naturalistic cohort.MethodsWe followed 696 Chinese patients with schizophrenia-spectrum disorders who received SGAs. Longitudinal targeted maximum likelihood estimation (LTMLE) was used to estimate the average treatment effects (ATEs) of continuous SGA treatment versus “no treatment” on metabolic outcomes, including total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride (TG), fasting glucose (FG), and body-mass-index (BMI), over 6-18 months at 3-month intervals. LTMLE accounted for both time-invariant and time-varying confounders. We also evaluated whether side-effects persisted after SGA discontinuation.ResultsThe average treatment effects (ATEs) of continuous SGA treatment on BMI and TG showed an inverted U-shaped pattern, peaking at 12 months and declining afterwards. Similar patterns were observed for TC and LDL, albeit the ATEs peaked at 15 months. For FG and HDL, the ATEs peaked at ∼6 months. The adverse impact of SGAs on BMI persisted even after medication discontinuation, yet other metabolic parameters did not show such lingering side-effects. Compared with other SGAs, clozapine and olanzapine showed greater metabolic side-effects.ConclusionsOur real-world study suggests that metabolic side-effects may stabilize with prolonged continuous treatment. Clozapine and olanzapine confer greater cardiometabolic risks than other SGAs. The side-effects of SGAs on BMI may persist after drug discontinuation. These insights may guide antipsychotic choice and help improve the management of metabolic side-effects.
Quantification of droplet and contact transmission risks among elementary school students based on network analyses using video-recorded data
Authors: Kikuchi Shuta , Nakajima Keisuke , Kato Yasuki , Takizawa Takeshi , Sugiyama Junichi , Mukai Taisei , Kakizawa Yasushi , Kurahashi Setsuya 
Published: October 2024

AbstractElementary schools are environments in which immunologically immature students come into close contact with each other and are susceptible to the spread of infectious diseases. Analyzing the behavior of multiple students has been challenging, and the relationship between infections remains unclear. In this study, we analyzed the relevance between droplet and contact transmission and the behavior of elementary school students using video-recorded data, network analyses, and simulations. The analysis of communication behavior revealed the diverse nature of interactions among students. By calculating the droplet transmission probabilities based on conversation duration, this study quantified the risk of droplet transmission in elementary schools. The analysis of contact behavior introduces a novel approach for constructing contact networks based on contact history. According to this method, items such as desks, shirts, and doors have the potential to be used as fomites for virus transmission. In addition, the reliability of the predictions was demonstrated through micro-simulations. Interestingly, the micro-simulations indicated that the majority of virus copies were transmitted through single items, emphasizing the importance of targeted hygiene measures. This study contributes significantly to the prevention of infectious diseases in elementary schools by providing evidence-based information on transmission pathways and behavior-related risks. Moreover, the insights from this study can guide the development of simulation models for analyzing infection risks in educational settings.
Alpha thalassemia/mental retardation X-linked (ATRX) protein expression in human pituitary neuroendocrine tumours and its reported correlation to prognosis and clinical outcomes: A systematic review
Authors: Wang Edward , Rotondo Fabio , Cusimano Michael D. 
Published: October 2024

AbstractMutations in Alpha thalassemia/mental retardation X-linked (ATRX) have been implicated in several cancers, including gliomas, sarcomas, neuroendocrine tumors, and other mesenchymal malignancies. ATRX loss contributes to oncogenesis, accelerates tumor growth, and reduces survival by disrupting epigenetic and telomere mechanisms. Additionally, ATRX loss can increase tumor sensitivity to treatment therapies. While research has explored ATRX expression in many cancers, data on its relationship to prognosis in pituitary neuroendocrine tumors (PitNETs) remain inconsistent. This systematic review aims to summarize all available studies on ATRX mutations and expression in PitNETs. A systematic search of PubMed, Scopus, and EMBASE databases was conducted to identify publications between 2014 and 2024 that investigated ATRX mutations or expression in PitNETs, following PRISMA 2020 guidelines. Of 30 identified studies, nine met the inclusion criteria, covering a total of 500 PitNETs. Only 19 tumors (3.6%) showed a loss of ATRX expression. Among these, 58% exhibited corticotropic pathology, while 26% displayed lactotrophic pathology. A small subset of tumors (32%) was classified as pituitary carcinomas with aggressive and proliferative characteristics. Additionally, 16% demonstrated the alternative lengthening of telomeres (ALT) phenotype, 53% had concurrent TP53 mutations, and 21% had elevated Ki67 indices, indicating a higher proliferative index. Although ATRX mutations are rare in PitNETs, tumors with ATRX loss tend to be more aggressive and exhibit proliferative and transformative properties. Due to the limited number of cases, further studies with larger, prospective cohorts are needed to better understand the role of ATRX loss in PitNET progression and aggressiveness.
A nationwide joint spatial modelling of simultaneous epidemics of dengue, chikungunya, and Zika in Colombia
Authors: Picinini Freitas Laís , Carabali Mabel , Schmidt Alexandra M. , Salazar Flórez Jorge Emilio , Ávila Monsalve Brayan , García-Balaguera César , Restrepo Berta N. , Jaramillo-Ramirez Gloria I. , Zinszer Kate 
Published: October 2024

AbstractBackgroundChikungunya, and Zika emerged in the 2010s in the Americas, causing simultaneous epidemics with dengue. However, little is known of theseAedes-borne diseases (ABDs) joint patterns and contributors at the population-level.MethodsWe applied a novel Poisson-multinomial spatial model to the registered cases of dengue (n=291,820), chikungunya (n=75,913), and Zika (n=72,031) by municipality in Colombia, 2014-2016. This model estimates the relative risk of total ABDs cases and associated factors, and, simultaneously, the odds of presence and contributors of each disease using dengue as a baseline category. This approach allows us to identify combined characteristics of ABDs, since they are transmitted by the same mosquitoes, while also identifying differences between them.FindingsWe found an increased ABDs risk in valleys and south of the Andes, the Caribbean coast, and borders, with temperature as the main contributor (Relative Risk 2.32, 95% Credible Interval, CrI, 2.05-2.64). Generally, dengue presence was the most probable among the ABDs, although that of Zika was greater on Caribbean islands. Chikungunya and Zika were more likely present than dengue in municipalities with less vegetation (Odds Ratio, OR, 0.75, 95%CrI 0.65-0.86, and 0.85, 95%CrI 0.74-0.99, respectively). Chikungunya tended to be present in more socially vulnerable areas than dengue (OR 1.20, 95%CrI 0.99-1.44) and Zika (OR 1.19, 95%CrI 0.95-1.48).InterpretationImportant differences between the ABDs were identified and can help guide local and context-specific interventions, such as those aimed at preventing cases importation in border and tourism locations and reducing chikungunya burden in socially vulnerable regions.
Telemedicine in Eye Care During the COVID-19 Pandemic: A Review of Patient & Physician Perspectives
Authors: Halajyan Christina Perjuhi , Thomas Jonathan , Xu Benjamin , Gluckstein Jeffrey , Jiang Xuejuan 
Published: October 2024

ABSTRACTPurposeThere has been an increase in the adoption of telemedicine during the COVID-19 pandemic. This review used systematic search and review criteria to assess the literature on patient and physician perspectives toward telemedicine for vision care during the pandemic.MethodsWe conducted a comprehensive search on PubMed, Embase, and Scopus using relevant MeSH terms to identify peer-reviewed studies examining telemedicine use in eye care during the pandemic. The search strategy encompassed three key concepts: COVID-19 or pandemic, telehealth or telemedicine, and eye care. Further screening of references and similar articles was conducted to identify additional relevant studies.ResultsWe identified 24 relevant studies published between 2020 and 2022. Of these, 15 focused on patients’ perspectives, while 12 explored physicians’ perspectives. Predominantly cross-sectional in design, these studies were mainly conducted during the initial wave of the pandemic (March 2020 to June 2020), primarily in urban locations and hospital settings. Patients were satisfied with telemedicine and considered it equally effective to in-person visits. Patients believed telemedicine was convenient, improved eye care access, and a beneficial triage tool. Physicians acknowledged telemedicine’s convenience for follow-up assessment and its ability to expand the capacity for emergency cases. However, both patients and physicians voiced concerns about the absence of ancillary examination and technological challenges.ConclusionOur review highlights the positive impact of telemedicine in eye care during the pandemic. Nonetheless, most studies were limited in sample size. They did not delve into potential disparities based on race/ethnicity, socioeconomic status, and geographic location, factors that could influence patient attitudes toward telemedicine. Further research is warranted to validate the findings from our selected studies and explore factors that influence the implementation of telemedicine, particularly across various eye care subspecialties.
miRNA Biomarkers in Prostate Cancer: Leveraging Machine Learning for Improved Diagnostic Accuracy
Authors: Singh Shweta , Pathak Abhay Kumar , Kural Sukhad , Kumar Lalit , Bhardwaj Madan Gopal , Yadav Mahima , Trivedi Sameer , Das Parimal , Gupta Manjari , Jain Garima 
Published: October 2024

ABSTRACTProstate cancer (PCa) diagnosis often relies on prostate-specific antigen (PSA) testing, but its high false-positive rates often lead to unnecessary biopsies. MicroRNAs (miRNAs) have emerged as promising non-invasive biomarkers for cancer detection due to their stability in biological fluid and disease specificity. Despite their potential, the clinical translation of miRNAs as non-invasive cancer biomarkers is hindered by several challenges - population-based variability, environmental Factors, methodological Inconsistencies, lack of standardization, normalization Issues, and complexity of the biological System. These factors significantly impact the consistency of miRNA expression readouts, particularly in terms of Ct-values, across different studies, which in turn affects the determination of cutoff values that are crucial in a diagnostic setup. This preliminary study offers a pilot demonstration for integrating miRNA biomarker expression with machine learning (ML), which can help identify patterns and improve classification, potentially reducing the reliance on fixed cutoff values in certain contexts and pave the path to wider clinical translation.We analyzed the expression of key miRNAs (miR-21-5p, miR-221-3p, and miR-141-3p) in blood samples from patients with PCa and benign prostatic hyperplasia (BPH). Utilizing a Random Forest classifier, we achieved an accuracy of 77.42%, a precision of 86.21%, a recall of 71.43%, and an AUC-ROC score of 0.78. The application of ML enabled us to leverage complex features, such as combinations and ratios of miRNA expression data, which enhanced the robustness and reliability of the diagnostic model. Additionally, bioinformatics analysis of the preferential features identified by the ML model confirmed the biological relevance of these miRNAs in PCa-related pathways, further supporting their potential as clinical biomarkers.In the future, ML is poised to significantly enhance diagnostic performance compared to traditional linear analyses of a limited set of biomarkers. While our study did not explore multiple populations or the effects of methodological variables, it highlights the potential of ML by demonstrating improved accuracy and eliminating the need for cutoff values. This capability could broaden the applicability of miRNA-based diagnostics, making them more reliable and actionable in clinical settings. However, to fully realize this potential, further validation with larger and more diverse cohorts is essential. Overall, this study lays the groundwork for utilizing ML-enhanced miRNA panels as powerful tools for the early and non-invasive diagnosis of PCa in future clinical practice.Graphical Abstract