bluuebunny/crossref_metadata_2025_split huggingface dataset:
New insights into the anti-inflammatory and anti-melanoma mechanisms of action of azelaic acid and other Fusarium solani metabolites via in vitro and in silico studies
Authors: Ismail Mona , Hassan Marwa H. A. , Mohamed Enas I. A. , Azmy Ahmed F. , Moawad Abeer , Mohammed Rabab , Zaki Mohamed A. 
Published: June 2024

AbstractMetabolites exploration of the ethyl acetate extract of Fusarium solani culture broth that was isolated from Euphorbia tirucalli root afforded five compounds; 4-hydroxybenzaldehyde (1), 4-hydroxybenzoic acid (2), tyrosol (3), azelaic acid (4), malic acid (5), and fusaric acid (6). Fungal extract as well as its metabolites were evaluated for their anti-inflammatory and anti-hyperpigmentation potential via in vitro cyclooxygenases and tyrosinase inhibition assays, respectively. Azelaic acid (4) exhibited powerful and selective COX-2 inhibition followed by fusaric acid (6) with IC50 values (2.21 ± 0.06 and 4.81 ± 0.14 μM, respectively). As well, azelaic acid (4) had the most impressive tyrosinase inhibitory effect with IC50 value of 8.75 ± 0.18 μM compared to kojic acid (IC50 = 9.27 ± 0.19 μM). Exclusive computational studies of azelaic acid and fusaric acid with COX-2 were in good accord with the in vitro results. Interestingly, this is the first time to investigate and report the potential of compounds 3–6 to inhibit cyclooxygenase enzymes. One of the most invasive forms of skin cancer is melanoma, a molecular docking study using a set of enzymes related to melanoma suggested pirin to be therapeutic target for azelaic acid and fusaric acid as a plausible mechanism for their anti-melanoma activity.
Site selection and prediction of urban emergency shelter based on VGAE-RF model
Authors: Wang Yong , Han Yaoyao , Luo An , Xu Shenghua , Chen Jian , Liu Wangwang 
Published: June 2024

AbstractAs urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability.
Optical, structural and antibacterial properties of phase heterostructured Fe2O3–CuO–CuFe2O4 nanocomposite
Authors: Alnehia Adnan , Hadi Muhammad , Alnahari Hisham , Al-Sharabi Annas 
Published: June 2024

AbstractThe synthesis of the Fe2O3–CuO–CuFe2O4 nanocomposite was effectively achieved through the sol–gel technique, utilizing ethanol as a reactive fuel. Investigation of the nanocomposite’s structure via X-ray Diffraction confirmed the coexistence of Fe2O3, CuO, and CuFe2O4 phases within the material. The Scherrer equation was applied to determine an average crystallite size ranging from 60 to 95 nm. UV–visible spectroscopy studies suggested the material possesses an approximate energy bandgap of 4 eV. Scanning Electron Microscopy provided insights into the nanocomposite’s surface morphology, which exhibited a porous and heterogeneous aggregation of particles in various sizes and shapes. When tested for antibacterial efficacy, the nanocomposite exhibited activity against gram-positive S. aureus with a maximum zone of inhibition (ZOI) measuring 9 mm at the highest concentration, whereas no inhibitory effect was detected against gram-negative E.coli.
Development of a single-chain variable antibody fragment against a conserved region of the SARS-CoV-2 spike protein
Authors: Gao Tingyu , Irie Atsushi , Kouwaki Takahisa , Oshiumi Hiroyuki 
Published: June 2024

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has prolonged the duration of the pandemic because of the continuous emergence of new variant strains. The emergence of these mutant strains makes it difficult to detect the virus with the existing antibodies; thus, the development of novel antibodies that can target both the variants as well as the original strain is necessary. In this study, we generated a high-affinity monoclonal antibody (5G2) against the highly conserved region of the SARS-CoV-2 spike protein to detect the protein variants. Moreover, we generated its single-chain variable antibody fragment (sc5G2). The sc5G2 expressed in mammalian and bacterial cells detected the spike protein of the original SARS-CoV-2 and variant strains. The resulting sc5G2 will be a useful tool to detect the original SARS-CoV-2 and variant strains.
Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning
Authors: Augustat Nick , Endres Dominik , Mueller Erik M. 
Published: June 2024

AbstractThe placebo-reward hypothesis postulates that positive effects of treatment expectations on health (i.e., placebo effects) and reward processing share common neural underpinnings. Moreover, experiments in humans and animals indicate that reward uncertainty increases striatal dopamine, which is presumably involved in placebo responses and reward learning. Therefore, treatment uncertainty analogously to reward uncertainty may affect updating from rewards after placebo treatment. Here, we address whether different degrees of uncertainty regarding the efficacy of a sham treatment affect reward sensitivity. In an online between-subjects experiment with N = 141 participants, we systematically varied the provided efficacy instructions before participants first received a sham treatment that consisted of listening to binaural beats and then performed a probabilistic reinforcement learning task. We fitted a Q-learning model including two different learning rates for positive (gain) and negative (loss) reward prediction errors and an inverse gain parameter to behavioral decision data in the reinforcement learning task. Our results yielded an inverted-U-relationship between provided treatment efficacy probability and learning rates for gain, such that higher levels of treatment uncertainty, rather than of expected net efficacy, affect presumably dopamine-related reward learning. These findings support the placebo-reward hypothesis and suggest harnessing uncertainty in placebo treatment for recovering reward learning capabilities.
Utilizing Bayesian inference in accelerated testing models under constant stress via ordered ranked set sampling and hybrid censoring with practical validation
Authors: Hashem Atef F. , Alotaibi Naif , Alyami Salem A. , Abdelkawy Mohamed A. , Elgawad Mohamed A. Abd , Yousof Haitham M. , Abdel-Hamid Alaa H. 
Published: June 2024

AbstractThis research investigates the application of the ordered ranked set sampling (ORSSA) procedure in constant-stress partially accelerated life-testing (CSPALTE). The study adopts the assumption that the lifespan of a specific item under operational stress follows a half-logistic probability distribution. Through Bayesian estimation methods, it concentrates on estimating the parameters, utilizing both asymmetric loss function and symmetric loss function. Estimations are conducted using ORSSAs and simple random samples, incorporating hybrid censoring of type-I. Real-world data sets are utilized to offer practical context and validate the theoretical discoveries, providing concrete insights into the research findings. Furthermore, a rigorous simulation study, supported by precise numerical calculations, is meticulously conducted to gauge the Bayesian estimation performance across the two distinct sampling methodologies. This research ultimately sheds light on the efficacy of Bayesian estimation techniques under varying sampling strategies, contributing to the broader understanding of reliability analysis in CSPALTE scenarios.
An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19
Authors: Zou Wei , Yao Xiujuan , Chen Yizhen , Li Xiaoqin , Huang Jiandong , Zhang Yong , Yu Lin , Xie Baosong 
Published: June 2024

AbstractThis study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-ZICU: − 0.821 (p-value: 0.412); Spiegelhalter-Zdeath: 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection.
Succinate promotes pulmonary fibrosis through GPR91 and predicts death in idiopathic pulmonary fibrosis
Authors: He Yijun , Han Yuanyuan , Zou Lijun , Yao Tingting , Zhang Yan , Lv Xin , Jiang Mao , Long Lingzhi , Li Mengyu , Cheng Xiaoyun , Jiang Guoliang , Peng Zhangzhe , Tao Lijian , Meng Jie , Xie Wei 
Published: June 2024

AbstractIdiopathic pulmonary fibrosis (IPF) is believed to be associated with a notable disruption of cellular energy metabolism. By detecting the changes of energy metabolites in the serum of patients with pulmonary fibrosis, we aimed to investigate the diagnostic and prognostic value of energy metabolites in IPF, and further elucidated the mechanism of their involvement in pulmonary fibrosis. Through metabolomics research, it was discovered that the TCA cycle intermediates changed dramatically in IPF patients. In another validation cohort of 55 patients with IPF compared to 19 healthy controls, it was found that succinate, an intermediate product of TCA cycle, has diagnostic and prognostic value in IPF. The cut-off levels of serum succinate were 98.36 μM for distinguishing IPF from healthy controls (sensitivity, 83.64%; specificity, 63.16%; likelihood ratio, 2.27, respectively). Moreover, a high serum succinate level was independently associated with higher rates of disease progression (OR 13.087, 95%CI (2.819–60.761)) and mortality (HR 3.418, 95% CI (1.308–8.927)). In addition, accumulation of succinate and increased expression of the succinate receptor GPR91 were found in both IPF patients and BLM mouse models of pulmonary fibrosis. Reducing succinate accumulation in BLM mice alleviated pulmonary fibrosis and 21d mortality, while exogenous administration of succinate can aggravate pulmonary fibrosis in BLM mice. Furthermore, GPR91 deficiency protected against lung fibrosis caused by BLM. In vitro, succinate promoted the activation of lung fibroblasts by activating ERK pathway through GPR91. In summary, succinate is a promising biomarker for diagnosis and prognosis of IPF. The accumulation of succinate may promote fibroblast activation through GPR91 and pulmonary fibrosis.
Estimating the COVID-19 prevalence from wastewater
Authors: Mohring Jan , Leithäuser Neele , Wlazło Jarosław , Schulte Marvin , Pilz Maximilian , Münch Johanna , Küfer Karl-Heinz 
Published: June 2024

AbstractWastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland–Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland–Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland–Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ($$0.208 \pm 0.031$$

0.208
±
0.031

) and a delay ($$5.07 \pm 2.30$$

5.07
±
2.30

days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.
Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning
Authors: Ahmed Md Sohel , Hanley Brenda J. , Mitchell Corey I. , Abbott Rachel C. , Hollingshead Nicholas A. , Booth James G. , Guinness Joe , Jennelle Christopher S. , Hodel Florian H. , Gonzalez-Crespo Carlos , Middaugh Christopher R. , Ballard Jennifer R. , Clemons Bambi , Killmaster Charlie H. , Harms Tyler M. , Caudell Joe N. , Benavidez Westrich Kathryn M. , McCallen Emily , Casey Christine , O’Brien Lindsey M. , Trudeau Jonathan K. , Stewart Chad , Carstensen Michelle , McKinley William T. , Hynes Kevin P. , Stevens Ashley E. , Miller Landon A. , Cook Merril , Myers Ryan T. , Shaw Jonathan , Tonkovich Michael J. , Kelly James D. , Grove Daniel M. , Storm Daniel J. , Schuler Krysten L. 
Published: June 2024

AbstractContinued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts. We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer (Odocoileus virginianus) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data. Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD. However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/.
Three-dimensional atrous inception module for crowd behavior classification
Authors: Choi Jong-Hyeok , Kim Jeong-Hun , Nasridinov Aziz , Kim Yoo-Sung 
Published: June 2024

AbstractRecent advances in deep learning have led to a surge in computer vision research, including the recognition and classification of human behavior in video data. However, most studies have focused on recognizing individual behaviors, whereas recognizing crowd behavior remains a complex problem because of the large number of interactions and similar behaviors among individuals or crowds in video surveillance systems. To solve this problem, we propose a three-dimensional atrous inception module (3D-AIM) network, which is a crowd behavior classification model that uses atrous convolution to explore interactions between individuals or crowds. The 3D-AIM network is a 3D convolutional neural network that can use receptive fields of various sizes to effectively identify specific features that determine crowd behavior. To further improve the accuracy of the 3D-AIM network, we introduced a new loss function called the separation loss function. This loss function focuses the 3D-AIM network more on the features that distinguish one type of crowd behavior from another, thereby enabling a more precise classification. Finally, we demonstrate that the proposed model outperforms existing human behavior classification models in terms of accurately classifying crowd behaviors. These results suggest that the 3D-AIM network with a separation loss function can be valuable for understanding complex crowd behavior in video surveillance systems.
Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers
Authors: Yousef Hibba , Feng Samuel F. , Jelinek Herbert F. 
Published: June 2024

AbstractType II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.
Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences
Authors: Lee Yun-Chen , Yu Jen-Chieh , Ni Kuan , Lin Yu-Chuan , Chen Ching-Tai 
Published: June 2024

AbstractAngiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inhibiting angiogenesis, are promising candidates in cancer treatment. We propose AAPL, a sequence-based predictor to identify AAPs with machine learning models of improved prediction accuracy. Each peptide sequence was transformed to a vector of 4335 numeric values according to 58 different feature types, followed by a heuristic algorithm for feature selection. Next, the hyperparameters of six machine learning models were optimized with respect to the feature subset. We considered two datasets, one with entire peptide sequences and the other with 15 amino acids from peptide N-termini. AAPL achieved Matthew’s correlation coefficients of 0.671 and 0.756 for independent tests based on the two datasets, respectively, outperforming existing predictors by a range of 5.3% to 24.6%. Further analyses show that AAPL yields higher prediction accuracy for peptides with more hydrophobic residues, and fewer hydrophilic and charged residues. The source code of AAPL is available athttps://github.com/yunzheng2002/Anti-angiogenic.
Spatio-temporal evolution and prediction of carbon balance in the Yellow River Basin and zoning for low-carbon economic development
Authors: Dong Linlin 
Published: June 2024

AbstractStudying the carbon effect of land use in watersheds is important for mitigating global warming, promoting coordinated emission reduction in different regions within the watersheds, and realizing high-quality development of the watersheds. Although a number of scholars have carried out relevant studies in the past, they mainly focused on carbon emissions, rarely involved the carbon balance formed by carbon sources and sinks, and lacked relevant studies on the development of low-carbon economy sub-region. Based on this, this study takes the Yellow River Basin as an example, explores the spatial and temporal evolution of carbon emissions from land use in counties in the Yellow River Basin from 1980 to 2020, and predicts the spatial pattern of carbon income and expenditure from land use under natural conditions in 2030 and 2060 using the PLUS model; and then superimposes on the main functional area planning, divides 735 counties in the Yellow River Basin into six low-carbon economic development subregions, and analyzes their economic development The model of their economic development is analyzed. The results show that: (1) the spatial and temporal differentiation of land use carbon balance in the Yellow River Basin has changed greatly over the past 40 years, (2) the spatial distribution pattern of land use carbon balance in the natural context in 2030 and 2060 is more similar to that in 1990, (3) the carbon emission reduction potentials and pattern optimization of the different low-carbon economic development subregions differ greatly, and they have different low-carbon economic development patterns. The results of this study provide a theoretical basis for scientifically and rationally formulating economic policies for low-carbon development in the counties of the Yellow River Basin, and also provide an important reference for related studies in other similar basins or regions in the world.