Miracle-dz/UniFAR-FTUs huggingface dataset:
Differences in performance in CAMCOG-R domains between old and oldest old patients with Alzheimer’s disease

ABSTRACT The purpose of this study is to evaluate the neuropsychological performance in a ≥90-year-old population with Alzheimer disease (AD) in comparison with younger elderly patients. We retrospectively studied all patients with AD attended in a specialized clinic between 1999 and 2011. Age, sex, educational level, and sensory loss data were collected. Neuropsychological evaluation included Mini-Mental State Examination and Global Deterioration Scale. We used the eight Cambridge Cognitive Assessment (CAMCOG-R) domains to evaluate and compare the neuropsychological performance in the younger than 90 years old (<90) and older than 90 years old (≥90) groups. We selected 2931 patients, 2897 <90 (98.83%) and 34 ≥90 years old (1.17%). The ≥90 group had significant lower punctuations in memory, praxis, and abstract thinking CAMOCG-R domains with 1.49, 0.75, and 0.58 less points, respectively, (p < 0.05). Neuropsychological characteristics of cognitive decline seem to be different in ≥90 compared to <90 years old patients. According to age, the biggest differences in the CAMCOG-R performance are in the memory, praxis, and abstract thinking domains.
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video

Learning-based, single-view depth estimation often generalizes poorly to unseen datasets. While learning-based, two-frame depth estimation solves this problem to some extent by learning to match features across frames, it performs poorly at large depth where the uncertainty is high. There exists few learning-based, multi-view depth estimation methods. In this paper, we present a learning-based, multi-view dense depth map and ego-motion estimation method that uses Recurrent Neural Networks (RNN). Our model is designed for 3D reconstruction from video where the input frames are temporally correlated. It is generalizable to single- or two-view dense depth estimation. Compared to recent single- or two-view CNN-based depth estimation methods, our model leverages more views and achieves more accurate results, especially at large distances. Our method produces superior results to the state-of-the-art learning-based, single- or two-view depth estimation methods on both indoor and outdoor benchmark datasets. We also demonstrate that our method can even work on extremely difficult sequences, such as endoscopic video, where none of the assumptions (static scene, constant lighting, Lambertian reflection, etc.) from traditional 3D reconstruction methods hold.
Occurrence of depressive tendency and associated social factors among elderly persons forced by the Great East Japan Earthquake and nuclear disaster to live as long-term evacuees: a prospective cohort study

Purpose This study examined the incidence of depression and associated factors among elderly persons from Iitate village after the March 2011 earthquake. Method This was a prospective cohort study. As a baseline survey, in May 2010 a self-assessment Basic Checklist (BCL) was distributed to 1611 elderly villagers, of which 1277 responded. Of these respondents, 885 without a tendency to depression (69.3%) were given a follow-up survey in May 2013. The BCL was used to assess depression tendency, instrumental activities of daily living (IADL), physical function, nutritional status, oral function, homeboundness, cognitive function and social activities. Univariate analysis was used to examine differences in risk between those with a presence of depression tendency (PDT) and those without (non-PDT) depending on demographic and BCL variables. Variables found to be significant were analysed by Poisson regression analysis. Results Of the 438 respondents in the second survey, 163 (37.2%) showed depression tendency. PDT risk was significantly increased by female gender, age, history of diabetes and cognitive disorder. It was significantly reduced by increased IADL. Engagement in social activities decreased PDT risk in rental accommodation. Discussion Renters faced a higher risk of PDT than persons evacuated in groups to purpose-built housing. The inclusion of social activities in the multivariate Poisson regression analysis weakened this effect. Female gender, a history of diabetes, reduced IADL and a tendency to cognitive disorder each independently affected PDT risk. These findings may inform future responses to earthquakes and the technical disasters that may accompany them.
Numerical analysis of the grain size distribution in the activation of dry debris flow by means of DEM

Debris flow is a process of granular nature that has been widely analysed with methodologies based upon continuum mechanics. However, those approaches do not take into account the real granular condition of the soil. Grain size distribution exerts an important control on the movement of debris flows. This behaviour can be studied by analyzing three variables: maximum tilt angle (θ), kinetic energy (Ek) and flow depth (Fd). These variables allow to obtain a deeper insight into the conditions that trigger the failure of slopes and its subsequent capacity of damage. This research resorts to the use of the discrete element technique, developed by [1], performing a parametrical study of the parameters controlling grain size distributions like: mean size (d50), coefficient of curvature (Cu) and maximum size (dmax). The results showed an important influence and also a strong interaction between Ek and Fd on the onset of granular flows. The angle θ reached on the surface has a variation less than 5o with extreme values of d50. Results reported herein, allow to recognize the influence of grain size distribution on the triggering of slides of granular materials.
Toxicogenetic Studies of Desplatsia dewevrei using Gene Expression of Blood, Pancreatic, and Intestinal Genes in Wistar rats

Background: Toxicity studies are relevant in assessing the side effects of chemical substances before they are incorporated into the process of drug development. Introduction: Desplatsia dewevrei is a scarce forest species believed by natives to be nutritive and therapeutic, without scientific evidence though. Thus, this study was aimed at investigating the possible toxicity of shortand long-term oral administration of D. dewevrei using Wistar rats. Methods: 0, 30 100, and 1000 mg/kg of D. dewevrei were daily administered p.o for 3 and 28 days to Wistar rats consisting of four animals (two females, two males) per group. Hemotoxicity and liver function tests were done using automated machines from ERMA Inc. RT-PCR method was used to study the regulation of intestinal glucose transporter 4 (GLUT4), glucose transporter 2 (GLUT2), glucagon-like peptide-1 (GLP-1), pancreatic insulin, KCJN5, and L-type voltage-gated calcium channel genes (CACNAIA). Results: No morphological or hematological signs of toxicity were observed. Liver function test showed an elevated level of highdensity lipoprotein (HDL-C) in the treatment group (100 mg/kg). The lethal dose (LD50) of D. dewevrei extracts were above 1000 mg/kg as no mortality was observed at the highest regimen dose used. Up-regulation of pancreatic insulin and down-regulation of intestinal GLUT-2 suggest that the plant may contain therapeutic constituents. Conclusion: Shortor long-term administration of D. dewevrei is relatively safe.
Association of N‐cadherin levels and downstream effectors of Rho GTPases with dendritic spine loss induced by chronic stress in rat hippocampal neurons

Chronic stress promotes cognitive impairment and dendritic spine loss in hippocampal neurons. In this animal model of depression, spine loss probably involves a weakening of the interaction between pre‐ and postsynaptic cell adhesion molecules, such as N‐cadherin, followed by disruption of the cytoskeleton. N‐cadherin, in concert with catenin, stabilizes the cytoskeleton through Rho‐family GTPases. Via their effector LIM kinase (LIMK), RhoA and ras‐related C3 botulinum toxin substrate 1 (RAC) GTPases phosphorylate and inhibit cofilin, an actin‐depolymerizing molecule, favoring spine growth. Additionally, RhoA, through Rho kinase (ROCK), inactivates myosin phosphatase through phosphorylation of the myosin‐binding subunit (MYPT1), producing actomyosin contraction and probable spine loss. Some micro‐RNAs negatively control the translation of specific mRNAs involved in Rho GTPase signaling. For example, miR‐138 indirectly activates RhoA, and miR‐134 reduces LIMK1 levels, resulting in spine shrinkage; in contrast, miR‐132 activates RAC1, promoting spine formation. We evaluated whether N‐cadherin/β‐catenin and Rho signaling is sensitive to chronic restraint stress. Stressed rats exhibit anhedonia, impaired associative learning, and immobility in the forced swim test and reduction in N‐cadherin levels but not β‐catenin in the hippocampus. We observed a reduction in spine number in the apical dendrites of CA1 pyramidal neurons, with no effect on the levels of miR‐132 or miR‐134. Although the stress did not modify the RAC–LIMK–cofilin signaling pathway, we observed increased phospho‐MYPT1 levels, probably mediated by RhoA–ROCK activation. Furthermore, chronic stress raises the levels of miR‐138 in accordance with the observed activation of the RhoA–ROCK pathway. Our findings suggest that a dysregulation of RhoA–ROCK activity by chronic stress could potentially underlie spine loss in hippocampal neurons. © 2015 Wiley Periodicals, Inc.
The GermaParl Corpus of Parliamentary Protocols

This paper introduces the GermaParl Corpus. We outline available data, the data preparation process for preparing corpora of parliamentary debates and the tools we used to obtain hand-coded annotations that serve as training data for classifying debates. Beyond introducing a resource that is valuable for research, we share experiences and best practices for preparing corpora of plenary protocols.
Multiclass Learning With Partially Corrupted Labels

Traditional classification systems rely heavily on sufficient training data with accurate labels. However, the quality of the collected data depends on the labelers, among which inexperienced labelers may exist and produce unexpected labels that may degrade the performance of a learning system. In this paper, we investigate the multiclass classification problem where a certain amount of training examples are randomly labeled. Specifically, we show that this issue can be formulated as a label noise problem. To perform multiclass classification, we employ the widely used importance reweighting strategy to enable the learning on noisy data to more closely reflect the results on noise-free data. We illustrate the applicability of this strategy to any surrogate loss functions and to different classification settings. The proportion of randomly labeled examples is proved to be upper bounded and can be estimated under a mild condition. The convergence analysis ensures the consistency of the learned classifier to the optimal classifier with respect to clean data. Two instantiations of the proposed strategy are also introduced. Experiments on synthetic and real data verify that our approach yields improvements over the traditional classifiers as well as the robust classifiers. Moreover, we empirically demonstrate that the proposed strategy is effective even on asymmetrically noisy data.
GPS/RFID Integration Using Feed Forward Time Delayed Neural Networks

: A vehicle-mounted GPS receiver used for positioning can undergo from signal blockage. To cure this problem, GPS/ RFID integration can be considered as an answer in some cases. Though, in the case of metropolitan areas where there are severe multipath conditions, the performance degrades noticeably. The last decade we have seen several proposals of techniques aiming at improving the precision of GPS positions. The difficulty of these techniques increases with the increase of the accurateness required. These techniques usually combine multiple techniques like fuzzy logic… etc. In this paper, we propose a new technique based just on neural network, which offers a improved performance while showing a lower complexity. The idea is to utilize a neural network, which emulates the behavior of a given estimator in order to change it. Here, we present simulations results, which confirm the performance and the robustness of our proposed scheme in a heterogeneous environment.
Do Young People Ever Sit Still? Variations in Accelerometer Counts, Muscle Activity and Heart Rate across Various Sedentary Activities in Youth

Evidence of adverse health effects of TV viewing is stronger than for overall sedentary behaviour in youth. One explanation may be that TV viewing involves less body movement than other sedentary activities. Variations in body movement across sedentary activities are currently unknown, as are age differences in such variations. This study examined body movement differences across various sedentary activities in children and adolescents, assessed by hip-, thigh- and wrist-worn accelerometers, muscle activity and heart rate. Body movement differences between sedentary activities and standing were also examined. Fifty-three children (aged 10–12 years) and 37 adolescents (aged 16–18 years) performed seven different sedentary activities, a standing activity, and a dancing activity (as a control activity) in a controlled setting. Each activity lasted 10 minutes. Participants wore an Actigraph on their hip and both wrists, an activPAL on their thigh and a heart rate monitor. The muscle activity of weight-bearing leg muscles was measured in a subgroup (n = 38) by surface electromyography. Variations in body movement across activities were examined using general estimation equations analysis. Children showed significantly more body movement during sedentary activities and standing than adolescents. In both age groups, screen-based sedentary activities involved less body movement than non-screen-based sedentary activities. This may explain the stronger evidence for detrimental health effects of TV viewing while evidence for child sedentary behaviour in general is inconsistent. Differences in body movement during standing and sedentary activities were relatively small. Future research should examine the potential health effects of differences in body movement between screen-based versus non-screen based and standing versus sedentary activities.
An SDN‐based scalable routing and resource management model for service provider networks

Volume of the Internet traffic has increased significantly in recent years. Service providers (SPs) are now striving to make resource management and considering dynamically changing large volume of network traffic. In this context, software defined networking (SDN) has been alluring the attention of SPs, as it provides virtualization, programmability, ease of management, and so on. Yet severe scalability issues are one of the key challenges of the SDN due to its centralized architecture. First of all, SDN controller may become the bottleneck as the number of flows and switches increase. It is because routing and admission control decisions are made per flow basis by the controller. Second, there is a signaling overhead between the controller and switches since the controller makes decisions on behalf of them. In line with the aforementioned explanations, this paper proposes an SDN‐based scalable routing and resource management model (SRRM) for SPs. The proposed model is twofold. SRRM performs routing, admission control, and signaling operations (RASOs) in a scalable manner. Additionally, resource management has also been accomplished to increase link use. To achieve high degree of scalability and resource use, pre‐established paths (PEPs) between each edge node in the domain are provided. The proposed controller performs RASOs based on PEPs. The controller also balances the load of PEPs and adjusts their path capacities dynamically to increase resource use. Experimental results show that SRRM can successfully perform RASOs in a scalable way and also increase link use even under heavy traffic loads.
The relation of education, occupation, and cognitive activity to cognitive status in old age: the role of physical frailty

ABSTRACT Background: It remains unclear so far whether the role of cognitive reserve may differ between physically frail compared to less frail individuals. Therefore, the present study set out to investigate the relation of key markers of cognitive reserve to cognitive status in old age and its interplay with physical frailty in a large sample of older adults. Methods: We assessed Mini-Mental State Examination (MMSE) in 701 older adults. We measured grip strength as indicator of physical frailty and interviewed individuals on their education, past occupation, and cognitive leisure activity. Results: Greater grip strength, longer education, higher cognitive level of job, and greater engaging in cognitive leisure activity were significantly related to higher MMSE scores. Moderation analyses showed that the relations of education, cognitive level of job, and cognitive leisure activity to MMSE scores were significantly larger in individuals with lower, compared to those with greater grip strength. Conclusions: Cognitive status in old age may more strongly depend on cognitive reserve accumulated during the life course in physically frail (compared to less frail) older adults. These findings may be explained by cross-domain compensation effects in vulnerable individuals.
The World Bank's Classification of Countries by Income

The World Bank has used an income classification to group countries for analytical purposes for many years. Since the present income classification was first introduced 25 years ago there has been significant change in the global economic landscape. As real incomes have risen, the number of countries in the low income group has fallen to 31, while the number of high income countries has risen to 80. As countries have transitioned to middle income status, more people are living below the World Bank's international extreme poverty line in middle income countries than in low income countries. These changes in the world economy, along with a rapid increase in the user base of World Bank data, suggest that a review of the income classification is needed. A key consideration is the views of users, and this paper finds opinions to be mixed: some critics argue the thresholds are dated and set too low; others find merit in continuing to have a fixed benchmark to assess progress over time. On balance, there is still value in the current approach, based on gross national income per capita, to classifying countries into different groups. However, the paper proposes adjustments to the methodology that is used to keep the value of the thresholds for each income group constant over time. Several proposals for changing the current thresholds are also presented, which it is hoped will inform further discussion and any decision to adopt a new approach.
Implementation of a Single-Image Haze Removal Using the FPGA

A comparison of the numbers of cloudy days and clear days shows a recent increase of the former because of fine or yellow dust. Although imaging systems are used in various fields, the image-quality degradation that is due to fog is an outstanding problem. In this paper, a hardware method for the removal of the haze from a single input image using the dark channel prior and the FPGA is proposed; furthermore, a high-quality depth map can be used for fog removal. The problem of the existing haze-removal algorithms is the recognition of the high RGB values as haze, resulting in a loss of the high RGB values. The use of the average atmospheric values, however, implements a high-level transmission map that prevents the loss of the high RGB values (recognized as haze) during the haze removal. In addition, an 8 x 5 matrix is created to improve the speed of the dark-channel calculation, resulting in a speed of more than 500 fps.
Fast Global Labelling for Depth-Map Improvement Via Architectural Priors

Depth map estimation techniques from cameras often struggle to accurately estimate the depth of large textureless regions. In this work we present a vision-only method that accurately extracts planar priors from a viewed scene without making any assumptions of the underlying scene layout. Through a fast global labelling, these planar priors can be associated to the individual pixels leading to more complete depth-maps specifically over large, plain and planar regions that tend to dominate the urban environment. When these depth-maps are deployed to the creation of a vision only dense reconstruction over large scales, we demonstrate reconstructions that yield significantly better results in terms of coverage while still maintaining high accuracy.
GEWEX Cloud System Study (GCSS) cirrus cloud working group: development of an observation-based case study for model evaluation

Abstract. The GCSS working group on cirrus focuses on an inter-comparison of model simulations ranging from very detailed microphysical and dynamical models through to general circulation models (GCMs). The past GCSS cirrus cloud inter-comparison highlighted the wide range in modelling results that was a surprise to the modelling community. That inter-comparison was idealised and, therefore, a key issue was that it did not benefit from observations to help distinguish between model performances. In this work, we aim to address this key issue by developing an observationally based case study to be used for the GCSS cirrus modelling inter-comparison study. We focused on developing a case that had sufficient observations with which to evaluate models, to help identify which models in the inter-comparison are performing well and highlight areas for model development. Furthermore, it will provide a base case for future model comparisons or testing of new or updated models. This paper outlines the modelling case development and the inter-comparison results will be presented in a follow-on paper. The case was based on the 9 March 2000 Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) during an intensive observation period (IOP). The case was developed utilising various observations including ARM SGP remote sensing including the MilliMeter Cloud Radar (MMCR), radiometers, radiosondes, aircraft observations, satellite observations, objective analysis and complemented with results from the Rapid Update Cycle (RUC) model as well as bespoke gravity wave simulations used to provide the best estimate for large scale forcing. The retrievals of ice water content, ice number concentration and fall velocity provide several constraints to evaluate model performances. Initial testing of the case has been reported using the UK Met Office Large Eddy Simulation Model (LEM) which suggests the case is appropriate for the model inter-comparison study. To our knowledge, this case offers the most detailed case study for cirrus comparison available and we anticipate this will offer significant benefits over past comparisons which have mostly been loosely based on observations.
Trust and information quality: performance gap analysis

This research considers information quality from the perspective of the dimensions of trust. This paper identifies 12 effective factors of information quality. These factors are investigated in order to gauge the gap between expected importance and perceived performance ratings of these factors within the process of information sharing. The research presents results of a survey comprising 39 organisations. Data analysis recognises four factors with critical gaps affecting information sharing. These are ‘level of responsiveness’, ‘information accessibility’, ‘information accuracy’ and ‘information timely as agreed’.