Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress.

Twenty-three percent of carbon emissions come from fossil fuels used in transportation. Electric vehicles are suggested as alternatives to fossil-fueled vehicles. Cars having vehicle integrated photovoltaics (VIPV) on the roof have recently been launched, aiming to increase fuel efficiency and increase maximum mileage by supplying electricity to the vehicle when needed. VIPV needs to be light in terms of efficiency. The use of polymeric materials, made of low-iron tempered glass on the front that contributes significantly to the module’s weight, is required instead. The use of a sandwich structure with polymer material achieves nine times stiffer rigidity than an aluminum sheet of the same weight. It can be used with a weight that is half that of glass through the lightweight and light-transmitting polymer material on the front side. The concentrator photovoltaic module structure is used to compensate for various angles of incidence on a moving car, and it is advantageous because it is easy to apply and has a low weight owing to its excellent flexibility. It is possible to reduce the weight from 20 kg to less than 10 kg by limiting the use of glass.

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Axl is a tyrosine kinase receptor, a negative regulator for innate immune responses and inflammatory bowel disease (IBD). The gut microbiota regulates intestinal immune homeostasis, but the role of Axl in the pathogenesis of IBD through the regulation of gut microbiota composition remains unresolved. In this study, mice with DSS-induced colitis showed increased Axl expression, which was almost entirely suppressed by depleting the gut microbiota with antibiotics. Axl−/− mice without DSS administration exhibited increased bacterial loads, especially the Proteobacteria abundant in patients with IBD, significantly consistent with DSS-induced colitis mice. Axl−/− mice also had an inflammatory intestinal microenvironment with reduced antimicrobial peptides and overexpression of inflammatory cytokines. The onset of DSS-induced colitis occurred faster with an abnormal expansion of Proteobacteria in Axl−/− mice than in WT mice. These findings suggest that a lack of Axl signaling exacerbates colitis by inducing aberrant compositions of the gut microbiota in conjunction with an inflammatory gut microenvironment. In conclusion, the data demonstrated that Axl signaling could ameliorate the pathogenesis of colitis by preventing dysbiosis of gut microbiota. Therefore, Axl may act as a potential novel biomarker for IBD and can be a potential candidate for the prophylactic or therapeutic target of diverse microbiota dysbiosis-related diseases.

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