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Why you should read this article: • To understand how the permutation test can be used to test null hypothesis • To appreciate how much easier it is to understand than significance tests based on t statistics • To illustrate how the permutation test requires fewer assumptions than t-tests. Background: Quantitative researchers can use permutation tests to conduct null hypothesis significance testing without resorting to complicated distribution theory. A permutation test can reach conclusions in hypothesis testing that are the same as those of better-known tests such as the t-test but is much easier to understand and implement. Aim: To introduce and explain permutation tests using two real examples of independent and dependent t-tests and their corresponding permutation tests. Discussion: This article traces the history of permutation tests, explains the possible reason for their absence in textbooks and offers a simple example of their implementation. It provides simple code written in the R programming language to generate the null distributions and P-values for the permutation tests. Conclusion: Permutation tests do not require the strict model assumptions of t-tests and can be robust alternatives. Implications for practice: Permutation tests are a useful addition to practitioners’ research repertoire for testing hypotheses. [ABSTRACT FROM AUTHOR]

• Distractions caused by fellow drivers (DFDs) have received little attention. • Associations between DFDs and other latent factors were evaluated using SEM. • DFDs are positively associated with stress among recipient drivers. • Stress is positively associated with anxiety-based performance deficits. • DFDs and stress are positively associated with crash propensity. Introduction: Aggressive behavior of drivers is a source of crashes and high injury severity. Aggressive drivers are part of the driving environment, however, excessive aggressive driving by fellow drivers may take the attention of the recipient drivers away from the road resulting in distracted driving. Such external distractions caused by the aggressive and discourteous behavior of other road users have received limited attention. These distractions caused by fellow drivers (DFDs) may agitate recipient drivers and ultimately increase crash propensity. Aggressive driving behaviors are quite common in South Asia and, thus, it is necessary to determine their contribution to distractions and crash propensity. Method: Our study aimed to evaluate the effects of DFDs using primary data collected through a survey conducted in Lahore, Pakistan. A total of 801 complete responses were obtained. Various hypotheses were defined to explore the associations between the latent factors such as DFDs, anxiety/stress (AS), anxiety-based performance deficits (APD), hostile behavior (HB), acceptability of vehicle-related distractions (AVRD), and crash propensity (CP). Structural Equation Modeling (SEM) was employed as a multivariate statistical technique to test these hypotheses. Results: The results supported the hypothesis that DFDs lead to AS among recipient drivers. DFDs and AS were further found to have positive associations with APDs. Whereas, there was a significant negative association between DFD, AS, and AVRD. As hypothesized, DFD and AS had positive associations with CP, indicating that distractions caused by aggressive behaviors leads to stress and consequently enhances crash propensity. Practical applications: The results of this study provide a statistically sound foundation for further exploration of the distractions caused by the aggressive behaviors of fellow drivers. Further, the results of this study can be utilized by the relevant authorities to alter aggressive driving behaviors and reduce DFDs. [ABSTRACT FROM AUTHOR]

Introduction: Epilepsy is the most common neurological disorder among humans after headaches. According to the World Health Organization, approximately 50–65 million individuals were diagnosed with epilepsy throughout the world, and around two million new cases of epilepsy are added to this figure every year. Methods: Designed as descriptive and cross‐sectional research, this study was performed on 132 elementary school teachers. Training on epilepsy and epileptic seizure was given to teachers. The pretest and posttest research data were collected with the face‐to‐face interview method. In this process, the epilepsy knowledge scale was used as well as a survey form that had questions designed to find out about teachers' personal characteristics. The Statistical Package for Social Science 25.0 was utilized in the statistical analysis of research data. In the research, the statistical significance was identified if the p‐value was below.05 (p <.05). Results: Of all teachers participating in the study, 59.1% were female, 90.2% were married, and 47.7% witnessed an epilepsy seizure before. The mean of teachers' pretest epilepsy knowledge scores was 8.43 ± 4.31 points before the training while the mean of their posttest epilepsy knowledge scores was 12.65 ± 2.48 points after the training. The difference between the means of pretest and posttest scores was statistically significant (p =.000). After the training, there was a statistically significant increase in means of scores obtained by teachers from each item of the epilepsy knowledge scale (p <.05). Conclusions: As there was a statistically significant improvement in levels of teachers' knowledge about both epilepsy and epileptic seizure after the training, it is recommended that the training about the approach to epilepsy and epileptic seizure be given to all teachers, and additionally, including these topics in the course curricula of universities is recommended. [ABSTRACT FROM AUTHOR]

Introduction: Healthcare workforce diversity is essential for increasing access and reducing racial/ethnic health disparities. We examined the growth and trends in physician assistant/associate (PA) workforce demographic composition by initial year of certification. Methods: Drawing on data from the National Commission on Certification of Physician Assistants, we aggregated gender, age (1975-2020), race/ethnicity, and underrepresented in medicine (URiM) (2000-2020). Descriptive statistics of demographics were calculated by the initial certification year and assessed for trends using the Cochran-Armitage test. Results: Analyses revealed an 11.7% annual growth rate in PAs earning initial certification and a change in gender composition (23.9% to 74.2% female) from 1975 to 2020. Between 2000 and 2020, we observed significant increases (all p<0.001) in the proportions of PAs who self-identify as Asian (5.7 percentage points), Hispanic/Latino(a) (3.5 percentage points), and multiracial (2.4 percentage points). However, there was a decline (all p<0.001) in the proportions of PAs who self-identify as Black/African American (1.2 percentage points), American Indian/Alaska Native (0.1 percentage points), Native Hawaiian/Pacific Islander (0.1 percentage points), and other race (1.1 percentage points). Trend analyses revealed that the proportion of PAs identifying as URiM decreased over the past 21 years. Conclusion: Ongoing assessment of the PA workforce demographics is essential to track the progress and effectiveness of diversification initiatives. [ABSTRACT FROM AUTHOR]

The study concentrates on two different genetic programming approaches for determining passenger car equivalent (PCE) values and observing the impact on capacity estimation at urban unsignalized intersections. Considering heterogeneous traffic conditions, a new PCE value is introduced to encompass sustainable modes of public transit vehicles, specifically slow-moving three-wheelers (SM3W), commonly known as E-Rickshaws. Since PCE value is considered an important parameter for capacity calculations, the present study considered 14 unsignalized intersections located in Ranchi city of India. An automatic plate recognition system is employed to have the count of vehicular traffic. The methodologies include age-layered population structure genetic programming (ALPSGP), and the offspring selection genetic programming (OSGP) approach that incorporates static and dynamic variables. Based on the significance test and ranking of the genetic programming (GP) models, the OSGP model is recommended as the most appropriate model for heterogeneous traffic. Sensitivity analysis reported that lagging headway () is the most contributing factor in PCE estimation. The PCE value of SM3W is found to be 0.81 and that could be incorporated as a new classification of vehicles in Indo-HCM. It is observed that evaluated capacity based on PCE values of OSGP performed admirably in both normal and congested traffic situations. [ABSTRACT FROM AUTHOR]

This study concerns the management of operational risk in truck transport using the reliability theory of risks. In this regard, the risk analysis of changes in the vehicle unavailability represents an important topic. In this study, the authors present their own method for analysing the phase changes in risk corresponding to successive sections (phases) of vehicle mileage. The presented risk analysis method is based on an integrated assessment of losses associated with the costs of incidental repairs and losses caused by lost income during vehicle downtime. This includes the following: assessment of differences in average risk and differences in the coefficient of variation in the time series of vehicle mileage phases, indicating the outliers and extremes of phase risk, identifying their physical causes and testing the statistical significance of phase risk differences. The proposed method is described mathematically and verified experimentally based on the operational data concerning trucks from two selected brands (20 trucks from each brand). We show that the method can be used to predict the continuity of transport services in the short term (one-year horizon). The method could also be useful to compare vehicles of different brands in the context of their sensitivity to operational risks. [ABSTRACT FROM AUTHOR]

The material developed focuses on the analysis of energy efficiency trends in Poland, utilising ODEX indicators for the sectors of industry, transport, households and in general, from 2011 to 2021. The objective of the study is to assess the progress made in energy efficiency and to forecast future trends in these sectors. The methods employed are based on statistical modelling of time series, taking into account sector-specific energy consumption dynamics. The following techniques were employed: linear regression, cluster analysis to identify patterns of change, statistical hypothesis testing for energy efficiency and simplified autoregressive models. The results demonstrated significant improvements in energy efficiency in the industrial sector, stability of the ODEX indicator in the transport sector and gradual improvements in households and overall. The prediction indicates an upward trend in the ODEX indicator in the short term, suggesting an increase in energy demand. However, it also predicts a decline in the long term, which may indicate the effectiveness of future energy efficiency strategies and investments. Consequently, the necessity for continued efforts to increase energy efficiency and further research into the factors influencing energy efficiency in different economic sectors is emphasised. [ABSTRACT FROM AUTHOR]

Air pollution is a growing concern in today's urbanized world, necessitating efficient and accurate methods for air quality monitoring. The proliferation of IoT devices has led to a surge in the generation of time-series data. With its high volume and complexity, this surge in time-series data necessitates cloud-based solutions for handling and analyzing this data effectively. However, existing methods for air quality monitoring face challenges in capturing the complex patterns and dynamics of air pollution, which often exhibit both linear and nonlinear characteristics. Air pollution data often exhibit both linear and nonlinear characteristics. Linearity and nonlinearity refer to the nature of the relationships within the data. Some aspects of air quality, such as pollutant concentrations, may follow linear patterns, while other factors, like the interaction of multiple pollutants and environmental conditions, exhibit nonlinear relationships. This complexity arises from the multifaceted nature of air quality dynamics, which various factors and interactions can influence. To address these challenges, this study introduces a novel hybrid time-series approach that combines the proven strengths of two well-established techniques: traditional time-series autoregressive integrated moving average (ARIMA) and soft computing adaptive neuro-fuzzy inference system (ANFIS). The hybrid model is designed to provide a comprehensive solution that accommodates the diverse characteristics of air quality time-series data. To assess the efficacy of our proposed model, we conducted extensive experiments using real-world air pollution datasets obtained from the Ministry of Environment, Forest and Climate Change of India, covering the period from January 2015 to July 2020. Our evaluation includes a range of performance metrics such as root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared logarithmic error (MSLE). Specifically, our model demonstrates exceptional accuracy, with notably low error values for key metrics such as air quality index (AQI) and PM2.5. Furthermore, we subjected our innovative hybrid model to rigorous statistical testing using the Diebold-Mariano test, establishing the significance and superiority of our approach. This research advances our understanding of air quality prediction and offers a valuable solution for mitigating the detrimental effects of air pollution on public health and the environment. [ABSTRACT FROM AUTHOR]

The escalating process of urbanization has raised concerns about incidents arising from overcrowding, necessitating a deep understanding of large human crowd behavior and the development of effective crowd management strategies. This study employs computational methods to analyze real-world crowd behaviors, emphasizing self-organizing patterns. Notably, the intersection of two streams of individuals triggers the spontaneous emergence of striped patterns, validated through both simulations and live human experiments. Addressing a gap in computational methods for studying these patterns, previous research utilized the pattern-matching technique, employing the Nelder-Mead Simplex algorithm for fitting a two-dimensional sinusoidal function to pedestrian coordinates. This paper advances the pattern-matching procedure by introducing Simulated Annealing as the optimization algorithm and employing a two-dimensional square wave for data fitting. The amalgamation of Simulated Annealing and the square wave significantly enhances pattern fitting quality, validated through statistical hypothesis tests. The study concludes by outlining potential applications of this method across diverse scenarios. [ABSTRACT FROM AUTHOR]

Background: Misconceptions about male nurses not only exacerbate the gender imbalance in the nursing profession but also negatively impact male nurses embarking on their careers. Currently, no tool exists to measure the gender biases toward males in nursing among nursing students in China. Consequently, the primary objective of this study is to assess the validity and reliability of the Chinese translation of the Gender Misconceptions of Men in Nursing (GEMINI) scale among nursing students. Methods: This cross-sectional study involved 1,102 nursing students from China who participated online. We utilized the Brislin translation technique with a forward-backward approach. To determine the factor structure within the Men in Nursing Gender Misconceptions Scale's Chinese version, both exploratory (EFA) and confirmatory factor analysis (CFA) were applied. The scale's internal consistency was measured through the Cronbach's alpha coefficient, corrected item-total correlation, and a retest reliability assessment. Results: The scale showed a content validity index of 0.938 and a retest reliability of 0.844. EFA indicated a two-factor structure for the translated instrument. CFA revealed a chi-square/degree of freedom of 3.837, an incremental fit index (IFI) of 0.952, a goodness-of-fit index (GFI) of 0.910, a comparative fit index (CFI) of 0.952, and an RMSEA of 0.073, all of which were within acceptable limits. The scale's Cronbach's α was 0.953, and the corrected item-total correlations ranged between 0.539 and 0.838. Gender-based misconceptions about men in nursing among students appeared to be influenced by their gender and whether they considered a nursing program as their first choice when applying for a major. Misconceptions about male nurses are greater among men and those who do not consider nursing programs as their first choice when applying for a major. Conclusions: The Chinese adaptation of the GEMINI scale showcased high reliability and validity. It stands as a potential instrument to gauge gender misconceptions concerning male nurses among Chinese nursing students. [ABSTRACT FROM AUTHOR]

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments. [ABSTRACT FROM AUTHOR]

Objective: Return to work and permanence in employment of women who survive breast cancer are topics that become important as incidence and survival rates increase. Self-efficacy as a modifiable element is of special interest in this process. The objective of this study is to measure the level of self-efficacy in female breast cancer survivors, according to sociodemographic, work and treatment characteristics and their relationship with return to work. Methods: This was a cross-sectional study, based on a survey of breast cancer patients about their demographic and work characteristics, the return to work process, permanence in the same job and the level of work self-efficacy. Differences in the level of self-efficacy with respect to characteristics were evaluated using bivariate analyses and hypothesis statistical testing. A value of p<0.05 was considered statistically significant. Results: One hundred and twenty-four women were included, 87.9% had returned to work, the majority (56.4%) between one and six months after treatment, 67.7% remained in the same job. Higher levels of work self-efficacy were related to a higher probability of returning to work and staying there, and a shorter time to return to work; these differences were statistically significant. Conclusion: Occupational health and risk prevention services must consider and strengthen work self-efficacy and organizational support in breast cancer survivors to achieve a successful return to work [ABSTRACT FROM AUTHOR]

Objective The objective of this study was to describe the utilization of physical therapist and occupational therapist services after rotator cuff repair (RCR) and examine variation in rehabilitation characteristics by profession. Methods This retrospective cohort study used the IBM MarketScan Commercial Claims and Encounters database. Eligible patients were 18 to 64 years old and had undergone outpatient RCR between 2017 and 2020. Physical therapist and occupational therapist services were identified using evaluation and treatment codes with profession-specific modifiers ("GP" or "GO"). Factors predicting utilization of formal rehabilitation and physical therapist versus occupational therapist services were examined; and univariable and multivariable analyses of days to initiate therapy, number of visits, and episode length by profession were completed. Results Among 53,497 patients with an RCR, 81.2% initiated formal rehabilitation (93.8% physical therapist, 5.2% occupational therapist, 1.0% both services). Patients in the Northeast and West (vs the South) were less likely to receive rehabilitation (odds ratio [OR] = 0.67 to 0.70) and less likely to receive occupational therapist services (OR = 0.39). Patients living in the Midwest (versus the South) were less likely to receive rehabilitation (OR = 0.79) but more likely to receive occupational therapist services (OR = 1.51). Similarly, those living in a rural (versus urban) area were less likely to utilize rehabilitation (OR = 0.89) but more likely to receive occupational therapist services (OR = 2.21). Additionally, receiving occupational therapist instead of physical therapist services was associated with decreased therapist visits (−16.89%), days to initiate therapy (−13.43%), and episode length (−13.78%). Conclusion Most patients in our commercially insured cohort utilized rehabilitation services, with a small percentage receiving occupational therapist services. We identified profession-specific variation in utilization characteristics that warrants further examination to understand predictors and associated outcomes. Impact Variation in rehabilitation utilization after RCR, including profession-specific and regional differences, may indicate opportunities to improve standardization and quality of care. [ABSTRACT FROM AUTHOR]

Parkinsonism‐dystonia‐2 PKDYS2 is an autosomal‐recessive disorder, caused by pathogenic biallelic variants in SLC18A2 which encodes the vesicular monoamine transporter (VMAT2) protein. PKDYS2 is a treatable neurotransmitter disease, and the rate of diagnosis of this disorder has increased significantly with the advance of genomic technologies. Our report highlights a novel pathologic variant in one case and a novel finding on MRI Brain, consisting of a normal symmetrical signal intensity in the dorsal brainstem and pons, and it substantiates the significance of genetic testing in the evaluation of children with developmental delays, which influences clinical decisions to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Urolithiasis is one of the most common urological problems worldwide. The fastest treatment modality to achieve stone clearance of ureteral stones is Ureteroscopic Lithotripsy (URSL). However, it is negated by both the cost burden and potential risk to the patient. Medical Expulsive Therapy (MET) is a treatment option for upto 10 mm stones but has failure rates of 40-60% in the literature. Aim: To analyse the various indications for surgery, whether MET was used or not, if used-its details, operative findings at ureteroscopy including the reason for the failure of MET. Materials and Methods: A cross-sectional study consisting of 72 patients with ureteric calculi undergoing URSL was analysed from June 2017 to December 2018 at Mazumdar Shaw Medical Centre, Bangalore, Karnataka, India. Indications were assessed at the time of admission. During ureteroscopy, factors like impaction, distal obstruction, and unusual findings were studied, which could have contributed to the failure of MET. The Chi-square test was used as a test of significance for categorical data. The Analysis of Variance (ANOVA) test was used as a test of significance to identify the mean difference between continuous variables. The p-value of <0.05 was considered statistically significant. Results: The mean age in the study was 41.65±13.4 years (range 20-69 years), and the mean stone size was 10.03±3.34 mm. Large stone was the most common indication (41, 56.9%, p=0.004), followed by failed MET (35, 48.6%). Even though MET could have been continued for four weeks in 17 patients (23.6%), they were taken up for surgery. The impaction rate was 70.8% (51), with 48.6% (35) being large impacted stones and 22.2% (16) being small impacted stones. The overall stone clearance rate was 68 out of 72 (94.4%). Conclusion: Large stone size (=10 mm) and failed MET were the main indications for surgery. One reason for the failure of MET was not waiting for a duration of four weeks. During ureteroscopy, impaction of the stone, irrespective of size, was the most common finding and was the reason for the failure of MET. Ureteric stones on MET should not be neglected as there are reasons for the failure of MET, and they will require URSL after four weeks. [ABSTRACT FROM AUTHOR]

Background: Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. Results: In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods. Conclusions: GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer. [ABSTRACT FROM AUTHOR]

Aims: To compare and evaluate marginal and internal adaptation and to assess the degree of conversion of alkasite restoration in relation with other restorative materials. Methods: on the buccal surface of 25 maxillary premolars a class V cavities were prepared. The teeth were randomly divided into five groups (n=5) that restored as: Group 1: alkasite without adhesive, Group 2: alkasite with adhesive, Group 3: Nanohybrid composite, Group 4: Glass ionomer cement, and Group 5: Resin modified glass ionomer cement. The teeth underwent 5000 cycles of thermocycling between 5° and 55°. Then, silver nitrate solution was infiltrated and Micro-Computed tomography analysis was performed using (LOTUS inVivo). For evaluating degree of conversion, 15 specimens in the form of a disc were prepared from Alkasite, nanohybrid composite and resin modified glass ionomer (n=5). All samples were analyzed using (FTIR) in an ATR Mode in three intervals (after 20 second. 24 hours and 7 days). The data had been analyzed using the Kruskal-Wallis, Dune and Wilcoxon tests at a 0.05 significance level. Result: A significant difference in marginal and internal adaptation were observed among restorations (p < 0.05). Superior marginal and internal adaptation was obtained in this study for alkasite whether with or without bonded over nanohybrid composite. However, RMGIC and GIC show more adaptation values among other tested groups. Also degree of monomer conversion percentage varied among restorations at different intervals. Conclusion: alkasite restorations wither with or without bonding have higher marginal and internal adaptation in comparison with nanohybrid composite but lower than that of GIC and RMGIC. Also alkasite restoration showed higher degree of conversion when compared with nanohybride composite and RMGIC after 20 second and 24 hours of polymerization. [ABSTRACT FROM AUTHOR]

Cronbach's alpha, widely used for measuring reliability, often operates within studies with sample information, suffering insufficient sample sizes to have sufficient statistical power or precise estimation. To address this challenge and incorporate considerations of both confidence intervals and cost-effectiveness into statistical inferences, our study introduces a novel framework. This framework aims to determine the optimal configuration of measurements and subjects for Cronbach's alpha by integrating hypothesis testing and confidence intervals. We have developed two R Shiny apps capable of considering up to nine probabilities, which encompass width, validity, and/or rejection events. These apps facilitate obtaining the required number of measurements/subjects, either by minimizing overall cost for a desired probability or by maximizing probability for a predefined cost. [ABSTRACT FROM AUTHOR]

The Omicron variants BA.1, BA.2, and BA.5 caused several waves of SARS-CoV-2 in Germany in 2022. In this comparative study, public health data on SARS-CoV-2 infections from Mecklenburg-Western Pomerania, Germany, between January and October 2022 were examined retrospectively using Pearson's chi-squared tests and Fisher's exact tests for testing for statistical significance. Compared to BA.5 infections, BA.1 and BA.2 infections affected younger individuals aged up to 19 years significantly more often, whereas BA.5 infections occurred significantly more frequently in patients between 40 and 59 years of age when compared to BA.1 and BA.2. Infections with all three variants predominantly caused flu-like symptoms; nevertheless, there were significant differences between the reported symptoms of BA.1, BA.2, and BA.5 infections. Especially, the symptoms of 'fever', 'severe feeling of sickness', 'loss of taste', and 'loss of smell' were significantly more often present in patients with BA.5 infections compared to BA.1 and BA.2 cases. Additionally, BA.2 and BA.5 cases reported significantly more often the symptoms of 'runny nose' and 'cough' than BA.1-infected cases. Our findings indicate remarkable differences in the clinical presentations among the sub-lineages, especially in BA.5 infections. Furthermore, the study demonstrates a powerful tool to link epidemiological data with genetic data in order to investigate their potential impact on public health. [ABSTRACT FROM AUTHOR]

This study examines the relationships between state-provided paid-leave availability and enrollment in public health and nutrition programs (SNAP, Medicaid, WIC) among single low-income women following a birth in the U.S. We hypothesize that women in paid leave states will be less likely to participate in publicly available health and nutrition programs. Data are from the Survey of Income and Program Participation (SIPP), a nationally representative panel survey data set (N = 1168). Descriptive tests of significance and probit regression models are used to examine the relationship between paid-leave availability and participation in SNAP, Medicaid, and WIC following a birth. A descriptive analysis suggests significantly lower enrollment in SNAP but not Medicaid or WIC for single low-income women in paid-leave states compared to those in non-paid-leave states. The finding of significantly lower post-birth SNAP participation in paid-leave states holds in probit models that include potentially relevant mother, household, and state controls. [ABSTRACT FROM AUTHOR]

Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems. [ABSTRACT FROM AUTHOR]

Background: Cortico-Steroid Injections (CSI) are commonly used to treat patients with Greater Trochanteric Pain Syndrome (GTPS) but it is unclear which patients will experience improvements in pain. Objectives: To identify factors that influence improvements in pain for patients with GTPS treated with CSI. Design: Systematic review. Methods: A search was undertaken of AMED, CINAHL, Cochrane Library, EMBASE, Medline and PEDro databases. Studies were eligible for inclusion if they investigated factors that influenced changes in pain experienced by patients with GTPS who received a CSI. Studies needed to include relevant summary statistics and tests of clinical significance. Risk Of Bias in Non-randomised Trials Of Interventions (ROBINS-I) and Risk Of Bias 2 (ROB2) tools were used to assess bias. Results: The search identified 466 studies, 8 were included in the final review with a total of 643 participants. There was no association between demographic variables such as age, sex, symptom duration or obesity and pain outcomes post-CSI. Having a co-existing musculoskeletal (MSK) condition such as knee osteoarthritis or sacroiliac/lumbar spine pain was associated with less pain reduction post-CSI. Injections into the Trochanteric Bursa were associated with longer lasting pain reduction than Gluteus Medius Bursa or extra-bursal injections. Image guidance of CSI maintained lower pain scores at six months but did not increase the duration of the therapeutic effect past six months. The presence of specific ultrasound scan features was not associated with differences in pain scores. Conclusions: Patients with co-existing MSK conditions may not respond to CSI as well as those without. Injections into the Greater Trochanteric Bursa may have longer lasting benefit. Further research is needed on the use of USS imaging findings and image guidance. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a common neurodegenerative disorder with a prolonged prodromal phase. Higher urinary bis(monoacylglycerol)phosphate (BMP) levels associate with LRRK2 (leucine-rich repeat kinase 2) and GBA1 (glucocerebrosidase) mutations, and are considered as potential noninvasive biomarkers for predicting those mutations and PD progression. However, their reliability has been questioned, with inadequately investigated genetics, cohorts, and population. In this study, multiple statistical hypothesis tests were employed on urinary BMP levels and sequences of 90 PD-risk single nucleotide polymorphisms (SNPs) from Parkinson's Progression Markers Institution (PPMI) participants. Those SNPs were categorized into four groups based on their impact on BMP levels in various cohorts. Variants rs34637584 G/A and rs34637584 A/A (LRRK2 G2019S) were identified as the most relevant on increasing urinary BMP levels in the PD cohort. Meanwhile, rs76763715 T/T (GBA1) was the primary factor elevating BMP levels in the prodromal cohort compared to its T/C and C/C variants (N370S) and the PD cohort. Proteomics analysis indicated the changed transport pathways may be the reasons for elevated BMP levels in prodromal patients. Our findings demonstrated that higher urinary BMP levels alone were not reliable biomarkers for PD progression or gene mutations but might serve as supplementary indicators for early diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Data-driven most powerful tests are statistical hypothesis decision-making tools that deliver the greatest power against a fixed null hypothesis among all corresponding data-based tests of a given size. When the underlying data distributions are known, the likelihood ratio principle can be applied to conduct most powerful tests. Reversing this notion, we consider the following questions. (a) Assuming a test statistic, say T, is given, how can we transform T to improve the power of the test? (b) Can T be used to generate the most powerful test? (c) How does one compare test statistics with respect to an attribute of the desired most powerful decision-making procedure? To examine these questions, we propose one-to-one mapping of the term "most powerful" to the distribution properties of a given test statistic via matching characterization. This form of characterization has practical applicability and aligns well with the general principle of sufficiency. Findings indicate that to improve a given test, we can employ relevant ancillary statistics that do not have changes in their distributions with respect to tested hypotheses. As an example, the present method is illustrated by modifying the usual t-test under nonparametric settings. Numerical studies based on generated data and a real-data set confirm that the proposed approach can be useful in practice. [ABSTRACT FROM AUTHOR]

This research aims to examine organizational justice's role as a mediator in the association between Islamic work ethic and work engagement among Sharia Bank employees. The respondents were 86 employees of Sharia Bank in Jakarta. The hypothesis was examined through a mediation test that involved four steps, including three simple regression and multiple regression. The results showed that organizational justice meditates the relationship between Islamic work ethic and work engagement among Sharia Bank employees. Employees who strongly uphold Islamic work ethics will have a positive perception of organizational justice, which, in turn, leads to higher engagement with their work. These findings have implications on how organizations can enhance employees' work engagement by promoting employees' positive perception of organizational justice. [ABSTRACT FROM AUTHOR]

The internal validity of a causal inference made based on an observational study is often subject to debate. The potential outcomes framework of causal inference stipulates that causal inference is essentially a missing data problem, and we follow this spirit to define the ideal sample as the combination of the observed data and the missing/counterfactual data for regression models. The robustness of a causal inference can be quantified by the probability of a robust inference for internal validity in regression, i.e., the PIVR, which is the probability of rejecting the null hypothesis again for the ideal sample provided the same null hypothesis has been already rejected for the observed sample. Drawing on the relationship between the PIVR and the mean counterfactual outcomes, we formalize a conceptual framework of quantifying the robustness of a regression-based causal inference based on a joint distribution about the mean counterfactual outcomes, holding the observed sample fixed. Interpretatively, the PIVR is the statistical power of the null hypothesis significance testing that is thought to be built on the ideal sample. We demonstrate the conceptual framework of quantifying the robustness of a regression-based causal inference with an empirical example. [ABSTRACT FROM AUTHOR]

Comparative sequence analysis permits unraveling the molecular processes underlying gene evolution. Many statistical methods generate candidate positions within genes, such as fast or slowly evolving sites, coevolving groups of residues, sites undergoing positive selection, or changes in evolutionary rates. Understanding the functional causes of these evolutionary patterns requires combining the results of these analyses and mapping them onto molecular structures, a complex task involving distinct coordinate referential systems. To ease this task, we introduce the site/group extended data format, a simple text format to store (groups of) site annotations. We developed a toolset, the SgedTools, which permits site/group extended data file manipulation, creating them from various software outputs and translating coordinates between individual sequences, alignments, and three-dimensional structures. The package also includes a Monte-Carlo procedure to generate random site samples, possibly conditioning on site-specific features. This eases the statistical testing of evolutionary hypotheses, accounting for the structural properties of the encoded molecules. [ABSTRACT FROM AUTHOR]

En-bloc retraction is a common procedure in orthodontic therapy. The application of palatal root torque moments is required to control incisor inclination during retraction, yet studies comparing forces and moments with respect to different mechanics are lacking. This study aimed to investigate the forces and moments during orthodontic en-bloc retraction using a robotic biomechanical simulation system, comparing two distinct approaches: (I) compound technique [stainless steel (SS) combined with nickel-titanium (NiTi)] using industrially pretorqued retraction-torque-archwires (RTA) in combination with NiTi closed coil springs; (II) conventional sliding mechanics using SS archwires with manually applied anterior twist bends in combination with elastic chains. Two dimensions (0.017" × 0.025" and 0.018" × 0.025") and ten archwires per group were investigated using 0.022" slot self-ligating brackets. Kruskal–Wallis tests with a significance level of α = 0.05 were conducted. The biomechanical simulation showed that en-bloc retraction was characterized by a series of tipping and uprighting movements, differing significantly regarding the examined mechanics. Collateral forces and moments occurred in all groups. Notably, RTA exhibited fewer extrusive forces. The most bodily movement was achieved with the compound technique and the 0.018" × 0.025" RTA. Sliding mechanics exhibited maximum palatal root torque moments of more than 20 Nmm, exceeding recommended values. [ABSTRACT FROM AUTHOR]

Objective: This study explores the contribution of nutrition knowledge to the health status of pre-school children's mothers in Niger State, North-Central, Nigeria. Design: The study is a descriptive cross-sectional design using a quantitative data collection method. Setting/participants: A multi-stage sampling technique was used to recruit 450 mothers of pre-school children across Niger state, Nigeria. Chi-square and linear regression were used to test the level of statistical significance (at p < 0.05). Socioeconomic and demographic information, anthropometric indices and nutrition knowledge were obtained using semi-structured questionnaires. Feeding patterns were also assessed using a qualitative 7-day dietary recall. Result: The results showed that the majority (63.8%) of the mothers were within 26–35 years, and more than half (51.6%) of the mothers lacked knowledge of a "balanced diet". Fruits, vegetables, and dairy products were the least consumed food group among the mothers (7.1% and 9.1%, respectively). Place of residence, occupation, and method of waste disposal were significantly associated with maternal minimum dietary diversity adequacy (p<0.05). Over half (57.6%) of the mothers were within the normal BMI range, and the mean waist/hip ratio was 0.82± 0.08. Social media/online was the most (36.4%) explored source of nutrition information among the mothers. This study shows no significant association between nutrition knowledge and adequacy of minimum dietary diversity among the mothers of preschool children (p = 0.09, χ2 = 13.682). Conclusion: Dietary diversity among mothers was associated with the socioeconomic status and BMI of the mothers, which were strong determinants of meal quality and health outcomes in Nigeria and other developing countries experiencing food insecurity. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, therefore understanding arterial stiffness is essential to developing innovative technologies to detect, monitor and treat them. The ubiquitous spread of photoplethysmography (PPG), a completely non-invasive blood-volume sensing technology suitable for all ages, highlights immense potential for arterial stiffness assessment in the wider healthcare setting outside specialist clinics, for example during routine visits to a General Practitioner or even at home with the use of mobile and wearable health devices. This study employs a custom-manufactured in vitro cardiovascular system with vessels of varying stiffness to test the hypothesis that PPG signals may be used to detect and assess the level of arterial stiffness under controlled conditions. Analysis of various morphological features demonstrated significant (p < 0.05) correlations with vessel stiffness. Particularly, area related features were closely linked to stiffness in red PPG signals, while for infrared PPG signals the most correlated features were related to pulse-width. This study demonstrates the utility of custom vessels and in vitro investigations to work towards non-invasive cardiovascular assessment using PPG, a valuable tool with applications in clinical healthcare, wearable health devices and beyond. [ABSTRACT FROM AUTHOR]

(1) Background: The relationship between sleep bruxism (SB) and psychological distress has been investigated in numerous studies and is heterogeneous. Different diagnostic procedures have been applied to determine SB. The aim of this study was to directly compare the association between psychological distress and SB diagnosed by different accepted methods. (2) Methods: Data of N = 45 subjects were analyzed, including group comparisons and correlation analyses. Following diagnostic methods for the determination of SB were used in one sample: self-report, clinical assessment, polysomnography with audio–video recording and a novel diagnostic sheet with analyzing software. Psychological distress was measured using the global severity index (GSI) of the Symptom Checklist-90-Standard (SCL-90-S). (3) Results: The GSI did not differ significantly between subjects with and without SB, regardless of the underlying diagnostic classification (p > 0.05). In-depth correlation analyses of self-report and clinical data revealed a weak-to-medium correlation with the GSI (r = 0.12–0.44). Due to non-normally distributed data, a test of statistical significance was not possible. Variables of instrumental methods such as the SB index (amount of SB activity per hour) of polysomnography (PSG) showed almost no correlation with psychological distress (r = −0.06–0.05). (4) Conclusions: Despite these limitations, the results provide an indication that the choice of diagnostic procedure may elucidate the variance in the correlation between SB and psychological distress. [ABSTRACT FROM AUTHOR]

Background: The human CXC chemokine receptor 2 (CXCR2) is a G protein-coupled receptor (GPCR) interacting with multiple chemokines (i.e., CXC chemokine ligands CXCL1-3 and CXCL5-8). It is involved in inflammatory diseases as well as cancer. Consequently, much effort is put into the identification of CXCR2 targeting drugs. Fundamental research regarding CXCR2 signaling is mainly focused on CXCL8 (IL-8), which is the first and best described high-affinity ligand for CXCR2. Much less is known about CXCR2 activation induced by other chemokines and it remains to be determined to what extent potential ligand bias exists within this signaling system. This insight might be important to unlock new opportunities in therapeutic targeting of CXCR2. Methods: Ligand binding was determined in a competition binding assay using labeled CXCL8. Activation of the ELR + chemokine-induced CXCR2 signaling pathways, including G protein activation, β-arrestin1/2 recruitment, and receptor internalization, were quantified using NanoBRET-based techniques. Ligand bias within and between these pathways was subsequently investigated by ligand bias calculations, with CXCL8 as the reference CXCR2 ligand. Statistical significance was tested through a one-way ANOVA followed by Dunnett's multiple comparisons test. Results: All chemokines (CXCL1-3 and CXCL5-8) were able to displace CXCL8 from CXCR2 with high affinity and activated the same panel of G protein subtypes (Gαi1, Gαi2, Gαi3, GαoA, GαoB, and Gα15) without any statistically significant ligand bias towards any one type of G protein. Compared to CXCL8, all other chemokines were less potent in β-arrestin1 and -2 recruitment and receptor internalization while equivalently activating G proteins, indicating a G protein activation bias for CXCL1,-2,-3,-5,-6 and CXCL7. Lastly, with CXCL8 used as reference ligand, CXCL2 and CXCL6 showed ligand bias towards β-arrestin1/2 recruitment compared to receptor internalization. Conclusion: This study presents an in-depth analysis of signaling bias upon CXCR2 stimulation by its chemokine ligands. Using CXCL8 as a reference ligand for bias index calculations, no ligand bias was observed between chemokines with respect to activation of separate G proteins subtypes or recruitment of β-arrestin1/2 subtypes, respectively. However, compared to β-arrestin recruitment and receptor internalization, CXCL1-3 and CXCL5-7 were biased towards G protein activation when CXCL8 was used as reference ligand. [ABSTRACT FROM AUTHOR]

Several egress pathways have been defined for many viruses. Among these pathways, extracellular vesicles (EVs) have been shown to function as vehicles of non-lytic viral egress. EVs are heterogenous populations of membrane-bound structures released from cells as a form of intercellular communication. EV-mediated viral egress may enable immune evasion and collective viral transport. Strains of nonenveloped mammalian orthoreovirus (reovirus) differ in cell lysis phenotypes, with T3D disrupting cell membranes more efficiently than T1L. However, mechanisms of reovirus egress and the influence of transport strategy on infection are only partially understood. To elucidate reovirus egress mechanisms, we infected murine fibroblasts (L cells) and non-polarized human colon epithelial (Caco-2) cells with T1L or T3D reovirus and enriched cell culture supernatants for large EVs, medium EVs, small EVs, and free reovirus. We found that both reovirus strains exit cells in association with large and medium EVs and as free virus particles, and that EV-enriched fractions are infectious. While reovirus visually associates with large and medium EVs, only medium EVs offer protection from antibody-mediated neutralization. EV-mediated protection from neutralization is virus strain- and cell type-specific, as medium EVs enriched from L cell supernatants protect T1L and T3D, while medium EVs enriched from Caco-2 cell supernatants largely fail to protect T3D and only protect T1L efficiently. Using genetically barcoded reovirus, we provide evidence that large and medium EVs can convey multiple particles to recipient cells. Finally, T1L or T3D infection increases the release of all EV sizes from L cells. Together, these findings suggest that in addition to exiting cells as free particles, reovirus promotes egress from distinct cell types in association with large and medium EVs during lytic or non-lytic infection, a mode of exit that can mediate multiparticle infection and, in some cases, protection from antibody neutralization. Author summary: The exit strategy that many viruses use to escape cells is unknown. Reovirus is a nonenveloped human virus and an ideal model system to understand virus exit strategies and their influence on infection. We found that two different reovirus strains, one that disrupts cell membranes and one that leaves cells largely intact, increase the release of extracellular vesicles (EVs) from cells. Both reovirus strains are released from cells as free particles and in association with EVs, which are membrane-bound structures that function in cell-to-cell communication. Depending on cell type and virus type, EVs can act like an 'invisibility cloak' that shields reovirus from antibodies. EVs can also bundle and ferry reovirus particles between cells. Although we used cells to examine the effects of reovirus association with EVs, it is possible that in mammalian hosts, EVs may shield reovirus from immune defenses and promote more efficient transmission and infection through a 'strength-in-numbers' strategy. Future work building on these findings will test the biological significance of EV-enclosed reovirus and may inform delivery strategies for oncolytic reoviruses to tumor sites. Broadly, these findings enhance our understanding of virus egress strategies and infection principles that may apply to other viruses that travel in EVs. [ABSTRACT FROM AUTHOR]

Background: p value is the most common statistic reported in scientific research articles. Choosing the conventional threshold of 0.05 commonly used for the p value in research articles, is unfounded. Many researchers have tried to provide a reasonable threshold for the p value; some proposed a lower threshold, eg, 0.005. However, none of the proposals has gained universal acceptance. Using the analogy between the diagnostic tests with continuous results and statistical inference tests of hypothesis, I wish to present a method to calculate the most appropriate p value significance threshold using the receiver operating characteristic curve (ROC) analysis. Results: As with diagnostic tests where the most appropriate cut-off values are different depending on the situation, there is no unique cut-off for the p significance threshold. Unlike the previous proposals, which mostly suggest lowering the threshold to a fixed value (eg, from 0.05 to 0.005), the most appropriate p significance threshold proposed here, in most instances, is much less than the conventional cut-off of 0.05 and varies from study to study and from statistical test to test, even within a single study. The proposed method provides the minimum weighted sum of type I and type II errors. Conclusions: Given the perplexity involved in using the frequentist statistics in a correct way (dealing with different p significance thresholds, even in a single study), it seems that the p value is no longer a proper statistic to be used in our research; it should be replaced by alternative methods, eg, Bayesian methods. [ABSTRACT FROM AUTHOR]

Background: Vaccination is a primary prevention approach to preventing disease by disconnecting the transmission chain. The current study utilized a BASNEF model framework to identify factors influencing subsequent doses of COVID-19 vaccination among older adults. Methods: This cross-sectional study was performed in the west of Iran in May 2022. The participants were selected via multi-stage sampling. Finally, 1120 participants contributed to the present study. The questionnaire consisted of three sections: a) Socio-demographic characteristics, b) cognitive impairments tests, and c) Questionnaire about the subsequent dose of COVID-19 vaccine uptake based on the BASNEF model. Data were analyzed using the software IBM AMOS-20 and SPSS-23 via one-way analysis of variance (ANOVA) and independent sample T-tests were used, too. The significance level of statistical tests was regarded as less than 0.05. Results: The presented results of analyzing 50% of the variance of vaccination intention as the dependent variable (R square = 0.497) and 10% of the behavior variance as the dependent variable (R square = 0.104) can be explained based on the BASNEF model. The enabling factors (β = 0.636, p < 0.001) and the intention (β = 0.322, p < 0.001) were important factors for subsequent doses of COVID-19 vaccine uptake in older adults. Conclusion: So, planning and implementing promotional intervention programs for older people (over 65; 80), females, illiterate, widows and divorced, good economic status, and urban areas is essential. It seems that enabling factors such as free vaccinations, vaccination inaccessible places such as public social security agencies, social supports such as involvement of the government and physicians, and improving information by the medium or knowledge-sharing experience, which can be further used to enhance the acceptance of subsequent doses of COVID-19 uptake in older adults. [ABSTRACT FROM AUTHOR]

AIM The aim of this study was to evaluate the effects of a virtual mindfulness meditation intervention on stress and anxiety levels of 145 nursing students. BACKGROUND Because of the twin demands of classroom and clinical workloads, nursing students experience greater amounts of stress and anxiety than the average college student. Mindfulness meditation is a promisingmethod to alleviate stress and anxiety. METHOD A pretest-posttest randomized controlled design was used. Participants received either weekly mindfulness meditation recordings or recordings on nursing information. Participants completed the Perceived Stress Scale and the Generalized Anxiety Disorder-7 Scale. RESULTS A two-way mixed analysis of variance and follow-up simple main effects tests revealed that participants in the experimental group, who received meditation recordings, experienced significantly lower levels of stress and anxiety on posttest surveys than the control group. CONCLUSION Mindfulness meditation can reduce stress and anxiety levels in nursing students. This can improve students' overall mental and physical well-being. [ABSTRACT FROM AUTHOR]

Popular theories that explain or predict behavioural intentions are based on people's attitudes and subjective norms. Their application is based on the (often implicit) assumption that people regard a subject (e.g., preventing the spread of COVID-19) as sufficiently important for them to formulate stable attitudes and subjective norms about it. As this assumption rarely holds for all people, the influence of attitudes and subjective norms in determining behavioural intentions changes depending on the importance of the subject. In other words, importance has a moderating effect on the relationship between intentions, attitudes, and subjective norms. We hypothesise that, as importance declines, the influence on intentions of attitudes decreases and the influence of subjective norms increases. This has important implications for efforts to encourage the adoption of preventative behaviours in relation to COVID-19 because promotional strategies designed to modify attitudes differ markedly from those designed to modify subjective norms. We test this hypothesis by analysing three different large-scale surveys about people's intentions, involvement, attitudes, and subjective norms regarding the spread of COVID-19 in New Zealand. The results support our hypothesis and highlight the importance of distinguishing between when the formation of behavioural intentions depends mainly on attitudes and when it depends mainly on subjective norms. [ABSTRACT FROM AUTHOR]

Today, supply chains operate in a very uncertain global environment, so one of the basic tasks is to maintain a higher level of resilience. Great threats to the resilience of supply chains, which occur at time intervals, are labelled in literature as health crises. The appearance of diseases, due to their nature may cause epidemics and pandemics’, which can greatly endanger stability and lead to interruptions in supply chain processes. A major problem cited in literature concerning the specifics of such situations relates to the limited ability to predict both the causes and consequences of such actions. Due to that, it is difficult to manage crisis situations, and as a logical consequence, there are immeasurable negative effects. The aim of this paper is to examine the negative effects of various epidemics and pandemics on production in the automotive industry. The analyses were employed with the aim of defining the extent to which an epidemic and a pandemic disrupt production activities in supply chains. In addition, it is important to consider the characteristic of viruses, as well as the differences in the average values of production in the years when the crisis occurred. In order to analyse the hypotheses, the authors used Wilcoxon’s test to check the statistical significance of changes in production levels. To examine the significance of the difference in average changes in production for the duration of the epidemics and the pandemics, the authors used Friedman’s test in SPSS. [ABSTRACT FROM AUTHOR]

Labor pain is a manifestation of the contraction (shortening) of the uterine muscles. Non-pharmacological treatment in pain management, namely deep breathing relaxation and counterpressure massage is an alternative method and a new trend that can be developed. This study investigated the effectiveness of deep breathing relaxation and Counterpressure massage in alleviating labor pain during active phase I. Employing a quasi-experimental design with a two-group pre-test post-test approach, 30 postpartum mothers from June to July 2021 were consecutively sampled. Deep breathing relaxation (5 times for 3-5 minutes) and counterpressure massage (3 times for 30-90 seconds) served as the independent variables, while pain intensity during the first active phase was the dependent variable. Utilizing the Wilcoxon and Mann Whitney tests at a 5% significance level, significant reductions in pain intensity were observed after both interventions (pvalues: 0.002 for deep breathing, 0.046 for counterpressure massage). These non-pharmacological methods, enhancing endorphin release, contribute to relaxation and pain intensity reduction during labor. The findings underscore the potential of integrating deep breathing relaxation and counterpressure massage as effective approaches in non-pharmacological labor pain management. [ABSTRACT FROM AUTHOR]

Obesity-induced dysregulation of hypothalamic neurons is not completely eliminated by restoring body weight, therefore the most urgent task of modern precision medicine is to predict the trajectory of development of metabolic disorders associated with obesity in children. The aim of the study was to determine the level of association of single-nucleotide variants of genes that determine eating behavior – Neuronal growth regulator 1, Fat mass and obesity associated gene, Glucagon-like peptide-1 receptor, ghrelin, leptin receptor, cholecystokinin, in the development of metabolically unhealthy obesity. 252 obese children aged 6-18 years were examined. The main group (n=152) consisted of children with metabolically unhealthy obesity (MUO) according to Identification and prevention of Dietary- and Lifestyle-induced Health Effects in Children and Infants 2014 criteria. The control group (n=100) consisted of children with metabolically healthy obesity (MHO). All children underwent a general clinical, immunobiochemical examination at the Synevo laboratory (Ukraine). Whole-genome sequencing (CeGat, Germany) was performed in 31 children of the primary and 21 children of the control group. Static analysis: variance analysis ANOVA, method of estimating data dispersion, ROC-analysis, method of testing statistical hypotheses. The level of single nucleotide variants association of anorexigenic hormone genes with MUO that exceeded the threshold accepted by 75% of the available data was, respectively, in ascending order: leptin receptor (LEPR) rs1137101 (40.38%), Glucagon-like peptide-1 receptor (GLP1R) rs1126476 (40.38%), GLP1R rs2235868 (42.31%), GLP1R rs1042044 (42.31%), LEPR rs3790435 (48.08%), cholecystokinin (CCK) rs754635 (50%), LEPR rs2186248 (55.76%), GLP1R rs6918287 (55.76%). Genotypes of the GLP1R gene, such as CC rs10305421 determine insulin resistance (F=5.6); GA/AA rs3765468 – meta-inflammation (F=5.8); AA rs6918287 – basal hyperglycemia (F=6.3) and triglyceridemia (F=51.3), p<0.05. Single-nucleotide variants of the gene GLP1R rs6918287, LEPR rs2186248, CCK rs754635 of the anorexic hormones that control eating behavior are highly associated with the presence of metabolically unhealthy obesity in children. [ABSTRACT FROM AUTHOR]

The problem of detecting hydrometeorological trends is still a concern. In this study, monthly, seasonal, and annual temperature and precipitation variations in the Van Province of Türkiye are assessed using both traditional such as Mann–Kendall (MK) and Spearman's Rho (SRHO) and graphical such as Innovative trend significance test (ITA-ST) and Innovative polygon trend analysis (IPTA) methods. In order to determine trend slope and change-point detection, Sen's Slope and Sequential Mann–Kendall tests are also used, respectively. The findings show that the MK, SRHO, and ITA-ST approaches detect a decreasing trend for annual total precipitation at the Erciş and Başkale stations. Additionally, the MAM and SON seasons at Erciş and the SON season at Gevaş stations also show a noticeably decreasing trend. All stations have an increasing trend at a 95% significant level for annual mean temperatures. Except Gevaş station, all four seasons have an increasing trend for temperature series. Results from the IPTA show that for precipitation data, the transition from May to June exhibits the highest trend length. In addition, for temperature data, the minimum trend length and slope are captured in the transition from July to August. In general, ITA-ST and IPTA are superior to MK and SRHO at identifying trends and that the outcomes of these methods are significantly more suitable to visual inspection and linguistic interpretation. Different hydro-climatological variables can be analyzed more flexibly and thoroughly utilizing innovative methodologies. [ABSTRACT FROM AUTHOR]

The paper makes a case that the current discussions on replicability and the abuse of significance testing have overlooked a more general contributor to the untrustworthiness of published empirical evidence, which is the uninformed and recipe-like implementation of statistical modeling and inference. It is argued that this contributes to the untrustworthiness problem in several different ways, including [a] statistical misspecification, [b] unwarranted evidential interpretations of frequentist inference results, and [c] questionable modeling strategies that rely on curve-fitting. What is more, the alternative proposals to replace or modify frequentist testing, including [i] replacing p-values with observed confidence intervals and effects sizes, and [ii] redefining statistical significance, will not address the untrustworthiness of evidence problem since they are equally vulnerable to [a]–[c]. The paper calls for distinguishing between unduly data-dependant 'statistical results', such as a point estimate, a p-value, and accept/reject H 0 , from 'evidence for or against inferential claims'. The post-data severity (SEV) evaluation of the accept/reject H 0 results, converts them into evidence for or against germane inferential claims. These claims can be used to address/elucidate several foundational issues, including (i) statistical vs. substantive significance, (ii) the large n problem, and (iii) the replicability of evidence. Also, the SEV perspective sheds light on the impertinence of the proposed alternatives [i]–[iii], and oppugns [iii] the alleged arbitrariness of framing H 0 and H 1 which is often exploited to undermine the credibility of frequentist testing. [ABSTRACT FROM AUTHOR]

Background. Anti-vascular endothelial growth factor (anti-VEGF) therapy via intravitreal injection is an effective treatment for patients with abnormal ocular neovascularization, such as age-related macular degeneration (AMD) and diabetic macular edema (DME). However, prolonged and frequent anti-VEGF treatment is associated with a risk of local and systemic adverse events, including geographic atrophy, cerebrovascular disease, and death. Furthermore, some patients do not adequately respond to anti-VEGF therapy. Hypoxia-inducible factor (HIF) is a transcription factor that controls the expression of hypoxia-responsive genes involved in angiogenesis, inflammation, and metabolism. The HIF/VEGF pathway plays an important role in neovascularization, and the inhibition of HIF activation could be an effective biomolecular target for neovascular diseases. The demand for disease prevention or treatment using functional foods such as superfoods has increased in recent years. Few reports to date have focused on the antineovascular effects of superfoods in the retinal pigment epithelium (RPE). In light of the growing demand for functional foods, we aimed to find novel HIF inhibitors from superfoods worked in RPE cells, which could be an adjuvant for anti-VEGF therapy. Methods. Seven superfoods were examined to identify novel HIF inhibitor candidates using luciferase assay screening. We used the human RPE cell line ARPE-19 and fetal human RPE (fhRPE) to investigate the biomolecular actions of novel HIF inhibitors using quantitative PCR and western blotting. Results. Under CoCl2-induced pseudohypoxic condition and 1% oxygen hypoxic incubation, camu-camu (Myrciaria dubia) showed HIF inhibitory effects determined by luciferase assays. Camu-camu downregulated HIF-1α and VEGFA mRNA expressions in a concentration-dependent manner. Camu-camu also inhibited HIF-1α protein expressions, and its inhibitory effect was greater than that of vitamin C, which is present at high levels in camu-camu. Conclusion. The camu-camu extract suppressed the activation of HIF and VEGF in RPE cells. This could assist anti-VEGF therapy in patients with abnormal ocular neovascularization. [ABSTRACT FROM AUTHOR]

Hikers and hillwalkers typically use the gradient in the direction of travel (walking slope) as the main variable in established methods for predicting walking time (via the walking speed) along a route. Research into fell-running has suggested further variables which may improve speed algorithms in this context; the gradient of the terrain (hill slope) and the level of terrain obstruction. Recent improvements in data availability, as well as widespread use of GPS tracking now make it possible to explore these variables in a walking speed model at a sufficient scale to test statistical significance. We tested various established models used to predict walking speed against public GPS data from almost 88,000 km of UK walking / hiking tracks. Tracks were filtered to remove breaks and non-walking sections. A new generalised linear model (GLM) was then used to predict walking speeds. Key differences between the GLM and established rules were that the GLM considered the gradient of the terrain (hill slope) irrespective of walking slope, as well as the terrain type and level of terrain obstruction in off-road travel. All of these factors were shown to be highly significant, and this is supported by a lower root-mean-square-error compared to existing functions. We also observed an increase in RMSE between the GLM and established methods as hill slope increases, further supporting the importance of this variable. [ABSTRACT FROM AUTHOR]

Background: To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial. Methods: We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics. Results: Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent. Conclusions: The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size. [ABSTRACT FROM AUTHOR]

Background: Alexithymia is the inability to identify and describe one's own emotions. Adolescents who suffer from Restrictive Eating Disorders (REDs) show a higher prevalence of alexithymia than the general population. Methods: The study explored the correlation between levels of alexithymia in mothers, fathers, and adolescents affected by REDs and patients' ability to recognize their emotions. The study also aimed to evaluate if patients' emotional distress can significantly impact the severity of their disorder and functioning measured by the Clinical Global Impression Scale - Severity (CGI-S) and the Children's Global Assessment Scale (CGAS). We enrolled 67 families of adolescents affected by REDs. Parents and patients' levels of alexithymia were assessed through the Toronto Alexithymia Scale (TAS-20). Spearman's correlation shows a statistically significant correlation between mothers and patients' levels of alexithymia. Results: Our findings also suggest that fathers and mothers' TAS scores correlate with each other. However, there is no statistically significant relationship between the influence of the TAS scores of fathers and sons/daughters. Conclusions: In conclusion, mothers' level of alexithymia could influence both fathers and patients' difficulty in identifying and describing their own emotions. This relationship can be investigated further when considering externally oriented thinking. However, the severity of the disease and overall functioning do not appear to be affected by patients' levels of alexithymia. [ABSTRACT FROM AUTHOR]

(1) Background: The aim of this prospective study was to evaluate measurement software in comparison with manual measurements using inter-observer and intra-observer variability on radiographs in the preoperative planning of total knee arthroplasty. (2) Methods: Two independent observers retrospectively measured the mechanical lateral proximal femoral angle (mLPFA), the mechanical lateral distal femoral angle (mLDFA), the joint line convergence angle (JLCA), the mechanical medial proximal tibial angle (mMPTA), the mechanical lateral distal tibial angle (mLDTA), the hip–knee angle or mechanical tibial–femoral axis angle (HKA), and the anatomical–mechanical angle (AMA) on 55 long-leg anteroposterior radiographs manually twice, followed by measurements using dedicated software. Variability between manual and computer-aided planning was assessed, and all measurements were performed a second time after 14 days in order to assess intra-observer variability. (3) Results: Concerning intra-observer variability, no statistically significant difference was observed regarding the software-based measurements. However, significant differences were noted concerning intra-observer variability when measuring the mLDFA and AMA manually. Testing for statistical significance regarding variability between manual and software-based measurements showed that the values varied strongly between manual and computer-aided measurements. Statistically significant differences were detected for mLPFA, mLDFA, mMPTA, and mLPTA on day 1, and mLPFA, mMPTA, and mLPTA on day 15, respectively. (4) Conclusions: Preoperative planning of leg axis angles and alignment using planning software showed less inter- and intra-observer variability in contrast to manual measurements, and results differed with respect to manual planning. We believe that the planning software is more reliable and faster, and we would recommend its use in clinical settings. [ABSTRACT FROM AUTHOR]

The Kaplan–Meier (KM) estimator is widely used in medical research to estimate the survival function from lifetime data. KM estimation is a powerful tool to evaluate clinical trials due to simple computational requirements, its use of a logrank hypothesis test, and the ability to censor patients. However, KM estimation has several constraints and fails to generalize to ordinal variables of clinical interest, such as toxicity and ECOG performance. We devised weighted trajectory analysis (WTA) to combine the advantages of KM estimation with the ability to visualize and compare treatment groups for ordinal variables and fluctuating outcomes. To assess statistical significance, we developed a new hypothesis test analogous to the logrank test. We demonstrated the functionality of WTA through 1000-fold clinical trial simulations of unique stochastic models of chemotherapy toxicity and schizophrenia disease course. With increments in sample size and hazard ratio, we compared the performance of WTA to KM estimation and the generalized estimating equation (GEE). WTA generally required half the sample size to achieve comparable power to KM estimation; advantages over the GEE included its robust nonparametric approach and summary plot. We also applied WTA to real clinical data: the toxicity outcomes of melanoma patients receiving immunotherapy and the disease progression of patients with metastatic breast cancer receiving ramucirumab. The application of WTA demonstrated that using traditional methods such as KM estimation can lead to both type I and II errors by failing to model illness trajectory. This article outlines a novel method for clinical outcome assessment that extends the advantages of Kaplan–Meier estimates to ordinal outcome variables. [ABSTRACT FROM AUTHOR]

The detrending-moving-average-based multivariate linear regression has been recently introduced as a new tool to probe into multiscale dependent behavior between multivariate data. It well solves the dependence among the nonstationary variates while classic multivariate linear regression dose not. The real-world applications demonstrate its versatility and robustness. However, the statistical significance of this new model needs to be further improved mathematically not just empirically. Hence, the main motivation of this paper is to provide a solid theoretical basis for the latest regression framework. Specifically, four propositions and three theorems are deduced theoretically to support the statistical significance test of detrending-moving-average-based multivariate linear regression model. These propositions and theorems revolve around hypothesis testing of each partial regression coefficients and regression model. They can examine the statistical significance at every given scales. Moreover, we also resort to synthetic time series to verify the performances of the proposed statistical test procedure. By using those statistical tests, we investigate the multiscale interdependence between the crude oil price and the exchange rates among oil-importing and oil-exporting countries. Some interesting findings show that the dependent behavior between the oil price and exchange rates varies at various scales, which cannot be achieved from classical multivariate regression model. • Statistical properties are derived mathematically for the model of DMA-based MLR. • Artificial experiments are also conducted around the statistical test of new model. • The scale-dependent co-movement behavior is investigated between crude oil price and exchange rate by the proposed methods. • The co-movement behavior is compared before and during the oil bust. [ABSTRACT FROM AUTHOR]