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- Frisoni, G. B., et al.
(författare)
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Dementia prevention in memory clinics: recommendations from the European task force for brain health services
- 2023
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Ingår i: Lancet Regional Health-Europe. - : Elsevier BV. - 2666-7762. ; 26
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Tidskriftsartikel (refereegranskat)abstract
- Observational population studies indicate that prevention of dementia and cognitive decline is being accomplished, possibly as an unintended result of better vascular prevention and healthier lifestyles. Population aging in the coming decades requires deliberate efforts to further decrease its prevalence and societal burden. Increasing evidence sup-ports the efficacy of preventive interventions on persons with intact cognition and high dementia risk. We report recommendations for the deployment of second-generation memory clinics (Brain Health Services) whose mission is evidence-based and ethical dementia prevention in at-risk individuals. The cornerstone interventions consist of (i) assessment of genetic and potentially modifiable risk factors including brain pathology, and risk stratification, (ii) risk communication with ad-hoc protocols, (iii) risk reduction with multi-domain interventions, and (iv) cognitive enhancement with cognitive and physical training. A roadmap is proposed for concept validation and ensuing clinical deployment.
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| 2. |
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| 3. |
- Magdalinou, N. K., et al.
(författare)
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Identification of candidate cerebrospinal fluid biomarkers in parkinsonism using quantitative proteomics
- 2017
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Ingår i: Parkinsonism & Related Disorders. - : Elsevier BV. - 1353-8020 .- 1873-5126. ; 37, s. 65-71
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Neurodegenerative parkinsonian syndromes have significant clinical and pathological overlap, making early diagnosis difficult. Cerebrospinal fluid (CSF) biomarkers may aid the differentiation of these disorders, but other than a-synuclein and neurofilament light chain protein, which have limited diagnostic power, specific protein biomarkers remain elusive. Objectives: To study disease mechanisms and identify possible CSF diagnostic biomarkers through discovery proteomics, which discriminate parkinsonian syndromes from healthy controls. Methods: CSF was collected consecutively from 134 participants; Parkinson's disease (n = 26), atypical parkinsonian syndromes (n = 78, including progressive supranuclear palsy (n = 36), multiple system atrophy (n = 28), corticobasal syndrome (n = 14)), and elderly healthy controls (n = 30). Participants were divided into a discovery and a validation set for analysis. The samples were subjected to tryptic digestion, followed by liquid chromatography-mass spectrometry analysis for identification and relative quantification by isobaric labelling. Candidate protein biomarkers were identified based on the relative abundances of the identified tryptic peptides. Their predictive performance was evaluated by analysis of the validation set. Results: 79 tryptic peptides, derived from 26 proteins were found to differ significantly between atypical parkinsonism patients and controls. They included acute phase/inflammatory markers and neuronal/synaptic markers, which were respectively increased or decreased in atypical parkinsonism, while their levels in PD subjects were intermediate between controls and atypical parkinsonism. Conclusion: Using an unbiased proteomic approach, proteins were identified that were able to differentiate atypical parkinsonian syndrome patients from healthy controls. Our study indicates that markers that may reflect neuronal function and/or plasticity, such as the amyloid precursor protein, and inflammatory markers may hold future promise as candidate biomarkers in parkinsonism. (C) 2017 Published by Elsevier Ltd.
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| 5. |
- Li, Zhixia, et al.
(författare)
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Calibrating VISSIM roundabout model using a critical gap and follow-up headway approach
- 2013
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Ingår i: Proceedings of the 16th International Conference Road Safety on Four Continents. - Linköping : Statens väg- och transportforskningsinstitut.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Roundabouts have been continuously constructed in the U.S. in recent years, as studies have shown their capability of reducing crash risk and severity when compared with signalized intersections. Despite of the safety benefits offered by roundabouts, operational efficiency is required be analyzed when considering building roundabouts. As a prevailing simulation platform for modeling roundabouts, VISSIM have been widely applied in practice to facilitate analyzing the operational performance of roundabouts. Considering that an essential prerequisite to preparing a VISSIM roundabout model is to calibrate the model by adjusting VISSIM parameters, comprehensive calibration guidance is of great importance to practitioners. Previous calibration research has conducted qualitative analysis to study the impact of VISSIM parameters on roundabout capacity. However, parameter values based on field data and quantitative calibration guidelines are more helpful to facilitate fast and accurate modeling of roundabouts. This paper addresses these important needs. Speed trajectories of free-flow entering vehicles were collected in the field using a radar sensor. Location, length, speed distribution, and deceleration rate parameters for the VISSIM Reduced Speed Areas (RSA) were determined through the analysis of the radar data. The impact of VISSIM parameters on critical gap and follow-up headway was quantitatively analyzed through sensitivity analysis of minimum gap for PR, speed distribution and deceleration rate for RSA, and additive and multiplicative settings for the Wiedemann 74 model. Numerical guidelines for calibrating VISSIM roundabout models were ultimately developed, and validated via a case study.
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| 6. |
- Zheng, Dongxi, et al.
(författare)
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Watch the traffic signal : a signal state extraction system for naturalistic driving videos
- 2016
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Konferensbidrag (refereegranskat)abstract
- Naturalistic driving studies (NDS) are revolutionizing road safety research. Naturalistic driving data provide a new window into driver behavior that promises a deeper understanding than was ever possible with crash data, roadside observations, or driving simulator experiments. NDS collect extensive vehicle network data, vehicle internal videos (e.g., drivers and passengers), and vehicle external videos (e.g., forward roadway). The video record of the driver and surrounding road situation often provide a more revealing account of driver behavior. Data size becomes a double edged sword for most NDS. Fully or partially automated procedures are needed for data reduction. Both intentional and unintentional violations of traffic signals can cause severe consequences, like fatal right-angle crashes. Knowing the signal state when the driver navigated through the intersection is the first step towards judging the driver’s compliance and analyzing deeper human factors issues. A system that codes traffic signal state from georeferenced front-view videos was developed for use with the Second Strategic Highway Research Program (SHRP2) NDS data. GPS coordinates and a free online map database are used to identify candidate frames from lengthy videos for computer vision processing. The computer vision algorithm uses color histograms and shape matching to detect traffic signals. Vehicle movement is used to select, from multiple signals the one corresponding to the vehicle’s traffic lane. Temporal relationship between frames is employed for the purpose of refining detection results. Experiments were conducted on daytime videos and showed reasonable performance given the severe challenges posed by the SHRP2 data. Misclassifications were primarily due to other vehicles’ taillights, reddish yellow signals, yellowish red signals, green traffic signs, etc. Misses were due to distant frames with very few pixels corresponding to the signals and reduced color conspicuity compared to background scenes (e.g., green signals and the sky look similar). The system is an insightful first step towards using computer vision techniques to support signal state coding of the large volumes of naturalistic driving video data. Challenges revealed by the experimental results are valuable knowledge for future improvement.
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