Research trends in combinatorial optimization

Acknowledgments This work has been partially funded by the Spanish Ministry of Science, Innovation, and Universities through the project COGDRIVE (DPI2017-86915-C3-3-R). In this context, we would also like to thank the Karlsruhe Institute of Technology. Open access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

Effects of El Nino events on Darwin's finch productivity

We studied the effects of heavy and prolonged rainfall associated with four El Nino events on the reproduction of Darwin's finches on the Galapagos island of Daphne Major. Rainfall varied in the El Nino years from 195 mm to 1359 mm, exceeding the maximum in the other years by 40% to 1000%. Two species were studied: Geospiza fortis, Medium Ground Finch, and G. scandens, Cactus Finch. Almost all eggs, nestlings, and fledglings produced by banded females were recorded in the El Nino years of 1983, 1987, 1991, and 1998. Finch production in these years was compared with production in 10 other years of breeding in the period 1976-1990; there was no breeding in three drought years, 1985, 1988, and 1989. Breeding differed in the two sets of years in several ways. More broods were produced in El Nino years than in non-El Nino years (maximum 10 clutches per female vs. five clutches), the period of breeding was longer (maximum eight months vs. four), average clutch sizes (range 2-6 eggs) were distinctly larger (four vs. three), and average egg and fledgling production per female was larger by a factor of four. The two species differed in some features of breeding, but differences were minor in comparison with the marked seasonal and annual variation. Finch production varied among El Nino years, being greatest in the year of most rain over the longest period (1983), and least in the next wettest year (1998). The surprisingly low production in 1998 is attributed in part to interactions with other finches, and in part to exceptionally high temperatures. Temperature, although postively correlated with rainfall, had an independent negative effect upon hatching and overall breeding success of G. fortis. Breeding by both species in the year of birth (hatch) occurred in two El Nino years with the most extended wet seasons: 1983 and 1987. Young breeders had lower clutch sizes and breeding success than did contemporaneously breeding older birds. Observations in different El Nino years show that finch population responses to major climatic perturbations such as elevated rainfall vary for two major reasons: the perturbations themselves vary in strength and duration, and responses to them are determined, in part, by preceding conditions. Those preceding conditions, in turn, are determined by whether drought or normal conditions precede the perturbation, and on the interval since the previous El Nino event. Thus, perturbations of natural systems can be fully understood only in a broad temporal context