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Background: Physiological signals, such as skin conductance, heart rate, and temperature, provide valuable insight into the physiological responses of students to stress during examination sessions., Objective: The primary objective of this research is to explore the effectiveness of physiological signals in predicting grades and to assess the impact of different models and feature selection techniques on predictive performance., Methods: We extracted a comprehensive feature vector comprising 301 distinct features from seven signals and implemented a uniform preprocessing technique for all signals. In addition, we analyzed different algorithmic selection features to design relevant features for robust and accurate predictions., Results: The study reveals promising results, with the highest scores achieved using 100 and 150 features. The corresponding values for accuracy, AUROC, and F1-Score are 0.9, 0.89, and 0.87, respectively, indicating the potential of physiological signals for accurate grade prediction., Conclusion: The findings of this study suggest practical applications in the field of education, where the use of physiological signals can help students cope with exam stress and improve their academic performance. The importance of feature selection and the use of appropriate models highlight the importance of engineering relevant features for precise and reliable predictions.

Hormonal cues regulate mammary development, but the consequent transcriptional changes and cell fate decisions are largely undefined. We show that knockout of the prolactin-regulated Ets transcription factor Elf5 prevented formation of the secretory epithelium during pregnancy. Conversely, overexpression of Elf5 in an inducible transgenic model caused alveolar differentiation and milk secretion in virgin mice, disrupting ductal morphogenesis. CD61+ luminal progenitor cells accumulated in Elf5-deficient mammary glands and were diminished in glands with Elf5 overexpression. Thus Elf5 specifies the differentiation of CD61+ progenitors to establish the secretory alveolar lineage during pregnancy, providing a link between prolactin, transcriptional events, and alveolar development.

The proliferative phase of mammary alveolar morphogenesis is initiated during early pregnancy by rising levels of serum prolactin and progesterone, establishing a program of gene expression that is ultimately responsible for the development of the lobuloalveoli and the onset of lactation. To explore this largely unknown genetic program, we constructed transcript profiles derived from transplanted mammary glands formed by recombination of prolactin receptor (Prlr) knockout or wild-type mammary epithelium with wild-type mammary stroma. Comparison with profiles derived from prolactin-treated Scp2 mammary epithelial cells produced a small set of commonly prolactin-regulated genes that included the negative regulator of cytokine signaling, Socs2 (suppressor of cytokine signaling 2), and the ets transcription factor, E74-like factor 5 (Elf5). Homozygous null mutation of Socs2 rescued the failure of lactation and reduction of mammary signal transducer and activator of transcription 5 phosphorylation that characterizes Prlr heterozygous mice, demonstrating that mammary Socs2 is a key regulator of the prolactin-signaling pathway. Reexpression of Elf5 in Prlr nullizygous mammary epithelium restored lobuloalveolar development and milk production, demonstrating that Elf5 is a transcription factor capable of substituting for prolactin signaling. Thus, Socs2 and Elf5 are key members of the set of prolactin-regulated genes that mediate prolactin-driven mammary development.