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In statistics, normal distribution has a fundamental role. This study analyses the statistical power of eleven tests of normality: Shapiro--Wilk (SW), Shapiro--Francia (SF), Filliben (F), Kolmogorov--Smirnov (KS), Anderson--Darling (AD), Kuiper (K), Cramér--von Mises (CvM), D'Agostino--Pearson (DP), Jarque--Bera (JB), Chi-square (CS) and Vasicek (V) test. The power comparison is obtained by sampling data from 5 distribution groups with Monte Carlo simulation: support (-∞, ∞) symmetric and asymmetric, support (0, ∞), support (0, 1), and contaminated distributions. We focus on the smaller 10, 20, 30, ..., 100 sample sizes given that econometrics often model macroeconomic and financial time- series within this range of samples. Results suggest that the regression and correlation-based normality tests, such as the Shapiro-Wilk and Filliben tests perform well on distributions with support (-∞, ∞). For distributions with (0, ∞) and (0, 1) support the Shapiro-Wilk and the Vasicek tests have the highest power while on contaminated samples the Jarque--Bera test outperforms them. The results could help statisticians and econometrics to apply the best-performing normality test having a prior assumption or information on the support of the data-generating process. [ABSTRACT FROM AUTHOR]

The Jarque–Bera test is commonly used in statistics and econometrics to test the hypothesis that sample elements adhere to a normal distribution with an unknown mean and variance. This paper proposes several modifications to this test, allowing for testing hypotheses that the considered sample comes from: a normal distribution with a known mean (variance unknown); a normal distribution with a known variance (mean unknown); a normal distribution with a known mean and variance. For given significance levels, α = 0.05 and α = 0.01 , we compare the power of our normality test with the most well-known and popular tests using the Monte Carlo method: Kolmogorov–Smirnov (KS), Anderson–Darling (AD), Cramér–von Mises (CVM), Lilliefors (LF), and Shapiro–Wilk (SW) tests. Under the specific distributions, 1000 datasets were generated with the sample sizes n = 25 , 50 , 75 , 100 , 150 , 200 , 250 , 500 , and 1000. The simulation study showed that the suggested tests often have the best power properties. Our study also has a methodological nature, providing detailed proofs accessible to undergraduate students in statistics and probability, unlike the works of Jarque and Bera. [ABSTRACT FROM AUTHOR]

Precipitation in northeastern Algeria is irregular, often exhibiting exceptional values. Extreme precipitation events, such as annual maximum daily precipitation (AMDP), can cause major disasters. Changes in global climate have increased the frequency and intensity of AMDP in many areas. This article examines the distribution and quantile mapping of AMDP, using the data from 180 precipitation stations covering eight watersheds in northeastern Algeria between 1970 and 2014. The watersheds, from east to west, are Coastal Algiers, Soummam, El Hoddna, Coastal Constantine, Kebir Rhumel, High Plateaus Constantine, Seybouse, and Medjerdah. By applying different goodness-of-fit tests, the chi-square (χ2), Kolmogorov–Smirnov (KS), Anderson–Darling test (AD), and log-likelihood ratio test, the best-fit distribution function for frequency analysis of AMDP was determined to be the generalized extreme value (GEV), with a 95% confidence level. The maximum value of AMDP for the data series generally varied between 60 and 150 mm, with higher values in the humid zone and lower values in the arid zone. The validity of the data used in this study was assessed through a series of tests, including the Wald–Wolfowitz test for independence, the Kendall test for stationarity, and the Wilcoxon test for homogeneity. Results of these tests demonstrated that a majority of the data met the conditions for independence, stationarity, and homogeneity. Results of goodness-of-fit test and graphical fit provided further support for the appropriateness of the GEV probability distribution function as the best fit for the data. Application of various goodness-of-fit tests for the first time on a significant number of stations in northeastern Algeria provided a better understanding of the uncertainties in frequency analysis and helped identify the most suitable distribution function for modeling AMDP in different climate regimes. The findings highlight the high-risk zones for extreme precipitation events and emphasize the need for increased investment in water resource infrastructure and management in order to address the challenges posed by climate change and population growth. This is particularly important in ensuring water security for agricultural and urban needs in northeastern Algeria. [ABSTRACT FROM AUTHOR]

Modelling traffic characteristics is the foundation for resolving various traffic and transportation issues. Among them, traffic speed has a significant impact on roadway crashes at blackspot (BS) locations. Speed is a random variable; several studies have recommended normal distribution to characterize the distribution of traffic speed for uninterrupted flow. However, a mixed-traffic situation causes heterogeneity, and the distribution of speeds deviates from the normal distribution. The present study investigates the distributions of traffic speeds for uninterrupted flow at 18 blackspot locations and individual vehicle types in mixed-traffic environments. Seven distribution models, namely Normal, Lognormal, Gamma, Logistic, Weibull, Burr, and Generalized Extreme Value (GEV), are considered to determine the speed characteristics. Different parametric distribution models are fitted to the vehicular speeds using maximum likelihood estimation (MLE) methods. Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and two penalized criteria, i.e., Akaike and Bayesian Information Criteria (AIC and BIC), are used as goodness-of-fit (GoF) measures to find the best-fitting distribution. The overall suitability of each predicted distribution is also determined using a novel ranking method. The test findings suggest that GEV and Burr are the most suitable empirical speed distributions, with GEV fitting best above 96%. When the heavy vehicle composition (truck, bus, and tractor) is below 10%, 10–14%, 15–20%, and above 20%, it follows the Weibull, Gamma, GEV, and Burr distributions, respectively, in a mixed traffic environment. • Speed distribution characteristics is analyzed for mixed traffic at black spot unsignalized intersections. • Seven continuous hypothesized distributions are considered. • KS, AD, AIC, BIC test is adopted to verify the aptness of the distribution models. • A ranking method is applied to evaluate the appropriateness of each distribution. • GEV and Burr distribution shows suitable fitting with 90% above fit percentage. [ABSTRACT FROM AUTHOR]

Purpose: Orbital rhabdomyosarcoma is a rare soft tissue sarcoma in childhood but with a good prognosis. Treatment usually includes surgery, chemotherapy, and radiotherapy. This study aimed to evaluate long-term alterations in teeth and cranial bones in children, adolescents, and young adults after oncologic treatment for childhood orbital rhabdomyosarcoma., Method: This was a cross-sectional study that evaluated patients treated for orbital rhabdomyosarcoma between 1988 and 2011. Demographic, clinical, and treatment data were collected during the study period; also, panoramic radiographs, cephalometric study, and photographs of the face were taken., Results: Eight long-term survivors were studied. Of those, 50% were male, 75% had less than 5 years of treatment, and 88% had only one of the orbits affected by the tumor. Regarding treatment, 50% received 50.4 Gy of radiotherapy in the orbit; the chemotherapy included vincristine, actinomycin D, and cyclophosphamide in 75% of the cases and also ifosfamide and etoposide in 25%. The children presented craniofacial alterations, mainly when radiotherapy occurred between 0 and 5 years old (p = 0.01). The mandibles also showed dental alterations, probably due to chemotherapy., Conclusion: In conclusion, orbital RMS patients treated with chemoradiotherapy, important dental, and facial bone alterations were found. The most significant were in the maxilla and close to the irradiation field. Dental and mandibular bone alterations were also found, indicating the probable chemotherapy action, as this region was not included in the irradiation field., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Previous studies found that it is promising to achieve the protective effects of dietary patterns on cardiovascular health through the modulation of gut microbiota. However, conflicting findings have been reported on how dietary patterns impact gut microbiota in individuals either established or at risk of cardiovascular disease (CVD). Our systematic review aimed to explore the effect of dietary patterns on gut microbiota composition and on risk factors for CVD in these populations. Methods: We systematically searched seven databases, including PubMed/MEDLINE, MEDLINE (Ovid), Embase (Ovid), CINHAL (EBSCO), Web of Science, CNKI (Chinese), and Wanfang (Chinese), covering literature from inception to October 2024. Studies were included if they focused on adults aged 18 years and older with CVD or at least two CVD risk factors, implemented dietary pattern interventions, and incorporated outcomes related to microbiome analysis. The risk of bias for included studies was assessed using the revised Cochrane risk of bias tool (RoB2) for randomized trials and the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) for non-randomized studies. Changes in the relative abundance of the gut microbiome were summarized at various taxonomic levels, including phylum, class, order, family, genus, and species. Random-effects meta-analysis was conducted to analyze the mean difference in cardiometabolic parameters pre- and post-intervention. Results: Nineteen studies were identified, including 17 RCT and two self-controlled trails. Risk of bias across the studies was mixed but mainly identified as low and unclear. The most frequently reported increased taxa were Faecalibacterium (N = 8) with plant-rich diets, Bacteroides (N = 3) with restrictive diets, and Ruminococcaceae UCG 005 and Alistipes (N = 9) with the polyphenol-rich diets. The most frequently reported decreased taxa were Parabacteroides (N = 7) with plant-rich diets, Roseburia (N = 3) with restrictive diets, and Ruminococcus gauvreauii group (N = 6) with the polyphenol-rich diets. Plant-rich diets showed a significant decrease in total cholesterol (TC) with a mean difference of -6.77 (95% CI, -12.36 to -2.58; I2 = 84.7%), while restrictive diets showed a significant decrease in triglycerides (TG) of -22.12 (95% CI, -36.05 to -8.19; I2 = 98.4%). Conclusions: Different dietary patterns showed distinct impacts on gut microbiota composition. Plant-rich diets promoted the proliferation of butyrate-producing bacteria, suggesting promising prospects for modulating gut microbiota and butyrate production through dietary interventions to enhance cardiovascular health. Further research is warranted to investigate the long-term effects of dietary patterns on clinical endpoints, such as CVD events or mortality. Review registration: Registration number: CRD42024507660 [ABSTRACT FROM AUTHOR]

Accurately predicting commuting flows is crucial for sustainable urban planning and preventing disease spread due to human mobility. While recent advancements have produced effective models for predicting these recurrent flows, the existing methods rely on datasets exclusive to a few study areas, limiting the transferability to other locations. This research broadens the utility of state-of-the-art commuting flow prediction models with globally available OpenStreetMap data while achieving prediction accuracy comparable to location-specific and proprietary data. We show that the types of buildings, residential and non-residential, are a strong indicator for predicting commuting flows. Consistent with theoretical and analytical models, our experiments indicate that building types, distance, and population are the determining characteristics for mobility related to commuting. Our experiments show that predicted flows closely match ground truth flows. Our work enables accurate flow prediction using building types to support applications such as urban planning and epidemiology. [ABSTRACT FROM AUTHOR]

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives. [ABSTRACT FROM AUTHOR]

This manuscript deals with a novel two-parameter stochastic distribution, obtained by transforming the Cauchy distribution, using generalized logistic mapping, into a unit interval. In this way, according to the well-known properties of the Cauchy distribution, a unit random variable with significantly accentuated values at the ends of the unit interval is obtained. Therefore, the proposed stochastic distribution, named the Cauchy–logistic unit distribution, represents a stochastic model that may be suitable for modeling phenomena and processes with emphasized extreme values. Key stochastic properties of the CLU distribution are examined, such as moments, entropy, modality, and symmetry conditions. In addition, a quantile-based parameter estimation procedure, an asymptotic analysis of the thus obtained estimators, and their Monte Carlo simulation study are conducted. Finally, the application of the proposed distribution in stochastic modeling of some real-world data with emphasized extreme values is provided. [ABSTRACT FROM AUTHOR]

Objective: Identify the most important sociodemographic and behavioral factors related to the diet of low-income adults with hypertension in order to guide the development of a community health worker (CHW) healthy eating intervention for low-income populations with hypertension. Design: In this cross-sectional analysis, dietary recalls were used to assess Healthy Eating Index-2020 (HEI-2020) total (range: 0 to 100 [best diet quality]) and component scores and sodium intake. Self-reported sociodemographic and behavioral data were entered into a Least Absolute Shrinkage and Selection Operator (LASSO) regression model to determine the relative importance of factors related to diet quality. Setting: Five community health centers in Boston, Massachusetts. Participants: Adults (>20 years old) with a hypertension diagnosis. Results: Participants (N = 291) were mostly female (65.0%), on Medicaid (82.8%), food insecure (59.5%), and Hispanic (52.2%). The mean (95% CI) HEI-2020 score was 63.0 (62.3, 65.7) Component scores were low for sodium and whole grains; mean (SE) sodium intake was 2676.9 (45.5) mg/day. The most important factors associated with lower HEI-2020 scores were: not having own housing, male gender, tobacco use, marijuana use, and skipping meals; the most important factors associated with higher HEI-2020 scores were Hispanic ethnicity and receipt of community food resources (5-fold cross-validated R2 = 0.17). Conclusions: In this population of low-income adults with hypertension, diet quality would be improved by reducing sodium and increasing whole grain intake. Healthy eating interventions among low-income populations should consider providing dietary guidance in the context of behavioral factors (e.g., meal skipping) and substance use (e.g., marijuana) and should address barriers to health eating through referral to community food resources (e.g., food pantries). [ABSTRACT FROM AUTHOR]

Background: The Mediterranean diet pattern has been consistently associated with health benefits but less is known about the association with environmental and economic sustainability in the United States (US). This information is needed to support sustainable policy agendas and provide consumers with evidence-based information needed to make informed food choices. This study fills this research gap by evaluating the environmental sustainability and diet cost associated with adherence to the Mediterranean diet pattern in the US. Methods: Dietary data from the National Health and Nutrition Examination Survey (2011–2018, n = 17,079) were merged with data on environmental impacts (greenhouse gas emissions, cumulative energy demand, water scarcity footprint), agricultural resource demand (land, fertilizer nutrients, and pesticides), and food prices from multiple publicly available databases. The Mediterranean Diet Score was used to evaluate adherence to the Mediterranean diet pattern. Multivariable linear regression models were used to evaluate the association between adherence to the Mediterranean diet pattern and environmental impacts, agricultural resource demand, and diet cost. Sensitivity analyses were used to evaluate adjustment of loss and waste and food-away-from-home prices. Results: Greater adherence to the Mediterranean diet pattern was associated with lower greenhouse gas emissions (p < 0.001), land use (p < 0.001), fertilizer nutrient use (p < 0.001), and pesticide use (p < 0.001), higher water scarcity footprint (p < 0.001) and diet cost (p < 0.001), and no change in cumulative energy demand (p = 0.147). These changes were driven primarily by reduced intake of animal-sourced foods such as beef dishes, meat sandwiches, and dairy, as well as decreased intake of refined carbohydrate foods such as refined grain dishes and soft drinks. Conclusions: This nationally representative study demonstrates that greater adherence to the Mediterranean diet pattern is associated with sustainability trade-offs. These findings have implications for the development of sustainable dietary guidelines and clinical practice guidelines that can be used to inform consumer food choices. [ABSTRACT FROM AUTHOR]

Background: The growth and drug response of tumors are influenced by their stromal composition, both in vivo and 3D-cell culture models. Cell-type inherent features as well as mutual relationships between the different cell types in a tumor might affect drug susceptibility of the tumor as a whole and/or of its cell populations. However, a lack of single-cell procedures with sufficient detail has hampered the automated observation of cell-type-specific effects in three-dimensional stroma-tumor cell co-cultures. Methods: Here, we developed a high-content pipeline ranging from the setup of novel tumor-fibroblast spheroid co-cultures over optical tissue clearing, whole mount staining, and 3D confocal microscopy to optimized 3D-image segmentation and a 3D-deep-learning model to automate the analysis of a range of cell-type-specific processes, such as cell proliferation, apoptosis, necrosis, drug susceptibility, nuclear morphology, and cell density. Results: This demonstrated that co-cultures of KP-4 tumor cells with CCD-1137Sk fibroblasts exhibited a growth advantage compared to tumor cell mono-cultures, resulting in higher cell counts following cytostatic treatments with paclitaxel and doxorubicin. However, cell-type-specific single-cell analysis revealed that this apparent benefit of co-cultures was due to a higher resilience of fibroblasts against the drugs and did not indicate a higher drug resistance of the KP-4 cancer cells during co-culture. Conversely, cancer cells were partially even more susceptible in the presence of fibroblasts than in mono-cultures. Conclusion: In summary, this underlines that a novel cell-type-specific single-cell analysis method can reveal critical insights regarding the mechanism of action of drug substances in three-dimensional cell culture models. [ABSTRACT FROM AUTHOR]

Background: The wild stocks of Pinctada maxima pearl oysters found off the coast of northern Australia are of critical importance for the sustainability of Australia's pearling industry. Locations inhabited by pearl oysters often have oil and gas reserves in the seafloor below and are therefore potentially subjected to seismic exploration surveys. The present study assessed the impact of a simulated commercial seismic survey on the transcriptome of pearl oysters. Animals were placed at seven distances (-1000, 0, 300, 500, 1000, 2000, and 6000 m) from the first of six operational seismic source sail lines. Vessel control groups were collected before the seismic survey started and exposed groups were collected after completion of six operational seismic sail lines (operated at varying distances over a four-day period). Samples from these groups were taken immediately and at 1, 3, and 6 months post-exposure. RNA-seq was used to identify candidate genes and pathways impacted by the seismic noise in pearl oyster mantle tissues. The quantified transcripts were compared using DESeq2 and pathway enrichment analysis was conducted using KEGG pathway, identifying differentially expressed genes and pathways associated with the seismic activity. Results: The study revealed the highest gene expression and pathway dysregulation after four days of exposure and a month post-exposure. However, this dysregulation diminished after three months, with only oysters at -1000 and 0 m displaying differential gene expression and pathway disruption six months post-exposure. Stress-induced responses were evident and impacted energy production, transcription, translation, and protein synthesis. Conclusion: Seismic activity impacted the gene expression and pathways of pearl oysters at distances up to 2000 m from the source after four days of exposure, and at distances up to 1000 m from the source one-month post-exposure. At three- and six-months post-exposure, gene and pathway dysregulations were mostly observed in oysters located closest to the seismic source at 0 and − 1000 m. Overall, our results suggest that oysters successfully activated stress responses to mitigate damage and maintain cellular homeostasis and growth in response to seismic noise exposure. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cardiac fibrosis, characterized by excessive extracellular matrix (ECM) deposition within the myocardium, poses a significant challenge in cardiovascular health, contributing to various cardiac pathologies. Ketone bodies (KBs), particularly β-hydroxybutyrate (β-OHB), have emerged as subjects of interest due to their potential cardioprotective effects. However, their specific influence on cardiac fibrosis remains underexplored. This literature review comprehensively examines the relationship between KBs and cardiac fibrosis, elucidating potential mechanisms through which KBs modulate fibrotic pathways. A multifaceted interplay exists between KBs and key mediators of cardiac fibrosis. While some studies indicate a profibrotic role for KBs, others highlight their potential to attenuate fibrosis and cardiac remodeling. Mechanistically, KBs may regulate fibrotic pathways through modulation of cellular components such as cardiac fibroblasts, macrophages, and lymphocytes, as well as extracellular matrix proteins. Furthermore, the impact of KBs on cellular processes implicated in fibrosis, including oxidative stress, chemokine and cytokine expression, caspase activation, and inflammasome signaling is explored. While conflicting findings exist regarding the effects of KBs on these processes, emerging evidence suggests a predominantly beneficial role in mitigating inflammation and oxidative stress associated with fibrotic remodeling. Overall, this review underscores the importance of elucidating the complex interplay between KB metabolism and cardiac fibrosis. The insights gained have the potential to inform novel therapeutic strategies for managing cardiac fibrosis and associated cardiovascular disorders, highlighting the need for further research in this area. [ABSTRACT FROM AUTHOR]

Background: The increasing extent and severity of uncharacteristic wildfire has prompted numerous policies and programs promoting landscape-scale fuels reduction. Aims: We used novel data sources to measure how recreation was influenced by fuels reduction efforts under the US Forest Service Collaborative Forest Landscape Restoration (CFLR) Program. Methods: We used posts to four social media platforms to estimate the number of social media user-days within CFLR landscapes and asked: (1) did visitation within CFLR Program landscapes between 2012 and 2020 change in a manner consistent with the pattern on nearby lands, and (2) was there a relationship between the magnitudes of specific fuel treatment activities within CFLR landscapes and visitation to that landscape? Key results: In aggregate, visitation to the CFLR landscapes changed at a rate mirroring the trend observed elsewhere. Within CFLR landscapes, pre-commercial thinning and pruning had slight positive influences on visitation whereas prescribed burning and managed wildfire had slight negative influences. Conclusions: Fuel treatments can have a modest influence on visitation, but we didnot find any wholesale changes in visitation within CFLR landscapes. Implications: Social media and other novel data sources offer an opportunity to fill in gaps in empirical data on recreation to better understand social-ecological system linkages. Novel data sources were used to measure how recreation was influenced by fuels reduction efforts under the US Forest Service Collaborative Forest Landscape Restoration (CFLR) Program. Visitation to the CFLR landscapes changed at a rate mirroring the trend observed elsewhere. Pre-commercial thinning and pruning had slight positive influences on visitation, whereas prescribed burning and managed wildfire had slight negative influences. We did not find any wholesale changes by fuel treatments within CFLR landscapes. [ABSTRACT FROM AUTHOR]

Distribution and intensity of rainfall varies over time and space. In this paper, we study probability models for spatial and temporal distribution of rainfall in western zone of Haryana. Exponential, gamma, Gumbel, lognormal and Weibull distributions were fitted to daily rainfall data of 34 years (1971 to 2005) covering 13 rainfall stations across western zone of Haryana. Maximum Likelihood (ML) method was used for estimating parameters of the probability distributions. Kolmogorov- Smirnov (KS), Anderson-Darling (AD) and Chi-square tests were used to test the goodness of fit of the fitted distributions. Lognormal distribution was found to be the best fit distribution for most of the stations based on KS and AD tests. Chi- Square test indicated that at some stations lognormal, gamma or Weibull distributions were found to fit the data well. However, no single distribution described the rainfall pattern of all the stations across western zone of Haryana. Multi-criteria decision making based on fuzzy majority approach was used for final selection of the best probability model among Exponential, Gamma, Lognormal and Weibull distribution for describing daily rainfall. Lognormal distribution was found to be best fitting distribution to describe daily rainfall in western zone of Haryana. [ABSTRACT FROM AUTHOR]

Background: The 24-h urine protein (24-hUP) excretion is the gold standard for evaluating proteinuria. This study aimed to evaluate the diagnostic efficacy of protein/creatinine ratio (PCR) for estimating 24-hUP at various levels of renal function and proteinuria levels. Methods: A cross-sectional study was conducted between December 2021 and December 2023 in Salvador, Bahia-Brazil, as an extension of previously published data from the TUNARI study. The study included 217 samples from 152 patients with various levels of renal function and proteinuria. PCR in isolated samples and 24-hUP were determined conventionally within a 24-h timeframe. Patients were classified into three groups according to the level of renal function (Group 1 = 10 to < 30 mL/min, Group 2 = 30–60 mL/min, and Group 3 = > 60 mL/min) and level of proteinuria (< 0.3 g/day, 0.3–3.5 g/day, and > 3.5 g/day). The data were analyzed using the Spearman correlation (rs), coefficient of determination (r2), Bland–Altman plots and receiver operating characteristic (ROC) curve. Likelihood ratios, positive (LR +), and negative (LR-) were derived from the sensitivity and specificity of PCR. Results: Mean age was 41.5 ± 15.7 years, 61.8% were women, 36.8% Black and 52% Mixed-race. Glomerulopathies constituted 80.3%; 46.1% with lupus nephritis. Of the total urine samples, we observed a high correlation between PCR in the total sample of 24-hUP sample (rs = 0.86, p < 0.001) across different levels of renal function. However, agreement between PCR and 24-hUP was reduced at higher levels of proteinuria. The ROC analysis showed an AUC of 0.95 (95% CI = 0.92, 0.98), sensitivity of 91% and specificity of 86.5% (LR + 6.7; LR- 0.1), with an optimal cut-off of 0.77. These results were similar across renal function levels. Proteinuria ≤ 0.3 g/day showed a high sensitivity of 83.3% and specificity of 90%, with an area under (AUC) of 0.85 (95% CI = 0.71; 0.94). In the 24-hUP range > 0.3–3.5 g/day, the sensitivity was 64.1%, the specificity was 84.6%, and the AUC was 0.76 (95% CI = 0.67; 0.84), PCR detected all cases > 3.5 g/day. Conclusions: PCR is a suitable measure to be used as an indicator of 24-hUP at different levels of renal function, but may have limitations at higher levels of proteinuria. Analysis of PCR by proteinuria level found that agreement as well as sensitivity decreases at higher levels, but it maintains good specificity and is able to identify nephrotic range proteinuria. [ABSTRACT FROM AUTHOR]

Building classification is crucial for population estimation, traffic planning, urban planning, and emergency response applications. Although essential, such data is often not readily available. To alleviate this problem, this work presents a comprehensive dataset by providing residential/non-residential building classification covering the entire United States. We developed a dataset of building types based on building footprints and the available OpenStreetMap information. The dataset is validated using authoritative ground truth data for select counties in the U.S., which shows a high precision for non-residential building classification and a high recall for residential buildings. In addition to the building classifications, this dataset includes detailed information on the OpenStreetMap data used in the classification process. A major result of this work is the resulting dataset of classifying 67,705,475 buildings. We hope that this data is of value to the scientific community, including urban and transportation planners. [ABSTRACT FROM AUTHOR]

Background: Cuproptosis, a recently identified type of programmed cell death triggered by copper, has mechanisms in Wilms tumor (WT) that are not yet fully understood. This research focuses on examining the link between WT and Cuproptosis-related genes (CRGs), with the goal of developing a predictive model for WT. Methods: Four gene expression datasets related to WT were sourced from the GEO database. Subsequently, expression profiles of CRGs were extracted for differential analysis and immune infiltration studies. Utilizing 105 WT samples, clusters related to Cuproptosis were identified. This involved analyzing associated immune cell infiltration and conducting functional enrichment analysis. Disease-characteristic genes were pinpointed using weighted gene co-expression network analysis. Finally, the WT risk prediction model was constructed by four machine learning methods: random forest, support vector machine (SVM), generalized linear and extreme gradient strength model. The best-performing machine learning model was chosen, and a nomogram was created. The effectiveness of this predictive model was validated using methods such as the calibration curve, decision curve analysis, and by appiying it to the TARGET-GTEx dataset. Results: Thirteen differentially expressed Cuproptosis-related genes were identified. The infiltration level of CD8 + T cells in WT children was lower than that in Normal tissue (NT) children, and the level of M0 infiltration of macrophages and T follicular helper cells was higher than that in NT children. In addition, two clusters of cuproptosis-related WT were identified. Enrichment analysis results indicated that genes in cluster 2 were primarily involved in cell division, nuclear division regulation, DNA biosynthesis process, ubiquitin-mediated proteolysis. The SVM model was judged to be the optimal model using 5 genes. Its accuracy was confirmed through a calibration curve and decision curve analysis, demonstrating satisfactory performance on the TARGET-GTEx validation dataset. Additional analysis revealed that these five genes exhibited high expression in both the TARGET-GTEx validation dataset and sequencing data. Conclusion: This research established a link between WT and Cuproptosis. It developed a predictive model for assessing the risk of WT and pinpointed five key genes associated with the disease. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between market risk measures and systemic risk in Indonesia's banking sector. It aims to inform policymakers and regulators in promoting financial stability. By examining how market risk measures, specifically total risk encompassing systematic and idiosyncratic components, influence systemic risk in Indonesia's banking system, this study addresses a notable research gap, particularly in emerging markets like Indonesia. This research contributes to the understanding of systemic risk dynamics in an emerging market context, offering insights into how market risk measures contribute to systemic risk. Employing a quantitative approach, this study utilizes secondary data from the Indonesia Stock Exchange and Datastream Refinitiv Eikon. Nonprobability sampling selects banks listed on the Indonesia Stock Exchange, with daily stock price data used to construct market risk measures. Systemic risk is measured using both CoVaR and MES, and regression analysis is employed to explore the relationship between market risk and systemic risk. The study reveals a significant positive association between total risk and systemic risk, highlighting the crucial role of idiosyncratic risk in this relationship. These findings underscore the importance of considering both systematic and idiosyncratic market risk when assessing systemic risk in Indonesia's banking sector. The study emphasizes the need for robust risk management practices and regulatory measures to ensure financial stability in emerging markets. [ABSTRACT FROM AUTHOR]

The design of a sampling unit, whether a simple plot or a subplot within a clustered structure, including shape and size, has received little attention in inferential forestry research. The use of auxiliary variables from remote sensing impacts the precision of estimators from both model-assisted and model-based inference perspectives. In both cases, model parameters are estimated from a sample of field plots and information from pixels corresponding to these units. In studies assisted by remote sensing, the shape of the plot used to fit regression models (typically circular) often differs from the shape of the population elements for prediction, where the area of interest is divided into equal tessellated parts. This raises interest in understanding the effect of the sampling unit shape on the mean of variables in forest stands of interest. Therefore, the objective of this study was to evaluate the effect of circular and square subplots, concentrically overlapped and arranged in an inverted Y cluster structure, over tree density, basal area, and aboveground biomass in a managed temperate forest in central Mexico. We used a Multivariate Generalised Linear Mixed Model, which considers the Gamma distribution of the variables and accounts for spatial correlation between Secondary Sampling Units nested within the Primary Sampling Unit. The main findings of this study indicate that the type of secondary sampling unit of the same area and centroid, whether circular or square, does not significantly affect the mean tree density (trees), basal area (m2), and aerial biomass. [ABSTRACT FROM AUTHOR]

Introduction: Obesity-related precocious puberty is induced by obesity, and acupoint catgut embedding (ACE) therapy is known to treat obesity. This study aims to validate the hypothesis that ACE can delay the onset of obesity-related precocious puberty. Methods: Female Sprague-Dawley rats, 21 days old, were randomly divided into three groups: the high-fat diet combined with ACE treatment group (ACE), the high-fat diet group (HFD), and the normal control diet group (NCD), with 8 rats in each group. The vaginal opening (VO) time was monitored, and serum levels of luteinizing hormone (LH), follicle-stimulating hormone (FSH), and total estradiol (E2) were measured, followed by statistical analysis. Results: Kaplan-Meier survival curves, with VO as the endpoint, showed that vaginal opening was delayed in the ACE group compared to the HFD group, with a statistically significant difference (p < 0.05). The changes in levels of FSH, LH, and E2 indicated that sexual development was delayed in the ACE group compared to the HFD group and was more similar to the NCD group. Discussion: Combining the vaginal opening time and changes in hormone levels, this study confirms the potential role of ACE in delaying the onset of obesity-related precocious puberty. [ABSTRACT FROM AUTHOR]

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Garnet-type oxide materials show high Li-ion conductivity and may be used as solid-state electrolytes in lithium-ion batteries to address safety concerns. In this study, Nb-doped Li7Nd2.8Ca0.2Zr1.8Nb0.2O12 (LNdCZNbO) and Ta-doped Li7Nd2.8Ca0.2Zr1.8Ta0.2O12 (LNdCZTaO) garnet-type compositions were prepared to examine the impact of Nb- and Ta-doping on ionic conductivity of Li7Nd3Zr2O12 (LNdZO). XRD patterns showed the crystallization of major phase of these garnet-structured oxides in tetragonal symmetry. Impedance measurements were recorded from room temperature to 450°C using a Novocontrol make impedance analyzer (Alpha-A high-performance frequency analyzer) in the frequency range of 1 Hz–40 MHz. The maximum total conductivity of parent composition LNdZO was 5.12 × 10−5 S cm−1 at 25°C. The compositions of LNdCZNbO and LNdCZTaO showed conductivity 7.05 × 10−4 and 8.23 × 10−4 S cm−1, respectively, at 25°C. On higher temperature of 350°C, these doped compositions, LNdCZNbO and LNdCZTaO, showed enhanced conductivity of 3.30 × 10−3 and 2.63 × 10−3 S cm−1, respectively, as compared to the parent LNdZO composition’s conductivity of 4.42 × 10−4 S cm−1. Analysis of the Cole–Cole plots fitting showed the nature of Li ionic conduction and the existence of bulk and grain boundary impedances in these compositions. The activation energy was found to be higher for the compositions of LNdCZNbO (0.18 ± 0.01 eV) and LNdCZTaO (0.17 ± 0.01 eV) in comparison with the activation energy of undoped composition Li7Nd3Zr2O12 (0.14 ± 0.00 eV) due to the change in garnet lattice by doping of Ca and Nb/Ta. [ABSTRACT FROM AUTHOR]

This systematic review sought to expand our knowledge of the increasing research studies focusing on the empirical link between school leadership and student achievement. Using five different data sources (Scopus, WoS, ERIC, Google Scholar, and Retrospective Reference Harvesting Procedure), we created a data set of 144 journal articles. Descriptive analysis of bibliographic data and content analysis of full texts were employed in the review process. The results revealed instructional leadership as the most frequently used school leadership model in the relevant research. There has been a growing tendency to use the direct effect model with the increase in the studies from non-English speaking western societies. Indeed, the use of rational and organizational variables has dominated the mediating effect studies, while moderating effect studies as well as exploratory and explanatory type of studies have not received much attention. Based on these results, a framework representing the broader picture of the current research on the relationship between school leadership and student learning is provided. In addition, several recommendations were made, aiming to build the research capacity in furthering our understanding of how school leadership practices impact student learning and achievement. [ABSTRACT FROM AUTHOR]

The Kashmir Valley has frequently endured devastating floods, presenting significant challenges for flood management due to unpredictable flood magnitudes and limited pre-disaster preparedness. A major difficulty in addressing these challenges is the lack of reliable flood frequency analysis (FFA) for effective planning and mitigation. This study seeks to overcome these issues by employing a rigorous quantitative analysis of annual peak discharge data over a 51-year period (1971–2021). One key challenge was the presence of low outliers, which could compromise the integrity of the data. To address this, the Multiple Grubbs-Beck test was applied to remove these outliers before conducting FFA. The study's original achievement lies in its application of multiple distribution models which include Gumbel (EV1), Generalized Extreme Variations (GEV), Log-Normal, Log Pearson III (LP III), Gamma and Normal distribution. Goodness-of-fit tests, including Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Chi-square at the 5 % significance level, along with visualization techniques such as Probability plots (PP), Quantile plots (QQ), and Probabilistic distribution (PD) graphs, were used to identify the most suitable distribution methods. The Log Pearson Type III (LP-III) was identified as the best fit for the Sangam gauge site (Upper Jhelum), the gamma distribution for Ram Munshibagh (Middle Jhelum), and the Generalized Extreme Value (GEV) and LP-III for Asham (Lower Jhelum). For Sangam, the estimated discharges for 2, 5, 10, 50, 100, 150, 200, and 250-year return periods were 549.63, 1028.43, 1471.34, 2907.64, 3758.92, 4338.61, 4790.99, and 5167.23 cumecs, respectively, using LP-III. For Ram Munshibagh, the discharges were 602.13, 911.03, 1107.04, 1512.12, 1674.35, 1767.0, 1831.87, and 1881.74 cumecs using the gamma distribution. For Asham, the discharges were 685.8, 998.0, 1193.3, 1593.2, 1750.6, 1839.4, 1901.0, and 1948.0 cumecs using the GEV distribution. The findings indicate that the Jhelum River cannot accommodate excess discharge for return periods of 5 years or more, underscoring the need for enhanced flood management strategies.

Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. We compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama and then quantified the impact of the observed gradients on simulated canopy-scale CO 2 and water fluxes. Comparison between observed and TBM trait gradients showed divergence that impacted canopy-scale simulations of water vapor and CO 2 exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top-of-canopy, leaf-level water-use efficiency was markedly higher at the top-of-canopy, and the decrease in maximum carboxylation rate from the top-of-canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within-individual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests., (© 2023 The Authors New Phytologist © 2023 New Phytologist Foundation This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Modelling traffic characteristics is the foundation for resolving various traffic and transportation issues. Among them, traffic speed has a significant impact on roadway crashes at blackspot (BS) locations. Speed is a random variable; several studies have recommended normal distribution to characterize the distribution of traffic speed for uninterrupted flow. However, a mixed-traffic situation causes heterogeneity, and the distribution of speeds deviates from the normal distribution. The present study investigates the distributions of traffic speeds for uninterrupted flow at 18 blackspot locations and individual vehicle types in mixed-traffic environments. Seven distribution models, namely Normal, Lognormal, Gamma, Logistic, Weibull, Burr, and Generalized Extreme Value (GEV), are considered to determine the speed characteristics. Different parametric distribution models are fitted to the vehicular speeds using maximum likelihood estimation (MLE) methods. Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and two penalized criteria, i.e., Akaike and Bayesian Information Criteria (AIC and BIC), are used as goodness-of-fit (GoF) measures to find the best-fitting distribution. The overall suitability of each predicted distribution is also determined using a novel ranking method. The test findings suggest that GEV and Burr are the most suitable empirical speed distributions, with GEV fitting best above 96%. When the heavy vehicle composition (truck, bus, and tractor) is below 10%, 10–14%, 15–20%, and above 20%, it follows the Weibull, Gamma, GEV, and Burr distributions, respectively, in a mixed traffic environment.