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To promote the rational use of Inonotus obliquus (chaga) from Eastern Canada, a mushroom consumed in infusion as a health drink, the extraction of its constituents was investigated. The extraction was carried out with water or ethanol at room temperature or by heating as well as following exposure to ultrasound. The extracts of the four seasons obtained with the four extraction methods were compared for their contents in carbon, nitrogen (N), sulfur (S), potassium (K), betulin, betulinic acid, potassium, flavonoids, and polyphenols. The antiradical effect as well as the photoprotective effects of all extracts were also investigated. The results show that there is no difference between the quantities extracted from the extracts of the 4 seasons. The results show a significant difference between the mass of the extracts obtained with water and ethanol. Betulinic acid was found to be more abundant in the extracts obtained with water while betulin was more abundant in the extracts obtained with ethanol. The mushroom and the extracts had high C contents, but low N and S contents, typical of protein-poor and carbohydrate-rich materials. Extracts were particularly rich in potassium, five times more than bananas. Heating favors the extraction of polyphenols and flavonoids. The aqueous extracts of chaga harvested in winter had the highest antiradical capacity. With a Sun Protection Factor (SPF) higher than 30 in the UVB wavelength, chaga extracts might be used as sunscreen. Extracts obtained with water had the highest SPF in general. The analysis of this mushroom further highlights this local product that deserves more attention for its potential benefits as a functional food/nutraceutical product. In addition to its nutritional values, this mushroom can also be used for its cosmetic qualities as it can be used as a sunscreen.

When preparing and executing goal-directed actions, neck proprioceptive information is critical to determining the relative positions of the body and target in space. While the contribution of neck proprioception for upper-limb movements has been previously investigated, we could not find evidence discerning its impact on the planning vs. online control of upper-limb trajectories. To investigate these distinct sensorimotor processes, participants performed discrete reaches towards a virtual target. On some trials, neck vibration was randomly applied before and/or during the movement, or not at all. The main dependent variable was the medio-lateral/directional bias of the reaching finger. The neck vibration conditions induced early leftward trajectory biases in some participants and late rightward trajectory biases in others. These different patterns of trajectory biases were explained by individual differences in the use of body-centered and head-centered frames of reference. Importantly, the current study provides direct evidence that sensory cues from the neck muscles contribute to the online control of goal-directed arm movements, likely accompanied by significant individual differences.

The feasibility of prediction of same-limb kinematics using a single inertial measurement unit attached to the same limb has been demonstrated using machine learning. This study was performed to see if a single inertial measurement unit attached to the tibia can predict the opposite leg’s kinematics (cross-leg prediction). It also investigated if there is a minimal or smaller data set in a convolutional neural network model to predict lower extremity running kinematics under other running conditions and with what accuracy for the intra- and inter-participant situations. Ten recreational runners completed running exercises under five conditions, including treadmill running at speeds of 2, 2.5, 3, and 3.5 m/s and level-ground running at their preferred speed. A one-predict-all scheme was adopted to determine which running condition could be used to best predict a participant’s overall running kinematics. Running kinematic predictions were performed for intra- and inter-participant scenarios. Among the tested running conditions, treadmill running at 3 m/s was found to be the optimal condition for accurately predicting running kinematics under other conditions, with R2 values ranging from 0.880 to 0.958 and 0.784 to 0.936 for intra- and inter-participant scenarios, respectively. The feasibility of cross-leg prediction was demonstrated but with significantly lower accuracy than the same leg. The treadmill running condition at 3 m/s showed the highest intra-participant cross-leg prediction accuracy. This study proposes a novel, deep-learning method for predicting running kinematics under different conditions on a small training data set.

Wearable sensors facilitate running kinematics analysis of joint kinematics in real running environments. The use of a few sensors or, ideally, a single inertial measurement unit (IMU) is preferable for accurate gait analysis. This study aimed to use a convolutional neural network (CNN) to predict level-ground running kinematics (measured by four IMUs on the lower extremities) by using treadmill running kinematics training data measured using a single IMU on the anteromedial side of the right tibia and to compare the performance of level-ground running kinematics predictions between raw accelerometer and gyroscope data. The CNN model performed regression for intraparticipant and interparticipant scenarios and predicted running kinematics. Ten recreational runners were recruited. Accelerometer and gyroscope data were collected. Intraparticipant and interparticipant R2 values of actual and predicted running kinematics ranged from 0.85 to 0.96 and from 0.7 to 0.92, respectively. Normalized root mean squared error values of actual and predicted running kinematics ranged from 3.6% to 10.8% and from 7.4% to 10.8% in intraparticipant and interparticipant tests, respectively. Kinematics predictions in the sagittal plane were found to be better for the knee joint than for the hip joint, and predictions using the gyroscope as the regressor were demonstrated to be significantly better than those using the accelerometer as the regressor.

Contrary to popular belief, we have known for many years that the endometrium is not a sterile environment and is considered to be a low-biomass milieu compared to the vagina. Numerous trials and studies have attempted to establish a valid sampling method and assess its physiological composition, but no consensus has been reached. Many factors, such as ethnicity, age and inflammation, can influence the microbiome. Moreover, it possesses a higher alpha-diversity and, therefore, contains more diverse bacteria than the vagina. For instance, Lactobacillus has been shown to be a predominant genus in the vaginal microbiome of healthy women. Consequently, even if a majority of scientists postulate that a predominance of Lactobacillus inside the uterus improves reproductive outcomes, vaginal contamination by these bacteria during sampling cannot be ruled out. Certain pathologies, such as chronic endometritis, have been identified as inflammation perpetrators that hinder the embryo implantation process. This pro-inflammatory climate created by dysbiosis of the endometrial microbiota could induce secondary inflammatory mediators via Toll-like receptors, creating an environment conducive to the development of endometriosis and even promoting carcinogenesis. However, studies to this day have focused on small populations. In addition, there is no clearly defined healthy uterine composition yet. At most, only a few taxa have been identified as pathogenic. As sampling and analysis methods become increasingly precise, we can expect the endometrial microbiota to be incorporated into future diagnostic tools and treatments for women’s health.