bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of mine water source based on TPE-LightGBM
Authors: Wang Man , Zhang Jianguo , Li Han , Zhang Bo , Yang Zhenwei 
Published: May 2024

AbstractMine water inrush is a serious threat to mine safety production. It is very important to identify water inrush source types quickly to prevent and control water damage. In this study, the aqueous chemical components Na+ + K+, Ca2+, Mg2+, Cl−, SO42− and HCO3− of different aquifers in Pingdingshan coalfield were selected as the characteristic values, and the Surface water, Quaternary pore water, Carboniferous limestone karst water, Permian sandstone water, and Cambrian limestone karst water were used as the labels. An intelligent water source discrimination model is proposed by combining data mining, classification models, and reinforcement learning. As outlier data in the samples may interfere with the model recognition ability, the data distribution range was analyzed using box plots, and 20 groups of abnormal samples were excluded. The processed water chemistry data were divided into 80% learning samples and 20% test samples, and the learning samples were fed into a light gradient boosting machine (LightGBM) for training. The tree-structured parson estimator (TPE) obtains the optimal values of the main parameters of LightGBM in a very short time. Substituting the hyperparameters back into the model yields a 13.9% improvement in the accuracy of the model, proving the effectiveness of the TPE algorithm. To further validate the performance of the model, TPE-LightGBM is compared and analyzed with a Random Search-Multi Layer Perceptron Machine (RS-MLP) and Genetic Algorithm-Extreme Gradient Boosting Tree (GA-SVM). The accuracy of TPE-LightGBM, RS-MLP, and GA-SVM is 0.931, 0.759, 0.724 in that order, and the generalization error RMSE is 0.415, 1.05, and 1.313 in that order. The results show that TPE-LightGBM is more advantageous in water source identification and is more resistant to overfitting. By calculating and comparing the information gain of each variable, the contribution of Ca2+ is the highest, so it is necessary to pay attention to the change in Ca2+ concentration. TPE-LightGBM’s high accuracy and generalization ability have a good prospect for the identification of sudden water source types.
Effects of dietary fermented Saccharomyces cerevisiae extract (Hilyses) supplementation on growth, hematology, immunity, antioxidants, and intestinal health in Nile tilapia
Authors: Abd El-Naby Asmaa S. , El Asely Amel M. , Hussein Mona N. , Khattaby Abd El-Rahman A. , Sabry Eman A. , Abdelsalam Mohamed , Samir Fatma 
Published: May 2024

AbstractThis study investigated the effects of dietary supplementation with the product Hilyses on growth performance, feed utilization, nutrient composition, hematological parameters, serum biochemistry, immune function, antioxidant status, and digestive enzyme activity in juvenile Nile tilapia (Oreochromis niloticus, initial body weight 4.24 ± 0.01 g). The fish were fed diets supplemented with Hilyses at concentrations of 0, 1, 2, or 3 g/kg for a period of 8 weeks. The results showed that supplementation with Hilyses at levels up to 2 g/kg diet significantly improved final body weight, weight gain, specific growth rate, feed efficiency ratio, protein efficiency ratio, apparent protein utilization, and energy utilization compared to the control diet without Hilyses. Carcass crude protein content and moisture were significantly higher in Hilyses-fed groups, while crude lipid content decreased at the 3 g/kg supplementation level. Hilyses supplementation enhanced various hematological parameters, including increased red blood cell count, total leukocyte count, hemoglobin concentration, hematocrit, and mean corpuscular volume. Serum biochemistry and immune function markers like total protein, albumin, complement component C3, IgM, and IgG were significantly elevated in the 2 and 3 g/kg Hilyses groups. Antioxidant enzyme activities (catalase, glutathione peroxidase, total superoxide dismutase) were enhanced, and lipid peroxidation was reduced, in the 2 g/kg Hilyses group. Digestive enzyme activities, particularly protease and lipase, were also improved with Hilyses supplementation. Histological examination showed reduced lipid deposition in the liver and increased branching of intestinal villi at the 2 g/kg Hilyses level. Overall, these results indicated that dietary Hilyses supplementation at 2 g/kg diet optimizes growth, feed utilization, nutrient composition, hematology, immunity, antioxidant status, and digestive function in juvenile Nile tilapia.
PIK3CA mutations in endocrine-resistant breast cancer
Authors: Schagerholm Caroline , Robertson Stephanie , Toosi Hosein , Sifakis Emmanouil G. , Hartman Johan 
Published: May 2024

AbstractAround 75% of breast cancer (BC) patients have tumors expressing the predictive biomarker estrogen receptor α (ER) and are offered endocrine therapy. One-third eventually develop endocrine resistance, a majority with retained ER expression. Mutations in the phosphatidylinositol bisphosphate 3-kinase (PI3K) catalytic subunit encoded by PIK3CA is a proposed resistance mechanism and a pharmacological target in the clinical setting. Here we explore the frequency of PIK3CA mutations in endocrine-resistant BC before and during treatment and correlate to clinical features. Patients with ER-positive (ER +), human epidermal growth factor receptor 2 (HER2)-negative primary BC with an ER + relapse within 5 years of ongoing endocrine therapy were retrospectively assessed. Tissue was collected from primary tumors (n = 58), relapse tumors (n = 54), and tumor-free lymph nodes (germline controls, n = 62). Extracted DNA was analyzed through panel sequencing. Somatic mutations were observed in 50% (31/62) of the patients, of which 29% occurred outside hotspot regions. The presence of PIK3CA mutations was significantly associated with nodal involvement and mutations were more frequent in relapse than primary tumors. Our study shows the different PIK3CA mutations in endocrine-resistant BC and their fluctuations during therapy. These results may aid investigations of response prediction, facilitating research deciphering the mechanisms of endocrine resistance.
Implementing vision transformer for classifying 2D biomedical images
Authors: Halder Arindam , Gharami Sanghita , Sadhu Priyangshu , Singh Pawan Kumar , Woźniak Marcin , Ijaz Muhammad Fazal 
Published: May 2024

AbstractIn recent years, the growth spurt of medical imaging data has led to the development of various machine learning algorithms for various healthcare applications. The MedMNISTv2 dataset, a comprehensive benchmark for 2D biomedical image classification, encompasses diverse medical imaging modalities such as Fundus Camera, Breast Ultrasound, Colon Pathology, Blood Cell Microscope etc. Highly accurate classifications performed on these datasets is crucial for identification of various diseases and determining the course of treatment. This research paper presents a comprehensive analysis of four subsets within the MedMNISTv2 dataset: BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST. Each of these selected datasets is of diverse data modalities and comes with various sample sizes, and have been selected to analyze the efficiency of the model against diverse data modalities. The study explores the idea of assessing the Vision Transformer Model’s ability to capture intricate patterns and features crucial for these medical image classification and thereby transcend the benchmark metrics substantially. The methodology includes pre-processing the input images which is followed by training the ViT-base-patch16-224 model on the mentioned datasets. The performance of the model is assessed using key metrices and by comparing the classification accuracies achieved with the benchmark accuracies. With the assistance of ViT, the new benchmarks achieved for BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST are 97.90%, 90.38%, 94.62% and 57%, respectively. The study highlights the promise of Vision transformer models in medical image analysis, preparing the way for their adoption and further exploration in healthcare applications, aiming to enhance diagnostic accuracy and assist medical professionals in clinical decision-making.
Controlling stimulus ambiguity reduces spurious creative ideation variance in a cyclic adaptation of the alternative uses task
Authors: Witczak Olga , Krzysik Iga , Bromberek-Dyzman Katarzyna , Thierry Guillaume , Jończyk Rafał 
Published: May 2024

AbstractIn the alternative uses task (AUT), a well-established creativity assessment, participants propose alternative uses for common items (e.g., a brick) within a 2–3 min timeframe. While idea evaluation is likely involved, the emphasis is strongly on idea generation. Here, we test the value of presenting a word overlapping an image compared to a word only prompt, and we introduce a cyclic adaptation of the AUT explicitly calling on participants to choose their best idea. In Experiment 1, as compared to word only, word + image prompts increased idea fluency but reduced idea originality and variability within a group of native Polish speakers. Thus, word + image prompts improve AUT baselining. In Experiment 2, different participants produced as many ideas as possible within two minutes (List) or their single best idea at the end of each of three 30 s ideation cycles (Cycle). Although originality did not differ between List and Cycle overall, the first three ideas in List were rated as less creative than the ideas in Cycle. Overall, we conclude that using disambiguating images reduces spurious interindividual variability in the AUT while introducing idea evaluation in the task allows us to assess creativity beyond idea generation.
Adherence to dietary recommendations mediates the effect of affective temperaments on infertility treatment outcomes
Authors: Szabo Georgina , Szigeti F. Judit , Sipos Miklos , Varbiro Szabolcs , Gonda Xenia 
Published: May 2024

AbstractAffective temperaments have been shown to robustly affect infertility treatment success. However, identification of possible mediating factors through which they exert their influence is still lacking. A growing number of results suggest that adherence to recommended treatments may be such a mediator, on the one hand, because affective temperaments are known to influence adherence and, on the other hand, because non-adherence negatively influences the treatment outcome. Recommended treatment of infertility involves, beyond medications, dietary and lifestyle changes. The aim of this retrospective cohort study was to evaluate whether adherence to physician-prescribed diet and physical activity recommendations mediates the effect of affective temperaments on infertility treatment outcomes. Among 308 women who underwent infertility treatment in an Assisted Reproduction Center, affective temperaments, adherence to diet, adherence to physical exercise, and infertility treatment success (clinical pregnancy) were assessed besides detailed medical history and demographic parameters. Associations between affective temperaments, adherence to diet and recommended physical activity, and assisted reproduction outcomes were analyzed using generalized linear models and causal mediation analysis. Adherence to physical activity didn’t have an effect, but diet adherence increased the odds of infertility treatment success by 130% suggesting its role as a potential mediator. Based on causal mediation analysis, higher depressive and anxious temperament scores were directly associated with 63% and 45% lower odds of achieving clinical pregnancy, respectively, with effects not mediated by diet adherence. Higher irritable temperament scores indirectly decreased the odds of achieving clinical pregnancy by 14%, mediated by diet adherence; while higher cyclothymic temperament scores decreased the odds of achieving clinical pregnancy both directly by 51% and indirectly, mediated by diet adherence by 11%. Our results suggest that diet adherence mediates the mechanism by which irritable and cyclothymic affective temperaments influence IVF treatment success. Since adherence is a modifiable risk factor of infertility treatment success, screening for affective temperaments may help to identify potentially high-risk non-adherent patient groups and offer patient-tailored treatment, which may help increase the chances of a successful pregnancy and live birth in women undergoing IVF treatment.
Flavivirus Zika NS4A protein forms large oligomers in liposomes and in mild detergent
Authors: Surya Wahyu , Honey Shwe Sin , Torres Jaume 
Published: May 2024

AbstractIn flaviviruses such as Dengue or Zika, non-structural (NS) NS4A protein forms homo-oligomers, participates in membrane remodelling and is critical for virulence. In both viruses, mature NS4A has the same length and three predicted hydrophobic domains. The oligomers formed by Dengue NS4A are reported to be small (n = 2, 3), based on denaturing SDS gels, but no high-resolution structure of a flavivirus NS4A protein is available, and the size of the oligomer in lipid membranes is not known. Herein we show that crosslinking Zika NS4A protein in lipid membranes results in oligomers at least up to hexamers. Further, sedimentation velocity shows that NS4A in mild detergent C14-betaine appears to be in fast equilibrium between at least two species, where one is smaller, and the other larger, than a trimer or a tetramer. Consistently, sedimentation equilibrium data was best fitted to a model involving an equilibrium between dimers (n = 2) and hexamers (n = 6). Overall, the large, at least hexameric, oligomers obtained herein in liposomes and in mild detergent are more likely to represent the forms of NS4A present in cell membranes.
LASSO-derived model for the prediction of bleeding in aspirin users
Authors: Liang Chen , Wanling Lei , Maofeng Wang 
Published: May 2024

AbstractAspirin is widely used for both primary and secondary prevention of panvascular diseases, such as stroke and coronary heart disease (CHD). The optimal balance between reducing panvascular disease events and the potential increase in bleeding risk remains unclear. This study aimed to develop a predictive model specifically designed to assess bleeding risk in individuals using aspirin. A total of 58,415 individuals treated with aspirin were included in this study. Detailed data regarding patient demographics, clinical characteristics, comorbidities, medical history, and laboratory test results were collected from the Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups at a ratio of 7:3. The larger group was used for model development, while the smaller group was used for internal validation. To develop the prediction model, we employed least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression. The performance of the model was assessed through metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The LASSO-derived model employed in this study incorporated six variables, namely, sex, operation, previous bleeding, hemoglobin, platelet count, and cerebral infarction. It demonstrated excellent performance at predicting bleeding risk among aspirin users, with a high AUC of 0.866 (95% CI 0.857–0.874) in the training dataset and 0.861 (95% CI 0.848–0.875) in the test dataset. At a cutoff value of 0.047, the model achieved moderate sensitivity (83.0%) and specificity (73.9%). The calibration curve analysis revealed that the nomogram closely approximated the ideal curve, indicating good calibration. The DCA curve demonstrated a favorable clinical net benefit associated with the nomogram model. Our developed LASSO-derived predictive model has potential as an alternative tool for predicting bleeding in clinical settings.
Transgenerational effects of early life stress on the fecal microbiota in mice
Authors: Otaru Nize , Kourouma Lola , Pugin Benoit , Constancias Florentin , Braegger Christian , Mansuy Isabelle M. , Lacroix Christophe 
Published: May 2024

AbstractStress in early life can affect the progeny and increase the risk to develop psychiatric and cardiometabolic diseases across generations. The cross-generational effects of early life stress have been modeled in mice and demonstrated to be associated with epigenetic factors in the germline. While stress is known to affect gut microbial features, whether its effects can persist across life and be passed to the progeny is not well defined. Here we show that early postnatal stress in mice shifts the fecal microbial composition (binary Jaccard index) throughout life, including abundance of eight amplicon sequencing variants (ASVs). Further effects on fecal microbial composition, structure (weighted Jaccard index), and abundance of 16 ASVs are detected in the progeny across two generations. These effects are not accompanied by changes in bacterial metabolites in any generation. These results suggest that changes in the fecal microbial community induced by early life traumatic stress can be perpetuated from exposed parent to the offspring.
Cryo-EM structure of cadmium-bound human ABCB6
Authors: Choi Seung Hun , Lee Sang Soo , Lee Hyeon You , Kim Subin , Kim Ji Won , Jin Mi Sun 
Published: May 2024

AbstractATP-binding cassette transporter B6 (ABCB6), a protein essential for heme biosynthesis in mitochondria, also functions as a heavy metal efflux pump. Here, we present cryo-electron microscopy structures of human ABCB6 bound to a cadmium Cd(II) ion in the presence of antioxidant thiol peptides glutathione (GSH) and phytochelatin 2 (PC2) at resolutions of 3.2 and 3.1 Å, respectively. The overall folding of the two structures resembles the inward-facing apo state but with less separation between the two halves of the transporter. Two GSH molecules are symmetrically bound to the Cd(II) ion in a bent conformation, with the central cysteine protruding towards the metal. The N-terminal glutamate and C-terminal glycine of GSH do not directly interact with Cd(II) but contribute to neutralizing positive charges of the binding cavity by forming hydrogen bonds and van der Waals interactions with nearby residues. In the presence of PC2, Cd(II) binding to ABCB6 is similar to that observed with GSH, except that two cysteine residues of each PC2 molecule participate in Cd(II) coordination to form a tetrathiolate. Structural comparison of human ABCB6 and its homologous Atm-type transporters indicate that their distinct substrate specificity might be attributed to variations in the capping residues situated at the top of the substrate-binding cavity.
Centroid of the bacterial growth curves: a metric to assess phage efficiency
Authors: Hosseini Nava , Chehreghani Mahdi , Moineau Sylvain , Charette Steve J. 
Published: May 2024

AbstractPhage replication can be studied using various approaches, including measuring the optical density (OD) of a bacterial culture in a liquid medium in the presence of phages. A few quantitative methods are available to measure and compare the efficiency of phages by using a single index based on the analysis of OD curves. However, these methods are not always applicable to non-canonical OD curves. Using the concept of center of area (centroid), we developed a metric called Centroid Index (CI), sensitive to the trend of the growth curves (OD distribution) including bacterial regrowth, which is not considered by the methods already available. We also provide a user-friendly software to facilitate the calculation of CI. This method offers an alternative and more precise way to determine phage efficiency by considering the OD variations over time, which may help in the selection of phages for biocontrol applications.
Autonomous mining through cooperative driving and operations enabled by parallel intelligence
Authors: Chen Long , Xie Yuting , He Yuhang , Ai Yunfeng , Tian Bin , Li Lingxi , Ge Shirong , Wang Fei-Yue 
Published: May 2024

AbstractAutonomous mining is promising to address several current issues in the mining sector, such as low productivity, safety concerns, and labor shortages. Although partial automation has been achieved in some mining operations, fully autonomous mining remains challenging due to its complexity and scalability in field environments. Here we propose an autonomous mining framework based on the parallel intelligence methodology, employing self-evolving digital twins to model and guide mining processes in the real world. Our framework features a virtual mining subsystem that learns from simulating real-world scenarios and generates new ones, allowing for low-cost training and testing of the integrated autonomous mining system. Through initial validation and extensive testing, particularly in open-pit mining scenarios, our framework has demonstrated stable and efficient autonomous operations. We’ve since deployed it across more than 30 mines, resulting in the extraction of over 30 million tons of minerals. This implementation effectively eliminates the exposure of human operators to hazardous conditions while ensuring 24-hour uninterrupted operation.
Structural regulation of NHC-protected copper(i) clusters through substitution for photoluminescence enhancement
Authors: Yang Yan-Yan , Hao Rui-Chen , Wu Qing-Qing , Luo Peng , Xu Jun , Dong Xi-Yan , Zhang Chuan-Xiang 
Published: May 2024

The Cu(i) clusters with Cu2, Cu3 and Cu4 kernel can be synthesized by simply changing the N-substituents onto benzimidazole ring, which could be explained by synergistic effect of steric and electronic properties. Among them, Cu2 exhibited admirable luminescence and processability.
Adaptive attack recognition method based on probability model for autonomous vehicle
Authors: Miao Zujia , Shao Cuiping , Li Huiyun , Cui Yunduan 
Published: May 2024

AbstractThe perception system is essential for autonomous vehicle safety and stability. However, on‐board sensors are vulnerable to external attacks, compromising driving strategies and security. Traditional attack detection methods, relying on static anomaly thresholds from data distributions, falter in complex driving scenarios. Recent studies have sought more nuanced detection techniques, including deep learning and inter‐vehicle communication, but these approaches face limitations related to data security and environmental dependency. This letter introduces an innovative, adaptive method for sensor attack detection in autonomous vehicles. By modelling the perception system's functions and employing the Gaussian process for probabilistic modelling, we generate dynamic uncertainty estimates that serve as adaptable anomaly boundaries. Our method's efficacy and resilience are validated through quantitative analysis of the real‐world KITTI dataset, demonstrating superior adaptive detection capabilities.