bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mechanical and physical assessment of epoxy, mineral, solvent-based, and water-soluble coating materials
Authors: Millán Ramírez Ginneth Patricia , Byliński Hubert , Niedostatkiewicz Maciej 
Published: August 2022

AbstractThis paper assesses the behavior of mineral, epoxy (EP), solvent, and water-soluble coatings when exposed to salt and regular water for 28 days. Also, it evaluates the pull-off adhesion strength of the same coating materials applied to concrete slabs saturated with oil and water and dried with two different processes: air-dried for 28 days and air-dried for 14 days plus 14 days in the oven at 70 °C. Properties such as carbonation, water absorption rate, pull-off adhesion strength were evaluated for all coatings, and tensile strength, Young’s modulus, and elongation percentage were calculated for mineral coatings. According to the results, the EP coating showed the best performance with the highest pull-off adhesion strength (2.55 MPa) and lowest absorption rate, about 0.02 ± 0.002 g/m2 day in saltwater and 0.03 ± 0.002 g/m2 day in regular water. In addition, EP coatings also presented the lowest carbonation rate and the highest suppress ratio. The excellent performance of epoxy coatings is mainly due to their low porosity and the ability to decrease chloride diffusion, making them better than other types of coatings investigated in this study.
Estimation of dust concentration by a novel machine vision system
Authors: Arjomandi Hamid Reza , Kheiralipour Kamran , Amarloei Ali 
Published: August 2022

AbstractThe dust phenomenon is one of the main environmental problems that it reversely affects human health and economical and social activities. In the present research, a novel algorithm has been developed based on image processing to estimate dust concentration. An experimental setup was implemented to create airborne dust with different concentration values from 0 to 2750 µg.m−3. The images of the different dust concentration values were acquired and analyzed by image processing technique. Different color and texture features were extracted from various color spaces. The extracted features were used to develop single and multivariable models by regression method. Totally 285 single variable models were obtained and compared to select efficient features among them. The best single variable model had a predictive accuracy of 91%. The features were used for multivariable modeling and the best model was selected with a predictive accuracy of 100% and a mean squared error of 1.44 × 10−23. The results showed the high ability of the developed machine vision system for estimating dust concentration with high speed and accuracy.
Evaluation of auto-segmentation for EBRT planning structures using deep learning-based workflow on cervical cancer
Authors: Wang Jiahao , Chen Yuanyuan , Xie Hongling , Luo Lumeng , Tang Qiu 
Published: August 2022

AbstractDeep learning (DL) based approach aims to construct a full workflow solution for cervical cancer with external beam radiation therapy (EBRT) and brachytherapy (BT). The purpose of this study was to evaluate the accuracy of EBRT planning structures derived from DL based auto-segmentation compared with standard manual delineation. Auto-segmentation model based on convolutional neural networks (CNN) was developed to delineate clinical target volumes (CTVs) and organs at risk (OARs) in cervical cancer radiotherapy. A total of 300 retrospective patients from multiple cancer centers were used to train and validate the model, and 75 independent cases were selected as testing data. The accuracy of auto-segmented contours were evaluated using geometric and dosimetric metrics including dice similarity coefficient (DSC), 95% hausdorff distance (95%HD), jaccard coefficient (JC) and dose-volume index (DVI). The correlation between geometric metrics and dosimetric difference was performed by Spearman’s correlation analysis. The right and left kidney, bladder, right and left femoral head showed superior geometric accuracy (DSC: 0.88–0.93; 95%HD: 1.03 mm–2.96 mm; JC: 0.78–0.88), and the Bland–Altman test obtained dose agreement for these contours (P > 0.05) between manual and DL based methods. Wilcoxon’s signed-rank test indicated significant dosimetric differences in CTV, spinal cord and pelvic bone (P < 0.001). A strong correlation between the mean dose of pelvic bone and its 95%HD (R = 0.843, P < 0.001) was found in Spearman’s correlation analysis, and the remaining structures showed weak link between dosimetric difference and all of geometric metrics. Our auto-segmentation achieved a satisfied agreement for most EBRT planning structures, although the clinical acceptance of CTV was a concern. DL based auto-segmentation was an essential component in cervical cancer workflow which would generate the accurate contouring.
Association between changes in handgrip strength and depression in Korean adults: a longitudinal panel study
Authors: Kim Hyunkyu , Jeong Wonjeong , Kim Seung Hoon , Park Yu Shin , Jang Sung-In , Park Eun-Cheol 
Published: August 2022

AbstractDepression in older adults is a global socioeconomic burden. Identifying factors, such as physical activity or exercise that can help prevent depression is important. We aimed to investigate the relationship between changes in handgrip strength and the presence of depression using longitudinal, nationwide data of older Korean adults. Data from the Korean Longitudinal Study of Aging were used in this study. A total of 6783 participants who had undergone a handgrip strength test and completed the short-form Center for Epidemiologic Studies Depression Scale (CESD-10-D) questionnaire from 2006 to 2018 were included. General estimating equations were used to assess the temporal effect of the changes in handgrip strength on depression. A decrease in handgrip strength was associated with high CESD-10-D scores (β = 0.1889 in men, β = 0.1552 in women). As a continuous variable, handgrip strength was negatively correlated with CESD-10-D scores(β = − 0.0166 in men, β = − 0.0196 in women). Changes in the handgrip strength were associated with depressive symptoms in our longitudinal study. Those who experienced a decrease in handgrip strength had severe depressive symptoms compared to those with unchanged or increased handgrip strength. These findings can be used to guide general health policies for the prevention of depression.
Interleukin 1 receptor antagonist relation to cardiovascular disease risk in patients with rheumatoid arthritis
Authors: Almeida-Santiago Cristina , Quevedo-Abeledo Juan Carlos , Hernández-Hernández Vanesa , de Vera-González Antonia , Gonzalez-Delgado Alejandra , González-Gay Miguel Ángel , Ferraz-Amaro Iván 
Published: August 2022

AbstractInterleukin (IL) 1, and its family member, IL-1 receptor antagonist (IL-1ra), are involved in the pathogenesis and inflammation perpetuation of patients with rheumatoid arthritis (RA). Besides, IL-1 has been linked to an increased risk and greater severity of cardiovascular (CV) disease. We aimed to study if IL-1ra is related to the CV manifestations—including lipid pattern and insulin resistance or subclinical atherosclerosis—that accompanies the disease in a large series of patients with RA. Cross-sectional study that encompassed 430 patients with RA. Serum IL-1ra levels were assessed. A multivariable analysis was performed to analyze the relation of IL-1ra to subclinical carotid atherosclerosis, and to traditional CV factors including a complete lipid molecules profile and insulin resistance or beta cell function indices. Body mass index, abdominal circumference, and the presence of obesity were significantly and positively associated with circulating IL-1ra. Similarly, erythrocyte sedimentation rate, and disease activity scores were significantly related to higher IL-1ra serum levels after adjustment for confounders. Neither carotid intima-media thickness nor the presence of carotid plaque were associated with serum levels of IL-1ra. However, after multivariable analysis circulating IL-1ra was independently and positively associated with higher serum levels of total cholesterol, triglycerides, and apolipoproteins B and C-III. Similarly, IL-1ra was related to higher levels of beta-cell function in the univariable analysis, although, in this case, significance was lost after adjustment. Among patients with RA, IL-1ra is associated with both disease activity and several traditional CV risk factors such as obesity and the presence of higher lipid levels. Our findings suggest that IL-1ra can represent a link between the inflammation and the CV disease risk that are present in patients with RA.
Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding
Authors: Tanin Mohammad Jafar , Saini Dinesh Kumar , Sandhu Karansher Singh , Pal Neeraj , Gudi Santosh , Chaudhary Jyoti , Sharma Achla 
Published: August 2022

AbstractIn wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 189 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.
GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing
Authors: Mathur Ravi , Fang Fang , Gaddis Nathan , Hancock Dana B. , Cho Michael H. , Hokanson John E. , Bierut Laura J. , Lutz Sharon M. , Young Kendra , Smith Albert V. ,   NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Silverman Edwin K. , Page Grier P. , Johnson Eric O. 
Published: August 2022

AbstractGenome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries.
Radical hydrodifluoromethylation of unsaturated C−C bonds via an electroreductively triggered two-pronged approach
Authors: Kim Seonyoung , Hwang Keon Ha , Park Hyeong Gyu , Kwak Jaesung , Lee Hyuk , Kim Hyunwoo 
Published: August 2022

AbstractDue to its superior ability in controlling pharmaceutical activity, the installation of difluoromethyl (CF2H) functionality into organic molecules has been an area of intensive research. In this context, difluoromethylation of C−C π bonds mediated by a CF2H radical have been pursued as a central strategy to grant access to difluoromethylated hydrocarbons. However, early precedents necessitate the generation of oxidative chemical species that can limit the generality and utility of the reaction. We report here the successful implementation of radical hydrodifluoromethylation of unsaturated C−C bonds via an electroreductively triggered two-pronged approach. Preliminary mechanistic investigations suggest that the key distinction of the present strategy originates from the reconciliation of multiple redox processes under highly reducing electrochemical conditions. The reaction conditions can be chosen based on the electronic properties of the alkenes of interest, highlighting the hydrodifluoromethylation of both unactivated and activated alkenes. Notably, the reaction delivers geminal (bis)difluoromethylated products from alkynes in a single step by consecutive hydrodifluoromethylation, granting access to an underutilized 1,1,3,3-tetrafluoropropan-2-yl functional group. The late-stage hydrodifluoromethylation of densely functionalized pharmaceutical agents is also presented.
Embedded chimera states in recurrent neural networks
Authors: Masoliver Maria , Davidsen Jörn , Nicola Wilten 
Published: August 2022

AbstractFully and partially synchronized brain activity plays a key role in normal cognition and in some neurological disorders, such as epilepsy. However, the mechanism by which synchrony and asynchrony co-exist in a population of neurons remains elusive. Chimera states, where synchrony and asynchrony coexist, have been documented only for precisely specified connectivity and network topologies. Here, we demonstrate how chimeras can emerge in recurrent neural networks by training the networks to display chimeras with machine learning. These solutions, which we refer to as embedded chimeras, are generically produced by recurrent neural networks with connectivity matrices only slightly perturbed from random networks. We also demonstrate that learning is robust to different biological constraints, such as the excitatory/inhibitory classification of neurons (Dale’s law), and the sparsity of connections in neural circuits. The recurrent neural networks can also be trained to switch chimera solutions: an input pulse can trigger the neural network to switch the synchronized and the unsynchronized groups of the embedded chimera, reminiscent of uni-hemispheric sleep in a variety of animals. Our results imply that the emergence of chimeras is quite generic at the meso- and macroscale suggesting their general relevance in neuroscience.
The Arctic has warmed nearly four times faster than the globe since 1979
Authors: Rantanen Mika , Karpechko Alexey Yu. , Lipponen Antti , Nordling Kalle , Hyvärinen Otto , Ruosteenoja Kimmo , Vihma Timo , Laaksonen Ari 
Published: August 2022

AbstractIn recent decades, the warming in the Arctic has been much faster than in the rest of the world, a phenomenon known as Arctic amplification. Numerous studies report that the Arctic is warming either twice, more than twice, or even three times as fast as the globe on average. Here we show, by using several observational datasets which cover the Arctic region, that during the last 43 years the Arctic has been warming nearly four times faster than the globe, which is a higher ratio than generally reported in literature. We compared the observed Arctic amplification ratio with the ratio simulated by state-of-the-art climate models, and found that the observed four-fold warming ratio over 1979–2021 is an extremely rare occasion in the climate model simulations. The observed and simulated amplification ratios are more consistent with each other if calculated over a longer period; however the comparison is obscured by observational uncertainties before 1979. Our results indicate that the recent four-fold Arctic warming ratio is either an extremely unlikely event, or the climate models systematically tend to underestimate the amplification.
The real‐time state identification of the electricity‐heat system based on Borderline‐SMOTE and XGBoost
Authors: Pei Xin , Mei Fei , Gu Jiaqi 
Published: August 2022

AbstractIt is meaningful to study the real‐time state monitoring and identification of integrated energy system and grasp its state in time for stable operation. A state identification method based on multi‐class data equalisation and extreme gradient boost (XGBoost) is proposed for integrated energy systems. First, Latin hypercube sampling is used to simulate the load at different moments. Different system states are set up and combined with the simulative load at different moments to determine the system operation state at different moments. Then, the energy flow model is used to calculate the system power flow under different states, and the feature indexes are obtained to form the original data set. Aiming at the unbalanced data, the oversampling technology is used to preprocess data to achieve the balance of data sets. The pre‐processed data is utilised to train the XGBoost, and the optimal hyperparameters of the model are obtained based on the K‐fold cross‐validation and grid search. Finally, the pre‐processed data set is used to verify the proposed method. The calculation results show the accuracy of the identification model reaches 87.79%. Compared with traditional methods, the model can accurately identify the operating state of the electricity–heat energy system at any time section.
An analog‐to‐digital converter calibration algorithm with clock jitter compensation based on bidirectional long‐short‐time‐memory
Authors: Zeng Jialong , Wan Jiangpeng , Chen Dongxu , Yang Xudong , Yu Zhongjun 
Published: August 2022

AbstractThis letter proposes a bidirectional long‐short‐time‐memory (bi‐LSTM)‐based analog‐to‐digital converter (ADC) background calibration method with a clock jitter compensation function. Different from traditional calibration algorithms, the proposed algorithm utilizes the sequence property of the signal by introducing an LSTM network. The network contains three parts, including a linear layer for input encoding, a refine layer for sequence processing, and a linear layer for output decoding. Two ADCs are used to train the network, one of which is the target to be calibrated, that is, a high‐speed high‐resolution ADC, and the other is a low‐speed ultra‐high‐resolution ADC producing the ideal signal. After the training stage, the network can be used as a postprocessor for the target ADC to compensate for the systemic errors and clock jitter, simultaneously. In the experiments, the proposed method is used to calibrate an ADC12D1600RFIUT, realizing the improvement of signal‐to‐noise distortion ratio (SNDR) from 43.41 to 66.82 dB and SFDR from 56.95 to 98.93 dB.
Pedestrians crossing intention anticipation based on dual‐channel action recognition and hierarchical environmental context
Authors: Ni Rongrong , Yang Biao , Wei Zhiwen , Hu Hongyu , Yang Changchun 
Published: August 2022

AbstractThe increase in car ownership has brought convenience to people's lives, but it has also led to a sharp increase in pedestrian–vehicle conflicts. Such conflicts can be avoided by braking when pedestrians in front of the car are detected. However, frequent braking reduces driving comfort and travel efficiency. Considering that pedestrian–vehicle conflicts always occur when pedestrians cross the road, this study proposes a multi‐factor fusion network (MFFN) to anticipate pedestrians' crossing intentions. MFFN contains a dual‐channel action recognition sub‐network to robustly identify pedestrians' actions by adaptively fusing pedestrians' skeleton and representation characteristics. Afterward, object‐level and semantic‐level perception of traffic scenes are realised by a hierarchical graph attention network and a lightweight semantic segmentation method, respectively. These hierarchical environmental contexts can provide critical clues for identifying pedestrians' crossing intentions. Then, a self‐attention mechanism is used to integrate various factors and produce intention identification results. The proposed MFFN is evaluated on public datasets PIE and JAAD. Experimental results show that the proposed method can accurately and reliably identify pedestrian crossing intentions. The accuracy, F1 score, AUC, and precision indicators on JAAD/PIE datasets are 0.912/0.876, 0.813/0.806, 0.896/0.889, and 0.802/0.788, respectively.
Hypertrophie musculaire : signe de bonne santé ou de maladie ?
Authors: Nadaj-Pakleza Aleksandra 
Published: August 2022

In order to explain muscle hypertrophy in a patient complaining of muscular symptoms one must take into account the patient’s daily professional or leisure physical activity.
If muscle hypertrophy cannot be attributed to active lifestyle, an underlying neuromuscular pathology should be suspected. The check-up includes a meticulous neurological examination (looking for myotonia, rippling, myoclonus, etc.), a CK level dosage, an electromyography (for myotonic discharges), and, if necessary, it should be completed by muscle biopsy and molecular examinations.
Several diagnostic hypotheses should be considered. Most are related to a genetic defect leading to dysfunction of the excitation-contraction coupling in the muscle. The diagnosis of a myotonic syndrome is the most common but caveolinopathy 3, RYR1-related myopathy or Brody’s disease are also possible.
Pathway Analysis of Patients with Severe Acute Respiratory
Syndrome
Authors: Khaleel Anas , Zakariya Abdullah Bassam , Niazi Mohammad , Qinna Nidal A. , Dayyih Wael Abu , Tarkhan Amneh H. 
Published: August 2022

Abstract
Background Coronaviruses are emerging threats for human health, as
demonstrated by the ongoing coronavirus disease 2019 (COVID-19) pandemic that is
caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
SARS-CoV-2 is closely related to SARS-CoV-1, which was the cause of the
2002–2004 SARS outbreak, but SARS-CoV-1 has been the subject of a
relatively limited number of studies. Understanding the potential pathways and
molecular targets of SARS-CoV-1 will contribute to current drug repurposing
strategies by helping to predict potential drug-disease associations.
Methods A microarray dataset, GSE1739, of 10 SARS patients and 4 healthy
controls was downloaded from NCBI’s GEO repository, and differential
expression was identified using NCBI’s GEO2R software. Pathway and
enrichment analysis of the differentially expressed genes was carried out using
Ingenuity Pathway Analysis and Gene Set Enrichment Analysis, respectively.
Results Our findings show that the drugs dexamethasone, filgrastim,
interferon alfacon-1, and levodopa were among the most significant upstream
regulators of differential gene expression in SARS patients, while neutrophil
degranulation was the most significantly enriched pathway.
Conclusion An enhanced understanding of the pathways and molecular targets
of SARS-CoV-1 in humans will contribute to current and future drug repurposing
strategies, which are an essential tool to combat rapidly emerging health
threats.