bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization and modeling of betamethasone removal from aqueous solutions using a SiO2/g-C3N5@NiFe2O4 nanophotocatalyst by RSM
Authors: Bagherlou Neda , Ghasemi Elnaz , Gharbani Parvin , Babazadeh Mirzaagha , Mehrizad Ali 
Published: January 2024

AbstractThis study presents the preparation of SiO2/g-C3N5@NiFe2O4 nanophotocatalyst for the removal of betamethasone from aqueous solutions. The SiO2/g-C3N5@NiFe2O4 nanophotocatalyst was synthesized using the solvothermal method, and its structure and optical properties were characterized and confirmed through XRD, FESEM, EDX, DRS, BET, VSM and PL analysis. Photocatalytic removal of betamethasone was optimized using a central composite design. The band gap of pure g-C3N5, NiFe2O4, and SiO2/g-C3N5@NiFe2O4 was obtained 2.4 eV, 2.7 eV, and 1.4 eV, respectively using the Tauc plot. The F-value of 909.88 and Lack of Fit F-value of 0.41 confirm the obtained model is significant. Also, the value of R2 = 0.9988 along with R2adja = 09977 demonstrates excellent model performance. Maximum removal efficiency of betamethasone was approximately 87.15% under the following optimal conditions: nanophotocatalyst dosage of 0.005 g/50 mL, a betamethasone concentration of 20 mg/L, and an irradiation time of 40 min under visible light. This performance closely aligns with the actual value of 80.65%. In conclusion, the SiO2/g-C3N5@NiFe2O4 nanophotocatalyst demonstrates excellent photocatalytic ability for the removal of betamethasone from aqueous solutions.
Constructing a finer-grained representation of clinical trial results from ClinicalTrials.gov
Authors: Shi Xuanyu , Du Jian 
Published: January 2024

AbstractRandomized controlled trials are essential for evaluating clinical interventions; however, selective reporting and publication bias in medical journals have undermined the integrity of the clinical evidence system. ClinicalTrials.gov serves as a valuable and complementary repository, yet synthesizing information from it remains challenging. This study introduces a curated dataset that extends beyond the traditional PICO framework. It links efficacy with safety results at the experimental arm group level within each trial, and connects them across all trials through a knowledge graph. This novel representation effectively bridges the gap between generally described searchable information and specifically detailed yet underutilized reported results, and promotes a dual-faceted understanding of interventional effects. Adhering to the “calculate once, use many times” principle, the structured dataset will enhance the reuse and interpretation of ClinicalTrials.gov results data. It aims to facilitate more systematic evidence synthesis and health technology assessment, by incorporating both positive and negative results, distinguishing biomarkers, patient-reported outcomes, and clinical endpoints, while also balancing both efficacy and safety outcomes for a given medical intervention.
Quantitative catalogue of mammalian mitotic chromosome-associated RNAs
Authors: Zhang Le , Hu Chuansheng , Xu Zeqian , Li Hua , Ye Bishan , Li Xinhui , Czajkowsky Daniel M. , Shao Zhifeng 
Published: January 2024

AbstractThe faithful transmission of a cell’s identity and functionality to its daughters during mitosis requires the proper assembly of mitotic chromosomes from interphase chromatin in a process that involves significant changes in the genome-bound material, including the RNA. However, our understanding of the RNA that is associated with the mitotic chromosome is presently limited. Here, we present complete and quantitative characterizations of the full-length mitotic chromosome-associated RNAs (mCARs) for 3 human cell lines, a monkey cell line, and a mouse cell line derived from high-depth RNA sequencing (3 replicates, 47 M mapped read pairs for each replicate). Overall, we identify, on average, more than 20,400 mCAR species per cell-type (including isoforms), more than 5,200 of which are enriched on the chromosome. Notably, overall, more than 2,700 of these mCARs were previously unknown, which thus also expands the annotated genome of these species. We anticipate that these datasets will provide an essential resource for future studies to better understand the functioning of mCARs on the mitotic chromosome and in the cell.
An RNA-seq atlas of mouse brain areas during fasting and diet-induced obesity
Authors: Todorov Petar V. , Klein Anders Bue , Egerod Kristoffer L. , Clemmensen Christoffer , Pers Tune H. 
Published: January 2024

AbstractMammalian energy homeostasis is primarilly regulated by the hypothalamus and hindbrain, with the hippocampus, midbrain nuclei, and other regions implicated by evidence from human genetics studies. To understand how these non-canonical brain regions respond to imbalances in energy homeostasis, we performed two experiments examining the effects of different diets in male C57BL6 mice. In our first study, groups of six pair-housed mice were given access to chow, high-fat diet or fasted for 16 hours. In our subsequent study, two groups of 10 mice were single-housed and given access to chow or fasted for 24 h. We recorded food intake for each cage, the change in body weight for each animal, and collected hypothalamus, hippocampus, superior colliculus, inferior colliculus, frontal cortex, and zona incerta-centric samples. We performed bulk RNA sequencing on 185 samples and validated them by a series of quality control assessments including alignment quality and gene expression profiling. We believe these studies capture the transcriptomic effects of acute fasting and high-fat diet in the rodent brain and provide a valuable reference.
Physical–chemical analysis of different types of flours available in the Romanian market
Authors: Bodor Katalin , Szilágyi József , Salamon Brigitta , Szakács Orsolya , Bodor Zsolt 
Published: January 2024

AbstractThe physical–chemical characteristics of different types of flours are essential for understanding their composition, nutritional value, and functional properties as well. The aim of this research was to identify the variability of the different wheat flours available in Romania. In this study 39 different wheat flours were selected and the following parameters were analyzed in the laboratory: moisture content, ash content, gluten content (wet and dry) and wet gluten spreading. The tested flours were classified into four different classes according to their ash content: 480 (ash content 0.48%) (N = 11), 550 (0.55%) (N = 9), 650 (0.65%) (N = 8), 1100 (1.1%) (N = 11). Mathematical and statistical methods were used to analyze the obtained results: descriptive statistics, box-plot, Spearman correlation and hierarchical cluster analysis. The results revealed that moisture content varied between 9.5 and 11.8%. In terms of ash content, the lowest and highest measured values were 0.427–2.04 g/100 g. The average wet gluten content of the studied flours varied between 30 and 32%, while the average dry gluten content was 12.8%. The findings indicate that the moisture content of all examined flour samples was within permissible levels for extended storage, aligning with established standards. Gluten is a key and essential parameter for bread making because influences the dough mixing and baking properties. The mineral content, represented by ash content, is influenced by cereal type and milling process, with wheat's ash content ranging between 1.5 and 2%. Flours with high wet gluten content (> 34%) can be used to improve the properties of lower quality flours. Further studies are necessary in order to determine the possible health effects of different cereal varieties.
Differentiating behavioral impact with or without vaccination certification under mass vaccination and non-pharmaceutical interventions on mitigating COVID-19
Authors: Cao Hu , Cao Longbing 
Published: January 2024

AbstractAs COVID-19 vaccines became widely available worldwide, many countries implemented vaccination certification, also known as a “green pass”, to promote and expedite vaccination on containing virus spread from the latter half of 2021. This policy allowed those vaccinated to have more freedom in public activities compared to more constraints on the unvaccinated in addition to existing non-pharmaceutical interventions (NPIs). Accordingly, the vaccination certification also induced heterogeneous behaviors of unvaccinated and vaccinated groups. This makes it essential yet challenging to model the behavioral impact of vaccination certification on the two groups and the transmission dynamics of COVID-19 within and between the groups. Very limited quantitative work is available for addressing these purposes. Here we propose an extended epidemiological model SEIQRD$$^2$$

2

to effectively distinguish the behavioral impact of vaccination certification on unvaccinated and vaccinated groups through incorporating two contrastive transmission chains. SEIQRD$$^2$$

2

also quantifies the impact of the green pass policy. With the resurgence of COVID-19 in Greece, Austria, and Israel in 2021, our simulation results indicate that their implementation of vaccination certification brought about more than a 14-fold decrease in the total number of infections and deaths as compared to a scenario with no such a policy. Additionally, a green pass policy may offer a reasonable practical solution to strike the balance between public health and individual’s freedom during the pandemic.
Long-lasting blocking of interoceptive effects of cocaine by a highly efficient cocaine hydrolase in rats
Authors: Wei Huimei , LeSaint Johnathan E. , Jin Zhenyu , Zhan Chang-Guo , Zheng Fang 
Published: January 2024

AbstractCocaine dependence is a serious world-wide public health problem without an FDA-approved pharmacotherapy. We recently designed and discovered a highly efficient long-acting cocaine hydrolase CocH5-Fc(M6). The present study examined the effectiveness and duration of CocH5-Fc(M6) in blocking interoceptive effects of cocaine by performing cocaine discrimination tests in rats, demonstrating that the duration of CocH5-Fc(M6) in blocking cocaine discrimination was dependent on cocaine dose and CocH5-Fc(M6) plasma concentration. Particularly, a dose of 3 mg/kg CocH5-Fc(M6) effectively attenuated discriminative stimulus effects of 10 mg/kg cocaine, cumulative doses of 10 and 32 mg/kg cocaine, and cumulative doses of 10, 32 and 56 mg/kg cocaine by ≥ 20% for 41, 19, and 10 days, and completely blocked the discriminative stimulus effects for 30, 13, and 5 days with corresponding threshold plasma CocH5-Fc(M6) concentrations of 15.9, 72.2, and 221 nM, respectively, under which blood cocaine concentration was negligible. Additionally, based on the data obtained, cocaine discrimination model is more sensitive than the locomotor activity to reveal cocaine effects and that CocH5-Fc(M6) itself has no long-term toxicity regarding behavioral activities such as lever pressing and food consumption in rats, further demonstrating that CocH5-Fc(M6) has the desired properties as a promising therapeutic candidate for prevenance of cocaine dependence.
CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection
Authors: Abdelrazek Mohammed , Abd Elaziz Mohamed , El-Baz A. H. 
Published: January 2024

AbstractIn this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.
Jurassic paleosurfaces with fecal mounds reveal the last supper of arenicolid worms
Authors: Mángano M. Gabriela , Buatois Luis A. , Piñuela Laura , Volkenborn Nils , Rodríguez-Tovar Francisco J. , García-Ramos José C. 
Published: January 2024

AbstractExceptional paleosurfaces preserving fecal casting mounds occur in the Upper Jurassic Lastres Formation of Spain. As in modern shorelines, these biogenic structures are associated with straight to sinuous-crested ripples showing the interplay of biological and physical processes in a low-energy marine environment. These trace fossils display characteristics, distribution, and densities like those of modern arenicolid populations (approximately 35 specimens per m2). Under close examination, these fecal casting mounds are morphologically undistinguishable from those produced by recent arenicolids (e.g. Arenicola marina, Abarenicola pacifica), providing evidence of the presence of these polychaetes in the Late Jurassic. As their modern counterparts, fossil arenicolids very likely modified their environment generating a seabed topography and impacting ancient benthic communities, sediment characteristics, and sediment biogeochemistry. Although the presence of oxic microhabitats and biogeochemical processes cannot be accurately measured in the fossil record, comparison with the work of modern populations allows to make inferences on sediment reworking and bioirrigation potential. In addition, association with grazing trails supports the idea of fertilization and modulation of food resources to other species. These paleosurfaces underscore the significance of high-fidelity snapshots in the fossil record (true substrates) to reconstruct past ecologies and sediment biogeochemistry. A new ichnotaxon, Cumulusichnus asturiensis n. igen. and n. isp., is defined.
Land subsidence analysis along high-speed railway based on EEMD-Prophet method
Authors: Dongwei Qiu , Yuci Tong , Yuzheng Wang , Keliang Ding , Tiancheng Liu , Shanshan Wan 
Published: January 2024

AbstractEnvironmental changes and ground subsidence along railway lines are serious concerns during high-speed railway operations. It is worth noting that AutoRegressive Integrated Moving Average (ARMA), Long Short-Term Memory (LSTM), and other prediction methods may present limitations when applied to predict InSAR time series results. To address this issue, this study proposes a prediction method that decomposes the nonlinear settlement time series of feature points obtained through InSAR technology using Ensemble Empirical Mode Decomposition (EEMD). Subsequently, multiple Intrinsic Mode Functions (IMFs) are generated, and each IMF is individually predicted using the Prophet forecasting model. Finally, we employ an equal-weight superimposition method to combine the results, resulting in the prediction of the InSAR settlement time series. The predicted values of each component are subsequently weighted equally and combined to derive the final prediction outcome. This paper selects InSAR monitoring data along a high-speed railway in inland China and uses the proposed method and ARMA and Prophet models to carry out comparative experiments. The experimental results show that compared with the ARMA and Prophet models, the method in this paper improves the root mean square error by 58.01% and 32.3%, and increases the mean absolute error by 62.69% and 33.78%, respectively. The predicted settlement values generated by our method exhibit better agreement with the actual InSAR monitoring values.
Machine learning-based prediction model for myocardial ischemia under high altitude exposure: a cohort study
Authors: Chen Yu , Zhang Xin , Ye Qing , Zhang Xin , Cao Ning , Li Shao-Ying , Yu Jie , Zhao Sheng-Tao , Zhang Juan , Xu Xin-Ming , Shi Yan-Kun , Yang Li-Xia 
Published: January 2024

AbstractHigh altitude exposure increases the risk of myocardial ischemia (MI) and subsequent cardiovascular death. Machine learning techniques have been used to develop cardiovascular disease prediction models, but no reports exist for high altitude induced myocardial ischemia. Our objective was to establish a machine learning-based MI prediction model and identify key risk factors. Using a prospective cohort study, a predictive model was developed and validated for high-altitude MI. We consolidated the health examination and self-reported electronic questionnaire data (collected between January and June 2022 in 920th Joint Logistic Support Force Hospital of china) of soldiers undergoing high-altitude training, along with the health examination and second self-reported electronic questionnaire data (collected between December 2022 and January 2023) subsequent to their completion on the plateau, into a unified dataset. Participants were subsequently allocated to either the training or test dataset in a 3:1 ratio using random assignment. A predictive model based on clinical features, physical examination, and laboratory results was designed using the training dataset, and the model's performance was evaluated using the area under the receiver operating characteristic curve score (AUC) in the test dataset. Using the training dataset (n = 2141), we developed a myocardial ischemia prediction model with high accuracy (AUC = 0.86) when validated on the test dataset (n = 714). The model was based on five laboratory results: Eosinophils percentage (Eos.Per), Globulin (G), Ca, Glucose (GLU), and Aspartate aminotransferase (AST). Our concise and accurate high-altitude myocardial ischemia incidence prediction model, based on five laboratory results, may be used to identify risks in advance and help individuals and groups prepare before entering high-altitude areas. Further external validation, including female and different age groups, is necessary.
Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity
Authors: Deng Jicai , Zhou Chenxing , Xiao Fei , Chen Jing , Li Chunlai , Xie Yubo 
Published: January 2024

AbstractA precise forecast of the need for blood transfusions (BT) in patients undergoing total hip arthroplasty (THA) is a crucial step toward the implementation of precision medicine. To achieve this goal, we utilized supervised machine learning (SML) techniques to establish a predictive model for BT requirements in THA patients. Additionally, we employed unsupervised machine learning (UML) approaches to identify clinical heterogeneity among these patients. In this study, we recruited 224 patients undergoing THA. To identify factors predictive of BT during the perioperative period of THA, we employed LASSO regression and the random forest (RF) algorithm as part of supervised machine learning (SML). Using logistic regression, we developed a predictive model for BT in THA patients. Furthermore, we utilized unsupervised machine learning (UML) techniques to cluster THA patients who required BT based on similar clinical features. The resulting clusters were subsequently visualized and validated. We constructed a predictive model for THA patients who required BT based on six predictive factors: Age, Body Mass Index (BMI), Hemoglobin (HGB), Platelet (PLT), Bleeding Volume, and Urine Volume. Before surgery, 1 h after surgery, 1 day after surgery, and 1 week after surgery, significant differences were observed in HGB and PLT levels between patients who received BT and those who did not. The predictive model achieved an AUC of 0.899. Employing UML, we identified two distinct clusters with significantly heterogeneous clinical characteristics. Age, BMI, PLT, HGB, bleeding volume, and urine volume were found to be independent predictors of BT requirement in THA patients. The predictive model incorporating these six predictors demonstrated excellent predictive performance. Furthermore, employing UML enabled us to classify a heterogeneous cohort of THA patients who received BT in a meaningful and interpretable manner.
Endocrine and dog factors associated with semen quality
Authors: Hallberg Ida , Olsson Hannah , Lau Angus , Wallander Stina , Snell Anna , Bergman Daniel , Holst Bodil Ström 
Published: January 2024

AbstractKnowledge of factors associated with semen quality may help in investigations of the aetiology and pathophysiology. We investigated the correlation between biomarkers for testicular cell function (anti-müllerian hormone, AMH, Inhibin B, testosterone, free androgen-index (testosterone/sex-hormone binding globulin), insulin like peptide 3, INSL-3), alkaline phosphate (ALP), canine prostate-specific esterase (CPSE), and heterophilic antibodies with dog variables, semen quality, and fertility. Blood and semen were collected from 65 Bernese Mountain Dogs. We evaluated total sperm count, motility and morphological parameters. The semen quality ranged from poor to excellent, with an average total sperm count of 1.1 × 109 and 50% morphologically normal spermatozoa (MNS). Age and abnormal testicular consistency correlated with decreased motility and MNS. Higher ALP correlated with higher total sperm count. AMH could not be detected in seminal plasma. AMH in blood correlated with head defects and high AMH concentration correlated with a severe decline in several semen parameters. Testosterone was negatively and CPSE positively correlated with age. No correlations were found for INSL-3, inhibin B, or heterophilic antibodies. Our findings contribute to the understanding of factors associated with semen quality in dogs, particularly related to Sertoli cell function.
Modified silica-based double-layered hydrophobic-coated stainless steel mesh and its application for oil/seawater separation
Authors: Deachophon Aunchalee , Bovornratanaraks Thiti , Poompradub Sirilux 
Published: January 2024

AbstractA double-layered hydrophobic-coated stainless steel mesh (CSSM) was successfully prepared by vapor deposition of polydimethylsiloxane (PDMS) to form aerosol silica (SiO2) particles on SSM followed by coating with the in situ modified SiO2 generated in the natural rubber (NR) latex for use in oil/seawater separation. The in situ SiO2 particles were modified with octyltriethoxysilane (OTES) or hexadecyltrimethoxysilane (HDTMS). Transmission electron microscopy, 29Si solid-state nuclear magnetic resonance, and Fourier transform infrared spectroscopy were used to determine the structure of the in situ modified SiO2 generated in the NR latex. Scanning electron microscopy and water contact angle analyses were applied to characterize the morphology and hydrophobicity of the CSSM, respectively. The presence of aerosol SiO2 particles from PDMS and in situ modified SiO2 by OTES (MSi-O) or HDTMS (MSi-H) generated in the NR could enhance the surface roughness and hydrophobicity of the CSSM. The hydrophobic CSSM was then applied for the separation of chloroform/seawater and crude oil/seawater mixtures. A high separation efficiency (up to 99.3%) with the PDMS/NR/MSi-H CSSM was obtained and the mesh was reusable for up to 20 cycles.