APPLYING SITUATION-SERVICE FIT TO PHYSICAL ENVIRONMENTS ENHANCED BY UBIQUITOUS INFORMATION SYSTEMS

Ubiquitous Information Systems (UIS) embedded in everyday environments are required to provide means for supporting complex behavioral task requirements by users. With the concept of a situation, we propose a knowledge level that can be used by users for understanding UIS-enhanced environments. It is discussed how the concept of situation differs from the concept of context. Situations are used to identify supporting services. At the core of this paper, we present how the Situation-Service Fit construct is used in early design stages (study 1) and later during the prototype phase (study 2). In study 2 we introduce an instrument for three more detailed fit constructs, i.e., Behavior-Service Fit, Modality-Service Fit, and Spatial-Service Fit. By additional evaluation of constructs known from various technology acceptance theories, we provide an innovative instrument for investigating UIS during the whole design cycle

Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning

Power outages and fluctuations represent serious crisis situations in energy-intensive process industry like glass and paper production, where substances such as oil, gas, wood fibers or chemicals are processed. Power disruptions can interrupt chemical reactions and produce tons of waste as well as damage of machine parts. But, despite of the obvious criticality, handling of outages in manufacturing focuses on commissioning of expensive proprietary power plants to protect against power outages and implicit gut feeling in anticipating potential disruptions. With AISOP, we introduce a model for AI-based scenario planning for predicting crisis situations. AISOP uses conceptual, well-defined scenario patterns to capture entities of crisis situations. Data streams are mapped onto these patterns for determining historic crisis scenarios and predicting future crisis scenarios by using inductive knowledge and machine learning. The model was exemplified within a proof of concept for energy-driven disruption prediction. We were able to evaluate the proposed approach by means of a set of data streams on weather and outages in Germany in terms of performance in predicting potential outages for manufacturers of paper industry with promising results

Occipital hypometabolism is a risk factor for conversion to Parkinson’s disease in isolated REM sleep behaviour disorder

Purpose: Isolated REM sleep behaviour disorder (iRBD) patients are at high risk of developing clinical syndromes of the α-synuclein spectrum. Progression markers are needed to determine the neurodegenerative changes and to predict their conversion. Brain imaging with 18F-FDG PET in iRBD is promising, but longitudinal studies are scarce. We investigated the regional brain changes in iRBD over time, related to phenoconversion.Methods: Twenty iRBD patients underwent two consecutive 18F-FDG PET brain scans and clinical assessments (3.7 ± 0.6 years apart). Seventeen patients also underwent 123I-MIBG and 123I-FP-CIT SPECT scans at baseline. Four subjects phenoconverted to Parkinson’s disease (PD) during follow-up. 18F-FDG PET scans were compared to controls with a voxel-wise single-subject procedure. The relationship between regional brain changes in metabolism and PD-related pattern scores (PDRP) was investigated.Results: Individual hypometabolism t-maps revealed three scenarios: (1) normal 18F-FDG PET scans at baseline and follow-up (N = 10); (2) normal scans at baseline but occipital or occipito-parietal hypometabolism at follow-up (N = 4); (3) occipital hypometabolism at baseline and follow-up (N = 6). All patients in the last group had pathological 123I-MIBG and 123I-FP-CIT SPECT. iRBD converters (N = 4) showed occipital hypometabolism at baseline (third scenario). At the group level, hypometabolism in the frontal and occipito-parietal regions and hypermetabolism in the cerebellum and limbic regions were progressive over time. PDRP z-scores increased over time (0.54 ± 0.36 per year). PDRP expression was driven by occipital hypometabolism and cerebellar hypermetabolism.Conclusions: Our results suggest that occipital hypometabolism at baseline in iRBD implies a short-term conversion to PD. This might help in stratification strategies for disease-modifying trials.</p

Four-YearFollow-upof [F-18]Fluorodeoxyglucose Positron Emission Tomography-Based Parkinson's Disease-Related Pattern Expression in 20 Patients With Isolated Rapid Eye Movement Sleep Behavior Disorder Shows Prodromal Progression

Background: Isolated rapid eye movement sleep behavior disorder is known to be prodromal for alpha-synucleinopathies, such as Parkinson's disease (PD) and dementia with Lewy bodies. The [18F]fluorodeoxyglucose-positron emission tomography (PET)–based PD-related brain pattern can be used to monitor disease progression. Objective: We longitudinally investigated PD-related brain pattern expression changes in 20 subjects with isolated rapid eye movement sleep behavior disorder to investigate whether this may be a suitable technique to study prodromal PD progression in these patients and to identify potential phenoconverters. Methods: Subjects underwent two [18F]fluorodeoxyglucose-PET brain scans ~3.7 years apart, along with baseline and repeated motor, cognitive, and olfactory testing within roughly the same time frame. Results: At baseline, 8 of 20 (40%) subjects significantly expressed the PD-related brain pattern (with z scores above the receiver operating characteristic–determined threshold). At follow-up, six additional subjects exhibited significant PD-related brain pattern expression (70% in total). PD-related brain pattern expression increased in all subjects (P = 0.00008). Four subjects (20%), all with significant baseline PD-related brain pattern expression, phenoconverted to clinical PD. Conclusions: Suprathreshold PD-related brain pattern expression and greater score rate of change may signify greater shorter-term risk for phenoconversion. Our results support the use of serial PD-related brain pattern expression measurements as a prodromal PD progression biomarker in patients with isolated rapid eye movement sleep behavior disorder

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder

Background and Objectives 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer’s disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. Methods We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. Results The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman’s rho =0.62, P=0.004). Conclusion In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages

Neuronally derived extracellular vesicle α-synuclein as a serum biomarker for individuals at risk of developing Parkinson disease

IMPORTANCE: Nonmotor symptoms of Parkinson disease (PD) often predate the movement disorder by decades. Currently, there is no blood biomarker to define this prodromal phase. OBJECTIVE: To investigate whether α-synuclein in neuronally derived serum-extracellular vesicles identifies individuals at risk of developing PD and related dementia. DESIGN, SETTING, and PARTICIPANTS: This retrospective, cross-sectional multicenter study of serum samples included the Oxford Discovery, Marburg, Cologne, and Parkinson’s Progression Markers Initiative cohorts. Participants were recruited from July 2013 through August 2023 and samples were analyzed from April 2022 through September 2023. The derivation group (n = 170) included participants with isolated rapid eye movement sleep behavior disorder (iRBD) and controls. Two validation groups were used: the first (n = 122) included participants with iRBD and controls and the second (n = 263) included nonmanifest GBA1N409S gene carriers, participants with iRBD or hyposmia, and available dopamine transporter single-photon emission computed tomography, healthy controls, and patients with sporadic PD. Overall the study included 199 participants with iRBD, 20 hyposmic participants with available dopamine transporter single-photon emission computed tomography, 146 nonmanifest GBA1N409S gene carriers, 21 GBA1N409S gene carrier patients with PD, 50 patients with sporadic PD, and 140 healthy controls. In the derivation group and validation group 1, participants with polysomnographically confirmed iRBD were included. In the validation group 2, at-risk participants with available Movement Disorder Society prodromal markers and serum samples were included. Among 580 potential participants, 4 were excluded due to alternative diagnoses. EXPOSURES: Clinical assessments, imaging, and serum collection. MAIN OUTCOME AND MEASURES: L1CAM-positive extracellular vesicles (L1EV) were immunocaptured from serum. α-Synuclein and syntenin-1 were measured by electrochemiluminescence. Area under the receiver operating characteristic (ROC) curve (AUC) with 95% CIs evaluated biomarker performance. Probable prodromal PD was determined using the updated Movement Disorder Society research criteria. Multiple linear regression models assessed the association between L1EV α-synuclein and prodromal markers. RESULTS: Among 576 participants included, the mean (SD) age was 64.30 (8.27) years, 394 were male (68.4%), and 182 were female (31.6%). A derived threshold of serum L1EV α-synuclein distinguished participants with iRBD from controls (AUC = 0.91; 95% CI, 0.86-0.96) and those with more than 80% probability of having prodromal PD from participants with less than 5% probability (AUC = 0.80; 95% CI, 0.71-0.89). Subgroup analyses revealed that specific combinations of prodromal markers were associated with increased L1EV α-synuclein levels. Across all cohorts, L1EV α-synuclein differentiated participants with more than 80% probability of having prodromal PD from current and historic healthy control populations (AUC = 0.90; 95% CI, 0.87-0.93), irrespective of initial diagnosis. L1EV α-synuclein was increased in at-risk participants with a positive cerebrospinal fluid seed amplification assay and was above the identified threshold in 80% of cases (n = 40) that phenoconverted to PD or related dementia. CONCLUSIONS AND RELEVANCE: L1EV α-synuclein in combination with prodromal markers should be considered in the stratification of those at high risk of developing PD and related Lewy body diseases