Vizualni identitet Koprivničko-križevačke županije

BackgroundPrevious studies using resting-state functional neuroimaging have revealed alterations in whole-brain images, connectome-wide functional connectivity and graph-based metrics in groups of patients with schizophrenia relative to groups of healthy controls. However, it is unclear which of these measures best captures the neural correlates of this disorder at the level of the individual patient.MethodsHere we investigated the relative diagnostic value of these measures. A total of 295 patients with schizophrenia and 452 healthy controls were investigated using resting-state functional Magnetic Resonance Imaging at five research centres. Connectome-wide functional networks were constructed by thresholding correlation matrices of 90 brain regions, and their topological properties were analyzed using graph theory-based methods. Single-subject classification was performed using three machine learning (ML) approaches associated with varying degrees of complexity and abstraction, namely logistic regression, support vector machine and deep learning technology.ResultsConnectome-wide functional connectivity allowed single-subject classification of patients and controls with higher accuracy (average: 81%) than both whole-brain images (average: 53%) and graph-based metrics (average: 69%). Classification based on connectome-wide functional connectivity was driven by a distributed bilateral network including the thalamus and temporal regions.ConclusionThese results were replicated across the three employed ML approaches. Connectome-wide functional connectivity permits differentiation of patients with schizophrenia from healthy controls at single-subject level with greater accuracy; this pattern of results is consistent with the 'dysconnectivity hypothesis' of schizophrenia, which states that the neural basis of the disorder is best understood in terms of system-level functional connectivity alterations

Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Tropical savanna ecosystems play a major role in the seasonality of the global carbon cycle. However, their ability to store and sequester carbon is uncertain due to combined and intermingling effects of anthropogenic activities and climate change, which impact wildfire regimes and vegetation dynamics. Accurate measurements of tropical savanna vegetation aboveground biomass (AGB) over broad spatial scales are crucial to achieve effective carbon emission mitigation strategies. UAV-lidar is a new remote sensing technology that can enable rapid 3-D mapping of structure and related AGB in tropical savanna ecosystems. This study aimed to assess the capability of high-density UAV-lidar to estimate and map total (tree, shrubs, and surface layers) aboveground biomass density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary least square regression models esti-mating AGBt were adjusted using 50 field sample plots (30 m × 30 m). The best model was selected under Akaike Information Criterion, adjusted coefficient of determination (adj.R2), absolute and relative root mean square error (RMSE), and used to map AGBt from UAV-lidar data collected over 1,854 ha spanning the three major vegetation formations (forest, savanna, and grassland) in Cerrado. The model using vegetation height and cover was the most effective, with an overall model adj-R2 of 0.79 and a leave-one-out cross-validated RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our estimations were assessed for each vegetation formation separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha (44.92%) for grasslands. These results prove the feasibility and potential of the UAV-lidar technology in Cerrado but also emphasize the need for further developing the estimation of biomass in grasslands, of high importance in the characterization of the global carbon balance and for supporting integrated fire management activities in tropical savanna ecosystems. Our results serve as a benchmark for future studies aiming to generate accurate biomass maps and provide baseline data for efficient management of fire and predicted climate change impacts on tropical savanna ecosystems

Domestic dog health worsens with socio-economic deprivation of their home communities

Dogs play an important role in infectious disease transmission as reservoir hosts of many zoonotic and wildlife pathogens. Nevertheless, unlike wildlife species involved in the life cycle of pathogens, whose health status might be a direct reflection of their fitness and competitive abilities, dog health condition could be sensitive to socio-economic factors impacting the well-being of their owners. Here, we compare several dog health indicators in three rural communities of Panama with different degrees of socio-economic deprivation. From a total of 78 individuals, we collected blood and fecal samples, and assessed their body condition. With the blood samples, we performed routine hematologic evaluation (complete blood counts) and measured cytokine levels (Interferon-γ and Interleukin-10) through enzyme-linked immunosorbent assays. With the fecal samples we diagnosed helminthiases. Dogs were also serologically tested for exposure to Trypanosoma cruzi and canine distemper virus, and molecular tests were done to assess T. cruzi infection status. We found significant differences between dog health measurements, pathogen prevalence, parasite richness, and economic status of the human communities where the dogs lived. We found dogs that were less healthy, more likely to be infected with zoonotic pathogens, and more likely to be seropositive to canine distemper virus in the communities with lower economic status. This study concludes that isolated communities of lower economic status in Panama may have less healthy dogs that could become major reservoirs in the transmission of diseases to humans and sympatric wildlife