bluuebunny/crossref_metadata_2025 huggingface dataset:
Astaxanthin Is Able to Prevent Alcohol-Induced Dysfunction of Liver Mitochondria
Authors: Krestinina Olga , Odinokova Irina , Sotnikova Linda , Krestinin Roman , Zvyagina Alena , Baburina Yulia
Published: October 2022

The search for new targets for the pathological action of ethanol remains an urgent task of biomedicine. Since degenerative changes in the liver are associated with the development of oxidative stress, antioxidants are promising agents for the treatment of alcohol-related diseases. In this work, we studied the ability of the carotenoid antioxidant, astaxanthin (AX), to prevent ethanol-induced changes in the liver of rats. It was shown that AX is able to protect the structure of mitochondria from degenerative changes caused by ethanol to improve mitochondrial functions. AX positively influences the activity and expression of proteins of the mitochondrial respiratory chain complexes and ATPase. In addition, a protective effect of AX on the rate and activity of mitochondrial respiration was demonstrated in the work. Thus, studies have shown that AX is involved in protective mechanisms in response to ethanol-induced mitochondrial dysfunction.
MCF-Net: Fusion Network of Facial and Scene Features for Expression Recognition in the Wild
Authors: Xu Hui , Kong Jun , Kong Xiangqin , Li Juan , Wang Jianzhong
Published: October 2022

Nowadays, the facial expression recognition (FER) task has transitioned from a laboratory-controlled scenario to in-the-wild conditions. However, recognizing facial expressions in the wild is challenging due to factors such as variant backgrounds, low-quality facial images, and the subjectiveness of annotators. Therefore, deep neural networks have increasingly been leveraged to learn discriminative representations for FER. In this work, we propose the Multi-cues Fusion Net (MCF-Net), a novel deep learning model with a two-stream structure for FER. Our model first proposes a two-stream coding network to extract face and scene representations. Then, an adaptive fusion module is employed to fuse the two different representations for final recognition. In the face coding stream, a Sparse Mask Attention Learning (SMAL) module is utilized to adaptively generate the corresponding sparse face mask according to the input image. Meanwhile, we employ a Multi-scale Attention (MSA) module for extracting fine-grained feature subsets, which can obtain richer multi-scale interaction information. In the scene coding stream, a Relational Attention (RA) module is applied to construct the hidden relationship between the face and contextual features of non-facial regions by capturing the pairwise similarity. In order to verify the effectiveness and accuracy of our model, a large number of experiments are carried out on two public large-scale static facial expression image datasets, CAER-S and NCAER-S. By comparing the performance of our MCF-Net with other methods, the proposed model achieves superior results on two in-the-wild FER benchmarks: CAER-S with an accuracy of 81.82% and NCAER-S with an accuracy of 45.59%.
Fatigue Detection of Air Traffic Controllers Based on Radiotelephony Communications and Self-Adaption Quantum Genetic Algorithm Optimization Ensemble Learning
Authors: Wu Nan , Sun Jingjuan
Published: October 2022

Air traffic controller (ATC) fatigue has become a major cause of air traffic accidents. Speech-based fatigue-state detection is proposed in this paper. The speech signal is preprocessed to further extract the Mel frequency cepstrum coefficient (MFCC) from speech discourse. The machine learning method is used in fatigue detection. However, single machine learning fatigue detection methods often have low detection accuracy. To solve this problem, an ensemble learning method based on self-adaption quantum genetic algorithm (SQGA) heterogeneous learning methods is proposed. Pattern-level and feature-level resampling are used to increase the differences in the base learner’s training dataset. To enlarge the diversity of single learners, k-nearest neighbor (KNN), Bayesian network (BN), back propagation neural network (BPNN) and support vector machine (SVM) are adopted for the heterogeneous ensemble. On this basis, finally, the detection result is obtained by weighted summation. The weight of each base learner was determined by SQGA. The SQGA method combines the quantum genetic algorithm with the adaptive strategy. The adaptive strategy includes adaptive adjustment of the quantum rotation gate, adaptive generation of crossover probability and adaptive generation of mutation probability. The experiments on real civil aviation radio land–air communication show that the proposed method can obtain 98.5% detection accuracy, with a 1.2% false and 3.0% missing report rate, whereas the SVM only obtains 94.0% detection accuracy, with a 5.4% false and 9.0% missing report rate.
Comparison of Rate of Force Development between Explosive Sustained Contractions and Ballistic Pulse-like Contractions during Isometric Ankle and Knee Extension Tasks
Authors: Kozinc Žiga , Pleša Jernej , Djurić Daniel , Šarabon Nejc
Published: October 2022

Background: The rate of force development (RFD) is a measure of explosive strength, commonly evaluated within the same repetition as peak force/torque, by instructing the participants to produce “fast” and “hard” contraction. Previous studies have indicated that attempting to achieve the maximal RFD and maximal force/torque within the same contraction may not be optimal. Methods: This study assessed the differences between explosive sustained (“fast-and-hard”) and ballistic (pulse-like) contractions on the RFD during isometric ankle and knee extensions in young, recreationally active participants (n = 33; age: 23.4 ± 5.6 years). The participants performed both contraction types for isometric ankle and knee extension tasks. The RFD was calculated for time intervals 50, 100, 150 and 200 ms from the contraction onset and also in between these time points (50–100, 100–150 and 150–200 ms). Findings: The results of this study showed a higher RFD in the ballistic contractions in later time intervals (100–150 and 150–200 ms) compared to that of the explosive sustained contractions (effect size (Hedge’s g) = 0.51–0.73). The differences between explosive sustained and ballistics contractions were larger and occurred for more outcome variables in females than males. Peak RFD was also larger in ballistic contractions, both for females (p = 0.010; Hedge’s g = 0.54) and males (p = 0.003; Hedge’s g = 0.78). The intra-session reliability of the RFD was similar for both contraction types, increasing from moderate to excellent with longer time intervals. Conclusion: Our results suggest that ballistic contractions should be used when the assessment of the RFD is the primary goal. When the aim is to assess both the RFD and peak force, it is suggested to use the combination of separate explosive sustained contractions and ballistic contractions in the protocol.
Spatial Evaluation of Machine Learning-Based Species Distribution Models for Prediction of Invasive Ant Species Distribution
Authors: Lee Wang-Hee , Song Jae-Woo , Yoon Sun-Hee , Jung Jae-Min
Published: October 2022

Recent advances in species distribution models (SDMs) associated with artificial intelligence (AI) and increased volumes of available data for model variables have allowed reliable evaluation of the potential distribution of any species. A reliable SDM requires suitable occurrence records and variables with optimal model structures. In this study, we developed three different machine learning-based SDMs [MaxEnt, random forest (RF), and multi-layer perceptron (MLP)] to predict the global potential distribution of two invasive ants under current and future climates. These SDMs showed that the potential distribution of Solenopsis invicta would be expanded by climatic change, whereas it would not significantly change for Anoplolepis gracilipes. The models were compared using model performance metrics, and the optimal model structure and spatial projection were selected. The MaxEnt exhibited high performance, while the MLP model exhibited low performance, with the largest variation by climate change. Random forest showed the smallest potential distribution area, but it was robust considering the number of occurrence records and changes in model variables. All the models showed reliable performance, but the difference in performance and projection size suggested that optimal model selection based on data availability, model variables, study objectives, or an ensemble approach was necessary to develop a comprehensive SDM to minimize modeling uncertainty. We expect that this study will help with the use of AI-based SDMs for the evaluation and risk assessment of invasive ant species.
Addition of by-Products and Flavored Hydrocolloids Filling in Californian-Style Black Olive to Reduce Acrylamide Formation
Authors: Fernández Antonio , Montero-Fernández Ismael , Pérez-Nevado Francisco , Martínez Manuel , Martín-Vertedor Daniel
Published: October 2022

Oxidized black table olives are frequently consumed in the Mediterranean diet. To improve food quality, the use of by-products is an emergent strategy that should be more studied. With a better understanding of the use of by-products, healthier food with the highest possible quality could be obtained, increasing its added value. Different by-products at different concentrations (1:10 and 1:100) and phenol-rich aromatized hydrocolloids were added to Californian-style black olive in order to evaluate its effect in acrylamide. In general, the addition of by-products before the sterilization process resulted in a significant reduction of the acrylamide content (10–60%). The highest inhibition was obtained with the phenolic extract at a 1:10 dilution. Furthermore, flavored olives stuffed with higher concentrations caused a greater reduction in acrylamide content. The ’Garlic’ and ‘Thyme’ flavors showed the greatest reduction of this toxic substance. The tasting panel considered that olives with a 1:10 dilution and olives filled with flavored hydrocolloids at low concentrations had a pleasant odor and positive sensory attributes. Orange peel was the recommended by-product to mitigate the effects of acrylamide since it obtained the highest score both in acrylamide inhibition and in the taste panel. As for the hydrocolloids, ‘Thyme’ showed the greatest inhibition effect but did not perform well in the taste panel, with similar effects to ‘Oregano’ and ‘Garlic.’ The current research attempts to underline the use of local by-products to obtain additives rich in antioxidant activity that could enhance oxidized black table olives, becoming a healthier, safer and better-quality products. Moreover, this connects with the idea of moving from a linear economy to a circular economy, obtaining a product of high economic value from a by-product.
Theoretical Investigation of Selection (Calculation) of Design Parameters of Modular Draft Device in Aggregation of Semitrailers
Authors: Bulgakov Volodymyr , Aboltins Aivars , Beloev Hristo , Nadykto Volodymyr , Pascuzzi Simone , Ruzhylo Mariia , Ivanovs Semjons
Published: October 2022

One of the directions for expanding the functional properties of modular draft devices (MDD) is their use with semitrailers (using a fifth-wheel coupling, installed on the technological module). The issue with this is due to their ability to create such a redistribution of the MDD operational mass along the semitrailer’s axles whereby the loss of control of the entire transport aggregate is possible. To solve this problem, regularities have been established to consider in choosing the design parameters of the MDD. It has been established by theoretical calculations that an increase in the angle of inclination of the central α, with negative values of the angle of inclination of the lower β links of the rear-mounted mechanism of the MDD’s power module (PM), leads to an increase in the vertical load of the wheels of its technological module. The transition of the longitudinal coordinate point of the semitrailer’s tractive resistance application from −0.30 m to +0.30 m, provided α > β, leads to an increase in the vertical load on the wheels of its technological module. Increasing the angles of inclination of the central and lower links of the rear linkage of the PM leads to an increase in the vertical load on the wheels of its front axle. An exception is the option in which the lower links of the specified linkage mechanism are set at a negative angle at the level of −10°. An increase in the value of the angle of inclination of the central value, with positive values of the angle of inclination of the lower links of the rear-mounted mechanism of the PM, and compliance with the condition α < β, leads to a decrease in the vertical load of the wheels of the technological module.
Nonlinear PID Controller Parameters Optimization Using Improved Particle Swarm Optimization Algorithm for the CNC System
Authors: Sun Xianghan , Liu Ning , Shen Rui , Wang Kexin , Zhao Zhijie , Sheng Xianjun
Published: October 2022

In this paper, a nonlinear PID (NLPID) controller is used to replace a traditional PID controller to overcome the influence of nonlinear factors in the computer numerical control (CNC) system. A particle swarm optimization based on a generalized opposition-based learning (G-PSO) algorithm is proposed to optimize the NLPID controller. The convergence speed and global optimization ability of the particle swarm optimization (PSO) algorithm are improved by introducing generalized opposition-based learning. The natural selection mutation is introduced into the G-PSO algorithm to further avoid the particles falling into local optimization. Different from the existing research, this paper designs a special fitness function according to the control objectives of improving system response speed and suppressing overshoot. By comparing the differential evolution (DE) algorithm, the ant lion optimizer (ALO) and the genetic algorithm (GA) through simulation, it is proven that the G-PSO algorithm has a faster convergence speed and better global optimization ability. Compared to Fuzzy PID and MRAC PID, G-PSO NLPID is shown to be more suitable for CNC systems. Additionally, it is proven through experiments that the rise time and settling time of the NLPID controller optimized by the G-PSO algorithm are 22.22% and 24.52% faster, respectively, than the traditional PID controller, and the system overshoot is successfully suppressed.
Monitoring In Vitro and In Vivo Aroma Release of Espresso Coffees with Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry
Authors: Romano Andrea , Cappellin Luca , Bogialli Sara , Pastore Paolo , Navarini Luciano , Biasioli Franco
Published: October 2022

This work presents in vitro and in vivo aroma release analysis of three espresso coffees carried out by PTR-ToF-MS headspace and nosespace analysis, respectively. The products were C. arabica coffees prepared using an espresso coffee machine: a low-caffeine C. arabica var. laurina light roast, a low-caffeine C. arabica var. laurina dark roast, and a single-origin coffee from Ethiopia which were roasted to a medium roast degree. Headspace analysis allowed for discrimination between coffees with a prediction accuracy of 92% or higher. Relevant discriminating compounds were related to the roasting degree and varietal compounds. Coffee nosespace consisted of 35 mass peaks overall. Despite this relatively low number of detected peaks, coffee discrimination was still possible with ≥93% accuracy. The compounds most relevant to the discrimination were those related to the roasting degree. Major differences—both qualitative and quantitative—were found between headspace and nosespace profiles.
MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
Authors: Ksibi Amel , Ayadi Manel , Soufiene Ben Othman , Jamjoom Mona M. , Ullah Zahid
Published: October 2022

The Kingdom of Saudi Arabia is considered to be one of the world leaders in olive production accounting for about 6% of the global olive production. Given the fact that 94% of the olive groves are mainly rain-fed using traditional methods of production, the annual olive production is witnessing a noticeable fluctuation which is worse due to infectious diseases and climate change. Thus, early and effective detection of plant diseases is both required and urgent. Most farmers use traditional methods, for example, visual inspection or laboratory examination, to identify plant diseases. Currently, deep learning (DL) techniques have been shown to be useful methods for diagnosing olive leaf diseases and many other fields. In this work, we use a deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pretrained CNN models, i.e., ResNet50 and MobileNet. Hence, we propose MobiRes-Net: A neural network that is a concatenation of the ResNet50 and MobileNet models for overall improvement of prediction capability. To build the dataset used in the study, 5400 olive leaf images were collected from an olive grove using a remote-controlled agricultural unmanned aerial vehicle (UAV) equipped with a camera. The overall performance of the MobiRes-Net model achieved a classification accuracy of 97.08% which showed its superiority over ResNet50 and MobileNet that achieved classification accuracies of 94.86% and 95.63%, respectively.
Management of the Diabetic Foot in People with Diabetes Mellitus Older than 65 Years
Authors: Astasio-Picado Álvaro , Toledano Vanessa Gómez , López-Sánchez Miriam , Jurado-Palomo Jesús , Cobos-Moreno Paula , Gómez-Martín Beatriz
Published: October 2022

Introduction: Diabetic foot is the infection, ulceration or destruction of the deep tissues of the foot in patients with diabetes mellitus. This pathology occurs when there are high levels of glucose in the blood, as well as other factors such as high blood pressure and hypercholesterolemia. These factors cause damage to the vessels and nerves, producing medium and long-term complications. This is a health problem that affects diabetic patients throughout their lives. Objective: To know the different techniques and strategies that allow to carry out an appropriate management of the diabetic foot. Methodology: A narrative review was carried out between November 2021 and July 2022 in the different databases of Pubmed (Medline), Scielo, Scopus and Web of Science (WOS), with a search string that combined the keywords derived from thesauri, namely Descriptors of Health Sciences (DeCS) and Medical Subject Headings (MeSH), combined with the Boolean operators “AND”, “NOT” and “NOT”. Results: After applying the article selection criteria and evaluating the quality of the methodology, a total of 22 articles were obtained. The results affirm the existence of promising therapies for the treatment of diabetic foot ulcers such as negative-pressure therapy and hyperbaric oxygen therapy. It is highlighted that the use of therapeutic footwear and health education are deficient aspects that must be reinforced. Conclusions: After comparing the different articles, it was possible to determine that negative-pressure therapy and hyperbaric oxygen therapy should be promoted as they are suitable for the treatment of diabetic foot ulcers. In addition, therapeutic footwear is a baseline in the diabetic foot approach. On the other hand, it is necessary to reinforce interdisciplinary work in this area and health education for patients suffering from this disease.
Cross-Examining Precipitation Products by Rain Gauge, Remote Sensing, and WRF Simulations over a South American Region across the Pacific Coast and Andes
Authors: Chen Mengye , Huang Yongjie , Li Zhi , Larico Albert Johan Mamani , Xue Ming , Hong Yang , Hu Xiao-Ming , Novoa Hector Mayol , Martin Elinor , McPherson Renee , Zhang Jiaqi , Gao Shang , Wen Yixin , Perez Andres Vitaliano , Morales Isaac Yanqui
Published: October 2022

Precipitation estimate is important for earth science studies and applications, and it is one of the most difficult meteorological quantities to estimate accurately. For regions such as Peru, reliable gridded precipitation products are lacking due to complex terrains and large portions of remote lands that limit the accuracy of satellite precipitation estimation and in situ measurement density. This study evaluates and cross-examines two high-resolution satellite-based precipitation products, a global rain-gauge interpolated precipitation product, and a Weather Research and Forecast (WRF) model that simulated precipitation for a ten-year period from 2010 to 2019 in the Peruvian Andes region across the Pacific coast, Andes, and in the Amazon. The precipitation estimates examined in this study are the Integrated Multi-SatellitE Retrievals for GPM (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Center product (GPCC), and a 3 km grid spacing WRF-based regional climate model (RCM) simulation. The evaluation and cross-examination were performed at sub-daily (6 h), daily, and monthly time scales, and at various spatial resolutions. The results show that the WRF simulation performs as well as, if not better than, GPM IMERG in the low precipitation and dry regions but becomes inaccurate in wet regions. GPM IMERG is more suitable for higher precipitation and wet regions, and MSWEP shows a systematic overestimation over the study area. It is therefore important to choose the most suitable precipitation product based on research needs and climate condition of the study for the challenging Peruvian Andes region.
Integrated All-Climate Heating/Cooling System Design and Preheating Strategy for Lithium-Ion Battery Pack
Authors: Wu Xiaogang , Wei Zhixin , Sun Yizhao , Sun Jinlei , Du Jiuyu
Published: October 2022

The continuous low temperature in winter is the main factor limiting the popularity of electric vehicles in cold regions. The best way to solve this problem is by preheating power battery packs. Power battery packs have relatively high requirements with regard to the uniformity of temperature distribution during the preheating process. Aimed at this problem, taking a 30 Ah LiFePO4 (LFP) pouch battery as the research object, a three-sided liquid cooling structure that takes into account the preheating of the battery module was designed. On the basis of analyzing the influence of the cooling plate arrangement, cooling liquid flow rate, liquid medium, and inlet temperature on the temperature consistency of the battery module, the orthogonal simulation method was used to formulate the optimal combination of factors for different cooling objectives. Using the designed preheating structure, a combined internal and external preheating strategy based on the available battery power is proposed. The research results show that the cooling plate arrangement scheme and the inlet temperature have obvious influences on the preheating effect, while the increase in the flow velocity of the preheating effect is saturated. The optimized external preheating structure can maintain the preheating temperature difference of the battery module at less than 5 °C. On this basis, the proposed combined internal and external preheating strategy saves 50% of the preheating time compared with three-sided preheating.
Aqueous Rechargeable Sodium-Ion Batteries: From Liquid to Hydrogel
Authors: Yang Mingrui , Luo Jun , Guo Xiaoniu , Chen Jiacheng , Cao Yuliang , Chen Weihua
Published: October 2022

Sodium-ion batteries stand out as a promising technology for developing a new generation of energy storage devices because of their apparent advantages in terms of costs and resources. Aqueous electrolytes, which are flame-resistant, inexpensive, and environmentally acceptable, are receiving a lot of attention in light of the present environmental and electronic equipment safety concerns. In recent decades, numerous improvements have been made to the performance of aqueous sodium-ion batteries (ASIBs). One particular development has been the transition from liquid to hydrogel electrolytes, whose durability, flexibility, and leakproof properties are eagerly anticipated in the next generation of flexible wearable electronics. The current review examines the most recent developments in the investigation and development of the electrolytes and associated electrode materials of ASIBs. An overview of new discoveries based on cycle stability, electrochemical performance, and morphology is presented along with previously published data. Additionally, the main milestones, applications, and challenges of this field are briefly discussed.
Nested Real-Time PCR Assessment of Vertical Transmission of Sandalwood Spike Phytoplasma (‘Ca. Phytoplasma asteris’)
Authors: Kirdat Kiran , Tiwarekar Bhavesh , Swetha Purushotham , Padma Sodaliyandi , Thorat Vipool , Manjula Kathiruguppe Nagappa , Kavya Narayan , Sundararaj Ramachandran , Yadav Amit
Published: October 2022

The Sandalwood Spike disease (SSD)-related to ‘Ca. Phytoplasma asteris’ has threatened the existence of sandalwood in India. The epidemiology of SSD is still poorly understood despite the efforts to understand the involvement of insect vectors in SSD transmission and alternate plant hosts over the last two decades. Apart from the transmission of SSD phytoplasma through insect vectors, the information on vertical transmission is entirely unknown. Over 200 seeds from SSD-affected trees and over 500 seedlings generated using commercially purchased seeds were screened for the presence of SSD phytoplasma to understand the vertical transmission in an insect-free environment. The end-point nested PCR and real-time nested PCR-based screening revealed an alarming rate of 38.66% and 23.23% phytoplasma positivity in one-month and four-month-old seedlings, respectively. These results were further validated by visualizing the phytoplasma bodies in sandalwood tissues using scanning electron microscopy. The presence of phytoplasma DNA in the seeds and seedlings is a concern for the commercial distribution of sandalwood seedlings in the current setup. This also poses a fear of spreading the disease to newer areas and negatively affecting the economy. The seedling mortality was also suspected to be associated with isolated bacterial and fungal isolates such as Erwinia, Curtobacterium, Pseudomonas, Rhodococcus, Aspergillus, Fusarium, and Neofusicoccum isolated using a culture-dependent approach. These findings strongly recommend the accreditation of commercial production of sandalwood seedlings curtailing SSD phytoplasma’s menace. Additionally, a new nested end-point and qRT PCR assays developed in this study proved valuable for the rapid screening of phytoplasma in many plant samples to detect phytoplasmas.
An Overview of Molecular Mechanisms in Fabry Disease
Authors: Amodio Federica , Caiazza Martina , Monda Emanuele , Rubino Marta , Capodicasa Laura , Chiosi Flavia , Simonelli Vincenzo , Dongiglio Francesca , Fimiani Fabio , Pepe Nicola , Chimenti Cristina , Calabrò Paolo , Limongelli Giuseppe
Published: October 2022

Fabry disease (FD) (OMIM #301500) is a rare genetic lysosomal storage disorder (LSD). LSDs are characterized by inappropriate lipid accumulation in lysosomes due to specific enzyme deficiencies. In FD, the defective enzyme is α-galactosidase A (α-Gal A), which is due to a mutation in the GLA gene on the X chromosome. The enzyme deficiency leads to a continuous deposition of neutral glycosphingolipids (globotriaosylceramide) in the lysosomes of numerous tissues and organs, including endothelial cells, smooth muscle cells, corneal epithelial cells, renal glomeruli and tubules, cardiac muscle and ganglion cells of the nervous system. This condition leads to progressive organ failure and premature death. The increasing understanding of FD, and LSD in general, has led in recent years to the introduction of enzyme replacement therapy (ERT), which aims to slow, if not halt, the progression of the metabolic disorder. In this review, we provide an overview of the main features of FD, focusing on its molecular mechanism and the role of biomarkers.
COVID-19 Severity and Thrombo-Inflammatory Response Linked to Ethnicity
Authors: Heissig Beate , Salama Yousef , Iakoubov Roman , Vehreschild Joerg Janne , Rios Ricardo , Nogueira Tatiane , Vehreschild Maria J. G. T. , Stecher Melanie , Mori Hirotake , Lanznaster Julia , Adachi Eisuke , Jakob Carolin , Tabe Yoko , Ruethrich Maria , Borgmann Stefan , Naito Toshio , Wille Kai , Valenti Simon , Hower Martin , Hattori Nobutaka , Rieg Siegbert , Nagaoka Tetsutaro , Jensen Bjoern-Erik , Yotsuyanagi Hiroshi , Hertenstein Bernd , Ogawa Hideoki , Wyen Christoph , Kominami Eiki , Roemmele Christoph , Takahashi Satoshi , Rupp Jan , Takahashi Kazuhisa , Hanses Frank , Hattori Koichi ,   on behalf of the LEOSS Study Group
Published: October 2022

Although there is strong evidence that SARS-CoV-2 infection is associated with adverse outcomes in certain ethnic groups, the association of disease severity and risk factors such as comorbidities and biomarkers with racial disparities remains undefined. This retrospective study between March 2020 and February 2021 explores COVID-19 risk factors as predictors for patients’ disease progression through country comparison. Disease severity predictors in Germany and Japan were cardiovascular-associated comorbidities, dementia, and age. We adjusted age, sex, body mass index, and history of cardiovascular disease comorbidity in the country cohorts using a propensity score matching (PSM) technique to reduce the influence of differences in sample size and the surprisingly young, lean Japanese cohort. Analysis of the 170 PSM pairs confirmed that 65.29% of German and 85.29% of Japanese patients were in the uncomplicated phase. More German than Japanese patients were admitted in the complicated and critical phase. Ethnic differences were identified in patients without cardiovascular comorbidities. Japanese patients in the uncomplicated phase presented a suppressed inflammatory response and coagulopathy with hypocoagulation. In contrast, German patients exhibited a hyperactive inflammatory response and coagulopathy with hypercoagulation. These differences were less pronounced in patients in the complicated phase or with cardiovascular diseases. Coagulation/fibrinolysis-associated biomarkers rather than inflammatory-related biomarkers predicted disease severity in patients with cardiovascular comorbidities: platelet counts were associated with severe illness in German patients. In contrast, high D-dimer and fibrinogen levels predicted disease severity in Japanese patients. Our comparative study indicates that ethnicity influences COVID-19-associated biomarker expression linked to the inflammatory and coagulation (thrombo-inflammatory) response. Future studies will be necessary to determine whether these differences contributed to the less severe disease progression observed in Japanese COVID-19 patients compared with those in Germany.
Nature’s Wind Turbines: The Measured Aerodynamic Efficiency of Spinning Seeds Approaches Theoretical Limits
Authors: Molteno Timothy C. A.
Published: October 2022

This paper describe a procedure to measure experimentally the power coefficient, Cp, of winged seeds, and apply this technique to seeds from the Norway maple (Acer platanoides). We measure Cp=56.9±2% at a tip speed ratio of 3.21±0.06. Our results are in agreement with previously published CFD simulations that indicate that these seeds—operating in low-Reynolds number conditions—approach the Betz limit (Cp=59.3%) the maximum possible efficiency for a wind turbine. In addition, this result is not consistent with the recent theoretical work of Okulov & Sørensen, which suggests that a single-bladed turbine with a tip-speed ratio of 3.2 can achieve a power efficiency of no more than 30%.
Recent Advances of Representative Optical Biosensors for Rapid and Sensitive Diagnostics of SARS-CoV-2
Authors: Xu Meimei , Li Yanyan , Lin Chenglong , Peng Yusi , Zhao Shuai , Yang Xiao , Yang Yong
Published: October 2022

The outbreak of Corona Virus Disease 2019 (COVID-19) has again emphasized the significance of developing rapid and highly sensitive testing tools for quickly identifying infected patients. Although the current reverse transcription polymerase chain reaction (RT-PCR) diagnostic techniques can satisfy the required sensitivity and specificity, the inherent disadvantages with time-consuming, sophisticated equipment and professional operators limit its application scopes. Compared with traditional detection techniques, optical biosensors based on nanomaterials/nanostructures have received much interest in the detection of SARS-CoV-2 due to the high sensitivity, high accuracy, and fast response. In this review, the research progress on optical biosensors in SARS-CoV-2 diagnosis, including fluorescence biosensors, colorimetric biosensors, Surface Enhancement Raman Scattering (SERS) biosensors, and Surface Plasmon Resonance (SPR) biosensors, was comprehensively summarized. Further, promising strategies to improve optical biosensors are also explained. Optical biosensors can not only realize the rapid detection of SARS-CoV-2 but also be applied to judge the infectiousness of the virus and guide the choice of SARS-CoV-2 vaccines, showing enormous potential to become point-of-care detection tools for the timely control of the pandemic.
Advances in Photoplethysmography for Personalized Cardiovascular Monitoring
Authors: Kim Seamin , Xiao Xiao , Chen Jun
Published: October 2022

Photoplethysmography (PPG) is garnering substantial interest due to low cost, noninvasiveness, and its potential for diagnosing cardiovascular diseases, such as cardiomyopathy, heart failure, and arrhythmia. The signals obtained through PPG can yield information based on simple analyses, such as heart rate. In contrast, when accompanied by the complex analysis of sophisticated signals, valuable information, such as blood pressure, sympathetic nervous system activity, and heart rate variability, can be obtained. For a complex analysis, a better understanding of the sources of noise, which create limitations in the application of PPG, is needed to get reliable information to assess cardiovascular health. Therefore, this Special Issue handles literature about noises and how they affect the waveform of the PPG caused by individual variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external factors (e.g., motion artifact, ambient light, and applied pressure to the skin). It also covers the issues that still need to be considered in each situation.
In Situ Growth Intercalation Structure MXene@Anatase/Rutile TiO2 Ternary Heterojunction with Excellent Phosphoprotein Detection in Sweat
Authors: Qiao Yuting , Liu Xianrong , Jia Zhi , Zhang Peng , Gao Li , Liu Bingxin , Qiao Lijuan , Zhang Lei
Published: October 2022

Abnormal protein phosphorylation may relate to diseases such as Alzheimer’s, schizophrenia, and Parkinson’s. Therefore, the real-time detection of phosphoproteins in sweat was of great significance for the early knowledge, detection, and treatment of neurological diseases. In this work, anatase/rutile TiO2 was in situ grown on the MXene surface to constructing the intercalation structure MXene@anatase/rutile TiO2 ternary heterostructure as a sensing platform for detecting phosphoprotein in sweat. Here, the intercalation structure of MXene acted as electron and diffusion channels for phosphoproteins. The in situ grown anatase/rutile TiO2 with n-n-type heterostructure provided specific adsorption sites for the phosphoproteins. The determination of phosphoprotein covered concentrations in sweat, with linear range from 0.01 to 1 mg/mL, along with a low LOD of 1.52 μM. It is worth noting that, since the macromolecular phosphoprotein was adsorbed on the surface of the material, the electrochemical signal gradually decreased with the increase of phosphoprotein concentration. In addition, the active sites in the MXene@anatase/rutile TiO2 ternary heterojunction and synergistic effect of the heterojunction were verified by first-principle calculations to further realize the response to phosphoproteins. Additionally, the effective diffusion capacity and mobility of phosphoprotein molecules in the ternary heterojunction structure were studied by molecular dynamics simulation. Furthermore, the constructed sensing platform showed high selectivity, repeatability, reproducibility, and stability, and this newly developed sensor can detect for phosphoprotein in actual sweat samples. This satisfactory sensing strategy could be promoted to realize the noninvasive and continuous detection of sweat.
Pilot-RCT Finds No Evidence for Modulation of Neuronal Networks of Auditory Hallucinations by Transcranial Direct Current Stimulation
Authors: Marquardt Lynn , Craven Alexander R. , Hugdahl Kenneth , Johnsen Erik , Kroken Rune Andreas , Kusztrits Isabella , Specht Karsten , Thomassen Anne Synnøve , Weber Sarah , Hirnstein Marco
Published: October 2022

Background: Transcranial direct current stimulation (tDCS) is used as treatment for auditory verbal hallucinations (AVH). The theory behind the treatment is that tDCS increases activity in prefrontal cognitive control areas, which are assumed to be hypoactive, and simultaneously decreases activity in temporal speech perception areas, which are assumed to be hyperactive during AVH. We tested this hypofrontal/hypertemporal reversal theory by investigating anatomical, neurotransmitter, brain activity, and network connectivity changes over the course of tDCS treatment. Methods: A double-blind, randomized controlled trial was conducted with 21 patients receiving either sham or real tDCS treatment (2 mA) twice daily for 5 days. The anode was placed over the left dorsolateral prefrontal cortex (DLPFC) and the cathode over the left temporo-parietal cortex (TPC). Multimodal neuroimaging as well as clinical and neurocognitive functioning assessment were performed before, immediately after, and three months after treatment. Results: We found a small reduction in AVH severity in the real tDCS group, but no corresponding neuroimaging changes in either DLPFCD or TPC. Limitations: The study has a small sample size. Conclusion: The results suggest that the currently leading theory behind tDCS treatment of AVH may need to be revised, if confirmed by studies with larger N. Tentative findings point to the involvement of Broca’s area as a critical structure for tDCS treatment.
Validating and Applying the Mathematical Models for Predicting Corporate Social Responsibility Behavior in Construction Firms: A Roadmap
Authors: Zhang Qian , Oo Bee Lan , Lim Benson Teck Heng
Published: October 2022

The prevalence of the sophisticated doctrine of corporate social responsibility (CSR) is increasing, given the perennial environmental concerns and social demands in the construction industry worldwide. Firms’ CSR implementation has been influenced by a broad spectrum of external impetuses and internal motives, yet fragmented assessments of such influences make the prediction and implementation of CSR in construction problematic. This study aimed to validate and apply mathematical models for predicting CSR practices in construction firms. Mobilizing integrated institutional theory, stakeholder theory, and self-determination theory, a questionnaire survey within the top-tier construction contractors was undertaken. Eight mathematical models were developed to predict the key dimensions of CSR practices, such as “government commitment” and “environmental preservation”, and validated by five subjective matter expert interviews. The results demonstrated the comprehensiveness, practicality, and robustness of the CSR prediction models in the construction industry. The results also highlighted the perceived importance of CSR practices; external coercive and normative forces, together with internal organizational culture, were the most influential factors directly enhancing construction firms’ CSR implementation. Conceptually, the findings refined CSR practice prediction in a construction management context. The proposed CSR assessment checklists can help practitioners improve the often-tenuous overall CSR performance and spur competitiveness in the construction market.
Development and Test of a New Fast Estimate Tool for Cooling and Heating Load Prediction of District Energy Systems at Planning Stage
Authors: Lyu Yan , Pan Yiqun , Huang Zhizhong
Published: October 2022

During the design and planning stage of a district energy system, the prediction of the cooling and heating loads is an important step. The accurate estimate of the load pattern can provide a basis for the configuration and optimization of the system. To meet the demand in practical application, this paper proposes a fast load prediction method for district energy systems based on a presimulated forward modelling database and KNN (K-nearest neighbor) algorithm and develops it into a practical tool. Owing to the absence of some design parameters at the planning stage, scenario analysis is also used to determine some input conditions for load prediction. In this paper, the scenarios cover three types of building: office, shopping mall and hotel. To test the performance of this new method, we randomly selected 15 virtual buildings (5 buildings for each type) with different design parameters and took their detailed BPS (building performance simulation) model as a benchmark to assess the prediction results of the new method. The index “ratio of the hours with effective prediction” is defined as the ratio of the hours whose relative error of hourly load prediction is less than 15% to the hours whose load is not 0 in the whole year, and the test result shows that this index is not less than 0.9 (90%) for the predicted cooling load of all 45 test cases and the predicted heating load of 25 of the 45 cases. As a research achievement with practical value, this paper accomplishes the programming work of the tool and makes it into a software. The application of this software in the actual project of district energy system is also presented. The performance of the new load prediction tool was compared with the traditional approach commonly used in engineering—the load estimation based on reference building models—and the result shows that the fast load estimate tool can provide the same level of prediction accuracy as traditional simulation methods.
Aftershock Fragility Assessment of Continuous RC Girder Bridges Using a Modified Damage Index
Authors: Wang Zhengnan , Deng Xiaowei , Luo Xiheng , Dang Xinzhi , Guo Junjun
Published: October 2022

Aftershock fragility is usually calculated conditioned on a range of potential post-mainshock damage states. The post-mainshock damage can be identified using damage indices, the latter being frequently associated with displacement-based parameters such as the maximum drift ratio or the residual displacement. However, when the reliable simulation of a structural system in a specific post-mainshock damage state is the objective of a numerical study, using such damage indicators may not assure the structure experiencing a homogeneous level of damage due to different mainshocks characteristics, which induce the aftershock fragility results unreliable. Along these lines, the current study presents a damage evaluation methodology mainly used for aftershock fragility assessment. It aims to reduce the variation of damage levels derived by using different mainshock seismic motions. The methodology presented herein includes: (i) the introduction of a damage index defined by comparing the monotonic pushover curve of the intact and post-earthquake damaged structure; (ii) the description of a finite element (FE)-based scheme that enables to quantify of the proposed damage index; and (iii) a deterioration-related modeling technique that can capture both strength and stiffness degrading performance of structural systems exposed to earthquake-induced excitations. The latter is essential to support the FE-based quantification scheme for the damage index. This methodology evaluation methodology can be primarily used for calculating the aftershock fragility assessment for a multi-span RC continuous girder bridge. The back-to-back incremental dynamic analysis framework uses a larger number of mainshock-aftershock artificial sequences to generate the aftershock fragility curves. The AS fragility results obtained via MBDI are compared with that via maximum drift ratio in terms of the ability to reduce the variation of residual capacities obtained using different mainshocks to induce a specific damage state but collapse by the same aftershock. The comparison shows a more robust relationship of MBDI with the residual capacity. It is found that MBDI, as well as its quantification approach proposed in this study, is a more effective damage predictor than the widely used displacement-based indices for AS fragility assessment.
Targeting Apoptosis in AML: Where Do We Stand?
Authors: Krawiec Kinga , Strzałka Piotr , Czemerska Magdalena , Wiśnik Aneta , Zawlik Izabela , Wierzbowska Agnieszka , Pluta Agnieszka
Published: October 2022

More than 97% of patients with acute myeloid leukemia (AML) demonstrate genetic mutations leading to excessive proliferation combined with the evasion of regulated cell death (RCD). The most prominent and well-defined form of RCD is apoptosis, which serves as a defense mechanism against the emergence of cancer cells. Apoptosis is regulated in part by the BCL-2 family of pro- and anti-apoptotic proteins, whose balance can significantly determine cell survival. Apoptosis evasion plays a key role in tumorigenesis and drug resistance, and thus in the development and progression of AML. Research on the structural and biochemical aspects of apoptosis proteins and their regulators offers promise for new classes of targeted therapies and strategies for therapeutic intervention. This review provides a comprehensive overview of current AML treatment options related to the mechanism of apoptosis, particularly its mitochondrial pathway, and other promising concepts such as neddylation. It pays particular attention to clinically-relevant aspects of current and future AML treatment approaches, highlighting the molecular basis of individual therapies.
Biodegradation of Congo Red Using Co-Culture Anode Inoculum in a Microbial Fuel Cell
Authors: Sharma Kalpana , Pandit Soumya , Thapa Bhim Sen , Pant Manu
Published: October 2022

Congo red is an azo dye widely used as a colouring agent in textile industries. It is a serious threat due to its carcinogenic effects. Its degradation has been challenging due to its complex yet stable structure. The present study was aimed to investigate the effective degradation of Congo red by bioremediating bacteria isolated from different environments. To investigate predominant microorganisms that degrade Congo red and its functions in microbial fuel cells (MFCs), strains isolated from cow dung (Enterococcus faecalis SUCR1) and soil (Pseudomonas aeruginosa PA1_NCHU) were used as a co-culture inocula. The remarkable results establish that E. faecalis as an excellent microbial source for the biological degradation of dye-contaminated wastewater treatment alongside bioactive treating wastewater with varied concentrations of congo red dye. The highest efficiency percentage of dye degradation was 98% after 3 days of incubation at pH 7 and 37 °C, whereas findings have shown that the decolorization at pH 5 and 6 was lower at 66% and 83.3%, respectively, under the same incubation conditions. Furthermore, the co-culture of E. faecalis SUCR1 and P. aeruginosa at a 1:1 ratio demonstrated improved power generation in MFCs. The maximum power density of 7.4 W/m3 was recorded at a 150 mg L−1 concentration of Congo red, indicating that the symbiotic relation between these bacterium resulted in improved MFCs performance simultaneous to dye degradation.