Kendall's Tau Research Articles

An adaptive test of the independence of high-dimensional data based on Kendall rank correlation coefficient

The paper considers the test on the components independence of high-dimensional continuous random vectors based on the Kendall rank correlation coefficient. Under the null hypothesis, it proves that the sum-of-powers type statistics constructed by the Kendall rank correlation coefficient are asymptotically normally distributed and independent when the sum of the orders is odd, and they are also independent with the extreme-value-type test statistics of the Kendall rank correlation coefficient. Based on the asymptotic independence property, it proposes an adaptive test procedure which combines p-values from the sum-of-powers-type test and extreme-value-type test. Some simulations reveal that the proposed test method can significantly enhance the statistical power of the test while keep the type I error well controlled, and it is powerful against a wide range of alternatives (including dense, sparse or moderately sparse signals). To illustrate the use of the proposed adaptive test method, two real data sets are also analysed.

Comprehensive analysis of compatibility and low-temperature rheological properties of SBS modified asphalt binder using entropy weight-TOPSIS method

A self-developed compatibilizer was added into SBS to realize its adequate dissolution and increase the SBS modified asphalt binder (SBSMA) compatibility. The compatibility and the low-temperature rheological properties and of SBSMA containing compatibilizer were investigated by combining the multiple perspectives. Moreover, the comprehensive evaluation of the compatibility and rheological properties for SBSMA was conducted using entropy weight-TOPSIS method, and correlation analysis between them was also analyzed using Kendall correlation analysis. Results show that the compatibility and low-temperature rheological properties of SBSMA is greatly increased by adding compatibilizer according to multiple evaluation indicators and comprehensive evaluation. The compatibility of the asphalt binder system modified by SBS is best when the compatibilizer dosage is 4 %. The SBSMA system presented the optimal low-temperature rheological properties with the compatibilizer dosage of 5 %. Moreover, the compatibility of SBSMA and low temperature performance are strongly correlated based on entropy weight-TOPSIS and the Kendall correlation analysis, where the Kendall's coefficient of concordance of the model is 1.0.

Assessment of four-dimensional flow MRI for prediction of varices risk in cirrhotic patients.

This study aims to validate the application of abdominal four-dimensional flow magnetic resonance imaging (MRI) for cirrhotic patients and quantify its effectiveness in assessing the hemodynamic impacts of cirrhosis to evaluate varices. All consecutive patients who underwent MRIs between September 2022 and June 2023 were enrolled. Groups were divided into varicose, non-varicose, and healthy groups. ANOVA and post hoc LSD-t tests were used for statistical analysis. The correlation between hemodynamic parameters and liver function grade was evaluated using Kendall's correlation coefficient. A total of 80 patients were included (53 cirrhotic, 27 healthy). Significant disparities were found in main portal vein flow (MPV-FR), splenic vein flow (SV-FR), and vessel diameters (MPV-VD, SV-VD) among the groups (p < 0.05). MPV-FR was higher in the varicose group (24.81 ± 8.52) compared to non-varicose (19.52 ± 5.07) and healthy groups (17.26 ± 5.48). The most robust assessment of variceal risk was achieved by combining the flow rates (FRs) and VDs of MPV and SV (AUC 0.83, 95% CI 0.72-0.94). The combined indices of FRs and VDs of MPV and SV effectively predict the occurrence of varicose veins in cirrhotic patients. Question Non-invasive prediction of variceal risk is essential for the clinical management of advanced chronic cirrhosis, yet existing clinical examinations are inadequate. Findings The effective assessment of variceal risk was achieved by combining the flow rates and vessel diameters of the main portal vein and splenic vein. Clinical relevance Four-dimensional flow MRI can reveal hemodynamic changes in cirrhotic patients and assist in identifying gastroesophageal varices, serving as a marker for varices risk prediction.

Uniform in Number of Neighbor Consistency and Weak Convergence of k-Nearest Neighbor Single Index Conditional Processes and k-Nearest Neighbor Single Index Conditional U-Processes Involving Functional Mixing Data

U-statistics are fundamental in modeling statistical measures that involve responses from multiple subjects. They generalize the concept of the empirical mean of a random variable X to include summations over each m-tuple of distinct observations of X. W. Stute introduced conditional U-statistics, extending the Nadaraya–Watson estimates for regression functions. Stute demonstrated their strong pointwise consistency with the conditional expectation r(m)(φ,t), defined as E[φ(Y1,…,Ym)|(X1,…,Xm)=t] for t∈Xm. This paper focuses on estimating functional single index (FSI) conditional U-processes for regular time series data. We propose a novel, automatic, and location-adaptive procedure for estimating these processes based on k-Nearest Neighbor (kNN) principles. Our asymptotic analysis includes data-driven neighbor selection, making the method highly practical. The local nature of the kNN approach improves predictive power compared to traditional kernel estimates. Additionally, we establish new uniform results in bandwidth selection for kernel estimates in FSI conditional U-processes, including almost complete convergence rates and weak convergence under general conditions. These results apply to both bounded and unbounded function classes, satisfying certain moment conditions, and are proven under standard Vapnik–Chervonenkis structural conditions and mild model assumptions. Furthermore, we demonstrate uniform consistency for the nonparametric inverse probability of censoring weighted (I.P.C.W.) estimators of the regression function under random censorship. This result is independently valuable and has potential applications in areas such as set-indexed conditional U-statistics, the Kendall rank correlation coefficient, and discrimination problems.

Temporal cue based categorization and speech perception in noise among pediatric cochlear implant users

ObjectivesVoice onset time (VOT) has been identified as a potential temporal cue for predicting children's performance in speech-in-noise tasks, yet the relationship between these two factors has never been explored among children using CI. Hence, the present study aimed to explore the performance of children using CI on temporal cue-based syllable categorization test and speech perception in noise and examine the relationship between the two. MethodsTemporal cue-based syllable categorization test was developed with the manipulation of /ba/ sound in 10 steps continuum with VOT varied between −74 ms to 26 ms. The developed test and revised speech in noise for Marathi-speaking children (0 and 5 dB SNR) were administered to thirty children with unilateral cochlear implant and thirty children with normal hearing, aged between 5 to 7 years. ResultsThe Mann-Whitney U test showed significant differences between groups in temporal cue-based categorization and speech in noise tests at 0 dB and 5 dB SNR. Kendall Tau B revealed a moderate correlation between implant age and scores on the temporal cue-based categorization and speech in noise tests at 0 dB SNR, with a strong correlation at 5 dB SNR. Additionally, there was a significant moderate relationship between temporal cue-based categorization and speech in noise test scores at both 0 dB and 5 dB SNR. ConclusionThe present study highlights the importance of temporal cues in speech perception and the need for temporal processing for children using cochlear implants. It reinforces the evidence that speech perception skills improve with implant age.

Label-Free Prediction of Tumor Metastatic Potential via Ramanome.

Assessing metastatic potential is crucial for cancer treatment strategies. However, current methods are time-consuming, labor-intensive, and have limited sample accessibility. Therefore, this study aims to investigate the urgent need for rapid and accurate approaches by proposing a Ramanome-based metastasis index (RMI) using machine learning of single-cell Raman spectra to rapidly and accurately assess tumor cell metastatic potential. Validation with various cultured tumor cells and a mouse orthotopic model of pancreatic ductal adenocarcinoma show a Kendall rank correlation coefficient of 1 compared to Transwell experiments and histopathological assessments. Significantly, lipid-related Raman peaks are most influential in determining RMI. The lipidomic analysis confirmed strong correlations between metastatic potential and phosphatidylcholine, phosphatidylethanolamine, cholesteryl ester, ceramide, and bis(monoacylglycero)phosphate, crucial in cell membrane composition or signal transduction. Therefore, RMI is a valuable tool for predicting tumor metastatic potential and providing insights into metastasis mechanisms.

Model for Inverting the Leaf Area Index of Green Plums by Integrating IoT Environmental Monitoring Data and Leaf Relative Content of Chlorophyll Values

The quantitative inversion of the leaf area index (LAI) of green plum trees is crucial for orchard field management and yield prediction. The data on the relative content of chlorophyll (SPAD) in leaves and environmental data from orchards show a significant correlation with LAI. Effectively integrating these two data types for LAI inversion is important to explore. This study proposes a multi−source decision fusion LAI inversion model for green plums based on their adjusted determination coefficient (MDF−ADRS). First, three statistical methods—Pearson, Spearman rank, and Kendall rank correlation analyses—were used to measure the linear relationships between variables, and the six environmental factors most highly correlated with LAI were selected from the orchard’s environmental data. Then, using multivariate statistical analysis methods, LAI inversion models based on environmental feature factors (EFs−PM) and SPAD (SPAD−PM) were established. Finally, a weight optimization allocation strategy was employed to achieve a multi−source decision fusion LAI inversion model for green plums. This strategy adaptively allocates weights based on the predictive performance of each data source. Unlike traditional models that rely on fixed weights or a single data source, this approach allows the model to increase the influence of a key data source when its predictive strength is high and reduce noise interference when it is weaker. This dynamic adjustment not only enhances the model’s robustness under varying environmental conditions but also effectively mitigates potential biases when a particular data source becomes temporarily unreliable. Our experimental results show that the MDF−ADRS model achieves an R2 of 0.88 and an RMSE of 0.39 in the validation set, outperforming other fusion methods. Compared to the EFs−PM and SPAD−PM models, the R2 increased by 0.19 and 0.26, respectively, and the RMSE decreased by 0.16 and 0.22. This model effectively integrates multiple sources of data from green plum orchards, enabling rapid inversion and improving the accuracy of green plum LAI estimation, providing a technical reference for monitoring the growth and managing the production of green plums.

The Correlation between Self-Efficacy of Hemodialysis Patients with Their Quality of Life

Background: Haemodialysis is often associated with decreased quality of life. Self-efficacy is a perception of patient confidence in ability activities. The correlation between self-efficacy and quality of life in the Indonesian sample is not yet explored in depth. Aim: To determine the correlation between self-efficacy and the quality of life in hemodialysis patients. Methods: Cross-sectional study. About 234 respondents were included. The General Self-Efficacy Scale (GSES) questionnaire has been used to measure self-efficacy, and the World Health Organization Quality of Life (WHOQOL-BREF) was used to measure the respondent's quality of life. Kendall's tau statistical approach was used to determine the correlation between self-efficacy and quality of life. Results: Majority of respondents with high self-efficacy as much as 68.8%, and majority of respondents with moderate and good quality of life as much as 48.3%. A significant correlation between self-efficacy and quality of life (p=0.000) with a correlation coefficient value of 0.491 was found. Conclusion: High level of self-efficacy would be a better quality of life of haemodialysis patients. Improving self-efficacy as a nursing care process is recommended as nursing outcomes in order improve the quality of life of haemodialysis patients. Identify self-efficacy predictor factors of haemodialysis patients are recommended for further research.

Application of AI to minimize information risk in tax control: Evidence from Bulgaria

The application of artificial intelligence (AI) in tax control is becoming more and more relevant in the conditions of globalization and digitalization of the economy, which creates a need for effective mechanisms for managing information risk. With the growing volume of data and the complexity of the tax processes, the risk of deviations, fraud and inconsistencies increases significantly. This article aims to examine applied AI tools and approaches that can minimize information risk in tax control, as well as to analyze their effectiveness through the prism of practice in Bulgaria. The methodology is based on correlation analysis, which measures the strength and direction of the linear relationship between pairs of variables. A Pearson correlation coefficient was used to evaluate the variable "Artificial Intelligence" and the different forms of tax control. Kendall's tau-b correlation coefficient was used to assess the linear relationship between the variables of collection, processing, verification, and management of tax information related to the application of AI. The main results show that AI has a positive and statistically significant effect on risk management and cybersecurity, suggesting the potential to improve data protection and reliability. However, the verification and revision of tax information needs to significantly impact AI, which points to the need for further development of these technologies for better integration. In conclusion, the application of AI offers significant opportunities to minimize information risk in tax control but requires targeted adaptation and refinement in specific aspects of control processes.

Correlation between Fundus Damage and Renal Function Deterioration in Chronic Kidney Disease Patients.

This study aimed to explore the correlation between the extent of fundus damage and the severity strategies for chronic kidney disease (CKD). We collected data from 118 CKD patients, including general information, renal function indicators, and fundoscopic examination results. The stages of CKD and degrees of fundus lesions were graded. SPSS 25.0 software facilitated the analysis of correlations using Kendall's tau-b correlation analysis and ordinal regression analysis. Statistically significant differences were observed among multiple CKD stages in the distribution of age, systolic blood pressure, diastolic blood pressure, hemoglobin, total cholesterol, homocysteine, cystatin C, serum creatinine, blood urea, eGFR, 24-hour urine protein, urine microalbumin, urine microalbumin/urine creatinine, and blood β2 microglobulin, complement C3. Notably, the levels of cytokeratin 19 fragment and transforming growth factor β significantly increased in all CKD stages. Kendall's tau-b correlation analysis revealed a significant positive correlation between CKD stage and fundus lesion grade. Ordinal regression analysis indicated that sex differences, total cholesterol levels, and hemoglobin levels were significant predictors of fundus lesion risk. Compared with patients at stage 5 CKD, the risk of fundus damage significantly lower in patients in stage 2 and stage 3, further demonstrating a positive correlation between renal function deterioration and increased risk of fundus damage. Routine fundus screening and early intervention for fundus lesions are vital for assessing CKD deterioration, providing new directions for future related research.

IDENTIFICATION OF PERIODONTAL PATHOGENS IN PATIENTS WITH CHRONIC GENERALIZED PERIODONTITIS DEPENDING ON THE SEVERITY OF INFLAMMATION

Introduction. The progression of chronic generalized periodontitis is associated with gram-negative anaerobic bacteria. Microbiological diagnostics of periodontal disease provides an opportunity for selecting the most effective therapeutic approach for both local and systemic conditions in patients. Aim of this study is to conduct a qualitative assessment of the presence of pathogenic microorganisms in the periodontal pockets of patients, based on the severity of inflammation. Materials and methods. The presence of five key pathogenic microorganisms in the periodontal pocket contents of 90 individuals aged 25-60 years (45 with grade I severity and 45 with grade II) was assessed using real-time polymerase chain reaction (PCR). The analysis was conducted with the CFX96™ Real-Time PCR Detection System (BIO-RAD) and the PeriodontScreen Real-TM reagent kit (Sacace Biotechnologies Srl, Italy). Non-parametric correlation statistical analysis was performed to calculate Kendall’s Tau and Gamma coefficients. In 27 patients (60%) with grade I severity, bacteria of a single species were identified. Two types of periodontal pathogens were detected in 13 patients (28.9%), with the most common combination being Tannerella forsythia and Aggregatibacter actinomycetemcomitans, found in 5 patients (11.1%); 5 patients (11.1%) showed the presence of three microbial species: Tannerella forsythia, Treponema denticola, and Porphyromonas gingivalis. Among the patients with grade II severity, 8 patients (17.8%) had bacteria of one species, specifically Prevotella intermedia. Two microbial species were identified in 9 patients (20%), while three species were present in 15 patients (33.3%). The most frequent combination included Prevotella intermedia, Tannerella forsythia, and Aggregatibacter actinomycetemcomitans, found in 5 patients (11.1%). Four types of microbes were detected in 13 patients (28.9%), with the predominant combination being Tannerella forsythia, Porphyromonas gingivalis, Treponema denticola, and Aggregatibacter actinomycetemcomitans (17.8%). None of the patients with grade II severity had all five periodontopathogenic agents. The correlation analysis confirmed that chronic periodontitis is strongly associated with the presence of Tannerella forsythia within microbial associations. Conclusions. The spectrum of periodontal pathogens affects the severity of inflammation in the periodontium. In grade II inflammation, associations of periodontal pathogens were detected by 82.2% of cases, while in grade I by in 40%. Tannerella forsythia was detected in 42.2% of patients with grade I inflammation, 15.6% of them as a mono-infection; in patients with grade II inflammation in 71.1%, in associations with Treponema denticola in 48.9%. Determining the spectrum of periodontal pathogens, taking into account their characteristics and associated virulence factors, is necessary for choosing the effective differentiated treatment approach.

Geometric morphometric approach to dental health in an 18th‐ to 19th‐century English infirmary

AbstractThis study evaluated correlations between poor dental health and craniomandibular morphology for the Radcliffe Infirmary population buried between 1770 and 1855. The Radcliffe Infirmary is situated in Oxford, 90 km northwest of London, in what used to be a rural area. The original report for this population observed high prevalence rates of dental caries, antemortem tooth loss (AMTL), and periodontal disease. Industrial societies are acknowledged to have highly processed foods that not only are detrimental to dental health but also require less mastication. This softening of diet is suggested to be the leading influence on the modern morphology of the skull. By using two‐dimensional geometric morphometrics (GM) and linear measurements of the skull, the Radcliffe population was evaluated for the effects of poor dental health and decreased wear on the masticatory structures. High frequencies of AMTL, periodontal disease, and caries and low amounts of dental wear were observed in this sample population. Caries and AMTL showed weak correlations with skull shape and craniomandibular dimensions according to the results of the morphometric analysis and Kendall's tau. Dental wear correlated with a wide bizygomatic breadth and a long narrow skull shape for the males only. Periodontal disease was the only dental health variable not correlated with shape or size. This study confirmed that the Radcliffe population ate a cariogenic diet and suggests that dental wear and pathology weakly correlates with craniomandibular shape and size. This unpredicted finding may be due to the limitations of two‐dimensional shape analysis, a small sample size, and the sampling strategy. Future research is highly encouraged to better understand the consequences of poor dental health for skull morphology.

Prediction uncertainty of evapotranspiration deficit index

Understanding uncertainty in drought characteristics is vital in water resource management. To demonstrate an effective presentation of the uncertainty in agricultural drought derived from the evapotranspiration deficit indices (ETDIs), this study simulated ETDIs using the general ETDI formula with 1000 parameter combinations at different locations in the Ruvu River basin, Tanzania. Then, principal component analysis was used to reduce ETDI simulations to proxies of simulations. Thirdly, the evaluation of Kendall tau correlations between the proxies of simulations and ETDI simulations produced behavioural simulations, which were used to calculate the 95 per cent prediction uncertainty (95PPU) of ETDIs and median ETDI. Finally, the 95PPU of ETDIs and median ETDI were characterized into drought durations. The findings show that the 95PPU bands of ETDIs and drought durations were very narrow but enclosed their median values. Therefore, the 95PPU and median from this approach comprehensively present drought characteristics derived from several ETDIs.

Predicting corrosion current density in magnesium alloy battery anodes: machine learning approach using rapid miner

ABSTRACT Magnesium (Mg) alloys are increasingly recognised for their potential as anode materials in batteries due to their high specific capacity and cost-effectiveness. However, their susceptibility to corrosion poses a significant challenge to practical applications. This study explores the effectiveness of various machine learning models – local polynomial regression, Decision Trees, Random Forest, Gradient Boost, and Extreme Gradient Boost – in predicting the corrosion behaviour of Mg alloy battery anodes in a 3.5 wt.% NaCl electrolyte. Experimental data on the corrosion current density of Mg alloys are collected and analysed using the RapidMiner tool for model training and evaluation. The findings highlight that the Extreme Gradient Boost (XGBoost) model excels among other machine learning techniques in accurately forecasting the corrosion rates of Mg alloys. XGBoost achieves an exceptional R2 value of 0.9931 in Tafel plots, indicating a robust fit to the data as evidenced by scatter plots. Furthermore, the XGBoost model demonstrates strong positive monotonic and ordinal relationships between actual and predicted corrosion current densities, as indicated by a Spearman correlation coefficient of 1 and a Kendall tau coefficient of 0.99. These outcomes underscore XGBoost's potential as a powerful tool for enhancing the understanding and management of corrosion in Mg alloy battery applications.

Understanding childhood obesity in Pakistan: exploring the knowledge, attitudes, practices of mothers, and influential factors. A cross-sectional study.

Childhood obesity is a significant public health issue with far-reaching implications. The World Health Organization reported that in 2020, around 38 million children under five were overweight or obese globally, and in 2016, 340 million children and adolescents aged 5-19 were affected. In Pakistan, the situation is alarming; 66.9% of school-going children in Punjab were overweight, and 5.8% were obese in 2022. This study explores mothers' knowledge, attitudes, and practices regarding childhood obesity in Pakistan and identifies factors contributing to this epidemic. Maternal perspectives are crucial as they significantly influence children's dietary habits, physical activity, and attitudes toward food and body image. A cross-sectional study was conducted from November 2023 to January 2024 at four medical centers: Fazaia Ruth Pfau Medical College Hospitals Karachi, Baqai Medical University Karachi, and Nishtar Medical University Multan. The study included 191 mothers with children aged 5 to 15 years. Data were collected using a structured questionnaire on sociodemographic characteristics, knowledge, attitudes, practices, and perceptions of childhood obesity. IBM-SPSS version 26.0 was used for data analysis, employing statistical tests like Kruskal-Wallis, Mann-Whitney U, Spearman, or Kendall Tau correlation to examine associations. Among the 191 mothers, 48.7% had education beyond intermediate, and 86.9% were housewives. The children's BMI distribution showed that 27.7% were obese and 21.5% overweight. Mothers had moderate knowledge (60.5%) about childhood obesity; 75.4% recognized its long-term health risks, and 62.8% associated it with diabetes. Attitudes were generally positive, with a 78.5% average score. Most mothers (73.8%) believed obesity could be controlled and had healthy practices (70.1% average score). However, 96.9% reported witnessing stigmatization of obese children, and 79.6% felt pressured by relatives about their child's weight. The findings indicate that while Pakistani mothers have moderate knowledge and positive attitudes toward childhood obesity, their practices are influenced by educational and socioeconomic factors. Addressing these disparities, enhancing public health initiatives, and mitigating stigmatization could improve childhood obesity management in Pakistan.

Digital financial inclusion, the belt and road initiative, and the Paris agreement: Impacts on energy transition grid costs

We investigate how digital financial inclusion, the Belt and Road Initiative, and the Paris Agreement influence the energy transition grid cost. We propose two new Kendall and Spearman wavelet cross-quantile correlation methods and utilize data from June 1, 2018, to July 31, 2024. Our findings indicate that digital financial inclusion, the Paris Agreement, and artificial intelligence significantly reduce grid costs in the short and long run. Additionally, the Belt and Road Initiative has substantial potential to decrease grid costs, particularly during bullish market conditions in the long run. Conversely, GCOVOL significantly increases grid costs, especially in the long run.

Impact‐Based Skill Evaluation of Seasonal Precipitation Forecasts

AbstractWe introduce an impact‐based framework to evaluate seasonal forecast model skill in capturing extreme weather and climate events over regions prone to natural disasters such as floods and wildfires. Forecasting hydroclimatic extremes holds significant importance in an era of increasing hazards such as wildfires, floods, and droughts. We evaluate the performance of five Copernicus Climate Change Service (C3S) seasonal forecast models (CMCC, DWD, ECCC, UK‐Met, and Météo‐France) in predicting extreme precipitation events from 1993 to 2016 using 14 indices reflecting timing and intensity (using absolute and locally defined thresholds) of precipitation at a seasonal timescale. Performance metrics, including Percent Bias, Kendall Tau Rank Correlation Score, and models' discrimination capacity, are used for skill evaluation. Our findings indicate that the performance of models varies markedly across regions and seasons. While models generally show good skill in the tropical regions, their skill in extra‐tropical regions is markedly lower. Elevated precipitation thresholds (i.e., higher intensity indices) correlate with heightened model biases, indicating deficiencies in modeling severe precipitation events. Our analysis using an impact‐based framework highlights the superior predictive capabilities of the UK‐Met and Météo‐France models in capturing the underlying processes that drive precipitation events, or lack thereof, across many regions and seasons. Other models exhibit strong performance in specific regions and/or seasons, but not globally. These results advance our understanding of an impact‐based framework in capturing a broad spectrum of extreme weather and climatic events, and inform strategic amalgamation of diverse models across different regions and seasons, thereby offering valuable insights for disaster management and risk analysis.

Detection of Hepatovirus A (HAV) in wastewater indicates widespread national distribution and association with socioeconomic indicators of vulnerability.

Wastewater-based epidemiology, which seeks to assess disease occurrence in communities through measurements of infectious disease biomarkers in wastewater, may represent a valuable tool for understanding the occurrence of hepatitis A infections in communities. In this study, we measured concentrations of Hepatovirus A (HAV) RNA, in samples from 191 wastewater treatment plants spanning 40 US states and the District of Columbia from September 2023 to June 2024 and compared the measurements with traditional measures of disease occurrence. Nationally, 13.76% of the 21,079 wastewater samples were positive for HAV RNA, and both concentrations and positivity rates were associated with NNDSS hepatitis A case data nationally (Kendall rank correlation coefficient = 0.20, concentrations; and 0.33, positivity rate; both P < 0.05). We further demonstrated that higher rates of wastewater HAV detection were positively associated with socioeconomic indicators of vulnerability including homelessness and drug overdose deaths (both P < 0.0001). Areas with above average levels of homelessness were 48% more likely to have HAV wastewater detections, while areas with above average levels of drug overdose deaths were 14% more likely to have HAV wastewater detections. Using more granular case data, we present a case study in the state of Maine that reinforces these results and suggests a potential lead time for wastewater over clinical case detection and exposure events. The ability to detect HAV RNA in wastewater before clinical cases emerge could allow public health officials to implement targeted interventions like vaccination campaigns.IMPORTANCEDespite the existence of a highly effective vaccine for hepatitis A, outbreaks in vulnerable populations remain common. The disease can be asymptomatic or subclinical, and disproportionately impacts populations with inadequate access to healthcare, leading to a severe underestimation of the occurrence of this viral infection. This study investigates the potential for wastewater measurements of biomarkers of the causative agent of hepatitis A (HAV RNA) to provide insights into disease occurrence. Results highlight the potential for wastewater-based epidemiology to be a complementary tool to traditional surveillance for monitoring and controlling HAV transmission.

Assessing on-site plastics fragmentation: Linking macroplastics litter to microplastics pollution in vegetable fields

This study proposes a novel approach to determine whether farmers act as a pollution source of microplastics through macroplastic mismanagement or if they are temporary hosts of in-transit microplastics. Using a case-study approach, it was hypothesized that 90 % of soil microplastics would trace back to macroplastic waste mismanaged by farmers. To determine the amount of soil microplastics, linear transects were established and microplastic litter was counted and classified. Additionally, composite soil samples were used to count and colour-classify microplastics. The Kendall correlation was used to differentiate between the colour ranks of macro- and microplastics to discriminate between plastic on-site fragmentation or exogenous plastic pollution. For this, 37 vegetable farms in central Chile were assessed after the growing season. Farms showed lower microplastic counts than previously reported for similar productive conditions (3.68 ± 3.28 u/kg). A median colour correlation of 0.43 (IQR = 0.18–0.55) was observed between macro- and microplastics. Only in 25 % of the studied fields did on-site plastic fragmentation predominate over exogenous plastic pollution. The applied approach is simple to implement and does not require specialized laboratory equipment. We hope that the proposed methodology will serve in environmental monitoring to evaluate adherence to good agricultural practices and to guide pollution-prevention measures.

Exploring Patient Empowerment in Major Depressive Disorder: Correlations of Trust, Active Role in Shared Decision-Making, and Symptomatology in a Sample of Italian Patients.

Background/Objectives: Empowerment in medicine and psychiatry involves patients gaining control over health-related decisions, improving treatment adherence, outcomes, and satisfaction. This concept is especially significant in psychiatric care due to the complex challenges of mental health conditions, including stigma and impairment of emotional and cognitive functioning. We aim to investigate the correlations between patient trust, decision-making involvement, symptom severity, and perceived empowerment among individuals with Major Depression. Methods: Patients with Major Depressive Disorder were recruited in the "Policlinico G. Rodolico" psychiatry outpatient clinic from November 2022 to June 2023. Inclusion criteria: ages 18-65, ability to consent, stable condition, psychiatric medication history, and recent consultation. Exclusion criteria: psychotic features, bipolar disorder, substance abuse, high suicide risk, and severe comorbidities. Measures included the User Scale for Measuring Empowerment in Mental Health Services (SESM), Trust in Oncologist Scale (TiOS), Clinical Decision-Making Style for Patients (CDMS-P), and Hamilton Depression Rating Scale (HAM-D). Analysis used Kendall's Tau correlation and Two-One-Sided Tests procedure. Results: Seventy-three patients completed the study. No relationship was found between decision-making involvement and perceived empowerment (τ = -0.0625; p = 0.448), or between trust in psychiatrists and empowerment (τ = 0.0747; p = 0.364). An inverse correlation existed between patient involvement in therapy management and trust (τ = -0.2505; p = 0.002). Depression severity inversely correlated with empowerment (τ = -0.2762; p = <.001), but not with trust or decision-making involvement. Conclusions: The lack of significant correlations suggests that decision-making involvement and trust alone may not suffice to enhance empowerment. Trust may encourage patient passivity, while skepticism might drive active involvement. Higher empowerment is associated with less depressive symptoms, highlighting its potential connection with patient outcomes.