A distributed wind downscaling technique for wave climate modeling under future scenarios

The aim of this study is to develop a Weibull-based distributed downscaling technique for wind field as forcing for the wave models to investigate the wave climate under future scenarios. For this purpose, the statistical downscaling approach modifies Weibull distribution parameters of the global circulation model wind speeds based on the corresponding features of wind data of ECMWF (European Center for Medium-Range Weather Forecasts). The proposed technique has the advantage of modifying the wind components in each grid point based on the corresponding values in the same grid point of ECMWF wind field. Hence, it is superior to other existing models due to considering the spatial variation. The previous models using inverse distance weighting suffer from heterogeneity and ignoring spatial variation in areas with high gradient of wind speed. Moreover, the Weibull-based technique outperforms the existing statistical downscaling techniques in terms of accuracy. Prior to investigate future distribution of wave characteristics, performance of the selected GCM was evaluated and compared against the corresponding models obtained from the available regional climate models. Future projections of wind fields (RCP4.5, RCP8.5) were downscaled for the period of 2081 to 2100 with the proposed model as driving force for wave modeling in the Persian Gulf. To investigate the impacts of climate change on wave characteristics, results of the wave simulations from a third generation wave model (SWAN) for future scenarios are compared with those of the historical period (1981–2000) in monthly, seasonal, and annual scales. Generally, for RCP8.5, the results indicate a decrease in future significant wave height and peak wave period about 15% and 5%, respectively. However, the change of wave direction is marginal. Moreover, wave models forced with RCP4.5 wind data provide slightly higher average values in terms of wave height and peak wave period compared to those of RCP8.5

The ER Stress/UPR Axis in Chronic Obstructive Pulmonary Disease and Idiopathic Pulmonary Fibrosis.

Cellular protein homeostasis in the lungs is constantly disrupted by recurrent exposure to various external and internal stressors, which may cause considerable protein secretion pressure on the endoplasmic reticulum (ER), resulting in the survival and differentiation of these cell types to meet the increased functional demands. Cells are able to induce a highly conserved adaptive mechanism, known as the unfolded protein response (UPR), to manage such stresses. UPR dysregulation and ER stress are involved in numerous human illnesses, such as metabolic syndrome, fibrotic diseases, and neurodegeneration, and cancer. Therefore, effective and specific compounds targeting the UPR pathway are being considered as potential therapies. This review focuses on the impact of both external and internal stressors on the ER in idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) and discusses the role of the UPR signaling pathway activation in the control of cellular damage and specifically highlights the potential involvement of non-coding RNAs in COPD. Summaries of pathogenic mechanisms associated with the ER stress/UPR axis contributing to IPF and COPD, and promising pharmacological intervention strategies, are also presented

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Future variability of wave energy in the Gulf of Oman using a high resolution CMIP6 climate model

There is a worldwide compromise toward increasing the proportion of renewable energy in future electricity production to mitigate the impacts of greenhouse gases. This study explores the sustainability of wave energy resources in the northern part of the Gulf of Oman, considering the impact of climate change using a Shared Socio-economic Pathway (SSP5-8.5) representing a high increase in CO2 concentration by 2100. Near-surface wind speed dataset from a high-resolution CNRM (CNRM-CM6-1-HR) global climate model was employed to force a third-generation wave model. A novel statistical bias-correction technique was developed based on Weibull distribution to generate high-resolution input wind for the wave model, and various criteria were employed to assess the sustainability of the wave energy in the study area. Comparing future projections of wave energy under SSP5-8.5 with those of historical simulations demonstrated the sustainability of the wave resources in the study area. The methodology of utilizing multiple criteria assessments, including accessibility, availability, and exploitable storage of wave energy predicts an increase ranging from 21 to 45% in the future wave power under a high emission scenario

Effect of river flow on the quality of estuarine and coastal waters using machine learning models

This study explores the river-flow-induced impacts on the performance of machine learning models applied for forecasting of water quality parameters in the coastal waters in Hilo Bay, Pacific Ocean. For this purpose, hourly recorded water quality parameters of salinity, temperature and turbidity as well as the flow data of the Wailuku River were used. Several machine learning models including artificial neural network, extreme learning machine and support vector regression have been employed to investigate the river-flow-induced impact on the water quality parameters from the current time up to 2 h ahead. Following the input structure of the machine learning models, two separate models based on including and excluding the river flow were developed for each variable to quantify the importance of the flow discharge on the accuracy of the forecasting models. The performance of different machine learning models was found to be close to each other and showing similar pattern considering accuracy and uncertainty of the forecasts. The results revealed that flow discharge influenced the water salinity and turbidity of the bay in which the models including the river flow as input variables had better performance compared with those excluding the flow time series. Among the water quality parameters investigated in this research, river flow made the most and least improvement on the efficiency of the models applied for forecasting of turbidity and water temperature, respectively. Overall, it was observed that water quality parameters can be properly forecasted up to several hours ahead providing a potentially valuable tool for environmental management and monitoring in coastal areas

The Prevalence of Asthma, Allergic Rhinitis and Eczema in North of Iran: the International Study of Asthma and Allergies in Childhood (ISAAC)

Objective: Asthma, allergic rhinitis and eczema as a common chronic disorder in childhood, has many epidemiologic variations in different geographic areas. Uniform and standard epidemiologic researches are able to clear and modify scientific questions in this field. We carried out this study to determine the prevalence and intensity of pediatric allergic disease in our region. Material & Methods: This analytical-cross sectional study was performed on 2 groups of children; the first group aged 6-7 years (n=3240) and the second group aged 12-14 years (n=3254) during 2002-03. According to ISAAC programming, sample size consisted of 3000 children in each group. From all students 99.3% of primary students and 88.8% of guidance students entered into study. Data was gathered by ISAAC first phase questionnaire and analyzed by SPSS 10 and Chi square test. Findings: The 12-month prevalence rates of symptoms were as follow: wheezing 16.8% and 21.7%, allergic rhinitis symptoms 14.5% and 19.9% and atopic dermatitis symptoms 4.5% and 8.2%, for younger and older age group, respectively. The prevalence of wheezing and current wheeze did not show differences according to sex (P>0.05) but it was significantly higher in students of guidance school (P<0.05). The prevalence of previous history of asthma, speech disorders, wheezing after physical exercises and dry cough at night, rhinoconjuntivitis, recurrent rhinitis, eczema with pruritus, recurrent lesions and history of eczema was significantly higher in boys and in students of guidance school (P<0.05). The prevalence of flexor lesion did not show a significant difference according to age (P>0.05) but in boys it was higher than in the girls (P<0.05). Conclusion: According to our findings asthma, allergic rhinitis and eczema have a moderate prevalence in this region of our countr

Identification of Candida species isolated from vulvovaginitis in Mashhad, Iran by Use of MALDI-TOF MS

Background and Purpose Vulvovaginal candidiasis (VVC) is a common problem in women. The purpose of this study was to identify of Candida species isolated from vulvovaginitis woman suffering vulvovaginitis refered to Ghaem Hospital, Mashhad, Iran, by use of MALDI-TOF mass spectrometry. &nbsp;Materials and Methods The 65 clinical samples isolated from Vulvovaginitis women were collected in Ghaem Hospital. All specimens were identified using phenotypic techniques such as microscopy and culture on Sabouraud dextrose agar and corn meal agar medium,Then, All isolates were detected and were processed for MALDI TOF MS identification. Results &nbsp;Of the 65 isolates analyzed, 61 (93.8%) were recognised by MALDI-TOF mass spectrometry and for four isolates (6.1%) only not relabile identifications were achieved. In this study, the most frequently isolated species were Candida albicans (58.5%), followed by Candida tropicalis (16.9%), Candida glabrata (7.7%), Candida parapsilosis (7.7%) and Candida guillermondii (3.1%). &nbsp;Conclusion presented results demonstrate that the MALDI TOF mass spectrometry is a fast and reliable technique, and has the potential to replace conventional phenotypic identification of Candida species and other yeast strains routinely isolated in clinical microbiology laboratories

The ER Stress/UPR Axis in Chronic Obstructive Pulmonary Disease and Idiopathic Pulmonary Fibrosis

Cellular protein homeostasis in the lungs is constantly disrupted by recurrent exposure to various external and internal stressors, which may cause considerable protein secretion pressure on the endoplasmic reticulum (ER), resulting in the survival and differentiation of these cell types to meet the increased functional demands. Cells are able to induce a highly conserved adaptive mechanism, known as the unfolded protein response (UPR), to manage such stresses. UPR dysregulation and ER stress are involved in numerous human illnesses, such as metabolic syndrome, fibrotic diseases, and neurodegeneration, and cancer. Therefore, effective and specific compounds targeting the UPR pathway are being considered as potential therapies. This review focuses on the impact of both external and internal stressors on the ER in idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) and discusses the role of the UPR signaling pathway activation in the control of cellular damage and specifically highlights the potential involvement of non-coding RNAs in COPD. Summaries of pathogenic mechanisms associated with the ER stress/UPR axis contributing to IPF and COPD, and promising pharmacological intervention strategies, are also presented