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

Optimal path planning using psychological profiling in drone-assisted missing person search

Search and rescue operations are all time-sensitive and this is especially true when searching for a vulnerable missing person, such as a child or elderly person suffering dementia. Recently, Police Scotland Air Support Unit has begun the deployment of drones to assist in missing person searches with success, although the efficacy of the search relies upon the expertise of the drone operator. In this paper, several algorithms for planning the search path are compared to determine which approach has the highest probability of finding the missing person in the shortest time. In addition to this, the use of á priori psychological profile information of the subject to create a probability map of likely locations within the search area was explored. This map is then used within a nonlinear optimization to determine the optimal flight path for a given search area and subject profile. Two optimization solvers were compared; genetic algorithms, and particle swarm optimization. Finally, the most effective algorithm was used to create a coverage path for a real-life location, for which Police Scotland Air Support Unit completed multiple test flights. The generated flight paths based on the predicted intent of the lost person were found to perform statistically better than those of the expert police operators

Country-level determinants of the severity of the first global wave of the COVID-19 pandemic : an ecological study

Acknowledgements We would like to thank Dr Kathryn Martin, who provided valuable advice in study design. Funding This work was supported by the Aberdeen Clinical Academic Training Scheme.Peer reviewedPublisher PD

An ecological study assessing the relationship between Public Health Policies and Severity of the COVID-19 Pandemic

Funding: This research received no external funding.Peer reviewedPublisher PD

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

Recovery of logged forest fragments in a human-modified tropical landscape during the 2015-16 El Nino

The past 40 years in Southeast Asia have seen about 50% of lowland rainforests converted to oil palm and other plantations, and much of the remaining forest heavily logged. Little is known about how fragmentation influences recovery and whether climate change will hamper restoration. Here, we use repeat airborne LiDAR surveys spanning the hot and dry 2015-16 El Nino Southern Oscillation event to measure canopy height growth across 3,300ha of regenerating tropical forests spanning a logging intensity gradient in Malaysian Borneo. We show that the drought led to increased leaf shedding and branch fall. Short forest, regenerating after heavy logging, continued to grow despite higher evaporative demand, except when it was located close to oil palm plantations. Edge effects from the plantations extended over 300 metres into the forests. Forest growth on hilltops and slopes was particularly impacted by the combination of fragmentation and drought, but even riparian forests located within 40m of oil palm plantations lost canopy height during the drought. Our results suggest that small patches of logged forest within plantation landscapes will be slow to recover, particularly as ENSO events are becoming more frequent. It is unclear whether tropical forest fragments within plantation landscapes are resilient to drought. Here the authors analyse LiDAR and ground-based data from the 2015-16 El Nino event across a logging intensity gradient in Borneo. Although regenerating forests continued to grow, canopy height near oil palm plantations decreased, and a strong edge effect extended up to at least 300m away.Peer reviewe

The impact of logging on vertical canopy structure across a gradient of tropical forest degradation intensity in Borneo

Forest degradation through logging is pervasive throughout the world's tropical forests, leading to changes in the three-dimensional canopy structure that have profound consequences for wildlife, microclimate and ecosystem functioning. Quantifying these structural changes is fundamental to understanding the impact of degradation, but is challenging in dense, structurally complex forest canopies. We exploited discrete-return airborne LiDAR surveys across a gradient of logging intensity in Sabah, Malaysian Borneo, and assessed how selective logging had affected canopy structure (Plant Area Index, PAI, and its vertical distribution within the canopy). LiDAR products compared well to independent, analogue models of canopy structure produced from detailed ground-based inventories undertaken in forest plots, demonstrating the potential for airborne LiDAR to quantify the structural impacts of forest degradation at landscape scale, even in some of the world's tallest and most structurally complex tropical forests. Plant Area Index estimates across the plot network exhibited a strong linear relationship with stem basal area (R2 = 0.95). After at least 11–14 years of recovery, PAI was ~28% lower in moderately logged plots and ~52% lower in heavily logged plots than that in old-growth forest plots. These reductions in PAI were associated with near-complete lack of trees >30-m tall, which had not been fully compensated for by increasing plant area lower in the canopy. This structural change drives a marked reduction in the diversity of canopy environments, with the deep, dark understorey conditions characteristic of old-growth forests far less prevalent in logged sites. Full canopy recovery is likely to take decades. Synthesis and applications. Effective management and restoration of tropical forests requires detailed monitoring of the forest and its environment. We demonstrate that airborne LiDAR can effectively map the canopy architecture of the complex tropical forests of Borneo, capturing the three-dimensional impact of degradation on canopy structure at landscape scales, therefore facilitating efforts to restore and conserve these ecosystems

Using Support Vector Machines with Multiple Indices of Diffusion for Automated Classification of Mild Cognitive Impairment

Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer's disease where the early identification of subjects with MCI is critical