bluuebunny/crossref_metadata_2025_split huggingface dataset:
Outcomes of early versus delayed invasive strategy in older adults with non-ST-segment elevation myocardial infarction
Authors: Kim Yong Hoon , Her Ae-Young , Rha Seung-Woon , Choi Cheol Ung , Choi Byoung Geol , Kim Ji Bak , Park Soohyung , Kang Dong Oh , Park Ji Young , Park Sang-Ho , Jeong Myung Ho 
Published: July 2022

AbstractWe evaluated the 3-year clinical outcomes following early invasive (EI) and delayed invasive (DI) strategies in older adults with non-ST-segment elevation myocardial infarction (NSTEMI) undergoing successful new-generation drug-eluting stents (DESs) implantation to reflect current real-world practice. Overall, 2437 older adults (age, ≥ 65 years) with NSTEMI were recruited from the Korea Acute Myocardial Infarction Registry-National Institute of Health. They were divided into two groups: EI (n = 1750) and DI (n = 687). The primary clinical outcome was the occurrence of major adverse cardiac and cerebrovascular events (MACCEs), defined by all-cause death, recurrent MI, any repeat coronary revascularization, and stroke. The secondary clinical outcome was stent thrombosis (ST). After multivariable-adjusted and propensity score-matched analyses, the primary and secondary clinical outcomes were not significantly different between the EI and DI groups. Even after the analysis was confined to those having complex lesions, these major clinical outcomes were similar between these two groups. The EI and DI strategies in older adults with NSTEMI receiving new-generation DES showed comparable results.Clinical Trial Registration: URL: http://cris.nih.go.kr/cris/en/; Unique identifier: KCT0000863.
Generalising from conventional pipelines using deep learning in high-throughput screening workflows
Authors: Garcia Santa Cruz Beatriz , Slter Jan , Gomez-Giro Gemma , Saraiva Claudia , Sabate-Soler Sonia , Modamio Jennifer , Barmpa Kyriaki , Schwamborn Jens Christian , Hertel Frank , Jarazo Javier , Husch Andreas 
Published: July 2022

AbstractThe study of complex diseases relies on large amounts of data to build models toward precision medicine. Such data acquisition is feasible in the context of high-throughput screening, in which the quality of the results relies on the accuracy of the image analysis. Although state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manually generating ground truth labels for model training hampers the day-to-day application in experimental laboratories. Alternatively, traditional computer vision-based solutions do not need expensive labels for their implementation. Our work combines both approaches by training a deep learning network using weak training labels automatically generated with conventional computer vision methods. Our network surpasses the conventional segmentation quality by generalising beyond noisy labels, providing a 25% increase of mean intersection over union, and simultaneously reducing the development and inference times. Our solution was embedded into an easy-to-use graphical user interface that allows researchers to assess the predictions and correct potential inaccuracies with minimal human input. To demonstrate the feasibility of training a deep learning solution on a large dataset of noisy labels automatically generated by a conventional pipeline, we compared our solution against the common approach of training a model from a small manually curated dataset by several experts. Our work suggests that humans perform better in context interpretation, such as error assessment, while computers outperform in pixel-by-pixel fine segmentation. Such pipelines are illustrated with a case study on image segmentation for autophagy events. This work aims for better translation of new technologies to real-world settings in microscopy-image analysis.
Prediction of benzimidazole therapy duration with PET/CT in inoperable patients with alveolar echinococcosis
Authors: Husmann Lars , Gruenig Hannes , Reiner Caecilia S. , Deibel Ansgar , Ledergerber Bruno , Liberini Virginia , Skawran Stephan , Muehlematter Urs J. , Messerli Michael , Hasse Barbara , Muellhaupt Beat , Huellner Martin W. 
Published: July 2022

AbstractAlveolar echinococcosis is a rare parasitic disease, most frequently affecting the liver, as a slow-growing tumor-like lesion. If inoperable, long-term benzimidazole therapy is required, which is associated with high healthcare costs and occasionally with increased morbidity. The aim of our study was to determine the role 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in staging of patients with alveolar echinococcosis and to identify quantitative imaging parameters related to patient outcome and/or duration of benzimidazole therapy. In this single-center retrospective cohort study, 47 PET/CT performed for staging in patients with confirmed alveolar echinococcosis were analysed. In 43 patients (91%) benzimidazole therapy was initiated and was successfully stopped after a median of 870 days (766–2517) in 14/43 patients (33%). In inoperable patients, tests for trend of survivor functions displayed clear trends for longer benzimidazole therapy duration (p = 0.05; n = 25), and for longer time intervals to reach non-detectable serum concentration of Em-18 antibodies (p = 0.01, n = 15) across tertiles of SUVratio (maximum standardized uptake value in the echinococcus manifestation compared to normal liver tissue). Hence, in inoperable patients with alveolar echinococcosis, PET/CT performed for staging may predict the duration of benzimidazole therapy.
Microsponge based gel as a simple and valuable strategy for formulating and releasing Tazarotene in a controlled manner
Authors: Khattab Alaa , Nattouf Abdulhakim 
Published: July 2022

AbstractThis study aims to deliver Tazarotene (TZR) in a controlled manner to reduce adverse effects in the form of a microsponge-based gel. It adopts the methodology of a similar study by the undersigned authors with respect to the drug Clindamycin. Under both studies, the methodology used is emulsion solvent diffusion. Accordingly, we altered the concentrations of polymer and emulsifier to generate four formulations of TZR microsponges. Additionally, we used two types of emulsifiers and two types of solvents to develop two further microsponge formulations. We then studied the physical properties of each formulation, as well as drug-polymer interactions. Echoing findings from our prior study of Clindamycin, we found that microsponge formulations coded by T1 and T3 had superior production yield and entrapment efficiency, and their particle size was suitable for dermal application. As in the prior Clindamycin study, each of the T1 and T3 microsponge formulations were incorporated into a Carbopol gel and evaluated in vitro. The optimal formulation was found to be the microsponge formulation gel T8, which released 87.63% of TZR over 12 h. No significant interactions between the drug and excipients were found through Fourier transform infrared spectroscopy and differential scanning calorimetry.
Fabric muscle with a cooling acceleration structure for upper limb assistance soft exosuits
Authors: Park Seong Jun , Choi Kyungjun , Rodrigue Hugo , Park Cheol Hoon 
Published: July 2022

AbstractSoft exosuits used for supporting human muscle strength must be lightweight and wearable. Shape memory alloy (SMA) spring-based fabric muscles (SFM) are light and flexible, making them suitable for soft and shape-conformable exosuits. However, SFMs have a slow actuation speed owing to the slow cooling rate of the SMA spring. This paper proposes a forced air-cooling fan-integrated fabric muscle (FCFM) that improves the cooling rate by arranging a thin-diameter SMA spring bundle with a high surface-area-to-volume ratio inside a breathable fabric with integrated fans. The relaxation time of an FCFM weighing 30 g and containing a 2.6 g SMA spring bundle, which contains 200 thin springs, was reduced by over 70.2% via forced-air cooling using the integrated fans. A 4 kg weight, which is 1530 times the mass of the SMA spring bundle, was hung from the FCFM and was repeatedly actuated in ten-second cycles. An upper limb assistive soft exosuit with FCFMs was fabricated and worn on a mannequin holding a dumbbell, and the arm extension time after flexion was improved by 4.5 times. Additionally, the assistive performance of the exosuits for repetitive tasks in specific scenarios was evaluated, and the strong potential of the proposed FCFM for soft exosuits was verified.
Dual immobilization of magnetite nanoparticles and biosilica within alginate matrix for the adsorption of Cd(II) from aquatic phase
Authors: Safari Mahdi , Rezaee Reza , Soltani Reza Darvishi Cheshmeh , Asgari Esrafil 
Published: July 2022

AbstractThe adsorption of cadmium ions by magnetite (Fe3O4)@biosilica/alginate (MBA nano-hybrid) was the main aim of the present investigation. Herein, MBA nano-hybrid was synthesized via chemical precipitation technique. As-synthesized MBA nano-hybrid was characterized using FT-IR, FESEM and XRD analyzes. Based on the results, the maximum adsorption capacity of the adsorbent for the removal of Cd(II) was obtained at the initial pH of 7.0. At the initial Cd(II) concentration of 40 mg/L, increasing the reaction time to 180 min led to the Cd adsorption of 35.36 mg/g. Since the removal of Cd(II) after the reaction time of 60 min was insignificant, the reaction time of 60 min was considered as optimum reaction time for performing the experimental runs. According to the results, Langmuir isotherm and pseudo-second order kinetic models were the best fitted models with high correlation coefficients (R2 > 0.99). The results of thermodynamic study indicated exothermic (positive ΔH°) and spontaneous nature (negative ΔG°) of the adsorption of Cd(II) on the surface of MBA nano-hybrid. Negligible reduction in the adsorption capacity of the nano-hybrid was observed (16.57%) after fifth experimental runs, indicating high reusability potential of the as-synthesized nano-hybrid adsorbent.
Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies
Authors: Wong Nathan C. K. , Meshkinfamfard Sepehr , Turbé Valérian , Whitaker Matthew , Moshe Maya , Bardanzellu Alessia , Dai Tianhong , Pignatelli Eduardo , Barclay Wendy , Darzi Ara , Elliott Paul , Ward Helen , Tanaka Reiko J. , Cooke Graham S. , McKendry Rachel A. , Atchison Christina J. , Bharath Anil A. 
Published: July 2022

Abstract
Background
Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home, but rely on subjective interpretation of a test line by eye, risking false positives and false negatives. Here, we report on the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity.

Methods
Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues.

Results
Automated analysis showed substantial agreement with human experts (Cohen’s kappa 0.90–0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7–99.4%) and sensitivity (90.1–97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets).

Conclusions
Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false-positive and false-negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests as a tool for improved accuracy for population-level community surveillance.
Exploring mechanisms of Chaihu-Shugan-San against liver fibrosis by integrated multi-omics and network pharmacology approach
Authors: Xie Zhihao , Xie Zhiying , Trujillo Nicolas Pineda , Yang Ting , Yang Chunxia 
Published: July 2022

AbstractChaihu-Shugan-San (CHSGS), a noted traditional Chinese medicine formula, has been used as a complementary and alternative therapy for liver fibrosis. However, the antifibrotic mechanisms of CHSGS still remain unclear. Thus, we used network pharmacology approach in combination with single cell and bulk transcriptomics to elucidate the antifibrotic mechanisms of CHSGS. We first screened out 134 bioactive ingredients of CHSGS through the defined criteria. Then, 1150 genes were predicted to be targets for CHSGS, while 625 liver fibrosis-associated genes were identified by single cell transcriptomics analysis. Next, 71 intersecting genes of CHSGS and liver fibrosis were defined as the therapeutic targets in CHSGS against liver fibrosis. Further, 21 core targets and 12 core ingredients of CHSGS against liver fibrosis were also identified. Meanwhile, enrichment analyses of core targets highlighted that the key mechanisms of CHSGS against liver fibrosis include modulation of inflammation responses, inhibition of angiogenesis, and regulation of ECM remodeling, of which the most important mechanism was the regulation of ECM remodeling. The molecular docking simulation validated strong binding affinity between the core targets and core ingredients. Furthermore, 62-gene signature may be used for determining the prognosis in cirrhotic patients based on the results of ssGSEA-Cox analysis. In conclusion, the present study revealed the multiple pharmacological targets and therapeutic mechanisms of CHSGS against liver fibrosis, which may thus serve as an effective antifibrotic therapy. Meanwhile, CHSGS may improve survival of patients with liver cirrhosis by the interaction of 62-gene signature.
Casting a wide NET: an update on uncontrolled NETosis in response to COVID-19 infection
Authors: Taylor Erin B. 
Published: July 2022

Abstract
Dysregulation of neutrophil extracellular trap (NET) formation has been shown to mediate disease pathology in multiple viral infections, including SARS-CoV-2. At the beginning of COVID-19 pandemic, Thierry and Roch wrote a perspective on the mechanisms by which severe SARS-CoV-2 infection may lead to uncontrolled NET formation that leads to acute respiratory distress syndrome (ARDS), systemic vascular permeability, and end organ damage. In this commentary, the progress that has been made in regards to the ideas postulated by the perspective will be discussed, with a focus on the therapeutics that target NET formation.
Spiking and chaotic behaviours of locally active memristor‐based neuron circuit
Authors: Lu Zhenzhou , Liang Yan , Dong Yujiao , Wang Shichang 
Published: July 2022

AbstractThis brief designs an artificial neuron circuit consisting of an inductor and an N‐type locally active memristor (LAM). When the presented neuron circuit is excited by the DC or sine voltage signal, it can pick up the most significant features of a biological neuron: all‐or‐none, spiking oscillation, and chaotic behaviours. Local activity and edge of chaos theories are applied to analyze the electrical characteristics of the neuron circuit. Furthermore, the spiking mechanism of the circuit exhibits is investigated. The circuit implementation of the proposed neuron circuit is presented. Both simulation and experimental results demonstrate the practicability of the proposed neuron circuit and the validity of the theoretical analysis.
3‐D thermal analysis for the valve‐side bushings of HVDC converter transformer and application in the evaluation of current‐carrying connections defects
Authors: Lin Mu , Liu Kai , Wu Zihao , Tang Hao , Liu Tong , Zhu Minghui , Wu Guangning 
Published: July 2022

AbstractTo analyze the overheating problem of the valve‐side bushings of high voltage direct current (HVDC) converter transformer, which is mainly caused by current‐carrying connections defects at copper–aluminium transition region in the bushing, this paper set up a 3‐D electromagnetic‐thermal analysis model for the valve‐side bushings, and a method was proposed to calculate the thermal distribution of the valve‐side bushings under the influence of current‐carrying connections defects. Based on this model, the details of an example were shown in this paper, and the numerical results agreed with the measured results within 1%. Then, the temperature field distribution characteristics of valve‐side bushing under different working current I, defect degree k, and ambient temperature Tamb were determined. According to the characteristics, the temperature difference ΔT and working current I of the bushing were applied to evaluate contact resistance at the copper–aluminium transition region of the bushing, which can be provided by a novel method to evaluate for current‐carrying connections defects in the valve‐side bushings.
Atmospheric turbulence degraded image restoration using a modified dilated convolutional network
Authors: Su Changdong , Wu Xiaoqing , Guo Yiming , Zhang Shitai , Wang Zhiyuan , Shi Dongfeng 
Published: July 2022

AbstractLong‐distance imaging is generally blurry and warped due to the presence of atmospheric turbulence, which harms the performance of the photoelectric system. Among these, real‐time restoration based on a single turbulence‐degraded image has always been a challenging topic that everyone is concerned about. The approach performed here optimizes the convolutional neural network using residual learning and smoothed dilated convolutions, which may increase the field of vision under the situation of limited GPU memory. To identify the model's performance, the authors employ training and test data with strong, medium, and weak levels synthesized by the Fried kernel, the real‐time data captured by the Ritchey–Chretien telescope, the Open Turbulent Images Set and the real comparative data. Furthermore, the proposed model is compared to previous state‐of‐the‐art approaches. The experimental results demonstrate that the proposed novel model can recover turbulently degraded images more effectively.
Plithogenic multi‐criteria decision making approach on airspace planning scheme evaluation based on ATC‐flight real‐time simulation
Authors: Liang Binbin , Han Songchen , Li Wei , Han Yunxiang , Liu Fan , Zhang Ying , Lin Chen 
Published: July 2022

AbstractAirspace planning scheme evaluation based on ATC‐Flight real‐time simulation which incorporates air traffic control simulator and flight simulator has become increasingly important. It has irreplaceable advantages of truly simulating and in‐depth evaluating airspace operation, especially for analyzing human factors. As a multi‐criteria decision making (MCDM) problem, it poses challenges which this study aims to overcome from three aspects. Firstly, the complexity of airspace and the characteristics of ATC‐Flight real‐time simulation are analyzed to build a multi‐criteria evaluation system. The criteria include air traffic flow, airspace operational performance, flight procedure quality, cost, controller workload and pilot workload. Secondly, a novel weighting method based on plithogenic set is presented to derive robust subjective criteria weights from a group of human decision makers. The subjective weights are then combined with objective weights using game theory to determine cumulative criteria weights. Thirdly, technique for order preference by similarity to ideal solution (TOPSIS) and grey correlation analysis are integrated to perform comprehensive evaluation and ranking. The first series of ATC‐Flight real‐time simulations in China are conducted to illustrate a case study. The results demonstrated that the proposed MCDM approach could improve the accuracy and discriminability of evaluation results, and outperformed other existing well‐known MCDM algorithms.
Curvature prediction of long‐period fibre grating based on random forest regression
Authors: Hu Xingliu , Si Haifei , Ye Quanyi , Zhang Yan 
Published: July 2022

AbstractThis study proposes a long‐period fibre grating (LPFG) curvature estimation method based on random forest regression (RFR) to address the shortcomings of the existing curvature evaluation method, namely, polynomial fitting; these shortcomings cause difficulty in achieving adequate model regularity and application universality. The resonant wavelength and resonant peak amplitude of the LPFG are used as input variables in this method to develop an RFR model for curvature estimation, allowing for accurate curvature prediction of the sample. The results show that the RFR‐based LPFG curvature prediction model can better characterise the input–output regression relationship than back‐propagation neural networks. The average R2 value of the RFR model is 0.9826, and the actual measured curvature value is highly correlated with the model predicted curvature value. Compared to that exhibited by back‐propagation neural networks, the RFR model exhibits higher accuracy for curvature estimation, with average values of 0.1314 and 0.1173 for root mean square and mean absolute errors, respectively. This method can provide a more comprehensive theoretical basis for the application of robot learning in the curvature measurement of LPFG and has practical value.
A robust spatio‐temporal prediction approach for wind power generation based on spectral temporal graph neural network
Authors: He Yuqin , Chai Songjian , Zhao Jian , Sun Yuxin , Zhang Xian 
Published: July 2022

AbstractAt present, the penetration of wind power generation is increasing remarkably worldwide, and the accurate wind power forecasting (WPF) is essential to ensure the reliability and economy of the power system. Most of the current work of WPF only capture temporal correlation in the time domain but ignore the spatial correlation. In this study, a spectral time graph neural network based on the maximum correlation criterion (MCC‐Stem‐GNN) is proposed to improve the accuracy of WPF for multiple sites and horizons. The self‐attentive mechanism in the MCC‐Stem‐GNN automatically learns the correlations between the multivariate sequences. Besides, this model combines the Graph Fourier Transform (GFT) to model spatial correlation and the Discrete Fourier Transform (DFT) to model temporal correlation. The effectiveness of the proposed robust deep learning framework is verified on the simulated wind energy dataset over 16 locations in Ohio, US through considering different sample contamination types and levels, comprehensive case study is carried out to show the superiority of the MCC‐Stem‐GNN over the benchmarks.