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Epitestosterone [E] has for a long time been considered as a biologically inactive androgen. However, recently a distinct antiandrogenic activity of this naturally occurring endogenous epimer of Testosterone has been demonstrated. Especially the ratios of testosterone/epitestosterone (T/E) seem to be key as inhibition of epitestosterone on androgen activity was postulated. As in autism, a higher androgen activity was implied. We, therefore, suggested higher levels of T/E ratios of children with autism versus children with typical development.

Urine probes of 22 girls with autism (BMI 18.7 ± 4.3; average age 12.3 ± 3.8 years) and a sample of 51 controls (BMI 17.0 ± 2.6; average age 11.9 ± 4 years), as well as 61 boys with autism (BMI 17.04 ± 2. average age 11.9 ± 2.5 years) and 61 control boys (BMI 17.0 ± 2.6; average age 11.1 ± 3.0 years), were analyzed with gas chromatography mass spectrometry.

The average T/E ratio of all boys with autism was 2.5 ± 1.8 versus 2.4 ± 1.3 in boys with typical developmentntal finding. Nevertheless, one suggestion might be that epitestosterone has the effect of a competitive inhibition on the androgen receptor, which would probably help to explain the higher prevalence of autism in boys as compared to girls. Presumably, as no significant difference was detected in boys, this effect might not be as relevant from a steroid hormone perspective, and other effects such as altered 17/20-hydroxylase activity as previously shown in boys and girls with autism seem to have more relevance. Analysis of larger samples, including plenty of metabolites and enzymatic cascades, as well as the role of backdoor pathway activity of androgen synthesis of girls with autism, are demanded in order to validate current findings of altered steroid hormones in autism.

Pseudoxanthoma elasticum (PXE), due to rare sequence variants in the

gene, is characterized by calcification of elastic fibers in several tissues/organs; however, the pathomechanisms have not been completely clarified. Although it is a systemic disorder on a genetic basis, it is not known why not all elastic fibers are calcified in the same patient and even in the same tissue. At present, data on soft connective tissue mineralization derive from studies performed on vascular tissues and/or on clinically affected skin, but there is no information on patients' clinically unaffected skin.

Skin biopsies from clinically unaffected and affected areas of the same PXE patient (n = 6) and from healthy subjects were investigated by electron microscopy. Immunohistochemistry was performed to evaluate p-SMAD 1/5/8 and p-SMAD 2/3 expression and localization.

In clinically unaffected skin, fragmented elastic fibers were prevalent, whereas calcified fibers were only rarely observed at the ultrastructural level. p-SMAD1/5/8 and p-SMAD2/3 were activated in both affected and unaffected skin.

These findings further support the concept that fragmentation/degradation is necessary but not sufficient to cause calcification of elastic fibers and that additional local factors (e.g., matrix composition, mechanical forces and mesenchymal cells) contribute to create the pro-osteogenic environment.

These findings further support the concept that fragmentation/degradation is necessary but not sufficient to cause calcification of elastic fibers and that additional local factors (e.g., matrix composition, mechanical forces and mesenchymal cells) contribute to create the pro-osteogenic environment.The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.Deep learning technology has improved the performance of vision-based action recognition algorithms, but such methods require a large number of labeled training datasets, resulting in weak universality. To address this issue, this paper proposes a novel self-deployable ubiquitous action recognition framework that enables a self-motivated user to bootstrap and deploy action recognition services, called FOLLOWER. Our main idea is to build a "fingerprint" library of actions based on a small number of user-defined sample action data. Then, we use the matching method to complete action recognition. The key step is how to construct a suitable "fingerprint". Thus, a pose action normalized feature extraction method based on a three-dimensional pose sequence is designed. FOLLOWER is mainly composed of the guide process and follow the process. Selleck Degrasyn Guide process extracts pose action normalized feature and selects the inner class central feature to build a "fingerprint" library of actions. Follow process extracts the pose action normalized feature in the target video and uses the motion detection, action filtering, and adaptive weight offset template to identify the action in the video sequence. Finally, we collect an action video dataset with human pose annotation to research self-deployable action recognition and action recognition based on pose estimation. After experimenting on this dataset, the results show that FOLLOWER can effectively recognize the actions in the video sequence with recognition accuracy reaching 96.74%.