Low P-values Research Articles

Shaping childhood obesity: behavioral and environmental risk factors associated with body mass index trajectories between 2 and 9 years in Samoan children.

Pacific children are at high obesity risk, yet the behavioral and environmental factors that contribute to obesity development in this setting remain poorly understood. We assessed associations between childhood risk factors for obesity with body mass index (BMI) trajectories between ages 2-9 years in Samoa. In a prospective cohort of 485 children from 'Upolu, we measured weight and height at ages 2-4 (2015), 3.5-8 (2017-18), and 5.5-11 years (2019-20). Modern dietary pattern adherence was assessed using factor analysis of primary caregiver-reported food frequency questionnaire data. Physical activity was estimated with the Netherlands Physical Activity Questionnaire. Socioeconomic resources were assessed using an 18-item household asset score. Urbanicity was based on village residence. Associations of these risk factors with predicted weight, height, and BMI (at 1-year intervals and velocity) were assessed using multilevel cubic spline regressions. Females had greater adjusted weight velocity with high modern dietary pattern adherence compared to low (p-value for interaction with age spline term 1 = 0.028 and age spline term 2 = 0.007). Starting at age 3 years, children with higher physical activity had higher BMI, but this association was not meaningful up to age 9 (all p-value > 0.05). Males with very high compared to low household assets had higher BMI from age 2 to 4 years (95% CI: 0.26-1.53 kg/m2, p = 0.006) and greater BMI velocity (p-value for interaction with age spline term 2 = 0.001). Males in the urban region had the greatest BMI gain after age 5 compared to the rural region (p-value for interaction with age spline term 2 = 0.014). High, centile-crossing BMI trajectories suggest that obesity prevention and intervention are needed among Samoan children before age 9 years. Positive associations between high modern dietary pattern adherence, greater asset ownership, and urbanization offer initial insights into who, and which behavioral risk factors, should be prioritized in implementing public health solutions.

Impact of Remittance Income on Women’s Empowerment in Nepal

Remittances are a crucial component of the global economy and a significant source of income for developing nations. They contribute significantly to the economic growth of recipient countries by providing investment opportunities, boosting consumption and women's empowerment, and reducing poverty. The objective of this study is to analyze the impact of remittance income on women’s empowerment in Nepal. The research used a descriptive cross-sectional design and collected primary data through a questionnaire. Five-point likert type scaling technique was used to obtain a response to each question. Data were collected by interviewing individuals who receive remittances from banks and cooperatives in Bhimad City Tanahun. Household were randomly selected as samples including 16 from Bhimad Municipality, 8 from Rishing Rural Municipality, 10 from Ghiriing Rural Municipality, and 6 from Myadge Rural Municipality. All the households in the study area receiving remittance income throughout the year 2023 were considered as the population of the study. Descriptive statistics were employed to present the data through mean, t-test, and percentages. The results of the study showed that remittances significantly contributed to women's financial independence, education, and healthcare access, poverty alleviation, enhancing decision-making power within households, and promoting participation in community development. The consistently high p-value was 17.30 and lowest p-value was 13.30, high and low p-value indicates that there was a strong consensus among respondents regarding the positive impact of remittances on women's empowerment. The findings have significant implications for researchers, policymakers, and practitioners. It highlights the need the targeted interventions that maximize the positive impact of remittances on women's empowerment while addressing potential challenges. Overall, the study emphasizes the need to recognize the critical role of remittances in promoting women's empowerment and the importance of developing effective strategies to harness their potential.

Bio-Adsorbent for Elimination of Reactive Dye from Aqueous Solution: Kinetic and Statistical Modelling Study

Introduction/Background: The use of various dyes to colour products is a general practice in various industries. The occurrence of these dyes in water, even at small concentrations, is highly noticeable and aesthetically objectionable. In the present study, the applicability of inexpensive and eco-friendly bio-adsorbent has been tested as an alternative substitution of the commercially available activated carbon for the removal of reactive dye from the aqueous solution. Materials and Methods: Bio-adsorbent prepared from pomegranate peel was successfully used to remove the reactive dye (Reactive Black 5) from the aqueous solution. The effects of major parameters such as pH, adsorbent dosage, and contact time on dye removal efficiency were studied. Statistical models were articulated based on selected variables to optimize the decolourisation efficiency of the adsorption process using a full factorial central composite design. Results: Dye removal efficiency of close to 100% was observed at a pH 12 using 1 g adsorbent/ 200 mL dye solution within a contact time of 60 min., yielding a virtually colourless solution. A fixed-bed column study with an initial dye concentration of 100 mg/L at bed depths of 4 cm, 8 cm, and 12 cm yielded a breakthrough time of 300 min, 570 min, and 780 min, respectively. Discussion: Langmuir, Freundlich, and Temkin isotherm models were applied to analyze the sorption equilibrium parameters. The experimental results of the analysis revealed that the Langmuir isotherm fits better than the other isotherms with a linear regression coefficient (R2) of 0.99. The high correlation coefficient (R² > 0.95) and low p-values (< 0.0001) indicate that the model and its terms are significant, making it effective for optimizing operational parameters and accurately predicting the response. Conclusion: The breakthrough curve serves as a tool to evaluate the effectiveness of the prepared adsorbent in real-world applications. The present work not only provides an alternative to commercially available activated carbon for dye wastewater colour removal but also emphasizes the importance of repurposing fruit residues, which could otherwise become environmental pollutants if improperly disposed of.

Analyzing Temporal Characteristics of Winter Catch Crops Using Sentinel-1 Time Series

Catch crops are intermediate crops sown between two main crop cycles. Their adoption into the cropping system has increased considerably in the last years due to its numerous benefits, in particular its potential in carbon fixation and preventing nitrogen leaching during winter. The growth period of catch crops in Germany is often marked by dense cloud cover, which limits land surface monitoring through optical remote sensing. In such conditions, synthetic aperture radar (SAR) emerges as a viable option. Despite the known advantages of SAR, the understanding of temporal behavior of radar parameters in relation to catch crops remains largely unexplored. Hence, in this study, we exploited the dense time series of Sentinel-1 data within the Copernicus Space Component to study the temporal characteristics of catch crops over a test site in the center of Germany. Radar parameters such as VV, VH, VH/VV backscatter, dpRVI (dual-pol Radar Vegetation Index) and VV coherence were extracted, and temporal profiles were interpreted for catch crops and preceding main crops along with in situ, temperature, and precipitation data. Additionally, we examined the temporal profiles of winter main crops (winter oilseed rape and winter cereals), that are grown parallel to the catch crop growing cycle. Based on the analyzed temporal patterns, we defined 22 descriptive features from VV, VH, VH/VV and dpRVI, which are specific to catch crop identification. Then, we conducted a Kruskal–Wallis test on the extracted parameters, both crop-wise and group-wise, to assess the significance of statistical differences among different catch crop groups. Our results reveal that there exists a unique temporal pattern for catch crops compared to main crops, and each of these extracted parameters possess a different sensitivity to catch crops. Parameters VV and VH are sensitive to phenological stages and crop structure. On the other hand, VH/VV and dpRVI were found to be highly sensitive to crop biomass. Coherence can be used to detect the sowing and harvest events. The preceding main crop analysis reveals that winter wheat and winter barley are the two dominant main crops grown before catch crops. Moreover, winter main crops (winter oilseed rape, winter cereals) cultivated during the catch crop cycle can be distinguished by exploiting the observed sowing window differences. The extracted descriptive features provide information about sowing, harvest, vigor, biomass, and early/late die-off nature specific to catch crop types. In the Kruskal–Wallis test, the observed high H-statistic and low p-value in several predictors indicates significant variability at 0.001 level. Furthermore, Dunn’s post hoc test among catch crop group pairs highlights the substantial differences between cold-sensitive and legume groups (p < 0.001).

A-035 Profiling of circulating-microRNAs' signatures in Pre-diabetic versus Diabetic patients

Abstract Background MicroRNAs (miRs) are small non-coding RNAs involved in the process of gene regulation, and hence affect various diseases including metabolic pathways in diabetes. Given their key roles, they are promising candidates as potential biomarkers or better understanding of disease etiology. In this study, our aim is to identify miRs signatures of plasma circulating-miRs related to glycemic control, vitamin D deficiency and platelet activation in diabetic, pre-diabetic versus non-diabetic patients. Methods Plasma samples were collected from 24 patients (both sexes) in total (eight non-diabetic (ND) control, twelve diabetic (DM), and four pre-diabetic (PD)), classified based on their glycemic control (HbA1c levels). miRs were extracted using the miRNeasy Serum/Plasma Advanced kit of Qiagen, run and analyzed on Next Generation Sequencing (NGS). Results Differential expression analysis revealed miRs expression variations between diabetic to pre-diabetic and non-diabetic groups. Expression levels of 131 miRs varied significantly between DM to ND group (92 down- and 39 up- regulated), and 141 miRs in PD compared to ND (97 down- and 39 up- regulated). Although DM and PD had similar differential expression, three miRs (has-miR4776-5p, -6778-3p, and 5002-3p) were over-expressed in PD with very low p-value. Differential expression comparisons accounting sex were performed showing significant differences between males to females that will be presented here. Conclusions Our findings supports the important involvement of circulating miRs in the regulation of glucose homeostasis and pathogenesis of diabetes.Further investigation of candidate miRs could identify promising new biomarkers for the early diagnosis and prevention of Diabetes and related health complications.

A pilot metabolomics study across the continuum of interstitial lung disease fibrosis severity.

Interstitial lung diseases (ILDs) include a variety of inflammatory and fibrotic pulmonary conditions. This study employs high-resolution metabolomics (HRM) to explore plasma metabolites and pathways across ILD phenotypes, including non-fibrotic ILD, idiopathic pulmonary fibrosis (IPF), and non-IPF fibrotic ILD. The study used 80 plasma samples for HRM, and involved linear trend and group-wise analyses of metabolites altered in ILD phenotypes. We utilized limma one-way ANOVA and mummichog algorithms to identify differences in metabolites and pathways across ILD groups. Then, we focused on metabolites within critical pathways, indicated by high pathway overlap sizes and low p-values, for further analysis. Targeted HRM identified putrescine, hydroxyproline, prolyl-hydroxyproline, aspartate, and glutamate with significant linear increases in more fibrotic ILD phenotypes, suggesting their role in ILD fibrogenesis. Untargeted HRM highlighted pathway alterations in lysine, vitamin D3, tyrosine, and urea cycle metabolism, all associated with pulmonary fibrosis. In addition, methylparaben level had a significantly increasing linear trend and was higher in the IPF than fibrotic and non-ILD groups. This study highlights the importance of specific amino acids, metabolic pathways, and xenobiotics in the progression of pulmonary fibrosis.

The impact of neighborhood socioeconomic deprivation on treatment and survival for patients with metastatic gastric cancer: An incidence-based retrospective cohort study.

110 Background: Studies have shown that socioeconomic deprivation (SED) is associated with reduced treatment and overall survival (OS) for gastric cancer. However, these studies have typically used large geographic areas for assessment of SED and aggregated all stages. Using a statewide cancer registry, the Area Deprivation Index, a granular measure of SED, was used to assess receipt of treatment and OS for metastatic gastric cancer patients. Methods: Using the incidence-based Florida Cancer Data System, gastric adenocarcinoma patients diagnosed from 2007-2015were identified (n=8,304). The Area Deprivation Index (ADI) is a composite measure of SED and validated dataset that ranks neighborhoods (census block groups) from 1-100 (higher scores=higher deprivation). Demographic, tumor, and treatment variables were assessed using descriptive statistics, regression, and survival analysis. Results: Overall, 3,050 stage IV patients met inclusion criteria; 53.9% were male and the median age was 67. When assessed by SED quartile, there were significant differences by race, ethnicity, insurance, and receipt of chemotherapy (p<0.05). For the cohort, 55.3% of patients received chemotherapy. However, 61.3% of the lowest deprivation cohort received chemotherapy compared to 50.4% in the highest deprivation cohort (p<0.001). After adjustment for potential confounders, the lowest deprivation quartile had increased odds of chemotherapy compared to the highest deprivation quintile (OR 1.52; 95% CI,1.20-1.92). Of patients who received chemotherapy, the median time in months to initiation was 1.1 (interquartile range 0.6-1.7). Median OS was 5.8 months. The median OS for the lowest and highest SED quartiles was 7.5 and 5.1 months, respectively (p<0.001). For patients who received and did not receive chemotherapy, median OS was 10.0 and 1.9 months, respectively. The median OS of the lowest and highest SED quartiles for those who received chemotherapy was 11.3 and 8.8 months, respectively (p<0.001). Conclusions: In an incidence-based, statewide cancer registry examining metastatic gastric cancer, just over half of patients received chemotherapy and median OS was poor. Additionally, higher SED was associated with lower odds of chemotherapy and worse OS. These data suggest that SED impacts metastatic gastric cancer outcomes and future studies are needed to understand the multilevel factors driving disparities. Lowest Deprivation Low Deprivation High Deprivation Highest Deprivation p-value N=752 N=780 N=747 N=718 Chemotherapy 0.003 No 262 (34.8%) 315 (40.4%) 306 (41.0%) 314 (43.7%) Yes 461 (61.3%) 424 (54.4%) 410 (54.9%) 362 (50.4%) Unknown 29 (3.9%) 41 (5.3%) 31 (4.1%) 42 (5.8%)

P > 0.05 is Good: The NORD-h Protocol for Several Hypothesis Analysis Based on Known Risks, Costs, and Benefits.

Statistical testing in medicine is a controversial and commonly misunderstood topic. Despite decades of efforts by renowned associations and international experts, fallacies such as nullism, the magnitude fallacy, and dichotomania are still widespread within clinical and epidemiological research. This can lead to serious health errors (e.g., misidentification of adverse reactions). In this regard, our work sheds light on another common interpretive and cognitive error: the fallacy of high significance, understood as the mistaken tendency to prioritize findings that lead to low p-values. Indeed, there are target hypotheses (e.g., a hazard ratio of 0.10) for which a high p-value is an optimal and desirable outcome. Accordingly, we propose a novel method that goes beyond mere null hypothesis testing by assessing the statistical surprise of the experimental result compared to the prediction of several target assumptions. Additionally, we formalize the concept of interval hypotheses based on prior information about costs, risks, and benefits for the stakeholders (NORD-h protocol). The incompatibility graph (or surprisal graph) is adopted in this context. Finally, we discuss the epistemic necessity for a descriptive, (quasi) unconditional approach in statistics, which is essential to draw valid conclusions about the consistency of data with all relevant possibilities, including study limitations. Given these considerations, this new protocol has the potential to significantly impact the production of reliable evidence in public health.

Application of Response Surface Methodology for concentration of fluorosilicic acid by extraction technique with benzyl alcohol as extractant

As a kind of alternative of natural fluorinated minerals and raw material for producing anhydrous hydrogen fluoride, fluorosilicic acid has attracted more and more attention. In order to raise its utilization rate, dilute fluorosilicic acid solution coming from chemical enterprises needs to be concentrated. The extraction technology with benzyl alcohol as extraction agent was used to concentrating fluorosilicic acid aqueous solution. In order to determine the optimizing operation conditions of the extracting process, single - factor experiments were carried out to confirm the significant affecting factors and their operating range. Then the Response Surface Methodology (RSM) was conducted to accomplish regression of a nonlinear quadratic polynomial model to the experimental data and statistical analysis. Based on these studies, the optimal technical parameters of extracting process are acquired by optimization of the model using the Design-Expert 13 software. The statistical analysis for the fitting response surface quadratic equation model indicated that the model was reliable and accurate with very low p-values (<0.0001), the predicted values with the model equation had remarkable consistency in experimental data. By a great number of optimizations carried out with the Design Expert 13 software based on the fitting model equation, the optimal extractive processing conditions for concentrating fluorosilicic acid aqueous solution were obtained: the phase ratio 7, the extraction time 120.11 min and the extraction temperature 39.70 °C. The optimal concentration of fluorosilicic acid is 26.68 %. In order to increase the concentration of fluorosilicic acid, three-stage cross-current extraction for dilute fluorosilicic acid solution was carried out at the optimizing extracting conditions, 17.43 wt% fluorosilicic acid solution can be concentrated up to 37.46 wt%. The extractant-benzyl alcohol can be recovered with vacuum distillation and recovery rate reached up to 98 %.

A methodology for gene level omics-WAS integration identifies genes influencing traits associated with cardiovascular risks: the Long Life Family Study.

The Long Life Family Study (LLFS) enrolled 4953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8 × 10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform ( https://nf-co.re/omicsgenetraitassociation ).

Integrating zirconia-gold hybrid into aptasensor capable of ultrasensitive and robust carcinoembryonic antigen determination

Aptasensors have emerged as a powerful strategy for clinical diagnostics due to their enhanced specificity and selectivity in detecting biomarkers. However, striking a delicate equilibrium between sensitivity, specificity, and stability remains a formidable challenge due to the biomarker's low abundance and clinical biospecimen's complexity. To address this, we developed a hybrid of zirconia-gold nanoparticles (ZrO2-AuNPs) to enhance the performance of aptasensors, resulting in improved sensitivity and stability. The introduction of ZrO2 resulted in a significant reduction in signal fluctuation, as demonstrated by a remarkable decrease in the relative standard deviation (RSD) from 22.60% to an impressive 1.95%, indicating improved stability; this enhancement was also observed in various ZrO2-AuNPs-aptasensors, yielding a minimal RSD of 2.61%. The sensitivity boosters (ZrO2 and AuNPs) amplified the electrochemical signal, leading to highly sensitive carcinoembryonic antigen (CEA) detection with a threshold as low as 42.504 fg/mL. For clinical application, the ZrO2-AuNPs-aptasensor demonstrated a strong correlation with clinical measurements, as evidenced by the regression equation y (A) = 1.005 × x - 0.025 (ng/mL) and a notable R2 value of 0.991, indicating a high predictive accuracy. Additionally, the aptasensor displayed remarkable diagnostic accuracy in distinguishing lung cancer from healthy samples, as evidenced by a perfect sensitivity of 1.0, a specificity of 0.913, and an impressive AUC value of 0.993 in the ROC analysis, a claim further confirmed by a significantly low p-value of 1.990 × 10-5 from a validation t-test. Moreover, we optimized various conditions, including temperature and pH, for the ZrO2-AuNPs-aptasensor, resulting in desirable reproducibility and selectivity. We anticipate that the ZrO2-AuNPs strategy will enhance the aptasensor's sensitivity and stability, potentially propelling advancements in clinical diagnostics.

Assessing the Availability of Digital Learning Infrastructures in NGO-Sponsored and Non-Sponsored Public Primary Schools in Buuri and Isiolo Sub-Counties

Purpose: To assess the availability of digital learning infrastructures in NGO-sponsored and non-sponsored public primary schools in Buuri and Isiolo sub-counties. Methodology: The study used deductive reasoning and adopted the descriptive survey research design. The study targeted 79 public primary schools in the 2 sub-counties, 79 headteachers, 79 ICT teachers, 2,192 Grade 6 learners, and their 79 Grade 6 class teachers. The researcher selected a 20% sample to get 16 schools, 16 headteachers, 16 ICT teachers, and 16 class teachers. Purposive sampling techniques were used to select 11 schools with NGO sponsorship while simple random sampling was used to select 5 schools without sponsorship for the comparative sample. Systematic random sampling was used to get a 15% sample from the learners – 329 Grade 6 learners. Data was collected using questionnaires, interview schedules, and observation checklists. Descriptive data was analyzed using percentages, mean, and standard deviation scores, while data from interviews was organized into themes and reported using quotes and narration and reported alongside the findings from descriptive data. Results: The results of the questionnaire revealed that 79.9% of learners in sponsored schools strongly agreed that their classrooms were built with robust materials compared to only 62.0% in non-sponsored schools. 70.0% of teachers in sponsored schools confirmed these findings strongly agreeing that their classrooms were built of concrete stones with metal doors and windows, compared to 60.0% in non-sponsored schools. This indicates a significant difference in infrastructure quality, with sponsored schools showing better construction (97.4% vs. 70.5%). Simple regression analysis showed that NGO-sponsored schools had lower p-values (p ≤ 0.01 for power availability and digital hardware, p = 0.05 for adequate gadgets) compared to non-sponsored schools (p = 0.05, 0.05, and 0.10 respectively). These results indicate significant differences in infrastructure availability, supporting the rejection of the null hypothesis. Conclusion: The conclusion made was that NGO-sponsored schools possess significantly superior digital learning infrastructures compared to their non-sponsored counterparts. The robust construction of classrooms, reliable electricity supply, and the availability of essential wiring and power sockets collectively create an environment that is highly conducive to effective digital literacy programs. This enhanced infrastructure provides a stable foundation for the integration and use of digital technologies in the school. The study recommends that efforts be made to increase NGO sponsorship in public primary schools to increase the chances of learners attending the schools getting access to essential digital skills. This could involve intentional efforts by the Ministry of Education to reach out to NGOs and pursue partnership opportunities in this direction

Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?

Synthetic data have been proposed as a solution for sharing anonymized versions of sensitive biomedical datasets. Ideally, synthetic data should preserve the structure and statistical properties of the original data, while protecting the privacy of the individual subjects. Differential Privacy (DP) is currently considered the gold standard approach for balancing this trade-off. The aim of this study is to investigate how trustworthy are group differences discovered by independent sample tests from DP-synthetic data. The evaluation is carried out in terms of the tests' Type I and Type II errors. With the former, we can quantify the tests' validity, i.e., whether the probability of false discoveries is indeed below the significance level, and the latter indicates the tests' power in making real discoveries. We evaluate the Mann-Whitney U test, Student's t-test, chi-squared test, and median test on DP-synthetic data. The private synthetic datasets are generated from real-world data, including a prostate cancer dataset (n = 500) and a cardiovascular dataset (n = 70,000), as well as on bivariate and multivariate simulated data. Five different DP-synthetic data generation methods are evaluated, including two basic DP histogram release methods and MWEM, Private-PGM, and DP GAN algorithms. A large portion of the evaluation results expressed dramatically inflated Type I errors, especially at levels of ϵ ≤ 1. This result calls for caution when releasing and analyzing DP-synthetic data: low p-values may be obtained in statistical tests simply as a byproduct of the noise added to protect privacy. A DP Smoothed Histogram-based synthetic data generation method was shown to produce valid Type I error for all privacy levels tested but required a large original dataset size and a modest privacy budget (ϵ ≥ 5) in order to have reasonable Type II error levels.

Modeling and design optimization of the performance of stone matrix asphalt mixtures containing low-density polyethylene and waste engine oil using the response surface methodology

In recent years, the use of waste plastic materials such as low-density polyethylene (LDPE) to modify asphalt binders and enhance mixture performance has garnered significant attention. One major concern with using such materials is the higher production temperature required, which necessitates the use of a bitumen extender agent, such as waste engine oil (WEO), to reduce the viscosity and mixing temperature. Therefore, using these stabilizing additives can reduce the consumption of virgin binder, especially for Stone Matrix Asphalt (SMA) mixtures, which require higher asphalt content. This study aimed to optimize the design of SMA modified by LDPE and WEO to minimize the optimum asphalt content (OAC) and mixing temperature while maximizing SMA performance. To achieve this, Response Surface Methodology (RSM) was utilized to develop the necessary models for optimizing design and predicting performance. The selected independent variables (factors) for the design include LDPE content, WEO content, OAC, and mixing temperature. Meanwhile, the responses (dependent variables) consist of Marshall stability, rut depth, tensile strength ratio, and resilient modulus, which were used to determine the best mix design. The findings demonstrated that the suggested models for the output variables can predict performance with a higher level of confidence. The Analysis of variance (ANOVA) demonstrated that predictive models were significant and well-fitted, with a coefficient of determination (R2) of higher than 0.80, an adequate precision value of greater than 4, and a low p-value (less than 0.05). The error percentage between the RSM-predicted and actual values was less than 5 %, indicating that RSM-established models can accurately and efficiently predict the SMA performance. The best mix design of the SMA mixture modified by LDPE and WEO was found to be 10 % LDPE, 3.91 % WEO, 5.92 % OAC, and mixing temperature of 152.24°C with a combined desirability of 82.8 %.

How Can Seasonality Influence the Performance of Recent Microwave Satellite Soil Moisture Products?

In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and soil properties. When long-term analysis is performed, these discrepancies are mitigated by the contribution of SM seasonality and are only evident when high-frequency variations (i.e., signal anomalies) are investigated. This study sought to examine the responsiveness of SM to seasonal variations in terrestrial ecoregions located in areas covered by the in situ Romanian Soil Moisture Network (RSMN). To achieve this aim, several remote sensing-derived retrievals were considered: (i) NASA’s Soil Moisture Active and Passive (SMAP) L4 V5 model assimilated product data; (ii) the European Space Agency’s Soil Moisture and Ocean Salinity INRA–CESBIO (SMOS-IC) V2.0 data; (iii) time-series data extracted from the H115 and H116 SM products, which are derived from the analysis of Advanced Scatterometer (ASCAT) data acquired via MetOp satellites; (iv) Copernicus Global Land Service SSM 1 km data; and (v) the “combined” European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) product v06.1. An initial assessment of the performance of these products was conducted by checking the anomaly of long-term fluctuations, quantified using the Absolute Variation of Local Change of Environment (ALICE) index, within a time frame spanning 2015 to 2020. These correlations were then compared with those based on raw data and anomalies computed using a moving window of 35 days. Prominent correlations were observed with the SMAP L4 dataset and across all ecoregions, and the Balkan mixed forests (646) exhibited strong concordance regardless of the satellite source (with a correlation coefficient RALICE &gt; 0.5). In contrast, neither the Central European mixed forests (No. 654) nor the Pontic steppe (No. 735) were adequately characterized by any satellite dataset (RALICE &lt; 0.5). Subsequently, the phenological seasonality and dynamic behavior of SM were computed to investigate the effects of the wetting and drying processes. Notably, the Central European mixed forests (654) underwent an extended dry phase (with an extremely low p-value of 2.20 × 10−16) during both the growth and dormancy phases. This finding explains why the RSMN showcases divergent behavior and underscores why no satellite dataset can effectively capture the complexities of the ecoregions covered by this in situ SM network.

Enhancing Biodegradation Efficiency of Reactive Black-5 Dye using Bacillus wiedmannii strain NAF4: Optimization of Degradation Parameters via Response Surface Methodology (RSM)

Reactive Black 5 (RB-5) dye is extensively used in industries such as textile, paper, and leather, raising environmental concerns due to its persistence and adverse effects. This study aimed to develop efficient and eco-friendly strategies for RB-5 dye removal from industrial wastewater. RB-5 dye-degrading bacteria, namely NAF1, NAF2, NAF3, and NAF4, were isolated from soil contaminated with textile effluents. Evaluation of their decolorization potential revealed NAF4 as the most effective, achieving a decolorization percentage of 89%, followed by NAF3 and the co-culture at 75% and 73%, respectively. NAF2 exhibited the lowest decolorization potential. The isolate NAF4 showed significant production of tyrosinase and laccase enzymes, with lower quantities of lignin peroxidase and azoreductase. Phylogenetic analysis confirmed the identities of the isolates as Bacillus, Pseudomonas, Escherichia, and Citrobacter. The decolorization potential exhibited by Bacillus wiedmannii NAF4 has instigated the investigation of its capacity to secrete degradative enzymes implicated in the breakdown of azo dyes. Substantial enzyme production was observed over a duration of 192 hours. The biodegradation potential of B. wiedmannii strain NAF4 for RB-5 dye was assessed using Surface Response Optimization (SRO) modeling. The optimized conditions for RB-5 degradation were determined to be an agitation speed of 115.777 rpm, pH of 7.449, inoculum size of 12.255, and temperature of 29.74°C. The SRO model exhibited high statistical significance with an F-value of 53.30 and low p-values (&lt;0.0001), as well as a correlation coefficient (R2) value of 0.9813. Validation studies confirmed the adequacy and precision of the model. The maximum RB-5 degradation achieved was 90.2291%. Three-dimensional plots visually represented the validated optimum conditions of variables and responses. The successful application of Response Surface Methodology (RSM) as an optimization tool highlights its effectiveness in enhancing degradation processes. This study provides insights into the potential applicability of RSM for optimizing degradation processes in various contexts. The developed eco-friendly strategies offer promising solutions for RB-5 dye removal from industrial wastewater, mitigating its environmental impact.In conclusion, the application of Response Surface Methodology (RSM) as an optimization tool proved effective in maximizing the degradation process by optimizing the degradation parameters. The success of this approach suggests its potential applicability in other contexts to enhance degradation processes.

Optimization of poplar wood shavings bio-pretreated with Coriolus versicolor to produce binderless fiberboard using response surface methodology

Free formaldehyde is released due to the addition of aldehyde-based adhesives during the production of fiberboard. This is harmful to human health and pollutes the environment, and for that reason binderless fiberboard has become a research hotspot. There have been reports about pretreatments with white-rot fungi or lignocellulase to produce binderless fiberboard, but there have been no such reports about optimizing the bio-pretreatment conditions. In this study, poplar wood shavings were used for fiberboard production, and the bio-pretreatment conditions with Coriolus versicolor were studied using response surface methodology. After single-factor optimization, the central levels of bran, molasses, and magnesium sulfate were obtained. Further optimization was carried out using Box-Behnken design to study the influence of the factors. A second-order polynomial equation was obtained, and the low p-value (0.001) implied that the model was highly significant. The optimized bio-pretreatment conditions for modulus of rupture (MOR) of the fiberboard were obtained by ridge analysis as 3.021 g of bran, 8.907 g of molasses, and 0.27 g of magnesium sulfate. Under the optimized conditions, MOR of fiberboard reached 27.21±0.64 MPa, which was 2.2 times that of the control fiberboard. Bio-pretreatment with C. versicolor should be a good choice to produce a high-strength binderless fiberboard.

The causal role between circulating immune cells and diabetic nephropathy: a bidirectional Mendelian randomization with mediating insights

Diabetic nephropathy (DN) is a critical inflammatory condition linked to diabetes, affecting millions worldwide. This study employs Mendelian randomization (MR) to explore the causal relationship between immune cell signatures and DN, analyzing over 731 immune signatures and incorporating data from 1400 metabolites to investigate potential mediators. Despite no statistically significant influence of DN on immunophenotypes after FDR correction, some phenotypes with unadjusted low P-values warranted mention, including CD34 on Hematopoietic Stem Cell (Myeloid cell Panel), CD45 on CD33− HLA DR− (Myeloid cell Panel). Furthermore, three immunophenotypes were identified to have a significant impact on DN risk: CD16−CD56 on HLA DR+ NK (TBNK Panel), CD45 on HLA DR+ T cell (TBNK Panel), and CD33dim HLA DR+ CD11b+ AC (Myeloid cell Panel). Our findings underscore the critical role of immune cells in DN, highlighting potential mediators and offering new insights into its underlying mechanisms.

Modelling and optimization of operating parameters for improved steam energy production in the food and beverage industry in a developing country

Efficient steam energy production was essential for reducing energy consumption and operational costs while enhancing productivity, particularly in industrial settings prone to explosions due to boiler parameter control issues. This challenge was especially acute in the food and beverage industry amid rising energy costs and stricter environmental regulations, highlighting the importance of optimizing steam energy production. This study focused on refining operational parameters in a steam production plant to maximize steam energy output. It utilized mathematical models and optimization tools to identify ideal operational conditions and investigate extreme scenarios. Design-Expert version 13.0 statistical software and Response Surface Methodology (RSM) via Centre Composite Design (CCD) were employed to create a comprehensive design matrix encompassing key variables like time, pressure levels, temperature, mass flow rate, and steam energy production across three experimental levels. The research revealed that increased pressure and time significantly boosted steam energy production by leveraging water’s energy content rise under initial conditions, thus improving efficiency by reducing required water mass circulation. Moreover, elevated temperature and extended operation enhanced economizer efficiency, leading to increased heat recovery and reduced steam generation. Steam generation also increased with temperature and time due to the pressure rise during boiling, necessitating more energy for steam conversion. An optimum yield of steam energy of 620 Cal was attained at a time, pressure, temperature, and mass flow rate of 1 h, 16.97 MPa, 249.5°C, and 59.85 kg/s, respectively. The mathematical model developed is accurate, reliable, responsive, and can replicate the experimental data due to the high F-value (24.48), low CV (0.94) low p-value (&amp;lt; 0.005), and high R2 (0.9821) value close to 1. This research promises to enhance the efficiency of steam energy production in the food and beverage industry by reducing the need for resource-intensive experimental procedures, thus lowering costs and resource consumption.

A new competing risks model with applications to blood cancer data

Competing risks models are invaluable probabilistic models in survival analysis, a particular part of medical research. Such models deal with research problems that involve multiple potential risk factors that compete with each other to cause death (i.e., failure from a statistical perspective). Competing risks models' flexibility must be thoroughly explored to guarantee suitability for multifaceted risk scenarios (e.g., modeling data of malicious diseases where factors such as treatment response and progression are intertwined). This article considers a new competing risks model called the additive generalized linear-exponential (AGLE) competing risks model. The proposed model is expected to be more robust and superior to other well-known models when modeling real-life data. A mathematical treatment for the properties of the new model is first considered. Afterward, model parameters estimation via various estimation methods is discussed. The critical role of estimating shape parameters in understanding blood cancer's survival and failure mechanisms is highlighted. Furthermore, the bias and root mean square error of different estimation methods are examined numerically through Monte Carlo simulations. The simulation study indicated that the maximum product of spacings estimation method for the model parameters has the optimal balance of bias and variance as the sample sizes increase. Two real-life blood cancer data sets are analyzed to illustrate the application of the proposed model. The analysis outcomes support the AGLE model's robustness, with low Kolmogorov-Smirnov statistics and high p-value, affirming its superior fit for blood cancer data compared to other well-known models.