Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

Spatial variation of fixed charge density in knee joint cartilage from sodium MRI – Implication on knee joint mechanics under static loading

The effects of fixed charge density (FCD) and cartilage swelling have not been demonstrated on cartilage mechanics on knee joint level before. In this study, we present how the spatial and local variations of FCD affects the mechanical response of the knee joint cartilage during standing (half of the body weight, 13 minutes) using finite element (FE) modeling. The FCD distribution of tibial cartilage of an asymptomatic subject was determined using sodium (23Na) MRI at 7T and implemented into a 3-D FE-model of the knee joint (Subject-specific model, FCD: 0.18±0.08 mEq/ml). Tissue deformation in the Subject-specific model was validated against experimental, in vivo loading of the joint conducted with a MR-compatible compression device. For comparison, models with homogeneous FCD distribution (homogeneous model) and FCD distribution obtained from literature (literature model) were created. Immediately after application of the load (dynamic response), the variations in FCD had minor effects on cartilage stresses and strains. After 13 minutes of standing, the spatial and local variations in FCD had most influence on axial strains. In the superficial tibial cartilage in the Subject-specific model, axial strains were increased up to +13% due to smaller FCD (mean −11%), as compared to the homogeneous model. Compared to the literature model, those were decreased up to −18% due to greater FCD (mean +7%). The findings demonstrate that the spatial and local FCD variations in cartilage modulates strains in knee joint cartilage. Thereby, the results highlight the mechanical importance of site-specific content of proteoglycans in cartilage

Colour and decay resistance and its relationships in Eperua grandiflora

$\bullet$ Eperua grandiflora, which is widely distributed in the French Guiana forest region, shows high variability in decay resistance. Further information concerning this wood quality parameter is necessary, but standard testing methods are complex and time-consuming. We assessed the use of colorimetry to determine durability in heartwood samples from a range of trees. $\bullet$ Eperua grandiflora colour parameters were measured using a CIELAB system, revealing that the tree effect was greater than the radial position and height effects. $\bullet$ The wood samples were exposed to Coriolus versicolor and Antrodia sp. according to two European standards (En 350-1 and XP CEN TS 15083-1). Eperua grandiflora is more susceptible to brown rot. These two standards did not give the same durability classes. The high variation in natural durability was due to the tree effect. $\bullet$ These two properties were found to be correlated and the assessment also distinguished the extreme durability classes but they are not sufficient to classify the class of durability of this species.Étude de la variabilité de la couleur, de la durabilité naturelle et recherche de corrélations chez Eperua grandiflora. $\bullet$ Eperua grandiflora, essence largement répandue dans les forêts de Guyane soufre d'un défaut majeur : une grande variabilité de sa durabilité naturelle à l'égard des champignons lignivores. Dans un premier temps, nous avons étudié la variabilité de la mesure de la couleur selon le système CIElab afin de vérifier si la colorimétrie peut être utilisée comme un indicateur de la durabilité naturelle. $\bullet$ Nous avons étudié la résistance de cette essence à l'échelle inter, et intra-arbres à l'égard de Coriolus versicolor et Antrodia sp., conformément aux normes européennes en vigueur : EN 350-1 et XP TS 15083-1. Les résultats ont révélé qu'Eperua grandiflora est plus sensible au champignon de pourriture brune. $\bullet$ De même, l'utilisation des normes a montré que l'on n'obtenait pas les mêmes classes de durabilité. La variabilité de la durabilité et de la couleur est plus importante à l'échelle inter-arbres qu'à l'échelle intra-arbre. $\bullet$ Enfin, ces deux propriétés sont corrélées mais nous ne pouvons envisager d'utiliser la colorimétrie comme indicateur de la durabilité naturelle, car elle ne permet de différencier que les classes extrêmes

Crowdsourcing digital health measures to predict Parkinson’s disease severity: The Parkinson’s Disease Digital Biomarker DREAM Challenge.

Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)