Using AI to Measure Parkinson's Disease Severity at Home

We present an artificial intelligence system to remotely assess the motor performance of individuals with Parkinson's disease (PD). Participants performed a motor task (i.e., tapping fingers) in front of a webcam, and data from 250 global participants were rated by three expert neurologists following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The neurologists' ratings were highly reliable, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings. Our machine learning model trained on these measures outperformed an MDS-UPDRS certified rater, with a mean absolute error (MAE) of 0.59 compared to the rater's MAE of 0.79. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care

High extraction efficiency source of photon pairs based on a quantum dot embedded in a broadband micropillar cavity

The generation of photon pairs in single quantum dots is based on a process that is, in its nature, deterministic. However, an efficient extraction of these photon pairs from a high-index semiconductor host material requires engineering of the photonic environment. We report on a micropillar-based device featuring an extraction efficiency of 69.4(10)$\%$ that is achieved by harnessing a broadband operation suitable for extraction of photon pairs emitted from a single quantum dot. Opposing the approaches that rely solely on Purcell enhancement to realize the enhancement of the extraction efficiency, our solution exploits a suppression of the emission into the modes other than the cavity mode. Our technological implementation requires modest fabrication effort enabling higher device yields that can be scaled up to meet the growing needs of quantum technologies. Furthermore, the design of the device can be further optimized to allow for an extraction efficiency of 85$\%$

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity:The Mobile Parkinson Disease Score

IMPORTANCE: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings. OBJECTIVES: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy. DESIGN, SETTING, AND PARTICIPANTS: This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months. MAIN OUTCOMES AND MEASURES: Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication. RESULTS: The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy. CONCLUSIONS AND RELEVANCE: Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics

Inter-cultural differences in response to a computer-based anti-bullying intervention

Background and purpose: Many holistic anti-bullying interventions have been attempted, with mixed success, while little work has been done to promote a 'self-help' approach to victimisation. The rise of the ICT curriculum and computer support in schools now allows for approaches that benefit from technology to be implemented. This study evaluates the cross-cultural effects of a computer-based anti-bullying intervention on primary school-aged children's knowledge about bullying and relevant coping strategies. Programme description: FearNot! is an interactive computer-based virtual learning environment designed for use as an anti-bullying intervention. It includes interactive virtual agents who assume the most common participant roles found in episodes of bullying. FearNot! was used by children over three consecutive weeks to allow its effectiveness to be evaluated in a longitudinal in situ programme. Sample: Two comparable samples were drawn from the UK and Germany. In the UK, 651 participants (aged 8-11) were recruited from primary schools in Hertfordshire, Coventry and Warwickshire, whereas the 535 German participants (aged 7-10) were sourced from Grundschulen in the Bayern and Hessen regions. Because of lack of parental consent, late joiners and absences/missing responses, data from 908 participants (UK 493; Germany 415) were analysed. Design and methods: A quasi-experimental, pre/post-tests control group design employed pre-published and bespoke questionnaires to collect data. Descriptive and inferential analyses were conducted. Results: UK students possessed higher coping strategy knowledge scores than German participants, but German children's scores improved over time and as a result of the FearNot! intervention. Conclusions: Overall, while not effective at increasing children's coping strategy knowledge in this study, the FearNot! intervention could prove a useful classroom tool to approach the issue of bullying as part of a wider initiative. Cultural differences at baseline and reactions to the intervention are discussed

Propagation loss in photonic crystal waveguides embedding InAs/GaAs quantum dots determined by direct spectral imaging

We determine the propagation loss of GaAs photonic crystal waveguides by spectral imaging of the spontaneous emission from the embedded InAs/GaAs quantum dots. The results are compared with the loss obtained by imaging the near field of the out-of-plane radiation of the waveguide mode propagating within the light cone. From the corresponding far field, we furthermore measure the mode wavevector, from which we determine the waveguide dispersion. Additionally, we show that spectral imaging allows to determine the relative efficiencies of the couplers. Using the same experiment, and detailed photonic simulations, we have determined the beta factor and the directionality of the emission of the QDs, finding beta factors up to 99% and high directionalities

Genomic Regions Associated with Multiple Sclerosis Are Active in B Cells

More than 50 genomic regions have now been shown to influence the risk of multiple sclerosis (MS). However, the mechanisms of action, and the cell types in which these associated variants act at the molecular level remain largely unknown. This is especially true for associated regions containing no known genes. Given the evidence for a role for B cells in MS, we hypothesized that MS associated genomic regions co-localized with regions which are functionally active in B cells. We used publicly available data on 1) MS associated regions and single nucleotide polymorphisms (SNPs) and 2) chromatin profiling in B cells as well as three additional cell types thought to be unrelated to MS (hepatocytes, fibroblasts and keratinocytes). Genomic intervals and SNPs were tested for overlap using the Genomic Hyperbrowser. We found that MS associated regions are significantly enriched in strong enhancer, active promoter and strong transcribed regions (p = 0.00005) and that this overlap is significantly higher in B cells than control cells. In addition, MS associated SNPs also land in active promoter (p = 0.00005) and enhancer regions more than expected by chance (strong enhancer p = 0.0006; weak enhancer p = 0.00005). These results confirm the important role of the immune system and specifically B cells in MS and suggest that MS risk variants exert a gene regulatory role. Previous studies assessing MS risk variants in T cells may be missing important effects in B cells. Similar analyses in other immunological cell types relevant to MS and functional studies are necessary to fully elucidate how genes contribute to MS pathogenesis

Hippocampal and Hippocampal-Subfield Volumes From Early-Onset Major Depression and Bipolar Disorder to Cognitive Decline

Background: The hippocampus and its subfields (HippSub) are reported to be diminished in patients with Alzheimer's disease (AD), bipolar disorder (BD), and major depressive disorder (MDD). We examined these groups vs healthy controls (HC) to reveal HippSub alterations between diseases. Methods: We segmented 3T-MRI T2-weighted hippocampal images of 67 HC, 58 BD, and MDD patients from the AFFDIS study and 137 patients from the DELCODE study assessing cognitive decline, including subjective cognitive decline (SCD), amnestic mild cognitive impairment (aMCI), and AD, via Free Surfer 6.0 to compare volumes across groups. Results: Groups differed significantly in several HippSub volumes, particularly between patients with AD and mood disorders. In comparison to HC, significant lower volumes appear in aMCI and AD groups in specific subfields. Smaller volumes in the left presubiculum are detected in aMCI and AD patients, differing from the BD group. A significant linear regression is seen between left hippocampus volume and duration since the first depressive episode. Conclusions: HippSub volume alterations were observed in AD, but not in early-onset MDD and BD, reinforcing the notion of different neural mechanisms in hippocampal degeneration. Moreover, duration since the first depressive episode was a relevant factor explaining the lower left hippocampal volumes present in groups