Low P-values Research Articles

Dimensions of Audit Committee Quality and CEO Compensation: Examining the Linear Relationship

This study examined the linear relationship between audit committee attributes, proxied with audit committee female diversity, audit committee diligent meeting, audit committee independence and audit committee size and Chief Executive Officer’s compensation, with firm size as a control variable. The study utilised data from 53 non-financial firms between 2013 and 2022 listed in the Nigerian Exchange Group. Using, STAT 14, the study also presented the descriptive statistics information, the correlation matrix and results of Random-effects GLS regression of the study. The results indicate that approximately 6.88% of the variation in Chief Executive Officer’s compensation can be explained by the combined effect of the independent variables, as evidenced by an R-squared value of 0.0688 and an adjusted R-squared of 0.0599. The model’s overall significance is confirmed by an F-statistic of 7.75 and a very low P-value (0.0000), we therefore reject the null hypothesis. The findings reveal that audit committee female diversity, audit committee diligent meeting, and audit committee independence do not have significant linear relationships with CEO compensation, as indicated by their respective P-values (0.542, 0.814, and 0.916). However, audit committee size displays a statistically significant negative linear relationship with CEO compensation (P = 0.000), suggesting that larger audit committees may be associated with lower CEO compensation due to increased oversight or stronger corporate governance structures. Consequently, further investigation with additional variables or alternative modelling approaches is recommended to gain deeper insights into other determinants of CEO compensation.

The Influence of Teamwork Skills and Challenges on Team - Based Learning of Nursing Students in a Selected University in Vietnam

Objectives: To explore the influence of teamwork skills and challenges on the team-based learning, in order to optimize the experiences and outcomes of team-based learning for nursing students. Methodology: A descriptive correlational study was conducted with the participation of 280 second, third, and fourth-year students studying in the Bachelor of Nursing program at a selected university in Da Nang city. Results: The findings of the study provide valuable insights into the factors influencing teamwork skills and the challenges faced by nursing students when team-based learning. The overall assessment of teamwork skills was answered by students with an average score of 4.03, indicating that students generally had "Agree" teamwork skills, with "communication skills" rated as "Strong Agree". The average score for the challenges encountered was 3.92, which is interpreted as "Usually". This suggests that students generally encountered these challenges frequently, but not always. Specific challenges identified included engagement, time management, motivation, personality conflicts, and social context, all rated as "Often" occurring. Student ratings of team-based learning were rated as "very good" with a mean of 3.91. The relationship between teamwork skills and team-based learning was found to be highly correlated, with Pearson's r = 0.51. The challenges and team-based learning were also highly correlated, with Pearson's r = 0.61. All the correlations were statistically significant, with a p-value of less than 0.001. Conclusions: These correlations were supported by high Pearson r coefficients and low p-values, indicating a very strong and significant positive relationship. Nursing students demonstrated strong teamwork skills, particularly in adaptability, coordination, decision-making, and communication. The results of this study also suggest that although team-based learning is generally effective, nursing students still face significant challenges, particularly in areas such as time management, motivation, and resolving personality conflicts.

Leveraging ChatGPT for Enhanced Aesthetic Evaluations in Minimally Invasive Facial Procedures.

In recent years, the application of AI technologies like ChatGPT has gained traction in the field of plastic surgery. AI models can analyze pre- and post-treatment images to offer insights into the effectiveness of cosmetic procedures. This technological advancement enables rapid, objective evaluations that can complement traditional assessment methods, providing a more comprehensive understanding of treatment outcomes. The study aimed to comprehensively assess the effectiveness of custom ChatGPT model, "Face Rating and Review AI," in facial feature evaluation in minimally invasive aesthetic procedures, particularly before and after Botox treatments. An analysis was conducted on the Web of Science (WoS) database, identifying 79 articles published between 2023 and 2024 on ChatGPT in the field of plastic surgery from various countries. A dataset of 23 patients from Kaggle, including pre- and post-Botox images, was used. The custom ChatGPT model, "Face Rating & Review AI," was used to assess facial features based on objective parameters such as the golden ratio, symmetry, proportion, side angles, skin condition, and overall harmony, as well as subjective parameters like personality, temperament, and social attraction. The WoS search found 79 articles on ChatGPT in plastic surgery from 27 countries, with most publications originating from the USA, Australia, and Italy. The objective and subjective parameters were analyzed using a paired t-test, and all facial features showed low p-values (<0.05). Higher mean scores on features such as the golden ratio (mean = 5.86, SD = 0.69), skin condition (mean = 3.78, SD = 0.73), and personality (mean = 5.0, SD = 0.79) indicate positive shifts after the treatment. The custom ChatGPT model "Face Rating and Review AI" is a valuable tool for assessing facial features in Botox treatments. It effectively evaluates objective and subjective attributes, aiding clinical decision-making. However, ethical considerations highlight the need for diverse datasets in future research to improve accuracy and inclusivity. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Validating instrument to assess science literacy and independent learning skills in high school

Inadequate science literacy and independent learning hinder students' ability to analyze scientific information. This study aimed to develop and validate a measurement instrument to assess science literacy and independent learning skills among high school students. The instrument was piloted with 20 twelfth-grade students at SMAN 03 Kota Yogyakarta to assess its reliability and validity. The validated instrument was administered to 20 eleventh-grade students at the same school to identify the profiles of these two skills and explore their relationship in the context of the nervous system. The validity analysis demonstrated that both the science literacy and independent learning instruments exhibited strong validity. Pearson correlation analysis revealed that 20 items in the independent learning questionnaire were strongly correlated with the total score. Rasch analysis of the science literacy test indicated low infit mean square values, appropriate p-values, and high point biserial values, suggesting well-functioning items. Cronbach's alpha reliability coefficients for both instruments were excellent. The profile analysis revealed a discrepancy, with many students showing high independent learning levels but low science literacy scores on the nervous system topic. This gap suggests that even students capable of independent learning may face challenges in grasping specific scientific concepts.

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.

Immunostaining Patterns of CD10 in Fibroadenomas and Phyllodes Tumours of Breast

Background: Accurate differentiation between fibroadenomas and phyllodes tumours (PTs) is challenging due to overlapping morphological features. CD10, an immunohistochemistry (IHC) marker, has shown promise in distinguishing these breast lesions. This study aimed to evaluate CD10 expression patterns in fibroepithelial lesions (FELs) of the breast. Methodology: This cross-sectional study was conducted in the Histopathology Department, at Dow University of Health Sciences over 1 year (01st Jan 2023 to 15th Dec 2023). Through the purposive sampling technique, the female patients having FELs were diagnosed by Consultant Pathologists as fibroadenomas or PT and were included in the study by employing specific histological parameters on H &amp; E slides. Immunohistochemistry with CD10 antibody was conducted on a total of 120 samples with an equal split of fibroadenomas, benign PT, borderline PT, and malignant PT (30 cases each), assessing stromal cell staining and categorizing expression as negative (&lt;10% positivity), weak (10–30% positivity), or strong (&gt;30% positivity). Data was statistically analyzed by using SPSS version 26 including reporting mean ± S.D for continuous data and utilizing the Chi-square test to assess associations, with significance set at p&lt;0.05. Results: Negative CD10 expression was observed in fibroadenomas and benign PT, while borderline and malignant PT exhibited weak and strong positive CD10 expression. The analysis revealed a highly significant association (p-value &lt; 0.001) between CD10 expression levels and the various forms of breast lesions. This strong statistical correlation indicates that CD10 could be an important biomarker for assessing the nature of breast lesions. A low p-value reinforces the reliability of the findings, suggesting that the observed differences in CD10 expression are unlikely to be due to random chance. Conclusion: The findings of the study suggest that CD10 expression could assist as a valuable marker for differentiating between FELs of the breast. Moreover, it may serve as an indicator of the aggressiveness and metastatic potential of PT.

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 &gt; 0.95) and low p-values (&lt; 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 &lt; 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&lt;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&lt;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&lt;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&lt;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

INFLUENCE OF GOOD ROADS ON POVERTY ALLEVIATION OF COOPERATIVE RURAL FARMERS IN BADAGRY LOCAL GOVERNMENT AREA OF LAGOS STATE

This study investigates how cooperative rural farmers' poverty can be reduced by having good roads in Badagry Local Government of Lagos State. A good road is essential for business and the timely marketing of agricultural products. In the study, a descriptive survey research design was used. In the study area, 120 farmers provided information via a structured questionnaire that was administered using a multistage sampling technique. The data were analyzed using multiple regression, frequency, percentage, and other inferential and descriptive statistics. Results show that 79.2% of roads used by the farmers were untarred while 20.8% were tarred. 28.3% of the farmers surveyed had a per capita expenditure on transportation below N20,001, while 42.5% fell within the range of N20,001 and N40,000. 76.7% spent more than N5,000 on transportation which invariably affected their profit. The reduction in poverty was positively impacted by road accessibility (t = 3.611; p = 0.000), as cooperative farmers would benefit from improved sales and profitability. The findings demonstrated a noteworthy positive correlation between road accessibility and quality of transportation and agricultural productivity and marketing (t = 5.321 for road accessibility and 6.881 for transportation quality). The low p-values (0.000) further suggest a robust and statistically significant relationship. These findings highlight the importance of well-developed road networks and transportation services in rural areas. As a result, it is advised that better road infrastructure boosts quality transportation services, supports and strengthens agriculture, and promotes strong relationships between cooperative farmers and carriers.

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.