Charging changes contact composition in binary sphere packings

Equal volume mixtures of small and large polytetrafluorethylene (PTFE) spheres are shaken in an atmosphere of controlled humidity which allows to also control their tribo-charging. We find that the contact numbers are charge-dependent: as the charge density of the beads increases, the number of same-type contacts decreases and the number of opposite-type contacts increases. This change is not caused by a global segregation of the sample. Hence, tribo-charging can be a way to tune the local composition of a granular material.Comment: 7 Pages, 5 Figure

Combined Imaging Markers Dissociate Alzheimer's Disease and Frontotemporal Lobar Degeneration – An ALE Meta-Analysis

To compare and dissociate the neural correlates of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), we combine and synthesize here recent comprehensive meta-analyses. Systematic and quantitative meta-analyses were conducted according to the QUOROM statement by calculating anatomical likelihood estimates (ALE). AD (n = 578) and the three subtypes of FTLD, frontotemporal dementia, semantic dementia (SD), and progressive non-fluent aphasia (n = 229), were compared in conjunction analyses, separately for atrophy and reductions in glucose metabolism. Atrophy coincided in the amygdala and hippocampal head in AD and the FTLD subtype SD. The other brain regions did not show any overlap between AD and FTLD subtypes for both atrophy and changes in glucose metabolism. For AD alone (n = 826), another conjunction analysis revealed a regional dissociation between atrophy and hypoperfusion/hypometabolism, whereby hypoperfusion and hypometabolism coincided in the angular/supramarginal gyrus and inferior precuneus/posterior cingulate gyrus. Our data together with other imaging studies suggest a specific dissociation of AD and FTLD if, beside atrophy, additional imaging markers in AD such as abnormally low parietal glucose utilization and perfusion are taken into account. Results support the incorporation of standardized imaging inclusion criteria into future diagnostic systems, which is crucial for early individual diagnosis and treatment in the future

Self-Learning Production Control using Algorithms of Artificial Intelligence

Manufacturing companies are facing an increasingly turbulent market - a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors

Virtual screening for PPAR-gamma ligands using the ISOAK molecular graph kernel and gaussian processes

For a virtual screening study, we introduce a combination of machine learning techniques, employing a graph kernel, Gaussian process regression and clustered cross-validation. The aim was to find ligands of peroxisome-proliferator activated receptor gamma (PPAR-y). The receptors in the PPAR family belong to the steroid-thyroid-retinoid superfamily of nuclear receptors and act as transcription factors. They play a role in the regulation of lipid and glucose metabolism in vertebrates and are linked to various human processes and diseases. For this study, we used a dataset of 176 PPAR-y agonists published by Ruecker et al. ..

Age Correction in Dementia – Matching to a Healthy Brain

In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of various dementia syndromes using imaging data. Some of these methods do not provide the possibility to integrate possible confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset (age<65 years) and late-onset (age≥65) patients with Alzheimer's disease (AD). Here, we propose a simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction. Additionally, we compare VBM results for the comparison of three different groups of AD patients differing in age with the same group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggest that the approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses. Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences

Face Masks Protect From Infection but May Impair Social Cognition in Older Adults and People With Dementia.

The coronavirus disease 2019 (COVID-19) pandemic will have a high impact on older adults and people with Alzheimer's disease and other dementias. Social cognition enables the understanding of another individual's feelings, intentions, desires and mental states, which is particularly important during the COVID-19 pandemic. To prevent further spread of the disease face masks have been recommended. Although justified for prevention of this potentially devastating disease, they partly cover the face and hamper emotion recognition and probably mindreading. As social cognition is already affected by aging and dementia, strategies must be developed to cope with these profound changes of communication. Face masking even could accelerate cognitive decline in the long run. Further studies are of uppermost importance to address face masks' impact on social cognition in aging and dementia, for instance by longitudinally investigating decline before and in the pandemic, and to design compensatory strategies. These issues are also relevant for face masking in general, such as in medical surroundings-beyond the COVID-19 pandemic

Dissociating memory networks in early Alzheimer's disease and frontotemporal lobar degeneration - a combined study of hypometabolism and atrophy

Introduction: We aimed at dissociating the neural correlates of memory disorders in Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD). Methods: We included patients with AD (n = 19, 11 female, mean age 61 years) and FTLD (n = 11, 5 female, mean age 61 years) in early stages of their diseases. Memory performance was assessed by means of verbal and visual memory subtests from the Wechsler Memory Scale (WMS-R), including forgetting rates. Brain glucose utilization was measured by [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and brain atrophy by voxel-based morphometry (VBM) of T1-weighted magnetic resonance imaging (MRI) scans. Using a whole brain approach, correlations between test performance and imaging data were computed separately in each dementia group, including a group of control subjects (n = 13, 6 female, mean age 54 years) in both analyses. The three groups did not differ with respect to education and gender. Results: Patients in both dementia groups generally performed worse than controls, but AD and FTLD patients did not differ from each other in any of the test parameters. However, memory performance was associated with different brain regions in the patient groups, with respect to both hypometabolism and atrophy: Whereas in AD patients test performance was mainly correlated with changes in the parieto-mesial cortex, performance in FTLD patients was correlated with changes in frontal cortical as well as subcortical regions. There were practically no overlapping regions associated with memory disorders in AD and FTLD as revealed by a conjunction analysis. Conclusion: Memory test performance may not distinguish between both dementia syndromes. In clinical practice, this may lead to misdiagnosis of FTLD patients with poor memory performance. Nevertheless, memory problems are associated with almost completely different neural correlates in both dementia syndromes. Obviously, memory functions are carried out by distributed networks which break down in brain degeneration

Wetting Heterogeneities in Porous Media Control Flow Dissipation

Pressure-controlled displacement of an oil-water interface is studied in dense packings of functionalized glass beads with well-defined spatial wettability correlations. An enhanced dissipation is observed if the typical extension ξ of the same-type wetting domains is smaller than the average bead diameter d. Three-dimensional imaging using x-ray microtomography shows that the frequencies n(s) of residual droplet volumes s for different ξ collapse onto the same curve. This indicates that the additional dissipation for small ξ is due to contact line pinning rather than an increase of capillary break-up and coalescence events