Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.

Objective: Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subphenotypes of Alzheimer's disease progression based on longitudinal real-world patient records.

Methods: The framework, dynaPhenoM, extracts coherent clinical topics across patient visits and employs a time-aware latent class analysis to characterize subphenotypes.

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology.

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question.

Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data.

Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping.

Effects of agriculture and nature reserves on avian behavior in northwestern Costa Rica.

Behavioral changes are often animals' first responses to environmental change and may act as a bellwether for population viability. Nonetheless, most studies of habitat conversion focus on changes in species occurrences or abundances. We analyzed >14,000 behavioral observations across 55 bird species in communities in northwestern Costa Rica to determine how land use affects reproductive, foraging, and other passive kinds of behaviors not associated with either foraging or reproduction.

Agriculture and hot temperatures interactively erode the nest success of habitat generalist birds across the United States.

Habitat conversion and climate change are fundamental drivers of biodiversity loss worldwide but are often analyzed in isolation. We used a continental-scale, decades-long database of more than 150,000 bird nesting attempts to explore how extreme heat affects avian reproduction in forests, grasslands, and agricultural and developed areas across the US. We found that in forests, extreme heat increased nest success, but birds nesting in agricultural settings were much less likely to successfully fledge young when temperatures reached anomalously high levels.

Multiple-region, N-mixture community model to assess associations of riparian area, fragmentation, and species richness.

The associations of habitat area and fragmentation with species richness long have been major topics within community ecology. Recent discussion has focused on properly assessing fragmentation independent of habitat area (fragmentation per se), and on whether fragmentation has significant negative or positive associations with species richness. We created a novel, multiple-region, N-mixture community model (MNCM) to examine the relations of riparian area and fragmentation with species richness of breeding birds in mountain ranges within the Great Basin, Nevada, USA.

A hierarchical N-mixture model to estimate behavioral variation and a case study of Neotropical birds.

Understanding how and why animals use the environments where they occur is both foundational to behavioral ecology and essential to identify critical habitats for species conservation. However, some behaviors are more difficult to observe than others, which can bias analyses of raw observational data. To our knowledge, no method currently exists to model how animals use different environments while accounting for imperfect behavior-specific detection probability.