A new possibilistic-based clustering method for probability density functions and its application to detecting abnormal elements.

Cluster analysis can also detect abnormalities besides building a basis for identifying elements into clusters. Detecting abnormalities is a highly developed feature in the field of unsupervised learning. However, existing studies have mainly focused on discrete data, not probability density functions. This paper enables a possibilistic approach to solving the clustering for probability density functions dealing with abnormal elements. First, the data are extracted using the density function. Then, they are passed through the proposed algorithm to produce a possibilistic partition. Finally, a decision rule is established to recognize which function is abnormal. We compare the proposed algorithm with baseline algorithms in clustering PDFs, such as k-means, FCF, and Self-Updated Clustering. The results of three numerical examples applied to the image are typical for this new method. Furthermore, The proposed algorithm reaches accuracy at 100% over simulated benchmark data and outperforms baseline methods. Additionally, two last examples apply to image data reaching G-mean up from 96 to 100% (Sensitivity: 92-100% and Specificity: 100%). The proposed method can be researched and used to understand the internal structures of big data in the digital age through the probability density functions.

Cardiac Effects of Renin-Angiotensin System Inhibitors in Nonproteinuric CKD.

BACKGROUND: In contrast to proteinuric chronic kidney disease (CKD), the relative cardioprotective benefits of antihypertensive medications in nonproteinuric CKD are unknown. We examined long-term cardiovascular outcomes and mortality in patients with nonproteinuric CKD treated with renin-angiotensin system inhibitors (RASIs) versus other antihypertensive medications. METHODS: Among participants of the CRIC study (Chronic Renal Insufficiency Cohort) without proteinuria, we used intention-to-treat analyses with inverse probability of treatment weighting and Cox proportional hazards modeling to determine the association of RASIs versus other antihypertensive medications with a composite cardiovascular outcome (myocardial infarction, stroke, heart failure hospitalization, and death) and mortality. Secondary analyses included per-protocol analyses accounting for continuous adherence and time-updated analyses accounting for the proportion of time using RASIs during follow-up. RESULTS: A total of 2806 participants met the inclusion criteria. In the intention-to-treat analyses, RASIs versus other antihypertensive medications were not associated with an appreciable difference in cardiovascular events (adjusted hazard ratio [aHR], 0.94 [95% CI, 0.80-1.11]) or mortality (aHR, 1.06 [95% CI, 0.88-1.28]). In the per-protocol analyses, RASIs were associated with a lower risk of adverse cardiovascular events (aHR, 0.78 [95% CI, 0.63-0.97]) and mortality (aHR, 0.64 [95% CI, 0.48-0.85]). Similarly, in the time-updated analyses, a higher proportion of RASI use over time was associated with a lower mortality risk (aHR, 0.33 [95% CI, 0.14-0.86]). CONCLUSIONS: Among individuals with nonproteinuric CKD, after accounting for time-updated use, RASIs are associated with fewer cardiovascular events and a lower mortality risk compared with other antihypertensive medications. Patients with nonproteinuric CKD may benefit from prioritizing RASIs for hypertension management.

Occurrence and potential diffusion of pine wilt disease mediated by insect vectors in China under climate change.

BACKGROUND: Pine wilt disease (PWD), a major international quarantined forest pest, causes serious ecological and economic damage to Pinus species in Asia and Europe. In China, PWD has spread northeasterly and northwesterly beyond its original northern limits. Consequently, an evaluation of the insect vector-mediated occurrence and potential diffusion of PWD is needed to identify important transmission routes and control the spread of disease. RESULTS: An optimized MaxEnt model was used to assess the current and future geographical distribution of Bursaphelenchus xylophilus and its insect vectors in China. The predicted suitable area for B. xylophilus colonization is currently 212.32 × 104 km2 and mainly concentrated in Central, East, Southwest and South China, although is anticipated to include the northwestern regions of China in the future. As for the insect vectors, Monochamus alternatus and M. saltuarius are expected to spread toward the northwest and southwest, respectively. The maximum predicted dispersion area of PWD mediated by M. alternatus, M. saltuarius and both species was 91.85 × 104, 218.76 × 104 and 29.99 × 104 km2, respectively, with potential diffusion areas being anticipated to increase in the future. Both the suitable probabilities and areas of B. xylophilus and its insect vectors were found to vary substantially along the latitudinal gradient, with the latitudinal range of these species being predicted to expand in the future. CONCLUSION: This is the first study to investigate the potential diffusion areas of PWD mediated by insect vectors in China, and our finding will provide a vital theoretical reference and empirical basis for developing more effective management strategies for the control of PWD in China. © 2024 Society of Chemical Industry.

Unmanned Aerial Vehicle-Assisted Terahertz-Visible Light Communication Systems: An In-Depth Performance Analysis.

This paper investigates the performance of dual-hop unmanned aerial vehicle (UAV)-assisted communication channels, employing a decode-and-forward (DF) relay architecture. The system leverages terahertz (THz) communication for the primary hop and visible light communication (VLC) for the secondary hop. We conduct an in-depth analysis by deriving closed-form expressions for the end-to-end (E2E) bit error rate (BER). Additionally, we use a Monte Carlo simulation approach to generate best-fitting curves, validating our analytical expressions. A performance evaluation through BER and outage probability metrics demonstrates the effectiveness of the proposed system. Specifically, our results indicate that the proposed system outperforms Free-Space Optics (FSO)-VLC and Radio-Frequency (RF)-VLC at a higher signal-to-noise ratio (SNR). The results of this study provide valuable insights into the feasibility and limitations of UAV-assisted THz-VLC communication systems.

Using Support Vector Machines to Classify Road Surface Conditions to Promote Safe Driving.

Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estimation of road surface conditions. The machine model was a support vector machine (SVM), which has been successfully applied in diverse fields, and kernel functions (linear, Gaussian, second-order polynomial) with a soft margin classification technique were also adopted. Two learner designs (one-vs-one, one-vs-all) extended their application to multi-class classification. In addition to this non-probabilistic classifier, this study calculated the posterior probability of belonging to each group by applying the sigmoid function to the classification scores obtained by the trained SVM. The results indicate that the classification errors of all the classifiers, excluding the one-vs-all linear learners, were below 3%, thereby accurately classifying road surface conditions, and that the generalization performance of all the one-vs-one learners was within an error rate of 4%. The results also showed that the posterior probabilities can analyze certain atmospheric and road surface conditions that correspond to a high probability of hazardous road surface conditions. Therefore, this study demonstrates the potential of data-driven learning models in classifying road surface conditions accurately.

Research on Mobile Robot Navigation Method Based on Semantic Information.

This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Second, the concept of voxels is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph-building and loop-closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

Adequacy of the Dosing and Infusion Time of Ceftazidime/Avibactam for the Treatment of Gram-Negative Bacterial Infections: A PK/PD Simulation Study.

Introduction: Recent studies suggested the potential benefits of extended infusion times to optimize the treatment efficacy of ceftazidime/avibactam, which indicated that the current pharmacokinetic/pharmacodynamic (PK/PD) target may not be sufficient, especially for severe infections. The purpose of this study is to assess the adequacy of dosing strategies and infusion durations of ceftazidime/avibactam when applying higher PK/PD targets. Methods: This study utilized published PK parameters to conduct Monte Carlo simulations. Different dosages including the recommended regimen based on renal function were simulated and evaluated by the probability of target attainment (PTA) and cumulative fraction of response (CFR). Different PK/PD targets were set for ceftazidime and avibactam. MIC distributions from various sources were used to calculate the CFR. Results: Multiple PK/PD targets have been set in this study, All recommended dosage could easily achieve the target of 50%fT ≥ MIC (ceftazidime) and 50%fT ≥ CT=1.0 mg/L (avibactam). However, for severe infection patients with normal renal function and augmented renal clearance at the recommended dosage (2000 mg/500 mg, every 8 hours), the infusion duration needs to be extended to 3 hours and 4 hours to achieve the targets of 100%fT ≥ MIC and 100%fT ≥ CT=1.0 mg/L. Only continuous infusion at higher dosages achieved 100%fT ≥ 4×MIC and 100%fT ≥ CT=4.0 mg/L targets to all currently recommended regimens. According to the varying MIC distributions, higher concentrations are needed for Pseudomonas aeruginosa, with the attainment rates vary across different regions. Conclusion: The current recommended dosing regimen of ceftazidime/avibactam is insufficient for severe infection patients, and continuous infusion is suggested.

A measure of the importance of moment for ball-strike counts in a baseball plate appearance.

This study constructs a discrete-time Markov Chain (DTMC) model for a baseball plate appearance (PA) employing Major League Baseball's pitch-by-pitch dataset. Based on the DTMC model, we propose a novel measure for a baseball PA, termed the Importance of Moment (IOM). The IOM quantifies the criticality of each ball-strike count situation, by assessing the probabilistic difference between the pitcher's and hitter's favourable outcomes (out vs reaching base). If the favours significantly vary right after a particular ball-strike count, then the count is deemed critical and is assigned a high IOM value. We empirically verify that IOM explains pitchers' behaviour of fastball speed. We then further investigate whether the behaviour of ace pitchers differs significantly from the majority. Several interesting properties are found from the analysis. Firstly, the path independence assumption generally holds, with the exception of the ball-strike count of 2B1S. Second, pitchers tend to throw the faster fastball at counts with higher IOM values. Lastly, ace pitchers are capable of pitching even faster fastball in two-strike situations in which IOM is high. The DTMC effectively models the probabilistic structure of a baseball PA, and the proposed IOM measure serves as a useful tool for explaining player behaviour.

Assessing Etiologic Heterogeneity for Multinomial Outcome with Two-Phase Outcome-Dependent Sampling Design.

Etiologic heterogeneity occurs when distinct sets of events or exposures give rise to different subtypes of disease. Inference about subtype-specific exposure effects from two-phase outcome-dependent sampling data requires adjustment for both confounding and the sampling design. Common approaches to inference for these effects do not necessarily appropriately adjust for these sources of bias, or allow for formal comparisons of effects across different subtypes. Herein, using inverse probability weighting (IPW) to fit a multinomial model is shown to yield valid inference with this sampling design for subtype-specific exposure effects and contrasts thereof. The IPW approach is compared to common regression-based methods for assessing exposure effect heterogeneity using simulations. The methods are applied to estimate subtype-specific effects of various exposures on breast cancer risk in the Carolina Breast Cancer Study.

Reassessing the Impact of Coffee Consumption on Liver Disease: Insights from a Large-Scale Cohort Study with IPTW Adjustment.

Coffee consumption is globally widespread and has become a lifestyle habit. This study investigated coffee consumption and liver-related survival in a large cohort of 455,870 individuals with UK biobank, categorized into without steatosis, metabolic dysfunction-associated steatotic liver disease (MASLD), and MASLD and increased alcohol intake (MetALD). Inverse probability of treatment weighting (IPTW) adjusted for confounding variables was used, followed by Kaplan-Meier analysis. Moderate coffee consumption (1-2 cups per day) was associated with lower all-cause mortality across the entire cohort, without the steatosis, MASLD (p < 0.0001), and MetALD cohorts (p = 0.0047 for pre-IPTW, p = 0.027 for post-IPTW). Before IPTW adjustment, consuming one or more cups of coffee per day appeared to significantly reduce liver-related mortality in the overall (p = 0.015) and MASLD cohorts (p = 0.011). However, post-IPTW application, no significant differences in liver-related mortality were observed between the coffee intake groups (p = 0.778, 0.319, 0.564, 0.238 for each group). While increased coffee consumption initially seemed to reduce liver-related mortality, after IPTW adjustment, only all-cause mortality significantly decreased (p < 0.0001 and p = 0.027). These findings suggest that previous studies might have overestimated the favorable effect of coffee intake on chronic liver disease due to confounding factors.

Biomarkers for identification of high-risk coronary artery plaques in patients with suspected coronary artery disease.

BACKGROUND: Patients with atherosclerotic plaques containing high-risk features have an increased likelihood of events and a worse prognosis. Whether increased levels of Troponin I (TnI) and C-reactive protein (CRP) are associated with the presence of high-risk coronary atherosclerotic plaques (HRP) is not well described. We assessed the association between 1) TnI and 2) CRP with quantified coronary plaque burden, luminal diameter stenosis, and HRP in patients with low/intermediate pre-test probability of obstructive coronary artery disease (CAD) referred for coronary computed tomography angiography (CCTA). METHODS: The CCTA from 1615 patients were analyzed using a semiautomatic software for coronary artery plaque characterization. Patients with high TnI (>6 â€‹ng/L) and high CRP (>2 â€‹mg/L) were identified. Associations of TnI and CRP with plaque burden, stenosis (≥50% luminal diameter stenosis on CCTA), and HRP were investigated. RESULTS: TnI and CRP were both positively correlated with total plaque burden (TnI rs â€‹= â€‹0.14, p â€‹< â€‹0.001; CRP rs â€‹= â€‹0.08, p â€‹< â€‹0.001). In multivariate logistic regression analyses, high TnI was associated with stenosis (OR 1.43, 95% confidence interval (CI) 1.03-1.99, p â€‹= â€‹0.034), the presence of HRP (OR 1.79, 95% CI: 1.17-2.74, p â€‹= â€‹0.008), and the subtypes of HRP; low attenuation plaque (OR 1.93, 95% CI: 1.24-3.00, p â€‹= â€‹0.003), and positive remodeling (OR 1.51, 95% CI: 1.07-2.13, p â€‹= â€‹0.018). For CRP, only stenosis and napkin ring sign correlated significantly. CONCLUSION: In patients with suspected CAD, TnI and CRP are associated with HRP features. These findings may suggest that inflammatory and particularly ischemic biomarkers might improve early risk stratification and affect patient management. GOV IDENTIFIER: NCT02264717.

Futility Interim Analysis Based on Probability of Success Using a Surrogate Endpoint.

In clinical trials with time-to-event data, the evaluation of treatment efficacy can be a long and complex process, especially when considering long-term primary endpoints. Using surrogate endpoints to correlate the primary endpoint has become a common practice to accelerate decision-making. Moreover, the ethical need to minimize sample size and the practical need to optimize available resources have encouraged the scientific community to develop methodologies that leverage historical data. Relying on the general theory of group sequential design and using a Bayesian framework, the methodology described in this paper exploits a documented historical relationship between a clinical "final" endpoint and a surrogate endpoint to build an informative prior for the primary endpoint, using surrogate data from an early interim analysis of the clinical trial. The predictive probability of success of the trial is then used to define a futility-stopping rule. The methodology demonstrates substantial enhancements in trial operating characteristics when there is a good agreement between current and historical data. Furthermore, incorporating a robust approach that combines the surrogate prior with a vague component mitigates the impact of the minor prior-data conflicts while maintaining acceptable performance even in the presence of significant prior-data conflicts. The proposed methodology was applied to design a Phase III clinical trial in metastatic colorectal cancer, with overall survival as the primary endpoint and progression-free survival as the surrogate endpoint.

PA-MSHA improves prognosis of patients undergoing radical cystectomy: a retrospective cohort study using inverse probability of treatment weighting.

Objective: To observe the effect of Pseudomonas aeruginosa mannose-sensitive hemagglutinin (PA-MSHA) on the prognosis and the incidence of lymphatic leakage in patients undergoing radical cystectomy (RC). Method: A total of 129 patients who underwent RC in Lanzhou University Second Hospital from 2013 to 2022 were enrolled in this study. They were divided into 43 patients treated with PA-MSHA and 86 patients in the control group. Inverse probability of treatment weighting (IPTW) was applied to reduce potential selection bias. Kaplan-Meier method and Cox regression analysis were used to analyze the effect of PA-MSHA on the survival of patients and the incidence of postoperative lymphatic leakage. Results: The PA-MSHA group exhibited improved overall survival (OS) and cancer-specific survival (CSS) rates compared to the control group. The 3-year and 5-year overall survival (OS) rates for the PA-MSHA group were 69.1% and 53.2%, respectively, compared to 55.6% and 45.3% for the control group (Log-rank=3.218, P=0.072). The 3-year and 5-year cancer-specific survival (CSS) rates for the PA-MSHA group were 73.3% and 56.5%, respectively, compared to 58.0% and 47.3% for the control group (Log-rank=3.218, P=0.072). Additionally, the 3-year and 5-year progression-free survival (PFS) rates for the PA-MSHA group were 74.4% and 56.8%, respectively, compared to 57.1% and 52.2% for the control group (Log-rank=2.016, P=0.156). Multivariate Cox regression analysis indicates that lymph node metastasis and distant metastasis are poor prognostic factors for patients, while the use of PA-MSHA can improve patients' OS (HR: 0.547, 95%CI: 0.304-0.983, P=0.044), PFS (HR: 0.469, 95%CI: 0.229-0.959, P=0.038) and CSS (HR: 0.484, 95%CI: 0.257-0.908, P=0.024). The same trend was observed in the cohort After IPTW adjustment. Although there was no significant difference in the incidence of postoperative lymphatic leakage [18.6% (8/35) vs. 15.1% (84.9%), P=0.613] and pelvic drainage volume [470 (440) ml vs. 462.5 (430) ml, P=0.814] between PA-MSHA group and control group, PA-MSHA could shorten the median retention time of drainage tube (7.0 d vs 9.0 d) (P=0.021). Conclusion: PA-MSHA may improve radical cystectomy in patients with OS, PFS, and CSS, shorten the pelvic drainage tube retention time.

Probabilistic inference and Bayesian-like estimation in animals: Empirical evidence.

Animals often make decisions without perfect knowledge of environmental parameters like the quality of an encountered food patch or a potential mate. Theoreticians often assume animals make such decisions using a Bayesian updating process that combines prior information about the frequency distribution of resources in the environment with sample information from an encountered resource; such a process leads to decisions that maximize fitness, given the available information. I examine three aspects of empirical work that shed light on the idea that animals can make such decisions in a Bayesian-like manner. First, many animals are sensitive to variance differences in behavioral options, one metric used to characterize frequency distributions. Second, several species use information about the relative frequency of preferred versus nonpreferred items in different populations to make probabilistic inferences about samples taken from populations in a manner that results in maximizing the likelihood of obtaining a preferred reward. Third, the predictions of Bayesian models often match the behavior of individuals in two main approaches. One approach compares behavior to models that make different assumptions about how individuals estimate the quality of an environmental parameter. The patch exploitation behavior of nine species of birds and mammals has matched the predictions of Bayesian models. The other approach compares the behavior of individuals who learn, through experience, different frequency distributions of resources in their environment. The behavior of three bird species and bumblebees exploiting food patches and fruit flies selecting mates is influenced by their experience learning different frequency distributions of food and mates, respectively, in ways consistent with Bayesian models. These studies lend support to the idea that animals may combine prior and sample information in a Bayesian-like manner to make decisions under uncertainty, but additional work on a greater diversity of species is required to better understand the generality of this ability.

Increased extinction probability and altered physiological characteristics in pirimicarb-tolerant Daphnia magna.

We evaluated the physiological characteristics of chemical-tolerant cladocerans. Over the course of 26 generations (F25), Daphnia magna was continuously exposed to pirimicarb (carbamate) solutions (0, 3.8, 7.5, and 15 µg/L) in sub-lethal or lethal levels. The 48 h EC50 values (29.2-29.9 µg/L) for 7.5 and 15 µg/L exposure groups were found to be nearly two times higher than that in the control (17.2 µg/L). Subsequently, we investigated whether the extinction probability changed when the chemical-tolerant daphnids were fed two different types of food, Chlorella vulgaris and Synechococcus leopoliensis. Furthermore, we ascertained how chemical tolerance influences respiration and depuration rates. The 48 h EC50 value was positively related to the extinction probability when the daphnids were fed S. leopoliensis. Because the measured lipid content of S. leopoliensis was three times lower than that of C. vulgaris, the tolerant daphnids struggled under nutrient-poor conditions. Respiration rates across all pirimicarb treatment groups were higher than those in the control group, suggesting that they may produce large amounts of energy through respiration to maintain the chemical tolerance. Since the pirimicarb depuration rate for 7.5 µg/L exposure groups was higher than that in the control, the altered metabolic/excretion rate may be one factor for acquiring chemical tolerance. These altered physiological characteristics are crucial parameters for evaluating the mechanisms of chemical tolerance and associated fitness costs.

Research on species categorical authentication of accelerants based on flame characteristics analysis.

Species categorical authentication of accelerants has traditionally relied on fire debris analysis. To explore a novel method for identifying the accelerants species, four commonly used accelerants for arson were loaded onto different substrates and ignited at different locations. The entire combustion process was recorded and flame characteristics were analyzed. The results showed that the probability density function (PDF) of flame apex angle counts within a certain period after ignition can be used to distinguish accelerant species, and this method is not affected by accelerant loading amount, ignition location, and substrate, demonstrating strong stability and universality, while the temporal variation of flame area and the value obtained by dividing half of the flame width by the flame height (tangent of flame cone angle) can effectively differentiate gasoline and diesel. The utilization of flame characteristics for identifying accelerants species holds significant implications for arson investigation.

Spatial invasion of cooperative parasites.

In this paper we study invasion probabilities and invasion times of cooperative parasites spreading in spatially structured host populations. The spatial structure of the host population is given by a random geometric graph on [0,1]n, n∈N, with a Poisson(N)-distributed number of vertices and in which vertices are connected over an edge when they have a distance of at most rN with rN of order N(ß-1)/n for some 0<ß<1. At a host infection many parasites are generated and parasites move along edges to neighbouring hosts. We assume that parasites have to cooperate to infect hosts, in the sense that at least two parasites need to attack a host simultaneously. We find lower and upper bounds on the invasion probability of the parasites in terms of survival probabilities of branching processes with cooperation. Furthermore, we characterize the asymptotic invasion time. An important ingredient of the proofs is a comparison with infection dynamics of cooperative parasites in host populations structured according to a complete graph, i.e. in well-mixed host populations. For these infection processes we can show that invasion probabilities are asymptotically equal to survival probabilities of branching processes with cooperation. Furthermore, we build on proof techniques developed in Brouard and Pokalyuk (2022), where an analogous invasion process has been studied for host populations structured according to a configuration model. We substantiate our results with simulations.

Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations.

We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.

Promoting Adolescent Safer Gambling Through Self-Referent Counterfactual Thinking and Knowledge of Probabilistic Outcome.

Research have continued to advocate for various safer gambling strategies (SGs) to curb initiation and sustenance of problem gambling through the conceptualization and development of SG tools. The present study aims to extend this advocacy by examining cognitive processes that promote adolescent responsible gambling (RG) in Nigeria. Young adolescents (N = 962, Mage =16.06, SD = 4.12) recruited from a high school population responded to measures of self-referent counterfactual thinking (S-RCFT), knowledge of probabilistic outcomes (KPO), and RG. Hayes PROCESS macro analysis result showed that S-RCFTs and KPO were positively associated with RG. More importantly, it was revealed that KPO amplified the S-RCFT-RG link and provided a plausible pathway to understanding the effect of S-RCFT on RG. This calls for SG advocacies to encourage adolescent gamblers to hold themselves responsible for responsible gambling, and to understand that gambling wins randomly occur. The limitations of the study and recommendations are highlighted.

[Comprehensive analysis of population mortality by cause: regional features].

Chronic non-communicable diseases (CNCDs) are the main most common causes of death both in the Russian Federation and in the Republic of Tatarstan. Thus, in the traditional structure of mortality in the Republic of Tatarstan in 2022, a "classical triad" is defined: diseases of the circulatory system are in first place (46% of all deaths), neoplasms are in second place (15%), and external causes are in third place. (7.3%). As throughout the world, during the coronavirus infection there was an increase in population mortality from all causes, mainly from diseases of the circulatory system (hereinafter referred to as CVD), as well as directly from coronavirus infection, which is the cause of almost every fifth death in the Republic of Tatarstan in 2021. The peak of coronavirus infection reduced the average age of death from cardiovascular causes by 4.5 years in men and by 4 years in women. It was revealed that in the Republic of Tatarstan the probability of death from diseases of the circulatory system and endocrine system in women is higher than in men; In men, on the contrary, there is a higher probability of death from respiratory diseases, cancer and external causes.