bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Outcomes of Acute Myocardial Infarction Patients Comorbidity With Hypertension and Hyperhomocysteinemia
Authors: Yan Jumei , Zhou Jiamin , Huang Jun , Zhang Hongyu , Deng Zilin , Du Yun 
Published: June 2021

Abstract
This study investigated the outcomes and major adverse cardiovascular events (MACEs) incurred by acute myocardial infarction (AMI) patients comorbiding with hypertension and hyperhomocysteinemia (HHcy) during hospitalization and 1-year follow-up. 648 consecutive AMI patients were divided into four categories: (1) hypertension with Hcy ≥ 15 umol/L; (2) hypertension with Hcy < 15 umol/L; (3) no-hypertension with Hcy ≥ 15 umol/L; (4) no-hypertension with Hcy < 15 umol/L. Information taken from these case files included gender, past medical history, vital signs, laboratory examination, electrocardiogram, coronary angiography, cardiac ultrasound, and medicine treatment. The primary endpoints were duration of coronary care units (CCU) stay, duration of in-hospital stay, and MACEs during follow-up. Our data show that hypertension and HHcy have a synergistic effect in AMI patients, AMI comorbiding with hypertension and HHcy patients had more severe multi-coronary artery disease and more frequent non-culprit coronary lesions complete clogging, had a higher prevalence of pro-brain natriuretic peptide, and significant decreases in the left ventricular ejection fraction. These patients had significant increases in the duration of CCU stay and in-hospital stay, had significant increase in the rate of MACEs, had significant decreases in the survival rate during follow-up.
Operando spectroscopic observation of dynamic-coupling oxygen on single-atomic iridium catalyst for acidic water oxidation
Authors: Su Hui , Zhou Wanlin , Zhou Wu , Li Yuanli , Zheng Li Rong , Zhang Hui , Liu Meihuan , Zhang Xiuxiu , Sun Xuan , Xu Yanzhi , Hu Fengchun , Zhang Jing , Hu Tiandou , Liu Qinghua , Wei Shiqiang 
Published: June 2021

Abstract
Uncovering the dynamics of active sites under working state is crucial to realizing increased activity, enhanced stability and reduced cost of oxygen evolution reaction (OER) electrocatalysts in proton exchange membrane electrolytes. Herein, we identify at atomic level a potential-driven dynamic-coupling oxygen on the hetero-nitrogen configured single-atomic Ir sites (HN-Ir NC) during OER working conditions to successfully endow the single-atomic Ir catalyst with an ultrahigh electrochemical acidic-OER activity. Using operando synchrotron radiation infrared and X-ray absorption spectroscopies, we directly observe in the experiment that a dynamic oxygen atom is formed at the Ir site with the O-hetero-Ir-N4 structure as more electrophilic active center and then effectively promote the generation of the key *OOH intermediates under working potentials, which is exceptionally favourable for the dissociation of H2O over Ir sites and resistance to over-oxidation and dissolution of the active sites.The optimal single-atomic HN-Ir NC catalyst delivers a large mass activity of 2860 A gmetal−1 and a huge turnover frequency of 5110 h− 1 at a low overpotential of 216 mV (10 mA cm− 2), 480˗510 times than that of commercial IrO2 catalyst. More importantly, the HN-Ir NC catalyst shows no evident deactivation after continuous 100 h OER operation in acidic medium.
Hydrogeochemistry of Hot Springs and the 2018 Mojiang M 5.9 Earthquake-Related Chemical Changes in the Simao Basin, China
Authors: Li Qilin , Zhao Ciping , Wang Yun , Zhou Yiying , Ran Hua 
Published: June 2021

Abstract
Due to strong tectonic activity, the Simao Basin frequently experiences earthquakes with a magnitude of 6 or greater. This study investigated the hydrochemical characteristics of 21 thermal springs in the Simao Basin from 2018 to 2020 to determine how the 2018 Mojiang M5.9 earthquake caused hydrochemical changes. The results showed that the Simao Basin contained saline spring waters, HCO3−-rich spring waters, and SO42−-rich spring waters. Stratum lithology characteristics in the area exerted obvious control over the water chemistry characteristics. Saline springs flowed through red beds and dissolved large amounts of halite, which is rich with Cl−and Na+. Ca2+ (Mg2+) and HCO3− in the hot spring waters were mainly derived from the dissolution of carbonate minerals, gypsum, and anhydrite in Triassic rocks. The higher SO42− content in the hot spring waters was influenced by pyrite contained in Ailaoshan metamorphic rocks. The reservoir temperatures (121–289° C) in the Simao Basin were estimated by the silica-enthalpy mixing model equation and silica-enthalpy diagram. The hot springs with higher reservoir temperatures (> 250° C) were mainly located at the edges of the basin, which may correlate with nearby deep faults that provided deep heat and metamorphic rocks with low permeabilities. The concentrations of Ca2+ and HCO3− in most springs increased obviously before the Mojiang M5.9 earthquake but decreased after the earthquake. Changes also occurred in the reservoir temperatures of some spring samples. These hydrogeochemical variations might be attributable to accelerated water-rock interactions and/or the mixing of second fluids due to the earthquake.
Classification of Brain Tumours in MR Images using Deep Spatiospatial Models
Authors: Chatterjee Soumick , Nizamani Faraz Ahmed , Nürnberger Andreas , Speck Oliver 
Published: June 2021

Abstract
A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning and magnetic resonance imaging is the principal imaging modality for diagnostic of brain tumours and their extent. Deep Learning methods in computer vision applications have shown significant improvement in recent years, most of which can be credited to the fact that a sizeable amount of data is available to train models on, and the improvements in the model architectures yielding better approximations in a supervised setting. Classifying tumours using such deep learning methods has made significant progress with the availability of open datasets with reliable annotations. Typically those methods are either 3D models, which use 3D volumetric MRIs or even 2D models considering each slice separately. However, by treating one spatial dimension separately or by considering the slices as a sequence of images over time, spatiotemporal models can be employed as "spatiospatial" models for this task. These models have the capabilities of learning specific spatial and temporal relationship, while reducing computational costs. This paper uses two spatiotemporal models, ResNet (2+1)D and ResNet Mixed Convolution, to classify different types of brain tumours. It was observed that both these models performed superior to the pure 3D convolutional model, ResNet18. Furthermore, it was also observed that pre-training the models on a different, even unrelated dataset before training them for the task of tumour classification improves the performance. Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0.9345 and a test accuracy of 96.98%, while at the same time being the model with the least computational cost.
Opioid Overdose Crises During the COVID-19 Pandemic: Implication of Health Disparities
Authors: Patel Ishika , Walter Lauren , Li Li 
Published: June 2021

Abstract
Background. Recent data suggests a disproportionate impact of opioid overdoses on Black Americans. The study aims to describe emergency department (ED) visits at a Southern, urban ED pertaining to opioid overdose and associated health disparities. Methods. Patients presenting to the ED at the University of Alabama at Birmingham Hospital with opioid overdoses from January 1 to October 31, 2019, and from January 1 to October 31, 2020, were identified from electronic medical records. Results. The total number of opioid overdose visits increased 9.7% (556 to 611) between January through October 2020 compared with 2019. Among patients who presented with opioid overdose, the mean ages were 50.3 years and 48.3 years, in 2019 and 2020, respectively. In both 2019 and 2020, more Blacks than whites were treated for opioid overdose in the ED (284 vs. 258 in 2019, and 306 vs. 271 in 2020) although 28 patients did not record their race in 2020. Consistently, more overdose deaths were observed in Blacks than in Whites in 2020. More individuals seeking opioid overdose treatment were single in both years. Conclusions. The study reported a greater number of visits for opioid overdoses from January to October of 2020 in an ED of a deep south region, as well as higher overdose deaths in Blacks. Our findings highlight the importance of substance use treatment, harm reduction, and overdose prevention efforts that should be immediately present to reduce opioid overdose especially for vulnerable populations in the South, i.e., Black community, and individuals experiencing singlehood.
Assessing the Presence and Position of Carotid Plaque Improves Risk Stratification for Cardiovascular Disease Prediction Among Patients With Hypertension
Authors: Zuo Hui-Juan , Song Xian-Tao , Wang Jin-Wen , Yang Hong-Xia , Lin Jie 
Published: June 2021

Abstract
Background: Ischemic cardiovascular disease (ISCVD) is a massive public health problem. ISCVD risk prediction models based on traditional risk factors as predictors is limited. Carotid atherosclerosis plays a fundamental value in the occurrence of ISCVD. The aim of this study was to evaluate the value of risk stratification plus carotid plaque improving the prediction of ISCVD. Methods: Between June 2016 and June 2017, 3998 subjects with hypertension were prospectively recruited and completed traditional risk factors survey and carotid ultrasound measurements in Anzhen Hospital, Beijing, China. Results: A total of 2010 (50.3%) subjects were detected carotid plaque. Among patients free from ISCVD (n=3479), there were 884 patients (25.4%) at high risk for ISCVD, and 868 (25.0%), 1727 (49.6%) was classified as intermediate risk or low risk according to Chinese cardiovascular risk score chart. The detected rate of carotid plaque was 64.7%, 53.7%, and 38.5% among patients at high risk to low risk, respectively. Carotid plaques and risk stratification alone or in combination were significantly associated with ischemic stroke, and negatively correlated with coronary heart disease (all P>0.05). Adding carotid plaque to risk stratification, the ischemic stroke prevalence increased from 5.3% to 9.1% in the low-risk group (P=0.001), 5.4% to 12.3% in the intermediate-risk group (P<0.001) and 8.2% to 14.4% than in the high-risk group (P=0.004). Intermediate risk plus carotid plaque (443/3998) were reclassified to a new high-risk group, high risk only (749/3998) and low risk plus carotid plaque (353/3998) were reclassified to a new intermediate risk group; and intermediate risk only (553/3998) were reclassified to a new low risk group. According to the reclassification, there were 1635 subjects (40.9%) at high risk, and 1102 (27.6%), 1261 (31.5%) was classified as intermediate risk or low risk. Conclusions: Carotid plaque has an important position as it plus risk stratification may improve the risk assessment of ischemic stroke and have resulted in reclassification.
Deep Learning-Based Breast Cancer Diagnosis at Ultrasound: Initial Application of Weakly-Supervised Algorithm Without Image Annotation Original Research
Authors: Kim Jaeil , Kim Hye Jung , Kim Chanho , Lee Jin Hwa , Kim Keum Won , Park Young Mi , Ki So Yeon , Kim You Me , Kim Won Hwa , Kim Hye Won 
Published: June 2021

Abstract
Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without image annotation. Weakly-supervised DL algorithms were implemented with three networks (VGG16, ResNet34, and GoogLeNet) and trained using 1000 unannotated US images (500 benign and 500 malignant masses). Two sets of 200 images (100 benign and 100 malignant masses) were used for internal and external validation sets. For comparison with fully-supervised algorithms, ROI annotation was performed manually and automatically. Diagnostic performances were calculated as the area under the receiver operating characteristic curve (AUC). Using the class activation map, we determined how accurately the weakly-supervised DL algorithms localized the breast masses. For internal validation sets, the weakly-supervised DL algorithms achieved excellent diagnostic performances, with AUC values of 0.92–0.96, which were not statistically different (all Ps > 0.05) from those of fully-supervised DL algorithms with either manual or automated ROI annotation (AUC, 0.92–0.96). For external validation sets, the weakly-supervised DL algorithms achieved AUC values of 0.86–0.90, which were not statistically different (Ps > 0.05) or higher (P = 0.04, VGG16 with automated ROI annotation) from those of fully-supervised DL algorithms (AUC, 0.84–0.92). In internal and external validation sets, weakly-supervised algorithms could localize 100% of malignant masses, except for ResNet34 (98%). The weakly-supervised DL algorithms developed in the present study were feasible for US diagnosis of breast cancer with well-performing localization and differential diagnosis.
Proofreading Experimentally Assigned Stereochemistry Through Q2MM Predictions in Pd-Catalyzed Allylic Aminations
Authors: Wahlers Jessica , Margalef Jessica , Hansen Eric , Bayesteh Armita , Helquist Paul , Diéguez Montserrat , Pàmies Oscar , Wiest Olaf , Norrby Per-Ola 
Published: June 2021

Abstract
The palladium-catalyzed enantioselective allylic substitution by carbon or nitrogen nucleophiles is a key transformation that is particularly useful for the synthesis of bioactive compounds. Unfortunately, the selection of a suitable ligand/substrate combination often requires significant screening effort. Here, we show that a transition state force field (TSFF) derived by the quantum-guided molecular mechanics (Q2MM) method can be used to rapidly screen ligand/substrate combinations. Testing of this method on 77 literature reactions revealed several cases where the computationally predicted major enantiomer differed from the one reported. Interestingly, experimental follow-up led to a reassignment of the experimentally observed configuration. This result demonstrates the power of mechanistically based methods to predict and, where necessary, correct the stereochemical outcome.
An Innovative Network based on Double Receptive Field and Recursive Bi-directional Long Short-Term Memory
Authors: Meng Peng-fei , Jia Shuang-cheng , Li Qian 
Published: June 2021

Abstract
In the scenes of character recognition, this paper studies the influence of network structure and parameters of “An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition (CRNN) ” on the recognition results in detail, and proposes an improved CRNN network based on double receptive field and recursive Bi-directional Long Short-Term Memory(BILSTM), which is named “An innovative network based on double receptive field and Recursive Bi-directional Long Short-Term Memory༈CRNN_RES༉”. In the CRNN_RES network, the innovations of this paper are adjusting the structure of CNN to enhance the feature extraction ability of the CNN network and using the shared parameter BILSTM network with recursive residuals to reduce the number of network parameters and improve the accuracy of the model prediction. In fact, the number of parameters of CRNN_RES network proposed in this paper is 7148325, which is 1182976 fewer than that of CRNN. On the same open datasets:ICDAR 2003༈IC03༉,ICDAR 2013༈IC13༉༌IIIT 5k-word༈IIIT5k༉, and Street View Text (SVT), the proposed method achieves 96.90%, 89.85%, 83.63%, and 82.96% recognition accuracy, which are higher than that of CRNN 1.40%, 3.15%, 5.43% ༌and 2.16%.
Long-term Effects of Alcohol Consumption on Cognitive Function in Seniors: A Cohort Study in China
Authors: Han Lizhen , Jia Jinzhu 
Published: June 2021

Abstract
Background
In the context of increasing global aging, the long-term effects of alcohol consumption on cognitive function in older adults were analyzed in order to provide rationalization recommendations and data support.
Methods
5,354 Chinese seniors aged 65–112 years were selected as the subjects, and their participation in the survey covered the time span from 1998–2018. Data cleaning and preprocessing was implemented by R software. The dynamic Cox model was applied for model construction and data analysis.
Results
The results of the dynamic Cox model suggested that seniors who drank alcohol were at higher risk of cognitive decline compared to those who never drank, and the effect was long-term. The risk was similarly exacerbated by perennial drinking habits. Compared to the liquor (≥ 38°), seniors who consumed liquors (< 38°) and rice wine had a relatively lower risk of cognitive decline. In addition, associations between marital status, frequency of food intake, activity participation and cognitive performance in seniors were also confirmed.
Conclusions
Alcohol consumption has a negative and long-term effects on cognitive function in seniors. For the elderly, we suggested that alcohol intake should be avoided as much as possible.
Development of a method for producing new generation of protein snacks using the processes of cryo and mechanical distruction
Authors: Pogarskaya Viktoriya , Yurieva Olga , Pogarskiy Aleksey , Balabai Kateryna , Maksymova Nadiya 
Published: June 2021

The aim of research is to develop a method for the production of a new generation of protein snacks using a protein vegetable and milk base and vegetable fortifiers from spicy and carotene-containing vegetables using cryo and mechanical destruction processes. The method is based on the use of a deep processing method as an innovation, which consists in a complex effect on vegetable and protein raw materials of cryo and mechanical destruction processes in modern quick-freezing and low-temperature crushing equipment. The proposed method allows to get new protein snacks with a high content of protein, BAR and phytocomponents in an easily digestible form.
A new generation of protein snacks has been developed to strengthen the immune system using cryogenic protein base and vegetable raw materials. Nano-additives from dried peas and specially processed soft brine cheese are used as a protein base. Pea nanoadditives obtained using non-enzymatic catalysis – mechanolysis, mechanical destruction contain 21.5 ... 23.0% of complete protein, which includes 49% of amino acids in a bound state and 51% in a free state. During the special processing of soft brine cheese with the help of mechanical destruction, 50 ... 55% of the bound amino acids of the protein are transformed into free α-amino acids in an easily digestible form.
The proposed method makes it possible to more fully reveal the biological potential of vegetable and protein raw materials, to transform protein, BAS and phytocomponents in a bound form into easily digestible nanocomponents into a free easily digestible form. In addition, the processed protein base and raw materials acquire new properties - structure formation, gelation, coloring ability.
The obtained protein snacks are natural, differ from the traditional ones by their high content of complete protein and the absence of synthetic impurities (preservatives, thickeners, colorants, etc.). In addition, 100 g of new products can satisfy the daily requirement for biologically active substances (β-carotene, L-ascorbic acid, phenolic compounds)
Removing co-occurring contaminants of arsenic and vanadium with full-scale arsenic adsorptive media systems
Authors: Sorg Thomas J. , Chen Abraham S. C. , Wang Lili , Lytle Darren A. 
Published: June 2021

Abstract
The U.S. Environmental Protection Agency conducted an Arsenic Demonstration Program (ADP) whereby 50 full, small-scale arsenic removal treatment systems were evaluated for removing arsenic to below the maximum contaminant level of 10 μg/L and their operating cost for a minimum of 1 year. The majority (28) of the systems installed were adsorptive media (AM) technology with the media replaced when exhausted. This paper reports on the results of two ADP projects and two laboratory rapid small-scale column tests (RSSCTs) using the iron-based media, Bayoxide E33 (E33) AM for the removal of arsenic (As) and the co-occurring contaminants (COCs) of vanadium and to a lesser degree fluoride (F) and nitrate (NO3). The ADP studies found that the AM effectively removed the COC of V, but with a lower removal capacity than of As. One ADP study found the AM to be ineffective for the removal of F and NO3. The RSSCT conducted on two other source waters also found vanadium to be removed by the same AM. The study results suggested the AM selectively sequence of As &gt; V &gt; F = N. The study also investigated the AM to achieve an As limit of 5 μg/L. The AM was found to reduce As to below 5 μg/L with around 30% shorter treatment run lengths.
Predicting submerged hydraulic jump characteristics using machine learning methods
Authors: Nasrabadi Mohsen , Mehri Yaser , Ghassemi Amin , Omid Mohammad Hossein 
Published: June 2021

Abstract
Hydraulic jump typically occurs downstream of hydraulic structures by converting the supercritical to subcritical flow regimes. If the tail-water depth is greater than the secondary depth of the hydraulic jump, the jump will be submerged (SHJ). In these conditions, the momentum equations will not have an analytical solution and a new solution is required. In this study, after dimensional analysis, an experimental study was conducted in a rectangular flume with a length of 9 m, a width of 0.5 m and a depth of 0.45 m in a wide range of Froude numbers (Fr = 3.5 to 11.5) and submergence ratios (Sr = 0.1 to 4). The data were then normalized and divided into two parts of training and testing. A new technique, DGMDH, was used to predict the submerged hydraulic jump characteristics. The results were then compared with the GMDH model. The results showed that the DGMDH model estimated the relative submergence depth, jump length, and relative energy loss with accuracy of R2 = 0.9944 and MAPE = 0.038, R2 = 0.9779 and MAPE = 0.0387, and R2 = 0.9932 and MAPE = 0.0192, respectively. While the accuracy of the GMDH model for relative submergence depth, jump length, and relative energy loss was respectively R2 = 0.9923 and MAPE = 0.043, R2 = 0.9671 and MAPE = 0.0527, and R2 = 0.9932 and MAPE = 0.0192. Due to superiority of the DGMDH model over the GMDH model, it is recommended to use this model to estimate the submerged hydraulic jump characteristics.