The isolation and characterization of tescalcin, a novel gene differentially expressed in the mouse testis during the early stages of gonadal differentiation

Gonadal development is an ideal model to study organogenesis because a variety of developmental processes can be studied during the differentiation of the bipotential primordium into testis or ovary. To better understand this process, Representational Difference Analysis of cDNA was used to identify genes that are differentially expressed in mouse gonads at 13.5 days post-coitus. The analysis led to the identification of three testis specific genes and a sequence that was only expressed in the ovary. The male genes identified: renin, Col9a3, and a novel gene termed tescalcin had patterns of expression that suggested a role in testis determination. ^ Studies of the tescalcin gene revealed that it is organized into eight exons and seven introns. The gene was located at 64 cM in mouse chromosome 5, where it spans approximately 35 Kb. Three mRNA variants resulting from alternative splicing of intron 5 were identified in mouse tissues. Gel mobility shift assays demonstrated that Sp1 and Sp3 from Y-1, msc-1, and MIN-6 cells nuclear extracts bind the GC-boxes within the tescalcin proximal promoter. Bisulfite sequencing analysis of tescalcin CpG island revealed that it is differentially methylated in male and female mouse embryonic gonads, and that hypermethylation of this region represses expression of tescalcin in the β-TC3 cell line. ^ The major tescalcin mRNA encodes a protein with 214 amino acids that contains a consensus EF-hand Ca2+-binding domain and an N-myristoylation motif. The amino acid sequence of tescalcin is highly conserved among various species, and it showed the highest homology with calcineurin B homologous proteins 1 and 2, and calcineurin B. Western blot analysis using antibodies generated against the tescalcin protein confirmed its presence in specific mouse tissues and cell lines. Immunohistochemical analysis of mouse embryos confirmed the pattern of expression of tescalcin mRNA in fetal testis. Using pull-down assays, glyceraidehydes-3-phosphate dehydrogenase was identified as an interacting and potential functional partner of tescalcin. ^ The identification and characterization of tescalcin as a novel embryonic testicular marker will contribute to the elucidation of the genetic pathways involved in testis development and likely to the understanding of pathological conditions such as sex reversal and infertility. ^

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^