Multiple indices of diffusion identifies white matter damage in mild cognitive impairment and Alzheimer's disease

The study of multiple indices of diffusion, including axial (DA), radial (DR) and mean diffusion (MD), as well as fractional anisotropy (FA), enables WM damage in Alzheimer's disease (AD) to be assessed in detail. Here, tract-based spatial statistics (TBSS) were performed on scans of 40 healthy elders, 19 non-amnestic MCI (MCIna) subjects, 14 amnestic MCI (MCIa) subjects and 9 AD patients. Significantly higher DA was found in MCIna subjects compared to healthy elders in the right posterior cingulum/precuneus. Significantly higher DA was also found in MCIa subjects compared to healthy elders in the left prefrontal cortex, particularly in the forceps minor and uncinate fasciculus. In the MCIa versus MCIna comparison, significantly higher DA was found in large areas of the left prefrontal cortex. For AD patients, the overlap of FA and DR changes and the overlap of FA and MD changes were seen in temporal, parietal and frontal lobes, as well as the corpus callosum and fornix. Analysis of differences between the AD versus MCIna, and AD versus MCIa contrasts, highlighted regions that are increasingly compromised in more severe disease stages. Microstructural damage independent of gross tissue loss was widespread in later disease stages. Our findings suggest a scheme where WM damage begins in the core memory network of the temporal lobe, cingulum and prefrontal regions, and spreads beyond these regions in later stages. DA and MD indices were most sensitive at detecting early changes in MCIa

Infections with Avian Pathogenic and Fecal Escherichia coli Strains Display Similar Lung Histopathology and Macrophage Apoptosis

The purpose of this study was to compare histopathological changes in the lungs of chickens infected with avian pathogenic (APEC) and avian fecal (Afecal) Escherichia coli strains, and to analyze how the interaction of the bacteria with avian macrophages relates to the outcome of the infection. Chickens were infected intratracheally with three APEC strains, MT78, IMT5155, and UEL17, and one non-pathogenic Afecal strain, IMT5104. The pathogenicity of the strains was assessed by isolating bacteria from lungs, kidneys, and spleens at 24 h post-infection (p.i.). Lungs were examined for histopathological changes at 12, 18, and 24 h p.i. Serial lung sections were stained with hematoxylin and eosin (HE), terminal deoxynucleotidyl dUTP nick end labeling (TUNEL) for detection of apoptotic cells, and an anti-O2 antibody for detection of MT78 and IMT5155. UEL17 and IMT5104 did not cause systemic infections and the extents of lung colonization were two orders of magnitude lower than for the septicemic strains MT78 and IMT5155, yet all four strains caused the same extent of inflammation in the lungs. The inflammation was localized; there were some congested areas next to unaffected areas. Only the inflamed regions became labeled with anti-O2 antibody. TUNEL labeling revealed the presence of apoptotic cells at 12 h p.i in the inflamed regions only, and before any necrotic foci could be seen. The TUNEL-positive cells were very likely dying heterophils, as evidenced by the purulent inflammation. Some of the dying cells observed in avian lungs in situ may also be macrophages, since all four avian E. coli induced caspase 3/7 activation in monolayers of HD11 avian macrophages. In summary, both pathogenic and non-pathogenic fecal strains of avian E. coli produce focal infections in the avian lung, and these are accompanied by inflammation and cell death in the infected areas

When a tree dies in the forest : scaling climate-driven tree mortality to ecosystem water and carbon fluxes

Altres ajuts: COST FP1106 network STReESS.Drought- and heat-driven tree mortality, along with associated insect outbreaks, have been observed globally in recent decades and are expected to increase in future climates. Despite its potential to profoundly alter ecosystem carbon and water cycles, how tree mortality scales up to ecosystem functions and fluxes is uncertain. We describe a framework for this scaling where the effects of mortality are a function of the mortality attributes, such as spatial clustering and functional role of the trees killed, and ecosystem properties, such as productivity and diversity. We draw upon remote-sensing data and ecosystem flux data to illustrate this framework and place climate-driven tree mortality in the context of other major disturbances. We find that emerging evidence suggests that climate-driven tree mortality impacts may be relatively small and recovery times are remarkably fast (~4 years for net ecosystem production). We review the key processes in ecosystem models necessary to simulate the effects of mortality on ecosystem fluxes and highlight key research gaps in modeling. Overall, our results highlight the key axes of variation needed for better monitoring and modeling of the impacts of tree mortality and provide a foundation for including climate-driven tree mortality in a disturbance framework

Deep Reinforcement Learning for Time-Critical Wilderness Search And Rescue Using Drones

Traditional search and rescue methods in wilderness areas can be time-consuming and have limited coverage. Drones offer a faster and more flexible solution, but optimizing their search paths is crucial. This paper explores the use of deep reinforcement learning to create efficient search missions for drones in wilderness environments. Our approach leverages a priori data about the search area and the missing person in the form of a probability distribution map. This allows the deep reinforcement learning agent to learn optimal flight paths that maximize the probability of finding the missing person quickly. Experimental results show that our method achieves a significant improvement in search times compared to traditional coverage planning and search planning algorithms. In one comparison, deep reinforcement learning is found to outperform other algorithms by over $160\%$, a difference that can mean life or death in real-world search operations. Additionally, unlike previous work, our approach incorporates a continuous action space enabled by cubature, allowing for more nuanced flight patterns.Comment: 16 pages, 19 figures. Submitte

GIS Data Driven Probability Map Generation for Search and Rescue Using Agents

Predicting the final resting location of a missing person is critical for search and rescue operations with limited resources. To improve the accuracy and speed of these predictions, simulated agents can be created to replicate the behavior of the missing person. In this paper, we introduce an agent-based model, to simulate various psychological profiles, that move over a physical landscape incorporating real-world data in their decision-making without relying on per-location training. The resultant probability density map of the missing person's location was the result of a combination of Monte Carlo simulations and mobility-time-based sampling. General trends in the data were comparable to historical data sets available. This work presents a flexible agent that can be employed by search and rescue that easily extends to various locations

Predictive probability density mapping for search and rescue using an agent-based approach with sparse data

Predicting the location where a lost person could be found is crucial for search and rescue operations with limited resources. To improve the precision and efficiency of these predictions, simulated agents can be created to emulate the behavior of the lost person. Within this study, we introduce an innovative agent-based model designed to replicate diverse psychological profiles of lost persons, allowing these agents to navigate real-world landscapes while making decisions autonomously without the need for location-specific training. The probability distribution map depicting the potential location of the lost person emerges through a combination of Monte Carlo simulations and mobility-time-based sampling. Validation of the model is achieved using real-world Search and Rescue data to train a Gaussian Process model. This allows generalization of the data to sample initial starting points for the agents during validation. Comparative analysis with historical data showcases promising outcomes relative to alternative methods. This work introduces a flexible agent that can be employed in search and rescue operations, offering adaptability across various geographical locations

Deep reinforcement learning for time-critical wilderness search and rescue using drones

Traditional search and rescue methods in wilderness areas can be time-consuming and have limited coverage. Drones offer a faster and more flexible solution, but optimizing their search paths is crucial for effective operations. This paper proposes a novel algorithm using deep reinforcement learning to create efficient search paths for drones in wilderness environments. Our approach leverages a priori data about the search area and the missing person in the form of a probability distribution map. This allows the policy to learn optimal flight paths that maximize the probability of finding the missing person quickly. Experimental results show that our method achieves a significant improvement in search times compared to traditional coverage planning and search planning algorithms by over 160%, a difference that can mean life or death in real-world search operations Additionally, unlike previous work, our approach incorporates a continuous action space enabled by cubature, allowing for more nuanced flight patterns