bluuebunny/crossref_metadata_2025_split huggingface dataset:
An individually adjusted approach for communicating epidemiological results on health and lifestyle to patients
Authors: Waaler Per Niklas , Bongo Lars Ailo , Rolandsen Christina , Lorem Geir F. 
Published: February 2024

AbstractIf scientific research on modifiable risk factors was more accessible to the general population there is a potential to prevent disease and promote health. Mobile applications can automatically combine individual characteristics and statistical models of health to present scientific information as individually tailored visuals, and thus there is untapped potential in incorporating scientific research into apps aimed at promoting healthier lifestyles. As a proof-of-concept, we develop a statistical model of the relationship between Self-rated-health (SRH) and lifestyle-related factors, and a simple app for conveying its effects through a visualisation that sets the individual as the frame of reference. Using data from the 6th (n = 12 981, 53.4% women and 46.6% men) and 7th (n = 21 083, 52.5% women and 47.5% men) iteration of the Tromsø population survey, we fitted a mixed effects linear regression model that models mean SRH as a function of self-reported intensity and frequency of physical activity (PA), BMI, mental health symptoms (HSCL-10), smoking, support from friends, and HbA1c ≥ 6.5%. We adjusted for socioeconomic and demographic factors and comorbidity. We designed a simple proof-of-concept app to register relevant user information, and use the SRH-model to translate the present status of the user into suggestions for lifestyle changes along with predicted health effects. SRH was strongly related to modifiable health factors. The strongest modifiable predictors of SRH were mental health symptoms and PA. The mean adjusted difference in SRH between those with 10-HSCL index = 1.85 (threshold for mental distress) and HSCL-10 = 1 was 0.59 (CI 0.61–0.57). Vigorous physical activity (exercising to exhaustion ≥ 4 days/week relative to sedentary) was associated with an increase on the SRH scale of 0.64 (CI 0.56–0.73). Physical activity intensity and frequency interacted positively, with large PA-volume (frequency ⨯ intensity) being particularly predictive of high SRH. Incorporating statistical models of health into lifestyle apps have great potential for effectively communicating complex health research to a general audience. Such an approach could improve lifestyle apps by helping to make the recommendations more scientifically rigorous and personalised, and offer a more comprehensive overview of lifestyle factors and their importance.
Green synthesis of collagen nanoparticles by Streptomyces xinghaiensis NEAA-1, statistical optimization, characterization, and evaluation of their anticancer potential
Authors: El-Sawah Asmaa A. , El-Naggar Noura El-Ahmady , Eldegla Heba E. , Soliman Hoda M. 
Published: February 2024

AbstractCollagen nanoparticles (collagen-NPs) are promising biopolymeric nanoparticles due to their superior biodegradability and biocompatibility. The low immunogenicity and non-toxicity of collagen-NPs makes it preferable for a wide range of applications. A total of eight morphologically distinct actinomycetes strains were newly isolated from various soil samples in Egypt. The cell-free supernatants of these strains were tested for their ability. These strains' cell-free supernatants were tested for their ability to synthesize collagen-NPs. Five isolates had the ability to biosynthesize collagen-NPs. Among these, a potential culture, Streptomyces sp. NEAA-1, was chosen and identified as Streptomyces xinghaiensis NEAA-1 based on 16S rRNA sequence analysis as well as morphological, cultural and physiological properties. The sequence data has been deposited at the GenBank database under the accession No. OQ652077.1. Face-centered central composite design (FCCD) has been conducted to maximize collagen-NPs biosynthesis. Maximum collagen-NPs was 8.92 mg/mL under the condition of 10 mg/mL of collagen concentration, initial pH 7, incubation time of 48 h and temperature of 35 °C. The yield of collagen-NPs obtained via FCCD optimization (8.92 mg/mL) was 3.32-fold compared to the yield obtained under non-optimized conditions (2.5 mg/mL). TEM analysis of collagen-NPs showed hollow sphere nanoscale particles with mean of 32.63 ± 14.59 nm in diameter. FTIR spectra showed major peaks of amide I, amide II and amide III of collagen and also the cell-free supernatant involved in effective capping of collagen-NPs. The biosynthesized collagen-NPs exhibited anti-hemolytic, antioxidant and cytotoxic activities. The inhibitory concentrations (IC50) against MCF-7, HeP-G2 and HCT116 cell lines were 11.62 ± 0.8, 19.60 ± 1.2 and 41.67 ± 2.2 µg/mL; respectively. The in-vivo investigation showed that collagen-NPs can suppress Ehrlich ascites carcinoma (EAC) growth in mice. The collagen-NPs/DOX combination treatment showed considerable tumor growth suppression (95.58%). Collagen-NPs evaluated as nanocarrier with a chemotherapeutic agent, methotrexate (MTX). The average size of MTX loaded collagen-NPs was 42.73 ± 3.5 nm. Encapsulation efficiency percentage (EE %) was 48.91% and drug loading percentage (DL %) was 24.45%.
Dissolved organic matter quantity and quality response of tropical rainforest headwater rivers to the transition from dry to wet season
Authors: Norouzi S. , Wagner T. , MacDonald A. , Bischoff J. , Brasche J. , Trojahn S. , Spray J. , Pereira R. 
Published: February 2024

AbstractDissolved organic matter (DOM) and its composition in aquatic ecosystems is a key indicator of ecosystem function and an important component of the global carbon cycle. Tropical rainforest headwaters play an important role in global carbon cycling. However, there is a large uncertainty on how DOM sources interact during mobilisation and the potential fate of associated carbon and nutrients. Using field techniques to measure dissolved organic carbon (DOC) concentration and composition, changes in DOM source from headwaters to larger downstream rivers were observed. This study shows that the hydrological connectivity, developed during the transition from dry to wet seasons, changes the DOM supply and transport across a tropical river catchment. The observed variability in the DOC-river discharge relationship provides further evidence of the changes in the DOM supply in a small headwater. This novel insight into the seasonal changes of the dynamics of DOM supply to the river helps understanding the mobilization of terrestrial DOM to tropical headwaters and its export from smaller to larger rivers. It also highlights the data gap in the study of smaller headwaters which may account for uncertainty in estimating the terrestrial carbon transported by inland waters.
Sustaining energetic communities: energy citizenship and participation in an age of upheaval and transition
Authors: Lennon Breffní , Dunphy Niall 
Published: February 2024

AbstractThe human use of energy is inherently understood and experienced through socially constructed frameworks. However, the degree of engagement with this topic on the part of humanities and the social sciences has until recently been uneven at best. This seems strange given current upheavals experienced in Europe and across the globe as the climate and biodiversity crises deepen. At the centre of all these crises is the energy system. Energy flows through various forms of natural and social circuitry (from production, to distribution and consumption) and these energyscapes are sited at the local, national, and transnational scales. The correlation between the (meta)physical flows taken by the various forms of energy we depend on—and the transitory social, cultural, economic, and political relationships that frame them—require much deeper study if we are to achieve the types of sustainable communities envisaged by the United Nations as part of its sustainable development goals (SDGs) for 2030. Arising from a review of current literature, this article presents recent research into the forming of citizen energy communities in Europe and the governance structures designed to facilitate their development. It also highlights the key drivers and barriers to citizen engagement with emergent, novel energetic communities.
Terahertz microscopy using laser feedback interferometry based on a generalised phase-stepping algorithm
Authors: Mohun Daniel , Sulollari Nikollao , Salih Mohammed , Li Lianhe H. , Cunningham John E. , Linfield Edmund H. , Davies A. Giles , Dean Paul 
Published: February 2024

AbstractIn this paper we report an improved method of coherent sensing through the use of a generalized phase-stepping algorithm to extract magnitude and phase information from interferometric fringes acquired by laser feedback interferometry (LFI). Our approach allows for significantly reduced optical sampling and acquisition times whilst also avoiding the need for fitting to complex models of lasers under optical feedback in post-processing. We investigate theoretically the applicability of this method under different levels of optical feedback, different laser parameters, and for different sampling conditions. We furthermore validate its use experimentally for LFI-based sensing using a terahertz (THz)-frequency laser in both far-field and near-field sensing configurations. Finally we demonstrate our approach for two-dimensional nanoscale imaging of the out-of-plane field supported by individual micro-resonators at THz frequencies. Our results show that fully coherent sensing can be achieved reliably with as little as 4 sampling points per imaging pixel, opening up opportunities for fast coherent sensing not only at THz frequencies but across the visible and infra-red spectrum.
Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection
Authors: Hoque Ahasanul , Anderson James V. , Rahman Mukhlesur 
Published: February 2024

AbstractBreeding programs require exhaustive phenotyping of germplasms, which is time-demanding and expensive. Genomic prediction helps breeders harness the diversity of any collection to bypass phenotyping. Here, we examined the genomic prediction’s potential for seed yield and nine agronomic traits using 26,171 single nucleotide polymorphism (SNP) markers in a set of 337 flax (Linum usitatissimum L.) germplasm, phenotyped in five environments. We evaluated 14 prediction models and several factors affecting predictive ability based on cross-validation schemes. Models yielded significant variation among predictive ability values across traits for the whole marker set. The ridge regression (RR) model covering additive gene action yielded better predictive ability for most of the traits, whereas it was higher for low heritable traits by models capturing epistatic gene action. Marker subsets based on linkage disequilibrium decay distance gave significantly higher predictive abilities to the whole marker set, but for randomly selected markers, it reached a plateau above 3000 markers. Markers having significant association with traits improved predictive abilities compared to the whole marker set when marker selection was made on the whole population instead of the training set indicating a clear overfitting. The correction for population structure did not increase predictive abilities compared to the whole collection. However, stratified sampling by picking representative genotypes from each cluster improved predictive abilities. The indirect predictive ability for a trait was proportionate to its correlation with other traits. These results will help breeders to select the best models, optimum marker set, and suitable genotype set to perform an indirect selection for quantitative traits in this diverse flax germplasm collection.
Non-contact assessment of cardiac physiology using FO-MVSS-based ballistocardiography: a promising approach for heart failure evaluation
Authors: Zhan Jing , Wu Xiaoyan , Fu Xuelei , Li Chenze , Deng Ke-Qiong , Wei Qin , Zhang Chao , Zhao Tao , Li Congcong , Huang Longting , Chen Kewei , Wang Qiongxin , Li Zhengying , Lu Zhibing 
Published: February 2024

AbstractContinuous monitoring of cardiac motions has been expected to provide essential cardiac physiology information on cardiovascular functioning. A fiber-optic micro-vibration sensing system (FO-MVSS) makes it promising. This study aimed to explore the correlation between Ballistocardiography (BCG) waveforms, measured using an FO-MVSS, and myocardial valve activity during the systolic and diastolic phases of the cardiac cycle in participants with normal cardiac function and patients with congestive heart failure (CHF). A high-sensitivity FO-MVSS acquired continuous BCG recordings. The simultaneous recordings of BCG and electrocardiogram (ECG) signals were obtained from 101 participants to examine their correlation. BCG, ECG, and intracavitary pressure signals were collected from 6 patients undergoing cardiac catheter intervention to investigate BCG waveforms and cardiac cycle phases. Tissue Doppler imaging (TDI) measured cardiac time intervals in 51 participants correlated with BCG intervals. The BCG recordings were further validated in 61 CHF patients to assess cardiac parameters by BCG. For heart failure evaluation machine learning was used to analyze BCG-derived cardiac parameters. Significant correlations were observed between cardiac physiology parameters and BCG's parameters. Furthermore, a linear relationship was found betwen IJ amplitude and cardiac output (r = 0.923, R2 = 0.926, p < 0.001). Machine learning techniques, including K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost, respectively, demonstrated remarkable performance. They all achieved average accuracy and AUC values exceeding 95% in a five-fold cross-validation approach. We establish an electromagnetic-interference-free and non-contact method for continuous monitoring of the cardiac cycle and myocardial contractility and measure the different phases of the cardiac cycle. It presents a sensitive method for evaluating changes in both cardiac contraction and relaxation in the context of heart failure assessment.
Genotype F of Echovirus 25 with multiple recombination pattern have been persistently and extensively circulating in Chinese mainland
Authors: Wang Xiaoyi , Cun Jianping , Li Shikang , Shi Yong , Liu Yingying , Wei Haiyan , Zhang Yong , Cong Ruyi , Yang Tingting , Wang Wenhui , Xiao Jinbo , Song Yang , Yan Dongmei , Yang Qian , Sun Qiang , Ji Tianjiao 
Published: February 2024

AbstractEchovirus 25 (E25), a member of the Enterovirus B (EV-B) species, can cause aseptic meningitis (AM), viral meningitis (VM), and acute flaccid paralysis (AFP). However, systematic studies on the molecular epidemiology of E25, especially those concerning its evolution and recombination, are lacking. In this study, 18 strains of E25, isolated from seven provinces of China between 2009 and 2018, were collected based on the Chinese hand, foot, and mouth disease (HFMD) surveillance network, and 95 sequences downloaded from GenBank were also screened. Based on the phylogenetic analysis of 113 full-length VP1 sequences worldwide, globally occurring E25 strains were classified into 9 genotypes (A–I), and genotype F was the dominant genotype in the Chinese mainland. The average nucleotide substitution rate of E25 was 6.08 × 10–3 substitutions/site/year, and six important transmission routes were identified worldwide. Seventeen recombination patterns were determined, of which genotype F can be divided into 9 recombination patterns. A positive selector site was found in the capsid protein region of genotype F. Recombination analysis and pressure selection analysis for genotype F showed multiple recombination patterns and evolution characteristics, which may be responsible for it being the dominant genotype in the Chinese mainland. This study provides a theoretical basis for the subsequent prevention and control of E25.
Graft-derived neurons and bystander effects are maintained for six months after human iPSC-derived NESC transplantation in mice’s cerebella
Authors: Mendonça Liliana S. , Henriques Daniel , Fernandes Vanessa , Moreira Ricardo , Brás João , Duarte Sónia , Schwamborn Jens C. , de Almeida Luís Pereira 
Published: February 2024

AbstractMachado-Joseph disease (MJD) is a neurodegenerative disorder characterized by widespread neuronal death affecting the cerebellum. Cell therapy can trigger neuronal replacement and neuroprotection through bystander effects providing a therapeutic option for neurodegenerative diseases. Here, human control (CNT) and MJD iPSC-derived neuroepithelial stem cells (NESC) were established and tested for their therapeutic potential. Cells’ neuroectodermal phenotype was demonstrated. Brain organoids obtained from the Control NESC showed higher mRNA levels of genes related to stem cells' bystander effects, such as BDNF, NEUROD1, and NOTCH1, as compared with organoids produced from MJD NESC, suggesting that Control NESC have a higher therapeutic potential. Graft-derived glia and neurons, such as cells positive for markers of cerebellar neurons, were detected six months after NESC transplantation in mice cerebella. The graft-derived neurons established excitatory and inhibitory synapses in the host cerebella, although CNT neurons exhibited higher excitatory synapse numbers compared with MJD neurons. Cell grafts, mainly CNT NESC, sustained the bystander effects through modulation of inflammatory interleukins (IL1B and IL10), neurotrophic factors (NGF), and neurogenesis-related proteins (Msi1 and NeuroD1), for six months in the mice cerebella. Altogether this study demonstrates the long-lasting therapeutic potential of human iPSC-derived NESC in the cerebellum.
A nationwide time-series analysis for short-term effects of ambient temperature on violent crime in South Korea
Authors: Heo Seulkee , Choi Hayon Michelle , Lee Jong-Tae , Bell Michelle L. 
Published: February 2024

AbstractPsychological theories on heat-aggression relationship have existed for decades and recent models suggest climate change will increase violence through varying pathways. Although observational studies have examined the impact of temperature on violent crime, the evidence for associations is primarily limited to coarse temporal resolution of weather and crime (e.g., yearly/monthly) and results from a few Western communities, warranting studies based on higher temporal resolution data of modern systemic crime statistics for various regions. This observational study examined short-term temperature impacts on violent crime using national crime data for the warm months (Jun.–Sep.) across South Korea (2016–2020). Distributed lag non-linear models assessed relative risks (RRs) of daily violent crime counts at the 70th, 90th, and 99th summer temperature percentiles compared to the reference temperature (10th percentile), with adjustments for long-term trends, seasonality, weather, and air pollution. Results indicate potentially non-linear relationships between daily summer temperature (lag0–lag10) and violent crime counts. Violent crimes consistently increased from the lowest temperature and showed the highest risk at the 70th temperature (~ 28.0 °C). The RR at the 70th and 90th percentiles of daily mean temperature (lag0–lag10), compared to the reference, was 1.11 (95% CI 1.09, 1.15) and 1.04 (95% CI 1.01, 1.07), indicating significant associations. Stratified analysis showed significant increases in assault and domestic violence for increases in temperature. The lagged effects, the influences of heat on subsequent crime incidence, did not persist 21 days after the exposure, possibly due to the displacement phenomenon. We found curvilinear exposure–response relationships, which provide empirical evidence to support the psychological theories for heat and violence. Lower public safety through increased violent crime may be an additional public health harm of climate change.
Exploring the relations of NLR, hsCRP and MCP-1 with type 2 diabetic kidney disease: a cross-sectional study
Authors: Fang Yaxuan , Wang Bin , Pang Bo , Zhou Zijun , Xing Yunze , Pang Pai , Zheng Dingyuan , Zhang Gang , Yang Bo 
Published: February 2024

AbstractType 2 diabetic kidney disease (T2DKD) is a common microvascular complication of type 2 diabetes mellitus (T2DM), and its incidence is significantly increasing. Microinflammation plays an important role in the development of T2DKD. Based on this, this study investigated the value of inflammatory markers including neutrophil–lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), monocyte chemoattractant protein-1 (MCP-1) in the prediction of T2DKD. This was a cross-sectional survey study. A total of 90 patients with T2DM, who were hospitalized in the nephrology and endocrinology departments of the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine from June 2021 to January 2022, were included and divided into three groups (A1, A2, A3) according to the urinary albumin-to-creatinine ratio (UACR). Observe and compare the basic information, clinical and laboratory data, and the inflammatory markers NLR, hs-CRP, MCP-1. Results revealed that high levels of NLR (OR = 6.562, 95% CI 2.060–20.902, P = 0.001) and MCP-1 (OR = 1.060, 95% CI 1.026–1.095, P < 0.001) were risk factors in the development of T2DKD. Receiver operating characteristic curve analysis showed that the area under curve of NLR and MCP-1 in diagnosing T2DKD were 0.760 (95% CI 0.6577–0.863, P < 0.001) and 0.862 (95% CI 0.7787–0.937, P < 0.001). Therefore, the inflammatory markers NLR and MCP-1 are risk factors affecting the development of T2DKD, which of clinical value may be used as novel markers of T2DKD.
Mapping urban mobility using vehicle telematics to understand driving behaviour
Authors: Xiang Junjun , Ghaffarpasand Omid , Pope Francis D. 
Published: February 2024

AbstractTelematics data, primarily collected from on-board vehicle devices (OBDs), has been utilised in this study to generate a thorough understanding of driving behaviour. The urban case study area is the large metropolitan region of the West Midlands, UK, but the approach is generalizable and translatable to other global urban regions. The new approach of GeoSpatial and Temporal Mapping of Urban Mobility (GeoSTMUM) is used to convert telematics data into driving metrics, including the relative time the vehicle fleet spends idling, cruising, accelerating, and decelerating. The telematics data is also used to parameterize driving volatility and aggressiveness, which are key factors within road safety, which is a global issue. Two approaches to defining aggressive driving are applied and assessed, they are vehicle jerk (the second derivative of vehicle speed), and the profile of speed versus acceleration/deceleration. The telematics-based approach has a very high spatial resolution (15–150 m) and temporal resolution (2 h), which can be used to develop more accurate driving cycles. The approach allows for the determination of road segments with the highest potential for aggressive driving and highlights where additional safety measures could beneficially be adopted. Results highlight the strong correlation between vehicle road occupancy and aggressive driving.