Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

© 2020 The Authors. It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases are often in the form of a “black box” where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model/pattern, known as a “latent phenotype,” with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data

Magnetic properties of Fe and Tb in TbxFe1-x amorphous films studied with soft X-ray circular and linear dichroism.

Item does not contain fulltex

Modelling changes in trophic and structural impacts of alien ecosystem engineers on a rocky-shore island

17 pages, 6 figures, 5 tables, supplementary materials https://doi.org/10.1016/j.ecolmodel.2020.109227Between 1980 and 2012, successive arrivals by three alien ecosystem engineers on a rocky shore community at Marcus Island on the west coast of South Africa led to substantial changes in species composition and diversity. An ecosystem analysis of this open intertidal system was developed using Ecopath with Ecosim to determine the impacts of these aliens and the services they provide on the native community. A baseline Ecopath model of the community in 2015 was generated using values of biomass, production/biomass, consumption/biomass and the dietary composition of 30 functional groups. Ecosim, a time-dynamic modelling routine, was then used to simulate the changes in biomass of native species. A 1980 model (pre-invasion) was constructed and 22 simulations were run up to 2015 by systematically adding (1) biomass time series for non-native species; (2) relative biomass time series for native species; (3) mediation functions that mimicked biomass impacts due to changes in substrate, shelter and feeding grounds created by the alien ecosystem engineers; and (4) the effects of wave action as a source of mortality. Positive or negative influences of these ecological processes on diversity and the final biomasses of all groups in 2015 were assessed. Trophic impacts by the alien species affected diversity and biomass at the end of all simulations, but the addition of shelter or a combination of all three ecosystem services provided by ecosystem engineers (shelter, substrate and feeding grounds) resulted in 2015 model ecosystems that most closely matched the diversity and individual group biomasses empirically measured on Marcus Island in 2015. Wave action had only a minor impact. Marcus Island's rocky shore community was therefore driven mainly by the fixed input of alien species biomass and made more realistic by the incorporation of their ecosystem services. However, structural complexity and zonation, explored in a follow-up paper invoking spatial modelling, need to be represented for a more complete realisation of the ecosystemFinancial contributions from the University of Cape Town, the Andrew Mellon Foundation, the South African Research Chair Initiative (funded through the South African Department of Science and Innovation (DSI) and administered by the South African National Research Foundation (NRF)), and the DSI-NRF Centre of Excellence for Invasion Biology are gratefully acknowledged. [...]. MC acknowledges partial funding from the European Union's Horizon research program grant agreement No 689518 for the MERCES projectWith the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)Peer reviewe