bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mechanisms for carbon stock driving and scenario modeling in typical mountainous watersheds of northeastern China
Authors: zhang Jin , zhang wenguang , Zhang Xinyan , Li Yuhong , Feng Mingming , Qi Peng , Huang Yiqiang , Li Rui , Jiang Ming , Zou Yuanchun , Wu Yi 
Published: December 2023

Abstract
Watershed ecosystems play a pivotal role in maintaining the global carbon cycle and reducing global warming by serving as vital carbon reservoirs for sustainable ecosystem management. In this study, we based on the "quantity-mechanism-scenario" frameworks to evaluate carbon stocks in mid to high latitudes alpine watersheds in China and explored the mechanisms of climate change and land use influence on the ability of watershed ecosystems to store carbon. The results showed that the carbon stock of the watershed increased by about 15.9 Tg from 1980 to 2020. Vegetation, precipitation, land-use change, and economic production have the strongest explanatory power for carbon stocks. Under different climate scenarios, it was found that the SSP2-4.5 scenario had a significant rise in carbon stock from 2020 to 2050, roughly 24.1 Tg. This increase was primarily observed in the southeastern region of the basin, with forest and grassland effectively protected. Conversely, according to the SSP5-8.5 scenario, the carbon stock would decrease by about 50.53 Tg with the expansion of cultivated and construction land in the basin's southwest part. Therefore, given the vulnerability of mid to high latitudes mountain watersheds, global warming trends continue to pose a greater threat to carbon sequestration in watersheds. Our findings carry important implications for tackling potential ecological threats in mid to high latitudes watersheds in the Northern Hemisphere and assisting policymakers in creating carbon sequestration plans, as well as for reducing climate change.
Sentinel – 5P derived Tropospheric NO 2 Concentration during the COVID-19 Pandemic Lockdown: Impacts of Urbanisation and Industrialisation in South Tamil Nadu, India
Authors: Rose R. S. Suja , Ponnukalai Bhavya 
Published: December 2023

Abstract
In emerging nations, rising urbanisation exacerbates existing issues like pollution. The biophysical and social conditions of cities are negatively impacted by unplanned urbanisation and the increase in industrialisation. Power plants, industries and vehicles are the substantial birthplaces of air pollution. The first instance of COVID-19 was discovered in China, the government implemented a curfew from March 26, 2020, to May 31, 2020, as part of a national COVID-19 pandemic prevention strategy. In the state of Tamil Nadu, in particular urban areas like Madurai, Virudhunagar, Tirunelveli, especially Thoothukudi, is experiencing a problem with air pollution as a result of booming industrial activity as well as urbanization. The impact of the recent environmental contamination is evaluated by a comprehensive regional and temporal study and it is considered vital. The confinement and isolation measures implemented during the COVID-19 pandemic impacted NO2 concentrations in 11 cities of southern Tamil Nadu is analysed. The impact of urbanization and industrialization on local air quality was assessed during the period of lockdown. Using remote sensing techniques, the study quantified the changes in atmospheric pollutant NO2 concentrations and represented them spatiotemporally which was observed during these 4 periods. In the study area, a general decline in NO2 content is seen. A significant number of places in the eastern region exhibit an increase in NO2 concentration as a result of lockdown relaxations. Therefore, NO2 pollution can be controlled if enough effort is put into it.
Giant Kerr nonlinearity of terahertz waves mediated by stimulated phonon polaritons in a microcavity chip
Authors: Wu Qiang , Huang Yibo , Lu Yao , Li Wei , Xu Xitan , Jiang Xinda , Ma Ruobin , Chen Lu , Ruan Ningjuan , Xu Jingjun 
Published: December 2023

Abstract

Optical Kerr effect, in which input light intensity linearly alters the refractive index, has enabled the generation of optical solitons, supercontinuum spectra, and frequency combs, playing vital roles in the on-chip devices, fiber communications, and quantum manipulations. Especially, terahertz Kerr effect, featuring fascinating prospects in future high-rate computing, artificial intelligence, and cloud-based technologies, encounters a great challenge due to the rather low power density and feeble Kerr response. Here, we demonstrate a giant terahertz frequency Kerr nonlinearity mediated by stimulated phonon polaritons. Under the influences of the giant Kerr nonlinearity, the power-dependent refractive index change would result in a frequency shift in the microcavity, which was experimentally demonstrated via the measurement of the resonant mode of a chip-scale lithium niobate Fabry-Pérot microcavity. Attributed to the existence of stimulated phonon polaritons, the nonlinear coefficient extracted from the frequency shifts are orders of magnitude larger than that of visible and infrared light, which is also theoretically demonstrated by nonlinear Huang equations. This work opens an avenue for many rich and fruitful terahertz Kerr effect based physical, chemical, and biological systems that have terahertz fingerprints.
Study on risk factors of diabetic peripheral neuropathy and development of a prediction model based on machine learning
Authors: Cui Qiyuan , Wang Li , Wang Xiaoyan , Zheng Yun , Lin Jiaxi , Liu Lu , Zhu Jinzhou , He Mingqing 
Published: December 2023

Abstract
(1) Background: Diabetic peripheral neuropathy (DPN) stands as a prevalent complication in individuals with diabetes. This study aims to develop and validate a machine learning-based model to predict the probability of DPN in patients diagnosed with type 2 diabetes mellitus.
(2) Methods: We conducted a retrospective analysis of data pertaining to 628 patients with type 2 diabetes mellitus who received treatment at the First Affiliated Hospital of Soochow University between 2022 and 2023. This dataset encompassed medical histories, physical examinations, and results from biochemical index tests. The cohort was divided into training and validation datasets at an 8:2 ratio randomly. Feature selection, parameter optimization, and model construction were carried out within the training set, while the validation set was employed to assess the predictive performance of the models. We utilized machine learning algorithms such as Gradient Boosting Machines (GBM), Random Forest (RF), Support Vector Machines (SVM), Naïve Bayes, Decision Trees (DT) and traditional logistic regression (LR). The performance of these models was evaluated through the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). To interpret the best-performing model, we employed Shapley Additive exPlanation (SHAP) Plots and Local Interpretable Model Agnostic Explanations (LIME).
(3) Results: The dataset, comprising 628 individuals from the First Affiliated Hospital of Soochow University, yielded significant variables following selection by the Boruta algorithm and logistic multivariate regression analysis. These significant variables included Age, HOMA-IR, Duration of diabetes, and (blood urea nitrogen) BUN. The GBM model outperformed the other models, demonstrating an accuracy of 0.9316, an F1-score of 0.9385, and an AUC of 0.9294. The validation set cohorts was further subdivided within the study, indicating that the GBM model remained an effective classifier in different subgroups.
(4) Conclusions: The GBM model was composed of age, HOMA-IR, duration of diabetes and BUN may assist doctors with the early identification of DPN in patients with type 2 diabetes mellitus.
Augmentation of Anaerobic Pentose Phosphate Pathway Dysregulates Tetrahydrobiopterin Metabolism in Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) Patients with Orthostatic Intolerance: A Pilot Study
Authors: Bulbule Sarojini , Gottschalk Carl Gunnar , Drosen Molly E , Peterson Daniel , Arnold Leggy A , Roy Avik 
Published: December 2023

Abstract
Tetrahydrobiopterin (BH4), an essential cofactor of amino acid metabolism, was found to be strongly elevated in ME/CFS patients with Orthostatic intolerance (ME + OI). However, the molecular mechanism of BH4 upregulation is poorly understood in ME + OI patients. Here, we report that the activation of the non-oxidative pentose phosphate pathway (PPP) plays a critical role in the biosynthesis of BH4 in ME + OI patients. Microarray-based gene screening followed by real-time PCR-based validation, ELISA assay, and finally enzyme kinetic studies of glucose-6-phosphate dehydrogenase (G6PDH), transaldolase (TALDO1), and transketolase (TK) enzymes revealed that the augmentation of anaerobic PPP is critical in the pathogenesis of ME + OI. Along with the upregulated anaerobic PPP enzymes, we observed that biopterin metabolites such as BH4 and dihydrobiopterin (BH2) are strongly upregulated suggesting the disruption of biopterin homeostasis in ME + OI patients. To explore the molecular role of anaerobic PPP in biopterin metabolism, we devised a novel cell culture strategy to induce non-oxidative PPP by treating human microglial cells with ribose-5-phosphate (R5P) under a hypoxic condition of 85%N2/10%CO2/5%O2 followed by the analysis of BH4 and BH2 upregulation via ELISA, immunoblot and dual immunocytochemical analyses. These results confirmed that the activation of non-oxidative PPP is indeed required for the upregulation of both BH4 and BH2. Moreover, the siRNA knocking down of the taldo1 gene strongly inhibited the expression of GTP cyclohydrolase 1 (GTPCH1) and subsequent production of BH4 and its metabolic conversion to BH2 in R5P-treated and hypoxia-induced C20 human microglia cells. To test the functional role of ME + OI plasma-derived biopterins, exogenously added plasma samples of ME + OI plasma with high BH4 upregulated inducible nitric oxide synthase (iNOS) and nitric oxide (NO) in human microglial cells indicating that the non-oxidative PPP-induced-biopterins could stimulate inflammatory response in ME + OI patients.
Optimization of in situ exopolysaccharides production by Lactobacillus helveticus MB2-1 in yogurt and its vitro functional characteristics
Authors: Ge Zhiwen , Wang Dan , Zhao Wenting , Wang Pan , Dong Mingsheng , Wang Junjuan , Zhao Yuanyuan , Zhao Xiaoyan 
Published: December 2023

Abstract
In order to improve the in situ exopolysaccharides (EPS) production of Lactobacillus helveticus MB2-1 in Sayram ketteki yogurt and explore its functional characteristics, the production optimization, rheology and microstructure of fermented yogurt, and emulsifying properties, antioxidant, antibiofilm activities of the in situ EPS were investigated. Based on the results of response surface methodology (RSM), the fermentation temperature 37℃, fermentation time 8.0 h and incubation amount 7.0% (w/w) was the optimum condition for L. helveticus MB2-1 growth and yogurt formation, and the obtained production of in situ EPS could be up to 340 mg/L. Under the optimized fermentation conditions, the rheology analysis illustrated that the Sayram ketteki yogurt fermented by L. helveticus MB2-1 formed better gel structure than the control yogurt. Besides, the in situ EPS presented fine emulsifying properties, which could be comparable to other biological emulsifiers. The in situ EPS exhibited potent antioxidant activities as well. Moreover, the antibiofilm activities of the in situ EPS on foodborne pathogens biofilm formation were obvious. Taken together, the results illustrated that the EPS produced in situ by L. helveticus MB2-1 in Sayram ketteki yogurt has the potentiality to be considered as a beneficial substitute for functional food products.
L. plantarum surface-displayed VP6 (PoRV) protein can prevent PoRV infection in piglets
Authors: Wang Junhong , Wang Haixu , Zhang Dongliang , Liu Fangyuan , Li Xiaoxu , Gao Ming , Cheng Mingyang , Bao Hongyu , Zhan Jiaxing , Zeng Yan , Wang Chunfeng , Cao Xin 
Published: December 2023

Abstract
Porcine rotavirus (PoRV) poses a threat to the development of animal husbandry and human health, leading to substantial economic losses. Among its components, VP6 protein constitutes a major portion of the viral particles and serves as the core structural protein of the virus. In this study, we first developed an antibiotics free and environmentally friendly vector called asd-araC-PBAD-alr (AAPA). Subsequently, we constructed recombinant Lactobacillus plantarum (L. plantarum) strains induced by arabinose to express VP6 and VP6-pFc fusion proteins. Our findings revealed that NC8/Δalr-pCXa-VP6-S and NC8/Δalr-pCXa-VP6-pFc-S strains were capable of enhancing body immunity and protecting suckling mice and piglets from rotavirus infection. The novel recombinant L. plantarum strains developed in this study hold promising potential as oral vaccines to enhance body immunity and present a new strategy for preventing PoRV infection.
T2DM/CKD genetic risk scores and the progression of diabetic kidney disease in T2DM subjects
Authors: Galuška David , Pácal Lukáš , Chalásová Katarína , Divácká Petra , Řehořová Jitka , Svojanovský Jan , Hubáček Jaroslav A. , Lánská Věra , Kaňková Kateřina 
Published: December 2023

Abstract

This study explored the predictive value of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression, Major Cardiovascular Events (MCVE), and All-Cause Mortality (ACM) in type 2 diabetes mellitus (T2DM) patients. We assessed 30 T2DM and CKD GWAS-derived single nucleotide polymorphisms (SNPs) in 400 Central European patients. Our analysis showed significant correlations between age, diabetes duration, diastolic blood pressure, total cholesterol, and eGFR with DKD progression (P < 0.05). No individual SNP strongly correlated with DKD progression, although CERS2 and SHROOM3 were near significance. WSF1 was associated with MCVE (P = 0.029), and CANCAS1, CERS2, and C9 were linked to ACM (P < 0.02). GRS did not surpass traditional clinical factors in predicting DKD, MCVE, or ACM. The model combining genetic and clinical factors only slightly improved the area under the curve (AUC) compared to the clinical model alone of 0.582 (95% CI 0.487–0.676) and 0.645 (95% CI 0.556–0.735), respectively, but this was not statistically significant (P = 0.06). Our findings underscore the complexity of genetic predictors in DKD progression and their interaction with clinical factors. Despite the potential of personalized medicine using genetic markers, our results suggest that conventional clinical factors are still crucial in predicting DKD in the Czech T2DM population. GWAS-derived GRSs did not show enhanced predictive capacity over standard clinical factors in this group.
Estimated transmission dynamics of SARS-CoV-2 variants from wastewater are robust to differential shedding
Authors: Beerenwinkel Niko , Dreifuss David , Huisman Jana , Rusch Johannes , Caduff Lea , Ganesanandamoorthy Pravin , Devaux Alexander , Gan Charles , Stadler Tanja , Kohn Tamar , Ort Christoph , Julian Timothy 
Published: December 2023

Abstract
The COVID-19 pandemic has accelerated the development and adoption of wastewater-based epidemiology. Wastewater samples can provide genomic information for detecting and assessing the spread of SARS-CoV-2 variants in communities and for estimating important epidemiological parameters such as the growth advantage of the variant. However, despite demonstrated successes, epidemiological data derived from wastewater suffers from potential biases. Of particular concern are differential shedding profiles that different variants of concern exhibit, because they can shift the relationship between viral loads in wastewater and prevalence estimates derived from clinical cases. Using mathematical modeling, simulations, and Swiss surveillance data, we demonstrate that this bias does not affect estimation of the growth advantage of the variant and has only a limited and transient impact on estimates of the effective reproduction number. Thus, population-level epidemiological parameters derived from wastewater maintain their advantages over traditional clinical-derived estimates, even in the presence of differential shedding among variants.
Relationships between prognosis and nutritional intake of Mycobacterium avium complex lung disease
Authors: Wakamatsu Kentaro , Nagata Nobuhiko , Honjo Satoshi , Kumazoe Hiroyuki , Hayashi Yuri , Katsuki Kouta , Hara Makiko , Nagaoka Aiko , Noda Naotaka , Kiyotani Ruriko , Fukui Izumi , Ose Mizuko , Ikematsu Yuki , Tatsuta Miyoko , Katahira Katsuyuki , Akasaki Takashi , Otsuka Junji , Maki Sanae , Izumi Miiru , Kawasaki Masayuki , Harada Yasuko 
Published: December 2023

Abstract

Objective Previous studies on prognostic factors in Mycobacterium avium complex (MAC) lung disease have consistently shown that nutritional status, specifically, low BMI, is a poor prognostic factor. However, there has been no study focusing on nutritional intake, and while several studies on prognosis have been published, none are prospective in design. This study aimed to prospectively examine relationships between prognosis and nutritional status and nutritional intake in patients with MAC lung disease.Patients and methods : This study included 174 outpatients and inpatients with MAC lung disease who were registered in our hospital between May 2010 and June 2016. At registration, sex, age, comorbidities, disease duration, bacterial strains, number of segments involved, disease type, treatment (with or without), nutritional status (body mass index [BMI]), waist circumference, lymphocyte count, serum albumin, serum cholinesterase, serum transferrin, total cholesterol, visceral fat area as assessed by abdominal CT, and nutritional intake were investigated, and their associations with all-cause mortality and disease-specific mortality risks were prospectively analyzed using Cox proportional hazard models.Results Older age, comorbidities, a large number of segments involved, fibrocavitary disease, low BMI, and low energy intake at registration were associated with all-cause mortality risk, whereas older age, a large number of segments involved, and low energy intake at registration were associated with disease-specific mortality risk.Conclusion Patients with MAC lung disease are characterized by low nutrient intake. It was suggested that low energy intake may be a risk factor for both all-cause and disease-specific deaths.Trial registration: This study was approved by the Ethics Committee of the National Hospital Organization Omuta Hospital (IRB approval number: 29 − 13). This study can be accessed via https://omuta.hosp.go.jp/profession/ethics_29.html
Analysis and Predictive Modeling of Perioperative Fever in Elderly Patients Undergoing Total Hip Arthroplasty for Femoral Neck Fractures
Authors: Yin Junxiang , Chen Xiangyu , Xu Kuixin , Qin Luyue , Yan Lianqi 
Published: December 2023

Abstract
Objective: To investigate the risk factors for perioperative fever in elderly patients undergoing primary total hip arthroplasty (THA) due to femoral neck fractures and establish a predictive model, providing insights for clinical practice.Methods: A retrospective study was conducted, collecting data from elderly patients (>60 years) who underwent unilateral primary hip arthroplasty for femoral neck fractures at the Northern People's Hospital of Jiangsu Province from January 2016 to January 2022. A total of 286 femoral neck fracture patients were selected after screening, including 190 without fever and 96 with fever. General information, including age, gender, body mass index (BMI), preoperative comorbidities (hypertension, diabetes, coronary heart disease), hospitalization duration, preoperative use of non-steroidal anti-inflammatory drugs, preoperative levels of white blood cells, neutrophils, hemoglobin, procalcitonin (PCT), erythrocyte sedimentation rate, albumin, and D-dimer, anesthesia method, operation duration, intraoperative blood loss, postoperative transfusion, and postoperative levels of white blood cells, neutrophils, hemoglobin, PCT, erythrocyte sedimentation rate, Alb, and D-dimer were collected for both groups. Multiple logistic regression analysis was performed to identify risk factors for postoperative fever after total hip arthroplasty, and the clinical predictive model's value was evaluated using the ROC curve.Results: The multiple logistic regression analysis revealed that anesthesia method, operation duration, intraoperative blood loss, postoperative PCT, and transfusion were risk factors for postoperative fever after total hip arthroplasty, while postoperative albumin level acted as a protective factor. A predictive model was constructed, and the ROC curve was plotted, with an AUC of 0.855 (sensitivity 0.847, specificity 0.708).Conclusion: The clinical predictive model based on postoperative albumin, anesthesia method, operation duration, intraoperative blood loss, postoperative PCT, and transfusion demonstrates excellent predictive value for the occurrence of postoperative fever after total hip arthroplasty.
Exploring the usefulness of the INLA model in predicting levels of crimes in the City of Johannesburg, South Africa
Authors: Coleman Toshka , Mokilane Paul , Holloway Jenny , Botha Nicolene , Koen Renee , Rangata Mapitsi , Dudeni-Tlhone Nontembeko 
Published: December 2023

Abstract
Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to optimize resource allocation and personnel management for crime prevention. Traditionally, the Poisson model has been the widely used model for crime prediction. However, recent statistical advancements introduce Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focusses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction, secondly developing a model using no demographic information other than clustering attributes as an exogenous variable, thirdly quantifying prediction uncertainty, and finally addressing data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model accurately ranked areas by crime levels, achieving a 29% Mean Absolute Percentage Error (MAPE) and 0.8 R-Squared value for crime predictions.