bluuebunny/crossref_metadata_2025_split huggingface dataset:
Medin co-aggregates with vascular amyloid-β in Alzheimer’s disease
Authors: Wagner Jessica , Degenhardt Karoline , Veit Marleen , Louros Nikolaos , Konstantoulea Katerina , Skodras Angelos , Wild Katleen , Liu Ping , Obermüller Ulrike , Bansal Vikas , Dalmia Anupriya , Häsler Lisa M. , Lambert Marius , De Vleeschouwer Matthias , Davies Hannah A. , Madine Jillian , Kronenberg-Versteeg Deborah , Feederle Regina , Del Turco Domenico , Nilsson K. Peter R. , Lashley Tammaryn , Deller Thomas , Gearing Marla , Walker Lary C. , Heutink Peter , Rousseau Frederic , Schymkowitz Joost , Jucker Mathias , Neher Jonas J. 
Published: November 2022

AbstractAggregates of medin amyloid (a fragment of the protein MFG-E8, also known as lactadherin) are found in the vasculature of almost all humans over 50 years of age1,2, making it the most common amyloid currently known. We recently reported that medin also aggregates in blood vessels of ageing wild-type mice, causing cerebrovascular dysfunction3. Here we demonstrate in amyloid-β precursor protein (APP) transgenic mice and in patients with Alzheimer’s disease that medin co-localizes with vascular amyloid-β deposits, and that in mice, medin deficiency reduces vascular amyloid-β deposition by half. Moreover, in both the mouse and human brain, MFG-E8 is highly enriched in the vasculature and both MFG-E8 and medin levels increase with the severity of vascular amyloid-β burden. Additionally, analysing data from 566 individuals in the ROSMAP cohort, we find that patients with Alzheimer’s disease have higherMFGE8expression levels, which are attributable to vascular cells and are associated with increased measures of cognitive decline, independent of plaque and tau pathology. Mechanistically, we demonstrate that medin interacts directly with amyloid-β to promote its aggregation, as medin forms heterologous fibrils with amyloid-β, affects amyloid-β fibril structure, and cross-seeds amyloid-β aggregation both in vitro and in vivo. Thus, medin could be a therapeutic target for prevention of vascular damage and cognitive decline resulting from amyloid-β deposition in the blood vessels of the brain.
Modelling the Mediterranean Sea ecosystem at high spatial resolution to inform the ecosystem-based management in the region
Authors: Piroddi Chiara , Coll Marta , Macias Diego , Steenbeek Jeroen , Garcia-Gorriz Elisa , Mannini Alessandro , Vilas Daniel , Christensen Villy 
Published: November 2022

AbstractCumulative pressures are rapidly expanding in the Mediterranean Sea with consequences for marine biodiversity and marine resources, and the services they provide. Policy makers urge for a marine ecosystem assessment of the region in space and time. This study evaluates how the whole Mediterranean food web may have responded to historical changes in the climate, environment and fisheries, through the use of an ecosystem modelling over a long time span (decades) at high spatial resolution (8 × 8 km), to inform regional and sub-regional management. Results indicate coastal and shelf areas to be the sites with highest marine biodiversity and marine resources biomass, which decrease towards the south-eastern regions. High levels of total catches and discards are predicted to be concentrated in the Western sub-basin and the Adriatic Sea. Mean spatial–temporal changes of total and commercial biomass show increases in offshore waters of the region, while biodiversity indicators show marginal changes. Total catches and discards increase greatly in offshore waters of the Western and Eastern sub-basins. Spatial patterns and temporal mean changes of marine biodiversity, community biomasses and trophic indices, assessed in this study, aim at identifying areas and food web components that show signs of deterioration with the overall goal of assisting policy makers in designing and implementing spatial management actions for the region.
Robust R-peak detection in an electrocardiogram with stationary wavelet transformation and separable convolution
Authors: Yun Donghwan , Lee Hyung-Chul , Jung Chul-Woo , Kwon Soonil , Lee So-Ryoung , Kim Kwangsoo , Kim Yon Su , Han Seung Seok 
Published: November 2022

AbstractR-peak detection is an essential step in analyzing electrocardiograms (ECGs). Previous deep learning models reported their performance primarily in a single database, and some models did not perform at the highest levels when applied to a database different from the testing database. To achieve high performances in cross-database validations, we developed a novel deep learning model for R-peak detection using stationary wavelet transform (SWT) and separable convolution. Three databases (i.e., the MIT-BIH Arrhythmia [MIT-BIH], the Institute of Cardiological Technics [INCART], and the QT) were used in both the training and testing models, and the MIT-BIH ST Change (MIT-BIH-ST), European ST-T, TELE and MIT-BIH Noise Stress Test (MIT-BIH-NST) databases were further used for testing. The detail coefficient of level 4 decomposition by SWT and the first derivative from filtered ECGs were used for model inputs, and the interval of 150 ms centered at marked peaks was used for labels. Separable convolution with atrous spatial pyramidal pooling was selected as the model’s architecture, and noise-augmented waveforms of 5.69 s duration (2048 size in 360 Hz) were used in training. The model performance was evaluated using cross-database validation. The F1 scores of the peak detection model were 0.9994, 0.9985, and 0.9999 in the MIT-BIH, INCART, and QT databases, respectively. When the above three databases were pooled, the F1 scores were 0.9993 for fivefold cross-validation and 0.9991 for cross-database validation. The model performance remained high for MIT-BIH-ST, European ST-T, and TELE, with F1 scores of 0.9995, 0.9988, and 0.9790, respectively. The model performance when trained by severe noise augmentation increased for the MIT-BIH-NST database (F1 scores from 0.9504 to 0.9759) and decreased for the MIT-BIH database (F1 scores from 0.9994 to 0.9991). The present SWT and separable convolution-based model for R-peak detection yields a high performance even for cross-database validations.
Impact assessment of particulate pollution on maternal mortality in Nigeria
Authors: Emetere M. E. , Oladimeji T. E. 
Published: November 2022

AbstractRecently, the World Health Organization reported that 20% of all global maternal deaths happened in Nigeria between 2005 and 2015. In developing countries, these maternal deaths are mainly from air pollution. Due to poor facilities and documentation, the extent of danger is not known. This research seeks to estimate the available pollutants and its direct and indirect impact on maternal mortality. Ten (10) years (2010–2019) datasets of black carbon, sulfur dioxide, dust, carbon monoxide, organic carbon particulates, sea-salts, and sulphate particulates were obtained from the second modern-era retrospective analysis for research and applications (MERRA-2). The dataset was obtained for the six geopolitical zones of Nigeria and analyzed using statistical tool, models, spatial interpolation, and risk analysis. The volumetric and radioecological risk was also analyzed. It was observed the dust content had minute volume of heavy metal and/or radionuclide particles that may be unharmful in the short term but lethal in the long term. The risk quotient and total dose rate per organism are given as 0.00000396 and 0.0000396 µGy h−1. The result in this manuscript corroborates existing data on maternal mortality in Nigeria. It is recommended that the safety of pregnant woman depends on significant efforts of authorities to enact and enforce environmental laws to mitigate air pollution.
Secure lightweight cryptosystem for IoT and pervasive computing
Authors: Abutaha Mohammed , Atawneh Basil , Hammouri Layla , Kaddoum Georges 
Published: November 2022

AbstractLarge volumes of sensitive data are being transferred among devices as the Internet of Things (IoT) grows in popularity. As a result, security measures must be implemented to ensure that unauthorized parties do not obtain access to the data. It is well acknowledged that IoT devices have restricted resources, such as limited battery life, memory, and hence reaction time. Classical encryption approaches and methods become inefficient for IoT devices due to memory limits. Large volumes of sensitive data are being transferred between devices as the Internet of Things (IoT) grows in popularity. This involves the implementation of security safeguards to ensure that unauthorized parties do not obtain access to the data. IoT devices are notorious for having limited resources, such as battery life, memory, and hence response time. Classical encryption approaches and methods become inefficient for IoT devices due to memory limits. As a result, a Lightweight cryptosystem that fits the needs of Lightweight devices and ubiquitous computing systems has emerged. The goal of this study is to present a Lightweight cryptosystem (LWC) that may be used as a plugin to secure data transfers in IoT devices and pervasive computing. To that goal, the researchers employ several simple measuring techniques. The suggested system was then implemented on a field-programmable gate array (FPGA) board using the Verilog programming language to demonstrate its appropriateness for actual security applications. FPGA is also utilized in hardware applications to assess the system’s resource usage and performance. Finally, a comparison of the proposed system with previous lightweight cryptography systems is performed to reinforce the major goal of this work, which is to present a new lightweight cryptosystem.
Parametric optimisation for the design of gravity energy storage system using Taguchi method
Authors: Elsayed Mostafa E. A. , Abdo Saber , Attia Ahmed A. A. , Attia El-Awady , Abd Elrahman M. A. 
Published: November 2022

AbstractGravitational energy storage systems are among the proper methods that can be used with renewable energy. However, these systems are highly affected by their design parameters. This paper presents a novel investigation of different design features of gravity energy storage systems. A theoretical model was developed using MATLAB SIMULINK to simulate the performance of the gravitational energy storage system while changing its design parameters. A parametric optimization study was also conducted using Taguchi and analysis of variance (ANOVA) techniques for optimizing the energy storage rate. Six parameters were studied; three are related to the piston design (diameter, height, and material density). The other parameters are the return pipe diameter, length, and charging/discharging time. Results revealed that the piston diameter and height are the two most significant parameters for the system performance compared to the other parameters, as they contributed by 35.11% and 30.28%, respectively. The optimization results indicated that the optimal piston diameter, height, and return pipe diameter were 0.25, 0.5, and 0.01 of the container height. The outcomes of this paper can significantly improve energy storage and power generation from renewable energy systems as it provides a reliable, economical, sustainable, and durable energy storage system.
Evaluation under real-life conditions of a lifestyle intervention for diabetes prevention developed by the municipal health services of Madrid, Spain
Authors: Martin-Ridaura Carmen , Ochoa-Esteban Darío , Berlinches-Zapero Carmen , Ruiz-Fernández Dolores , Sanz-Martín Vanessa , Gavira-Izquierdo Rosario , March Sebastià , López-Toribio María , Ceinos-Arcones Mercedes ,   The ALAS Project Working Group, Rivas-Hernández Dolores , Pino-Vega Saray , Melero-Rubio Juan Manuel , Bordel-Nieto Francisco , Caballero-Jauregui Milagros , Corella-Monzon Isabel , Pino-Días Raquel , Cutanda-Rodriguez Carmen , Sánchez-Muñoz Sergio , Fernández-Garrido José Manuel , Morales-López Carmen , Majarrez-Arias María Jesús , Sancho Santiago , Fernández Nuria Calle , Martínez-Cortes Mercedes , García-Crespo Pilar , León-Dominguez Carmen María , Pascual Marina , Herrera Raquel 
Published: November 2022

AbstractThe Diet, Physical Activity and Health (Alimentación, Actividad física y Salud, ALAS) program is an intervention implemented by the municipal health services of Madrid with the objective of reducing weight and preventing diabetes in high-risk population by improving diet and physical activity. The ALAS program combines individual visits with a 10-session group workshop that takes place over a 6-month period. This study evaluated the effectiveness of the ALAS intervention implemented under real-life conditions between 2016 and 2019. The intervention was evaluated with a pre- and post-intervention study with follow-up performed 6 and 12 months from the start of the program. The analyzed outcomes were a 5–10% reduction in the initial weight, body mass index (BMI), waist circumference and a change in glycemic status in prediabetic participants. Statistical models were adjusted by sociodemographic variables. The participants were recruited from municipal community health centers or referred by municipal occupational health services. Between 2016 and 2019, 1629 people participated in the program. At 6 months, 85% of the participants had lost weight; 43% had lost 5% or more of their initial weight, and 12% had lost 10% or more. Regarding BMI, 22.3% of participants who were initially obese were no longer obese, and 15.2% of the overweight participants achieved normal weight. A total of 35.1% of the prediabetic participants reverted to normoglycemic status. The intervention was found to be more effective for men, for those who completed the intervention and those who accessed the program through the occupational health route. Among the participants who accessed the intervention via the community, the intervention was more effective in those with a high educational level. The evaluation demonstrated the effectiveness of the ALAS program for reducing weight and the risk of developing Type 2 diabetes when applied under real-life conditions. The effectiveness of the intervention differed according to gender, access route and educational level of the participants.
Numerical analysis of the relationship between mixing regime, nutrient status, and climatic variables in Lake Biwa
Authors: Zhou Jinxin , Yoshida Takero , Kitazawa Daisuke 
Published: November 2022

AbstractAs awareness of climate-related freshwater quality problems increases, more research is needed to better understand how climate impacts water quality. Climate has significant impacts on the mixing regime and nutrient status of lakes. However, the relationship between climatic variables, mixing regime, and nutrient status in large monomictic lakes requires further study. Here we applied a three-dimensional ecosystem model to the large monomictic Lake Biwa, where hypoxia had recurred over the past 60 years. The model was validated using monitoring data, and the relationships among mixing regime, nutrient status, and climatic variables have been investigated. The turnover timing, which represented the mixing regime, varied by 36 days and depended most on wind speed but least on air temperature. In the early period prior to waste-water treatment there was a strong linear relationship between phosphorus and deep-water dissolved oxygen (DO) concentrations. Following this period, the relationship weakened but remained significant. In addition, we find a weak relationship between turnover timing and deep-water DO concentrations. We suggest that later turnover timing may favor lower deep-water DO concentrations, which in turn may favor release of legacy nutrients from sediments. Although waste-water treatment has improved conditions in the lake, climate change induced alteration of turnover timing may adversely influence water quality. Maintaining water quality under continued warming may require more rigorous controls on phosphorus loading to the lake.
Near field and far field plasmonic enhancements with bilayers of different dimensions AgNPs@DLC for improved current density in silicon solar
Authors: Hekmat Maryam , Shafiekhani Azizollah , Khabir Mehdi 
Published: November 2022

AbstractThe effect of a bilayer of different dimension silver nanoparticles (Ag NPs) on light trapping in silicon solar cells is investigated. Here, we report on the improved performance of silicon solar cells by integrating two layers of silver nanoparticles of different sizes. We experimentally examine the plasmonic near-field and far-field effects of bilayer Ag NPs embedded within an anti-reflective DLC layer on silicon solar cells' optical and electrical characteristics. Field-Emission Scanning Electron Microscopy drove the two-dimensional differences in the size of Ag NPs. The surface plasmon resonance of the two-dimensional nanoparticles was estimated from the absorption optical spectra. External quantum efficiency measurements showed that near-field or far-field plasmonic effects altered with the Ag NPs size. The development of far fields was confirmed by measuring the solar cell performance under AM 1.5 G illumination. The impact of the far-field in the cell containing two layers of Ag NPs, which outer layer is larger dimensions NPs, improves the current density up to 38.4 mA/cm2 (by 70% compared to the bare reference cell).
Paradoxical suboptimal vitamin D levels in a Mediterranean area: a population-based study
Authors: Díaz-Rizzolo D. A. , Kostov B. , Gomis R. , Sisó-Almirall A. 
Published: November 2022

AbstractPolicies in sunny countries, such as those in the Mediterranean area, do not promote vitamin D supplementation despite some studies might suggest the high prevalence of sub-optimal levels. The objective was to determine the vitamin D levels by 25-hydroxyvitamin D (25(OH)D) of a Mediterranean population and their characteristics. This population-based study included a database of public health system from all individuals living in Catalonia > 18 years who had some measure of 25(OH)D between January 2018 and April 2021. More than half million people were classified based on 25(OH)D measurements to study their characteristics. Three vitamin D categories were created: < 20 ng/ml deficiency, 20–30 ng/ml insufficiency and > 30 ng/ml optimal. Less than 10% of the population residing in Catalonia had recent 25(OH)D determinations and the majority of determinations were in ≥ 45 years and in women. Around 80% of young people with determination had sub-optimal levels but the prevalence of vitamin D supplementation prescription increased with age which was associated with better values of 25(OH)D. In a Mediterranean area 25(OH)D determinations were low despite the high prevalence of suboptimal levels in the population with recent determination. In addition, the measurements were especially concentrated in people ≥ 45 years of age and in women who were, in addition, the groups to whom the most vitamin D supplementation was prescribed. On the contrary, young people presented few determinations of 25(OH)D and, although majority of them showed sub-optimal levels, vitamin D supplementation was not prescribed in most cases.
Imaging-based representation and stratification of intra-tumor heterogeneity via tree-edit distance
Authors: Cavinato Lara , Pegoraro Matteo , Ragni Alessandra , Sollini Martina , Erba Paola Anna , Ieva Francesca 
Published: November 2022

AbstractPersonalized medicine is the future of medical practice. In oncology, tumor heterogeneity assessment represents a pivotal step for effective treatment planning and prognosis prediction. Despite new procedures for DNA sequencing and analysis, non-invasive methods for tumor characterization are needed to impact on daily routine. On purpose, imaging texture analysis is rapidly scaling, holding the promise to surrogate histopathological assessment of tumor lesions. In this work, we propose a tree-based representation strategy for describing intra-tumor heterogeneity of patients affected by metastatic cancer. We leverage radiomics information extracted from PET/CT imaging and we provide an exhaustive and easily readable summary of the disease spreading. We exploit this novel patient representation to perform cancer subtyping according to hierarchical clustering technique. To this purpose, a new heterogeneity-based distance between trees is defined and applied to a case study of prostate cancer. Clusters interpretation is explored in terms of concordance with severity status, tumor burden and biological characteristics. Results are promising, as the proposed method outperforms current literature approaches. Ultimately, the proposed method draws a general analysis framework that would allow to extract knowledge from daily acquired imaging data of patients and provide insights for effective treatment planning.
Causal attribution fractions, and the attribution of smoking and BMI to the landscape of disease incidence in UK Biobank
Authors: Webster Anthony J. 
Published: November 2022

AbstractUnlike conventional epidemiological studies that use observational data to estimate “associations” between risk factors and disease, the science of causal inference has identified situations where causal estimates can be made from observational data, using results such as the “backdoor criteria”. Here these results are combined with established epidemiological methods, to calculate simple population attribution fractions that estimate the causal influence of risk factors on disease incidence, and can be estimated using conventional proportional hazards methods. A counterfactual argument gives an attribution fraction for individuals. Causally meaningful attribution fractions cannot be constructed for all risk factors or confounders, but they can for the important established risk factors of smoking and body mass index (BMI). Using the new results, the causal attribution of smoking and BMI to the incidence of 226 diseases in the UK Biobank are estimated, and summarised in terms of disease chapters from the International Classification of Diseases (ICD-10). The diseases most strongly attributed to smoking and BMI are identified, finding 11 with attribution fractions greater than 0.5, and a small number with protective associations. The results provide new tools to quantify the causal influence of risk factors such as smoking and BMI on disease, and survey the causal influence of smoking and BMI on the landscape of disease incidence in the UK Biobank population.
Quantitative muscle MRI captures early muscle degeneration in calpainopathy
Authors: Forsting Johannes , Rohm Marlena , Froeling Martijn , Güttsches Anne-Katrin , Südkamp Nicolina , Roos Andreas , Vorgerd Matthias , Schlaffke Lara , Rehmann Robert 
Published: November 2022

AbstractTo evaluate differences in qMRI parameters of muscle diffusion tensor imaging (mDTI), fat-fraction (FF) and water T2 time in leg muscles of calpainopathy patients (LGMD R1/D4) compared to healthy controls, to correlate those findings to clinical parameters and to evaluate if qMRI parameters show muscle degeneration in not-yet fatty infiltrated muscles. We evaluated eight thigh and seven calf muscles of 19 calpainopathy patients and 19 healthy matched controls. MRI scans were performed on a 3T MRI including a mDTI, T2 mapping and mDixonquant sequence. Clinical assessment was done with manual muscle testing, patient questionnaires (ACTIVLIM, NSS) as well as gait analysis. Average FF was significantly different in all muscles compared to controls (p < 0.001). In muscles with less than 8% FF a significant increase of FA (p < 0.005) and decrease of RD (p < 0.004) was found in high-risk muscles of calpainopathy patients. Water T2 times were increased within the low- and intermediate-risk muscles (p ≤ 0.045) but not in high-risk muscles (p = 0.062). Clinical assessments correlated significantly with qMRI values: QMFM vs. FF: r = − 0.881, p < 0.001; QMFM versus FA: r = − 0.747, p < 0.001; QMFM versus MD: r = 0.942, p < 0.001. A good correlation of FF and diffusion metrics to clinical assessments was found. Diffusion metrics and T2 values are promising candidates to serve as sensitive early and non-invasive methods to capture early muscle degeneration in non-fat-infiltrated muscles in calpainopathies.