Comparative computational analysis of orthoconic antiferroelectric liquid crystals: DFT analysis.

CONTEXT: The study undertakes a comparative analysis of four distinct semi-fluorinated chiral Organic Active Ferroelectric Liquid Crystals (OAFLCs). The comparative analysis of the compounds is done by using various parameters, including thermodynamic, non-linear optical, electrical, atomic charge distribution, and atomic orientations. We use optimization algorithms to look at chemical reactivity, electrical properties, intermolecular interactions, and static hyperpolarizability. Sample 4 is the best choice for a wide range of display applications. This research contributes to understanding the nuanced properties of semi-fluorinated chiral OAFLCs and highlights Sample 4's potential for novel applications in display technology, owing to its superior stability and optimized properties. This study helps to enhance our understanding of the comparative analysis of semi-fluorinated chiral OAFLCs for potential advancements in display technologies by incorporating findings from key studies. METHOD: The simulations are performed using density functional theory (DFT) with the B3LYP functional for predicting molecular properties, and Vibrational Energy Distribution Analysis (VEDA) software is used to perform the vibrational analysis of the molecules.

Association between the inflammatory burden index and rheumatoid arthritis and its all-cause mortality: data from NHANES 1999-2018.

Background and aims: Rheumatoid arthritis (RA) is a prevalent chronic autoimmune disease characterized by chronic inflammation. The Inflammatory Burden Index (IBI) is a newly proposed comprehensive inflammation index used to assess systemic inflammation. The relationship between IBI and RA, as well as its all-cause mortality, remains unclear. The objective of this study was to examine the correlation between IBI and RA and to analyze the association between IBI and all-cause mortality in RA. Methods: The study comprehensively analyzes adult data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999 to 2018. The participants' IBI was calculated using the formula IBI = CRP * neutrophils/lymphocytes. Three models were constructed to investigate the relationship between IBI and the prevalence of RA. Nonlinear relationships were determined using restricted cubic spline curves. Stratified analyses and interaction tests were used to explore the relationship between RA and IBI in different subgroups. The same data analyses were applied to investigate the association between IBI and RA all-cause mortality. Results: The data analyses revealed a stable positive and nonlinear correlation between IBI and the risk of RA, as well as a positive, nonlinear, J-shaped association between IBI and RA all-cause mortality. The correlation and association were consistent across most subgroups, and multiple covariates had no effect on the results. No significant effect of multiple covariates on the association was found through interaction tests. Conclusion: Our study has demonstrated a positive correlation between the prevalence of RA and all-cause mortality with the IBI index. This suggests that lower levels of inflammation in the body are associated with a reduced risk of RA prevalence and all-cause mortality. Further prospective studies are required to explore the mechanisms involved.

A Longitudinal Examination of Blood Sugar Dynamics in Diabetes and Non-Diabetes Using Growth Curve Model: The Sabzevar Persian Cohort Study.

Background: Diabetes mellitus is a chronic metabolic disorder with substantial implications for public health. Understanding the factors influencing blood sugar fluctuations is crucial for effective diabetes management and prevention. This study aimed to evaluate factors associated with blood sugar changes in diabetic patients and healthy individuals attending the Sabzevar Persian Cohort Center, employing the growth curve model. Materials and Methods: Data related to 589 diabetic patients and 589 non-diabetic patients participating in the Persian cohort study of Sabzevar were used. Due to the repetition of blood sugar measurements for each individual over time, we use the conditional latent growth curve model to examine intra-individual changes and variables that affect these changes over time. Results: The linear latent growth curve model, fitted with independent variables, exhibited a superior fit. The slope of the line for the diabetic group was measured at 1.78, while for the non-diabetic group, it was estimated to be -0.29. Within the diabetic group, the influence of age, the presence of fatty liver, and history of congenital heart disease (CHD) had a significant impact on the baseline (the intercept), and the effect of body mass index (BMI) on the changing trend of the response variable (slope) was also significant. In the non-diabetic group, significant effects were observed for age variables, BMI, family history of diabetes, and history of stroke in the family. Conclusion: Overall, the linear latent growth curve model showed good performance in the evaluation of the factors related to blood sugar changes in diabetic patients and healthy people.

Associations Between Anthropogenic Factors, Meteorological Factors, and Cause-Specific Emergency Department Admissions.

Unpredictable emergency department (ED) admissions challenge healthcare systems, causing resource allocation inefficiencies. This study analyses associations between air pollutants, meteorological factors, and 2,655,861 cause-specific ED admissions from 2014 to 2018 across 12 categories. Generalized additive models were used to assess non-linear associations for each exposure, yielding Incidence Rate Ratios (IRR), while the population attributable fraction (PAF) calculated each exposure's contribution to cause-specific ED admissions. IRRs revealed increased risks of ED admissions for respiratory infections (IRR: 1.06, 95% CI: 1.01-1.11) and infectious and parasitic diseases (IRR: 1.09, 95% CI: 1.03-1.15) during increased rainfall (13.21-16.97 mm). Wind speeds >12.73 km/hr corresponded to increased risks of ED admissions for respiratory infections (IRR: 1.12, 95% CI: 1.03-1.21) and oral diseases (IRR: 1.58, 95% CI: 1.31-1.91). Higher concentrations of air pollutants were associated with elevated risks of cardiovascular disease (IRR: 1.16, 95% CI: 1.05-1.27 for PM10) and respiratory infection-related ED admissions (IRR: 2.78, 95% CI: 1.69-4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28°C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM10 concentrations >60 µg/m3 were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered.

Quantifying exercise intensity with fractal correlation properties of heart rate variability: a study on incremental and constant-speed running.

The short-term scaling exponent of detrended fluctuation analysis (DFAα1) applied to interbeat intervals may provide a method to identify ventilatory thresholds and indicate systemic perturbation during prolonged exercise. The purposes of this study were to (i) identify the gas exchange threshold (GET) and respiratory compensation point (RCP) using DFAα1 values of 0.75 and 0.5 from incremental exercise, (ii) compare DFAα1 thresholds with DFAα1 measures during constant-speed running near the maximal lactate steady state (MLSS), and (iii) assess the repeatability of DFAα1 between MLSS trials. Twelve runners performed an incremental running test and constant-speed running 5% below, at, and 5% above the MLSS, plus a repeat trial at MLSS. During 30-min running trials near MLSS, DFAα1 responses were variable (i.e., 0.27-1.24) and affected by intensity (p = 0.031) and duration (p = 0.003). No difference in DFAα1 was detected between MLSS trials (p = 0.597). In the early phase (~ 8 min), DFAα1 measures at MLSS (0.71 [0.13]) remained higher than the DFAα1 identified at RCP from the incremental test (0.57 [0.13]; p = 0.024). In addition, following ~ 18 min of constant speed running at MLSS, DFAα1 measures (0.64 [0.14]) remained higher than 0.5 (p = 0.011)-the value thought to demarcate the boundaries between heavy and severe exercise intensities. Accordingly, using fixed DFAα1 values associated with the RCP from incremental exercise to guide constant-speed exercise training may produce a greater than expected exercise intensity, however; the dependency of DFAα1 on intensity and duration suggest its potential utility to quantify systemic perturbations imposed by continuous exercise.

Non-linear relationship between TyG index and the risk of prediabetes in young people: a 5-year retrospective cohort study in Chinese young adults.

Objective: Given the limited evidence on the relationship between the triglyceride-glucose (TyG) index and the risk of prediabetes among young adults, our study aimed to investigate the potential impact of the TyG index on the future development of prediabetes in young individuals. Methods: This retrospective cohort study included 125,327 healthy adults aged 20 to 45 years. We utilized Cox proportional hazards regression models, combined with cubic spline functions and smooth curve fitting, to assess the relationship between baseline TyG index and the risk of prediabetes among young adults, exploring its non-linear association. A series of sensitivity analyses and subgroup analyses were conducted to ensure the robustness of our findings. Results: After adjusting for covariates, the study found a positive correlation between the TyG index and the risk of prediabetes (HR=1.81, 95%CI: 1.54-2.13, p<0.0001). The risk of prediabetes increased progressively across quartiles of the TyG index (Q1 to Q4), with Q4 showing a significantly higher risk compared to Q1 (adjusted HR=2.33, 95% CI=1.72-3.16). Moreover, a non-linear relationship was identified between the TyG index and the risk of prediabetes, with an inflection point at 9.39. To the left of the inflection point, the HR was 2.04 (95% CI: 1.69 to 2.46), while to the right, the HR was 0.89 (95% CI: 0.48 to 1.65). Conclusion: Our study reveals a non-linear relationship and a saturation effect between the TyG index and the development of prediabetes among young individuals in China, with an inflection point at 9.39. Understanding this non-linear relationship can assist clinicians in identifying young individuals at high risk and implementing targeted interventions to reduce their risk of progressing to diabetes.

The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability.

Objective The present study investigated how breathing stimuli affect both non-linear and linear metrics of the autonomic nervous system (ANS). Approach The analyzed dataset consisted of 70 young, healthy volunteers, in whom arterial blood pressure (ABP) was measured noninvasively during 5-minute sessions of controlled breathing at three different frequencies: 6, 10, and 15 breaths/min. CO2 concentration and respiratory rate were continuously monitored throughout the controlled breathing sessions. The ANS was characterized using non-linear methods, including Phase-Rectified Signal Averaging (PRSA) for estimating heart acceleration and deceleration capacity (AC, DC), multiscale entropy (MSEn), approximate entropy (ApEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn), as well as time and frequency domains (low frequency, LF; high-frequency, HF; total power, TP) of heart rate variability (HRV). Main Results Higher breathing rates resulted in a significant decrease in end-tidal CO2 concentration (p < 0.001), accompanied by increases in both ABP (p<0.001) and heart rate (p<0.001). A strong, linear decline in AC and DC (p<0.001 for both) was observed with increasing respiratory rate. All entropy metrics increased with breathing frequency (p<0.001). In the time-domain, HRV metrics significantly decreased with breathing frequency (p<0.01 for all). In the frequency-domain, HRV LF and HRV HF decreased (p = 0.038 and p = 0.040, respectively), although these changes were modest. There was no significant change in HRV TP with breathing frequencies. Significance Alterations in CO2 levels, a potent chemoreceptor trigger, and changes in HR most likely modulate ANS metrics. Non-linear PRSA and entropy appear to be more sensitive to breathing stimuli compared to frequency-dependent HRV metrics. Further research involving a larger cohort of healthy subjects is needed to validate our observations. .

Physiologically based in vitro - In vivo correlation of modified release oral formulations with non-linear intestinal absorption: A case study using mirabegron.

Establishing an in vitro - in vivo correlation (IVIVC) for oral modified release (MR) formulations would make it possible to substitute an in vitro dissolution test for human bioequivalence (BE) studies when changing the formulation or manufacturing methods. However, the number of IVIVC applications and approvals are reportedly low. One of the main reasons for failure to obtain IVIVCs using conventional methodologies may be the lack of consideration of the dissolution and absorption mechanisms of drugs in the physiological environment. In particular, it is difficult to obtain IVIVC using conventional methodologies for drugs with non-linear absorption processes. Therefore, the aim of the present study was to develop a physiologically based biopharmaceutics model (PBBM) that enables Level A IVIVCs for mirabegron MR formulations with non-linear absorption characteristics. Using human pharmacokinetic (PK) data for immediate-release formulations of mirabegron, the luminal drug concentration-dependent membrane permeation coefficient was calculated through curve fitting. The membrane permeation coefficient data were then applied to the human PK data of the MR formulations to estimate the in vivo dissolution rate by curve fitting. It was assumed that in vivo dissolution could be described using a zero-order rate equation. Furthermore, a Levy plot was generated using the estimated in vivo dissolution rate and the in vitro dissolution rate obtained from the literature. Finally, the dissolution rate of the MR formulations from the Levy plot was applied to the PBBM to predict the oral PK of the mirabegron MR formulations. This PB-IVIVC approach successfully generated linear Levy plots with slopes of almost 1.0 for MR formulations with different dose strengths and dissolution rates. The Cmax values of the MR formulations were accurately predicted using this approach, whereas the prediction errors for AUC exceeded the Level A IVIVC criteria. This can be attributed to the incomplete description of colonic absorption in the current PBBM.

Strain-Induced Structural Rearrangement Towards a White-Light-Emitting Adamantane-Type Cluster Dimer.

Group 14/16 adamantane-type hybrid clusters of the type [(RT)4E6] (T = group 14 element, E = group 16 element, R = organic group) have been reported to emit white-light when irradiated in an amorphous state with a continuous-wave (CW) infrared laser diode. This effect is enhanced if the cluster core is varied from a binary to a more complex composition. To further explore this phenomenon, we synthesized clusters with a multinary R/R'-T/T'-E/E' composition, including isolobal replacement of E with CH2, in [(2-NpSi){CH2Sn(S)Ph}3] (1, Np = naphthyl). When expanding one of the CH2 moieties to a C2H4 group, thus generating a R/R'-T/T'-E/E'/E'' cluster composition, we unexpectedly observed a dimerization of the initially formed, yet non-isolable adamantane-like cluster [(2-NpSi){CH2Sn(S)Ph}2{C2H4Sn(S)Ph}] (2) to [(2-NpSi){CH2Sn(S)Ph}2{C2H4Sn(S)Ph}]2 (3), exhibiting a heretofore unprecedented cluster architecture. Both monomeric 1 and dimeric 3, show white-light emission as thin films. The nonlinear optical response of the compounds was also modelled with DFT methods.

Analysing tie-break performance of professional tennis players at Grand Slam matches.

A tiebreak in tennis is one of the critical moments where players are expected to excel under mental pressure and maintain high level of performance. Despite the importance of tiebreak points, research exploring the performance of male and female players during such match phrase remains limited. This study aimed to investigate i) the overall tiebreak performance of male and female players in relation to the outcome, ii) to examine their point-level performance by considering different contextual variables. A total of 535 tiebreaks comprising 6380 points from the 2016-2021 US Open men's and women's singles matches were collected. The difference in match performance between winning and losing players within the entire tiebreak game was explored. A subsequent decision tree analysis was then used to analyse the effect of the contextual and performance variables on tiebreak point-by-point outcome. The results showed that male and female Winning players outperformed the Losing players in 1st Serve, Serve Width and Net approach performance. The analysis of point-level performance showed that Net point, Score scene, and Point server substantially impacted tennis players' tiebreak outcome. These findings provide valuable insight for coaches and players, informing tiebreak tactics tailoring and training in relevance to different match status.

Nationwide Analysis of the Relationship Between Low Ambient Temperature and Acute Aortic Dissection-related Hospitalizations.

AIMS: Acute aortic dissection (AAD) is a life-threatening cardiovascular emergency. Therefore, identifying modifiable risk factors for AAD is of great public health significance. An association between ambient temperature (AT) and AAD has been reported; however, not all findings have been elucidated. This study examined the association between AAD-related hospitalization and AT using data from the Japanese Registry of All Cardiac and Vascular Diseases Diagnostic Procedure Combination (JROAD-DPC), which is a nationwide claims-based database. METHODS: This nationwide time-stratified case-crossover study evaluated data of hospitalized patients with AAD from 1,119 certified hospitals between 2012 and 2020 using the JROAD-DPC database. Conditional logistic regression and distributed lag non-linear models were used to investigate the association between average daily temperature and AAD-related hospitalization. RESULTS: Among the 96,812 cases analyzed. The exposure-response curve between AT and AAD-related hospitalization showed an increase in the odds ratio for lower temperatures, with a peak at timed -10°C (odds ratio: 2.28, 95% confidence interval: 1.92-2.71, compared with that at 20°C). The effects of temperature on lag days 0 and 1 were also significant.Stratified analyses showed a greater association between AT and AAD-related hospitalization for the following variables: older age (≥75 years), female sex (44.4%, the mean age ± SD was 76 ± 12 years), low body mass index (<22), winter season, and warmer regions. CONCLUSIONS: Low AT is associated with an increased risk of AAD-related hospitalization. Several susceptible groups are affected by cold temperatures and have a higher risk of hospitalization.

This study examined the association between hospitalization due to acute aortic dissection (AAD) and ambient temperature using data from the Japanese Registry of All Cardiac and Vascular Diseases Diagnostic Procedure Combination, which is a nationwide claims-based database. Key findings:Low ambient temperature is associated with an increased risk of AAD-related hospitalization, with a greater association with older age and female sex.Other predisposing factors for the above association include lower body mass index, winter seasons, and warmer regions.

Non-linear relationships in clinical research.

True linear relationships are rare in clinical data. Despite this, linearity is often assumed during analyses, leading to potentially biased estimates and inaccurate conclusions. In this introductory paper, we aim to first describe - in a non-mathematical manner - how to identify non-linear relationships. Various methods are then discussed that can be applied to deal with non-linearity, including transformations, polynomials, splines, and Generalized Additive Models (GAMs), along with their strengths and weaknesses. Finally, we illustrate the use of these methods with a practical example from nephrology, providing guidance on how to report the results from non-linear relationships.

Achieving climate sustainability in the Republic of Congo: The role of economic growth, biomass energy consumption, rule of law and government effectiveness- a NARDL Approach.

This study explores the relationship between economic growth (GDP), biomass energy consumption (BEC), Rule of Law, and Government Effectiveness on climate change (CO2 emissions) in the Republic of Congo from 1990 to 2020. We employed a nonlinear autoregressive distributed Lag (NARDL) model to analyse data from World Bank databases. Higher GDP leads to lower CO2 emissions in the long run. Increased BEC also reduces emissions, but a decrease can have a small negative impact. Interestingly, a stronger Rule of Law and Government Effectiveness is associated with higher CO2 emissions in the short run, potentially due to relaxed environmental regulations. However, a stronger Rule of Law and Government Effectiveness leads to lower emissions in the long run, suggesting a potential shift towards sustainable practices. These findings provide valuable insights for policymakers aiming to achieve economic growth and climate stability in the Republic of Congo.

Bifurcation study, phase portraits and optical solitons of dual-mode resonant nonlinear Schrodinger dynamical equation with Kerr law non-linearity.

This study investigates the dynamic characteristics of the dual-mode resonant non-linear Schrodinger equation with a Bhom potential. Hydrodynamics, nonlinear optical fibre communication, elastic media, and plasma physics are just a few of the mathematical physics and engineering applications for this model. The study aims to achieve two main objectives: first, to discuss bifurcation analysis, and second, to extract optical soliton solutions using the extended hyperbolic function method. The study successfully derives various wave solutions, including bright, singular, periodic singular and dark solitons, based on the governing model. The findings conferred in this article show a crucial advancement in understanding the propagation of waves in non-linear media. Additionally, bifurcation of phase portraits of ordinary differential equation consistent with the partial differential equation under consideration is conducted. We also highlight specific constraint conditions that ensure the presence of these obtained solutions. The existing literature shows that these methods are first time applied on this model.

Elevated triglyceride-glucose index associated with increased risk of diabetes in non-obese young adults: a longitudinal retrospective cohort study from multiple Asian countries.

Objective: Previous studies have confirmed a positive correlation between the Triglyceride-Glucose (TyG) index and future risk of diabetes. However, evidence of this association in non-obese young populations remains limited. This study aims to investigate the relationship between the TyG index and the future risk of diabetes among non-obese young adults. Methods: This retrospective cohort study included 113,509 non-obese young adults from China and 9,549 from Japan. The mean age was 35.73 ± 6.38 years, and 56,469 participants (45.89%) were male. The median follow-up duration was 3.38 years. The association between baseline TyG index and risk of diabetes was examined using Cox proportional hazards regression models. Non-linear relationships between the TyG index and risk of diabetes were identified using cubic splines and smoothed curve fitting in the Cox models. Sensitivity and subgroup analyses were also conducted. Results: After adjusting for covariates, the results indicated a positive correlation between the TyG index and risk of diabetes in non-obese young adults (HR=3.57, 95% CI: 2.92-4.36, P<0.0001). A non-linear relationship was observed with an inflection point at 7.3. The HR to the right of this inflection point was 3.70 (95% CI: 3.02-4.52, P<0.0001), while to the left, it was 0.34 (95% CI: 0.06-1.88, P=0.2161). The robustness of our findings was confirmed through a series of sensitivity analyses and subgroup analyses. Conclusion: This study reveals a positive and non-linear association between the TyG index and risk of diabetes among non-obese young adults. Interventions aimed at reducing the TyG index by lowering triglycerides or fasting glucose levels could substantially decrease the future likelihood of developing diabetes in this population.

A non-linear association between low-density lipoprotein cholesterol and the risk of diabetic kidney disease in patients with type 2 diabetes in China.

Objective: To explore the intrinsic relationship between low-density lipoprotein cholesterol (LDL-C) and diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D) in China. Methods: This cross-sectional study included 1,313 patients with type 2 diabetes treated at the Affiliated Jinhua Hospital of Zhejiang University School of Medicine, located in Jinhua, China. The data were combined from two periods, 2017 and 2020-2021. Participants were categorized into groups with and without DKD. The relationship between LDL-C levels and DKD was evaluated employing logistics regression analysis and restricted cubic spline (RCS) curves. Results: Generally, there was no statistical difference in LDL-C levels between DKD and non-DKD groups, however, a significantly non-linear relationship (Pnon-linear = 0.011) was observed between LDL-C levels and DKD prevalence after adjusting for confounding factors according to the RCS analysis. Two optimal cut-points of 2.97 and 3.61 mmol/L were selected out using random forest algorithm. With the middle LDL-C concentration (2.97-3.61 mmol/L) as the reference, the odds ratios for low (<2.97 mmol/L) and high (>3.61 mmol/L) concentrations were 1.45 (1.08-1.96) and 1.47 (1.01-2.15) respectively, after adjusting for confounding factors in the multivariate analyses. Notably, this association was more pronounced among female participants in the subgroup analyses. Conclusion: A non-linear association was observed between LDL-C levels and the risk of DKD in patients with T2D in China. LDL-C levels below 2.97 mmol/L may elevate the risk of DKD, particularly in female patients with T2D.

Gait pattern modification based on ground contact adaptation using the robot-assisted training platform (RATP).

Robot-assisted rehabilitation and training systems are utilized to improve the functional recovery of individuals with mobility limitations. These systems offer structured rehabilitation through precise human-robot interaction, outperforming traditional physical therapy by delivering advantages such as targeted muscle recovery, optimization of walking patterns, and automated training routines tailored to the user's objectives and musculoskeletal attributes. In our research, we propose the development of a walking simulator that considers user-specific musculoskeletal information to replicate natural walking dynamics, accounting for factors like joint angles, muscular forces, internal user-specific constraints, and external environmental factors. The integration of these factors into robot-assisted training can provide a more realistic rehabilitation environment and serve as a foundation for achieving natural bipedal locomotion. Our research team has developed a robot-assisted training platform (RATP) that generates gait training sets based on user-specific internal and external constraints by incorporating a genetic algorithm (GA). We utilize the Lagrangian multipliers to accommodate requirements from the rehabilitation field to instantly reshape the gait patterns while maintaining their overall characteristics, without an additional gait pattern search process. Depending on the patient's rehabilitation progress, there are times when it is necessary to reorganize the training session by changing training conditions such as terrain conditions, walking speed, and joint range of motion. The proposed method allows gait rehabilitation to be performed while stably satisfying ground contact constraints, even after modifying the training parameters.

A comparative analysis of mutual information methods for pairwise relationship detection in metagenomic data.

BACKGROUND: Construction of co-occurrence networks in metagenomic data often employs correlation to infer pairwise relationships between microbes. However, biological systems are complex and often display qualities non-linear in nature. Therefore, the reliance on correlation alone may overlook important relationships and fail to capture the full breadth of intricacies presented in underlying interaction networks. It is of interest to incorporate metrics that are not only robust in detecting linear relationships, but non-linear ones as well. RESULTS: In this paper, we explore the use of various mutual information (MI) estimation approaches for quantifying pairwise relationships in biological data and compare their performances against two traditional measures-Pearson's correlation coefficient, r, and Spearman's rank correlation coefficient, ρ. Metrics are tested on both simulated data designed to mimic pairwise relationships that may be found in ecological systems and real data from a previous study on C. diff infection. The results demonstrate that, in the case of asymmetric relationships, mutual information estimators can provide better detection ability than Pearson's or Spearman's correlation coefficients. Specifically, we find that these estimators have elevated performances in the detection of exploitative relationships, demonstrating the potential benefit of including them in future metagenomic studies. CONCLUSIONS: Mutual information (MI) can uncover complex pairwise relationships in biological data that may be missed by traditional measures of association. The inclusion of such relationships when constructing co-occurrence networks can result in a more comprehensive analysis than the use of correlation alone.

Geographical Patterns in Mortality Impacts Due To Heatwaves of Different Characteristics in Spanish Cities.

The impact of heatwaves (HWs) on human health is a topic of growing interest due to the global magnification of these phenomena and their substantial socio-economic impacts. As for other countries of Southern Europe, Spain is a region highly affected by heat and its increase under climate change. This is observed in the mean values and the increasing incidence of extreme weather events and associated mortality. Despite the vast knowledge on this topic, it remains unclear whether specific types and characteristics of HW are particularly harmful to the population and whether this shows a regional interdependency. The present study provides a comprehensive analysis of the relationship between HW characteristics and mortality in 12 Spanish cities. We used separated time series analysis in each city applying a quasi-Poisson regression model and distributed lag linear and non-linear models. Results show an increase in the mortality risk under HW conditions in the cities with a lower HW frequency. However, this increase exhibits remarkable differences across the cities under study not showing any general pattern in the HW characteristics-mortality association. This relationship is shown to be complex and strongly dependent on the local properties of each city pointing out the crucial need to examine and understand on a local scale the HW characteristics and the HW-mortality relationship for an efficient design and implementation of prevention measures.

Empathy and Autism: Establishing the Structure and Different Manifestations of Empathy in Autistic Individuals Using the Perth Empathy Scale.

PURPOSE: There is a common mischaracterisation that autistic individuals have reduced or absent empathy. Measurement issues may have influenced existing findings on the relationships between autism and empathy, and the structure of the empathy construct in autism remains unclear. METHODS: The present study sought to address these gaps by examining the structure and psychometric properties of the Perth Empathy Scale (PES) in autistic individuals (N = 239) compared to non-autistic individuals (N = 690). RESULTS: Our moderated non-linear factor analysis revealed that the multidimensional empathy construct manifested similarly in autistic and non-autistic individuals, with the PES displaying good validity and reliability. Moreover, the results revealed that autistic individuals reported reduced cognitive empathy and reduced affective empathy for positive and negative emotions. However, there was greater heterogeneity of empathic tendencies in the autistic sample, indicating that these mean differences may not be generalisable for all autistic individuals. CONCLUSION: The present study highlights that the PES is suitable for assessing empathy across autistic and non-autistic individuals. This work with the PES also provides greater nuance to our understanding of empathy and autism, and based on these findings, we propose the empathy heterogeneity hypothesis of autism as a new way of describing empathy in autism.