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A mechanism known as Pavlovian-to-instrumental transfer (PIT) describes a phenomenon by which the values of environmental cues acquired through Pavlovian conditioning can motivate instrumental behavior. PIT may be one basic mechanism of action control that can characterize mental disorders on a dimensional level beyond current classification systems. Therefore, we review human PIT studies investigating subclinical and clinical mental syndromes. The literature prevails an inhomogeneous picture concerning PIT. While enhanced PIT effects seem to be present in non-substance-related disorders, overweight people, and most studies with AUD patients, no altered PIT effects were reported in tobacco use disorder and obesity. Regarding AUD and relapsing alcohol-dependent patients, there is mixed evidence of enhanced or no PIT effects.
Additionally, there is evidence for aberrant corticostriatal activation and genetic risk, e.g., in association with high-risk alcohol consumption and relapse after alcohol detoxification. In patients with anorexia nervosa, stronger PIT effects elicited by low caloric stimuli were associated with increased disease severity.
In patients with depression, enhanced aversive PIT effects and a loss of action-specificity associated with poorer treatment outcomes were reported. Schizophrenic patients showed disrupted specific but intact general PIT effects. Patients with chronic back pain showed reduced PIT effects.
We provide possible reasons to understand heterogeneity in PIT effects within and across mental disorders. Further, we strengthen the importance of reliable experimental tasks and provide test-retest data of a PIT task showing moderate to good reliability.
Finally, we point toward stress as a possible underlying factor that may explain stronger PIT effects in mental disorders, as there is some evidence that stress per se interacts with the impact of environmental cues on behavior by selectively increasing cue-triggered wanting.
To conclude, we discuss the results of the literature review in the light of Research Domain Criteria, suggesting future studies that comprehensively assess PIT across psychopathological dimensions.
Background
Elderly patients are a growing population in cardiac rehabilitation (CR). As postural control declines with age, assessment of impaired balance is important in older CR patients in order to predict fall risk and to initiate counteracting steps. Functional balance tests are subjective and lack adequate sensitivity to small differences, and are further subject to ceiling effects. A quantitative approach to measure postural control on a continuous scale is therefore desirable. Force plates are already used for this purpose in other clinical contexts, therefore could be a promising tool also for older CR patients. However, in this population the reliability of the assessment is not fully known.
Research question
Analysis of test-retest reliability of center of pressure (CoP) measures for the assessment of postural control using a force plate in older CR patients.
Methods
156 CR patients (> 75 years) were enrolled. CoP measures (path length (PL), mean velocity (MV), and 95% confidence ellipse area (95CEA)) were analyzed twice with an interval of two days in between (bipedal narrow stance, eyes open (EO) and closed (EC), three trials for each condition, 30 s per trial), using a force plate. For test-retest reliability estimation absolute differences (& UDelta;: T0-T1), intraclass correlation coefficients (ICC) with 95% confidence intervals, standard error of measurement and minimal detectable change were calculated.
Results
Under EO condition ICC were excellent for PL and MV (0.95) and good for 95CEA (0.88) with & UDelta; of 10.1 cm (PL), 0.3 cm/sec (MV) and 1.5 cm(2 )(95CEA) respectively. Under EC condition ICC were excellent (> 0.95) for all variables with larger & UDelta; (PL: 21.7 cm; MV: 0.7 cm/sec; 95CEA: 2.4 cm(2))
Significance
In older CR patients, the assessment of CoP measures using a force plate shows good to excellent test retest reliability.
The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.