Kendall's Tau Research Articles

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.

Prediction of Capillary Pressure Curves Based on Particle Size Using Machine Learning

Capillary pressure curves are usually obtained through mercury injection experiments, which are mainly used to characterize pore structures. However, mercury injection experiments have many limitations, such as operation danger, a long experiment period, and great damage to the sample. Therefore, researchers have tried to predict capillary pressure data based on NMR data, but NMR data are expensive and unstable to obtain. This study aims to accurately predict capillary pressure curves. Based on rock particle size data, various machine learning methods, such as traditional machine learning and artificial neural networks, are used to build prediction models and predict different types of capillary pressure curves, aiming at studying the best prediction algorithm. In addition, through adjusting the amount of particle size characteristic data, the best amount of particle size characteristic data is explored. The results show that three correlation coefficients of the four optimal algorithms can reach more than 0.92, and the best performance is obtained using the Levenberg–Marquardt method. The prediction performance of this algorithm is excellent, with the three correlation coefficients being all higher than 0.96 and the root mean square error being only 5.866. When partial particle size characteristics are selected, the training performance is gradually improved with an increase in the amount of feature data, but it is far less than the performance of using all the features. When the interpolation increases the particle size characteristics, the best performance is achieved when the feature data volume is 50 groups and the root mean square error is the smallest, but the Kendall correlation coefficient decreases. This study provides a new way to obtain capillary pressure data accurately.

Diagnostic Approaches of Palatogingival Groove: A Systematic Review

Palatogingival Grooves (PGG) constitute developmental anatomical irregularities primarily situated along the palatal aspects of the roots of maxillary incisors. Literature is sparse on diagnostic approaches of PGG. Our study aimed to alleviate clinicians the process of recognizing anomaly. Systematic electronic searches of scientific literature without publication date or language restriction were undertaken in 2023.11.03 research was conducted in PubMed, Willey Online Library, Science Direct and Medline Ovid. Using specific keywords 54 articles with 67 cases were included in our study according to inclusion and exclusion criteria. Pre-made Cochrane data extraction templates were used for frequency evaluation, nonparametric Spearman’s, Kendall rank correlation coefficients, Mann-Whitney U Test and Odds ratio was assessed by statistic to evaluate significant correlations. Most quantitively expressed symptoms of PGG included increased tooth mobility, appearance of purulent discharge, sinus tract and periodontal pocket with an average mean of 9,14 ± 2,30 mm along with painful vertical per-cussion. However, statistical assessment showed inverse correlation between in-creased mobility and painful percussion. Furthermore, quantitively, deeper perio-dontal pockets were accompanied by more prominent gingival alterations, however only one opposing significant correlation was noted between the depth of per-iodontal pocket and existence of additional root. Frequency evaluation revealed that IPR was the main diagnostic tool used in majority of cases. Deeper periodontal pocket was assessed by conjunction of two or three tools. CBCT was used most with deeper periodontal pocket averaging 9,75 ± 1,02 mm. In conclusion, quantita-tive data suggest that periodontal pockets averaging 9,14 ± 2,30 mm, I or II-degree mobility, painful percussion and purulent discharge are the main PGG characteristics, with sparce significant correlations between them. IPR was noted as the main roentgenological modality as should be used firstly when suspecting PGG, yet both quantitative and statistical data suggest that CBCT is ought to be used to alleviate the assessment.

A Risk Tool for Evaluating Overuse Injury and Return-to-Play Time Periods in Youth and Collegiate Athletes: Preliminary Study.

Overuse injuries in youth athletes are associated with risks, including sports specialization, biological maturation, female sex, and workload measures. As no assessment tool exists to evaluate risk accumulation, we developed a novel risk factor scoring system (Sport Training Assessment of Risk [STAR]) to assess participants' risk of overuse injury and explore association with return-to-play (RTP) time periods. (1) STAR will reach an acceptable predictive threshold in the assessment of overuse injury in youth athletes. (2) Higher STAR scores will be associated with increased RTP time periods after injury. Longitudinal cohort study. Level 3. Youth athletes with an injury sustained during competitive sport completed questionnaires. Association of questionnaire variables with injury risk type was evaluated via logistic regression analyses, and unweighted and weighted versions of a total risk score were developed. RTP was defined by physician clearance per electronic medical record review. Mantel-Haenszel chi-square tests and Kendall's rank correlation coefficients were used to assess the relationship between weighted total risk score and RTP time periods. The weighted STAR model was analyzed with receiver operating characteristic (ROC) curves. The weighted STAR model trended toward an acceptable level of prediction for overuse (nonserious + serious) injury (area under the ROC curve [AUC], 0.66; 95% CI, 0.61-0.71), but was less predictive for serious overuse injury (AUC, 0.63; 95% CI, 0.55-0.71). Weighted total risk score was weakly associated with return to full play (ρ = 0.11; P < 0.01), and potentially with return to modified play (ρ = -0.08; P = 0.04). STAR may be a feasible tool for assessing overuse injury risk and RTP time periods in youth athletes but requires further development, as it did not reach an acceptable predictive threshold in this preliminary study. Clinicians can use STAR to assess overuse injury risk in youth athletes.

The Relationship between Traditional Acoustic Measures and Cepstral Analysis of Voice.

In this study, the correlations between traditional acoustic measures (TAMs) and cepstral analysis (CA) were explored in Persian. This investigation was a cross-sectional study including 179 dysphonic (n=141) and normophonic (n=38) speakers. The TAMs (jitter, shimmer, and noise-to-harmonic ratio) and CA (cepstral peak prominence, and cepstral peak prominence-smoothed) values were obtained during vowel prolongation, reading a standard sentence, and a non-standard running speech sample using Praat software. The difference of acoustic measures between normophonic and dysphonic speakers and intercorrelation among acoustic measures and correlation between the acoustic measures and perceived dysphonia levels was analyzed with independent t test, Mann-Whitney U test, Pearson, Spearman, and Kendall's Tau-b correlation tests using IBM SPSS Statistics. The findings showed that dysphonic speakers had higher TAM values and lower CA values than normophonic speakers. (P &lt; 0.05). In dysphonic speakers, a large correlation was discovered among all acoustic measurements (r = 0.52 to 0.96; P &lt; 0.05), while in various perceived dysphonic speakers, there was a correlation of varying strength (r = 0.25 to 0.97; P &lt; 0.05). Ultimately, there was a significant small-to-large correlation between the acoustic measures and perceived dysphonia levels (r = 0.34-0.58; P &lt; 0.05). This research demonstrated that Persian speakers with dysphonia experienced a rise in TAM and a corresponding reduction in CA. In the future, multi-parametric indices can be developed using both TAM and CA to include various aspects of vocal production and yield a single, comprehensive value.

Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.

Studies have shown that patients have difficulty understanding medical jargon in electronic health record (EHR) notes, particularly patients with low health literacy. In creating the NoteAid dictionary of medical jargon for patients, a panel of medical experts selected terms they perceived as needing definitions for patients. This study aims to determine whether experts and laypeople agree on what constitutes medical jargon. Using an observational study design, we compared the ability of medical experts and laypeople to identify medical jargon in EHR notes. The laypeople were recruited from Amazon Mechanical Turk. Participants were shown 20 sentences from EHR notes, which contained 325 potential jargon terms as identified by the medical experts. We collected demographic information about the laypeople's age, sex, race or ethnicity, education, native language, and health literacy. Health literacy was measured with the Single Item Literacy Screener. Our evaluation metrics were the proportion of terms rated as jargon, sensitivity, specificity, Fleiss κ for agreement among medical experts and among laypeople, and the Kendall rank correlation statistic between the medical experts and laypeople. We performed subgroup analyses by layperson characteristics. We fit a beta regression model with a logit link to examine the association between layperson characteristics and whether a term was classified as jargon. The average proportion of terms identified as jargon by the medical experts was 59% (1150/1950, 95% CI 56.1%-61.8%), and the average proportion of terms identified as jargon by the laypeople overall was 25.6% (22,480/87,750, 95% CI 25%-26.2%). There was good agreement among medical experts (Fleiss κ=0.781, 95% CI 0.753-0.809) and fair agreement among laypeople (Fleiss κ=0.590, 95% CI 0.589-0.591). The beta regression model had a pseudo-R2 of 0.071, indicating that demographic characteristics explained very little of the variability in the proportion of terms identified as jargon by laypeople. Using laypeople's identification of jargon as the gold standard, the medical experts had high sensitivity (91.7%, 95% CI 90.1%-93.3%) and specificity (88.2%, 95% CI 86%-90.5%) in identifying jargon terms. To ensure coverage of possible jargon terms, the medical experts were loose in selecting terms for inclusion. Fair agreement among laypersons shows that this is needed, as there is a variety of opinions among laypersons about what is considered jargon. We showed that medical experts could accurately identify jargon terms for annotation that would be useful for laypeople.

Determination of Factors Affecting Farmers' Preference for Corn Seed by Conjoint Analysis

Modeling consumer preferences among multi-attribute alternatives has been one of the main activities in consumer research. Conjoint analysis is a multivariate statistical analysis technique that reveals the acceptance of criteria such as yield, price and adaptability that are important in consumers' usage habits and preferences. In this study, it was aimed to determine the degree of importance given by grain corn growers to seed traits in corn seed purchase. For this purpose, conjoint analysis was applied and preferences in seed purchase were determined according to the data obtained from the survey conducted among farmers. The survey questions used in the research were applied to 123 farmers. Kendall's Tau value, which is the representativeness of the model, was 0.935 (p

Automated differentiation of calcifications and their clusters on the mammography image: the outcomes of the computer aided diagnosis system module

INTRODUCTION: Previously we developed the computer aided detection system (CAD) for mammography MammCheck II that increased the detection rate of small and difficult to detect breast carcinomas (BC). However this system was not specifically designed for calcification detection and discrimination. On the other hand, the calcifications had no influence on the CAD capability to detect BCs that appeared as a focal lesions.OBJECTIVE: To develop the approach for automated differentiation of benign and suspicious calcifications on the mammography images and assess its clinical value.MATERIALS AND METHODS: For the developed software testing we used a set of 390 mammography images with calcifications of all possible types (278 images with benign and 112 images with suspicious calcifications). For classification we used linear support vector machine (SVM) model, that was trained on the set of 126 images (70 — benign and 56 — suspicious). We developed two SVM models: with no vascular calcification analysis and with it. Statistics: for comparison between the normally distributed samples we used the Student’s T-test, for non-normally distributed — Wilcoxon signed-rank or Chi-square tests. For correlation testing of normally distributed samples the Paerson coefficient was calculated, for non-normally distributed samples — the Spearman or Kendall correlation coefficients. The statistical significance corresponded to Р-values &lt;0,05.RESULTS: During the testing of the first model version with no vascular calcification analysis we discovered the similarity of small early vascular calcifications and the suspicious ones. As a result this model falsely classified 14 of 23 (60.87%) vascular calcification clusters as suspicious. Therefore the model was improved. The final discrimination results for all calcification types (both benign and suspicious) obtained with the help of improved model were the following: true positive conclusions — 375/390 (96.15%), false positive conclusions — 15/390 (3.84%). In both cases when suspicious calcifications were classified as benign the wrong results were een only on one mammography view. At the same time, on another view the suspicious calcifications were correctly classified.DISCUSSION: During the CAD development it seems important not only mark the suspicious areas but also suppress false positive markings corresponding to the obviously benign lesions. However it is important during this operation not to suppress the true positive markings. Therefore such systems are inevitably characterized by a certain shift to decreased prognostic value of suspicious markings at the expense of the highest possible prognostic value of benign markings. In our viewpoint, the developed approach meets this requirement. Moreover, its integration into the CAD allows to suppress the markings of soft tissue lesions associated with typical benign calcifications, appeared on the previous processing steps. This capability may decrease the false positive rate of the main CAD module.CONCLUSION: The developed approach to benign and suspicious calcification discrimination (version with vascular calcification analysis) on the mammography image provided the sensitivity — 98.21%, specificity — 95.32%, negative predictive value (benign marking) — 99.25%, positive predictive value (suspicious marking) — 89.43%.

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-to-quantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within the data but also integrates expert knowledge, achieving accurate evaluation of drilling risks.

Perilymphatic fistula: a historical overview of publication trends

BackgroundPerilymph fistula (PLF) can cause symptoms of dizziness, vertigo, and fluctuating hearing. We hypothesized that publications on PLF have decreased in recent years relative to other inner ear disorders with overlapping symptoms.MethodsWe performed a Scopus search using the terms “perilymphatic fistula” OR “perilymph fistula,” limited to original studies or reviews involving human subjects published in English before 2022. We noted the senior author's institution, country affiliation, and publishing journal. The Kendall rank correlation coefficient test was used to analyze the trend of each variable over the past 30 years. Using the same search strategy, we compared these data to those from vestibular neuritis (VN), Meniere's disease (MD), superior semicircular canal dehiscence (SSCD), and benign paroxysmal positional vertigo (BPPV).ResultsSix hundred and ninety-eight PLF publications were returned. The top publishing country was the United States (n = 334), the top publishing journal was the American Journal of Otology/Otology and Neurotology (n = 68), and the top publishing institution was the University of Pittsburgh (n = 15). In the past 30 years (n = 501), there was no trend in the frequency of annual PLF publications (τ = −0.150, p = 0.265). Still there were positive trends in publications on vestibular neuritis (τ = 0.724, p &amp;lt; 0.001), Meniere's disease (τ = 0.587, p &amp;lt; 0.001), superior canal dehiscence syndrome (τ = 0.840, p &amp;lt; 0.001), and benign paroxysmal positional vertigo (τ = 0.882, p &amp;lt; 0.001) 5,398 PLF cases were identified, of which 4,356 specified the etiology; the majority identified an inciting insult (74.8%, n = 3,257).ConclusionsThe absence of diagnostic criteria for PLF may hinder its appeal to researchers as similar syndromes have had diagnostic consensus documents set forth. Given the greater number of cases with inciting insults, efforts to define a diagnostic criterion should focus on this subtype.

Effect of asymptomatic intervertebral flexion patterns on lumbar disc pressure: A finite element analysis study.

Movement patterns may be a factor for manipulating the lumbar load, although little information is yet available in the literature about the relationship between this variable and intervertebral disc pressure (IDP). A finite element model of the lumbar spine (49-year-old asymptomatic female) was used to simulate intervertebral movements (L2-L5) of 127 asymptomatic participants. The data from participants that at least completed a simulation of lumbar vertebral movement during the first 53% of a movement cycle (flexion phase) were used for further analyses. Then, for each vertebral angular motion curve with constant spatial peaks, different temporal patterns were simulated in two stages: (1) in lumbar pattern exchange (LPE), each vertebral angle was simulated by the corresponding vertebrae of other participants data; (2) in vertebral pattern exchange (VPE), vertebral angles were simulated by each other. The k-mean algorithm was used to cluster two groups of variables; peak and cumulative IDP, in both stages of simulations (i.e., LPE and VPE). In the second stage of the simulation (VPE), Kendall's tau was utilized to consider the relationship between different temporal patterns and IDPs for each individual lumbar level. Cluster analyses showed that the temporal movement pattern did not exhibit any effect on the peak IDP while the cumulative IDP changed significantly for some patterns. Earlier involvement in lumbar motion at any level led to higher IDP in the majority of simulations. There is therefore a possibility of manipulating lumbar IDP by changing the temporal pattern with the same ROM, in which optimal distribution of the loads among lumbar levels may be applied as preventive or treatment interventions. Evaluating load benefits, such as load, on biomechanically relevant lumbar levels, dynamically measured by quantitative fluoroscopy, may help inform interventional exercises.

Research Attitude, Achievement Motivation, and Self-Efficacy Among Post-Graduate Students Specializing in Primary Education

This study investigates the research attitude, achievement motivation, and self-efficacy concepts among post-graduate students specializing in primary education. The research aims to understand the interplay between these constructs and their implications for academic success and research engagement. A quantitative research design was employed, and data were collected using structured questionnaires from a sample of 62 post-graduate students. Descriptive statistics, and correlation analyses were conducted to examine the relationships between research attitude, achievement motivation, and self-efficacy. Statistical analyses showed non-significant correlations: between research attitude and achievement motivation (Kendall's tau_b = -.048, p = .881; Spearman's rho = -.036, p = .939), achievement motivation and self-efficacy (Kendall's tau_b = -.109, p = .708; Spearman's rho = -.204, p = .629), and research attitude and self-efficacy (Kendall's tau_b = .293, p = .362; Spearman's rho = .360, p = .427) among post-graduate students. The findings contribute to a better understanding of the factors influencing post-graduate students' research experiences and offer insights for academic institutions to enhance support mechanisms for student researchers. The implications of the study underscore the importance of fostering positive research attitudes, strengthening achievement motivation, and enhancing self-efficacy among post-graduate students specializing in primary education. Targeted interventions can optimize academic support and promote professional development in research-intensive environments

Aggressive Behaviour Risk Assessment Tool for Hospitalised Patients in Non-Psychiatric Inpatient Units.

To refine and validate an electronic version of the Aggressive Behaviour Risk Assessment Tool (ABRAT) and determine the sensitivity and specificity for identifying potentially violent patients in non-psychiatric inpatient units. A prospective cohort study design was used. All patients admitted or transferred to three inpatient units of an acute care hospital in Nebraska, USA, from 7 February to 9 April 2023, were included. The 10-item ABRAT assessments were performed daily for the first 3 days of admission. The violent events were collected until discharge in three categories: Physical aggression towards others, physical aggression towards property and verbal intimidation/threat towards others. Kendall's tau tests and a multivariate logistic regression procedure were performed to select a parsimonious set of items that best predict violent events. Of 1179 patients, 69 had ≥1 violent event (5.9%). The revised six-item tool with item weighting was named ABRAT for Hospitalised Patients (ABRAT-H). The area under the curve from the Receiver Operating Characteristics analysis was 0.82. The sensitivity and specificity at a cutoff score of two were 68.1% and 85.2%, respectively. As ABRAT-H scores increased, the percentage of violent patients also increased and for patients with scores ≥5, 55.2% became violent. ABRAT-H appears to be useful for identifying potentially violent patients in non-psychiatric inpatient units with satisfactory sensitivity and specificity. The availability of ABRAT-H may help provide focused preventive measures that target patients at high risk for violence and reduce violent events. A majority of the nursing workforce is employed in acute care hospital setting, and the availability of ABRAT-H can further enhance the culture of a safe work environment and have positive impacts not only on the nurses' physical and mental health but also on the quality of patient care. We have adhered to relevant STROBE guidelines for reporting observational studies. No Patient or Public Contribution.

The Relationship between Social Support and Self-Esteem with Academic Resilience of Junior High School Students

This study aims to examine the relationship between social support and self-esteem and academic resilience in junior high school (SMP) students. Academic resilience is a student's ability to endure and adapt to academic challenges and stress, which is important to their academic success. Social support and self-esteem are thought to play an important role in increasing students' academic resilience. This research uses quantitative methods with a correlational design. The research subjects were junior high school students at several schools in Krian, totaling 351 students. The research instrument is a questionnaire that measures students' levels of social support, self-esteem and academic resilience. The data analysis used was Kendalls Tau B. The results of the study showed that there was no significant positive relationship between social support and students' academic resilience. In addition, self-esteem was also found to have a significant positive relationship with academic resilience. Students who have strong social support from family, friends and the school environment and have high self-esteem tend to have better academic resilience. The conclusion of this research is that social support and self-esteem play an important role in increasing the academic resilience of junior high school students. Therefore, it is important for schools and families to provide adequate support and build students' self-esteem to help them overcome academic challenges and achieve academic success.

If EQ-5D-5L Mobility Dimension Ratings Are High, Is Life-Space Assessment a Clinically Sensible Next Step? Data from a Population Survey.

Background: Impaired health states can limit a person's mobility, often progressively for people with life-limiting illnesses. Quantifying mobility changes is crucial for individual clinical care and service planning. Objective: To explore any correlation between EQ-5D-5L's mobility dimension ratings and Life-Space Assessment (LSA) from a population sample. Methods: An online population survey of Australian adults, nationally-representative by key demographics. An analysis of variance examined each level of the EQ-5D-5L mobility dimension rating against its LSA scores; Kendall's Tau assessed correlation. Results: Participants (n = 6366) were 53% women, mean age 46.1 years (SD 18.6), and mean LSA score 78.0 (SD = 27.5; possible range 0-120). At each EQ-5D-5L mobility dimension level there was a significant difference between LSA scores (p < 0.001), and a moderate negative correlation (Kendall's tau b = -0.342) between the two measures. Conclusion: Given the relationship defined, EQ-5D-5L mobility dimension ratings may prompt clinicians to consider further evaluation with the more detailed Life-Space Assessment. .

Microbial source tracking and identification of fecal contamination patterns in saltwater estuaries between base- and storm-flow events using dual genome-based approaches

Fecal contamination from natural and anthropogenic sources poses significant threats to saltwater estuaries, particularly after storms or heavy rainfall. Monitoring fecal contamination is essential for protecting these vulnerable ecosystems having important ecological and economic values. In this study, we investigated the abundance, sources, and potential causes of fecal contamination at three marine and seven freshwater stations across Vaughn Bay (WA, USA), a shellfish growing district, during base- and storm-flow events. Additionally, we evaluated the performance of fecal indicator bacteria (FIB) quantification, optical brightener assessment, and qPCR analysis for fecal contamination quantification. We compared the effectiveness of qPCR-based microbial source tracking (MST), which targeted a broad range of hosts including, such as humans, birds, cows, horses, ruminants, dogs, and pigs, with sequencing-based MST in identifying fecal contamination sources. Both MST analysis approaches identified birds and humans as the primary sources of fecal contamination. For marine water stations, freshwater creeks VBU001, VBU002, and VB047, along with drain VB007, were identified as the main sources of human-derived fecal contamination in Vaughn Bay, based on Kendall's tau analysis (τ: 0.58–0.97). This information indicates that the septic systems in the catchment areas of these creeks and drains require further investigation to achieve effective fecal contamination control. Optical brightener, FIB enumeration and qPCR quantification results were generally higher during storm-flow events, although they showed poor correlation with each other (Pearson r < 0.40), likely due to physiological and phylogenetic differences among the target organisms of these methods. However, the sequencing-based method faces challenges in precise quantitative identification of differences in fecal contamination between base- and storm-flow events. Due to its high-throughput and cost-effectiveness, we recommend using sequencing-based analysis for large-scale identification of the primary sources of fecal contamination in water environments, followed by targeted qPCR quantification of MST markers for more precise assessments.

Hierarchy and mobility of Latin America and Caribbean container ports

Research on port hierarchies within a maritime region is crucial for comprehending the dynamics of seaborne trade and the broader trends within the maritime sector and related supply chains. This study investigates the evolution of the Latin America and the Caribbean (LAC) container ports and regional sub-systems from 2000 to 2022, analyzing traffic dynamics, shifts in port throughput rankings, and market concentration trends in and among the different coastal regions. Conceptually, this analysis benefits from the notion of evolutionary phases, revealing the stages of a port system's development. Methodologically, 49 LAC ports of international interest are categorized into different coastal regions and tiers based on throughput sizes. Various metrics, including the Gini coefficient, Lorenz curve, Herfindahl-Hirschman Index (HHI), and Kendall Tau (τ), assess port hierarchy and related mobility since 2000. The observed shifts in the hierarchy and market structures, coupled with the fluctuating positions of critical ports, underscore the transition that LAC container ports dynamics experienced in the 21st century. Following a particularly dynamic and volatile evolution observed in the 2000s, the financial crisis of 2008/09 was a critical juncture leading to the transition of the LAC port dynamics from the ‘emerging’ to the ‘maturity’ phase. On the contrary, the pandemic (2019) has not affected the pre-existing hierarchy or the moderate market concentration that was already in progress. The LAC region comprises port sub-systems (coastal ranges) subjected to different tendencies towards (de)concentration, yet mobility in port rankings within each sub-system decreases. The study also reveals the coexistence of this transition with a structural transformation of port governance structures, i.e., granting of concessions of operations to third parties. In 20 of these ports and the above-average mobility of ports are operated by Global Terminal Operators (GTOs), and calls for further research on the (non) causality of these phenomena.

A novel and effective method for characterizing time series correlations based on martingale difference correlation.

Analysis of correlation between time series is an essential step for complex system studies and dynamical characteristics extractions. Martingale difference correlation (MDC) theory is mainly concerned with the correlation of conditional mean values between response variables and predictor variables. It is the generalization and deepening of the Pearson correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and other statistics. In this paper, on the basis of phase space reconstruction, the generalized dependence index (GDI) is proposed by using MDC and martingale difference divergence matrix theories, which can measure the degree of dependence between time series more effectively. Moreover, motivated by the theoretical framework of the refined distance correlation method, the corresponding dependence measure (DE) is employed in this paper to construct the DE-GDI plane, so as to comprehensively and intuitively distinguish different types of data and deeply explore the operating mechanism behind the relevant time series and complex systems. According to the performances tested by the different simulated and real-world data, our proposed method performs relatively reasonably and reliably in dependence measuring and data distinguishing. The proposal of this complex data clustering method can not only recognize the features of complex systems but also distinguish them effectively so as to acquire more relevant detailed information.

Estimating Students' Academic Success in the Preclinical Stage of Undergraduate Medical Education Using the Admission Test Approach.

Medical schools face substantial challenges in objectively selecting the best applicants, and the admission process can impact medical students' academic performance. This study aimed to estimate the students' academic success in the preclinical stage of undergraduate medical education using admission tests. This cross-sectional study was conducted on 1,193 students' records from the 2014 to 2019 cohorts. The students' admission data comprised the cohort, sex, admission track, psychological test, and academic tests. The academic success was based on the student's end-year academic evaluation. Data were analyzed using contingency and Kendall's tau b tests with IBM® SPSS® Statistics version 16.0 for Windows. Most of the 1,193 preclinical medical students' records included in the study were females (68.1%), from the regular admission track (78.5%), from the considered psychology test category (52.8%), and had an academic admission test of less than or equal to the median. (51.6%). Most students (89.7%) met all the academic requirements to pass the end-year evaluation. The bivariate analyses showed significant correlations between academic success and cohort (p<0.001), psychology test (p=0.005), and academic test (p<0.001). The analyses showed no significant correlation between academic success and sex (p=0.324), and admission track (p=0.128). This study indicated that cohort and psychology tests could estimate the student's academic success at the preclinical stage of undergraduate medical education. The admission criteria related to the academic tests during the admission process should be re-evaluated, so that the academic tests could select the best students among the applicants.