Non-random length distribution of individual telomeres in immunodeficiency, centromeric instability and facial anomalies syndrome, type I.

Mutations in the de novo DNA methyltransferase DNMT3B lead to Immunodeficiency, Centromeric Instability and Facial anomalies (ICF) syndrome, type I. This syndrome is characterized, among other hypomethylated genomic loci, by severe subtelomeric hypomethylation that is associated with abnormally short telomere length. While it was demonstrated that the mean telomere length is significantly shorter in ICF type I cells, it is unknown whether all telomeres are equally vulnerable to shortening. To study this question we determined by combined telomere-FISH and spectral karyotyping the relative length of each individual telomere in lymphoblastoid cell lines (LCLs) generated from multiple ICF syndrome patients and control individuals. Here we confirm the short telomere lengths, and demonstrate that telomere length variance in the ICF patient group is much larger than in the control group, suggesting that not all telomeres shorten in a uniform manner. We identified a subgroup of telomeres whose relatively short lengths can distinguish with a high degree of certainty between a control and an ICF metaphase, proposing that in ICF syndrome cells, certain individual telomeres are consistently at greater risk to shorten than others. The majority of these telomeres display high sequence identity at the distal 2 kb of their subtelomeres, suggesting that the attenuation in DNMT3B methylation capacity affects individual telomeres to different degrees based, at least in part, on the adjacent subtelomeric sequence composition.

Developing a length of stay prediction model for newborns, achieving better accuracy with greater usability.

BACKGROUND: One in ten newborn children is born prematurely. The elongated length of stay (LOS) of these children in the Neonatal Intensive Care Unit (NICU) has important implications on hospital occupancy figures, healthcare and management costs, as well as the psychology of parents. In order to allow accurate planning and resource allocation, this study aims to create a generalizable and robust model to predict the NICU LOS of preterm newborns. METHODS: Data were collected from a large tertiary center NICU between 2011 and 2018 and relates to 5,362 newborns. The selected model was externally validated using a data set of 8,768 newborns from another tertiary center NICU. This report compares several models, such as Random Forest (RF), quantile RF, and other feature selection methods, including LASSO and AIC step-forward selection. In addition, a novel step-forward selection based on False Discovery Rate (FDR) for quantile regression is presented and evaluated. RESULTS: A high-orderquantile regression model for predicting preterm newborns' LOS that uses only four features available at birth had more attractive properties than other richer ones. The model achieved a Mean Absolute Error (MAE) of 6.26 days on the internal validation set (average LOS 27.04) and an MAE of 6.04 days on the external validation set (average LOS 29.32). The suggested model surpassed the accuracy obtained by models in the literature. It is shown empirically that the FDR-based selection has better properties than the AIC-based step-forward selection approach. CONCLUSION: This paper demonstrates a process to create a predictive model for NICU LOS in preterm newborns, where each step is reasoned. We obtain a simple and robust model for NICU LOS prediction, which achieves far better results than the current model used for financing NICUs. Utilizing this model, we have created an easy-to-use online web application to ease parents' worries and to assist NICU management: https://tzviel.shinyapps.io/calcuLOS.

Exploration in the Presence of Mother in Typically and Non-typically Developing Pre-walking Human Infants.

In previous phenotyping studies of mouse and rat exploratory behavior we developed a computational exploratory data analysis methodology including videotaping, tracking, preparatory methods for customized data analysis, a methodology for improving the replicability of results across laboratories, and algorithmic design for exposing the natural reference places (origins) used by animals during exploration. We then measured the animals' paths in reference to these origins, revealing robust, highly replicable modules termed excursions, which are performed from the origin into the environment and back to the origin. Origin-related exploration has been claimed to be phylogenetically conserved across the vertebrates. In the current study we use the same methodology to examine whether origin-related exploration has also been conserved in human pre-walking typically developing (TD) and a group of non-typically developing (NTD) infants in the presence of their stationary mother. The NTDs had been referred to a center for the early treatment of autism in infancy by pediatric neurologists and clinicians. The TDs established a reference place (origin) at mother's place and exhibited a modular partitioning of their path into excursions performed in reference to mother, visiting her often, and reaching closely. In contrast, the NTDs did not establish a distinct origin at the mother's place, or any other place, and did not partition the exploratory path into excursions. Once this difference is validated, the differences between the human infant groups may serve as an early referral tool for child development specialists. The absence of distinct modularity in human infants at risk of autism spectrum disorder can guide the search for animal models for this disorder in translational research.

The Importance of Nonlinear Transformations Use in Medical Data Analysis.

BACKGROUND: The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked. OBJECTIVE: Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages. METHODS: We describe 10 commonly encountered data types used in the medical field and the relevant transformations for each data type. Data from the Alzheimer's Disease Neuroimaging Initiative study, Parkinson's disease hospital cohort, and disease-simulating data were used to demonstrate the approach and its benefits. RESULTS: Symmetry-targeted monotone transformations were applied, and the advantages gained in variance, stability, linearity, and clustering are demonstrated. An open source application implementing the described methods was developed. Both linearity of relationships and increase of stability of variability improved after applying proper nonlinear transformation. Clustering simulated nonsymmetric data gave low agreement to the generating clusters (Rand value=0.681), while capturing the original structure after applying nonlinear transformation to symmetry (Rand value=0.986). CONCLUSIONS: This work presents the use of nonlinear transformations for medical data and the importance of their semi-automated choice. Using the described approach, the data analyst increases the ability to create simpler, more robust and translational models, thereby facilitating the interpretation and implementation of the analysis by medical practitioners. Applying nonlinear transformations as part of the preprocessing is essential to the quality and interpretability of results.