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Physical activity improves quality of life, physical health and mental health, and is also an important protective factor against highly prevalent age-related diseases such as cardiovascular diseases, diabetes, cancer and mental health. With age, physical activity tends to decrease, leading down a vicious cycle that increases vulnerability to disease in the elderly. In the following, we trained neural network architectures to predict age from 115,456 one week-long 100Hz wrist accelerometer recordings from the UK Biobank (R-Squared=63.5±2.4%; root mean squared error=4.7±0.1 years). We achieved this performance by preprocessing the raw data as 2,271 scalar features, 113 time series and four images. We also considered the raw signal at different time scales (weekly activity patterns vs. gait). We then defined accelerated aging for a participant as being predicted older than one’s actual age and aimed to characterize these participants. We performed a genome wide association on the accelerated aging phenotypes to estimate its heritability (h_g2=12.3±0.9%) and identified nine single nucleotide polymorphisms in seven genes associated with it (e.g HIST1H4L, involved in chromatin organization). Similarly, we identified biomarkers (e.g blood pressure), clinical phenotypes (e.g chest pain), diseases (e.g hypertension), environmental (e.g smoking) and socioeconomic (e.g income and education) variables associated with accelerated aging. We conclude that physical activity-derived biological age is a complex phenotype with both genetic and non-genetic factors.

It is hypothesized that there are inter-individual differences in biological aging; however, differences in aging among (heart images vs. electrophysiology) and across (e.g., brain vs heart) physiological dimensions have not been systematically evaluated and compared. We analyzed 676,787 samples from 502,211 UK Biobank participants aged 37-82 years with deep learning approaches to build a total of 331 chronological age predictors on different data modalities such as videos (e.g. heart magnetic resonance imaging [MRI]), images (e.g. brain, liver and pancreas MRIs), time-series (e.g. electrocardiograms [ECGs], wrist accelerometer data) and scalar data (e.g. blood biomarkers) to characterize the multiple dimensions of aging. We combined these age predictors into 11 main aging dimensions, 31 subdimensions and 84 sub-subdimensions ensemble models based on specific organ systems. Heart dimension features predict chronological age with a testing root mean squared error (RMSE) and standard error of 2.83±0.04 years and musculoskeletal dimension features predict age with a RMSE of 2.65±0.04 years. We defined “accelerated” agers as participants whose predicted age was greater than their chronological age and computed the correlation between these different definitions of accelerated aging. We found that most aging dimensions are modestly correlated (average correlation=.139±.090) but that dimensions that are biologically related tend to be more positively correlated. For example, we found that heart anatomical (from MRI) accelerated aging and heart electrical (from ECG) accelerated aging are correlated (average Pearson of .249±.005). Overall, most dimensions of aging are complex traits with both genetic and non-genetic correlates. We identified 9,697 SNPs in 3,318 genes associated with accelerated aging and found an average GWAS-based heritability for accelerated aging of 26.1±7.42% (e.g. heart aging: 35.2±1.6%). We used GWAS summary statistics to estimate genetic correlation between aging dimensions and we found that most aging dimensions are genetically not correlated (average correlation=.104±.149). However, on the other hand, specific dimensions were genetically correlated, such as heart anatomical and electrical accelerated aging (Pearson rho .508±.089 correlated [r_g]). Finally, we identified biomarkers, clinical phenotypes, diseases, family history, environmental variables and socioeconomic variables associated with accelerated aging in each aging dimension and computed the correlation between the different aging dimensions in terms of these associations. We found that environmental and socioeconomic variables are similarly associated with accelerated aging across aging dimensions (average correlations of respectively .639±.180 and .607±.309). Dimensions are weakly correlated with each other, highlighting the multidimensionality of the aging process. Our results can be interactively explored on the following website:https://www.multidimensionality-of-aging.net/

The evolution of composite resins stratification on anterior teeth is due to advances in the mechanical and optical properties of these materials. However, to obtain an optimal results, it is necessary to use a rigorous and precise method. This requires an observation of the colorimetric properties of the adjacent teeth. In addition, the use of composite enamel and dentine, combined with a finishing and polishing steps to recreate the internal and external architecture. Once these conditions are met, the practitioner is likely to succeed in a functional restoration similar to the natural tooth.

In a ternary system composed of aluminum phosphate, zinc oxide, and orthophosphoric acid, we have prepared microporous ceramics for orthopedic and dental uses. Firstly, we have prepared the ceramics with uncontrollable porosity by sintering blocs of the zinc phosphate cement drawn from the ternary system. Then, based on a binary system consisted of aluminum phosphate, zinc oxide, and some porogenic additives, we have prepared ceramics with good and homogeneous porosity structure. In the second issue, the preparation is made by a simple mixture of powders. Thus, after homogenization, compaction and sintering at high temperatures, the resulted ceramics developed high mechanical resistances. Besides, antibacterial tests on the ceramics have been conducted and shown good biological performances. The mechanical behavior as well as the biological properties candidate the ceramics to be a good alternative to the hard tissue. Eventually, all preparations are controlled and investigated using several technics such as FTIR, XRD, SEM, TAG-TAD, mechanical test, and an antibacterial activity test., Moroccan Journal of Chemistry, Vol 7, No 4 (2019)

International audience

Le déploiement à large échelle de Compteurs à Lait Electroniques Portables avec enregistrement de paramètres de la cinétique d’émission du lait, dans le cadre du contrôle de performances, permet d’explorer l’intérêt de nouveaux phénotypes d’aptitude à la traite des chèvres. Une étude a été conduite sur 5 820 chèvres issues de 16 élevages caprins de l’ouest de la France afin de mieux appréhender l’aptitude et les conditions de traite des animaux en tant que facteurs de risque potentiels des infections de la mamelle. La modélisation des cinétiques d’éjection du lait a permis d’identifier différentes classes de cinétiques se différenciant notamment par les temps de traite et la présence d’un ou deux plateaux. Ces informations couplées aux données morphologiques et de contrôle de performances pourraient être valorisées à la fois pour identifier des individus ou situations à risque et pour mieux adapter les installations et réglages du matériel de traite (dimensionnement des canalisations, détermination des seuils de dépose automatique, …).