This study investigates the trend in monthly and annual rainfall, minimum and maximum temperature (T_min and T_max) using the Mann–Kendall (MK) test and Sen’s slope (SS) method and evaluates the significance of their variability for maize, sorghum and millet yields in northern Togo employing multiple regression analysis. The historical data of Kara, Niamtougou, Mango and Dapaong weather stations from 1977 to 2012 were used. Four non-parametric methods—Alexandersson’s Standard Normal Homogeneity Test (SNHT), Buishand’s Range Test (BRT), Pettitt’s Test (PT) and Von Neumann’s Ratio Test (VNRT)—were applied to detect homogeneity in the data. For the data which were serially correlated, a modified version of the MK test (pre-whitening) was utilised. Results showed an increasing trend in the annual rainfall in all four locations. However, this trend was only significant at Dapaong (p < 0.1). There was an increasing trend in T_max at Kara, Mango and Niamtougou, unlike Dapaong where T_max revealed a significant decreasing trend (p < 0.01). Similarly, there was an increasing trend in T_min at Kara, Mango and Dapaong, unlike Niamtougou where T_min showed a non-significant decreasing trend (p > 0.05). Rainfall in Dapaong was found to have increased (7.79 mm/year) more than the other locations such as Kara (2.20 mm/year), Niamtougou (4.57 mm/year) and Mango (0.67 mm). T_max increased by 0.13, 0.13 and 0.32 °C per decade at Kara, Niamtougou and Mango, respectively, and decreased by 0.20 per decade in Dapaong. Likewise, T_min increased by 0.07, 0.20 and 0.02 °C per decade at Kara, Mango and Dapaong, respectively, and decreased by 0.01 °C per decade at Niamtougou. Results of multiple regression analysis revealed nonlinear yield responses to changes in rainfall and temperature. Rainfall and temperature variability affects rainfed cereal crops production, but the effects vary across crops. The temperature has a positive effect on maize yield in Kara, Niamtougou and Mango but a negative effect on sorghum in Niamtougou and millet in Dapaong, while rainfall has a negative effect on maize yield in Niamtougou and Dapaong and millet yield in Mango. In all locations, rainfall and temperature variability has a significant effect on the cereal crop yields. There is, therefore, a need to adopt some adaptation strategies for sustainable agricultural production in northern Togo.

This paper defines the two main causes of global warming in the UK and seeks to cut climate change using green hydrogen made from renewable electricity sources. Combustion vehicles powered by fossil gasoline and diesel emit a third of UK climate-warming CO2 while buildings heated by natural gas provide a quarter. First, current UK grid problems are defined: Electricity grid, gas grid and petroleum grid. Then experiments on the private energy community of Keele University allow electrolysis demonstrations producing green hydrogen for buildings and vehicle refueling. Next, the model supply chain is planned and tested. Finally, experiments and calculations are outlined, analyzing the optimum system design criteria proposed. We conclude that economic green hydrogen can displace diesel and gasoline in vehicle transport, while also displacing natural gas for combined heat and power in buildings. In addition, the prospect in 2023 is that profits can now be made all along the green hydrogen supply chain, such that new businesses involved in clean communities can beat the National Grid monopoly and other large dominant fossil grid companies.

One of the oldest problems in physics is that of calculating the motion of N particles under a specified mutual force: the N-body problem. Much is known about this problem if the specified force is non-relativistic gravity, and considerable progress has been made by considering the problem in one spatial dimension. Here I review what is known about the relativistic gravitational N-body problem. Reduction to one spatial dimension has the feature that gravitational radiation is absent, thereby allowing for a clear comparison between the physics of one-dimensional relativistic and non-relativistic self-gravitating systems. After describing how to obtain a relativistic theory of gravity coupled to N point particles, I discuss in turn the 2-body, 3-body, 4-body, and N-body problems. Quite general exact solutions can be obtained for the 2-body problem, unlike the situation in general relativity in 3 spatial dimensions for which only highly specified solutions exist. The 3-body problem exhibits mild forms of chaos, and provides one of the first theoretical settings in which relativistic chaos can be studied. For N4 other interesting features emerge. Relativistic self-gravitating systems have a number of interesting problems awaiting further investigation, providing us with a new frontier for exploring relativistic many-body systems.

The Mann-Whitney effect is a measure for comparing survival distributions between two groups. The Mann-Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann-Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent each other, the traditional expression of the Mann-Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann-Witney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands (https://nkosuke.shinyapps.io/shiny_survival/). Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets.

In this work, we extend the fundamental results of Schu to the class of monotone asymptotically nonexpansive mappings in modular function spaces. In particular, we study the behavior of the Fibonacci-Mann iteration process defined by $$x_{n+1} = t_n T^{\phi(n)}(x_n) + (1-t_n)x_n,$$ for $n \in \mathbb{N}$, when $T$ is a monotone asymptotically nonexpansive self-mapping.

Global warming may increase the potential evapotranspiration (Etp), thereby affecting the amount of clean water resources available in a region. In this study, the systematic biases in temperature outputs of 24 global climate models (GCMs) under 3 emission scenarios are corrected by using 8 bias correction methods. The trend of Etp in Yangzi River Delta in the 21st century is projected by using 4 Etp calculation formulas. The uncertainty of the projections is estimated and decomposed by using multi-way analysis of variance. The influence of uncertainty on the projected change signal is quantified by using the signal-to-noise ratio. The results show that, relative to the reference period (1971~2000), the annual mean daily Etp in Yangzi River Delta will increase by 0.14~0.17mm/d during the period 2021~2050 and by 0.21~0.41mm/d during the period 2061~2090, respectively. During 2021~2050, the uncertainty of Etp increase projections is dominantly contributed by the main effects of GCM (63%) and Etp calculation formula (24%). During 2061~2090, it is mainly contributed by the main effect of GCM (36%), followed by the main effects of emission scenario (34%) and Etp calculation formula (18%). The increase projections of Etp are generally reliable and robust during the two projection periods.

To make wind power more competitive, it is necessary to reduce turbine downtime and reduce costs associated with wind turbine Operation and Maintenance (O&amp;M). Incorporating machine learning in developing condition-based predictive maintenance methodologies for wind turbines can enhance their efficiency and reliability. This paper presents a monitoring method that utilizes Base Density for the Support Vector Machine (BDSVM) and the evolutionary Fourier spectra of vibrations. This method allows smart monitoring of the function evolution of the turbine. A complex optimal function (FO) for 5-degree order has been developed that will be the boundary function of the BDSVM to timely determine the magnitude, frequency, and place of the failure occurring in wind turbine drive.

Long term Precipitation and temperature variations are one of the main determinants of climate variability of one’s area. The aim of this study is to determine trends variation in climatic elements of temperature and precipitation in the southern zone of Tigray regional state, Ethiopia. The station is assumed for the study of climatic records over southern zone of the region in detection for probable trends. The daily, monthly and annual precipitation totals and temperature observed at korem meteorological station were used for the period of 1981-2010 for Precipitation and 1985 – 2010 for minimum and maximum temperature. Summary of descriptive statistics and Mann Kendall test methods were employed for the observed data analysis to demonstrate any existence of possible trends. The main findings of the study indicated that the mean and maximum temperature had a general increasing trend; however, minimum temperature showed decreasing trend. In general annual temperature from 1985 – 2010 of the area showed a warming trend. Moreover analysis of the 30 years (1981-2010) annual precipitation showed a coefficient of variation ranging from 33.77 – 233 %. It indicated that the precipitation dissemination is not normal with large year to year variances.

Field electron emission, or electron tunneling through a potential barrier under the influence of a strong electrostatic field, is of broad interest to the accelerator physics community. For example, it is the source of undesirable dark currents in resonant cavities, providing a limit to high-field operation. The classical approach to field electron emission is the Fowler-Nordheim (FN) framework, which incorporates a simplified surface potential and various assumptions. Here we build a more realistic model using the potential and charge densities derived from a density-functional theory (DFT) calculation. We examine the correction factors associated with each model assumption. Compared to the FN framework, our results can be extended up to 80 GV/m, a limit that has been reached in laser-induced strong field emission scenarios.

Polar Mesospheric Summer Echoes (PMSEs) are radar echoes that are observed in the mesosphere during the Arctic summer months in the polar regions. By studying PMSE, researchers can gain insights into physical and chemical processes that occur in the upper atmosphere specifically in the 80 to 90 km altitude range. In this paper, we employ fully convolutional networks such as UNET and UNET++ for the purposes of segmenting PMSE from the EISCAT VHF dataset. First, experiments are performed to find suitable weights and hyperparameters for UNET and UNET++. Second, different loss functions are tested to find one suitable for our task. Third, as the number of PMSE samples used is relatively small that can lead to poor generalization. To address this, image-level and object-level augmentation methods are employed. Four, we briefly explain our findings by employing layerwise relevance propagation.

In this paper, we present a framework to study the spatial structure of noctilucent clouds formed by ice particles in the upper atmosphere at mid and high latitude during summer. We study noctilucent cloud activity in optical images taken from three different locations and under different atmospheric conditions. In order to identify and distinguish noctilucent cloud activity from other objects in the scene, we employ linear discriminant analysis (LDA) with feature vectors ranging from simple metrics to higher-order local autocorrelation (HLAC), and histogram of oriented gradients (HOG). Finally, we propose a Convolutional Neural Networks (CNN) based method for the detection of noctilucent clouds. The results clearly indicate that the CNN based approach outperforms LDA based methods used in this article. Furthermore, we outline suggestions for future research directions to establish a framework that can be used for synchronizing the optical observations from ground based camera systems with echoes measured with radar systems like EISCAT in order to obtain independent additional information on the ice clouds.

Cool-season grasses are the most common forage types in livestock operations and amenities. Several of the cool-season grasses establish mutualistic associations with an endophytic fungus of the Epichloe genus. The grasses and endophytic fungi have evolved over a long period of time to form host-fungus specific relationships that confer protection for the grass against various stressors in exchange for housing and nutrients to the fungus. This review provides an overview of the mechanisms by which Epichloe endophytes and grasses interact, including molecular pathways for secondary metabolite production. It also outlines specific mechanisms by which the endophyte helps protect the plant from various abiotic and biotic stressors. Finally, the review provides information on how Epichloe infection of grass and stress affect the rhizosphere environment of the plant.

Evidence shows that weightloss improves insulin sensitivity but few studies have examined the effect of macronutrient composition independently of weight loss on direct measures of insulin sensitivity. We randomised 89 overweight or obese women to either a standard diet (StdD) that was intended to be low in fat and relatively high in carbohydrate (n=42) or to a relatively high protein (up to 30% of energy), relatively high fibre (>30g/day) diet (HPHFib) (n=47) for 10 weeks. Advice regarding strict adherence to energy intake goals was not given. Insulin sensitivity and secretion was assessed by a novel method - the Dynamic Insulin Sensitivity and Secretion Test (DISST). Although there were significant improvements in body composition and most cardiometabolic risk factors on HPHFib, insulin sensitivity was reduced by 19.3% (95% CI: 31.8, 4.5%; p=0.013) in comparison with StdD. We conclude that the reduction in insulin sensitivity after a diet relatively high in both protein and fibre, despite cardiometabolic improvements, suggests insulin sensitivity may reflect metabolic adaptations to dietary composition for maintenance of glucose homeostasis, rather than impaired metabolism.

Despite the extensive research conducted on ruminal methanogens and anti-methanogenic intervention strategies over the last 50 years, most of the currently researched enteric CH4 abatement approaches have shown limited efficacy. This is largely because of the complex nature of animal production and the ruminal environment, host genetic variability of CH4 production, and an incomplete understanding of the role of the ruminal microbiome in enteric CH4 emissions. Recent sequencing-based studies suggest the presence of methanogenic archaea in extra-gastrointestinal tract tissues, including respiratory and reproductive tracts of cattle. While these sequencing data require further verification by culture-dependent methods, the consistent identification of methanogens with relatively greater frequency in the airway and urogenital tract of cattle plus increasing appreciation of the microbiome-gut-organ axis highlight the potential interactions between ruminal and extra-gastrointestinal methanogenic communities. Thus, a traditional singular focus on ruminal methanogens may not be sufficient, and a holistic approach which takes into consideration of the transfer of methanogens between ruminal, extra-gastrointestinal, and environmental microbial communities is of necessity to develop more efficient and long-term ruminal CH4 mitigation strategies. In the present review, we provide a holistic survey of the methanogenic archaea present in different anatomical sites of cattle and discuss potential seeding sources of the ruminal methanogens.

A definitive transcriptome atlas for the non-coding expressed elements of pathogenic mycobacteria does not exist. Incomplete lists of non-coding transcripts can be obtained for some of the reference genomes (e.g. Mycobacterium tuberculosis H37Rv) but to what extent these transcripts have homologues in closely related species or even strains is not clear. This has implications for the analysis of transcriptomic data; non-coding parts of the transcriptome are often ignored in the absence of formal, reliable annotation. Here, we review the state of our knowledge of non-coding RNAs in pathogenic mycobacteria, emphasising the disparities in the information included in commonly used databases. We then proceed to review ways of combining computational solutions for predicting the non- coding transcriptome with experiments that can help refine and confirm these predictions.

Ovarian cancer (OC) is the most lethal gynecologic malignancy. Cannabis sativa is being used to treat different medical conditions. We sought to examine the effectiveness of combinations of cannabis compounds against OC. Cytotoxic activity was determined by XTT assay on HTB75 and HTB161 cell lines. Apoptosis and cell cycle were determined by fluorescence-activated cell sorting (FACS). Gene expression was determined by quantitative PCR. The two most active fractions, F5 and F7, from a high Δ9–tetrahydrocannabinol (THC) cannabis strain extract and their standard mix (SM) showed cytotoxic activity against OC cells. The most effective phytocannabinoid combination was THC+cannabichromene (CBC)+cannabigerol (CBG). F5, F7 and SM affected cell cycle, led to cell apoptosis and to a marked reduction in cell migration. Moreover, these fractions act in synergy with niraparib, and were ~50 fold more cytotoxic to OC cells than to normal keratenocytes. Niraparib+F7 treatment was effective on OC patient's cells. F7 and the niraparin+fraction (F5 and F7) treatments reduced Mitogen-Activated Protein Kinase 4 (MAPK4) gene expression; this reduction may act in synergy with the niraparib inhibition of Poly (ADP-ribose) polymerase 1 (PARP1) activity. Combinations of cannabis compounds and niraparib should be examined for efficacy in pre-clinical studies and clinical trials.

This work presents the first evidence that dissolved globular proteins change the arrangement of hydrogen bonds in water, with different proteins showing quantitatively different effects. Using ATR-FTIR (Attenuated Total Reflection – Fourier Transform Infrared) spectroscopic analysis of OH-stretch bands, we obtain quantitative estimates of the relative amounts of the previously reported four subpopulations of water structures coexisting in a variety of aqueous solutions. Where solvatochromic dyes can measure the properties of solutions of non-ionic polymers, the results correlate well with ATR-FTIR measurements. In protein solutions to which solvatochromic dye probes cannot be applied, NMR (Nuclear Magnetic Resonance) spectroscopy was used for the first time to estimate the hydrogen bond donor acidity of water. We found strong correlations between the solvent acidity and arrangement of hydrogen bonds in aqueous solutions for several globular proteins. Even quite similar proteins are found to change water properties in dramatically different ways.

Functional copy number alterations (fCNAs) are DNA copy number changes with concordant differential gene expression. These are less likely to be bystander genetic lesions and could serve as robust and reproducible tumor biomarkers. To identify candidate fCNAs in neuroendocrine tumors (NETs), we integrated chromosomal microarray (CMA) and RNA-seq differential gene expression data from 31 pancreatic (pNET) and 33 small bowel neuroendocrine tumors (sbNET). Tumors were resected from 47 early disease progression (24 months) patients. Candidate fCNAs that accurately differentiated these groups in this discovery cohort were then replicated using fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded (FFPE) tissues in a larger validation cohort of 60 pNETs and 82 sbNETs (52 early and 65 late disease progression samples). Logistic regression analysis determined the predictive ability of these biomarkers as well as the assay performance metrics of sensitivity, specificity, and area under the curve. Our results indicate that copy number changes at chromosomal loci 4p16.3, 7q31.2, 9p21.3, 17q12, 18q21.2, and 19q12 may be used as diagnostic and prognostic NET biomarkers. This involves a rapid, cost-effective approach to determine primary tumor site for patients with metastatic liver NETs and to guide risk stratified therapeutic decisions.

The sudden and abrupt rise of COVID-19 became a challenge for the world economy. In this paper, we investigate the changes of a trend of mutual trade between the EU-15 countries and China during the demanding times of the COVID-19 crisis. We use monthly data for Chinese export to the EU (2018:01 – 2020:05) and import from the EU (2018:01 – 2020:07) relying on the data from open source Trademap. We employ descriptive statistics to analyze the trade. Overall, there is the obvious decline of 13-32 per cent in worldwide trade as predicted by the WTO. This affected China as the main trading partner of electronic devices and medical supplies. The trade between the EU and China has decreased, but the major change in demand brought the alteration in commodities structure and re-orientation of Chinese export production. In the first five months of 2020, we have witnessed strong engagement of the Chinese economy in new highly demanded production - mainly to articles strongly linked with healthcare and medical equipment. Thus, we have observed that the Chinese were very flexible in changing the structure of export which was triggered by COVID-19 crisis. This flexibility is worth further exploration.

Fine-mesh exclusion netting is a potential alternative to organic and conventional insecticide application to control numerous pests of fruit crops. We tested whether fine-mesh exclusion netting would reduce pest abundance and increase marketable yield compared to organic spinosad insecticide sprays in an organically managed blackberry field. At the completion of flowering, we covered blackberry rows (N = 3) with fine-mesh exclusion netting (ProtekNet) and treated alternating rows (N = 3) with an organic spinosad insecticide (Entrust™). Fine-mesh exclusion reduced green June beetle (Cotinus nitida Linnaeus) and bird presence and marginally reduced Japanese beetle (Popillia japonica Newman) presence on blackberry canes compared to organic spinosad insecticide treatment. Exclusion netting reduced the capture of spotted-wing Drosophila (Drosophila suzukii Matsumara; SWD) in baited traps in the fourth week of exclusion, and reduced the overall number of SWD adults emerging from harvested blackberry fruits. Marketable yield in the fine-mesh exclusion treatments was two times higher than the organic spinosad insecticide treatment. These results suggest that fine-mesh exclusion netting is a functional pest control alternative to organic insecticide treatment.

In Bangladesh, climate change is a major concern because of its geophysical location and climate dependent agriculture. As sessile organisms, crops plants have to face difficulties often in this environmentally vulnerable country. Therefore, this study examines the seasonal trend of two climatic parameters viz. temperature (maximum and minimum) and rainfall over a period of 1983 to 2013. Besides, this study provides insight into the relationship between climatic parameters and crop yield of two major crops viz. rice and wheat during 1997-2013. To assess the relationship of climatic parameters with time and yield using Pearson correlation analysis, time series data used at an aggregate level. SPSS software utilized for this analysis. The cropping seasons such as rice growing seasons Aus (summer rice), Aman (autumn rice) and Boro (winter rice) exhibited a significant increase in maximum and minimum temperature. Rainfall found to have a decreasing trend for all the seasons. This study also revealed that the climatic parameters had significant effects on rice yield, but these results varied among three rice crops. Maximum temperature had positive effects on all rice yields, especially on Aus and Aman. Minimum temperature had a negative effect on Aman rice yield but a positive effect on Aus rice yield. Wheat yield negatively associated with temperature. Rainfall exhibited negative relation with both rice and wheat yield.

Defect detection and identification from fruits and vegetables are particularly challenging for Indian agriculture. Defect Detection is a process to identify the defects or damages in vegetables and fruits, based on the shapes, colors and textures. The local market finds it difficult to cope with the defects and other infections in fruits and vegetables as quality evaluations and classification of vegetables and fruits have become tedious process. Recently, several approaches based on Image processing, Machine Learning and Artificial Intelligence methods have been proposed for the purpose of defect detection. On the basis of classifying the types of defects, related pathogens, and physical and morphological characteristics descriptors, we review the different approaches based on a corpus of 57 articles between 2016 and 2021. In the process of describing the defect analysis, steps from the target articles, algorithms, and methods including qualitative and quantitative evaluation are mainly summarized. The aim of this current review work is to present-day novel images and collects recent defective area calculation methods to detect surface defects of fruits and vegetables using RGB images and to classify whether the fruit is defected or fresh. A rigorous evaluation of many new algorithms provided for quality assurance by researcher’s probes of vegetables and fruits have been conducted in this work. This review work conveys that using the recent identification features will help to decrease the disadvantages in fruit storeroom owing to storage of the affected vegetables and fruits, ie. Preventing the spread of defects and other infections from the infected fruits and vegetables to the fresh ones.

Available water resources in the Middle East, as one of the most water-scarce regions of the world, have undergone extra pressure due to climatic change, population growth, and economic development during the past decades. The objective of this study is to detect the trends and quantify the changes in aridity with respect to precipitation and potential evapotranspiration in 20 countries of the Middle East and the adjacent area. A Pixel-wised trend analysis was conducted on precipitation, potential evapotranspiration, and aridity index for 71 years from 1948 to 2018. A nonparametric Mann-Kendall test was used over 14106 points in the study area to detect the trends at monthly and annual time scales. Results showed statistically significant (|Z| >1.96) upward trends in aridity (a downward trend in aridity index) up to 96 percent from December through September in most parts of the region. Aridity in October and November had a downward tendency in most parts of the study area. At the annual time scale, 62.5 percent of the statistically significant trends in aridity were found to be upward (up to 96 percent increase in aridity) due to the combined effects of the decrease in precipitation and the increase in potential evapotranspiration and 37.5 percent of the detected trends were downward (up to 61 percent decrease in aridity). The highest and the lowest trends in aridity were found in the north of Sudan (96 percent increase in aridity) and Eastern Arabia (61 percent decrease in aridity), respectively.

The study focused on two climatic variables i.e. precipitation and stream flow for analysing change in trend of rainfall for the River Ssezibwa Catchment area and stream flow of River Ssezibwa. This Catchment is found in the districts of Mukono and Buikwe in Uganda. In this area agriculture is majorly dependent on rainfall and irrigation on a small scale. However, rainfall occurrence has become unpredictable over the past few years as result of the changes in patterns of weather. This has caused severe effects on the agricultural cropping system as well as caused negative effects on the natural water resources. Stream flow data of 57 years (1960 – 2017) and rainfall data for 35 years (1982 – 2017) on a daily basis was analysed to find out the trend and detect change point. Trend analysis was done by using the non-parametric analysis while the change point detection was carried out by using the Pettit test (1979).Magnitude of trend for the time series data was carried out using Sen’s Slope estimator and Mann – Kendall test was done to determine the trend. Results from the statistical analysis highlighted that; for stream flow the trend was generally positive and change point detected to be in the year 2000 while for rainfall data analysis indicated that the trend was predominantly negative and change point was in the year 1998.

Objective: To visualize and analyze the published literature on diabetes and pyroptosis based on a bibliometric approach, so as to provide a comprehensive picture of the hot research directions and dynamic progress in this field. Methods: This study was based on the web of science core collection database to conduct a comprehensive search of the published literature in the field of diabetes and Pyroptosis from January 1985 to August 2022, including the published research literature in this field, as well as a visual analysis of the number of citations, year of publication, journal, author, research institution, country, and research topic. Results: A total of 139 literature on research related to diabetes and cellular scorch from 2011 to 2022 were retrieved, with a total of 3009 citations and a maximum of 255 citations for a single article, which had a first authorSchmid-Burgk, JL The first author of this article is from Germany; among 20 publishing countries, China leads with 100 articles; among 222 publishing institutions, Harbin Medical University leads with 18 articles and 184 citations; among 980 authors, Chen, X from China tops the list of high-impact authors with 5 articles and 29 citations; Among the 98 journals, "CELL DEATH DISEASE" ranked first in both volume and high impact journals with 4 articles and 29 citations; among 349 keywords, "pyroptosis" ranked first with a cumulative frequency of 65 times. The cluster analysis was divided into three categories, chronic complications of diabetes and pyroptosis (67 articles), diabetes and pyroptosis (60 articles), and diabetes combined with other diseases and pyroptosis (12 articles), and the number of articles related to diabetes and its chronic complications increased rapidly from 2019, among which, diabetic cardiomyopathy (27 articles) had the highest number of articles. Conclusions: Based on a comprehensive analysis of published literature in the field of diabetes and pyroptosis from 2011-2022, this study achieved a visual analysis of studies with significant and outstanding contributions to the field, thus framing a picture showing the development and changes in the field. At the same time, this study provides research information and direction for clinicians and investigators to conduct diabetes and pyroptosis related research in the future.

Respiratory Syncytial Virus (RSV) causes severe inflammation and airway pathology in children and the elderly by infecting the epithelial cells of the upper and lower respiratory tract. RSV replication is sensed by intracellular pattern recognition receptors upstream of the IRF and NF-B transcription factors. These proteins coordinate an innate inflammatory response via Bromodomain containing protein 4 (BRD4), a protein that functions as a scaffold for unknown transcriptional regulators. To better understand the pleiotropic regulatory function of BRD4, we examine the BRD4 interactome and identify how RSV infection dynamically alters it. To accomplish these goals, we leverage native immunoprecipitation and Parallel Accumulation – Serial Fragmentation (PASEF) mass spectrometry to examine BRD4 complexes isolated from human alveolar epithelial cells in the absence or presence of RSV infection. In addition, we explore the role of BRD4’s acetyl-lysine binding bromodomains in mediating these interactions by using a highly selective competitive bromodomain inhibitor. We identify 101 proteins that are significantly enriched in the BRD4 complex and are responsive to both RSV-infection and BRD4 inhibition. These proteins are highly enriched in transcription factors and transcriptional coactivators. Among them, we identify members of the AP1 transcription factor complex, a complex important in innate signaling and cell stress responses. We independently confirm the BRD4/AP1 interaction in primary human small airway epithelial cells. We conclude that BRD4 recruits multiple transcription factors during RSV infection in a manner dependent on acetyl-lysine binding domain interactions. This data suggests that BRD4 recruits transcription factors to target its RNA processing complex to regulate gene expression in innate immunity and inflammation.

Abstract:High-pressure water jet technology has received extensive attention and application due to its high efficiency and environmental protection characteristics. The high-pressure water jet nozzle is its core component, and the research on the high-pressure water jet nozzle is conducive to improving the jet performance and improving the jet efficiency. This paper introduces the characteristics of water jet and the working principle of pure water jet, abrasive water jet, pulsed water jet and cavitation water jet, reviews the theoretical analysis and experiment of the predecessor's nozzle, briefly explains the application of high-pressure water jet and looks forward to the development trend of this technology.

With the development of information technology and the wide application of building information modelling technology, the construction industry continues to make digital changes. Managers are trying to apply smart construction site management to promote the upgrading of production modes. We used CiteSpace software to analyze 1707 articles from China National Knowledge Infrastructure, Web of Science and Scopus to understand the current research hotspots of global scholars in this field. Results show that: (1) The number of studies on smart construction sites increases rapidly by year. Researchers from China, America and the UK have the most influential studies. (2) The cooperation between researchers and institutions is not close enough. In addition, the directions of research in the field are still scattered; (3) Chinese scholars are good at building intelligent platforms and evaluating intelligent systems from multiple perspectives. International scholars are willing to pursue technological innovation, allowing the continuous development of intelligent construction technology. (4) The applications in this field have not been popularized despite relatively perfect technology and typical cases. Future scholars should gradually improve the theoretical basis and industry standards of smart construction sites, promote the development of intelligent construction technology and establish the evaluation standards of qualified smart sites. This study will provide scholars in this field with the theoretical basis and research directions for further in-depth research, help construction companies to understand the development status and trend of smart construction sites and accelerate the intelligent transformation of construction companies.

This report provides a comprehensive overview of occupational asthma and respiratory hypersensitivity, focusing on some chemicals used in the workplace Includes numerous case studies and research papers related to occupational exposure methacrylates, such as HEMA-HPMA, and respiratory hypersensitivity are discussed And the relationship is explored The book also examines asthma pathophysiology, emerging drug delivery systems used to treat asthma, and the potential of nanoparticles , dendrimers and micellar systems have to improve pulmonary drug delivery in detail The worldwide prevalence of asthma is highlighted, with higher rates in high-GDP countries. The book highlights the need to improve asthma diagnosis, treatment and biomarkers, particularly in rural and low-income countries where the disease is often underdiagnosed and inadequately treated, and discusses the role of inflammation in asthma, in particular secondary inflammation characterized by specific cytokines and inflammation cells In addition, the paper explores various drug delivery methods, such as parenteral and parenteral drugs, pressurized inhalers (pMDIs), dry powder inhalers ( DPIs), nebulizers, nanoparticles, liposomes, micellar systems, microparticles , dendrimers, and gene therapy . These systems aim to increase pulmonary delivery of drugs and improve clinical outcomes. The potential of dendrimer bilirubin-based nanoparticles and micellar systems in the treatment of asthma and lung inflammatory diseases, including asthma, has been discussed in summary, this paper provides an overview of occupational asthma and respiratory hypersensitivity, and this discussion highlights the association between methacrylate exposure and respiratory hypersensitivity.

Systematic traders employ algorithmic strategies to manage their investments. As a result of the deterministic nature of such strategies, it is possible to determine their exact responses to any conceivable set of market conditions. Consequently, sensitivity analysis can be conducted to systematically uncover undesirable strategy behavior and enhance strategy robustness by adding controls to reduce exposure during periods of poor performance / unfavorable market conditions or increase exposure during periods of strong performance / favorable market conditions. In this study, we formulate both a simple systematic trend-following strategy (i.e., trading model) to simulate investment decisions, and a market model to simulate the evolution of instrument prices. We then map the relationship between market model parameters under various conditions and strategy performance. We focus, in particular, on identifying the performance impact of changes in both serial dependence in price variability and changes in the trend. The long-range serial dependence of the true range worsens performance of the simple classic trend-following strategy. During periods of strong performance, the dispersion of trading outcomes increases significantly as long-range serial dependence increases.

Magnesium alloy stents have been extensively studied in the field of biodegradable metal stents due to their exceptional biocompatibility, biodegradability and excellent biomechanical properties. Nevertheless, the specific in vivo service environment causes magnesium alloy stents to degrade rapidly and fail to provide sufficient support for certain time. This paper presents an overview of the development history and protection strategies for magnesium alloy stents. Alloying, optimizing stent structure and preparing protective coating have improved the corrosion resistance of magnesium alloy stents. Based on the corrosion mechanism of magnesium alloy stents, as well as their deformation during use and environmental characteristics, we present some novel solution strategies aiming at reducing the corrosion rate of magnesium alloys and enhancing the comprehensive performance of magnesium alloy stents. These strategies include adapting protective coatings for the deformation of the stents, preparing rapid endothelialization coatings to enhance the service environment of the stent, and constructing coatings with self-healing functions. It is hoped that this review can help to understand the development of magnesium alloy cardiovascular stents and solve the problems related to magnesium alloy stents in clinical applications at the early implantation stage.

The recent happenings in the world such as flood and wild forest fire were as a result of climate effect as being envisaged by scientists. It is urgent now to adopt a source of energy that will eliminate this effect on our universe. Solar energy is the major energy means that is abundant which could be utilized. In this review, the prospects of solar energy exploration were studied in Nigeria which include assessments, economic viability and hybrid systems. Findings show higher potential in the North as compared to the Southern region. Additionally, potential of offshore solar energy system was simulated by considering 2002, 2003 and 2004 data sets from Era5-land base. Their monthly mean, seasonal changes and annual mean value were estimated. The algebraic annual solar radiance for 2002, 2003 and 2004 were 34,914.732 kWh/m2, 33,898.316 kWh/m2 and 34,338.324 kWh/m2. Suggestions were made due to the present status of solar energy utilization that will enhance its maximum usage and development. One of these is the establishment of a functioning financial scheme and the database for all renewable energy systems. When all these are put in place, the energy supply will increase, climate effect will be reduced, and the economy will be boosted.

The observed data of COVID-19 progression in Pakistan for first 50 days from the first patient been reported has shown quite an unusual trend which is in opposition to clear exponential spread pattern of any infectious disease. The data of positive cases of 50 days of disease progression has been collected from COVID-19 dashboard of Pakistan and analyzed to see the graphical trend and to forecast the behaviour of disease progression for next 30 days. Mathematical equations regarding exponential growth are used to analyse the disease progression and different possible trajectories are plotted to understand the approximate trend pattern. The possible projections estimated 20k-456k positive cases within 80 days of disease spread in Pakistan. Although, the disease progression pattern is not perfectly exponential, it is still threatening a major fraction of susceptible population and demands effective strategic planning and control.

Policymakers, academics, and industry players have been focused on determining whether connected health (CH) is a fad or a trend by looking at its sustainability. Although the significance of innovation in healthcare is gradually rising, a definitive identification and systematic comprehension of the core drivers, structure, content, and pattern of innovation in CH are missing. To bridge this gap, this study re-examines and analyses CH from the perspectives of its industrial chain and structure, to assess its future prospects and sustainability by focusing on how its structures and participants act in the ecosystem. This study involves an inductive theory building approach based on multi-stage, semi-structured interviews (n=60 in total). The results indicate that the core drivers, constituents, and components of CH need to be identified and restructured. A valid discourse, which is missing in the current literature, should be proposed with regard to the sustainability of CH. A sustainable business model innovation (BMI) system and the methods employed to achieve sustainability are suggested to discover indicators for future success. This study enriches the current CH understanding from a technology perspective and suggests some implications for practitioners as well as policymakers to enhance sustainable development in the healthcare sector.

Prevalence of overweight and obesity are escalating globally among the general population. However, there are limited evidence on the trends and prevalence of overweight /obesity in the military setting. Increased rates of injuries, low work productivity, incurred higher healthcare costs and prematurely discharge from service are consequences of overweight/obesity among the military. Therefore, we aimed to systematically describe the trends and prevalence of overweight and obesity in the military population, by regions and military service branches. Databases including PubMed, Scopus, Web of Science and Google Scholar were used. Inclusion criteria were military population (inclusive of all service branches) with BMI reported as the overweight/obesity indicator. Population related to military but not exclusively military personnel such as the retirees, the veterans and their dependents were excluded. From the 27 included studies, 13 were published between year 2010 to 2014 and half were conducted in the United States. Overall, studies showed increasing trend in the prevalence of overweight (30% to 50%) and obesity (2% to 30%); only one study showed a decreasing trend of 0.9% and 0.6% for the prevalence of overweight and obesity respectively. However, the rates of increment differed between countries, where Asian countries demonstrated lower overweight prevalence compared to the United States and the European countries. In conclusion, the military population is not protected from overweight and obesity. Interventions should be implemented to prevent overweight / obesity among the military personnel.

The interband transitions of urania (UO2) are validated independently through cathode luminescence of UO2. A picture emerges consistent with density functional theory. While theory is generally consistent with experiment, it is evident that the choice of functional can significantly alter the band gap and some details of the band structure, in particular at the conduction band minimum. Strictly ab initio predictions of the optical properties of the actinide compounds, based on density functional theory alone continues to be somewhat elusive.

Background: The standard surgical approach to amputation has failed to evolve significantly over the past century. Consequently, standard amputations often fall short with regards to improving the quality-of-life (QoL) for patients. A modified amputation has been designed at both the transtibial (TTA) and transfemoral (TFA) level that incorporates agonist-antagonist myoneural interface (AMI) constructs to provide patients with a novel alternative to standard amputation. Here, we present the patient reported outcome measures (PROMs) amongst our lower extremity AMI amputees and demonstrate an overall significant improvement in their physical and mental wellbeing. Methods: Four PROMs surveys, 1) EQ-5D-3L, 2) Lower Extremity Functional Scale (LEFS), 3) PROMIS-57, 4) Short Form-36 (SF-36), and 5) Sickness Impact Profile (SIP), were administered to lower extremity AMI amputees pre-operatively (baseline) and at 6 weeks, 3 months, 6 months, 9 months, 12 months, 24 months, 36 months, and 48 months post-operatively between June 24th, 2018, and December 15th, 2022. The surveys were either administered during a clinic visit or emailed at the encounter timepoint. Study data was collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at Brigham and Women’s Hospital. Analyses of collated data were performed by fitting a linear mixed model using SAS version 9.4 (SAS Institute, Cary, NC). Results: The cohort’s baseline and 12-month post-operative responses were compared to determine score improvement. The cohort demonstrated a significantly improved EQ-5D-3L index value (p<0.0001) and EQ VAS (p<0.0001) at 12-months post-operatively (n=22) when compared to baseline (n=28). The LEFS score significantly improved at 12-months post-operatively compared to baseline (p<0.0001) with a mean of 59.04 ± 3.14 (SE) at 12-months and a mean of 31.96 ± 2.84 (SE) at baseline. AMI patients demonstrated significantly improved scores across PROMIS-57 domains at 12-months compared to baseline scores; the p-value was <0.0001 for all domains except for the Depression domain where p=0.0181. The health domain scores significantly improved from baseline to 12-months post-operation for all SF-36 domains. Lastly, the SIP indicated a significant decrease in overall dysfunction from baseline to 12-months post-operation (p<0.0001). Conclusions: Modified lower extremity amputation with AMI construction has the potential to provide amputees with increased quality-of-life when compared to the pre-operative state. However, further investigation is necessary to determine whether the patient reported outcome measures of the AMI amputee cohort are superior to those who receive a standard amputation.

The article reviews the constituent’s physico-chemical parameters in the water of the Mansagar Lake of the Jaipur City. The water quality is also investigated using evaluated physico-chemical parameters with in-depth study of their distribution as per sample location-wise and season-wise. A detailed discussion on the associationship among parameters (pH, Conductivity, turbidity, dissolved Oxygen, chemical and Biochemical Oxygen demand, etc.) using the Pearson’s analytical method is also presented. The strength of the article is the graphical presentation of evaluated physico-chemical parameters and the water quality indices for sample station-wise and the season-wise. To our best of understanding, first time such details have been clubbed together and discussion in brief for studied lake.

Urbanization and industrialization processes in Mongolia have been significant and rapid for the last half-century. During this period, changes in political and economic systems, growth in the population, and the occasional harsh climate conditions were subject to fluctuations in the natural resource usage. The total Ecological Footprint (EF) in Mongolia has increased from 6.8 million global hectares (gha) in 1961 to 14.6 million gha in 2012. However, Biocapacity (BC) has decreased from 50.6 million gha in 1961 to 39.0 million gha in 2012. The study shows that grazing land Footprint and carbon uptake land Footprint are the two major contributors of the recent intensified use of biological resources. To ensure stable economic development and sustainable use of natural resources, environmental planning is required to consider both the population’s pressure on the environment and the ecosystem’s regeneration capacity, simultaneously. We have proposed a few possible strategies for sustainable utilization of grazing land Footprint and carbon Footprint. For grazing land Footprint, efficient management of both herding practice and number of animals should be considered. In case of carbon Footprint, it is estimated that with the improved combustion efficiencies of coal-based power plants and the maximum use of renewable energy, carbon dioxide (CO₂) emissions in Mongolia can be reduced up to 30% compared to the base line business as usual case in 2030.

Fe[C5H5N]2[N(CN)2]2 (1) was synthesized from a reaction of stoichiometric amounts of NaN(CN)2 and FeCl2·4 H2O in a methanol/pyridine solution. Single-crystal and powder diffraction show that 1 crystallizes in the monoclinic space group I2/m (no. 12), different from Mn[C5H5N]2[N(CN)2]2 (P21/c, no. 14) due to tilted pyridine rings, with a = 7.453(7) Å, b = 13.167(13) Å, c = 8.522(6) Å, β = 114.98(6)° and Z = 2. ATR-IR, AAS, and CHN measurements confirm the presence of dicyanamide and pyridine. Thermogravimetric analysis shows that π-stacking interactions of the pyridine rings play an important role in structural stabilization. Based on DFT-optimized structures, a chemical bonding analysis was performed using a local-orbital framework by projection from a plane-wave basis. The resulting bond orders and atomic charges are in good agreement with the expectations based on the structure analysis. SQUID magnetic susceptibility measurements show a high-spin state FeII compound with predominantly antiferromagnetic exchange interactions at lower temperatures.

The rapid expansion of China's A-share market, coupled with the burgeoning volume of financial research reports, presents a unique challenge to traditional stock price prediction methodologies. This study introduces FAST-SCAN, a novel framework designed to leverage natural language processing (NLP) and time series analysis for the efficient and accurate prediction of stock market trends. By integrating advanced sentiment analysis, primarily through the utilization of the RoBERTa model, with dynamic time series forecasting, FAST-SCAN aims to distill actionable insights from the vast corpus of financial research reports. This approach not only enhances the speed and efficiency of financial analysis but also addresses the time-sensitive nature of market-influencing factors, thereby providing a competitive edge in stock price prediction. The framework demonstrates a notable improvement in predictive performance, achieving a 20% annualized return and a 7.19% RankIC, marking a significant advancement over conventional statistical analysis methods. Through its innovative combination of NLP and predictive analytics, FAST-SCAN paves the way for a more informed and strategic approach to investment in the A-share market, emphasizing the importance of timely, data-driven decision-making in the financial industry.

Study of spatiotemporal dynamics of temperature is vital to assess changes in climate, especially in the Himalayan region where livelihoods of billions of people living downstream depends on water coming from the melting of snow and glacier ice. To this end, temperature trend analysis is carried out in Narayani river basin, a major river basin of Nepal characterized by three climatic regions: tropical, subtropical and alpine. Temperature data from six stations located within the basin were analyzed. The elevation of these stations ranges from 460 to 3800 m a.s.l. and the time period of available temperature data ranges from 1960–2015. Multiple regression and empirical mode decomposition (EMD) methods were applied to fill in missing data and to detect trends. Annual as well as seasonal trends were analyzed and a Mann-Kendall test was employed to test the statistical significance of detected trends. Results indicate significant cooling trends before 1970s, and warming trends after 1970s in the majority of the stations. The warming trends range from 0.028 ∘" style="position: relative;">∘C year−1" style="position: relative;">−1 to 0.035 ∘" style="position: relative;">∘C year−1" style="position: relative;">−1 with a mean increasing trend of 0.03 ∘" style="position: relative;">∘C year−1" style="position: relative;">−1 after 1971. Seasonal trends show highest warming trends in the monsoon season followed by winter, pre-monsoon, and the post-monsoon season. However, difference in warming rates between different seasons was not significant. An average temperature lapse rate of −0.006 ∘" style="position: relative;">∘C m−1" style="position: relative;">−1 with the steepest value (−0.0064 ∘" style="position: relative;">∘C m−1" style="position: relative;">−1) in pre-monsoon season and least negative (−0.0052 ∘" style="position: relative;">∘C m−1" style="position: relative;">−1) in winter season was observed for this basin. A comparative analysis of the gap-filled data with freely available global climate datasets show reasonable correlation thus confirming the suitability of the gap filling methods.

This work highlights that monitoring the microbiological quality of animal products, such as raw sheep’s milk and cheese, is necessary for food safety. Sheep’s milk and its derivatives are not legislated in Brazil. Thus, present study aimed to evaluate: (i) the hygienic-sanitary quality of raw sheep’s milk and cheese produced in southern Brazil; (ii) the presence of enterotoxins and Staphylococcus spp. in these products; and (iii) the antimicrobial susceptibility profile and resistance genes of the isolated Staphylococcus spp. Thirty-five raw sheep’s milk and cheese samples were evaluated. Microbiological quality and the presence of enterotoxins were determined by Petrifilm and VIDAS SET2 methods, respectively. Antimicrobial susceptibility tests were conducted using VITEK 2 equipment and the disc diffusion method. The presence of resistance genes tet(L), sul1, sul2, ermB, tetM, AAC(6)', tetW, and strA were evaluated by PCR. In total, 39 Staphylococcus spp. were isolated. The tetM, ermB, strA, tetL, sul1, AAC(6)’, and sul2 resistance genes were detected in 82%, 59%, 36%, 28%, 23%, 3%, and 3% of isolates, respectively. The results showed that both products contained Staphylococcus spp. and these strains were resistant to antimicrobials as well carriers of resistance genes. These results highlight Brazil's need for specific legislation regarding the pro-duction and marketing of these products.

The author attempts to reconstruct the climatic vicissitudes of the Mediterranean to explain why only broadly distributed Eurasian forest species have penetrated the Mediterranean peninsulas. He has carried out a bibliographic survey of the status of breeding forest birds in Italy over 15 decades (1872-2022) in order to establish an objective long-term trend (stable, increasing, decreasing, etc.). The number of breeding forest birds in Italy amounts to 61; their distribution, with a few exceptions, indicates that they are widespread in Eurasia, but only a small percentage of Eurasian forest species have colonized Italy and the other Mediterranean peninsulas, namely 49 in Iberian, 61 in Italian and 64 in Balkan peninsulas; a small percentage of them (between 10.9 and 14.3%) belongs to trans-Saharan migrants. The similarity between the forest species on the three peninsulas (Iberian, Italian and Balkan) results between 0.45 and 0.48, indicating a certain differ-ence in the overall fauna on the three territories. Not all species have penetrated southwards into the three peninsulas; for example, some that stopped in the Italian Alps have instead arrived to the forests of Greece, at a latitude corresponding to southern Italy, or species that in Italy stopped in the northern Apennines in the other two peninsulas have instead arrived far south. Iberian peninsula and the island of Corsica hold three endemic species among breeding forest birds, Italian and Balkan peninsulas have not endemic species. Overall, the Mediterranean presently hosts mainly neo-endemic taxa among forest bird species; the only paleo-endemics can be considered the three species of nuthatches living in Corsica, Algeria, and Turkey (other than Caucasus and the islet of Lesvos) and Le Vaillant’s woodpecker in the Maghreb (North Africa). Italian forests cover ca. 40% of land surface and since 1980’ are increasing, but 22% of them do not have a natural origin. However, it is difficult to know the true increase of forests, because some of them are fired every year. The presence of some ecologically demanding forest birds depends on the age of the trees, permanent open spaces and other characteristics at the edge of woodland.

Disasters related to climate change on our water resources are on the rise in terms of scale and severity. Therefore, predicting groundwater levels (GWL) is a crucial means to aid adaptive capacity towards disasters related to climate change in our water resources. In this study Gradient Boosting (GB) regression modelling approach for GWL prediction as a function of rainfall and antecedent GWL is used. Firstly, we sought to demonstrate the effects of rainfall changes on our groundwater resources through a Mann-Kendall trend analysis. Secondly, we evaluated the relationship between the input and response variables and determined the optimal lag times between the variables using autocorrelations and cross-correlations. Lastly a predictive model was developed for eight groundwater stations in the Upper Crocodile. 50 % of the groundwater stations revealed declining trends, while 25% had no trends and the other 25% showed an increasing trend. Generally low cross-correlation maximum (CCmax) were obtained, with the highest CCmax being 0.299 at an optimal lag of 2-month. While the highest autocorrelation was 0.969 at a 1-month lag. The best groundwater predictive model had R2 and MSE of 0.66 and 0.06, respectively. The stations that generally performed better had both high autocorrelation and cross-correlation coefficients. GB model performed satisfactorily in predicting GWL for most of the stations in the study area. Therefore, GB can be used for GWL prediction in the Upper Crocodile.

Trend patterns of melanoma skin in Serbia from 1999 to 2020 *Rancic K. Natasa 1,2, Tiodorovic B. Danica 1,4, Ilic V. Mirko 2, Biljana N. Kocic 1,2, Maksimovic S. Natasa 3, Ilija D. Kocic 4, Dusan P. Miljkovic 4, Otasevic A. Suzana 1,2 Introduction. Melanoma of the skin represents about 1% of all skin cancers and it causes nearly 80% of skin cancer deaths. Due to its increasing incidence, particularly in the most developed countries it represents a great challenge for primary prevention. Objective. To explore patterns and analyze incidence trends of melanoma in Central Serbia from 1999 to 2020. Materials and Methods. Descriptive study was done and data were obtained from the Population-based Cancer Registry of Serbia. The joinpoint regression analysis was done to assess the incidence trend of the melanoma skin: the annual percent change (APC) along with its 95% confidence interval (95% CI) was calculated. Results. A total number of 9952 new melanoma cases were registered melanoma (5204 in males and 4748 in females) during the observed period. Males represented 52.3% of all patients, with male to female ratio of incidence 2.09:1. A non significant increasing trend of incidence in males from 1999 to 2013 with APC of 2.53% (95%CI: from -18.2 to 21.9, p=1) and significantly increasing trend from 2013 to 2020 with the APC of 11.29 % (95%CI: from 1.4 to 5.8, p<0.001) was established. An incidence trend in females has stabilized in the whole studied period, with APC of 0.42% (95%CI: from -3.3 to 6.8, p=0.1). Melanoma of the skin was not common before the age of 30. Out of all cases and about of 76% new cases were diagnosed in persons ≥ 50 years of age. Conclusion. The incidence trend of new melanoma cases significantly increase in the whole population from 1999 to 2020. The significant increasing of incidence trend in males and stabilization of incidence trend in females was established. Males had significantlly higher incidence rates than femalesand over of 76% new melanoma cases were diagnosed in persons above 50 years of age.

Abstract Depending upon altitudinal gradient in the Himalayas, the rate of climate change varies from lowland to upland. The Chitwan Annapurna Landscape (CHAL) is the central part of the Himalayas and covers all bioclimatic zones. Analysis of time series data (1970-2019) of temperature and precipitation was carried out in seven bioclimatic zones extending from lowland Terai to higher Himalayas. The non-parametric Mann-Kendall test was applied to determine the trend, which was quantified by Sen’s slope. Annual and decade interval average temperature, precipitation trends, and lapse rate were analyzed in each bioclimatic zone. Out of seven bioclimatic zones, four zones showed a decreasing precipitation trend (lower tropical, upper tropical, upper subtropical, and alpine bioclimatic zones)at the rate of 1.8, 1.98, 2.06, and 1.80 mm/year, and in lower sub-tropical, temperate, and lower subalpine bioclimatic zones, increasing at the rate of 0.45, 1.81 and 1.28mm/year, respectively. Precipitation did not show any particular trend at decade intervals. The average annual temperature at different bioclimatic zones clearly indicates that temperature at higher elevations is significantly increasing more than at lower elevations. In lower tropical bioclimatic zone (LTBZ), upper tropical bioclimatic zone(UTBZ), lower subtropical bioclimatic zone (LSBZ), upper subtropical bioclimatic zone(USBZ), and temperate bioclimatic zone(TBZ), the average temperature increased by 0.022, 0.030, 0.036, 0.042 and 0.051oC/year, respectively. The decade level temperature scenario revealed that the hottest decade was from 1999-2009. The average temperature was found as 24.1, 21.8, 19.7, 17.5, and 13.3oC in LTBZ, UTBZ, LSBZ, USBZ, and TBZ, respectively, and the average annual precipitation in LTBZ, UTBZ, LSBZ, USBZ, TBZ, LBZ, and ABZ was 2002.1, 2613.1, 2223.9, 3146.9, 1447.2, 952.1, and 361.7mm/year, respectively, in CHAL. With the impact of climate change site and region-specific, this information highlights the need to mitigate climate change in different bioclimatic zones.

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are increasingly being collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN's recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R2 scores up to 0.908, 0.823 and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like ANN and SVR, the predictive accuracy of the kPCA-RNN model was at least 8 %, 17 % and 21 % better than the comparative models in these 3 cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

With advances in Building Information Modeling (BIM), Virtual Reality (VR) and Augmented Reality (AR) technologies have many potential applications in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry, relative to other industries, has been slow in adopting AR/VR technologies, partly due to lack of feasibility studies examining the actual cost of implementation versus an increase in profit. The main objectives of this paper are to understand the industry trends in adopting AR/VR technologies and identifying gaps within the industry. The identified gaps can lead to opportunities for developing new tools and finding new use cases. To achieve these goals, two rounds of a survey at two different time periods (a year apart) were conducted. Responses from 158 industry experts and researchers were analyzed to assess the current state, growth, and saving opportunities for AR/VR technologies for the AEC industry. The findings demonstrate that older generations are significantly more confident about the future of AR/VR technologies and they see more benefits in AR/VR utilization. Furthermore, the research results indicate that Residential and commercial sectors have adopted these tools the most, compared to other sectors and institutional and transportation sectors had the highest growth from 2017 to 2018. Industry experts anticipated a solid growth in the use of AR/VR technologies in 5 to 10 years, with the highest expectations towards healthcare. Ultimately, the findings show a significant increase in AR/VR utilization in the AEC industry from 2017 to 2018.

Plants and its corresponding botanical preparations have been used for centuries due to their remarkable potentialities in both treatment and prevention of numerous affections. Hundreds of biologically active constituents are present in each whole plant matrice, working in synergism and conferring both its own protection against invaders and even providing promissory bioactive effects for human beings. The worldwide population has devoted increasing attention and preference by medicinal plants use for health promotion and disease prevention, and more recently for oxidative-stress related disorders protection. Indeed, oxidative stress-related disorders, like cardiovascular and (neuro) degenerative disorders, and even cancer have raised exponentially. Although oxidative stress is in itself intrinsic to our own metabolism, allarming sources of free radicals are daily affecting us. In fact, plant-derived bioactives present a broad spectrum of biological effects, and its antioxidant, anti-inflammatory and more recently anti-aging effects have been considered a hot topic among the medical and scientific community. Nonetheless, and although its numerous biological effects, it should not also be forgotten that some bioactive molecules are prone to oxidation and can even exert pro-oxidant effects. In this sense, the objective of the present review is to provide a detailed overview on plant-derived bioactives in oxidative stress-related disorders. Specifically, the role of phytochemicals as antioxidants and pro-oxidant agents is carefully addressed, as is its therapeutic relevance in disease prevention. Finally, an eye-opening look in overall evidence in humans is also ensured.

China is a traditional agricultural country, and the integration of rural industries has become an essential policy direction for the current strategy of agricultural modernization in China. In recent years, the agricultural industrial chains across various regions of China have consistently expanded and extended. The multifunctionality of agriculture is continuously unearthed, and the principal entities managing agriculture have become increasingly diversified and have grown significantly. New industries and forms in agriculture emerge incessantly. Accurately and profoundly understanding the inherent features of the integration of industries in rural China, establishing a scientific and rational measurement index system in line with the current status and development goals of rural China, and employing appropriate measurement methods to assess its development level and process, is instrumental in comprehending the level of integrated development in various regions. This aids in identifying challenges in the developmental process and proposing targeted developmental strategies. Hence, this paper, rooted in the integration of rural industries, cross-industry, industry extension, industry agglomeration, and industry penetration, sets up an measurement index system. Using provincial panel data from 2011 to 2020, the paper measures the level of integrated development in rural industries across China and its 31 provinces. Based on the measurement results, further techniques such as kernel density estimation and Gini coefficient decomposition have been used to analyze the spatial distribution characteristics and dynamic trends of the integrated development of rural industries in China. Research indicates that during the sample period, the overall level of integrated development in rural industries in various provinces in China has shown an upward trend. Development among the eastern, central, and western regions is highly uneven, but this disparity has been gradually narrowing in recent years. Furthermore, the levels of integrated development in different provinces exhibit significant spatial agglomeration effects. The development level of neighboring regions significantly impacts the province's development status. In the process of integrated development of rural industries in China, there's a trend where "it's easier to downgrade than to upgrade," with very few regions achieving leapfrog development.

This study aims to identify the knowledge structure and research trends of soccer psychology research during the last 33 years (1990–2022). Data were obtained from 1,863 papers from the Web of Science database. The data were collected through keyword text mining and data preprocessing to determine the keywords needed for analysis. Based on the keywords, latent Dirichlet allocation-based topic modeling analysis was performed to analyze the topic distribution of papers and explore research trends by topic area. The topic modeling process included four topic area and fifty topics. The “Coaching Essentials in Football” topic area had the highest frequency, but it was not statistically identified as a trend. However, coaching, including training, is expected to continue to be an important research topic, as it is a key requirement for success in the highly competitive elite football world. Interest in the research field of “Psychological Skills for Performance Development” has waned in recent years. This may be due to the predominance of other subject areas rather than a lack of interest. Various high-tech interventions and problem-solving attempts are being made in this field, providing opportunities for qualitative and quantitative expansion. “Motivation, cognition, and emotion” is a largely underrated subject area in soccer psychology. This could be because survey-based psychological evaluation attempts have decreased as the importance of rapid field application has been emphasized in recent soccer-related studies. However, measuring psychological factors contributes to the study of football psychology through a new methodology and theoretical background. Recognizing the important role of psychological factors in player performance and mental management, as well as presenting new research directions and approaches that can be directly applied to the field, will advance soccer psychology research.

The purpose of the study is to control the ratio of programs of different genres when forming the broadcast grid in order to increase and maintain the rating of the channel. In the multichannel environment, television rating control consists of selecting such content, ratings of which are completely restored after advertising. A hybrid approach combining the benefits of semantic training and fuzzy relational equations in simplification of the expert recommendation systems construction is proposed. The problem of retaining the television rating can be attributed to the problems of fuzzy resources control. The increase or decrease trends of the demand and supply are described by primary fuzzy relations. The rule-based solutions of fuzzy relational equations connect significance measures of the primary fuzzy terms. Rules refinement by solving fuzzy relational equations allows avoiding labor-intensive procedures for the generation and selection of expert rules. The solution set generation corresponds to the granulation of the television time, where each solution represents the time slot and the granulated rating of the content. In automated media planning, generation of the weekly TV program in the form of the granular solutions provides the decrease of the time needed for the programming of the channel broadcast grid.

Abstract: A recent approach to radiotherapy for prostate cancer is the administration of high doses of radiation to the prostate while minimizing the risk of side effects. Thus, image-guided radiotherapy utilizes advanced imaging techniques and is a feasible strategy for increasing the radiation dose. New radioactive particles are another approach to achieving high doses and safe procedures. Prostate brachytherapy is currently considered as a combination therapy. Spacers are useful to protect adjacent organs, specifically the rectum, from excessive radiation exposure.

The article is aimed at a brief comparison and analysis of the results of queries to IEEE Xplore and the leading abstract databases Scopus and Web of Science to identify research trends. Some errors in the Author Keywords in Web of Science have been revealed. Therefore, a more detailed analysis was conducted by comparing different types of key terms for IEEE Xplore and Scopus platforms only. I used IEEE Access journal metadata as indexed on both platforms. The sample match for IEEE Xplore and Scopus was achieved by comparing DOI. The IEEE Xplore metadata contains more types of key terms, which provides an advantage in analyzing research trends. Using NSPEC Controlled Terms from expert-compiled vocabulary provides a more stable data, which gives an advantage when considering the change of terms over time. Apriori, an algorithm for finding association rules, has been used to compare co-occurrence of terms for a more detailed description of sample subjects on both platforms. VOSviewer was used to analyze trends in scientific research based on IEEE Xplore data. The 2011-2021 ten-year period was divided into two sub-intervals for comparing the occurrence of Author Keywords, IEEE Terms, and NSPEC Controlled Terms. Using the IEEE conference proceedings bibliometric data, I illustrated the importance of context in estimating the rate of growth of publishing activity on a topic of interest.

This paper is an attempt to present a COVID-19 prediction model for India. Lockdown plays an important role in the arrest of community spread of the disease. This was evident from the study of other countries such as Russia, Belgium and Germany, where peak cases were recorded within a month of the imposition of lockdown, that it showed an immediate positive effect. However, in India, even after 65 days of lockdown, there is no decrease in the number of daily new cases reported. There were many models prepared for India and almost all of them were proven wrong by the increase in the number of cases. The model in this paper is prepared using the COVID-19 trend in other countries, population density and the pandemic bell curve. Based on the available data until 24^th May 2020, two scenarios have been presented. In one, the peak shall be obtained when the number of daily new cases per million reaches 190 and in the second when the daily new cases per million reach 724. One model predicts the number of cases to reach 1 million by mid-July 2020. The other model predicts the number of cases to peak by mid-July with the total cases reaching 20 million. The predicted cases were compared with the actual cases recorded for the period 25^th May to 11^th June 2020. It was observed that the actual values matched quite reasonably with the predicted values.

GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

The New Zealand housing sector is experiencing rapid growth that boosts the national economy but also results in the loss of valuable resources. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: ARIMA and multiple linear regression analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions.

In order to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional network (TCN) is proposed. Firstly, construct a high-dimensional feature set in the multi-domain of vibration signals, and use comprehensive evaluation indicators to preliminarily screen performance degradation indexes with good sensitivity and strong trend. Secondly, the kernel principal component analysis (KPCA) method is adopted to eliminate redundant information between multi-domain features, and construct a health index (HI) based on convolutional auto-encoder (CAE) network. Thirdly, a TCN-based performance degradation trend prediction model is constructed, and direct multi-step prediction is used to predict the performance degradation trend of the monitored object. On this basis, the validity of the proposed method is verified using the bearing public data, and it is successfully applied to performance degradation trend prediction of rolling contact fatigue specimen. The results show that the feature set can be reduced from 14 dimensions to 4 dimensions by using KPCA, while 98.33% of the information of the original feature set is retained. Furthermore, the method of constructing HI based on CAE network is effective. The change process of the HI constructed truly reflects the performance degradation process of the rolling contact fatigue specimen. Compared with the two commonly used HI construction methods, auto-encoding (AE) network and gaussian mixture model (GMM), this method has obvious advantages. At the same time, the prediction model based on TCN can accurately predict the performance degradation of the rolling contact fatigue specimen with the root mean square error 0.0146 and the mean absolute error 0.0105, which has better performance and higher prediction accuracy than the prediction model based on the long short-term memory (LSTM) network and the gated recurrent unit (GRU). This method has general significance and may be extended to the performance degradation prediction of other mechanical equipment/parts.

A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3,283 abstracts). Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. It is suggested that all disci-plines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.

Cereal crops, such as oats and barley, possess a number of valuable properties that meet the requirements for functional diet components. This review summarized the available information about bioactive compounds of oat and barley grain. The results of studying the structure and physicochemical properties of the cell wall polysaccharides of barley and oat are presented. The main components of the flavonoids formation pathway are shown and data, concerning anthocyanins biosynthesis in various barley tissues, are discussed. Moreover, we analyzed the available information about structural and regulatory genes of anthocyanin biosynthesis in Hordeum vulgare L. genome, including β-glucan biosynthesis genes in Avena sativa L species. However, there is not enough knowledge about genes responsible for biosynthesis of β-glucans and corresponding enzymes and plant polyphenols. The review also covers contemporary studies about collections of oat and barley genetic resources held by VIR. This review intended to provide information on the processes of biosynthesis of biologically active compounds in cereals that will promote further researches devoted to transcription factors controlling expression of structural genes and their role in other physiological processes in higher plants. Found achievements will allow breeders to create new highly productive varieties with the desirable properties.

For the purpose of clean energy hydropower development, the construction of reservoirs has been continuously promoted in the Jinsha River basin for many years, and how the reservoirs affect the water and sediment process is a very necessary topic to study. This study is based on the gauged water and sediment data (span from the 1960’s to 2020) at Shigu, Panzhihua, and Xiangjiaba stations downstream located in the trunk channel of the river, and uses Mann-Kendall trend test method and double cumulative curve method to comprehensively judge the variation trends of runoff and suspended sediment load and reveal their credible mutation years. The linear regression method is used to reveal the variation characteristics of the relationship between water and sediment before and after the abrupt change years. The results show that the variations in runoff at Shigu and Panzhihua stations have significant and relatively obvious increasing trends, respectively, and that at Panzhihua station has a mutation year of 1985. The runoff at Xiangjiaba Station slightly increased but not significantly. The variation of suspended sediment load showed a temporal and spatial differentiation. The variation of sediment discharge at Shigu Station showed an increasing trend with a mutation year of 1997. For Panzhihua Station, it showed an increasing trend before 1998, but has significantly decreased since 1998. The fluctuation of sediment transport at Xiangjiaba Station was significant before 1998, but the trend is unclear. In the period of 1998−2020, it showed a significant decreasing trend, especially since 2013, when the mean annual suspended sediment load only accounted for 0.61% of its multi-year average. The variations of mean annual sediment concentration and sediment inflow coefficient at the hydrological stations is consistent with the variation trend of sediment transport. Before 2013, the correlation between water and sediment was strong, but thereafter it was extremely weak. The two sudden years of 1998 and 2013 are consistent with the year when large reservoirs were built in the river basin. The construction of large reservoirs and their large amount of sediment retention are the key reasons for the sudden changes in the water-sediment relationship and the sharp decrease in sediment transport in the downstream reach of the reservoir dam. The climate and underlying surface changes in the study area are not significant, and their impact on the water and sediment processes in the watershed is limited.

In the paper, we measure the digital financial inclusion index of 31 provinces in China from 2011 to 2020 based on three dimensions: coverage breadth, depth of use and digitalization degree. By means of weighted Dagum Gini coefficient and quantile standardization, we explored the degree of imbalance and insufficiency of the development of digital inclusive finance in China and four major regions and its structural causes. Using Kernel density estimation method and Markov chain analysis method, we further investigates the evolution trend of imbalance and insufficiency. The study finds that (1) the Digital Inclusive Financial Index in China and the four major regions rise significantly, with the COVID-19 epidemic reducing its growth rate. Of these, the eastern region has the highest development level. (2) The imbalance level of digital inclusive finance development obviously has reduced. The level of imbalance is highest within the eastern region, and the development gap between the eastern and western regions is the widest. The imbalance of overall development is mainly due to the regional imbalance. The imbalance of coverage breadth and depth of use is the main structural cause of unbalanced development in the four major regions. There is a trend of bipolarization or multipolarization in China and the other three major regions, with the exception of the central region. (3) The western region is the least developed. The development shortcoming of digital inclusive finance in China and the four major regions is the breadth of coverage. There are "Club Convergence" and "Matthew Effect" in the eastern, central and western regions.

Drought severity still remains a serious concern across sub-Saharan Africa (SSA) due to the destructive impact on multiple sectors of our society The interannual variability and trends in the changes of self-calibrated Palmer Drought Severity Index based on Penman–Monteith (scPDSIPM) and Thornthwaite (scPDSITH) methods for potential evapotranspiration (PET), precipitation (P) and normalized difference vegetation index (NDVI) anomalies, and sea surface temperature (SST) anomaly were investigated through statistical analysis of modelled and remote sensing data. It is shown that scPDSIPM and scPDSITH differed in the representation of drought characteristics over SSA. The scPDSI and remotely-sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980-2012. The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis reveals a strong relationship between scPDSI variability and P, and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of scPDSI variability with SST anomalies indicates significant positive and negative relationships, respectively. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies.

In order for the body to function properly, it needs not only food, but also nutrients and non-nutritive bioactive compounds that have an immunomodulating effect. This applies not only to healthy people, but especially to people with accompanying chronic diseases, including type 2 diabetes. Unfortunately, the current food industry and the use of highly processed food promote nutritional deficiencies. Many studies confirm their occurrence in patients with type 2 diabetes. The article presents the influence of selected nutrients on the functioning of the immune system, which ensures homeostasis of the body, with particular emphasis on type 2 diabetes. The role of macroelements, microelements, vitamins and selected substances, such as omega-3 acids, coenzyme Q10 and alpha-lipoic acid. The minimum scope of tests that should be performed in patients to directly or indirectly determine the degree of malnutrition in this group of patients is presented.

Parasite control strategies such as blanket anthelmintic treatment of all horses in a herd or interval treatment regimen based on egg reappearance remain widely followed practices for the last several decades. These practices have resulted in the fast emergence of resistance in equine strongyles to all major classes of drugs on the market. In this regard, the guidelines set by the American Association of Equine Practitioners advocate evidence-based strongyle control in horses. It recommends targeted treatment of all heavy egg shedders [&gt; 500 eggs per gram (EPG) of feces] while the low shedders (0-200 EPG) are left untreated to maintain susceptible parasite refugia in a herd. As 50-75% of adult horses in a herd are low shedders, preventing them from unnecessary anthelmintic exposure is critical to tackling resistance. There are various fecal egg count (FEC) techniques with many modifications and variations in use, but none is identified as a gold standard and methods comparison studies are lacking. We hypothesized that the diagnostic performance of commonly used quantitation methodologies differs and the FEC methods must be evaluated to determine the gold standard. In this regard, we performed methods comparison studies using polystyrene beads with size and specific gravity comparable to strongyle eggs, as proxy. The linearity of 12 commonly used FEC methodologies (3 techniques with 4 different solutions) was studied using bead standards in the clinically applicable range (63 to 1,000) to determine their suitability for strongyle egg counts. Deming regression analysis identified Mini-FLOTAC with sugar (specific gravity 1.33) as the test with the greatest coefficient of determination. All tests underestimated the true bead count, and we determined a correction factor (CF) for each test to estimate the true count. CF negates the preference for a gold standard test. Finally, we analyzed the validity of CF for 5 tests with R2&gt;0.95 to accurately quantify strongyle eggs from 40 different horses. Overall, this study identified methodologies with the highest diagnostic performance and the limitations to standardizing routine FEC tests to promote uniformity in implementing AAEP parasite control guidelines.

The COVID-19 pandemic continues to ravage the world, with the United States being highly affected. A vaccine provides the best hope for a permanent solution to controlling the pandemic. However, to be effective, a vaccine must be accepted and used by a large majority of the population. Structural equation modelling was used to analyze the relationships of several factors with attitudes toward potential COVID-19 vaccination. The survey was administered to 316 respondents across the United States by a survey corporation. Prior vaccine usage and attitudes predicted attitudes towards COVID-19 vaccination. Assessment of the severity of COVID-19 for the United States was also predictive. Approximately 68% of all respondents were supportive of being vaccinated for COVID-19, but side effects, efficacy, and length of testing remained concerns. Longer testing, increased efficacy and development in the United States were significantly associated with increased vaccine acceptance. Messages promoting COVID-19 vaccination should seek to alleviate the concerns of those who are already vaccine-hesitant. Messaging directed at the benefits of vaccination for the United States as a country would address the second predictive factor. Enough time should be taken to allay concerns about both short and long-term side effects before a vaccine is released.

Abstract: This paper presents a comprehensive analysis of links between satellite-measured vegetation vigor and climate variables in Armenian mountain grassland ecosystems in years 1984–2018. NDVI is derived from MODIS and Landsat data, temperature and precipitation data are from meteorological stations. Two study sites were selected, representing arid and semi-arid grassland vegetation types, respectively. Various trend estimators including Mann-Kendall (MK) and derivatives were combined for vegetation change analysis at different time scales. Results suggest that temperature and precipitation had negative and positive impacts on vegetation growth, respectively, in both areas. NDVI-to-precipitation correlation was significant but with an apparent time-lag effect that was further investigated. No significant general changes were observed in vegetation along the observed period. Further comparisons between results from corrected and uncorrected data led us to conclude that MODIS and Landsat data with BRDF, topographic and atmospheric corrections applied are best suited for analyzing relationships between NDVI and climatic factors for the 2000-2018 period in grassland at a very local scale, but in the absence of correction tools and information, uncorrected data can still provide meaningful results. Future refinements will include removal of anthropogenic impact, and deeper investigation of time-lag effects of climatic factors on vegetation dynamics.

Because of ambiguous and widely debated observations concerning incidence trend and management of TC, we performed this analysis. We drew attention to some events, such as “cancer screening activity,” introduction of noninvasive follicular neoplasm with papillary-like nuclear features (NIFTP) to TC types, possibility of papillary thyroid microcarcinoma (PTMC) active surveillance (AS), occurrence of personalized medicine in TC management and finally COVID-19 pandemic time. Because of the opinion that all changes have been made mostly by PTC, we compared it to the rest of types of TC in terms of incidence, clinical and pathological characteristics and treatment. The prevalence of TC significantly increased from 5.15% in 2008 to 13.84% in 2015 and then significantly decreased to 1.33% in 2022 when the pandemic lasted (p<0.0001). A similar trend was observed for PTC, when the incidence significantly increased to 13.99% in 2015 and then decreased to 1.38% in 2022 (p<0.0001). At that time, the NIFTP category was introduced, and observation of PTMC began. The prevalence of FTC and MTC also increased until 2015 and then decreased. Significant differences in age, types of surgery, necessity of reoperation, pTNM, between PTCs and other types of TCs were observed. The average age was significantly lower in PTC patients than in patients with the remaining types of TC (p<0.0001). Four milestones, including NIFTP introduction, the possibility of PTMC AS, personalized cancer medicine and the COVID-19 pandemic, may have influenced the general statistics of TC.

Our research proposes two different training and modelling approaches of Generative Pre-trained Transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. Our findings indicate that integrating GPT insights into electricity price trend forecasting can result in more precise predictions and a deeper understanding of market dynamics. Through our research, we aim to provide insights to understand the capabilities of GPT solutions, and how those can be used to enhance prediction accuracy, ultimately supporting informed decision-making for stakeholders across the Spanish electricity market and companies whose margins heavily depend on electricity costs and price volatility.

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASS) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks like real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASS using bibliographic analysis to inform policy and future research directions in this evolving field. We analyze 3,363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. Findings reveal a rapidly growing field dominated by image recognition research. Keywords like "datum," "image," and "detection" suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with "Image analysis and classification research" as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASS and informing future policy and research agendas for the integration of electric propulsion systems in the maritime industry.

The power converter is the significant device in a wind power system. Wind turbine will be shut down and off grid immediately with the occurrence of the IGBT module open-circuit fault of power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies of power converter, there are few single and double IGBT modules open-circuit fault diagnosis methods producing negative results including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed. Above all, collecting the three-phase current varying with wind speed of 22 failure states including a normal state of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are used as the input of deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

Esophageal adenocarcinoma (EAC) detection relies on endoscopy-biopsy diagnosis, with routine endoscopic surveillance recommended for Barrett’s esophagus (BE) patients. Here, we examine the utility of blood biomarkers in patient risk stratification by translating the EAC blood biomarker Jacalin lectin binding complement C9 (JAC-C9) into a novel microfluidic immunoassay, the EndoScreen Chip. Cohort evaluation (n=46) showed elevated serum total C9 and JAC-C9 in EAC. Logistic regression modeling demonstrated that addition of C9 and JAC-C9 to patient risk factors (age, body mass index and heartburn/reflux history) improved EAC prediction from AUROC of 0.838 to 0.931. Serum JAC-C9 strongly predicted EAC (vs BE OR= 4.6, 95% CI: 1.6-15.6, p = 0.014; vs Healthy OR=4.1, 95% CI:1.2-13.7, p = 0.024) while total C9 was moderately predictive for BE (vs EAC OR=1.4; 95% CI: 1.0-1.8, p = 0.032; vs Healthy OR=0.8; 95% CI: 0.6-1.0, p = 0.039). This translational study demonstrates the potential utility of blood biomarkers in improving triaging for diagnostic endoscopy.

Due to satisfactory mechanical properties, low specific weight and other advantages, a substantial utilization of laminated structural composites in many components and equipments of several industry sectors, especially in the past two decades, has been made. This class of materials has a good potential to be an important ally to support scientists and engineers with the main current world challenge: an energy transition process that must occur with sustainability, eco-friendly materials and based on an efficient reduction of greenhouse gas emissions. In this context, a microanalysis (scale length: 10μm to 1mm) of mechanical behavior that considers the multiphase characteristics, at leastwise a matrix and a reinforcement phase, of this kind of composites must be explored, i. e., a heterogeneous material is assumed to predict, for example, the effective tensors of stiffness or compliance by a homogenization process, modeling to damage analyses, studies about progressive delamination and other specific aspects of a composite can be previously investigated using micromechanical approaches. Therefore, the understanding of fundamental micromechanics as well as some advanced models are essential to apply these materials in structural industry components. In this sense, this systematic literature review is designed to investigate the main basic and advanced models of laminated composites micromechanics and using this overall study, have been developed, in the end, an analysis of research trends to the topic, in which, lines to future researches are indicated.

Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the sub-district level and aggregated to monthly, annual and seasonal rainfall totals and the number of rainy days. The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. Non-Parametric Mann-Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann-Kendall tests (Pre-Whitening, Trend Free Pre-Whitening, Bias Corrected Pre-Whitening and two variants of Variance Correction Approaches) were applied. A significant increasing summer rainfall trend is observed in 6 out of 27 stations. Significant decreasing trends are observed at two stations in the south-west monsoon season and at two stations in the north-east monsoon season. To identify the trend change-points in the time series, distribution-free Cumulative SUM test and sequential Mann-Kendall tests were applied. Two open-source library packages were developed in R language namely, ‘modifiedmk’ and ‘trendchange’ to implement the statistical tests mentioned in this paper. The study will benefit water resource management, drought mitigation, socio-economic development and sustainable agricultural planning in the region.

This paper performs non-parametric Mann Kendall (MK) trend analysis of historical hydroclimatic data (1961-2016), an ensemble climate model validation and a computation of 16 Expert Team on Climate Change Detection and Indices (ETCCDI) temperature and rainfall extremes indices. The climate indices are evaluated using MK test and annual trend analysis for two Representative Concentration Pathways (RCP4.5 & RCP8.5) future scenarios from 2020 to 2045 over Mono River Basin (MRB) in Togo. The annual and seasonal trend analyses are assessed on historical potential evapotranspiration, mean temperature, rainfall and discharge data. Results show positive and negative trends of hydroclimatic data over MRB from1961 to 2016. Mean temperatures increase significantly in most of the stations while a negative non-significant trend is noticed for rainfall. Meanwhile, the discharge presents a significant seasonal and annual trend for three gauge stations (Corrokope, Nangbéto and Athiémé). Validation of the ensemble climate models reveals that the model under-estimates observations at Sokode, Atkakpamé and Tabligbo stations, however linear regression and spatial correlation coefficients are higher than 0.6. Moreover, the percentage of bias between climate model and observations are less than 15% at most of the stations. Finally, the computation of extreme climatic indices under RCP4.5 and RCP8.5 scenarios shows a significant annual trend of some extreme climatic indices of rainfall and temperature at selected stations between 2020 and 2045 in the MRB. Therefore, relevant governmental politics are needed to elaborate strategies and measures to cope with projected climate changes impacts in the country.

(1) Background: Until December 2021, French Guiana (FG), located in South America, faced four consecutive COVID-19 epidemic waves. This study sought to analyze the mortality trend of severe COVID-19 patients admitted to the referral ICU of FG. (2) Methods: We conducted a prospective, observational, and non-interventional study in ICU at Cayenne Hospital. We included 383 patients older than 18 admitted with SARS-CoV-2-related pneumonia hospitalized from May 2020 to December 2021. The study covers three periods. Period 1 (Waves 1 and 2, original variant), period 2 (Wave 3, gamma variant), and period 3 (Wave 4, delta variant). (3) Results: The median age was 63 years (52-70). Frailty was diagnosed in 36 patients over 70 (32.4%). Only 4.8% of patients were vaccinated. The median ICU LOS was 10 days (6-19). Hospital mortality was 37.3%. It was 30.9% in period 1, 36.6% in period 2 (p=0.329 vs. period 1), and 47.1% in period 3 (0.015 vs. period 1). In multivariate analysis, independent factors associated with hospital mortality were age more than 40 years (]40-60 years] OR=5.2, 95%CI: 1.4-19.5; (]60-70 years] OR=8.5 95%CI: 2.2-32; (]70+ years] OR=17.9, 95%CI: 4.5-70.9), frailty (OR=5.6, 95%CI: 2.2-17.2), immunosuppression (OR=2.6, 95%CI: 1.05-6.7), and MV use (OR=11, 95%CI: 6.1-19.9). This model had an overall sensitivity of 72%, a specificity of 80.4%, a positive predictive value of 68.7%, and a negative predictive value of 82.8%. (4) Conclusions: The mortality of severe COVID-19 patients in French Amazonia was higher during the delta variant wave. This over-death is explained by the virulence of the responsible SARS-CoV-2 variant and the under-vaccination coverage of the studied population.

Spatial interpolation of meteorological data can have immense implications on risk management and climate change planning. Kriging with external drift (KED) is a spatial interpolation variant that uses auxiliary information in the estimation of target variable at unobserved locations. However, the traditional KED methods with linear trend functions may not be able to capture the complex and non-linear interdependence between target and auxiliary variables, which can lead to an inaccurate estimation. In this work, a novel KED method using least squares support vector regression (LSSVR) is proposed. This machine learning algorithm is employed to construct trend functions regardless of the type of variable interrelations being considered. To evaluate the efficiency of the proposed method (KED with LSSVR) relative to the traditional method (KED with a linear trend function), a systematic simulation study for estimating the monthly means temperature and pressure in Thailand in 2017 was conducted. The KED with LSSVR is shown to have superior performance over the KED with the linear trend function.

In the ongoing Covid-19 pandemic, in the global data on the case fatality ratio and other indices reflecting death rate, there is a consistent downward trend from mid-April to mid-August. The downward trend can be an illusion caused by biases and limitations of data or it could faithfully reflect a declining death rate. A variety of explanations for this trend are possible, but a systematic analysis of the testable predictions of the alternative hypotheses has not yet been attempted. We state six testable alternative hypotheses, analyse their testable predictions using public domain data and evaluate their relative contributions to the downward trend. We show that a decline in the death rate is real; changing age structure of the infected population and evolution of the virus towards reduced virulence are the most supported hypotheses and together contribute to major part of the trend. The testable predictions from other explanations including altered testing efficiency, time lag, improved treatment protocols and herd immunity are not consistently supported, or do not appear to make a major contribution to this trend although they may influence some other patterns of the epidemic.

A vast majority of the countries is under the economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, which is an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We have considered an autoregressive model with a non-linear time trend component that approximately converted into the linear trend using the spline function. The spline function split the COVID-19 series into different piecewise segments between respective knots and fitted the linear time trend. First, we obtain the number of knots with its locations in the COVID-19 series and then the estimation of the best-fitted model parameters are determined under Bayesian setup. The results advocate that the proposed model/methodology is a useful procedure to convert the non-linear time trend into a linear pattern of newly coronavirus case for various countries in the pandemic situation of COVID-19.

Mississippi youth are demographically unique compared to that of the nation. The purpose of this study was to determine the trend of drug use among adolescents of the United States and Mississippi, and to estimate the differences of drug use prevalence as a whole, and by gender and by race. National and Mississippi YRBSS data from 2001 to 2021 were obtained for analysis. Summary statistics, prevalence ratio, and survey Chi-squared tests of independence statistics were generated for the comparison for all students, and by gender and race separately. Trend analysis was conducted using logistic regression combined with joinpoint regression. Six survey questions being studied were: have you ever used marijuana, inhalant, heroin, methamphetamines, injected drugs, and were you offered, sold, or given an illegal drug on school property during the last 12 months. Survey packages in R were used to account for the complex sampling design of YRBSS data. On the national level, all six drug related risk behaviors being studied showed significant decrease from 2001 to 2021. In Mississippi however, only “ever used marijuana” showed a trend of decrease, while three remain unchanged, and two increased. 2021 YRBSS data shows that Mississippi adolescents exhibited significantly higher prevalence of drug use, and are more likely to be offered, sold or given an illegal drug on school property. Our study suggests an urgent need for drug use intervention among Mississippi adolescents. More concerted actions at the community, school and government level are needed for reducing youth drug use and controlling the drug traffic on school property.

In the last two decades, the application of composite structures in many industrial sectors has been made progressively. For the current major scientific and technological challenge, the energy transition with sustainable and eco-friendly initiatives, the polymer matrix laminated composites have been revealed as an excellent alternative for the renewable energy sector. In this context, the knowledge about mechanical behavior of multiphase materials is required. Then, researches related to macro, mesomechanical and multiscale approaches are very important to improve accuracy of the models to predict effective mechanical properties and to develop a better understanding about complex phenomena such as progressive damage, creep and fatigue. Due to theses needs, this research is a synthesis of fundamentals and advanced topics concerning to global modeling of laminated composite structures, layerwise analyzes and micro-macromechanical models, in which mainly fiber, matrix, interface and damage characteristics are studied and used in the macroscale approach. At the end of this study, some final remarks and a trend analysis are proposed based on literature consulted, in other words, expectations for the future works are made by the authors.

This is a single tertiary center in southwest Romania, population-based, study. We retrospectively compared data obtained in two periods: January 2008 - December 2013 and January 2018 - De-cember 2023, respectively. The global incidence of transposition of great arteries (TGA) - the ter-minated cases added to the ones resulting in live-born pregnancies, remained almost constant. The live-born incidence of TGA decreased (0.34% to 0.013%). The prenatal detection rate increased slightly (71.42% to 87.5%). The median gestational age at the diagnosis decreased, from 29.3 ges-tational weeks (mean 25.4) to 13.4 weeks (mean 17.2). The proportion of terminated pregnancies significantly increased (14.28% to 75%, p = 0.019).

This study was aim to assess the spatial-temporal trends of urban expansion with land use/land cover change dynamics over a period of 30 years in Addis Ababa. The source of data were Land sat satellite imagery freely available by the United States Geological Survey, Addis Ababa City plan map obtained from Addis Ababa city governance planning and development commission, and Ethiopia Space Science and Geospatial Agency. The data obtained were analysed by using QGIS, and ERDAS software. The finding showed that over the last thirty years Addis Ababa city was highly spreads with (Urban Expansion Intensity Index(UEII)=1.524%); building-up land increased faster (dynamic degree (K) = -6.22 and Annual Urban Expansion Rate(AUER) is 3.56. From the finding, Addis Ababa is spreading speedily.

Background: Aedes albopictus, the Asian Tiger Mosquito, has become a prevalent pest in Italy, causing severe nuisance and posing a threat of transmission of arboviruses introduced by infected travellers. In this study, we investigated the influence of weather parameters on the seasonal population density of Aedes albopictus. Methods: A Bayesian approach was employed to identify the best meteorological predictors of species trend, using the eggs collected monthly from 2010 to 2022 by the Emilia-Romagna regional monitoring network. Results: The findings show that the winter-spring period (January to May) plays a crucial role in the size of the first generation and seasonal development of the species. Conclusions: A temperate winter and a dry and cold March, followed by a rainy and hot spring and a rainy July, seem to favour the seasonal development of Ae. albopictus.

This study numerically evaluated the long-term and stable trends of meandering channel planform at different mean annual subcritical flows based on a depth-averaged linearized meander evolution model. The calculation cases included an idealized sine-generated meander with moderate max-deflection angle and a typical natural meander - Jiyun River (in China). Within the increase of the selection of the mean annual width-depth ratio in a certain scope, the results showed some specific phase characteristics of channel centerline evolution: (1) the meander straightening trend became weaker in phase 1 (B/H ≤ 16.5) and gradually turned into the meander developing trend in phase 2 (B/H ≤ 27) with the overall immunity to flow magnitude; (2) the symmetric development form along the transverse direction perpendicular to the flow average direction was broken starting from the downstream tail in phase 3 (B/H ≤ 28.9), meantime accompanied by the highlight of flow effects when the mean annual B/H exceeded 28; (3) in phase 4 (B/H ≤ 29.5), there occurred obvious incipience and jump process of the channel sensitivity responding to flow when the selected B/H crossed 29.3, which were very similar to the transition process from the laminar flow to turbulent flow. Besides, the initial sinuosity and curvature-ratio indicator could contribute much to the channel evolutionary state, as demonstrated in the comparison between natural Jiyun River case and the correspondingly idealized sine-generated meander case with the same level of mean annual B/H. This research provides innovation spaces for deeper meander mechanism exploration and effective riparian management.

For the Philippines, a country exposed to multiple natural hazards like severe wind, sustainable development includes resiliency. Severe wind hazard is brought by tropical cyclones in the Western Pacific, known as typhoons, that frequent the Philippines. Therefore, adequately evaluating the wind hazard and its impact is crucial for sustainable building design. Acknowledging the impacts of climate change on said hazards would require adaptation to its consequences which necessitate a deeper understanding on the changing behavior of typhoons in recent years. For this study, detailed wind information from the Japan Meteorological Agency from 1977-2021, the Holland-B parameter, and the radius of maximum wind speed for each typhoon, are determined for simulation of the regional cyclonic wind field. The analysis of the Holland-B parameters, which represent the steepness of the pressure gradient and tropical cyclone convection, suggest that the Holland-B parameters have been increasing since 2011. The regional wind fields caused by the typhoons also suggest an increasing trend in severe wind hazard. Seasonality for the location of severe wind hazard is also observed, with the Southern Philippines experiencing an increase (decrease) during the Northeast (Southwest) Monsoon season, and the Northern Philippines experiencing an increase (decrease) during the Southwest (Northeast) Monsoon season.

The purpose is to verify trends of scientific production from 2010 to 2020, considering the best universities of the United States, China, the European Union (EU) and private companies. The top 30 universities in 2020 in China, the EU, and the US and private companies were selected from the SCImago institutions ranking (SIR). The positions in 2020, 2015, 2010 in SIR and three sub-indicators were analyzed by means of non-parametric statistics, taking into consideration the effect of time and group on rankings. American and European Union universities have lost positions to Chinese universities and even more to private companies, which have improved. In 2020, private companies have surpassed all other groups considering Innovation as sub-indicator. The loss of leadership of European and partly American universities mainly concerns research linked to the production of patents. This can lead to future risks of monopoly that may elude public control and cause a possible loss of importance of research not linked to innovation.

Degradation is an inevitable companion in the operation of solid oxide fuel cell (SOFC) systems since it directly deteriorates the reliability of the system’s operation and the system’s durability. Both are seen as barriers that limit the extensive commercial use of SOFC systems. Therefore diagnosis and prognosis are valuable tools that can contribute to raising the reliability of the system operation, efficient health management, increased durability and implementation of predictive maintenance techniques. Remaining useful life (RUL) prediction has been extensively studied in many areas like batteries and proton-exchange membrane fuel cell (PEM) systems, and a range of different approaches has been proposed. On the other hand, results available in the domain of SOFC systems are still relatively limited. Moreover, methods relying on detailed process models and models of degradation turned out to have limited applicability for in-field applications. Therefore, in this paper, we propose an effective, data-driven approach to predicting RUL where the trend of the health index is modelled by an adaptive linear model, which is updated at all times during the system operation. This allows for a closed-form solution of the probability distribution of the RUL, which is the main novelty of this paper. Such a solution requires no computational load and is as such very convenient for the application in ordinary low-cost control systems. The performance of the approach is demonstrated first on the simulated case studies and then on the data obtained from a long-term experiment on a laboratory SOFC system. From the tests conducted so far it turns out that, the quality of the RUL prediction is usually rather low at the beginning of the system operation, but then gradually improves while the system is approaching the end-of-life (EOL), making it a viable tool for prognosis.

Tree growth is under the combined influence of abiotic and biotic factors. Trees with different sizes may respond differently to these factors, implying that tree heterogeneity in tree size may also modulate the overall growth trend. To test this hypothesis, we focused on the radial growth trends of natural subalpine forests on the Tibetan Plateau. We first extended the iterative growth model (IGM) to the tree-ring scale (IGMR) to test the applicability of the generalized metabolic growth theory to tree growth. As predicted by the IGMR, the radial growth of trees at the aggregate scale is constrained by a unimodal pattern. Using the IGMR, we reconstructed the historical best growth trajectory (HBGT) of trees within the same community based on the tree with the largest radius and/or longest age in the community. From the average difference between the HBGT and the current radial growth rate of trees with different sizes, we constructed an indicator that can measure the overall variation in tree radial growth. Based on this indicator, we found a negative effect of tree size heterogeneity on the overall variability of tree growth across elevations. Further analysis also revealed that the radial growth rate of trees on the Tibetan Plateau has increased significantly compared to the past, where growing season average temperature and annual minimum temperature were negatively and positively correlated with tree growth below and above the treeline, respectively. Our study not only confirmed that the overall variability of tree growth depends on tree size heterogeneity but also proposed an indicator that reveals net changes in tree radial growth rate relative to the past. These theoretical advances facilitate the understanding of changes in the extent of subalpine forests.

Health care workers (HCWs), specifically dentists, are at the front line for acquiring blood-borne virus infections. The highest proportion of occupational transmission is through percutaneous injuries via hollow-bore needles. Several studies around the world have reported that hepatitis viruses and human immunodeficiency virus are the main pathogens for most cases of occupationally acquired blood-borne infection. We aim to investigate the prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis E virus (HEV), and human immunodeficiency virus (HIV) among Mexican dentists. Methods. We included 159 dentists who attended the annual meeting at the Medica Sur Clinic & Foundation held in Mexico City in May 2016. A survey was applied in order to obtain data of occupational exposure to blood-borne viruses (BBV). Serum samples were screened serologically using enzyme-linked immunosorbent assays. Results. Two dentists (1.2%) were positive for antibodies against HCV antigen, one (0.6%) was positive for antibodies against HBV antigen and three (1.8%) were positive for the detection of IgG antibodies against HEV. Two cases (1.2%) were positive for antibodies against HIV. Conclusions. The infection by HEV was the most prevalent among dentists. However, the prevalence of BBV in dentists was similar to that in the general population.

Melanophryniscus admirabilis (admirable red-belly toad) is a microendemic and critically endangered species found exclusively along 700 meters of the Forqueta River, in a fragment of Atlantic Forest in southern Brazil. One of the greatest concerns regarding the conservation of this species is the extensive use of pesticides in areas near their natural habitat. In recent years, the adaptation and persistence of animal species in impacted environments have been associated with microbiota. Therefore, the current study aimed to characterize the oral bacterial community of wild M. admirabilis and to address the question of how this community might contribute to toad’s adaptation in the anthropogenic environment and its general metabolic capabilities. In the present study, 11 oral samples collected from wild M. admirabilis were characterized and analyzed via high-throughput sequencing. A total of 181,350 sequences were obtained, resulting in 16 phyla, 34 classes, 39 orders, and 77 families. Proteobacteria dominated (53%) the oral microbiota of toads followed by Firmicutes (18%), Bacteroidetes (17%), and Actinobacteria (5%). No significant differences in microbial community profile from among the samples were reported, suggesting that the dietary restriction may directly influence the bacterial composition. Functional inference of microbiome was performed using PICRUSt2 and important pathways, such as xenobiotic degradation pathways to pesticides and aromatic phenolic compounds were detected, suggesting that the bacterial communities may have important roles in M. admirabilis health and survival in the anthropogenic environment. Overall, our results have important implications for the conservation and management of this microendemic and critically endangered species.

We studied the weekly number and the growth/decline rates of COVID-19 deaths in the period October 31, 2022 – February 9, 2023, in Italy, finding that that COVID-19 winter wave reached its peak during the three holyday weeks extending from December 16, 2022 to January 5, 2023, and it was definitely trending downward, returning to the same number of deaths of the end of October 2022, in the week February 3 – 9, 2023. During this period of fifteen weeks, that wave caused a number of deaths as large as 8,526. Its average growth rate was +7.89% deaths per week (ten weeks), while the average weekly decline rate was -12.32% (five weeks). Currently, Italy is experiencing a new COVID-19 wave, with the latest six weekly bulletins (October 26, 2023 – December 14, 2023) showing that the deaths have climbed from 148 to 322. The weekly growth rate has risen by +14.08% deaths, on average. In the hypothesis that this 2023-2024 wave have a duration similar to that of 2022-2023, with: i) a comparable extension of both the growing period and of the decline period and ii) similar growth/decline rates, a prediction can be cast regarding the number of COVID-19 deaths in the period November 2023 - January 2024. They could reach the amount of 4100-4200

Tuberculosis cause of death worldwide and the leading cause from a single infectious agent, ranking above Human immunodeficiency virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS). The aim of this study is to ascertain the trend of tuberculosis prevalence and the effect of HIV prevalence onl Tuberculosis case in some West African countries from 2000 to 2016 using count panel data regression models. The data used annual HIV and Tuberculosis cases spanning from 2000 to 2016 extracted from online publication of World health Organization (WHO). Panel Poisson regression model and Negative binomial regression model for fixed and random effects were used to analyzed the count data, the result revealed a positive trend in TB cases while increased in HIV cases leads to increase in TB cases in West African countries. Among the competing models used in this study, Panel Negative Binomial Regression Model with fixed effect emerged the best model with log likelihood value of -1336.554. This study recommended that Government and NGOs need more strategies to fight against HIV menace in West Africa as this will in turn reduced TB cases in West Africa.

After globalization, South Korea’s retail landscape has been saturated with large-scale, corporate type retails. However, recently new commercial districts composed of small retail units are returning and bringing about a change. This study sought to take note of this phenomenon and identify its features and meanings from the perspective of urban ecology. A density-distribution analysis was conducted to investigate how they were forms, and an analysis of traces on the Internet and an analysis on the types of businesses were done to identify sociocultural characteristics. Results showed that they had similar type of locations and growth patterns, that they harmoniously congregated in a form of smaller-individual stores, and that their use of similar names for their stores had an impact of branding their entire districts. It was also noted that a shared culture through social networking services served as a growth boost for their unfavorable location. The spontaneous formation of such commercial districts can be an outcome of an urban ecological process geared toward blank niches burgeoning in the current retail structure of Korea. The causes and conditions found in the cases reveal meaningful policy implications for cities facing the same urban crisis diversity.

To analyze the viral titers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and respiratory syncytial virus (RSV) at the anterior nasal site (ANS) and nasopharyngeal site (NS), evaluate their virological dynamics, and validate the usefulness of a newly developed two antigen-detecting rapid antigen diagnostic test (Ag-RDT) that simultaneously detects SARS-CoV-2 and RSV using clinical specimens. This study included 195 asymptomatic to severely ill patients. Overall, 668 specimens were collected simultaneously from the ANS and NS. The cycle threshold (Ct) values calculated from real-time polymerase chain reaction were used to analyze temporal changes in viral load and evaluate the sensitivity and specificity of the Ag-RDT. The mean Ct values for SARS-CoV-2-positive, ANS, and NS specimens were 28.8, 28.9, and 28.7, respectively. The mean Ct values for RSV-positive, ANS, and NS specimens were 28.7, 28.8, and 28.6, respectively. SARS-CoV-2 and RSV showed the same trend in viral load, although the viral load of NS was higher than that of ANS. The sensitivity and specificity of the newly developed Ag-RDT were excellent in specimens collected up to 10 days after the onset of SARS-CoV-2 infection and up to 6 days after the onset of RSV infection.

Tooth decay, also known as caries, is a significant medical problem that harms teeth. Treatment is based on the removal of the carious material and then filling the cavity left in the tooth, most commonly with amalgam or composite resin. The consequences of filling failure include repeating the filling, or performing another treatment, such as a root canal, or extraction. Dental amalgam contains mercury, and there is a global effort to reduce its use. However, no consensus has been reached using randomized clinical trials, regarding whether amalgam or composite resin materials are more durable, and which is the best restorative material. To determine which material is superior, we performed a retrospective cohort study using a large database where members of the 58 dental clinics with 440 dental units were treated. The number of failures of the amalgam compared to composite resin restorations between 2014 and 2021 were compared. Our data included information from over 650,000 patients. Between 2014 - 2021, 260,905 patients were treated. 19,692 out of 113,281 first amalgam restorations failed (17.49%), whereas significantly fewer composite restorations failed (11.98%) 65,943 out of 555,671. This study indicates that composite is superior to amalgam and therefore it is reasonable to cease using mercury containing amalgam.

In this article, we first assessment of peste des petits ruminants (PPR) antibodies in vaccinated pregnant ewes of Kazakh breed fine-fleeced immunized with the PPR vaccine and the duration of maternal immunity in their lambs. Ewes in the last trimester of pregnancy of gestation were immunized with a vaccine from the Nigeria 75/1 strain of the PPR virus (PPRV) produced by the Research Institute of Biological Safety Problems (RIBSP), Kazakhstan. Serum samples from lambs born from vaccinated and unvaccinated ewes were collected a week after birth and at intervals of 7 days for 18 weeks after birth. Serum samples collected from lambs were tested for PPR antibodies using competitive ELISA and Virus neutralization test (VNT). Maternal antibodies (MAs) in lambs born from vaccinated ewes were detected up to 18 weeks with a tendency to decrease starting at week 14 and by the end of the experiment receded below the protective level (&lt;1:8). In the blood serum of a 14-week-old lamb with MAs (1:8), post vaccination with a field dose (103 TCID50) of the vaccine against PPR, the titers of protective antibodies to the PPRV increased to 1:16 on 14 day post vaccination and lamb protected from infection upon field PPRV. A lamb of the same age with MAs in the 1:8 titer was 100% protected from infection with the field PPRV. Therefore, it is recommended that lambs of the Kazakh fine-wool breed be immunized from the age of 14 weeks or older to avoid a period of susceptibility.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.