A new First-Order mixture integer-valued threshold autoregressive process based on binomial thinning and negative binomial thinning

In this paper, we introduce a new first-order mixture integer-valued threshold autoregressive process, based on the binomial and negative binomial thinning operators. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares (CLS) and conditional maximum likelihood (CML) estimators are derived and the asymptotic properties of the estimators are established. The inference for the threshold parameter is obtained based on the CLS and CML score functions. Moreover, the Wald test is applied to detect the existence of the piecewise structure. Simulation studies are considered, along with an application: the number of criminal mischief incidents in the Pittsburgh dataset.Comment: 34 pages;5 figure

Causal relationship between gut microbiota and risk of gastroesophageal reflux disease: a genetic correlation and bidirectional Mendelian randomization study

BackgroundNumerous observational studies have identified a linkage between the gut microbiota and gastroesophageal reflux disease (GERD). However, a clear causative association between the gut microbiota and GERD has yet to be definitively ascertained, given the presence of confounding variables.MethodsThe genome-wide association study (GWAS) pertaining to the microbiome, conducted by the MiBioGen consortium and comprising 18,340 samples from 24 population-based cohorts, served as the exposure dataset. Summary-level data for GERD were obtained from a recent publicly available genome-wide association involving 78 707 GERD cases and 288 734 controls of European descent. The inverse variance-weighted (IVW) method was performed as a primary analysis, the other four methods were used as supporting analyses. Furthermore, sensitivity analyses encompassing Cochran’s Q statistics, MR-Egger intercept, MR-PRESSO global test, and leave-one-out methodology were carried out to identify potential heterogeneity and horizontal pleiotropy. Ultimately, a reverse MR assessment was conducted to investigate the potential for reverse causation.ResultsThe IVW method’s findings suggested protective roles against GERD for the Family Clostridiales Vadin BB60 group (P = 0.027), Genus Lachnospiraceae UCG004 (P = 0.026), Genus Methanobrevibacter (P = 0.026), and Phylum Actinobacteria (P = 0.019). In contrast, Class Mollicutes (P = 0.037), Genus Anaerostipes (P = 0.049), and Phylum Tenericutes (P = 0.024) emerged as potential GERD risk factors. In assessing reverse causation with GERD as the exposure and gut microbiota as the outcome, the findings indicate that GERD leads to dysbiosis in 13 distinct gut microbiota classes. The MR results’ reliability was confirmed by thorough assessments of heterogeneity and pleiotropy.ConclusionsFor the first time, the MR analysis indicates a genetic link between gut microbiota abundance changes and GERD risk. This not only substantiates the potential of intestinal microecological therapy for GERD, but also establishes a basis for advanced research into the role of intestinal microbiota in the etiology of GERD

Association between urinary excretion of protein-bound uremic toxins and upper urinary tract calculus

Objective·To investigate the relation between urinary excretion of protein-bound uremic toxins (PBUTs) and upper urinary tract calculus.Methods·Residents aged 18‒80 years in the community of Haitou, Danzhou city in Hainan Province were recruited. Basic information and diet for the last 3 d of the subjects were recorded. Their fasting sera and 24-hour urine samples were collected, and they also underwent ultrasound examination of kidneys and ureters. The subjects with upper urinary calculi detected by ultrasound or a clear history of upper urinary calculi were selected as the calculus group, and the others as the non-calculus group. The biochemical indicators related to the formation of calculus in blood and urine were detected, and the levels of PBUTs, including indoxyl sufate (IS), indole-3-acetic acid (IAA), and p-cresol sulfate (PCS) in blood and urine, as well as oxalic acid and citric acid in urine were detected by high-performance liquid chromatography. The related factors of upper urinary tract calculus formation were analyzed by multivariate Logistic regression. The correlations of urine PBUTs with urine uric acid, oxalic acid, and citric acid were analyzed by Spearman correlation test.Results·A total of 117 participants were screened out with 54 people in the calculus group and 63 people in the non-calculus group. There were no significant differences between the two groups in terms of gender, age, serum indicators, and prevalence of complications such as hypertension, diabetes, and hyperuricemia/gout. The 24-hour urine pH, calcium, uric acid, and chlorine in the calculus group were significantly higher than those in the non-calculus group (all P<0.05), while IS was significantly lower than that in the non-calculus group (P<0.05). Multivariate Logistic regression analysis showed that urinary IS (OR=0.929, 95%CI 0.875‒0.986, P=0.016) was related to the calculus formation independently, in addition to urinary calcium. The Spearman correlation analysis results showed that the levels of IAA (r=0.420, P=0.000) and PCS (r=0.307, P=0.001) in 24-hour urine were positively correlated with oxalic acid, PCS was positively correlated with uric acid (r=0.297, P=0.002), and IS was positively correlated with citric acid (r=0.289, P=0.002).Conclusion·In the population, a decrease in urinary excretion of IS may be an independent risk factor for the formation of upper urinary tract calculus, and PBUTs levels are correlated with levels of uric acid, oxalic acid, and citric acid

A Time-Varying Mixture Integer-Valued Threshold Autoregressive Process Driven by Explanatory Variables

In this paper, a time-varying first-order mixture integer-valued threshold autoregressive process driven by explanatory variables is introduced. The basic probabilistic and statistical properties of this model are studied in depth. We proceed to derive estimators using the conditional least squares (CLS) and conditional maximum likelihood (CML) methods, while also establishing the asymptotic properties of the CLS estimator. Furthermore, we employed the CLS and CML score functions to infer the threshold parameter. Additionally, three test statistics to detect the existence of the piecewise structure and explanatory variables were utilized. To support our findings, we conducted simulation studies and applied our model to two applications concerning the daily stock trading volumes of VOW