Survival estimation for singly type one censored sample based on generalized Rayleigh distribution

This paper interest to estimation the unknown parameters for generalized Rayleigh distribution model based on censored samples of singly type one . In this paper the probability density function for generalized Rayleigh is defined with its properties . The maximum likelihood estimator method is used to derive the point estimation for all unknown parameters based on iterative method , as Newton – Raphson method , then derive confidence interval estimation which based on Fisher information matrix . Finally , testing whether the current model ( GRD ) fits to a set of real data , then compute the survival function and hazard function for this real data

Baysian and NonBaysian Methods to Estimate the two parameters of Logistic Distribution

In this paper ,the problem of point estimation for the two parameters of logistic distribution has been investigated using simulation technique. The rank sampling set estimator method which is one of the Non_Baysian procedure and Lindley approximation estimator method which is one of the Baysian method were used to estimate the parameters of logistic distribution. Comparing between these two mentioned methods by employing mean square error measure and mean absolute percentage error measure .At last simulation technique used to generate many number of samples sizes to compare between these methods

Turbid of Water By Using Fuzzy C- Means and Hard K- Means

في هذا البحث طبقنا طريقتين الأولى Fuzzy C Means (FCM)  و الثانية Hard K Means (KM) لمعرفة ايهما الأفضل حيث طبقنا كلتاهما على مجموعة من البيانات التي جمعت من وزارة التخطيط عن تلوث (عكرة ) المياه لخمس مناطق في بغداد وذلك لمعرفة أي من هذه  المناطق اقل تلوث (عكرة )  في الماء الصافي  وبعد ان نحدد المنطقة الأقل تلوث (عكره) في الماء الصافي  نطبق طريقة Hard K Means (KM) لمعرفة أي الأشهر خلال السنة اقل تلوث (عكرة) في الماء الصافي في المنطقة المحددة .In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area

Comparison between Modified Weighted Pareto Distribution and Many other Distributions

  In 2020 one of the researchers in this paper, in his first research, tried to find out the Modified Weighted Pareto Distribution of Type I by using the Azzalini method for weighted distributions, which contain three parameters, two of them for scale while the third for shape.This research compared the distribution with two other distributions from the same family; the Standard Pareto Distribution of Type I and the Generalized Pareto Distribution by using the Maximum likelihood estimator which was derived by the researchers for Modified Weighted Pareto Distribution of Type I, then the Mont Carlo method was used–that is one of the simulation manners for generating random samples data in different sizes ( n= 10,30,50), and in different initial values for each Pareto distribution family being used in the research. A comparison was done by using Akaike Information Criteria, Corrected Akaike Information Criteria, and Bayesian Information Criteri

Islet Autoantibody Measurements from Dried Blood Spots on Filter Paper Strongly Correlate to Serum Levels

Type 1 diabetes (T1D) is increasing in incidence and predictable with measurement of serum islet autoantibodies (iAb) years prior to clinical disease onset. Identifying iAb positive individuals reduces diabetic ketoacidosis and identifies individuals for T1D prevention trials. However, large scale screening for iAb remains challenging as assays have varying sensitivities and specificities, insulin autoantibodies remain difficult to measure and venipuncture is generally required to obtain serum. We developed an approach to reliably measure all four major iAb, including insulin autoantibodies, from dried blood spots (DBS) on filter-paper. By spiking iAb positive serum into iAb negative whole blood in a dose titration, we optimized the conditions for autoantibody elution from filter paper as measured by fluid phase radioimmunoassays. After assessing stability of measuring iAb from DBS over time, we then screened iAb from DBS and the corresponding serum in new-onset T1D (n = 52), and controls (n = 72) which included first-degree relatives of T1D patients. iAb measured from eluted DBS in new-onset T1D strongly correlated with serum measurements (R2 = 0.96 for mIAA, GADA = 0.94, IA-2A = 0.85, ZnT8A = 0.82, p<0.01 for each autoantibody). There were no false positives in control subjects, and 5/6 with previously unknown iAb positivity in sera were detected using DBS. With further validation, measuring iAb from DBS can be a reliable method to screen for T1D risk

Combinatorial detection of autoreactive CD8+ T cells with HLA-A2 multimers: a multi-centre study by the Immunology of Diabetes Society T Cell Workshop

Aims/hypothesis: Validated biomarkers are needed to monitor the effects of immune intervention in individuals with type 1 diabetes. Despite their importance, few options exist for monitoring antigen-specific T cells. Previous reports described a combinatorial approach that enables the simultaneous detection and quantification of multiple islet-specific CD8+ T cell populations. Here, we set out to evaluate the performance of a combinatorial HLA-A2 multimer assay in a multi-centre setting. Methods: The combinatorial HLA-A2 multimer assay was applied in five participating centres using centralised reagents and blinded replicate samples. In preliminary experiments, samples from healthy donors were analysed using recall antigen multimers. In subsequent experiments, samples from healthy donors and individuals with type 1 diabetes were analysed using beta cell antigen and recall antigen multimers. Results: The combinatorial assay was successfully implemented in each participating centre, with CVs between replicate samples that indicated good reproducibility for viral epitopes (mean %CV = 33.8). For beta cell epitopes, the assay was very effective in a single-centre setting (mean %CV = 18.4), but showed sixfold greater variability across multi-centre replicates (mean %CV = 119). In general, beta cell antigen-specific CD8+ T cells were detected more commonly in individuals with type 1 diabetes than in healthy donors. Furthermore, CD8+ T cells recognising HLA-A2-restricted insulin and glutamate decarboxylase epitopes were found to occur at higher frequencies in individuals with type 1 diabetes than in healthy donors. Conclusions/interpretation Our results suggest that, although combinatorial multimer assays are challenging, they can be implemented in multiple laboratories, providing relevant T cell frequency measurements. Assay reproducibility was notably higher in the single-centre setting, suggesting that biomarker analysis of clinical trial samples would be most successful when assays are performed in a single laboratory. Technical improvements, including further standardisation of cytometry platforms, will likely be necessary to reduce assay variability in the multi-centre setting

Microbial antigen mimics activate diabetogenic CD8 T cells in NOD mice

Both animal model and human studies indicate that commensal bacteria may modify type 1 diabetes (T1D) development. However, the underlying mechanisms by which gut microbes could trigger or protect from diabetes are not fully understood, especially the interaction of commensal bacteria with pathogenic CD8 T cells. In this study, using islet-specific glucose-6-phosphatase catalytic subunit–related protein (IGRP)–reactive CD8 T cell receptor NY8.3 transgenic nonobese diabetic mice, we demonstrated that MyD88 strongly modulates CD8+ T cell–mediated T1D development via the gut microbiota. Some microbial protein peptides share significant homology with IGRP. Both the microbial peptide mimic of Fusobacteria and the bacteria directly activate IGRP-specific NY8.3 T cells and promote diabetes development. Thus, we provide evidence of molecular mimicry between microbial antigens and an islet autoantigen and a novel mechanism by which the diabetogenicity of CD8+ T cells can be regulated by innate immunity and the gut microbiota

Regulation of type 1 diabetes development and B-cell activation in nonobese diabetic mice by early life exposure to a diabetogenic environment

Microbes, including viruses, influence type 1 diabetes (T1D) development, but many such influences remain undefined. Previous work on underlying immune mechanisms has focussed on cytokines and T cells. Here, we compared two nonobese diabetic (NOD) mouse colonies, NODlow and NODhigh, differing markedly in their cumulative T1D incidence (22% vs. 90% by 30 weeks in females). NODhigh mice harbored more complex intestinal microbiota, including several pathobionts; both colonies harbored segmented filamentous bacteria (SFB), thought to suppress T1D. Young NODhigh females had increased B-cell activation in their mesenteric lymph nodes. These phenotypes were transmissible. Co-housing of NODlow with NODhigh mice after weaning did not change T1D development, but T1D incidence was increased in female offspring of co-housed NODlow mice, which were exposed to the NODhigh environment both before and after weaning. These offspring also acquired microbiota and B-cell activation approaching those of NODhigh mice. In NODlow females, the low rate of T1D was unaffected by cyclophosphamide but increased by PD-L1 blockade. Thus, environmental exposures that are innocuous later in life may promote T1D progression if acquired early during immune development, possibly by altering B-cell activation and/or PD-L1 function. Moreover, T1D suppression in NOD mice by SFB may depend on the presence of other microbial influences. The complexity of microbial immune regulation revealed in this murine model may also be relevant to the environmental regulation of human T1D

Intestinal Lactobacillus in health and disease, a driver or just along for the ride?

Metagenomics and related methods have led to significant advances in our understanding of the human microbiome. Members of the genus Lactobacillus, although best understood for essential roles in food fermentations and applications as probiotics, have also come to the fore in a number of untargeted gut microbiome studies in humans and animals. Even though Lactobacillus is only a minor member of the human colonic microbiota, the proportions of those bacteria are frequently either positively or negatively correlated with human disease and chronic conditions. Recent findings on Lactobacillus species in human and animal microbiome research, together with the increased knowledge on probiotic and other ingested lactobacilli, have resulted in new perspectives on the importance of this genus to human health