bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data
Authors: De Silva Senuri , Alli-Shaik Asfa , Gunaratne Jayantha 
Published: June 2024

AbstractComprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.
Stiffness analysis and structural optimization design of an air spring for ships
Authors: Cheng Yuqiang , Gao Hua , Ma Jianguo , Shuai Changgeng 
Published: June 2024

AbstractAn air spring (AS) for ships must have the structural strength of its bellows enhanced considerably to ensure its reliability under high internal pressure and strong impact. In this case, the stiffness of the bellows gradually dominates the overall stiffness of the AS. Nevertheless, the parameterization calculation of stiffness for an AS mainly focuses on its pneumatic stiffness. The bellows stiffness is normally analyzed by virtue of equivalent simplification or numeric simulation. There is not an effective parameterization calculation model for the stiffness of the bellows, making it difficult to achieve the structural optimization design of the bellows. In this paper, the shell theory was borrowed to build a mechanical model for the bellows. Subsequently, the state vector of the bellows was solved by precision integration and boundary condition. Iteration was conducted to identify the complex coupling relationship between the vector of the bellows and other parameters. On this basis, the parameterization calculation method was introduced for the stiffness of the bellows to obtain the vertical and horizontal stiffness of the AS. After that, a dual-membrane low-stiffness structure was designed to analyze the dominating factors affecting the strength and stiffness of the AS, which highlighted the way to the low-stiffness optimization design of high-strength ASs. In the end, three prototypes and one optimized prototype were tested to verify the correctness of the parameterization design model for stiffness as well as the effectiveness of the structural optimization design.
Rapid phenotypic differentiation in the iconic Japanese knotweed s.l. invading novel habitats
Authors: Yuan Wei , Pigliucci Massimo , Richards Christina L. 
Published: June 2024

AbstractUnderstanding the mechanisms that underlie plant invasions is critical for management and conservation of biodiversity. At the same time, invasive species also provide a unique opportunity to study rapid adaptation to complex environmental conditions. Using four replicate reciprocal transplant experiments across three habitats, we described patterns of phenotypic response and assessed the degree of local adaptation in knotweed populations. We found plants from beach habitats were generally smaller than plants from marsh and roadside habitats when grown in their home habitat. In the marsh habitat, marsh plants were generally larger than beach plants, but not different from roadside plants. There were no differences among plants grown in the roadside habitat. We found mixed evidence for local adaptation: plants from the marsh habitat had greater biomass in their “home” sites, while plants from beaches and roadsides had greater survival in their “home” sites compared to other plants. In sum, we found phenotypic differentiation and some support for the hypothesis of rapid local adaptation of plants from beach, marsh and roadside habitats. Identifying whether these patterns of differentiation result from genetic or heritable non-genetic mechanisms will require further work.
Severity influences categorical likelihood communications: A case study with Southeast Asian weather forecasters
Authors: Liefgreen Alice , Jenkins Sarah C. , Osman Sazali , Moron Lorenzo A. , Monteverde Maria Cecilia A. , Cayanan Esperanza O. , Hoang Lam , Tran Diep Quang , Ngo Huong , Putra Agie Wandala , Novikarany Riefda , Ayuliana Sefri , Beckett Rebecca , Harris Adam J. L. 
Published: June 2024

AbstractRisk assessments are common in multiple domains, from finance to medicine. They require evaluating an event’s potential severity and likelihood. We investigate the possible dependence of likelihood and severity within the domain of impact-based weather forecasting (IBF), following predictions derived from considering asymmetric loss functions. In a collaboration between UK psychologists and partners from four meteorological organisations in Southeast Asia, we conducted two studies (N = 363) eliciting weather warnings from forecasters. Forecasters provided warnings denoting higher likelihoods for high severity impacts than low severity impacts, despite these impacts being described as having the same explicit numerical likelihood of occurrence. This ‘Severity effect’ is pervasive, and we find it can have a continued influence even for an updated forecast. It is additionally observed when translating warnings made on a risk matrix to numerical probabilities.
Advantages of CoS2 nano-particles on the corrosion resistance and adhesiveness of epoxy coatings
Authors: Deyab M. A. , Alghamdi Majed M. , El-Zahhar Adel A. , El-Shamy Omnia A. A. 
Published: June 2024

AbstractResearchers face significant challenges because of the inadequate corrosion resistance and weak adherence of epoxy (EP) coatings. We deal with these issues here by means of a novel nano-composite coating (EP/nano-CoS2). In order to create a composite coating (EP/nano-CoS2), CoS2 nanoparticles (nano-CoS2) were prepared and incorporated to an epoxy (EP) resin. The synthesized CoS2 was characterized using XRD and FT-IR spectroscopic techniques. The mean particle size was determined using Scherer equation and found to be 19.38 nm. The zeta potential was also determined (− 9.78 mV). Electrochemical impedance spectroscopies (EIS) as well as pull-off assessments were used to quantify the EP/nano-CoS2 coating’s anti-corrosion capabilities and adhesive power. The findings demonstrate that the EIS variables of the EP/nano-CoS2 composite coating are markedly improved when compared to raw EP coating. The corrosion resistance or even adhesion of EP protective layer can be markedly increased by using the synthesized nanoparticles as nano-fillers.
Design optimization of large-scale bifacial photovoltaic module frame using deep learning surrogate model
Authors: Han Dongwoon , Kim Seongtak 
Published: June 2024

AbstractRecently, the wafers used in solar cells have been increasing in size, leading to larger module sizes and weights. The increased weight can cause deflection of photovoltaic (PV) module, which may lead to decreased cell efficiency. In this study, we developed a deep neural network (DNN)-based finite element (FE) surrogate model to obtain the optimal frame design factors that can improve deflection in large-scale bifacial PV module. Initially, an FE model was constructed for large-scale bifacial PV module. Based on this, the FE surrogate model was trained using 243 FEA datasets generated within the proposed range of factors. Furthermore, it was improved through Bayesian optimization and k-fold validation. As a result, the final loss value was $$3.743 \times 10^{-4}$$

3.743
×

10

-
4

, and the average mean absolute percentage error (MAPE) and coefficient of determination ($$R^2$$

R
2

) values for deflection and weight were 0.0017, 0.9972 for the training set, and 0.0020, 0.9962 for the test set, respectively. This indicates that the trained FE surrogate model possesses significant accuracy. After generating 1 million datasets within the range of frame design factors, the trained model was used to obtain predictions. Based on this data, the frame design factors that minimize both deflection and weight were identified as about a = 1.5, b = 13.7, c = 1.5, d = 3.0, e = 4.3. At this point, the deflection was 11.1 mm, and the weight was 3.6 kg. After altering the frame shape with the derived factors, FEA was conducted. The results matched for both deflection and weight, with almost no error. At this point, the weight increased by approximately 12.8% compared to the existing, while the deflection decreased by about 9.6%. Additionally, we analyzed the relationship between deflection and weight for each factor and secured the basis for the derived results. Consequently, our FE surrogate model accurately predicted the FEA results and quickly identified the optimal factors that minimize deflection and weight.
A retrospective study of deep learning generalization across two centers and multiple models of X-ray devices using COVID-19 chest-X rays
Authors: Fernández-Miranda Pablo Menéndez , Fraguela Enrique Marqués , de Linera-Alperi Marta Álvarez , Cobo Miriam , del Barrio Amaia Pérez , González David Rodríguez , Vega José A. , Iglesias Lara Lloret 
Published: June 2024

AbstractGeneralization of deep learning (DL) algorithms is critical for the secure implementation of computer-aided diagnosis systems in clinical practice. However, broad generalization remains to be a challenge in machine learning. This research aims to identify and study potential factors that can affect the internal validation and generalization of DL networks, namely the institution where the images come from, the image processing applied by the X-ray device, and the type of response function of the X-ray device. For these purposes, a pre-trained convolutional neural network (CNN) (VGG16) was trained three times for classifying COVID-19 and control chest radiographs with the same hyperparameters, but using different combinations of data acquired in two institutions by three different X-ray device manufacturers. Regarding internal validation, the addition of images from an external institution to the training set did not modify the algorithm’s internal performance, however, the inclusion of images acquired by a device from a different manufacturer decreased the performance up to 8% (p < 0.05). In contrast, generalization across institutions and X-ray devices with the same type of response function was achieved. Nonetheless, generalization was not observed across devices with different types of response function. This factor was the key impediment to achieving broad generalization in our research, followed by the device’s image-processing and the inter-institutional differences, which both reduced generalization performance to 18.9% (p < 0.05), and 9.8% (p < 0.05), respectively. Finally, clustering analysis with features extracted by the CNN was performed, revealing a substantial dependence of feature values extracted by the pre-trained CNN on the X-ray device which acquired the images.
Seismic hazard prediction of the Hunhe Fault in the Shen-Fu New District
Authors: Wang Zijun , Zhao Boming , Wan Bo 
Published: June 2024

AbstractEarthquake prevention and disaster mitigation are crucial aspects of social welfare that significantly impact national public security. This paper presents a seismic risk assessment and hazard prediction of the Hunhe Fault in the Shengyang-Fushun (Shen-Fu) New District. The target area is at risk of seismic damage due to two major branch ruptures, namely, F9 and F1; these ruptures have the potential to generate maximum earthquakes with a magnitude of 6.0 in the next 50 to 100 years. A three-dimensional underground velocity structure and asperity source model were established for the target faults. Subsequently, a hybrid technique combining deterministic and empirical approaches was employed to simulate the broadband strong ground motion of the target region in anticipation of the occurrence of expected scenario earthquakes. The distributions of peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD) for the area are provided, and the results indicate that densely populated urban areas could experience PGA values close to 280 cm/s2 along the fault traces. This study provides a reliable basis for engineering construction and urban planning in the Shen-Fu New District.
Rapid entomological assessment in eight high malaria endemic regencies in Papua Province revealed the presence of indoor and outdoor malaria transmissions
Authors: Rozi Ismail E. , Permana Dendi H. , Syahrani Lepa , Asih Puji B. S. , Zubaidah Siti , Risandi Rifqi , Wangsamuda Suradi , Dewayanti Farahana K. , Demetouw Michael R. , Mabui Silas , Robaha Marthen M. F. , Sumiwi Maria E. , Bangs Michael J. , Lobo Neil F. , Hawley William A. , Syafruddin Din 
Published: June 2024

AbstractMalaria in eastern Indonesia remains high despite significant reduction and elimination in other parts of the country. A rapid entomological assessment was conducted in eight high malaria endemic regencies of Papua Province, Indonesia, to expedite malaria elimination efforts in this region. This study aims to characterize specific, actionable endpoints toward understanding where and when malaria transmission is happening, where interventions may function best, and identify gaps in protection that result in continued transmission. The entomological assessment included identifying potential vectors through human landing catch (HLC), indoor morning and night resting collections, identification of larval sites through surveillance of water bodies, and vector incrimination toward understanding exposure to malaria transmission. Human landing catches (HLCs) and larval collections identified 10 Anopheles species, namely Anopheles koliensis, Anopheles punctulatus, Anopheles farauti, Anopheles hinesorum, Anopheles longirostris, Anopheles peditaeniatus, Anopheles tesselatus, Anopheles vagus, Anopheles subpictus and Anopheles kochi. The most common and abundant species found overall were An. koliensis and An. punctulatus, while An. farauti was found in large numbers in the coastal areas of Mimika and Sarmi Regencies. Vector incrimination on Anopheles collected from HLCs and night indoor resting demonstrated that An. koliensis and An. punctulatus carried Plasmodium in Keerom, Jayapura, and Sarmi Regencies. Analysis of HLCs for the most common species revealed that the An. koliensis and An. punctulatus, bite indoors and outdoors at equal rates, while An. farauti predominantly bite outdoors. Larval surveillance demonstrated that most water bodies in and surrounding residential areas contained Anopheles larvae. This study demonstrated indoor and outdoor exposure to mosquito bites and gaps in protection, enabling exposure to infectious bites in all regencies. This explains why current malaria control efforts focusing on indoor protection have failed to substantially reduce malaria incidence in the region. Optimization of insecticide-treated bed nets (ITNs), as well as installment of mosquito screens in houses, may further reduce indoor transmission. For outdoor transmission, the use of community-centric approaches to reduce or eliminate larval sources within and surrounding the village through the guidance of locally stationed entomologists, along with Social and Behavior Change mediated health education towards the local adoption of mosquito protection tools during outdoor activities, may reduce malaria transmission.
Pan-cancer multi-omics analysis of PTBP1 reveals it as an inflammatory, progressive and prognostic marker in glioma
Authors: Ye Zheng , Zhong Yan , Zhang Zhiyuan 
Published: June 2024

AbstractPTBP1 is an oncogene that regulates the splicing of precursor mRNA. However, the relationship between PTBP1 expression and gene methylation, cancer prognosis, and tumor microenvironment remains unclear. The expression profiles of PTBP1 across various cancers were derived from the TCGA, as well as the GTEx and CGGA databases. The CGGA mRNA_325, CGGA mRNA_301, and CGGA mRNA_693 datasets were utilized as validation cohorts. Immune cell infiltration scores were approximated using the TIMER 2.0 tool. Functional enrichment analysis for groups with high and low PTBP1 expression was conducted using Gene Set Enrichment Analysis (GSEA). Methylation data were predominantly sourced from the SMART and Mexpress databases. Linked-omics analysis was employed to perform functional enrichment analysis of genes related to PTBP1 methylation, as well as to conduct protein functional enrichment analysis. Single-cell transcriptome analysis and spatial transcriptome analysis were carried out using Seurat version 4.10. Compared to normal tissues, PTBP1 is significantly overexpressed and hypomethylated in various cancers. It is implicated in prognosis, immune cell infiltration, immune checkpoint expression, genomic variation, tumor neoantigen load, and tumor mutational burden across a spectrum of cancers, with particularly notable effects in low-grade gliomas. In the context of gliomas, PTBP1 expression correlates with WHO grade and IDH1 mutation status. PTBP1 expression and methylation play an important role in a variety of cancers. PTBP1 can be used as a marker of inflammation, progression and prognosis in gliomas.
Prevalence and death rate of birth defects from population-based surveillance in Hunan Province, China, 2010–2020
Authors: Zhou Xu , Xie Donghua , Jiang Yurong , Fang Junqun 
Published: June 2024

AbstractTo describe the prevalence and death rate of birth defects from population-based surveillance in Hunan Province, China. Data were obtained from the population-based Birth Defects Surveillance System in Hunan Province, China (2010–2020). The surveillance population included all live births, stillbirths, infant deaths, and legal terminations of pregnancy from 28 weeks of gestation to 42 days after birth between 2010 and 2020 when the mother resided in the surveillance area (Liuyang County and Shifeng District, Hunan Province). The prevalence of birth defects is the number of birth defects per 1000 infants (‰). The death rate of birth defects is the number of deaths attributable to birth defects per 100 birth defects (%). The prevalence and death rate with 95% confidence intervals (CI) were calculated by the log-binomial method. Crude odds ratios (ORs) were calculated to examine the association of each demographic characteristic with birth defects. Our study included 228,444 infants, and 4453 birth defects were identified, with a prevalence of 19.49‰ (95%CI 18.92–20.07). Congenital heart defects were the most common specific defects (5.29‰), followed by limb defects (4.01‰). Birth defects were more common in males than females (22.34‰ vs. 16.26‰, OR = 1.38, 95%CI 1.30–1.47), in premature birth than not (91.82‰ vs. 16.14‰, OR = 6.16, 95%CI 5.72–6.65), in birth weight < 2500 g (98.26‰ vs. 16.22‰, OR = 6.61, 95%CI 6.11–7.15) or > 4000 g (19.48‰ vs. 16.22‰, OR = 1.21, 95%CI 1.03–1.42) than birth weight 2500–4000 g, in hospitalized deliveries than other institutions (22.16‰ vs. 11.74‰, OR = 1.91, 95%CI 1.76–2.07), in multiple births than singletons (28.50‰ vs. 19.28‰, OR = 1.49, 95%CI 1.27–1.76), in maternal age < 20 years (26.33‰ vs. 18.69‰, OR = 1.42, 95%CI 1.15–1.76) or >  = 35 years (24.31‰ vs. 18.69‰, OR = 1.31, 95%CI 1.18–1.45) than maternal age 25–29 years, and in number of pregnancies >  = 4 (22.91‰ vs. 18.92‰, OR = 1.22, 95%CI 1.10–1.35) than the first pregnancy. A total of 747 deaths attributable to birth defects were identified, including 603 (80.72%) stillbirths, 75 (10.04%) deaths within 7 days after birth, 46 (6.16%) deaths in 7–27 days after birth, 23 (3.08%) deaths in 28–42 days after birth. The death rate of birth defects was 16.78% (95%CI 15.57–17.98). Deaths attributable to birth defects accounted for 51.09% (747/1462) of all deaths. Central nervous system defects had the highest death rate (90.27%), and neonatal genetic metabolic defects had the lowest death rate (0.39%). In summary, we have described the prevalence and epidemiology of birth defects from population-based surveillance in Hunan Province, China, 2010–2020. There were differences in the prevalence and death rate of birth defects between population-based surveillance and hospital-based surveillance.
Distributed transformer for high order epistasis detection in large-scale datasets
Authors: Graça Miguel , Nobre Ricardo , Sousa Leonel , Ilic Aleksandar 
Published: June 2024

AbstractUnderstanding the genetic basis of complex diseases is one of the most important challenges in current precision medicine. To this end, Genome-Wide Association Studies aim to correlate Single Nucleotide Polymorphisms (SNPs) to the presence or absence of certain traits. However, these studies do not consider interactions between several SNPs, known as epistasis, which explain most genetic diseases. Analyzing SNP combinations to detect epistasis is a major computational task, due to the enormous search space. A possible solution is to employ deep learning strategies for genomic prediction, but the lack of explainability derived from the black-box nature of neural networks is a challenge yet to be addressed. Herein, a novel, flexible, portable, and scalable framework for network interpretation based on transformers is proposed to tackle any-order epistasis. The results on various epistasis scenarios show that the proposed framework outperforms state-of-the-art methods for explainability, while being scalable to large datasets and portable to various deep learning accelerators. The proposed framework is validated on three WTCCC datasets, identifying SNPs related to genes known in the literature that have direct relationships with the studied diseases.
Solitary wave solutions of the time fractional Benjamin Bona Mahony Burger equation
Authors: Pavani K. , Raghavendar K. , Aruna K. 
Published: June 2024

Abstract
The present study examines the approximate solutions of the time fractional Benjamin Bona Mahony Burger equation. This equation is critical for characterizing the dynamics of water waves and fluid acoustic gravity waves, as well as explaining the unidirectional propagation of long waves in nonlinear dispersive systems. This equation also describes cold plasma for hydromagnetic and audio waves in harmonic crystals. The natural transform decomposition method is used to obtain the analytical solution to the time fractional Benjamin Bona Mahony Burger equation. The proposed method uses the Caputo, Caputo Fabrizio, and Atangana Baleanu Caputo derivatives to describe the fractional derivative. We utilize a numerical example with appropriate initial conditions to assess the correctness of our findings. The results of the proposed method are compared to those of the exact solution and various existing techniques, such as the fractional homotopy analysis transform method and the homotopy perturbation transform technique. As a result, bell shaped solitons are discovered under the influence of hyperbolic functions. By comparing the outcomes with tables and graphs, the findings demonstrate the efficacy and effectiveness of the suggested approach.