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Ethnopharmacological relevance: Two types of traditional Chinese formulas of botanical drugs are prescribed for treating perimenopausal syndrome (PMS), a disorder in middle-aged women during their transition to menopause. One is for treating PMS as kidney deficiency (KD) due to senescence and declining reproductive functions, and the other is for treating it as liver qi stagnation (LQS) in association with stress and anxiety. Despite the time-tested prescriptions, an objective attestation to the effectiveness of the traditional Chinese treatment of PMS is still to be established and the associated molecular mechanism is still to be investigated.Materials and methods: A model for PMS was generated from perimenopausal rats with chronic restraint stress (CRS). The effectiveness of traditional Chinese formulas of botanical drugs and a combination of two of the formulas was evaluated based on 1H NMR plasma metabolomic, as well as behavioral and physiological, indicators. To investigate whether the formulas contained ligands that could compensate for the declining level of estrogen, the primary cause of PMS, the ligand-based NMR technique of saturation transfer difference (STD) was employed to detect possible interacting molecules to estrogen receptors in the decoction.Results: Each prescription of the classical Chinese formula moderately attenuated the metabolomic state of the disease model. The best treatment strategy however was to combine two traditional Chinese formulas, each for a different etiology, to adjust the metabolomic state of the disease model to that of rats at a much younger age. In addition, this attenuation of the metabolomics of the disease model was by neither upregulating the estrogen level nor supplementing an estrogenic compound.Conclusion: Treatment of PMS with a traditional Chinese formula of botanical drugs targeting one of the two causes separately could ameliorate the disorder moderately. However, the best outcome was to treat the two causes simultaneously with a decoction that combined ingredients from two traditional prescriptions. The data also implicated a new paradigm for phytotherapy of PMS as the prescribed decoctions contained no interacting compound to modulate the activity of estrogen receptors, in contrast to the treatment strategy of hormone replacement therapy.

Collective design and innovation are crucial in organizations. To investigate how the collective design and innovation processes would be affected by the diversity of knowledge and background of collective individual members, we conducted three collaborative design task experiments which involved nearly 300 participants who worked together anonymously in a social network structure using a custom-made computer-mediated collaboration platform. We compared the idea generation activity among three different background distribution conditions (clustered, random, and dispersed) with the help of the “doc2vec” text representation machine learning algorithm. We also developed a new method called “Idea Geography” to visualize the idea utility terrain on a 2D problem domain. The results showed that groups with random background allocation tended to produce the best design idea with the highest utility values. It was also suggested that the diversity of participants’ backgrounds distribution on the network might interact with each other to affect the diversity of ideas generated. The proposed idea geography successfully visualized that the collective design processes did find the high utility area through exploration and exploitation in collaborative work.

Leaders are often identified in empirical studies by either their position in an organizationally defined hierarchy or by survey responses, yet such methods conflate behavioral antecedents and outcomes with behaviors themselves. Furthermore, without an external standard for comparison, it cannot be known to what extent differences in leader assignment or emergence between demographic or other categories are due to behavioral differences or biases in the assessment, selection, training, or rating processes. In this study, we propose the ``interruption network'' as a model of small group structure that is (a) grounded in social status theory, (b) definable in both lab and field groups, (c) minimally impacted by rater bias, and (d) based on assessed behaviors rather than antecedents or outcomes. We show that analysis of interruption networks suggests that the often-reported male bias in leadership attributions is found in the ratings but not in the behavior of the observed individuals: males and females may engage in leadership behaviors equally as often, but males are attributed leadership significantly more. Use of the interruption network to represent small group social status therefore extends well-established research on non-verbal behaviors to an explicitly group-oriented context, supporting theory-based unobtrusive assessment and new ways to address important questions in applied psychology.

Light-weight devices have become ubiquitous in our daily life, such as smartphones, smart monitors, and other smart devices in our home. As light-weight devices are becoming popular, the demand for sophisticated human-computer interaction (HCI) applications for light-weight devices is also increasing. One particularly promising HCI application for light-weight devices is facial expression recognition (FER), since it may open up possibilities of various medical, psychological or psychiatric monitoring. However, its high computational demand has prevented widespread adoption of FER on light-weight devices. To address this issue, here we aim at decreasing computational overhead of FER by reducing the number of facial landmarks. We calculated mutual information of facial landmarks’ movements and detected their clusters using hierarchical agglomerative clustering (HAC). We also applied a genetic algorithm (GA)-inspired landmark selection method to filter out low-utility features from each facial landmark cluster. The selected features were provided to a support vector machine (SVM) classifier to classify facial expressions, and its performance was compared among several different algorithm settings. Results showed that our proposed method achieved classification accuracy similar to the classifier that used the original full-featured dataset, with improved performance robustness and computational time reduced by 63.5%.