Changes in the mechanical properties of the thigh and lower leg muscle-tendon units during the early follicular and early luteal phases.

Background: This study aimed to determine changes in the muscle and tendon stiffness of the thigh and lower leg muscle-tendon units during the early follicular and early luteal phases, and check for possible relations between muscle and tendon stiffness in each phase. Methods: The sample consisted of 15 female university students with regular menstrual cycles. The basal body temperature method, ovulation kit, and salivary estradiol concentration measurement were used to estimate the early follicular and early luteal phases. A portable digital palpation device measured muscle-tendon stiffness in the early follicular and early luteal phases. The measurement sites were the rectus femoris (RF), vastus medialis (VM), patellar tendon (PT), medial head of gastrocnemius muscle, soleus muscle, and Achilles tendon. Results: No statistically significant differences in the thigh and lower leg muscle-tendon unit stiffness were seen between the early follicular and early luteal phases. Significant positive correlations were found between the stiffness of the RF and PT (r = 0.608, p = 0.016) and between the VM and PT (r = 0.737, p = 0.002) during the early luteal phase. Conclusion: The present results suggest that the stiffness of leg muscle-tendon units of the anterior thigh and posterior lower leg do not change between the early follicular and early luteal phases and that tendons may be stiffer in those women who have stiffer anterior thigh muscles during the early luteal phase.

Anatomical study of type classification and surface area of attachment sites for tibialis anterior tendon.

BACKGROUND: The purpose of this study was to clarify the attachment types of the tibialis anterior tendon (TAT) in Japanese fixed cadavers and to determine the attachment site area in three dimensions. METHODS: We examined 100 feet from 50 Japanese cadavers. The TAT was classified according to differences in the number of fiber bundles as: Type I, with one fiber bundle; Type II, with two fiber bundles; and Type III, with three fiber bundles. The attachment site area of the TAT was measured using a three-dimensional scanner. RESULTS: Cases were Type II in 95% and Type III in 5%, with no cases of Type I identified. In Type II, mean attachment site areas were 85.2 ± 18.2 mm2 for the medial cuneiform bone (MCB) and 72.4 ± 19.0 mm2 for the first metatarsal bone (1 MB), showing a significantly larger area for MCB than for 1 MB. CONCLUSIONS: These findings suggest the possibility of ethnic differences in TAT attachment types and suggest that TAT attachments in Japanese individuals are highly likely to be Type II, with rare cases of Type III. Accurate measurement of attachment site areas is possible with appropriate three-dimensional measurements.

Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods.

Climate change can increase the number of uprooted trees. Although there have been an increasing number of machine learning applications for satellite image analysis, the estimation of deracinated tree area by satellite image is not well developed. Therefore, we estimated the deracinated tree area of forests via machine-learning classification using Landsat 8 satellite images. We employed support vector machines (SVMs), random forests (RF), and convolutional neural networks (CNNs) as potential machine learning methods, and tested their performance in estimating the deracinated tree area. We collected satellite images of upright trees, deracinated trees, soil, and others (e.g., waterbodies and cities), and trained them with the training data. We compared the accuracy represented by the correct classification rate of these methods, to determine the deracinated tree area. It was found that the SVM and RF performed better than the CNN for two-class classification (deracinated and upright trees), and the correct classification rates of all methods were up to 93%. We found that the CNN and RF performed significantly higher for the four- and two-class classification compared to the other methods, respectively. We conclude that the CNN is useful for estimating deracinated tree areas using Landsat 8 satellite images.