RESUMO
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. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.
RESUMO
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. Specifically, we quantified differences in behaviors between farms, privately owned forests, and protected areas and implemented a novel modeling framework to account for variation in detection among behaviors. This framework entailed estimating abundances of birds performing different behaviors while allowing detection probabilities of individuals to vary by behavior. Birds were 1.2 times more likely to exhibit reproductive behaviors in forest than in agriculture and 1.5 times more likely to exhibit reproductive behaviors in protected areas than in private forests. Species were not always most abundant in the habitats where they were most likely to exhibit foraging or reproductive behaviors. Finally, species of higher conservation concern were less abundant in agriculture than in forest. Together, our results highlight the importance of behavioral analyses for elucidating the conservation value of different land uses.
Efectos de la agricultura y las reservas naturales sobre el comportamiento de las aves en el noroeste de Costa Rica Resumen Los cambios conductuales suelen ser la primera respuesta de los animales ante el cambio ambiental y pueden funcionar como un barómetro para la viabilidad poblacional. Sin embargo, la mayoría de los estudios sobre la conversión del hábitat se enfocan en cambios en la presencia o abundancia de las especies. Analizamos más de 14,000 observaciones conductuales en las comunidades de 55 especies de aves del noroeste de Costa Rica para determinar cómo el uso de suelo afectó el comportamiento reproductivo, de forrajeo y otras formas pasivas no asociadas con las dos anteriores. En específico, cuantificamos las diferencias en el comportamiento entre granjas, bosques de propiedad privada y áreas protegidas e implementamos un marco novedoso de modelado para justificar la variación en la detección entre los comportamientos. Este marco implicó estimar la abundancia de aves que realizaban diferentes comportamientos mientras permitía que variaran las probabilidades de detección de individuos según el comportamiento. Fue 1.2 veces más probable que las aves exhibieran comportamiento reproductivo en el bosque que en las zonas agrícolas y 1.5 veces más probable que exhibieran estos comportamientos en las áreas protegidas que en los bosques privados. Las especies no siempre fueron las más abundantes en los hábitats en donde era más probable que exhibieran comportamientos reproductivos o de forrajeo. Por último, las especies de mayor preocupación para la conservación fueron menos abundantes en las zonas agrícolas que en los bosques. En conjunto, nuestros resultados resaltan la importancia del análisis conductual para ilustrar el valor de conservación de los diferentes usos de suelo.
Assuntos
Agricultura , Aves , Conservação dos Recursos Naturais , Animais , Costa Rica , Aves/fisiologia , Comportamento Animal , Florestas , Ecossistema , ReproduçãoRESUMO
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. Species that build exposed cup nests and species of higher conservation concern were particularly vulnerable to maximum temperature anomalies in agricultural settings. Finally, future projections suggested that ongoing climate change may exacerbate the negative effects of habitat conversion on avian nesting success, thereby compromising conservation efforts in human-dominated landscapes.
Assuntos
Agricultura , Aves , Mudança Climática , Temperatura Alta , Comportamento de Nidação , Animais , Humanos , Biodiversidade , Aves/fisiologia , Florestas , Reprodução , Estados Unidos , Pradaria , Conservação dos Recursos NaturaisRESUMO
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. Our MNCM accounts for imperfect detection in count data at the survey-point level while allowing comparisons of species richness among regions in which those points are embedded. We used individual canyons within mountain ranges as regions in our model and measured riparian area and the Normalized Landscape Shape Index, a metric of fragmentation that is independent of total riparian area. We found that riparian area, but not its fragmentation, was a primary predictor of canyon-level species richness of both riparian obligates and all species. The relationship between riparian area and riparian obligate species richness was nonlinear: canyons with ≥25 ha woody riparian vegetation had relatively high species richness, whereas species richness was considerably lower in canyons with <25 ha. Our MNCM can be used to calculate other metrics of diversity that require abundance estimates. For example, Simpson's evenness of riparian obligate species had a weak negative association with riparian area and was not associated with fragmentation. Projections of future riparian contraction suggested that decreases in species richness are likely to be greatest in canyons that currently have moderate (~10-25 ha) amounts of riparian vegetation. Our results suggest that if a goal of management is to maximize the species richness of breeding birds in montane riparian areas in the Great Basin, it may be more effective to focus on total habitat area than on fragmentation of patches within canyons, and that canyons with at least moderate amounts of riparian vegetation should be prioritized.
Assuntos
Biodiversidade , Rios , Animais , Melhoramento Vegetal , Ecossistema , AvesRESUMO
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. We developed an extension of a binomial N-mixture model (hereafter the behavior N-mixture model) to estimate the probability of a given behavior occurring in a particular environment while accounting for imperfect detection. We then conducted a simulation to validate the model's ability to estimate the effects of environmental covariates on the probabilities of individuals performing different behaviors. We compared our model to a naïve model that does not account for imperfect detection, as well as a traditional N-mixture model. Finally, we applied the model to a bird observation data set in northwest Costa Rica to quantify how three species behave in forests and farms. Simulations and sensitivity analyses demonstrated that the behavior N-mixture model produced unbiased estimates of behaviors and their relationships with predictor variables (e.g., forest cover, habitat type). Importantly, the behavior N-mixture model accurately characterized uncertainty, unlike the naïve model, which often suggested erroneous effects of covariates on behaviors. When applied to field data, the behavior N-mixture model suggested that Hoffmann's woodpecker (Melanerpes hoffmanii) and Inca dove (Columbina inca) behaved differently in forested versus agricultural habitats, while turquoise-browed motmot (Eumomota superciliosa) did not. Thus, the behavior N-mixture model can help identify habitats that are essential to a species' life cycle (e.g., where individuals nest, forage) that nonbehavioral models would miss. Our model can greatly improve the appropriate use of behavioral survey data and conclusions drawn from them. In doing so, it provides a valuable path forward for assessing the conservation value of alternative habitat types.