bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving text mining in plant health domain with GAN and/or pre-trained language model
Authors: Jiang Shufan , Cormier Stéphane , Angarita Rafael , Rousseaux Francis
Published: February 2023

The Bidirectional Encoder Representations from Transformers (BERT) architecture offers a cutting-edge approach to Natural Language Processing. It involves two steps: 1) pre-training a language model to extract contextualized features and 2) fine-tuning for specific downstream tasks. Although pre-trained language models (PLMs) have been successful in various text-mining applications, challenges remain, particularly in areas with limited labeled data such as plant health hazard detection from individuals' observations. To address this challenge, we propose to combine GAN-BERT, a model that extends the fine-tuning process with unlabeled data through a Generative Adversarial Network (GAN), with ChouBERT, a domain-specific PLM. Our results show that GAN-BERT outperforms traditional fine-tuning in multiple text classification tasks. In this paper, we examine the impact of further pre-training on the GAN-BERT model. We experiment with different hyper parameters to determine the best combination of models and fine-tuning parameters. Our findings suggest that the combination of GAN and ChouBERT can enhance the generalizability of the text classifier but may also lead to increased instability during training. Finally, we provide recommendations to mitigate these instabilities.
Precision and personalized medicine: What their current definition says and silences about the model of health they promote. Implication for the development of personalized health
Authors: Delpierre Cyrille , Lefèvre Thomas
Published: February 2023

The US National Human Genome Research Institute defines precision medicine as follows: “Precision medicine (generally considered analogous to personalized medicine or individualized medicine) is an innovative approach that uses information about an individual's genomic, environmental, and lifestyle information to guide decisions related to their medical management. The goal of precision medicine is to provide a more precise approach for the prevention, diagnosis, and treatment of disease.” In this perspective article, we question this definition of precision medicine and the risks linked to its current practice and development. We highlight that in practice, precision medicine is based on the use of large volumes of biological data for individual purposes mostly in line with the biomedical model of health, which carries the risk of the biological reductionism of the person. A more comprehensive, precise, and even “personal” approach to health would require taking into account environmental, socio-economic, psychological, and biological determinants, an approach more in line with the biopsychosocial model of health. The role of environmental exposures, in a broad sense, is highlighted more and more, notably in the field of exposome research. Not considering the conceptual framework in which precision medicine is deployed leads to the concealment of the different responsibilities that can be mobilized within the health system. Anchoring precision medicine in a model that does not limit its definition to its biological and technical components makes it possible to envisage a personalized and more precise medicine, integrating a greater share of interventions centered on the skills and life contexts of individuals.
Spousal Similarities in Cardiovascular Risk Factors in Northern China: A Community-Based Cross-Sectional Study
Authors: Lin Binbin , Pan Li , He Huijing , Hu Yaoda , Tu Ji , Zhang Ling , Cui Ze , Ren Xiaolan , Wang Xianghua , Nai Jing , Shan Guangliang
Published: February 2023

Objectives: The aim of this study was to explore spousal similarities in cardiovascular risk factors in northern China.

Methods: We conducted a cross-sectional study of married couples from Beijing, Hebei, Gansu, and Qinghai provinces between 2015 and 2019. A total of 2,020 couples were included in the final analyses. The spousal similarities for metabolic indicators and cardiovascular risk factors (including lifestyle factors and cardiometabolic diseases) were evaluated using Spearman’s correlation and logistic regression analyses, respectively.

Results: All metabolic indicators showed positive spousal correlations (p < 0.001), with the strongest for fasting blood glucose (r = 0.30) and the lowest for high-density lipoprotein cholesterol (r = 0.08). Significant husband-wife associations were observed for several cardiovascular risk factors except for hypertension in multivariable models, with the strongest association for physical inactivity (odds ratios with 95% confidence intervals of 3.59 [2.85, 4.52] and 3.54 [2.82, 4.46] for husbands and wives, respectively). In addition, the interaction of age with spousal overweight/obesity status was statistically significant, and the association was stronger in people ≥50 years.

Conclusion: There were spousal similarities in cardiovascular risk factors. The finding may have public health implications that targeted screening and interventions for spouses of people with cardiovascular risk factors.
Urban Gardening in a Changing Climate: A Review of Effects, Responses and Adaptation Capacities for Cities
Authors: Tomatis Francisco , Egerer Monika , Correa-Guimaraes Adriana , Navas-Gracia Luis Manuel
Published: February 2023

Climate change is impacting the ecological, social and technological aspects of urban gardens. Gardens experience threats (e.g., water scarcity) but are also responding through adaptation strategies (e.g., selecting drought-resilient plants). A synthetic overview of how urban gardens are affected by climate change and responding to climate change is unclear. Here, we systematically reviewed articles and book chapters published in the last two decades (2000–2022) to illustrate the relationship between climate change and urban gardening. From 72 documents analyzed with Nvivo Software, we found that there has been an increase in academic publications. Universities from the US (14) and Germany (9) universities are the dominant producers. Evidence shows that climate change can have negative impacts on cities, people and urban food. Suggestions on how to build the adaptation capacity of urban gardens include collecting rainwater, changing plant selection, changing planting times, applying vegetative cover on the soil and other practices. For cities, community and allotment gardens are helpful for adaptation, mitigation and resilience. This includes the capacity to regulate the microclimate, to reduce urban heat island effects and to buffer urban floods, the power to capture carbon, the ability to create social networks and other socio-environmental benefits for urban climate planning.
Identification of Personality-Related Candidate Genes in Thoroughbred Racehorses Using a Bioinformatics-Based Approach Involving Functionally Annotated Human Genes
Authors: Yokomori Tamu , Ohnuma Aoi , Tozaki Teruaki , Segawa Takao , Itou Takuya
Published: February 2023

Considering the personality traits of racehorses (e.g., flightiness, anxiety, and affability) is considered essential to improve training efficiency and decrease accident frequency, especially when retraining for a second career that may involve contact with inexperienced personnel after retiring from racing. Studies on human personality-related genes are frequently conducted; however, such studies are rare in horses because a consistent methodology for personality evaluation is lacking. Using the recently published whole genome variant database of 101 Thoroughbred horses, we compared horse genes orthologous to human genes related to the Big Five personality traits, and identified 18 personality-related candidate genes in horses. These genes include 55 variants that involve non-synonymous substitutions that highly impact the encoded protein. Moreover, we evaluated the allele frequencies and functional impact on the proteins in terms of the difference in molecular weights and hydrophobicity levels between reference and altered amino acids. We identified 15 newly discovered genes that may affect equine personality, but their associations with personality are still unclear. Although more studies are required to compare genetic and behavioral information to validate this approach, it may be useful under limited conditions for personality evaluation.
Effects of Dietary Supplementation of L-Carnitine and Mannan-Oligosaccharides on Growth Performance, Selected Carcass Traits, Content of Basic and Mineral Components in Liver and Muscle Tissues, and Bone Quality in Turkeys
Authors: Kwiecień Małgorzata , Jachimowicz-Rogowska Karolina , Krupa Wanda , Winiarska-Mieczan Anna , Krauze Magdalena
Published: February 2023

The study aimed to determine the effect of L-carnitine and Bio-Mos administration on selected production performance, slaughter parameters, elemental and mineral content of liver, breast and thigh muscles, and physical, morphometric, strength and bone mineral composition parameters of turkeys. The experiment was conducted on 360 six-week-old Big-6 turkey females, randomly divided into three groups of 120 birds each (six replicates of 20 birds). The turkeys of the control group were fed standard feed without additives; group II was fed with drinking water, a preparation containing L-carnitine at a dose of 0.83 mL/L, while group III was provided mixed feed with 0.5% Bio-Mos. The addition of L-carnitine and Bio-Mos increased body weight at 16 weeks (p = 0.047) and reduced the proportion of fat in the breast muscle (p = 0.029) and liver (p = 0.027). It also modified the content of some minerals in breast muscle, thigh muscle, liver, and bone. Furthermore, the addition of L-carnitine and Bio-Mos increased bone mass and length and modified the value of selected morphometric and strength parameters. The results indicate a positive effect of the applied feed additives on selected rearing indices and carcass quality while improving the elasticity and fracture toughness of the femur. There is a need for further research to determine optimal doses of L-carnitine and Bio-Mos in poultry nutrition.
Relevance of Pharmacogenomics to the Safe Use of Antimicrobials
Authors: Daly Ann K.
Published: February 2023

There has been widespread implementation of pharmacogenomic testing to inform drug prescribing in medical specialties such as oncology and cardiology. Progress in using pharmacogenomic tests when prescribing antimicrobials has been more limited, though a relatively large number of pharmacogenomic studies on aspects such as idiosyncratic adverse drug reactions have now been performed for this drug class. Currently, there are recommendations in place from either National Regulatory Agencies and/or specialist Pharmacogenomics Advisory Groups concerning genotyping for specific variants in MT-RNR1 and CYP2C19 before prescribing aminoglycosides and voriconazole, respectively. Numerous additional pharmacogenomic associations have been reported concerning antimicrobial-related idiosyncratic adverse drug reactions, particularly involving specific HLA alleles, but, to date, the cost-effectiveness of genotyping prior to prescription has not been confirmed. Polygenic risk score determination has been investigated to a more limited extent but currently suffers from important limitations. Despite limited progress to date, the future widespread adoption of preemptive genotyping and genome sequencing may provide pharmacogenomic data to prescribers that can be used to inform prescribing and increase the safe use of antimicrobials.
Theoretical Analysis of Deformation and Internal Forces of Used Piles Due to New Static-Pressure Pile Penetration
Authors: Cui Jifei , Ouyang Peihao , Zhang Jiani , Yang Zhenkun
Published: February 2023

Evaluation of the impact of new static-pressure pile penetration on used piles is vitally important for the reutilization of the used piles. The cavity expansion theory in semi-infinite soil is adopted to obtain the displacement field of the surrounding soil caused by new pile penetration, and then the displacement is applied to the used pile based on a two-stage method to analyze the deformation and internal force of the used pile. The effects of constraint conditions of the used pile, the pile rigidity and the soil modulus on the response of the used pile are considered. Meanwhile, numerical analysis is adopted to verify the effectiveness of the theoretical method. The influence of the distance between the new and used piles and the radius of the new pile is analyzed, and the measures to reduce the influence of new pile penetration on existing piles are proposed. The results show that the form of pile end only affects the deformation near the pile end. With the increase in pile diameter, the existing pile deformation gradually increases. As the distance between the existing pile and new pile increases, the existing pile deformation decreases significantly.
Discovering and Ranking Relevant Comment for Chinese Automatic Question-Answering System
Authors: Cheng Siyuan , Yin Didi , Hou Zhuoyan , Shi Zihao , Wang Dongyu , Fu Qiang
Published: February 2023

Intelligent customer service system is timely, efficient, and accurate, which is more and more popular in grid electric power companies, and the amount of customer consultation is increasing day by day. It is infeasible for human customer service to answer these questions on time, so an automatic question-answering system is of great help to the grid electric power company. The customer queries from the grid electric power company customer service is very different from open-domain questions: the problems questioned by customer tend to be for a specific device or system within the enterprise operation problem. Most grid electric companies provide customers with a communication platform where customers can get guidance on using equipment and the business process. The comments from communication platforms are valuable resources for answering customer questions. In our work, we use three neural network models which excavate potential answers to customer queries from comments. One of the key challenges, however, is the difficulty of matching customer questions with comments. To solve this problem, we propose a method based on deep learning to find the comments related to customer questions to generate more accurate and reliable answers. Experiments can prove that our method performed well in the customer service of grid electric power company.
Detection of Operator Fatigue in the Main Control Room of a Nuclear Power Plant Based on Eye Blink Rate, PERCLOS and Mouse Velocity
Authors: Dai Licao , Li Yu , Zhang Meihui
Published: February 2023

Fatigue affects operators’ safe operation in a nuclear power plant’s (NPP) main control room (MCR). An accurate and rapid detection of operators’ fatigue status is significant to safe operation. The purpose of the study is to explore a way to detect operator fatigue using trends in eyes’ blink rate, number of frames closed in a specified time (PERCLOS) and mouse velocity changes of operators. In experimental tasks of simulating operations, the clustering method of Toeplitz Inverse Covariance-Based Clustering (TICC) is used for the relevant data captured by non-invasive techniques to determine fatigue levels. Based on the determined results, the data samples are given labeled fatigue levels. Then, the data of fatigue samples with different levels are identified using supervised learning techniques. Supervised learning is used to classify different fatigue levels of operators. According to the supervised learning algorithm in different time windows (20 s–60 s), different time steps (10 s–50 s) and different feature sets (eye, mouse, eye-plus-mouse) classification performance show that K-Nearest Neighbor (KNN) perform the best in the combination of the above multiple indexes. It has an accuracy rate of 91.83%. The proposed technique can detect operators’ fatigue level in real time within 10 s.
Short-Term Load Forecasting of the Greek Electricity System
Authors: Stamatellos George , Stamatelos Tassos
Published: February 2023

Short-term load forecasting is an essential instrument in power system planning, operation, and control. It is involved in the scheduling of capacity dispatch, system reliability analysis, and maintenance planning for turbines and generators. Despite the high level of development of advanced types of machine learning models in commercial codes and platforms, the prediction accuracy needs further improvement, especially in certain short, problematic time periods. To this end, this paper employs public domain electric load data and typical climatic data to make 24-hour-ahead hourly electricity load forecasts of the Greek system based on two types of robust, standard feed-forward artificial neural networks. The accuracy and stability of the prediction performance are measured by means of the modeling error values. The current prediction accuracy levels of mean absolute percentage error, mean value μ = 2.61% with σ = 0.33% of the Greek system operator for 2022, attained with noon correction, are closely matched with a simple feed-forward artificial neural network, attaining mean value μ = 3.66% with σ = 0.30% with true 24-hour-ahead prediction. Specific instances of prediction failure in cases of unexpectedly high or low energy demand are analyzed and discussed. The role of the structure and quality of input data of the training datasets is demonstrated to be the most critical factor in further increasing the accuracy and reliability of forecasting.
The Modular Gait Design of a Soft, Earthworm-like Locomotion Robot Driven by Ultra-Low Frequency Excitation
Authors: Qi Zhifeng , Sun Xiuting
Published: February 2023

In complex and extreme environments, such as pipelines and polluted waters, gait programming has great significance for multibody segment locomotion robots. The earthworm-like locomotion robot is a representative multibody bionic robot, which has the characteristics of low weight, multibody segments, and excellent movement performance under the designed gait. The body segment cell can realize large deformation under ultra-low frequency excitation. The multibody segment robot can locomote under ultra-low frequency excitation with appropriate shifts. In this paper, a modular gait design principle for a soft, earthworm-like locomotion robot is proposed. The driven modules defined by modular gait generation correspond to the peristaltic wave transmissions of the excitation in the robot for different modular gait modes. A locomotion algorithm is presented to simulate the locomotion of the earthworm-like robot under different locomotion gaits. Moreover, the locomotion speeds are obtained for different modular gait modes. The results show that locomotion speed is related to the original state of the body segments and modular gaits. As the initial actuated segments and driven modules (which correspond to the excitation frequency and shift) increase, faster movement speeds can be realized, which resolves the speed saturation of the earthworm-like robot. The proposed modular gait design method gives a new gait generation principle for the improvement of the locomotion performance of soft, earthworm-like robots.
Identification Procedure for Design Optimization of Gravitational Electromagnetic Energy Harvesters
Authors: Lo Monaco Mirco , Russo Caterina , Somà Aurelio
Published: February 2023

Energy harvesting is a promising technique for supplying low-power devices as an alternative to conventional batteries. Energy harvesters can be integrated into Autonomous Internet of Things (AIoT) systems to create a wireless network of sensor nodes for real-time monitoring of assets. This paper shows a design and optimization methodology for gravitational vibration-based electromagnetic energy harvesters (GVEHs) of different sizes considering the design constraints of its real application. The configuration, analytical model, and electro-mechanical coupling of these devices are described in detail. A numerical model is developed in the Ansys Maxwell FEM environment to derive the non-linear stiffness and damping of the asymmetric magnetic suspension. Experimental laboratory tests on three harvester prototypes are compared to numerical results of dynamic simulations in MATLAB/Simulink for the validation of the proposed model through error estimation. The fully-parametric validated model is used to perform sensitivity analyses on the device’s mechanical characteristics of natural frequency and magnet equilibrium position by varying the fixed and moving magnets dimensions. The set of magnets composing the magnetic spring is chosen complying with the application design constraints of size and resonance frequency tuning. Coil parameters of length and number of turns are optimized for maximum output power generation. The optimized device simulated performances are compared to other devices in the literature in terms of NPD, a significant index that evaluates power density under different excitation amplitudes. The optimized harvester presents the highest NPD value of 2.61, achieving an improvement of 52% with respect to the best harvester amongst the three tested prototypes.
The Temperature Interval of the Liquid–Glass Transition of Amorphous Polymers and Low Molecular Weight Amorphous Substances
Authors: Darmaev Migmar V. , Ojovan Michael I. , Mashanov Alexey A. , Chimytov Timur A.
Published: February 2023

We present calculation results of the temperature interval δTg characterizing the liquid–glass transition in amorphous materials obtained on the basis of available data of the empirical parameters C1 and C2 in the Williams–Landel–Ferry (WLF) viscosity equation. We consider the unambiguous dependence of the relative transition temperature interval δTg/Tg on the fraction of the fluctuation volume fg frozen at the glass transition temperature Tg utilizing Sanditov’s model of delocalized atoms. The parameter f = ΔVe/V, which determines the molecular mobility characteristic of delocalized atoms in the liquid–glass transition region, is weakly dependent on the nature of most vitreous substances and can be found as fg = 1/C1. We show that the temperature interval δTg is less than 1% of the Tg for most amorphous substances. This result conforms with Simon’s classical idea of a small temperature range in which the structure freezes. The structural relaxation time τg at Tg of polymers and chalcogenide glasses is also calculated.
A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting
Authors: Khan Junaid , Lee Eunkyu , Balobaid Awatef Salem , Kim Kyungsup
Published: February 2023

Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level prediction is a critical aspect of water resource management and requires accurate and efficient modelling techniques. This study reviews the most commonly used conventional numerical, machine learning, and deep learning models for predicting GWL. Significant advancements have been made in terms of prediction efficiency over the last two decades. However, while researchers have primarily focused on predicting monthly, weekly, daily, and hourly GWL, water managers and strategists require multi-year GWL simulations to take effective steps towards ensuring the sustainable supply of groundwater. In this paper, we consider a collection of state-of-the-art theories to develop and design a novel methodology and improve modelling efficiency in this field of evaluation. We examined 109 research articles published from 2008 to 2022 that investigated different modelling techniques. Finally, we concluded that machine learning and deep learning approaches are efficient for modelling GWL. Moreover, we provide possible future research directions and recommendations to enhance the accuracy of GWL prediction models and improve relevant understanding.
Performance Evaluation of an A Band Differential Absorption LIDAR Model and Inversion for the Ocean Surface Pressure from Low-Earth Orbit
Authors: Hong Guanglie , Dong Yu , Di Huige
Published: February 2023

Remote sensing of ocean surface pressure from space is very important, and differential absorption LIDAR and differential absorption radar are only two kinds of remote sensing instruments with this potential. The differential absorption LIDAR works with the integral path mode from the spacecraft in the 400 km low-Earth orbit. The differential optical depth of the oxygen A-band is measured, and then the ocean surface pressure is obtained using a circle-iterative calculation. Performance evaluation of the differential absorption LIDAR model was based on feasibility to the advanced system parameters of the space instrument, whilst weak echo pulse energy at ocean surface yielded random errors in the surface pressure measurement. On the other hand, uncertain atmospheric temperature profiles and water vapor mixture profiles resulted in a primary systematic error in the surface pressure. The error of the surface pressure is sensitive to the jitter of the central frequency of laser emission. Under a strict implementation of the error budget, the time resolution is 6.25 s and the along-orbit distance resolution is 44 km, 625 echoes from ocean surface was cumulatively averaged. Consequently, if the jitter of the central frequency of laser emission exceeded 10 MHz, controlling the error of the surface pressure below 0.1% proved almost hopeless; further, the error could be expected to within 0.1–0.2%; however, the error limited within 0.2–0.3% is an achievable indicator.
The Hemispheric Asymmetry of Gravity Wave Impact on the Polar Mesospheric Cloud, Based on the Aeronomy of Ice in the Mesosphere Satellite
Authors: Qiu Shican , Wang Ning , Soon Willie , Herrera Victor Manuel Velasco , Yang Chengyun , Dou Xiankang
Published: February 2023

In this research, we analyze the gravity wave (GW) energy density, ice water content (IWC), particle radius, and cloud albedo data of 16 polar mesospheric cloud (PMC) seasons in the Northern Hemisphere (NH) from 2007 to 2014 and Southern Hemisphere (SH) from 2007/2008 to 2014/2015, based on observations from the Cloud Imaging and Particle Size and Solar Occultation For Ice Experiment instruments. The influence of GW activity on the formation of PMCs is studied by hemispheric contrast. In the NH, the GW flux generally starts to increase significantly around the summer solstice during the 8 PMC seasons. In 6/8 of these seasons, the IWC is positively correlated with the variation of GW. When the GW activity is enhanced to reach the maximum, the IWC will start to increase and reach the peak within 0–23 days. In comparison, in the SH, the GW peaks around 55 days after the solstice. The timing of PMC appearance also varies, with the IWC starting to grow 20 days after the solstice and the GW increasing in 55 days after the solstice. In particular, the IWC starts even earlier than the solstice in the seasons of 2012/2013 and 2013/2014.
Role of NADPH Oxidase-Derived ROS-Mediated IL-6/STAT3 and MAPK/NF-κB Signaling Pathways in Protective Effect of Corilagin against Acetaminophen-Induced Liver Injury in Mice
Authors: Liu Fu-Chao , Lee Hung-Chen , Liao Chia-Chih , Chou An-Hsun , Yu Huang-Ping
Published: February 2023

Acetaminophen (APAP) overdose causes acute liver injury via oxidative stress, uncontrolled inflammatory response, and subsequent hepatocyte death. Nicotinamide adenine dinucleotide phosphate oxidase (NOX) is a potent source of cellular reactive oxygen species (ROS) and may contribute to oxidative stress in many inflammatory processes. Corilagin, a component of Phyllanthus urinaria, possesses antioxidant, anti-inflammatory, and hepatoprotective effects. We evaluated the mechanisms underlying the protective effect of corilagin against acetaminophen-induced liver injury. Mice were intraperitoneally administrated 300 mg/kg APAP or equal volume of saline (control), with or without various concentrations of corilagin (0, 1, 5, or 10 mg/kg) administered after 30 min. All animals were sacrificed 16 h after APAP administration, and serum and liver tissue assays including histology, immunohistochemistry, and Western blot assay were performed. Corilagin post-treatment significantly attenuated APAP-induced liver injury (p < 0.005), inflammatory cell infiltration, hepatic proinflammatory cytokine levels, and hepatic oxidative stress. Furthermore, corilagin attenuated the protein levels of NOX1, NOX2, signal transducer and activator of transcription 3 (STAT3), and nuclear factor kappa B (NF-κB) in APAP-induced liver injury. These results indicated that the antioxidant, anti-inflammatory, and protective effects of corilagin in APAP-induced liver injury might involve the regulation of interleukin (IL)-6/STAT3 and mitogen-activated protein kinase (MAPK)/NF-κB signaling pathways through NOX-derived ROS.
Diagnosis of Acute Aortic Syndromes on Non-Contrast CT Images with Radiomics-Based Machine Learning
Authors: Ma Zhuangxuan , Jin Liang , Zhang Lukai , Yang Yuling , Tang Yilin , Gao Pan , Sun Yingli , Li Ming
Published: February 2023

We aimed to detect acute aortic syndromes (AAS) on non-contrast computed tomography (NCCT) images using a radiomics-based machine learning model. A total of 325 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from 2 medical centers in China to form the internal cohort (230 patients, 60 patients with AAS) and the external testing cohort (95 patients with AAS). The internal cohort was divided into the training cohort (n = 135), validation cohort (n = 49), and internal testing cohort (n = 46). The aortic mask was manually delineated on NCCT by a radiologist. Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to filter out nine feature parameters; the Support Vector Machine (SVM) model showed the best performance. In the training and validation cohorts, the SVM model had an area under the curve (AUC) of 0.993 (95% CI, 0.965–1); accuracy (ACC), 0.946 (95% CI, 0.877–1); sensitivity, 0.9 (95% CI, 0.696–1); and specificity, 0.964 (95% CI, 0.903–1). In the internal testing cohort, the SVM model had an AUC of 0.997 (95% CI, 0.992–1); ACC, 0.957 (95% CI, 0.945–0.988); sensitivity, 0.889 (95% CI, 0.888–0.889); and specificity, 0.973 (95% CI, 0.959–1). In the external testing cohort, the ACC was 0.991 (95% CI, 0.937–1). This model can detect AAS on NCCT, reducing misdiagnosis and improving examinations and prognosis.
A Systematic Review on the Potential of Aspirin to Reduce Cardiovascular Risk in Schizophrenia
Authors: Dao Joseph , Saran Savreen , Wang Melody , Michael Christina , Phan Nhu-y , Bellon Alfredo
Published: February 2023

Cardiovascular disease (CVD), including heart disease and stroke, continues to be the leading cause of death worldwide. Patients with mental health disorders, including schizophrenia (SCZ) are known to have an increased risk for CVD. Given the association with metabolic syndrome, patients with SCZ are often prescribed metformin and statins but its impact remains unsatisfactory. The use of aspirin (ASA) to decrease cardiovascular risk in the general population has been thoroughly investigated and clear guidelines are currently in place. Since adjuvant treatment with ASA could possibly decrease CVD risk and mortality in SCZ, we conducted a systematic review of the literature to determine the state of the current literature on this subject. Our systematic review points to gaps in the literature on CVD prevention in SCZ and illustrates an obvious need for further research. Although several studies have shown increased CVD risk in SCZ, to date, no research has been conducted on the utilization of CVD preventative treatment such as ASA for SCZ.
Size of Craniectomy Predicts Approach-Related Shear Bleeding in Poor-Grade Subarachnoid Hemorrhage
Authors: Vychopen Martin , Wach Johannes , Lampmann Tim , Asoglu Harun , Vatter Hartmut , Güresir Erdem
Published: February 2023

Decompressive craniectomy is an option to decrease elevated intracranial pressure in poor-grade aneurysmal subarachnoid hemorrhage (SAH) patients. The aim of the present study was to analyze the size of the bone flap according to approach-related complications in patients with poor-grade SAH. We retrospectively analyzed poor-grade SAH patients (WFNS 4 and 5) who underwent aneurysm clipping and craniectomy (DC or ommitance of bone flap reinsertion). Postoperative CT scans were analyzed for approach-related tissue injury at the margin of the craniectomy (shear bleeding). The size of the bone flap was calculated using the De Bonis equation. Between 01/2012 and 01/2020, 67 poor-grade SAH patients underwent clipping and craniectomy at our institution. We found 14 patients with new shear bleeding lesion in postoperative CT scan. In patients with shear bleeding, the size of the bone flap was significantly smaller compared to patients without shear bleeding (102.1 ± 45.2 cm2 vs. 150.8 ± 37.43 cm2, p > 0.0001). However, we found no difference in mortality rates (10/14 vs. 23/53, p = 0.07) or number of implanted VP shunts (2/14 vs. 18/53, p = 0.2). We found no difference regarding modified Rankin Scale (mRS) 6 months postoperatively. In poor-grade aneurysmal SAH, the initial planning of DC—if deemed necessary —and enlargement of the flap size seems to decrease the rate of postoperatively developed shear bleeding lesions.
A Multi-Criteria Decision-Making Model for Selecting the Best Project Delivery Systems for Offsite Construction Projects
Authors: Assaf Mohamed , Hussein Mohamed , Abdelkhalek Sherif , Zayed Tarek
Published: February 2023

Off-site construction (OSC) is an innovative construction method that transfers most of the site-based work to a more controlled environment. Construction waste minimization, speedy schedules, higher sustainability, and better quality are some of the perceived benefits of OSC. Therefore, significant research attention has been given to OSC. However, minimal research attention has been given to procurement management in OSC, which could impact its pace of adoption. Existing studies on the procurement methods of OSC projects have overlooked several criteria related to OSC that impact the selection of the appropriate procurement methods (i.e., design-build, construction management, etc.). In addition, the literature lacks decision-making tools to assist OSC practitioners in selecting the appropriate procurement method. In this regard, this study contributes to the body of knowledge by (1) identifying the criteria that impact the selection of OSC procurement methods; (2) developing a multi-criteria decision-making (MCDM) model to select the appropriate OSC procurement methods. The developed MCDM model uses a hybrid approach of analytic network process (ANP) and evidential reasoning (ER). The ANP, which considers the interdependencies among the collected OSC procurement criteria, is used to calculate the relative importance weights through questionnaire surveys. The ER method evaluates various OSC procurement methods in accordance with the criteria importance weights. The results indicate that project quality, cost control, and funding arrangement are the prominent selection factors. On the other hand, the model reveals that the integrated project delivery (IPD) and construction management (CM) methods have the highest utility scores. The MCDM model has been validated by comparing the results with similar studies. The present study could assist OSC practitioners in selecting the appropriate procurement method for OSC projects.
A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning
Authors: Qin Yilun , Tang Qizhi , Xin Jingzhou , Yang Changxi , Zhang Zixiang , Yang Xianyi
Published: February 2023

Rapid and accurate identification of moving load is crucial for bridge operation management and early warning of overload events. However, it is hard to obtain them rapidly via traditional machine learning methods, due to their massive model parameters and complex network structure. To this end, this paper proposes a novel method to perform moving loads identification using MobileNetV2 and transfer learning. Specifically, the dynamic responses of a vehicle–bridge interaction system are firstly transformed into a two-dimensional time-frequency image by continuous wavelet transform to construct the database. Secondly, a pre-trained MobileNetV2 model based on ImageNet is transferred to the moving load identification task by transfer learning strategy for describing the mapping relationship between structural response and these specified moving loads. Then, load identification can be performed through inputting bridge responses into the established relationship. Finally, the effectiveness of the method is verified by numerical simulation. The results show that it can accurately identify the vehicle weight, vehicle speed information, and presents excellent strong robustness. In addition, MobileNetV2 has faster identification speed and requires less computational resources than several traditional deep convolutional neural network models in moving load identification, which can provide a novel idea for the rapid identification of moving loads.
Development of Deep Learning with RDA U-Net Network for Bladder Cancer Segmentation
Authors: Lee Ming-Chan , Wang Shao-Yu , Pan Cheng-Tang , Chien Ming-Yi , Li Wei-Ming , Xu Jin-Hao , Luo Chi-Hung , Shiue Yow-Ling
Published: February 2023

In today’s high-order health examination, imaging examination accounts for a large proportion. Computed tomography (CT), which can detect the whole body, uses X-rays to penetrate the human body to obtain images. Its presentation is a high-resolution black-and-white image composed of gray scales. It is expected to assist doctors in making judgments through deep learning based on the image recognition technology of artificial intelligence. It used CT images to identify the bladder and lesions and then segmented them in the images. The images can achieve high accuracy without using a developer. In this study, the U-Net neural network, commonly used in the medical field, was used to extend the encoder position in combination with the ResBlock in ResNet and the Dense Block in DenseNet, so that the training could maintain the training parameters while reducing the overall identification operation time. The decoder could be used in combination with Attention Gates to suppress the irrelevant areas of the image while paying attention to significant features. Combined with the above algorithm, we proposed a Residual-Dense Attention (RDA) U-Net model, which was used to identify organs and lesions from CT images of abdominal scans. The accuracy (ACC) of using this model for the bladder and its lesions was 96% and 93%, respectively. The values of Intersection over Union (IoU) were 0.9505 and 0.8024, respectively. Average Hausdorff distance (AVGDIST) was as low as 0.02 and 0.12, respectively, and the overall training time was reduced by up to 44% compared with other convolution neural networks.
Molecular Links between Cancer and Metabolic Diseases: New Perspectives and Therapeutic Strategies for Cancer Prevention and Treatment by Targeting Nutritional Patterns and Metabolic Alterations
Authors: Zaiou Mohamed , Morjani Hamid
Published: February 2023

Cancer-related mortality is reported to be elevated in cases with metabolic dysfunction [...]
Measuring the Wellbeing of Cancer Patients with Generic and Disease-Specific Instruments
Authors: Chen Gang , Bulamu Norma B. , McGrane Ellen , Richardson Jeff
Published: February 2023

Different wellbeing measures have been used among cancer patients. This study aimed to first investigate the sensitivity of health state utility (HSU), capability, and subjective wellbeing (SWB) instruments in cancer. A cancer-specific instrument (QLQ-C30) was included and transferred onto the cancer-specific HSU scores. Furthermore, it examined the relative importance of key life domains explaining overall life satisfaction. Data were drawn from the Multi-instrument Comparison survey. Linear regression was used to explore the extent to which the QLQ-C30 sub-scales explain HSU and SWB. Kernel-based Regularized Least Squares (KRLS), a machine learning method, was used to explore the life domain importance of cancer patients. As expected, the QLQ-C30 sub-scales explained the vast majority of the variance in its derived cancer-specific HSU (R2 = 0.96), followed by generic HSU instruments (R2 of 0.65–0.73) and SWB and capability instruments (R2 of 0.33–0.48). The cancer-specific measure was more closely correlated with generic HSU than SWB measures, owing to the construction of these instruments. In addition to health, life achievements, relationships, the standard of living, and future security all play an important role in explaining the overall life satisfaction of cancer patients.
Canine-Inspired Chemometric Analysis of Volatile Organic Compounds in Urine Headspace to Distinguish Prostate Cancer in Mice and Men
Authors: Woollam Mark , Siegel Amanda P. , Munshi Adam , Liu Shengzhi , Tholpady Sunil , Gardner Thomas , Li Bai-Yan , Yokota Hiroki , Agarwal Mangilal
Published: February 2023

Canines can identify prostate cancer with high accuracy by smelling volatile organic compounds (VOCs) in urine. Previous studies have identified VOC biomarkers for prostate cancer utilizing solid phase microextraction (SPME) gas chromatography–mass spectrometry (GC-MS) but have not assessed the ability of VOCs to distinguish aggressive cancers. Additionally, previous investigations have utilized murine models to identify biomarkers but have not determined if the results are translatable to humans. To address these challenges, urine was collected from mice with prostate cancer and men undergoing prostate cancer biopsy and VOCs were analyzed by SPME GC-MS. Prior to analysis, SPME fibers/arrows were compared, and the fibers had enhanced sensitivity toward VOCs with a low molecular weight. The analysis of mouse urine demonstrated that VOCs could distinguish tumor-bearing mice with 100% accuracy. Linear discriminant analysis of six VOCs in human urine distinguished prostate cancer with sensitivity = 75% and specificity = 69%. Another panel of seven VOCs could classify aggressive cancer with sensitivity = 78% and specificity = 85%. These results show that VOCs have moderate accuracy in detecting prostate cancer and a superior ability to stratify aggressive tumors. Furthermore, the overlap in the structure of VOCs identified in humans and mice shows the merit of murine models for identifying biomarker candidates.
Challenges of Anti-Mesothelin CAR-T-Cell Therapy
Authors: Zhai Xuejia , Mao Ling , Wu Min , Liu Jie , Yu Shicang
Published: February 2023

Chimeric antigen receptor (CAR)-T-cell therapy is a kind of adoptive T-cell therapy (ACT) that has developed rapidly in recent years. Mesothelin (MSLN) is a tumor-associated antigen (TAA) that is highly expressed in various solid tumors and is an important target antigen for the development of new immunotherapies for solid tumors. This article reviews the clinical research status, obstacles, advancements and challenges of anti-MSLN CAR-T-cell therapy. Clinical trials on anti-MSLN CAR-T cells show that they have a high safety profile but limited efficacy. At present, local administration and introduction of new modifications are being used to enhance proliferation and persistence and to improve the efficacy and safety of anti-MSLN CAR-T cells. A number of clinical and basic studies have shown that the curative effect of combining this therapy with standard therapy is significantly better than that of monotherapy.
Biochar Derived from Palm Waste Supported Greenly Synthesized MnO2 Nanoparticles as a Novel Adsorbent for Wastewater Treatment
Authors: Taha Amel , Daffalla Samah
Published: February 2023

Water pollution with dye effluents from different industries is a broadly established environmental and health problem that needs serious attention. In this study, making use of Acacia nilotica seed extract, greenly synthesized MnO2 nanoparticles were loaded on the surface of biochar derived from palm waste (MnO2/PF), with specific surface areas of 70.97 m2/g. Batch experiments were adopted, aiming to evaluate the performance of palm fronds, biochar, and the MnO2/PF adsorbents in methyl orange (MO) removal from an aqueous solution. The feedstock and synthesized biochars were comprehensively characterized using XRD, SEM-EDX, FTIR, and BET surface area techniques. Moreover, the influences of the modification of palm fronds, initial dye concentrations, pH, and adsorbent dosage on MO uptake were examined. The results demonstrated that MnO2/PF biochar nanocomposite led to an increase in the removal efficiency by 6 and 1.5 times more than those of palm fronds and biochar, respectively. In addition, it was found that the second-order kinetic model presented the kinetic adsorption very well. This paper demonstrates that the depositing of greenly synthesized MnO2 nanoparticles on the date palm waste biochar forms a novel adsorbent (MnO2/PF) for the removal of MO from aqueous solutions. Furthermore, this adsorbent was easy to synthesize under moderate conditions without the need for chemical capping agents, and would thus be cost-effective and eco-friendly.
Carcinogenic Effects of Areca Nut and Its Metabolites: A Review of the Experimental Evidence
Authors: Senevirathna Kalpani , Pradeep Roshan , Jayasinghe Yovanthi Anurangi , Jayawickrama Shalindu Malshan , Illeperuma Rasika , Warnakulasuriya Saman , Jayasinghe Ruwan Duminda
Published: February 2023

Oral cancers (OC) are among the most frequent malignancies encountered in Southeast Asia, primarily due to the prevalent habit of betel quid (BQ) and smokeless tobacco use in this region. Areca nut (AN), the primary ingredient in BQ, contains several alkaloids, including arecoline, arecaidine, guvacoline, and guvacine. These have been associated with both the AN abuse liability and carcinogenicity. Additionally, variations in AN alkaloid levels could lead to differences in the addictiveness and carcinogenic potential across various AN-containing products. Recent studies based on animal models and in vitro experiments show cellular and molecular effects induced by AN. These comprise promoting epithelial-mesenchymal transition, autophagy initiation, tissue hypoxia, genotoxicity, cytotoxicity, and cell death. Further, clinical research endorses these undesired harmful effects in humans. Oral submucosal fibrosis, a potentially malignant disease of the oral cavity, is predominantly reported from the geographical areas of the globe where AN is habitually chewed. OC in chronic AN users presents a more aggressive phenotype, such as resistance to anti-cancer drugs. The available evidence on the carcinogenicity of AN based on the findings reported in the recently published experimental studies is discussed in the present review.
Researching Teacher Work Motivation in Ghana through the Lens of COVID-19
Authors: Adarkwah Michael Agyemang
Published: February 2023

Teachers, particularly in developing contexts, were vulnerable populations during the COVID-19 pandemic. As natural parental figures for students, they had to reconcile the dual role of ensuring the safety and health of students and their own and their family’s well-being. The external crisis of COVID-19 heightened the negative experiences of teachers in their work environments during both online and physical instruction. This qualitative phenomenological study involving thirty (30) secondary school teachers in Ghana took a comprehensive and fresh look at how COVID-19 impacted the work motivation of teachers. It was found that teachers suffered a great deal of stress in the wake of the pandemic and had to face mounting concerns about their working conditions. The low morale of teachers precipitated by COVID-19 made them develop attrition intentions. However, intrinsic and altruistic traits such as passion, the feeling of responsibility, and the desire to contribute to society and foster student development made teachers resilient towards the deleterious effects of the pandemic to promote optimal teaching. Future studies should investigate the installation of support structures that strengthen the motivation of teachers in unforeseen crises.