Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks

Data preprocessing is a crucial part of any machine learning pipeline, and it can have a significant impact on both performance and training efficiency. This is especially evident when using deep neural networks for time series prediction and classification: real-world time series data often exhibit irregularities such as multi-modality, skewness and outliers, and the model performance can degrade rapidly if these characteristics are not adequately addressed. In this work, we propose the EDAIN (Extended Deep Adaptive Input Normalization) layer, a novel adaptive neural layer that learns how to appropriately normalize irregular time series data for a given task in an end-to-end fashion, instead of using a fixed normalization scheme. This is achieved by optimizing its unknown parameters simultaneously with the deep neural network using back-propagation. Our experiments, conducted using synthetic data, a credit default prediction dataset, and a large-scale limit order book benchmark dataset, demonstrate the superior performance of the EDAIN layer when compared to conventional normalization methods and existing adaptive time series preprocessing layers

Functional polymorphisms within the inflammatory pathway regulate expression of extracellular matrix components in a genetic risk dependent model for anterior cruciate ligament injuries

Objectives: To investigate the functional effect of genetic polymorphisms of the inflammatory pathway on structural extracellular matrix components (ECM) and the susceptibility to an anterior cruciate ligament (ACL) injury. Design: Laboratory study, case–control study. Methods: Eight healthy participants were genotyped for interleukin (IL)1B rs16944 C > T and IL6 rs1800795 G > C and

Prospecting environmental mycobacteria: combined molecular approaches reveal unprecedented diversity

Background: Environmental mycobacteria (EM) include species commonly found in various terrestrial and aquatic environments, encompassing animal and human pathogens in addition to saprophytes. Approximately 150 EM species can be separated into fast and slow growers based on sequence and copy number differences of their 16S rRNA genes. Cultivation methods are not appropriate for diversity studies; few studies have investigated EM diversity in soil despite their importance as potential reservoirs of pathogens and their hypothesized role in masking or blocking M. bovis BCG vaccine. Methods: We report here the development, optimization and validation of molecular assays targeting the 16S rRNA gene to assess diversity and prevalence of fast and slow growing EM in representative soils from semi tropical and temperate areas. New primer sets were designed also to target uniquely slow growing mycobacteria and used with PCR-DGGE, tag-encoded Titanium amplicon pyrosequencing and quantitative PCR. Results: PCR-DGGE and pyrosequencing provided a consensus of EM diversity; for example, a high abundance of pyrosequencing reads and DGGE bands corresponded to M. moriokaense, M. colombiense and M. riyadhense. As expected pyrosequencing provided more comprehensive information; additional prevalent species included M. chlorophenolicum, M. neglectum, M. gordonae, M. aemonae. Prevalence of the total Mycobacterium genus in the soil samples ranged from 2.3×107 to 2.7×108 gene targets g−1; slow growers prevalence from 2.9×105 to 1.2×107 cells g−1. Conclusions: This combined molecular approach enabled an unprecedented qualitative and quantitative assessment of EM across soil samples. Good concordance was found between methods and the bioinformatics analysis was validated by random resampling. Sequences from most pathogenic groups associated with slow growth were identified in extenso in all soils tested with a specific assay, allowing to unmask them from the Mycobacterium whole genus, in which, as minority members, they would have remained undetected

Inherited retinal disorders in South Africa and the clinical impact of evolving technologies

Retinal degenerative disorders (RDDs) encompass a group of inherited diseases characterised by vision loss. The genetic and clinical complexity poses a challenge in unravelling the molecular genetic aetiology of this group of disorders. Furthermore, the population diversity in South Africa (SA) presents researchers with a particularly complicated task. Rapid advances in the development of cutting-edge technological platforms over the past two decades, however, have assisted in overcoming some of the challenges. The RDD research team has utilised these escalating technologies, which has facilitated a corresponding increase in molecular diagnoses. A biorepository has been established and comprises ~3 200 patient DNA samples archived with many forms of RDD (including retinitis pigmentosa, macular dystrophies, Stargardt disease, Leber congenital amaurosis, Usher syndrome and Bardet Biedl syndrome). A comprehensive review is presented of the SA journey spanning 25 years, into elucidating the molecular genetic basis of various forms of RDD in SA

An exploration into the impact of exposure to community violence and hope on children's perceptions of well-being: a South African perspective

The study aims to explore the relationship between exposure to community violence, hope, and well-being. More specifically, the study aims to ascertain whether hope is a stronger predictor of well-being than exposure to violence. Stratified random sampling was used to select a sample of 566 adolescents aged 14–17 years, from both high violence and low violence areas in Cape Town, South Africa. A questionnaire consisting of Snyder’s Children’s Hope Scale, the Recent Exposure to Violence Scale and the KIDSCREEN-52 was used. Data analysis techniques included descriptive statistics, correlations, and multiple regression. A positive, significant relationship was found between children’s hope and their well-being. Although exposure to community violence was found to be significantly correlated with wellbeing, the relationship was negligible.While exposure to community violence and hope were found to be significant predictors of well-being, hope emerged as a stronger predictor of child well-being than exposure to community violence.Department of HE and Training approved lis