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Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Compendium of Veterinary Sciences / Compendio de Ciencias Veterinarias is the property of Facultad de Ciencias Veterinarias de la Universidad Nacional de Asuncion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There has been a steep increase in the use of machine learning for various healthcare applications like the diagnosis of diseases, drug discovery, medical image analysis, etc. Machine learning solutions are proven to be more efficient and less time-consuming than conventional approaches. In this paper, we leverage the advantages of machine learning models to enable a humanoid robot to assist mental health patients. Facial expression and human voice are some of the most demonstrative ways to analyze human emotions, especially for the mentally challenged. We carry out this assistance by constantly monitoring the patient's voice (or audio) and facial expressions to predict human emotions. To implement the model of audio monitoring, we train three different machine learning and deep learning models to compare and choose the better model. Similarly, for facial recognition, we train a deep learning model using a specific dataset to predict facial expressions from the video captured in real time. We then integrate the better-performing machine learning models into a web interface for demonstration. [ABSTRACT FROM AUTHOR]

Copyright of Compendium of Veterinary Sciences / Compendio de Ciencias Veterinarias is the property of Facultad de Ciencias Veterinarias de la Universidad Nacional de Asuncion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ginecología y Obstetricia de México is the property of Federacion Mexicana de Ginecologia y Obstetricia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rho GTPases are critical elements involved in the regulation of signal transduction cascades from extracellular stimuli to cytoskeleton. The Rho guanine nucleotide exchange factors (RhoGEFs) have been implicated in direct activation of these GTPases. Here, we describe a novel RhoGEF, denominated EhGEF3 from the parasite Entamoeba histolytica, which encodes a 110 kDa protein containing the domain arrangement of a Dbl homology domain in tandem with a pleckstrin homology domain, the DH domain of EhGEF3 is closely related with the one of the Vav3 protein. Biochemical analysis revealed that EhGEF3 is capable of stimulating nucleotide exchange on the E. histolytica EhRacA and EhRho1 GTPases in vitro, however only a partial GEF activity toward Cdc42 was observed. Conserved residue analysis showed that the N816 and L817 residues are critical for EhGEF3 activity. Cellular studies revealed that EhGEF3 colocalises with EhRacA in the rear of migrating cells, probably regulating the retraction of the uroid and promoting the activation of these GTPase during the chemotactic response toward fibronectin, and that EhGEF3 also regulates EhRacA activation during the capping of cell receptors. These results suggest that EhGEF3 should have a direct role in activating EhRacA, and in bringing the activated GTPase to specific target sites such as the uroid. Cell Motil. Cytoskeleton 2007. © 2007 Wiley‐Liss, Inc. [ABSTRACT FROM AUTHOR]