bluuebunny/crossref_metadata_2025_split huggingface dataset:
BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation
Authors: Welch E. Celeste , Lu Chenhao , Sung C. James , Zhang Cunxian , Tripathi Anubhav , Ou Joyce 
Published: December 2024

AbstractIn the past several years, a few cervical Pap smear datasets have been published for use in clinical training. However, most publicly available datasets consist of pre-segmented single cell images, contain on-image annotations that must be manually edited out, or are prepared using the conventional Pap smear method. Multicellular liquid Pap image datasets are a more accurate reflection of current cervical screening techniques. While a multicellular liquid SurePath™ dataset has been created, machine learning models struggle to classify a test image set when it is prepared differently from the training set due to visual differences. Therefore, this dataset of multicellular Pap smear images prepared with the more common ThinPrep® protocol is presented as a helpful resource for training and testing artificial intelligence models, particularly for future application in cervical dysplasia diagnosis. The “Brown Multicellular ThinPrep” (BMT) dataset is the first publicly available multicellular ThinPrep® dataset, consisting of 600 clinically vetted images collected from 180 Pap smear slides from 180 patients, classified into three key diagnostic categories.
Gastrointestinal carriage of carbapenemase-producing enterobacterales among inpatient and outpatient children in Kenya
Authors: Githii Susan , Maingi John M. , Nyaga Teresia , Ndungu Cecilia , Nyongesa Kelvin Wangira , Musyoki Abednego Moki 
Published: December 2024

AbstractGastrointestinal carriage of antimicrobial-resistant bacteria, especially carbapenemase-producing Enterobacterales (CPE), presents a critical public health threat globally. However, in many resource-constrained countries, epidemiological data on CPE is limited. Here, we assessed gastrointestinal carriage and associated factors of CPE among inpatient and outpatient children (≤ 5 years). This hospital-based cross-sectional study was conducted at Thika Level 5 Hospital in Kenya from February to June 2023. In total, 540 participants were recruited from outpatient (270) and inpatient (270) children, excluding those admitted for < 48 h and outpatients with ≤ 3 months hospitalization history. Demographic data were collected using a questionnaire, and stool or swab samples were cultured following standard microbiology methods. Automated platforms were used for isolates identification and antimicrobial susceptibility testing. Gastrointestinal carriage rate of CPE was 9.6%, 95% confidence interval (CI): 6.39–13.79% (26/270) among the inpatients and 5.9%, 95% CI: 3.42–9.45% (16/270) among the outpatients. The carbapenemase-producing Enterobacterales were predominated by multidrug-resistant Escherichia coli from inpatients (22/22, 100%) and outpatients (8/10, 80%). The colonization rate was higher among inpatients who were presenting with chills (aOR = 10.57, p = 0.008). We report a high gastrointestinal carriage of CPE among children (≤ 5 years). Strict adherence to colonization screening, and antimicrobial stewardship policies are critical to control CPE dissemination in the current study area and beyond.
Customer churn prediction model based on hybrid neural networks
Authors: Liu Xinyu , Xia Guoen , Zhang Xianquan , Ma Wenbin , Yu Chunqiang 
Published: December 2024

AbstractIn today’s competitive market environment, accurately identifying potential churn customers and taking effective retention measures are crucial for improving customer retention and ensuring the sustainable development of an organization. However, traditional machine learning algorithms and single deep learning models have limitations in extracting complex nonlinear and time-series features, resulting in unsatisfactory prediction results. To address this problem, this study proposes a hybrid neural network-based customer churn prediction model, CCP-Net. In the data preprocessing stage, the ADASYN sampling algorithm balances the sample sizes of churned and non-churned customers to eliminate the negative impact of sample imbalance on the model performance. In the feature extraction stage, CCP-Net uses Multi-Head Self-Attention to learn the global dependencies of the input sequences, combines with BiLSTM to capture the long-term dependencies in the sequential data, and uses CNN to extract the local features, and ultimately generates the prediction results. Experimental results of cross-validation on Telecom, Bank, Insurance, and News datasets show that CCP-Net outperforms the comparison algorithms in all performance metrics. For example, CCP-Net achieves a Precision of 92.19% on the Telecom dataset, 91.96% on the Bank dataset, 95.87% on the Insurance dataset, and 95.12% on the News dataset, which compares to other hybrid neural network models, the performance improvement of CCP-Net ranges from 1% to 3%. These results indicate that the design of the CCP-Net model effectively improves the accuracy and robustness of churn prediction, enabling it to be widely applied to different industries, especially in the financial, telecommunication, and media fields, to provide more comprehensive and effective churn management strategies for enterprises.
Isolation and characterization of Saprolegnia parasitica from cage-reared Pangasianodon hypophthalmus and its sensitivity to different antifungal compounds
Authors: Rathod Sanjaykumar Karsanbhai , Das Basanta Kumar , Tandel Ritesh Shantilal , Chatterjee Sohini , Das Nilemesh , Tripathi Gayatri , Kumar Saurav , Panda Satyen Kumar , Patil Prasanna Kumar , Manna Sanjib Kumar 
Published: December 2024

AbstractSaprolegniasis is one of the most dangerous fungal diseases of fish, causing significant mortality in fish hatcheries and young ones. The present study aimed to isolate and characterize the causative fungus from fingerlings of Pangasianodon hypophthalmus cultured intensively in freshwater cages in Indian reservoirs and to determine minimum inhibitory concentrations of different antifungal compounds against the fungal hyphae and zoospores. The fungal isolates grown on potato dextrose agar showed an abundance of gemmae, elongated mycelia, non-septate hyphae, primary zoospores, mature zoosporangia with numerous zoospores, cysts with bundles of long hairs and were further identified as Saprolegnia parasitica following PCR amplification and sequencing of internal transcribed spacer region. S. parasitica showed temperature-sensitive optimum growth in a narrow window of 12–24 ℃, which might drive its experimental pathogenesis as well as natural infections in the winter months. In vitro sensitivity testing established negligible inhibitory activity of fluconazole, boric acid, sodium thiosulfate, and potassium permanganate while clotrimazole arrested the spore and hyphal growths at 2 mgL-1 concentration suggesting potential of the imidazole antifungal in treating S. parasitica infection in fish. The present study will serve as the baseline information for developing therapeutic and management strategies for controlling saprolegniasis in the economically significant iridescent catfish.
A machine learning tool for early identification of celiac disease autoimmunity
Authors: Dreyfuss Michael , Getz Benjamin , Lebwohl Benjamin , Ramni Or , Underberger Daniel , Ber Tahel Ilan , Steinberg-Koch Shlomit , Jenudi Yonatan , Gazit Sivan , Patalon Tal , Chodick Gabriel , Shoenfeld Yehuda , Ben-Tov Amir 
Published: December 2024

AbstractIdentifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagnostic delays or remain undiagnosed. Using anonymized outpatient data from the electronic medical records of Maccabi Healthcare Services, we developed and evaluated five machine learning models to classify patients as at-risk for CD autoimmunity prior to first documented diagnosis or positive serum tissue transglutaminase (tTG-IgA). A train set of highly seropositive (tTG-IgA > 10X ULN) cases (n = 677) with likely CD and controls (n = 176,293) with no evidence of CD autoimmunity was used for model development. Input features included demographic information and commonly available laboratory results. The models were then evaluated for discriminative ability as measured by AUC on a distinct set of highly seropositive cases (n = 153) and controls (n = 41,087). The highest performing model was XGBoost (AUC = 0.86), followed by logistic regression (AUC = 0.85), random forest (AUC = 0.83), multilayer perceptron (AUC = 0.80) and decision tree (AUC = 0.77). Contributing features for the XGBoost model for classifying a patient as at-risk for undiagnosed CD autoimmunity included signs of anemia, transaminitis and decreased high-density lipoprotein. This model’s ability to distinguish cases of incident CD autoimmunity from controls shows promise as a potential clinical tool to identify patients with increased risk of having undiagnosed celiac disease in the community, for serologic screening.
Predicting fine-scale downstream migratory movement of Atlantic salmon smolt (Salmo salar) in front of a hydropower plant
Authors: Bærum Kim M. , Silva Ana T. , Baktoft Henrik , Gjelland Karl Ø. , Økland Finn , Forseth Torbjørn 
Published: December 2024

AbstractThe Atlantic salmon (Salmo salar) is an iconic species of significant ecological and economic importance. Their downstream migration as smolts represents a critical life-history stage that exposes them to numerous challenges, including passage through hydropower plants. Understanding and predicting fine-scale movement patterns of smolts near hydropower plants is therefore essential for adaptive and effective management and conservation of this species. We present a spatially explicit individual-based model for predicting the movement of Atlantic salmon smolts in regulated rivers in Norway, parameterised for smolt movements in the River Mandal and the River Orkla. The model is rooted in statistically derived relationships between observed smolt swimming behaviour and the hydraulic variables they encounter. The aim of the model was to provide fast yet representative swimming patterns past hydropower plants, based on the hydraulic conditions experienced by the smolts. The model outperformed a ‘drift-only’ model in portraying observed swim tracks when comparing simulated and observed tracks. It was found to represent smolt swimming behaviour well. Our results show that by constructing swim models using relatively simple and general statistical relationships between smolt swimming behaviour and the hydraulic environment, we can produce fast and relevant outputs for an adaptive management process, aimed at exploring how physical implementations or changes in flow regulations might affect smolt populations.
Remote disruption of intestinal homeostasis by Mycobacterium abscessus is detrimental to Drosophila survival
Authors: Touré Hamadoun , Durand Nicolas , Rincheval Vincent , Girard-Misguich Fabienne , Guénal Isabelle , Herrmann Jean-Louis , Szuplewski Sébastien 
Published: December 2024

AbstractMycobacterium abscessus (Mabs), an intracellular and opportunistic pathogen, is considered the most pathogenic fast-growing mycobacterium, and causes severe pulmonary infections in patients with cystic fibrosis. While bacterial factors contributing to its pathogenicity are well studied, the host factors and responses that worsen Mabs infection are not fully understood. Here, we report that Mabs systemic infection alters Drosophila melanogaster intestinal homeostasis. Mechanistically, Mabs remotely induces a self-damaging oxidative burst, leading to excessive differentiation of intestinal stem cells into enterocytes. We demonstrated that the subsequent increased intestinal renewal is mediated by both the Notch and JAK/STAT pathways and is deleterious to Drosophila survival. In conclusion, this work highlights that the ability of Mabs to induce an exacerbated and self-damaging response in the host contributes to its pathogenesis.
Prenatal exposure to diethylstilbestrol has multigenerational effects on folliculogenesis
Authors: Rogers Rachael E. , Fowler Kate A. , Pask Andrew J. , Mattiske Deidre M. 
Published: December 2024

AbstractDiethylstilbestrol (DES) is an estrogenic endocrine disrupting chemical (EDC) that was prescribed to millions of pregnant women worldwide, leading to increased rates of infertility in the exposed offspring. We have previously demonstrated that this reduced fertility persists for multiple generations in the mouse. However, how altered ovarian function contributes to this infertility is unknown. Therefore, this study sought to determine if DES exposure promotes two common ovarian disorders, primary ovarian insufficiency (POI) and polycystic ovary syndrome, contributing to the reduced fertility in DES offspring. Moreover, we investigated if these impacts are transgenerational. Gestating mice were exposed to 100 µg/kg DES, and ovarian morphology was observed in F1-F3 female descendants. F1 females trended towards fewer primordial and more secondary follicles and similarly, F2 females had fewer primordial and significantly more secondary follicles compared to controls. No differences in follicle proportions were observed in the F3. Moreover, DES exposure did not increase follicular cysts. These results show that DES accelerates folliculogenesis, indicative of a POI phenotype and that this is likely contributing to the reduced fertility observed in DES descendants. Moreover, this study highlights the ability of estrogenic EDCs to disrupt folliculogenesis, which may exacerbate the onset of POI in women already at risk.
Prediction of peak particle velocity using hybrid random forest approach
Authors: Yan Yu , Guo Jiwei , Bao Shijie , Fei Honglu 
Published: December 2024

AbstractBlasting excavation is widely used in mining, tunneling and construction industries, but it leads to produce ground vibration which can seriously damage the urban communities. The peak particle velocity (PPV) is one of main indicators for determining the extent of ground vibration. Owing to the complexity of blasting process, there is controversy over which parameters will be considered as the inputs for empirical equations and machine learning (ML) algorithms. According to current researches, the burden has controversial impact on the blast-induced ground vibration. To judge whether the burden affects blast-induced ground vibration, the data of ground vibration considering burden have been recorded at the Wujiata coal mine. Correlation coefficient is used to analyze the relationship between variables, the correlation between the distance from blasting center to monitored point (R) and peak particle velocity (PPV) is greatest and the value of correlation coefficient is − 0.67. This study firstly summarizes the most common empirical equations, and a new empirical equation is established by dimension analysis. The new equation shows better performance of predicting PPV than most other empirical equations by regression analysis. Secondly, the machine learning is confirmed the applicability of predicting PPV. Based on the performance assessments, regression error characteristic curve and Uncertainty analysis in the first round of predicting PPV, the random forest (RF) and K-Nearest Neighbors (KNN) show better performance than other four machine learning algorithms. Then, in the second round, based on the artithmetic optimization algorithm (AOA), the optimized random forest (AOA-RF) model as the most accurate model compared with the optimized K-Nearest Neighbors (AOA-KNN) presented in the literature. Finally, the points of predicted PPV which have been informed of danger are marked based on Chinese safety regulations for blasting.
Heterostructure growth, electrical transport and electronic structure of crystalline Dirac nodal arc semimetal PtSn4
Authors: Beynon Edward L. , Barker Oliver J. , Veal Tim D. , O’Brien Liam , O’Sullivan Marita 
Published: December 2024

AbstractTopological semimetals have recently garnered widespread interest in the quantum materials research community due to their symmetry-protected surface states with dissipationless transport which have potential applications in next-generation low-power electronic devices. One such material, $$\hbox {PtSn}_{4}$$, exhibits Dirac nodal arcs and although the topological properties of single crystals have been investigated, there have been no reports in crystalline thin film geometry. We examined the growth of $$\hbox {PtSn}_{4}$$ heterostructures on a range of single crystals by optimizing the electron beam evaporation of Pt and Sn and studied the effect of vacuum thermal annealing on phase and crystallinity. The electrical resistivity was fitted to a modified Bloch–Grüneisen model with a residual resistivity of 79.43(1) $$\mu \Omega$$cm at 2K and a Debye temperature of 200K. Nonlinear Hall resistance indicated the presence of more than one carrier type with an effective carrier mobility of 33.6 $$\hbox {cm}^2\, \hbox {V}^{-1}\, \hbox {s}^{-1}$$ and concentration of 1.41 $$\times 10^{21}\, \hbox {cm}^{-3}$$ at 300 K. X-ray photoemission spectra were in close agreement with convolved density of states and a work function of 4.7(2) eV was determined for the $$\hbox {PtSn}_{4}$$ (010) surface. This study will facilitate measurements that require heterostructure geometry, such as spin and topological Hall effect, and will facilitate potential device incorporation in future quantum technologies.
Spatiotemporal dynamics of the oropharyngeal microbiome in a cohort of Ivorian school children
Authors: Diallo K. , Missa K. F. , Tuo K. J. , Tiemele L. S. , Ouattara A. F. , Gboko K. D. T. , Gragnon B. G. , Bla K. B. , Ngoi J. M. , Wilkinson R. J. , Awandare G. A. , Bonfoh B. 
Published: December 2024

AbstractThe respiratory tract harbours microorganisms of the normal host microbiota which are also capable of causing invasive disease. Among these, Neisseria meningitidis a commensal bacterium of the oropharynx can cause meningitis, a disease with epidemic potential. The oral microbiome plays a crucial role in maintaining respiratory health. An imbalance in its composition is associated with increased risk of invasive disease. The main objective of this study was to evaluate changes in the spatio-temporal dynamics of the oropharyngeal microbiota considering meningococcal carriage in a cohort of 8–12-year-old school children within (Korhogo) and outside (Abidjan) of the meningitis belt of Côte d’Ivoire. A significant geographic difference in the oropharyngeal microbiome was identified between the two study sites in terms of bacterial abundance and diversity (p < 0.001), with greater diversity in children in Abidjan than in Korhogo. Meningococcal carriage was low in the cohort with eight Neisseria carriers identified in Korhogo (3.64%) including one Neisseria meningitidis (0.45%). No Neisseria were detected in Abidjan indicating geographical differences in carriage (p = 0.006). Negative correlations were also found between Neisseria abundance and humidity. Meningococcal carriage was very low during the study; however, Neisseria carriage differed between the two study areas, with a higher frequency in children in Korhogo. Analysis of the oropharyngeal microbiome showed significant differences between children followed in Abidjan and Korhogo with higher microbial diversity in Abidjan, which is generally associated with better health status. Significant correlations between Neisseria or other pathogens carriage and climatic variables (Temperature, Relative humidity, and Wind speed) were also demonstrated, indicating an important role of climate in the carriage of these bacteria; an important element to note in the current context of climate change.
PARP inhibition radiosensitizes BRCA1 wildtype and mutated breast cancer to proton therapy
Authors: Ben Kacem Mariam , Bright Scott J. , Moran Emma , Flint David B. , Martinus David K. J. , Turner Broderick X. , Qureshi Ilsa , Kolachina Rishab , Manandhar Mandira , Marinello Poliana C. , Shaitelman Simona F. , Sawakuchi Gabriel O. 
Published: December 2024

AbstractAggressive breast cancers often fail or acquire resistance to radiotherapy. To develop new strategies to improve the outcome of aggressive breast cancer patients, we studied how PARP inhibition radiosensitizes breast cancer models to proton therapy, which is a radiotherapy modality that generates more DNA damage in the tumor than standard radiotherapy using photons. Two human BRCA1-mutated breast cancer cell lines and their isogenic BRCA1-recovered pairs were treated with a PARP inhibitor and irradiated with photons or protons. Protons (9.9 and 3.85 keV/µm) induced higher cell kill independent of BRCA1 status. PARP inhibition amplified the cell kill effect to both photons and protons (9.9 and 3.85 keV/µm) independent of BRCA1 status. Numbers of γH2AX foci, micronuclei, and cGAS-positive micronuclei were significantly higher in BRCA1-mutated cells. Cell cycle distribution and stress-induced senescence were not affected by PARP inhibition in our cell lines. In vivo, the combination of protons (3.99 keV/µm) and PARP inhibition induced the greatest tumor growth delay and the highest survival. We found that PARP inhibition increases radiosensitization independent of BRCA1 status for both protons and photons. The combination of protons and PARP inhibition was the most effective in decreasing clonogenic cell survival, increasing DNA damage, and delaying tumor growth.
Experimental and explainable machine learning approach on thermal conductivity and viscosity of water based graphene oxide based mono and hybrid nanofluids
Authors: Kanti Praveen Kumar , Paramasivam Prabhu , Wanatasanappan V. Vicki , Dhanasekaran Seshathiri , Sharma Prabhakar 
Published: December 2024

AbstractThis study explores the thermal conductivity and viscosity of water-based nanofluids containing silicon dioxide, graphene oxide, titanium dioxide, and their hybrids across various concentrations (0 to 1 vol%) and temperatures (30 to 60 °C). The nanofluids, characterized using multiple methods, exhibited increased viscosity and thermal conductivity compared to water, with hybrid nanofluids showing superior performance. Graphene oxide nanofluids displayed the highest thermal conductivity and viscosity ratios, with increases of 52% and 177% at 60 °C and 30 °C, respectively, for a concentration of 1 vol% compared to base fluid. Similarly, graphene oxide-TiO2 hybrid nanofluids achieved thermal conductivity and viscosity ratios exceeding 43% and 144% compared to the base fluid at similar conditions. This data highlights the significance of nanofluid concentration in influencing thermal conductivity, while temperature was found to have a more pronounced effect on viscosity. To tackle the challenge of modeling the thermophysical properties of these hybrid nanofluids, advanced machine learning models were applied. The Random Forest (RF) model outperformed others (Gradient Boosting and Decision Tree) in both the cases of thermal conductivity and viscosity with greater adaptability to handle fresh data during model testing. Further analysis using shapely additive explanations based on cooperative game theory revealed that relative to temperature, nanofluid concentration contributes more to the predictions of the thermal conductivity ratio model. However, the effect of nanofluid concentration was more dominant in the case of viscosity ratio model.