bluuebunny/crossref_metadata_2025 huggingface dataset:
DA-FER: Domain Adaptive Facial Expression Recognition
Authors: Bie Mei , Xu Huan , Liu Quanle , Gao Yan , Song Kai , Che Xiangjiu
Published: May 2023

Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learning algorithms is limited. To address this issue, a domain adaptive learning strategy is proposed in this paper. The approach uses a public dataset with sufficient samples as the source domain and a small sample dataset as the target domain. Furthermore, the maximum mean discrepancy with kernel mean embedding is utilized to reduce the disparity between the source and target domain data samples, thereby enhancing expression recognition accuracy. The proposed Domain Adaptive Facial Expression Recognition (DA-FER) method integrates the SSPP module and Slice module to fuse expression features of different dimensions. Moreover, this method retains the regions of interest of the five senses to accomplish more discriminative feature extraction and improve the transfer learning capability of the network. Experimental results indicate that the proposed method can effectively enhance the performance of expression recognition. Specifically, when the self-collected Selfie-Expression dataset is used as the target domain, and the public datasets RAF-DB and Fer2013 are used as the source domain, the performance of expression recognition is improved to varying degrees, which demonstrates the effectiveness of this domain adaptive method.
Protective Effects of Aquilaria agallocha and Aquilaria malaccensis Edible Plant Extracts against Lung Cancer, Inflammation, and Oxidative Stress—In Silico and In Vitro Study
Authors: Nahar Jinnatun , Boopathi Vinothini , Rupa Esrat Jahan , Awais Muhammad , Valappil Anjali Kariyarath , Morshed Md Niaj , Murugesan Mohanapriya , Akter Reshmi , Yang Dong Uk , Mathiyalagan Ramya , Yang Deok Chun , Jung Seok-Kyu
Published: May 2023

The family Thymelaeaceae, which includes huge evergreen trees that are sparsely distributed in tropical rainforests, includes the genus Aquilaria. Numerous medical conditions, including inflammation, cancer, and oxidative stress have been traditionally treated using Aquilaria agallocha and Aquilaria malaccensis. In this study, we evaluated in silico and biological activity with A. agallocha and A. malaccensis sample for more conformation. Raw 264.7 macrophage cells and HacaT cells were used, together with the MTT, ROS, NO, and wound healing assays, to investigate the possible cytotoxicity in A549 lung cancer. Thus, A. agallocha and A. malaccensis showed significant cytotoxicity against A549 cancer cells at 1000 µg/mL. Furthermore, we observed an elevated ROS level in cancer cells. The wound healing assay showed cancer cell inhibition activity. While BCL-2 decreased in the intrinsic route, p53, Bax, Caspase 3, and Caspase 9 were elevated by A.A and A.M. Additionally, we have also conducted an in silico evaluation followed by molecular dynamics (MD) simulations, along with ADMET and biological activity prediction to further validate the experimental results. In normal cells, both samples showed less toxicity at 1000 µg/mL and suppressed the LPS-treated NO and ROS levels against the inflammation. Additionally, A.A and A.M suppressed the pro-inflammatory gene expression of COX-2, iNOS, TNF-α, IL-6, and IL-8 in RAW 264.7 cells. On the other hand, A.A and A.M extract effectively suppressed oxidative stress by increasing the antioxidative gene expression in H2O2-induced HaCat cells at 50 μg/mL. This study revealed that the plant extracts from A. agallocha and A. malaccensis could exert a cytotoxic effect on lung adenocarcinoma cells through the activation of an intrinsic signaling pathway. Moreover, it could be a potential source of anti-inflammatory, antioxidant, and anti-cancer agents after consideration of in vivo and clinical studies.
Multiview Fusion Using Transformer Model for Recommender Systems: Integrating the Utility Matrix and Textual Sources
Authors: Ho Thi-Linh , Le Anh-Cuong , Vu Dinh-Hong
Published: May 2023

Recommender systems are challenged with providing accurate recommendations that meet the diverse preferences of users. The main information sources for these systems are the utility matrix and textual sources, such as item descriptions, users’ reviews, and users’ profiles. Incorporating diverse sources of information is a reasonable approach to improving recommendation accuracy. However, most studies primarily use the utility matrix, and when they use textual sources they do not integrate them with the utility matrix. This is due to the risk of combined information causing noise and reducing the effectiveness of good sources. To overcome this challenge, in this study we propose a novel method that utilizes the Transformer Model, a deep learning model that efficiently integrates textual and utility matrix information. The study suggests feature extraction techniques suitable for each information source and an effective integration method in the Transformer model. The experimental results indicate that the proposed model significantly improves recommendation accuracy compared to the baseline model (MLP) for the Mean Absolute Error (MAE) metric, with a reduction range of 10.79% to 31.03% for the Amazon sub-datasets. Furthermore, when compared to SVD, which is known as one of the most efficient models for recommender systems, the proposed model shows a decrease in the MAE metric by a range of 34.82% to 56.17% for the Amazon sub-datasets. Our proposed model also outperforms the graph-based model with an increase of up to 108% in Precision, a decrease of up to 65.37% in MAE, and a decrease of up to 59.24% in RMSE. Additionally, experimental results on the Movielens and Amazon datasets also demonstrate that our proposed model, which combines information from the utility matrix and textual sources, yields better results compared to using only information from the utility matrix.
Yb-Doped All-Fiber Amplifier with Low-Intensity Noise in mHz Range Oriented to Space-Borne Gravitational Wave Detection
Authors: Wang Zaiyuan , Wang Jiehao , Li Fan , Li Yuhang , Tian Long , Liu Qiang
Published: May 2023

We present a low-intensity noise single-frequency Yb-doped all-fiber amplifier oriented to space-borne gravitational wave detection. Relative intensity noise (RIN) below −70 dBc/Hz @ 1 mHz~1 Hz was achieved by virtue of feedback-loop-based intensity noise suppression. Based on systematic noise analysis and experimental investigation, we found that the pump noise and temperature-dependent noise of the fiber splitter and the photodetector contributed mainly to the RIN of the fiber amplifier. Therefore, we carefully designed a feedback-loop-based Yb-doped all-fiber amplifier, and finely stabilized the temperature of the pump diode, fiber splitters, and photodetectors. Consequently, the RIN can be suppressed down to −72.5 dBc/Hz around 1 mHz. This low-intensity all-fiber Yb-doped amplifier can be used for space-borne gravitational-wave detection.
Computational Dynamics of Multi-Rigid-Body System in Screw Coordinate
Authors: Zhao Jing-Shan , Wei Song-Tao , Sun Xiao-Cheng
Published: May 2023

This paper investigates the kinematics and dynamics of multi-rigid-body systems in screw form. The Newton–Euler dynamics equations are established in screw coordinates. All forces and torques of the multi-rigid-body system can be solved straightforwardly since they are explicit in the form of screw coordinates. The displacement and acceleration are unified in matrix form, which associates the kinematics and dynamics with variable of velocity. A one-step numerical algorithm only is needed to solve the displacements and accelerations. As a result, all absolute displacements, velocities, and accelerations are directly obtained by one kinematic equation. The kinematics and dynamics of Gough–Stewart platform validate this the method. In this paper, the kinematics and dynamics are carried out with the example of a Gough–Stewart platform, which represents the most complex multi-rigid-body system, to verify the computational dynamics method. The proposed algorithm is also fit for the kinematics and dynamics modeling of other multi-rigid-body systems.
Center-to-Center Distance’s Effect between Vertical Square Tubes of a Horizontal Array on Natural Convection Heat Transfer
Authors: Alsuhaibani Zeyad , Ali Mohamed , Saleh Nader S.
Published: May 2023

An experimental study on natural convection heat transfer from the outer surface of a horizontal array of vertical square tubes in the air is investigated. The array consists of three vertical square tubes at equally different center-to-center distances. Each tube has a square cross-section with a side length of 2.00 cm, 100 cm length, and is filled with sand. Each tube is heated by inserting an internal heating element with a constant heat flux at the center. Five center-to-center separation distance to hydraulic diameter ratios (S/D) are used at different heat flux ranges of 70–360 W/m2. Results show that at small S/D, the Nusselt number of any tube in the array is lower than that of the single tube up to a specific S/D and then increases as the ratio increases. Empirical correlations are obtained for each tube in the array at different S/D using the modified Rayleigh numbers only. General correlations using S/D as a parameter are obtained for each tube, and an overall general correlation using both S/D and the tube number (n) as parameters is obtained. The difference between the predicted and experimental Nusselt numbers is in the reasonable range even at high Rayleigh numbers.
A Systematic Literature Review of Information Security in Chatbots
Authors: Yang Jing , Chen Yen-Lin , Por Lip Yee , Ku Chin Soon
Published: May 2023

Chatbots have become increasingly popular in recent years, but they also present security risks and vulnerabilities that need to be addressed. This systematic literature review examines the existing research relating to information security in chatbots, identifying the potential threats, proposed solutions, and future directions for research. The review finds that chatbots face various security threats, including malicious input, user profiling, contextual attacks, and data breaches, and that solutions such as blockchain technology, end-to-end encryption, and organizational controls can be used to mitigate these concerns. The review also highlights the importance of maintaining user trust and addressing privacy concerns for the successful adoption and continued use of chatbots. A taxonomy developed in this review provides a useful framework for categorizing the articles and their findings. The review concludes by identifying future research directions that include developing more sophisticated authentication and authorization mechanisms, exploring the use of privacy-enhancing technologies, and improving the detection and prevention of security threats, among others. This review contributes to the growing body of literature on information security in chatbots and can guide future research and practice in this field.
Effect of Subject-Specific Region of Interest on Motor Imagery Brain–Computer Interface
Authors: Mohamed Eltaf Abdalsalam , Adam Ibrahim Khalil , Yusoff Mohd Zuki
Published: May 2023

A brain–computer interface (BCI), as a solution to disabled people’s concerns, has drawn attention in biomedical engineering over the last decade. However, the most existing brain–computer interface systems are based on the time or frequency domain of feature extraction, and it is associated with inaccurate detection of event-related desynchronization (ERD). In this study, a new algorithm relating to subject-specific regions of interest (ROIs) with intrinsic time-scale decomposition (ITD) was investigated to achieve satisfactory classification accuracy. ROI-based discrete wavelet transform (DWT) combined with an artificial neural network was used to validate the ROI-based ITD method. Experimentally recorded data of motor imagery movement tasks (right hand, left hand, both hands and both feet) were collected from 15 subjects. The parameters of the subject-specific regions of interest were investigated and optimized. An optimal condition was observed at a specific region of interest and the accuracy increased by 12.76 to 15.17% compared to that without ROI estimation. ITD showed higher classification accuracy, sensitivity, specificity and Kappa coefficient of 9.47%, 8.99%, 9.79% and 12.09%, respectively, for the four classes of motor imagery movements compared to DWT. The developed ITD model was validated using the dataset from BCI Competition IV. On average, ITD with ROIs showed 8.56% and 7.32% higher classification accuracy compared to common spatial patents (CSP) and DWT with ROIs.
Multimodal Classification of Anxiety Based on Physiological Signals
Authors: Vaz Mariana , Summavielle Teresa , Sebastião Raquel , Ribeiro Rita P.
Published: May 2023

Multiple studies show an association between anxiety disorders and dysregulation in the Autonomic Nervous System (ANS). Thus, understanding how informative the physiological signals are would contribute to effectively detecting anxiety. This study targets the classification of anxiety as an imbalanced binary classification problem using physiological signals collected from a sample of healthy subjects under a neutral condition. For this purpose, the Electrocardiogram (ECG), Electrodermal Activity (EDA), and Electromyogram (EMG) signals from the WESAD publicly available dataset were used. The neutral condition was collected for around 20 min on 15 participants, and anxiety scores were assessed through the shortened 6-item STAI. To achieve the described goal, the subsequent steps were followed: signal pre-processing; feature extraction, analysis, and selection; and classification of anxiety. The findings of this study allowed us to classify anxiety with discriminatory class features based on physiological signals. Moreover, feature selection revealed that ECG features play a relevant role in anxiety classification. Supervised feature selection and data balancing techniques, especially Borderline SMOTE 2, increased the performance of most classifiers. In particular, the combination of feature selection and Borderline SMOTE 2 achieved the best ROC-AUC with the Random Forest classifier.
CANPerFL: Improve In-Vehicle Intrusion Detection Performance by Sharing Knowledge
Authors: Hoang Thien-Nu , Islam Md Rezanur , Yim Kangbin , Kim Daehee
Published: May 2023

The Controller Area Network (CAN) is a widely used communication protocol in automobiles, but it is vulnerable to various types of attacks. To address this issue, researchers have been exploring the use of intrusion detection systems (IDS) for the CAN bus. Deep learning and machine learning have been proven to be powerful tools for detecting intrusions accurately and quickly. However, deep learning models require large amounts of data to achieve optimal performance, which can be challenging in the case of a CAN bus IDS. To overcome this challenge, we propose a novel machine learning-based IDS called CANPerFL that uses a personalized federated learning scheme to aggregate datasets from different car models. By building a universal model trained on a small amount of data from each manufacturer, we can provide global knowledge that can be transferred to improve the performance of each participant. To demonstrate the efficiency of the proposed model, we collected a real CAN dataset consisting of three different car models: KIA, BMW, and Tesla. The experimental results show that the proposed model increases F1 scores by 4% overall, compared to baselines. Moreover, the proposed system provides significant advantages when the local dataset of each participant is relatively small. According to our experiments, the proposed models can achieve F1 scores of more than 90% with at least 30k training samples on each client. Finally, we show empirically that each participant takes benefits from joining the CANPerFL system.
Emissions of Volatile Compounds and Solids during Additive Manufacturing by the FFF Method
Authors: Dostatni Ewa , Osiński Filip , Karwasz Anna
Published: May 2023

The rapid development of the additive manufacturing industry is a great opportunity both for the development of the industry in the context of rapid prototyping and for hobbyists using 3D printers at home. At the same time, such a rapid technological development poses a significant challenge for specialists dealing with sources of pollutant emissions to the environment as well as for Occupational Health and Safety (OHS) specialists dealing with the assessment of chemical hazards in the workplace. In this article, the authors demonstrate the legitimacy of the concern regarding issues relating to emissions from the 3D printing process, in particular FFF (fused filament fabrication). We carried out tests on the emission of volatile organic compounds from the printer chamber and compared them with the results of analyses conducted for the same materials in laboratory conditions by heating in a vial. They showed that the spectrometric analysis differed in both cases, which proved a clear influence of the parameters of additive manufacturing on both the amount and type of the emitted substances. The article also presents the sizes and shapes of the solid particles emitted during the process and aggregated during pollutant analyses.
Development of a NOx Calculation Model for Low-Speed Marine Diesel Engines Based on Soft Measurement Technology
Authors: Du Shenglin , Gong Man , Wang Qinpeng
Published: May 2023

With the increasing level of intelligence of marine engines, there is an increasing demand for the online monitoring of engines, and marine NOx emissions have been of great concern. In this paper, a NOx simulation model is developed based on virtual measurement technology, which can calculate and predict NOx emissions based on the current operating state parameters of low-speed two-stroke diesel engines. First, the calibrated 3D simulation model is used to design the experiments to obtain the simulation experimental samples. Based on the NOx generation mechanism and diesel engine work-related parameters, the relevant factors were selected as alternative input parameters for the NOx emission model. The correlation analysis was then performed on the input parameters using the grey relational analysis correlation method and the Pearson correlation coefficient, and the principal component analysis method was used to reduce the dimensionality of the relevant factors by minimizing the loss of important information in reducing the complexity of the whole model. Then, the structure-related parameters of the backpropagation neural network (BPNN) were adaptively optimized using the group method of data handling (GMDH) to improve the accuracy of the NOx soft measurement model. Finally, the developed GMDH–BP model was validated with data and compared with the error evaluation index of BPNN and BPNN optimized by genetic algorithm (GA), and the developed NOx simulation model demonstrated high prediction accuracy under the same hyperparameter settings. The result provides technical support for the subsequent realization of the real-time online monitoring of NOx emissions from low-speed marine diesel engines without NOx sensors.
Performance Improvement of Microchannel Heat Exchangers with Modified Louver Fins under Frosting Conditions
Authors: Xiong Tong , Liu Guoqiang , Yan Gang
Published: May 2023

Microchannel heat exchangers (MCHX) are increasingly being used in refrigeration and heat pumps due to their superior thermal-hydraulic properties. However, when the MCHX surface temperature drops below the freezing point, frost will accumulate on the fin surface, which significantly affects the heat transfer performance. In this study, a modified MCHX with extended windward fins was developed to improve the frosting performance. The thermal-hydraulic performance of the modified MCHX and conventional MCHX were compared and evaluated under frosting conditions. Results show that the extended fins on the windward side capture a large amount of frost and delay the rapid blockage of air flow passage by frost. The modified MCHX structure makes the frost more evenly distributed. During the 60 min frosting cycle, the total heat transfer capacity and the mass of the accumulated frost of the modified MCHX are 9.6–49.7% and 10.3–46.9% higher than the conventional MCHX, respectively. Furthermore, the modified MCHX has greater potential to improve the thermal-hydraulic performance under the condition of more uneven frost layer distribution. The purpose of this work is to provide useful guidance for the optimal design of MCHX under frosting conditions.
Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach
Authors: Bhatti Mughair Aslam , Song Zhiyao , Bhatti Uzair Aslam , Ahmad Naushad
Published: May 2023

Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R2 value of more than 72% average in each year and decreased MAE value, which was better than other studies’ deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM2.5 by 14%, PM10 by 18%, NO2 by 14%, and SO2 by 16% during the COVID-B period. This study found an increase in O3 by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM10 by 42%, PM2.5 by 97%, NO2 by 89%, SO2 by 36%, CO by 58%, O3 by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality.
PM2.5 Concentration Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Various Machine Learning Methods Based on Meteorological Data
Authors: Duan Min , Sun Yufan , Zhang Binzhe , Chen Chi , Tan Tao , Zhu Yihua
Published: May 2023

The escalating issue of air pollution in China’s rapidly developing urban areas has prompted increased attention to the role of meteorological conditions in PM2.5 pollution. This study examines the spatiotemporal distribution of PM2.5 concentrations and their relationship with meteorological factors in six major Chinese urban agglomerations from 2017 to 2020, using daily average data. Statistical and spatial analysis techniques are employed, alongside the construction of eight machine learning models for prediction purposes. The study also compares the feature importance of various meteorological factors impacting PM2.5 concentrations. Results reveal significant regional differences in both average PM2.5 levels and meteorological influences. The Multilayer Perceptron (MLP) model demonstrates the highest prediction accuracy for PM2.5 concentrations. According to the MLP model’s feature importance identification, temperature is the most significant factor affecting PM2.5 concentrations across all urban agglomerations, while wind speed and precipitation have the least impact. Contributions from air pressure and dew point temperature, however, vary among different urban agglomerations. This research considers the impact of urban agglomerations and meteorological conditions on PM2.5 and also offers valuable artificial intelligence-based insights into the key meteorological factors influencing PM2.5 concentrations in diverse regions, thereby informing the development of effective air pollution control policies.
An MCDM Approach to Analytically Identify the Air Pollutants’ Impact on Health
Authors: Bhardwaj Rashmi , Garg Shanky
Published: May 2023

Air pollution is one of the deadliest and most important concerns of our era, and it not only impacts our environment but also our health. The consequences of poor air quality are not limited to just our lungs or our heart but also our brain and resulting in increased mortality rate of many countries every year. There are many effluents/pollutants present in the air that are harmful and cause diseases in humans which eventually lead to an increase in morbidity and mortality. Therefore, there is a need to identify those factors and evaluate the effect of pollution caused by air on the health of humans which is a prerequisite for the implementation of policies in preventing pollution. In this study, we model and evaluate the harmful impact of pollution caused by air on the health of humans by using a multi-criteria decision-making approach (MCDM). We have proposed a novel coupled model of the double modified (criteria importance through intercriteria correlation) CRITIC—technique for order of preference by similarity to ideal solution (TOPSIS) method (DMCTM) to identify and evaluate the factors of air pollution and its effect on health which overcome the disadvantage of bias while collecting the subjective data in the traditional TOPSIS method. To get a clear view of the framework proposed, a case study is conducted based on the methodology proposed in which we find that Xinxiang is the most polluted city in China among the five studied cities with SO2 as the major contributor, and the city experienced more pollution levels in 2022 and least in 2016, whereas there is a slight fluctuation in life expectancy with air pollution in the years 2015 and 2023.
On the Usage of Battery Equivalent Series Resistance for Shuntless Coulomb Counting and SOC Estimation
Authors: De Angelis Alessio , Carbone Paolo , Santoni Francesco , Vitelli Michele , Ruscitti Luca
Published: May 2023

In this paper, a feasibility study of a shuntless coulomb counting method for estimating the state of charge (SOC) of a battery is presented. Contrary to conventional coulomb counting, the proposed method does not require an external resistive shunt; it instead only requires voltage measurements performed on the battery under test while it is operating. The current is measured indirectly using the battery’s equivalent series resistance (ESR). The method consists of a preliminary calibration phase where the ESR and the open-circuit voltage of the battery are measured for different SOCs and stored in look-up tables (LUTs). Then, in the subsequent operational phase, the method uses these LUTs together with the measured voltage at the battery terminals to estimate the SOC. The performance of the proposed method is evaluated on a sample lithium polymer (LiPo) battery, using a realistic current profile derived from the Worldwide Harmonized Light-Duty Vehicles Test Procedure (WLTP). The results of this experimental evaluation demonstrate a SOC estimation root-mean-square error of 0.82% and a maximum SOC error of 1.45%. These results prove that the proposed method is feasible in a practical scenario.
Computational Modeling of Diffusion-Based Delamination for Active Implantable Medical Devices
Authors: Nguyen Minh-Hai , Onken Adrian , Wulff Anika , Foremny Katharina , Torgau Patricia , Schütte Helmut , Hild Sabine , Doll Theodor
Published: May 2023

Delamination at heterogeneous material interfaces is one of the most prominent failure modes in active implantable medical devices (AIMDs). A well-known example of an AIMD is the cochlear implant (CI). In mechanical engineering, a multitude of testing procedures are known whose data can be used for detailed modeling with respect to digital twins. Detailed, complex models for digital twins are still lacking in bioengineering since body fluid infiltration occurs both into the polymer substrate and along the metal-polymer interfaces. For a newly developed test for an AIMD or CI composed of silicone rubber and metal wiring or electrodes, a mathematical model of these mechanisms is presented. It provides a better understanding of the failure mechanisms in such devices and their validation against real-life data. The implementation utilizes COMSOL Multiphysics®, consisting of a volume diffusion part and models for interface diffusion (and delamination). For a set of experimental data, the necessary diffusion coefficient could be derived. A subsequent comparison of experimental and modeling results showed a good qualitative and functional match. The delamination model follows a mechanical approach. The results of the interface diffusion model, which follows a substance transport-based approach, show a very good approximation to the results of previous experiments.
The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases
Authors: Sun Tianze , Wang Jinzuo , Suo Moran , Liu Xin , Huang Huagui , Zhang Jing , Zhang Wentao , Li Zhonghai
Published: May 2023

Due to the high prevalence and rates of disability associated with musculoskeletal system diseases, more thorough research into diagnosis, pathogenesis, and treatments is required. One of the key contributors to the emergence of diseases of the musculoskeletal system is thought to be changes in the biomechanics of the human musculoskeletal system. However, there are some defects concerning personal analysis or dynamic responses in current biomechanical research methodologies. Digital twin (DT) was initially an engineering concept that reflected the mirror image of a physical entity. With the application of medical image analysis and artificial intelligence (AI), it entered our lives and showed its potential to be further applied in the medical field. Consequently, we believe that DT can take a step towards personalized healthcare by guiding the design of industrial personalized healthcare systems. In this perspective article, we discuss the limitations of traditional biomechanical methods and the initial exploration of DT in musculoskeletal system diseases. We provide a new opinion that DT could be an effective solution for musculoskeletal system diseases in the future, which will help us analyze the real-time biomechanical properties of the musculoskeletal system and achieve personalized medicine.
Gender Differences in Lower Extremity Stiffness during a Single-Leg Landing Motion in Badminton
Authors: Zhang Yanan , Hu Zhe , Li Bairan , Qiu Xuan , Li Ming , Meng Xiangwei , Kim Sukwon , Kim Youngsuk
Published: May 2023

In general, at the same level of exercise, female athletes are three to six times more likely to injure an anterior cruciate ligament (ACL) than male athletes. Female athletes also had higher rates of ACL injury than males in a single-leg landing task after a backcourt backhand side overhead stroke in badminton. In many previous studies, stiffness of the musculoskeletal system in the lower limbs has been reported as a potential factor contributing to differences in ACL injury rates between genders. The purpose of this study was to describe the differences between genders in leg and knee stiffness in male and female athletes during a single-leg landing action after the backhand side overhead shot in the backcourt. Eight male athletes and eight female athletes participated in this test. Leg stiffness and knee stiffness were calculated separately for male and female athletes during the landing phase. The results showed that both absolute and normalized leg stiffness were lower in female athletes than in male athletes (p < 0.05). And both absolute and normalized knee stiffness were also lower than male athletes (p < 0.05). The low leg stiffness and knee stiffness demonstrated by females in this single-leg drop task compared to male athletes may indicate that females have lower dynamic leg stability than males during the drop, which may lead to hypermobility of the knee joint and may put females at a higher risk of injury in this high-risk maneuver for non-contact cruciate ligament injuries.
Association between Antihyperlipidemic Agent Use and Age-Related Macular Degeneration in Patients with Hyperlipidemia: A Population-Based Retrospective Cohort Study
Authors: Chen Chun-Hao , Lin Hsiu-Chen , Lin Hsiu-Li , Keller Joseph Jordan , Wang Li-Hsuan
Published: May 2023

Several studies have indicated that lipoproteins might contribute to the pathogenesis of age-related macular degeneration (AMD). In this population-based retrospective cohort study, patients with hyperlipidemia were divided into two groups (study groups I and II) based on whether or not they were receiving antihyperlipidemic agents. The comparison group included patients without hyperlipidemia who were randomly selected and matched with study group II patients. A Cox proportional hazard model was used to evaluate the risk of AMD among the groups. Patients with hyperlipidemia receiving antihyperlipidemic agents (study group I, n = 15,482) had a significantly increased AMD risk (adjusted hazard ratio (HR) = 1.23, 95% confidence interval (CI) = 1.04–1.45) compared to those not receiving antihyperlipidemic agents (study group II, n = 15,482). However, with an increase in cumulative exposure, a reduced risk of AMD was observed in patients using a defined daily dose of more than 721, with an adjusted HR of 0.34 (95% CI = 0.22–0.53, p < 0.001). Additionally, the adjusted HR of AMD for study group II was 1.40 (95% CI = 1.20–1.63, p < 0.001) relative to the comparison group (n = 61,928). In conclusion, the study results indicated that patients with hyperlipidemia have a higher AMD risk than patients without hyperlipidemia. Furthermore, patients with hyperlipidemia who received antihyperlipidemic agents had a significantly increased AMD risk. However, a dose-dependent reduction in the risk of AMD was observed in patients with hyperlipidemia using statins or/and fibrates.
DAPTA, a C-C Chemokine Receptor 5 (CCR5), Leads to the Downregulation of Notch/NF-κB Signaling and Proinflammatory Mediators in CD40+ Cells in Experimental Autoimmune Encephalomyelitis Model in SJL/J Mice
Authors: Alghibiwi Hanan , Ansari Mushtaq A. , Nadeem Ahmed , Algonaiah Majed Ali , Attia Sabry M. , Bakheet Saleh A. , Albekairi Thamer H. , Almudimeegh Sultan , Alhamed Abdullah S. , Shahid Mudassar , Alwetaid Mohammad Y. , Alassmrry Yasseen A. , Ahmad Sheikh F.
Published: May 2023

Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system characterized by motor deficits, cognitive impairment, fatigue, pain, and sensory and visual dysfunction. CD40, highly expressed in B cells, plays a significant role in MS pathogenesis. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS has been well established, as well as its relevance in MS patients. This study aimed to evaluate the therapeutic potential of DAPTA, a selective C-C chemokine receptor 5 (CCR5) antagonist in the murine model of MS, and to expand the knowledge of its mechanism of action. Following the induction of EAE, DAPTA was administrated (0.01 mg/kg, i.p.) daily from day 14 to day 42. We investigated the effects of DAPTA on NF-κB p65, IκBα, Notch-1, Notch-3, GM-CSF, MCP-1, iNOS, and TNF-α in CD40+ spleen B cells using flow cytometry. Furthermore, we also analyzed the effect of DAPTA on NF-κB p65, IκBα, Notch-1, Notch-3, GM-CSF, MCP-1, iNOS, and TNF-α mRNA expression levels using qRT-PCR in brain tissue. EAE mice treated with DAPTA showed substantial reductions in NF-κB p65, Notch-1, Notch-3, GM-CSF, MCP-1, iNOS, and TNF-α but an increase in the IκBα of CD40+ B lymphocytes. Moreover, EAE mice treated with DAPTA displayed decreased NF-κB p65, Notch-1, Notch-3, GM-CSF, MCP-1, iNOS, and TNF-α and but showed increased IκBα mRNA expression levels. This study showed that DAPTA has significant neuroprotective potential in EAE via the downregulation of inflammatory mediators and NF-κB/Notch signaling. Collectively, DAPTA might have potential therapeutic targets for use in MS treatment.
A Novel Repetition Frequency-Based DNA Encoding Scheme to Predict Human and Mouse DNA Enhancers with Deep Learning
Authors: Alakuş Talha Burak
Published: May 2023

Recent studies have shown that DNA enhancers have an important role in the regulation of gene expression. They are responsible for different important biological elements and processes such as development, homeostasis, and embryogenesis. However, experimental prediction of these DNA enhancers is time-consuming and costly as it requires laboratory work. Therefore, researchers started to look for alternative ways and started to apply computation-based deep learning algorithms to this field. Yet, the inconsistency and unsuccessful prediction performance of computational-based approaches among various cell lines led to the investigation of these approaches as well. Therefore, in this study, a novel DNA encoding scheme was proposed, and solutions were sought to the problems mentioned and DNA enhancers were predicted with BiLSTM. The study consisted of four different stages for two scenarios. In the first stage, DNA enhancer data were obtained. In the second stage, DNA sequences were converted to numerical representations by both the proposed encoding scheme and various DNA encoding schemes including EIIP, integer number, and atomic number. In the third stage, the BiLSTM model was designed, and the data were classified. In the final stage, the performance of DNA encoding schemes was determined by accuracy, precision, recall, F1-score, CSI, MCC, G-mean, Kappa coefficient, and AUC scores. In the first scenario, it was determined whether the DNA enhancers belonged to humans or mice. As a result of the prediction process, the highest performance was achieved with the proposed DNA encoding scheme, and an accuracy of 92.16% and an AUC score of 0.85 were calculated, respectively. The closest accuracy score to the proposed scheme was obtained with the EIIP DNA encoding scheme and the result was observed as 89.14%. The AUC score of this scheme was measured as 0.87. Among the remaining DNA encoding schemes, the atomic number showed an accuracy score of 86.61%, while this rate decreased to 76.96% with the integer scheme. The AUC values of these schemes were 0.84 and 0.82, respectively. In the second scenario, it was determined whether there was a DNA enhancer and, if so, it was decided to which species this enhancer belonged. In this scenario, the highest accuracy score was obtained with the proposed DNA encoding scheme and the result was 84.59%. Moreover, the AUC score of the proposed scheme was determined as 0.92. EIIP and integer DNA encoding schemes showed accuracy scores of 77.80% and 73.68%, respectively, while their AUC scores were close to 0.90. The most ineffective prediction was performed with the atomic number and the accuracy score of this scheme was calculated as 68.27%. Finally, the AUC score of this scheme was 0.81. At the end of the study, it was observed that the proposed DNA encoding scheme was successful and effective in predicting DNA enhancers.
A Review on Photonic Sensing Technologies: Status and Outlook
Authors: Butt Muhammad A. , Kazanskiy Nikolay L. , Khonina Svetlana N. , Voronkov Grigory S. , Grakhova Elizaveta P. , Kutluyarov Ruslan V.
Published: May 2023

In contemporary science and technology, photonic sensors are essential. They may be made to be extremely resistant to some physical parameters while also being extremely sensitive to other physical variables. Most photonic sensors may be incorporated on chips and operate with CMOS technology, making them suitable for use as extremely sensitive, compact, and affordable sensors. Photonic sensors can detect electromagnetic (EM) wave changes and convert them into an electric signal due to the photoelectric effect. Depending on the requirements, scientists have found ways to develop photonic sensors based on several interesting platforms. In this work, we extensively review the most generally utilized photonic sensors for detecting vital environmental parameters and personal health care. These sensing systems include optical waveguides, optical fibers, plasmonics, metasurfaces, and photonic crystals. Various aspects of light are used to investigate the transmission or reflection spectra of photonic sensors. In general, resonant cavity or grating-based sensor configurations that work on wavelength interrogation methods are preferred, so these sensor types are mostly presented. We believe that this paper will provide insight into the novel types of available photonic sensors.
On-Field Test of Tuberculosis Diagnosis through Exhaled Breath Analysis with a Gas Sensor Array
Authors: Ketchanji Mougang Yolande Christelle , Endale Mangamba Laurent-Mireille , Capuano Rosamaria , Ciccacci Fausto , Catini Alexandro , Paolesse Roberto , Mbatchou Ngahane Hugo Bertrand , Palombi Leonardo , Di Natale Corrado
Published: May 2023

Tuberculosis (TB) is among the more frequent causes of death in many countries. For pulmonary TB, early diagnosis greatly increases the efficiency of therapies. Although highly sensitive tests based on nucleic acid amplification tests (NAATs) and loop-mediated isothermal amplification (TB-LAMP) are available, smear microscopy is still the most widespread diagnostics method in most low–middle-income countries, and the true positive rate of smear microscopy is lower than 65%. Thus, there is a need to increase the performance of low-cost diagnosis. For many years, the use of sensors to analyze the exhaled volatile organic compounds (VOCs) has been proposed as a promising alternative for the diagnosis of several diseases, including tuberculosis. In this paper, the diagnostic properties of an electronic nose (EN) based on sensor technology previously used to identify tuberculosis have been tested on-field in a Cameroon hospital. The EN analyzed the breath of a cohort of subjects including pulmonary TB patients (46), healthy controls (38), and TB suspects (16). Machine learning analysis of the sensor array data allows for the identification of the pulmonary TB group with respect to healthy controls with 88% accuracy, 90.8% sensitivity, 85.7% specificity, and 0.88 AUC. The model trained with TB and healthy controls maintains its performance when it is applied to symptomatic TB suspects with a negative TB-LAMP. These results encourage the investigation of electronic noses as an effective diagnostic method for future inclusion in clinical practice.
Insight into the Mode of Action of 8-Hydroxyquinoline-Based Blockers on the Histamine Receptor 2
Authors: Patel Amisha , Marquez-Gomez Paola L. , Torp Lily R. , Gao Lily , Peralta-Yahya Pamela
Published: May 2023

Histamine receptor 2 (HRH2) blockers are used to treat peptic ulcers and gastric reflux. Chlorquinaldol and chloroxine, which contain an 8-hydroxyquinoline (8HQ) core, have recently been identified as blocking HRH2. To gain insight into the mode of action of 8HQ-based blockers, here, we leverage an HRH2-based sensor in yeast to evaluate the role of key residues in the HRH2 active site on histamine and 8HQ-based blocker binding. We find that the HRH2 mutations D98A, F254A, Y182A, and Y250A render the receptor inactive in the presence of histamine, while HRH2:D186A and HRH2:T190A retain residual activity. Based on molecular docking studies, this outcome correlates with the ability of the pharmacologically relevant histamine tautomers to interact with D98 via the charged amine. Docking studies also suggest that, unlike established HRH2 blockers that interact with both ends of the HRH2 binding site, 8HQ-based blockers interact with only one end, either the end framed by D98/Y250 or T190/D186. Experimentally, we find that chlorquinaldol and chloroxine still inactivate HRH2:D186A by shifting their engagement from D98 to Y250 in the case of chlorquinaldol and D186 to Y182 in the case of chloroxine. Importantly, the tyrosine interactions are supported by the intramolecular hydrogen bonding of the 8HQ-based blockers. The insight gained in this work will aid in the development of improved HRH2 therapeutics. More generally, this work demonstrates that Gprotein-coupled receptor (GPCR)-based sensors in yeast can help elucidate the mode of action of novel ligands for GPCRs, a family of receptors that bind 30% of FDA therapeutics.
Anti-Disturbance of Scale-Free Spiking Neural Network against Impulse Noise
Authors: Guo Lei , Guo Minxin , Wu Youxi , Xu Guizhi
Published: May 2023

The bio-brain presents robustness function to external stimulus through its self-adaptive regulation and neural information processing. Drawing from the advantages of the bio-brain to investigate the robustness function of a spiking neural network (SNN) is conducive to the advance of brain-like intelligence. However, the current brain-like model is insufficient in biological rationality. In addition, its evaluation method for anti-disturbance performance is inadequate. To explore the self-adaptive regulation performance of a brain-like model with more biological rationality under external noise, a scale-free spiking neural network(SFSNN) is constructed in this study. Then, the anti-disturbance ability of the SFSNN against impulse noise is investigated, and the anti-disturbance mechanism is further discussed. Our simulation results indicate that: (i) our SFSNN has anti-disturbance ability against impulse noise, and the high-clustering SFSNN outperforms the low-clustering SFSNN in terms of anti-disturbance performance. (ii) The neural information processing in the SFSNN under external noise is clarified, which is a dynamic chain effect of the neuron firing, the synaptic weight, and the topological characteristic. (iii) Our discussion hints that an intrinsic factor of the anti-disturbance ability is the synaptic plasticity, and the network topology is a factor that affects the anti-disturbance ability at the level of performance.