bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitative Comparison of Personal Cooling Garments in Performance and Design: A Review
Authors: Zhou Yiying , Lou Lun , Fan Jintu
Published: October 2023

Personal cooling garments (PCGs) have gained increasing attention as a promising solution to alleviate heat stress and enhance thermal comfort in hot and humid conditions. However, limited attention has been paid to the influence of clothing design on cooling performance. This review highlights the influence of design factors and provides a quantitative comparison in cooling performance for different types of PCGs, including air cooling garments, evaporative cooling garments, phase-change cooling garments, and liquid cooling garments. A detailed discussion about the relationship between design factors and the cooling performance of each cooling technique is provided based on the available literature. Furthermore, potential improvements and challenges in PCG design are explored. This review aims to offer a comprehensive insight into the attributes of various PCGs and promote interdisciplinary collaboration for improving PCGs in both cooling efficiency and garment comfort, which is valuable for further research and innovation.
CFD-DEM Simulation of Slugging and Non-Slugging Fast Fluidization of Fine Particles in a Micro Riser
Authors: Wu Guorong , Li Yanggui
Published: October 2023

The discrete element method (DEM) coupled with computational dynamics (CFD) has been considered one of the most sensitive ways of studying the micro fluidized bed. This article proposes a so-called particle circumstance-dependent drag model that is dependent on a particle’s complex circumstances. Slugging and non-slugging fast fluidization in a micro fluidized bed is modeled with the use of the CFD-DEM. The results show that the formation and the fragmentation of clusters in a slugging fast fluidized state are clearly captured, and both have time synchronization. However, with the increase in gas velocity, the boundary of the dense and dilute phases turns blurry and the slugs disappear. Furthermore, there exists a relatively serious backmixing of particles in the slugging fast fluidization, while the backmixing effect weakens in the non-slugging fast fluidization. Moreover, the outlet solid flux decreases compared with those in the big fluidized beds for the slugging fast fluidized bed due to the micro size effect, while the micro size effect on the solid flux is not distinct for the non-slugging fast fluidized bed. Last but not least, the radial porosity with slugging exhibits a weakened core-annulus structure compared with the correlated radial porosity in the big fluidized beds. The radial porosity without slugging tends to approach the correlated core-annulus structure.
Production of Biohydrogen from Microalgae Biomass after Wastewater Treatment and Air Purification from CO2
Authors: Velmozhina Ksenia , Shinkevich Polina , Zhazhkov Viacheslav , Politaeva Natalia , Korablev Vadim , Vladimirov Iaroslav , Morales Tania Carbonell
Published: October 2023

This article explores the feasibility of producing biohydrogen from microalgae following their use in wastewater purification from food industry facilities and the removal of high levels of carbon dioxide (CO2) from the air. The authors investigated various methods for disrupting the cell membranes of microalgae and their impact on biohydrogen yield. The microalgae biomass obtained after wastewater and air purification underwent pre-treatment using physicochemical and chemical methods, including microwave radiation, acid treatment, and thermal treatment. The highest hydrogen production occurred during thermal and acid treatments of biomass with the addition of starch (44.24 mL/L of suspension). The use of microwave radiation for processing did not yield significant results. A comparison of the biohydrogen values obtained from untreated and treated biomass revealed that treatment enhances biohydrogen yield.
Numerical Simulation of Fluidization Behavior and Chemical Performance for Hydrochlorination of Silicon Tetrachloride in a Fluidized Bed Reactor
Authors: Guo Qijun , Dai Enrui , Xu Shigang , Yang Yuehong , Yang Ni , Xie Gang , Nie Zhifeng
Published: October 2023

Exploring the fluidization behaviors and chemical performance in silicon tetrachloride (SiCl4) hydrochlorination processes within a fluidized bed reactor (FBR) poses significant challenges. In this study, we developed an Eulerian-granular model (EGM) by integrating the Eulerian–Eulerian two-fluid model with the kinetic theory of granular flow (KTGF). The effect of fluidization velocities on the flow regime, heat transfer, and chemical reaction performance were investigated. The applicability of the simulation method and the validity of the model were confirmed through comprehensive comparisons, including the simulated values of the maximum bed expansion height (Hmax) with theoretical values derived from empirical formulas and the simulated gas temperature profile with Hsu’s experimental data. The results indicate that the present EGM can be feasible to describe the variation of the flow regime within the FBR. An increase in bed voidage over time, coinciding with transitions in the flow regime, can be observed. Particularly noteworthy was the attainment of a more uniform distribution of SiCl4 under the bubbling fluidization state. Furthermore, the FBR possess high heat transfer characteristics, and the reaction gas can reach the set temperature of the bed after entering a small distance (about 10 mm). The presence of circulating bubbles within the FBR enhances the mixing uniformity of the SiCl4 reaction gas and silicon particles, particularly in the central and upper regions of the bed under the bubbling fluidization state. As a result, the predicted highest concentration of SiHCl3 was 13.08% and the conversion rate of SiCl4 was 28.97% under the bubbling fluidization state. Our results can provide a theoretical basis for further understanding of the hydrochlorination process of SiCl4 within the FBR.
Predicting Myalgic Encephalomyelitis/Chronic Fatigue Syndrome from Early Symptoms of COVID-19 Infection
Authors: Hua Chelsea , Schwabe Jennifer , Jason Leonard A. , Furst Jacob , Raicu Daniela
Published: October 2023

It is still unclear why certain individuals after viral infections continue to have severe symptoms. We investigated if predicting myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) development after contracting COVID-19 is possible by analyzing symptoms from the first two weeks of COVID-19 infection. Using participant responses to the 54-item DePaul Symptom Questionnaire, we built predictive models based on a random forest algorithm using the participants’ symptoms from the initial weeks of COVID-19 infection to predict if the participants would go on to meet the criteria for ME/CFS approximately 6 months later. Early symptoms, particularly those assessing post-exertional malaise, did predict the development of ME/CFS, reaching an accuracy of 94.6%. We then investigated a minimal set of eight symptom features that could accurately predict ME/CFS. The feature reduced models reached an accuracy of 93.5%. Our findings indicated that several IOM diagnostic criteria for ME/CFS occurring during the initial weeks after COVID-19 infection predicted Long COVID and the diagnosis of ME/CFS after 6 months.
Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing
Authors: Igwe Victor , Salehi Bahram , Mahdianpari Masoud
Published: October 2023

Rapid impacts from both natural and anthropogenic sources on wetland ecosystems underscore the need for updating wetland inventories. Extensive up-to-date field samples are required for calibrating methods (e.g., machine learning) and validating results (e.g., maps). The purpose of this study is to design a dataset generation approach that extracts training data from already existing wetland maps in an unsupervised manner. The proposed method utilizes the LandTrendr algorithm to identify areas least likely to have changed over a seven-year period from 2016 to 2022 in Minnesota, USA. Sentinel-2 and Sentinel-1 data were used through Google Earth Engine (GEE), and sub-pixel water fraction (SWF) and normalized difference vegetation index (NDVI) were considered as wetland indicators. A simple thresholding approach was applied to the magnitude of change maps to identify pixels with the most negligible change. These samples were then employed to train a random forest (RF) classifier in an object-based image analysis framework. The proposed method achieved an overall accuracy of 89% with F1 scores of 91%, 81%, 88%, and 72% for water, emergent, forested, and scrub-shrub wetland classes, respectively. The proposed method offers an accurate and cost-efficient method for updating wetland inventories as well as studying areas impacted by floods on state or even national scales. This will assist practitioners and stakeholders in maintaining an updated wetland map with fewer requirements for extensive field campaigns.
A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar
Authors: Zhang Hua , Liu Yiheng , Dong Qinghai , Liu Ning , Chang Kai , Wang Xuemei , Lyu Xiaode
Published: October 2023

Deploying Passive Multistatic Radar (PMR) on mobile platforms provides covert and cost-effective monitoring over a large area, offering certain advantages in countermeasure. However, mobile PMR faces significant challenges, such as Doppler distortion and phase deviations. A multi-parameter space target detection method is proposed for mobile PMR to achieve target detection in three-dimensional environments. By estimating the Doppler Frequency Rate (DFR), applying bistatic range phase compensation, and implementing azimuth time integration, frame division, and data fusion, the detection accuracy and the Signal-to-Noise Ratio (SNR) are improved. Simulation results indicate that the proposed method significantly enhances the SNR and produces accurate detection results, demonstrating its efficacy.
Early Crop Mapping Using Dynamic Ecoregion Clustering: A USA-Wide Study
Authors: Wang Yiqun , Huang Hui , State Radu
Published: October 2023

Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. This paper presents a new method for early crop mapping for the entire conterminous USA (CONUS) land area using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data with a dynamic ecoregion clustering approach. Ecoregions, geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. We conducted our dynamic ecoregion clustering by analyzing soil, climate, elevation, and slope data. This analysis facilitated the division of the cropland area within the CONUS into distinct ecoregions. Unlike static ecoregion clustering, which generates a single ecoregion map that remains unchanged over time, our dynamic ecoregion approach produces a unique ecoregion map for each year. This dynamic approach enables us to consider the year-to-year climate variations that significantly impact crop growth, enhancing the accuracy of our crop mapping process. Subsequently, a Random Forest classifier was employed to train individual models for each ecoregion. These models were trained using the time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m NDVI and EVI data retrieved from Google Earth Engine, covering the crop growth periods spanning from 2013 to 2017, and evaluated from 2018 to 2022. Ground truth data were sourced from the US Department of Agriculture’s (USDA) Cropland Data Layer (CDL) products. The evaluation results showed that the dynamic clustering method achieved higher accuracy than the static clustering method in early crop mapping in the entire CONUS. This study’s findings can be helpful for improving crop management and decision-making for agricultural activities by providing early and accurate crop mapping.
Assessment of Active Ground Subsidence in the Dibrugarh and Digboi Areas of Assam, Northeast India, Using the PSInSAR Technique
Authors: Lakhote Abhishek , Kothyari Girish Ch , Patidar Atul Kumar , Changmai Jayshree , Borgohain Rashmi , Choudhury Tanupriya , Um Jung-Sup
Published: October 2023

Ground deformation on a regional to local scale is the consequence of a wide range of natural processes such as tectonic and anthropogenic activities. Globally, the over-extraction of groundwater and hydrocarbon exploitation are the primary causes of ground subsidence. The current study demonstrates regional scale ground subsidence analysis of the Dibrugarh and Digboi regions of Brahmaputra alluvial plain, Assam, Northeast India. To understand the ongoing surface deformation satellite base, the RADAR technique has been applied using SENTINEL-1A data, which were acquired between 15 October 2015 to 25 January 2022. The assessment carried out via the time series analysis of the radar data suggests that the Dibrugarh area is subsiding at a rate of ~5 mm/yr, whereas the Digboi is deforming at a much faster rate (±22 mm/yr) than Dibrugarh. The presence of active faults in the subsurface and associated deformation is another reason for active ground subsidence. The outcomes of the current study validate that the study area is currently undergoing active subsurface deformation caused by both endogenic as well as exogenic processes. Furthermore, our Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) and satellite-based analysis suggest that the over-exploitation of the natural resources is enhancing the rate of deformation in the Brahmaputra alluvial plain in the northeast of India.
Perceptions of Cheating and Doping in E-Cycling
Authors: Richardson Andrew , Berger Nicolas , Smith Phillip
Published: October 2023

E-cycling is a growing area of cycling appealing to competitive cyclists and fitness enthusiasts. Zwift is the most popular e-cycling platform, with approx. 1 million subscribers and is a virtual environment that hosts regular races, including the UCI e-cycling world championships. The popularity of Zwift has given rise to cases of cheating and hacking the system to gain an advantage in e-racing. As a result, some high-profile professional riders have faced bans. We set out to understand the thoughts and concerns e-cyclists have about cheating, hacking, and doping in e-cycling. A total of 337 females and 1130 males were recruited over a 7-week period via social networking sites to complete an online survey. Forty-four per cent had experienced cheating during e-racing, which made them feel angry, annoyed, disappointed, and cheated. However, 15% of those who experienced cheating said they did not care, possibly because many see e-racing as a game or training tool rather than a competitive event. Eighty-seven per cent of participants were in favour of enforcing a ban on cheaters in e-cycling, while 34% wanted cheaters to be banned during in-person cycling events too. Results indicate that many e-cyclists have experienced cheating and would like clearer rules and bans for cheaters during e-races.
Urban Living Labs: A Higher Education Approach to Teaching and Learning about Sustainable Development
Authors: Morales Ivetheyamel , Segalás Jordi , Masseck Torsten
Published: October 2023

This study focuses on the use of urban living labs (ULLs) as a teaching and learning strategy toward education for sustainable development (ESD) in higher education institutions (HEIs). The article presents an exhaustive literature review on the ESD approach in HEIs, the conceptualization and understanding of ULLs, and the use of ULLs as learning environments. Several ULL case studies in HEIs that seek to foster ESD through innovation and experimentation in real-world settings are presented. Each case describes the type, approach, characteristics, results, limitations, and challenges in relation to sustainability. It highlights the need for HEIs to adapt to the ESD approach and become role models for sustainability. It is concluded that ULLs are closely related to ESD and the SDGs, provide a practical and applied learning environment for students, encourage the active participation of students in identifying and solving sustainability problems in their local community, and encourage interdisciplinary collaboration between students and academics from different disciplines. All in all, ULLs can be an effective teaching and learning strategy in HEIs toward ESD. In addition, the lack of specific empirical results on the evaluation of ULL as teaching and learning tools toward ESD in HEIs is highlighted, which justifies the need for further research in this field.
Top Quarks from Tevatron to the LHC
Authors: Jung Andreas , Kieseler Jan
Published: October 2023

Recent measurements in the top quark sector at the CERN Large Hadron Collider are discussed. This review discusses the most recent measurements of inclusive and differential top quark cross-sections in strong and electroweak production of top quarks and related measurements, such as top quark properties, as well as searches, including EFT approaches.
Existence and General Decay of Solutions for a Weakly Coupled System of Viscoelastic Kirchhoff Plate and Wave Equations
Authors: Hajjej Zayd
Published: October 2023

In this paper, a weakly coupled system (by the displacement of symmetric type) consisting of a viscoelastic Kirchhoff plate equation involving free boundary conditions and the viscoelastic wave equation with Dirichlet boundary conditions in a bounded domain is considered. Under the assumptions on a more general type of relaxation functions, an explicit and general decay rate result is established by using the multiplier method and some properties of the convex functions.
Effects of Transporter Inhibitors and Chemical Analogs on the Uptake of Antimonite and Antimonate by Boehmeria nivea L.
Authors: Lu Yi , Peng Fangyuan , Wang Yingyang , Li Haipu , Yang Zhaoguang
Published: October 2023

Antimony (Sb) is a non-essential metalloid that can be taken up by plants from contaminated soils and thus enter the food chain and threaten human health. Boehmeria nivea L. (ramie) is a promising phytoremediation plant for Sb-polluted soils. However, the mechanisms of antimonite (SbIII) and antimonate (SbV) uptake by ramie remain unclear. In this study, a hydroponic system was established to investigate how different substances affect the uptake of SbIII or SbV by ramie, including an energy inhibitor (malonic acid), an aquaglyceroporin inhibitor (silver nitrate), an SbV analog (phosphate—PV), and SbIII analogs (arsenite—AsIII, glycerol, silicic acid—Si, and glucose). The results indicated that ramie primarily transported Sb by increasing the Sb concentration in the bleeding sap, rather than increasing the weight of the bleeding sap. After 16 h of Sb exposure, the absolute amount of transported Sb from the roots to the aboveground parts was 1.90 times higher under SbIII than under SbV. The addition of malonic acid significantly inhibited the uptake of SbV but had limited effects on SbIII, indicating that SbV uptake was energy dependent. PV addition significantly reduced SbV uptake, while the addition of AsIII, glycerol, and Si obviously inhibited SbIII uptake. This suggested that the uptake of SbV might be via low-affinity P transporters and SbIII might use aquaglyceroporins. These findings deepen the understanding of Sb uptake pathways in ramie, contribute to a better comprehension of Sb toxicity mechanisms in ramie, and establish a foundation for identifying the most effective Sb uptake pathways, which could further improve the efficiency of phytoremediation of Sb-polluted soils.
Environmental Geochemical Analysis in the Yanomami Indigenous Land, Mucajaí River Basin, State of Roraima, Brazil
Authors: Jacques Patricia Duringer , Viglio Eduardo Paim , de Oliveira d’El Rei Pinto Daniel
Published: October 2023

The Yanomami Indigenous Land in the Amazon has a long history of illegal artisanal gold mining, leading to concerns about mercury (Hg) contamination. This study has conducted a geochemical analysis to assess Hg contamination from these mining activities. Geological materials, including river water and stream sediments, were collected from 14 predetermined points based on the Geological Survey of Brazil’s methodology. The results revealed that water samples did not show Hg contamination above the limits set by the National Council of the Environment (Conama) Resolution 357. However, two stream sediment samples, particularly PJS009 and PJS010 collected from the Mucajaí River, exceeded the Conama Resolution 454’s limit of 0.17 mg/kg. A Hg content of 0.344 mg/kg was found in the sediment sample PJS009, the one collected further upstream in the Mucajaí River, and 1.386 mg/kg was found in sample PJS010, also in the Mucajaí River in the region shortly before the Fumaça Waterfall, indicating that the sediments of the Mucajaí River may be contaminated with Hg from the Fumaça Waterfall upstream.
First Principles Description of Plasma Expansion Using the Expanding Box Model
Authors: Echeverría-Veas Sebastián , Moya Pablo S. , Lazar Marian , Poedts Stefaan
Published: October 2023

Multi-scale modeling of expanding plasmas is crucial for understanding the dynamics and evolution of various astrophysical plasma systems such as the solar and stellar winds. In this context, the Expanding Box Model (EBM) provides a valuable framework to mimic plasma expansion in a non-inertial reference frame, co-moving with the expansion but in a box with a fixed volume, which is especially useful for numerical simulations. Here, fundamentally based on the Vlasov equation for magnetized plasmas and the EBM formalism for coordinates transformations, for the first time, we develop a first principles description of radially expanding plasmas in the EB frame. From this approach, we aim to fill the gap between simulations and theory at microscopic scales to model plasma expansion at the kinetic level. Our results show that expansion introduces non-trivial changes in the Vlasov equation (in the EB frame), especially affecting its conservative form through non-inertial forces purely related to the expansion. In order to test the consistency of the equations, we also provide integral moments of the modified Vlasov equation, obtaining the related expanding moments (i.e., continuity, momentum, and energy equations). Comparing our results with the literature, we obtain the same fluids equations (ideal-MHD), but starting from a first principles approach. We also obtained the tensorial form of the energy/pressure equation in the EB frame. These results show the consistency between the kinetic and MHD descriptions. Thus, the expanding Vlasov kinetic theory provides a novel framework to explore plasma physics at both micro and macroscopic scales in complex astrophysical scenarios.
A Cell-Adapted Live-Attenuated Vaccine Candidate Protects Pigs against the Homologous Strain VNUA-ASFV-05L1, a Representative Strain of the Contemporary Pandemic African Swine Fever Virus
Authors: Truong Quang Lam , Wang Lihua , Nguyen Tuan Anh , Nguyen Hoa Thi , Tran Son Danh , Vu Anh Thi , Le Anh Dao , Nguyen Van Giap , Hoang Phuong Thi , Nguyen Yen Thi , Le Thi Luyen , Van Thang Nguyen , Huynh Thi My Le , Lai Huong Thi Lan , Madera Rachel , Li Yuzhen , Shi Jishu , Nguyen Lan Thi
Published: October 2023

African swine fever (ASF) is a lethal and highly contagious transboundary animal disease with the potential for rapid international spread. Currently, there is no ASF vaccine commercially available. All infected animals must be isolated and culled immediately upon the confirmation of the presence of the virus. Studies leading to the rational development of protective ASF vaccines are urgently needed. Here, we generated a safe and efficacious live-attenuated vaccine (LAV) VNUA-ASFV-LAVL2 by serially passaging a field isolate (VNUA-ASFV-05L1, genotype II) in porcine alveolar macrophages (PAMs, 65 passages) and an immortalized porcine alveolar macrophage cell line (3D4/21, 55 passages). VNUA-ASFV-LAVL2 can efficiently replicate in both PAMs and 3D4/21 cells. It provides 100% protection, even with the low dose of 102 HAD50, to the vaccinated pigs against the challenge of contemporary pandemic ASFV field isolate. Pigs vaccinated with this LAV in a dose range of 102 to 105 HAD50 remained clinically healthy during both the 28-day observation period of immunization and the 28-day observation period of challenge. VNUA-ASFV-LAVL2 was eliminated from blood by 28 days post-inoculation (DPI), and from feces or oral fluids by 17 DPI. Although the vaccine strain in serum remained a safe and attenuated phenotype after five passages in swine, a reversion-to-virulence study using blood or tissue homogenates at peak viremia will be conducted in the future. ASFV-specific IgG antibodies and significant cellular immunity were detected in vaccinated pigs before the ASFV challenge. These results indicate that the VNUA-ASFV-LAVL2 strain is a safe and efficacious LAV against the genotype II ASFV strain responsible for current ASF outbreaks in Asia.
Expression of Toll-like Receptors on the Immune Cells in Patients with Common Variable Immune Deficiency after Different Schemes of Influenza Vaccination
Authors: Kostinova Aristitsa Mikhailovna , Latysheva Elena Alexandrovna , Akhmatova Nelly Kimovna , Vlasenko Anna Egorovna , Skhodova Svetlana Anatolyevna , Khromova Ekaterina Alexandrovna , Linok Andrey Viktorovich , Poddubikov Arseniy Alexandrovich , Latysheva Tatyana Vasilievna , Kostinov Mikhail Petrovich
Published: October 2023

Background: for the first time, the effect of one and two doses of adjuvanted influenza vaccines on toll-like receptors (TLRs) in patients with common variable immunodeficiency (CVID) was studied and compared (primary vaccination with one vs. two doses, primary vs. repeated vaccination). Materials and methods: Six patients received one dose of quadrivalent adjuvanted influenza vaccine during the 2018–2019 and 2019–2020 influenza seasons, and nine patients with CVID received two doses of trivalent inactivated influenza vaccine during 2019–2020. Expression of TLRs was measured by flow cytometry. Results: The expression of toll-like receptors in patients with CVID was noted both with repeated (annual) administration of the influenza vaccine and in most cases was accompanied by an increase in the proportion of granulocytes (TLR3 and TLR9), lymphocytes (TLR3 and TLR8), and monocytes (TLR3 and TLR9). When carried out for the first time as a simultaneous vaccination with two doses it was accompanied by an increase in the proportion of granulocytes, lymphocytes expressing TLR9, and on monocytes—TLR3 and TLR9. Conclusion: in CVID patients, the use of adjuvanted vaccines is promising, and research on the influence of the innate immunity and more effective regimens should be continued.
Interventions to Vaccinate Zero-Dose Children: A Narrative Review and Synthesis
Authors: Ingle Erin A. , Shrestha Priyanka , Seth Aparna , Lalika Mathias S. , Azie Jacinta I. , Patel Rena C.
Published: October 2023

Zero-dose children, or children who have not received any routine vaccination, are a priority population for global health policy makers as these children are at high risk of mortality from vaccine-preventable illnesses. We conducted a narrative review to identify potential interventions, both within and outside of the health sector, to reach zero-dose children. We reviewed the peer-reviewed and grey literature and identified 27 relevant resources. Additionally, we interviewed six key informants to enhance the synthesis of our findings. Data were organized into three priority settings: (1) urban slums, (2) remote or rural communities, and (3) conflict settings. We found that zero-dose children in the three priority settings face differing barriers to vaccination and, therefore, require context-specific interventions, such as leveraging slum health committees for urban slums or integrating with existing humanitarian response services for conflict settings. Three predominant themes emerged for grouping the various interventions: (1) community engagement, (2) health systems’ strengthening and integration, and (3) technological innovations. The barriers to reaching zero-dose children are multifaceted and nuanced to each setting, therefore, no one intervention is enough. Technological interventions especially must be coupled with community engagement and health systems’ strengthening efforts. Evaluations of the suggested interventions are needed to guide scale-up, as the evidence base around these interventions is relatively small.
The Relationship between COVID-19 Severity in Children and Immunoregulatory Gene Polymorphism
Authors: Kozak Kateryna , Pavlyshyn Halyna , Kamyshnyi Oleksandr , Shevchuk Oksana , Korda Mykhaylo , Vari Sandor G.
Published: October 2023

Coronavirus disease (COVID-19) and its outcomes remain one of the most challenging problems today. COVID-19 in children could be asymptomatic, but can result in a fatal outcome; therefore, predictions of the disease severity are important. The goal was to investigate the human genetic factors that could be associated with COVID-19 severity in children. Single-nucleotide polymorphisms of the following genes were studied: ACE2 (rs2074192), IFNAR2 (rs2236757), TYK2 (rs2304256), OAS1 (rs10774671), OAS3 (rs10735079), CD40 (rs4813003), FCGR2A (rs1801274) and CASP3 (rs113420705). In the case–control study were 30 children with mild or moderate course of the disease; 30 with severe COVID-19 symptoms and multisystem inflammatory syndrome in children (MIS-C) and 15 who were healthy, and who did not have SARS-CoV-2 (PCR negative, Ig G negative). The study revealed that ACE2 rs2074192 (allele T), IFNAR2 rs2236757 (allele A), OAS1 rs10774671 (allele A), CD40 rs4813003 (allele C), CASP3 rs113420705 (allele C) and male sex contribute to severe COVID-19 course and MIS-C in 85.6% of cases. The World Health Organization reported that new SARS-CoV-2 variants may cause previously unseen symptoms in children. Although the study has limitations due to cohort size, the findings can help provide a better understanding of SARS-CoV-2 infection and proactive pediatric patient management.
Sentiment Analysis toward the COVID-19 Vaccine in the Main Latin American Media on Twitter: The Cases of Argentina, Chile, Colombia, Mexico, and Peru
Authors: Córdoba-Cabús Alba , García-Borrego Manuel , Ceballos Yaiza
Published: October 2023

This article analyzes the media coverage of the COVID-19 vaccine by major media outlets in five Latin American countries: Argentina, Colombia, Chile, Mexico, and Peru. For this purpose, the XLM-roBERTa model was applied and the sentiments of all tweets published between January 2020 and June 2023 (n = 24,243) by the five outlets with the greatest online reach in each country were analyzed. The results show that the sentiment in the overall media and in each nation studied was mostly negative, and only at the beginning of the pandemic was there some positivity. In recent months, negative sentiment has increased twelvefold over positive sentiment, and has also garnered many more interactions than positive sentiment. The differences by platform and country are minimal, but there are markedly negative media, some more inclined to neutrality, and only one where positive sentiment predominates. This paper questions the role of journalism in Latin America during a health crisis as serious as that of the coronavirus, in which, instead of the expected neutrality, or even a certain message of hope, the media seem to have been dragged along by the negativity promoted by certain discourses far removed from scientific evidence.
Transcriptional Analysis of lncRNA and Target Genes Induced by Influenza A Virus Infection in MDCK Cells
Authors: Liu Geng , Pei Mengyuan , Wang Siya , Qiu Zhenyu , Li Xiaoyun , Ma Hua , Ma Yumei , Wang Jiamin , Qiao Zilin , Ma Zhongren , Liu Zhenbin
Published: October 2023

Background: The MDCK cell line is the primary cell line used for influenza vaccine production. Using genetic engineering technology to change the expression and activity of genes that regulate virus proliferation to obtain high-yield vaccine cell lines has attracted increasing attention. A comprehensive understanding of the key genes, targets, and molecular mechanisms of viral regulation in cells is critical to achieving this goal, yet the post-transcriptional regulation mechanism involved in virus proliferation—particularly the effect of lncRNA on influenza virus proliferation—is still poorly understood. Therefore, this study used high-throughput RNA-seq technology to identify H1N1 infection-induced lncRNA and mRNA expression changes in MDCK cells and explore the regulatory relationship between these crucial lncRNAs and their target genes. Results: In response to H1N1 infection in MDCK cells 16 h post-infection (hpi) relative to uninfected controls, we used multiple gene function annotation databases and initially identified 31,501 significantly differentially expressed (DE) genes and 39,920 DE lncRNAs (|log2FC| > 1, p < 0.05). Among these, 102 lncRNAs and 577 mRNAs exhibited predicted correlations with viral response mechanisms. Based on the magnitude of significant expression differences, related research, and RT-qPCR expression validation at the transcriptional level, we further focused on 18 DE mRNAs and 32 DE lncRNAs. Among these, the differential expression of the genes RSAD2, CLDN1, HCLS1, and IFIT5 in response to influenza virus infection was further verified at the protein level using Western blot technology, which showed results consistent with the RNA-seq and RT-qPCR findings. We then developed a potential molecular regulatory network between these four genes and their six predicted lncRNAs. Conclusions: The results of this study will contribute to a more comprehensive understanding of the molecular mechanism of host cell non-coding RNA-mediated regulation of influenza virus replication. These results may also identify methods for screening target genes in the development of genetically engineered cell lines capable of high-yield artificial vaccine production.
Projection and Uncertainty Analysis of Future Temperature Change over the Yarlung Tsangpo-Brahmaputra River Basin Based on CMIP6
Authors: Xu Zhangchao , Chen Linyan , Qin Peng , Ji Xuan
Published: October 2023

Accurate predictions of future climate change are significant to both human social production and development. Accordingly, the changes in the daily maximum (Tmax) and minimum temperatures (Tmin) in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB), along with its three sub-regions (Tibetan Plateau—TP, Himalayan Belt—HB, and Floodplain—FP) were evaluated here using the Bayesian model average (BMA) results from nine climate models in the CMIP6 under four future scenarios, and the corresponding uncertainty of the projected results was analyzed. The results showed the following: (1) The BMA can simulate the Tmax and Tmin of the YBRB well. (2) Future Tmax and Tmin over the YBRB exhibited an overall fluctuating upward trend. Even under the most ideal sustainable development scenario examined (SSP126), the average Tmax (Tmin) over the YBRB was projected to increase by 3.53 (3.38) °C by the end of this century. (3) Although the future changes in the YBRB are predicted to fall below the global average, the future temperature difference in the YBRB will increase further. (4) The uncertainty increased with prediction time, while spatially, the regions with the uncertainty were the TP > HB > FP. These findings can provide a reference for the YBRB climate change adaptation strategies.
SPI and SPEI Drought Assessment and Prediction Using TBATS and ARIMA Models, Jordan
Authors: Hasan Nivin Abdelrahim , Dongkai Yang , Al-Shibli Fayha
Published: October 2023

Drought is a complex threat where its propagation is not yet controllable, causing more environmental, social, and economic damage. This research assesses the effects of incessant warming and decrescent precipitation by calculating SPI and SPEI from 1985 to 2021 in the Amman –Zarqa Basin based on five grid points on time and space scales. The study applied the Pearson Correlation Coefficient (PCC) between each one-to-one index at different time scales and the Mann–Kendall test (MKT) to determine trends with different data sources to measure the inferential capturing of historical drought features. Machine learning algorithms are used to predict near-future droughts from 2022 to 2025. TBATS and ARIMA models run diverse input datasets, including observations, CSIC, and CMIP6-ssp126 datasets. The longest drought duration was 14 months. Drought severity and average intensity were found to be −24.64 and −1.76, −23.80 and −1.83, −23.57 and −1.96, and −23.44 and −2.0 where the corresponding drought categories were SPI-12 Sweileh, SPI-9 Sweileh, SPI-12 Wadi Dhullal, SPI-12 Amman Airport, respectively. The dominant drought event occurred between Oct 2020 and Dec 2021. CMIP6-ssp126 can capture the drought occurrence and severity by measuring SPI but did not capture the severity magnitude as the observations (SPI was −2.87 by observation and −1.77 by CMIP6). There are significant differences in drought dimensions between SPI and SPEI, where SPI was more sensitive to drought assessment than SPEI. Using CMIP6-ssp126, ARIMA was more accurate than TBATS, as well as using the observed historical SPEI and CSIC across all stations. The performance metrics ME, RMSE, MAE, and MASE implied significantly promising forecasting models with values of −0.0046, 0.278, 0.179, and 0.193, respectively, for ARIMA and −0.0181, 0.538, 0.416, and 0.466, respectively, for TBATS. The outcomes suggest an increased risk of drought incidents and, consequently, water deficits in the future. Hybrid modelling is suggested for more consistency and robustness of forecasting approaches.
Stochastic Precipitation Generation for the Xilingol League Using Hidden Markov Models with Variational Bayes Parameter Estimation
Authors: Zhang Shenyi , Tuerde Mulati , Hu Xijian
Published: October 2023

Precipitation modeling holds significant importance in various fields such as agriculture, animal husbandry, weather derivatives, hydrology, and risk and disaster preparedness. Stochastic precipitation generators (SPGs) represent a class of statistical models designed to generate synthetic data capable of simulating dry and wet precipitation stretches for a long duration. The construction of Hidden Markov Models (HMMs), which treat latent meteorological circumstances as hidden states, is an efficient technique for simulating precipitation. Considering that there are many choices of emission distributions used to generate positive precipitation, the characteristics of different distributions for simulating positive precipitation have not been fully explored. The paper includes a simulation study that demonstrates how the Pareto distribution, when used as the distribution for generating positive precipitation, addresses the limitations of the exponential and gamma distributions in predicting heavy precipitation events. Additionally, the Pareto distribution offers flexibility through adjustable parameters, making it a promising option for precipitation modeling. We can estimate parameters in HMMs using forward–backward algorithms, Variational Bayes Expectation-Maximization (VBEM), and Stochastic Variational Bayes (SVB). In the Xilingol League, located in the central part of the Inner Mongolia Autonomous Region, China, our study involved data analysis to identify crucial locations demonstrating a robust correlation and notable partial correlation between the Normalized Difference Vegetation Index (NDVI) and annual precipitation. We performed fitting of monthly dry days ratios and monthly precipitation using seasonal precipitation and year-round precipitation data at these crucial locations. Subsequently, we conducted precipitation predictions for the daily, monthly, and annual time frames using the new test dataset observations. The study concludes that the SPG fits the monthly dry-day ratio better for annual daily precipitation data than for seasonal daily precipitation data. The fitting error for the monthly dry day ratio corresponding to annual daily precipitation data is 0.053 (exponential distribution) and 0.066 (Pareto distribution), while for seasonal daily precipitation data, the fitting error is 0.14 (exponential distribution) and 0.15 (Pareto distribution). The exponential distribution exhibits the poorest performance as a model for predicting future precipitation, with average errors of 2.49 (daily precipitation), 40.62 (monthly precipitation), and 130.40 (annual precipitation). On the other hand, the Pareto distribution demonstrates the best overall predictive performance, with average errors of 0.69 (daily precipitation), 34.69 (monthly precipitation), and 66.42 (annual precipitation). The results of this paper can provide decision support for future grazing strategies in the Xilingol League.
A New Multi-Objective Genetic Programming Model for Meteorological Drought Forecasting
Authors: Reihanifar Masoud , Danandeh Mehr Ali , Tur Rifat , Ahmed Abdelkader T. , Abualigah Laith , Dąbrowska Dominika
Published: October 2023

Drought forecasting is a vital task for sustainable development and water resource management. Emerging machine learning techniques could be used to develop precise drought forecasting models. However, they need to be explicit and simple enough to secure their implementation in practice. This article introduces a novel explicit model, called multi-objective multi-gene genetic programming (MOMGGP), for meteorological drought forecasting that addresses both the accuracy and simplicity of the model applied. The proposed model considers two objective functions: (i) root mean square error and (ii) expressional complexity during its evolution. While the former is used to increase the model accuracy at the training phase, the latter is assigned to decrease the model complexity and achieve parsimony conditions. The model evolution and verification procedure were demonstrated using the standardized precipitation index obtained for Burdur City, Turkey. The comparison with benchmark genetic programming (GP) and multi-gene genetic programming (MGGP) models showed that MOMGGP provides the same forecasting accuracy with more parsimony conditions. Thus, it is suggested to utilize the model for practical meteorological drought forecasting.
Does Tobacco Use Enhance the Risk of SARS-CoV-2 Infection: Evidence from Eastern Indian Population
Authors: Rai Arpita , Mehta Nishant , Kumar Ansul , Majhee Lakhan , Verma Pratik , Singh Priyanka , Haque Zeya Ul
Published: October 2023

Background: Tobacco consumption causes altered immune and inflammatory responses which lead to various respiratory diseases such as asthma and chronic obstructive pulmonary disease, as well as cardiovascular and cerebrovascular disorders. Studies have only confirmed the harmful effects of tobacco consumption on the severity of COVID-19. The present study aimed to explore the association between tobacco consumption and the initiation of COVID-19. Methods: This retrospective cohort study was conducted to explore the relationship between tobacco consumption and COVID-19. A brief closed-ended, self-structured questionnaire was prepared to record participants’ responses. The Participants included the individuals who visited Rajendra Institute of Medical Sciences (RIMS), Ranchi, India for the COVID-19 diagnostic test. The statistical analysis was performed using SPSS software (version 24). The chi-square test and logistic regression analysis were also used to predict the odds of getting infected with COVID-19. Findings: A total of 521 valid responses were obtained and subjected to analysis. Moreover, 256 participants (49.13%) were COVID-19 positive and 57 participants (10.94%) were tobacco users. The odds ratio of tobacco consumption was higher in COVID-19-positive patients compared to COVID-19-negatives (OR=1.78; 95% CI 1.01, 3.13). The current tobacco users had a higher risk of developing COVID-19 as compared to the former users (OR=4.8; 95% CI 1.39, 16.61). The frequency and duration of tobacco use also affected the COVID-19 infectivity rate but these were statistically insignificant. Conclusion: The COVID-19 positivity rate was significantly higher in tobacco users, especially in current tobacco users as compared to former users. Nevertheless, gender and occupation had no significant effect on COVID-19 incidence in this study.
Determining the Burden of Suicidal Behaviors Using the DALY Approach: A Case Study in Iran (2018-2021)
Authors: Bahram Nejad Ali , Behzadi Anahita , Ostad Ahmadi Zakeih , Barouni Mohsen
Published: October 2023

Background: Suicide is a significant and growing concern in health systems worldwide. It is considered a crucial part of the comprehensive mental health action plan in every country. Kerman, one of the largest provinces of Iran with a relatively high population, has witnessed an increasing trend in this phenomenon, especially during the COVID-19 pandemic. Methods: This was a cross-sectional study conducted in urban and rural areas of Kerman. Suicide data for 2018–2021 were acquired from the Psychosocial Health and Addiction Prevention Group of the Deputy for Health at Kerman University of Medical Sciences. The burden resulting from suicide during these years was measured using the disability-adjusted life years (DALY) index. Findings: During these four years, 23701 suicide attempts were recorded in Kerman, with 59% and 41% of the suicide attempts made by men and women, respectively, and 668 (2.82%) attempts leading to death. The highest rate (68%) was observed in the 15–29 age range and the lowest rate (1.1%) was seen in people older than 60. Poisoning (89.3% of the attempts) was the most common suicide method. The suicide burden in Kerman in 2021 was 4417 according to the DALY index, which is 162.6 per 100000 people; men and women endure 38% and 62% of this burden, respectively. The highest DALY rates were seen in the 15–29 and 30–44 age groups. Conclusion: The burden resulting from suicide highlights the necessity of taking immediate measures to prevent this behavior, especially among vulnerable groups.
Willingness to Quit and Associated Factors Among Tobacco Users Attending Outpatient Departments of a Tertiary Care Hospital in Delhi, India
Authors: Goyal Mohit , Khokhar Anita , Lukhmana Shveta , Debnath Aninda , Srivastava Namita
Published: October 2023

Background: Tobacco use has become a modern-day epidemic which significantly impacts health, socioeconomic status, and environmental sustainability. The readiness to quit or stop using tobacco is a crucial first step in changing one’s behavior. Hence, the current study sought to assess the prevalence of willingness to quit and associated factors among tobacco users. Methods: This study was conducted on 425 tobacco users selected using multi-stage random sampling from the outpatient departments (OPDs) of a tertiary care hospital in Delhi, India. A pre-designed, interviewer-administered questionnaire was used to elicit information. Logistic regression was performed to assess the effect of independent factors on the willingness to quit. Findings: The mean age of the study participants was 39.37 years (S.D.=±12.99). The majority of the participants were male (400, 94.1%), and 25 (6.9%) were female. Overall, the prevalence of willingness to quit in the current study was 70% among the study participants. The results of the multivariable analysis showed that those belonging to urban areas, tobacco users with a duration of≤10 years, and those who received advice from a doctor to quit had a significantly higher willingness to quit than their counterparts. However, age, gender, marital status, education, religion, age of initiation of tobacco use, and nicotine dependence were not found to have a statistically significant relationship with the willingness to quit tobacco products. Conclusion: Willingness to quit was high among the study participants. The data in this study suggested that belonging to urban areas, duration of tobacco use, and doctor’s advice to quit are important factors which need to be considered when framing future tobacco cessation programs.