Beta slashed generalised half-normal distribution

In this paper we propose the beta slashed generalised half-normal distribution, which includes some important distributions such as the half-normal, slashed half-normal and generalised slashed half-normal distributions. Explicit expressions for the cumulative distribution and characteristic functions are derived. The maximum likelihood estimates of the parameters are obtained via the EM algorithm and the value of the proposed model is illustrated with an application on fatigue data.This work was based on research supported by STATOMET, Department of Statistics, University of Pretoria.http://www.sastat.org.za/journal/informationhttp://www.journals.co.za/content/journal/sasjam2018Statistic

A comparison of data mining and spatial techniques : an application to property data

The improvement of data management and data capturing techniques has led to the availability of large amounts of data for analysis. This is especially true in the field of spatial data, where data is indexed by location. Traditionally, spatially correlated data has been analysed using methods that rely on the spatial component of the data. This article will compare the results of using traditional spatial methods such as Kriging and geographically weighted regression against the use of other statistical data mining methods, given the large amount of data available. Using a dataset containing property values for the Tshwane Metropolitan area, different spatial and statistical models will be applied for predictive purposes in order to determine which model represents the data most accurately. Finally, these methods will be combined using stacking, to determine whether the combination of models has better predictive abilities than the single models.http://www.sastat.org.za/journal.htmam201

Phase I interim results of a phase I/II study of the IgG-Fc fusion COVID-19 subunit vaccine, AKS-452

To address the coronavirus disease 2019 (COVID-19) pandemic caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a recombinant subunit vaccine, AKS-452, is being developed comprising an Fc fusion protein of the SARS-CoV-2 viral spike protein receptor binding domain (SP/RBD) antigen and human IgG1 Fc emulsified in the water-in-oil adjuvant, Montanide™ ISA 720. A single-center, open-label, phase I dose-finding and safety study was conducted with 60 healthy adults (18–65 years) receiving one or two doses 28 days apart of 22.5 µg, 45 µg, or 90 µg of AKS-452 (i.e., six cohorts, N = 10 subjects per cohort). Primary endpoints were safety and reactogenicity and secondary endpoints were immunogenicity assessments. No AEs ≥ 3, no SAEs attributable to AKS-452, and no SARS-CoV-2 viral infections occurred during the study. Seroconversion rates of anti-SARS-CoV-2 SP/RBD IgG titers in the 22.5, 45, and 90 µg cohorts at day 28 were 70%, 90%, and 100%, respectively, which all increased to 100% at day 56 (except 89% for the single-dose 22.5 µg cohort). All IgG titers were Th1-isotype skewed and efficiently bound mutant SP/RBD from several SARS-CoV-2 variants with strong neutralization potencies of live virus infection of cells (including alpha and delta variants). The favorable safety and immunogenicity profiles of this phase I study (ClinicalTrials.gov: NCT04681092) support phase II initiation of this room-temperature stable vaccine that can be rapidly and inexpensively manufactured to serve vaccination at a global scale without the need of a complex distribution or cold chain

Beta slashed generalised half-normal distribution

In this paper we propose the beta slashed generalised half-normal distribution, which includes some important distributions such as the half-normal, slashed half-normal and generalised slashed half-normal distributions. Explicit expressions for the cumulative distribution and characteristic functions are derived. The maximum likelihood estimates of the parameters are obtained via the EM algorithm and the value of the proposed model is illustrated with an application on fatigue data

Mixtures of semi-parametric generalised linear models

The mixture of generalised linear models (MGLM) requires knowledge about each mixture component’s specific exponential family (EF) distribution. This assumption is relaxed and a mixture of semi-parametric generalised linear models (MSPGLM) approach is proposed, which allows for unknown distributions of the EF for each mixture component while much of the parametric structure of the traditional MGLM is retained. Such an approach inherently allows for both symmetric and non-symmetric component distributions, frequently leading to non-symmetrical response variable distributions. It is assumed that the random component of each mixture component follows an unknown distribution of the EF. The specific member can either be from the standard class of distributions or from the broader set of admissible distributions of the EF which is accessible through the semi-parametric procedure. Since the inverse link functions of the mixture components are unknown, the MSPGLM estimates each mixture component’s inverse link function using a kernel smoother. The MSPGLM algorithm alternates the estimation of the regression parameters with the estimation of the inverse link functions. The properties of the proposed MSPGLM are illustrated through a simulation study on the separable individual components. The MSPGLM procedure is also applied on two data sets.STATOMET, the Bureau for Statistical and Survey Methodology at the University of Pretoria.https://www.mdpi.com/journal/symmetryam2023Statistic

Fitting non-parametric mixture of regressions : introducing an EM-type algorithm to address the label-switching problem

The non-parametric Gaussian mixture of regressions (NPGMRs) model serves as a flexible approach for the determination of latent heterogeneous regression relationships. This model assumes that the component means, variances and mixing proportions are smooth unknown functions of the covariates where the error distribution of each component is assumed to be Gaussian and hence symmetric. These functions are estimated over a set of grid points using the Expectation- Maximization (EM) algorithm to maximise the local-likelihood functions. However, maximizing each local-likelihood function separately does not guarantee that the local responsibilities and corresponding labels, obtained at the E-step of the EM algorithm, align at each grid point leading to a label-switching problem. This results in non-smooth estimated component regression functions. In this paper, we propose an estimation procedure to account for label switching by tracking the roughness of the estimated component regression functions. We use the local responsibilities to obtain a global estimate of the responsibilities which are then used to maximize each local-likelihood function. The performance of the proposed procedure is demonstrated using a simulation study and through an application using real world data. In the case of well-separated mixture regression components, the procedure gives similar results to competitive methods. However, in the case of poorly separated mixture regression components, the procedure outperforms competitive methods.DATA AVAILABILITY STATEMENT : Publicly available datasets were analyzed in this study. This data can be found here: https://databank.worldbank.org/source/world-development-indicators/ accessed on 15 February 2022.STATOMET, the Bureau for Statistical and Survey Methodology at the University of Pretoria.https://www.mdpi.com/journal/symmetryam2023Statistic

A New Coding System for Metabolic Disorders Demonstrates Gaps in the International Disease Classifications ICD-10 and SNOMED-CT, Which Can Be Barriers to Genotype-Phenotype Data Sharing

<p>Data sharing is essential for a better understanding of genetic disorders. Good phenotype coding plays a key role in this process. Unfortunately, the two most widely used coding systems in medicine, ICD-10 and SNOMED-CT, lack information necessary for the detailed classification and annotation of rare and genetic disorders. This prevents the optimal registration of such patients in databases and thus data-sharing efforts. To improve care and to facilitate research for patients with metabolic disorders, we developed a new coding system for metabolic diseases with a dedicated group of clinical specialists. Next, we compared the resulting codes with those in ICD and SNOMED-CT. No matches were found in 76% of cases in ICD-10 and in 54% in SNOMED-CT. We conclude that there are sizable gaps in the SNOMED-CT and ICD coding systems for metabolic disorders. There may be similar gaps for other classes of rare and genetic disorders. We have demonstrated that expert groups can help in addressing such coding issues. Our coding system has been made available to the ICD and SNOMED-CT organizations as well as to the Orphanet and HPO organizations for further public application and updates will be published online (www.ddrmd.nl and www.cineas.org).</p>

Development of an IgG-Fc fusion COVID-19 subunit vaccine, AKS-452.

AKS-452 is a biologically-engineered vaccine comprising an Fc fusion protein of the SARS-CoV-2 viral spike protein receptor binding domain antigen (Ag) and human IgG1 Fc (SP/RBD-Fc) in clinical development for the induction and augmentation of neutralizing IgG titers against SARS-CoV-2 viral infection to address the COVID-19 pandemic. The Fc moiety is designed to enhance immunogenicity by increasing uptake via Fc-receptors (FcγR) on Ag-presenting cells (APCs) and prolonging exposure due to neonatal Fc receptor (FcRn) recycling. AKS-452 induced approximately 20-fold greater neutralizing IgG titers in mice relative to those induced by SP/RBD without the Fc moiety and induced comparable long-term neutralizing titers with a single dose vs. two doses. To further enhance immunogenicity, AKS-452 was evaluated in formulations containing a panel of adjuvants in which the water-in-oil adjuvant, Montanide™ ISA 720, enhanced neutralizing IgG titers by approximately 7-fold after one and two doses in mice, including the neutralization of live SARS-CoV-2 virus infection of VERO-E6 cells. Furthermore, ISA 720-adjuvanted AKS-452 was immunogenic in rabbits and non-human primates (NHPs) and protected from infection and clinical symptoms with live SARS-CoV-2 virus in NHPs (USA-WA1/2020 viral strain) and the K18 human ACE2-trangenic (K18-huACE2-Tg) mouse (South African B.1.351 viral variant). These preclinical studies support the initiation of Phase I clinical studies with adjuvanted AKS-452 with the expectation that this room-temperature stable, Fc-fusion subunit vaccine can be rapidly and inexpensively manufactured to provide billions of doses per year especially in regions where the cold-chain is difficult to maintain

A randomized phase I/II safety and immunogenicity study of the Montanide-adjuvanted SARS-CoV-2 spike protein-RBD-Fc vaccine, AKS-452

Background: Previous interim data from a phase I study of AKS-452, a subunit vaccine comprising an Fc fusion of the respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor binding domain (SP/RBD) emulsified in the water-in-oil adjuvant, Montanide™ ISA 720, suggested a good safety and immunogenicity profile in healthy adults. This phase I study was completed and two dosing regimens were further evaluated in this phase II study. Methods: This phase II randomized, open-labelled, parallel group study was conducted at a single site in The Netherlands with 52 healthy adults (18 – 72 years) receiving AKS-452 subcutaneously at one 90 µg dose (cohort 1, 26 subjects) or two 45 µg doses 28 days apart (cohort 2, 26 subjects). Serum samples were collected at the first dose (day 0) and at days 28, 56, 90, and 180. Safety and immunogenicity endpoints were assessed, along with induction of IgG isotypes, cross-reactive immunity against viral variants, and IFN-γ T cell responses. Results: All AEs were mild/moderate (grades 1 or 2), and no SAEs were attributable to AKS-452. Seroconversion rates reached 100% in both cohorts, although cohort 2 showed greater geometric mean IgG titers that were stable through day 180 and associated with enhanced potencies of SP/RBD-ACE2 binding inhibition and live virus neutralization. AKS-452-induced IgG titers strongly bound mutant SP/RBD from several SARS-CoV-2 variants (including Omicrons) that were predominantly of the favorable IgG1/3 isotype and IFN-γ-producing T cell phenotype. Conclusion: These favorable safety and immunogenicity profiles of the candidate vaccine as demonstrated in this phase II study are consistent with those of the phase I study (ClinicalTrials.gov: NCT04681092) and suggest that a total of 90 µg received in 2 doses may offer a greater duration of cross-reactive neutralizing titers than when given in a single dose