Continuous Stress Detection Based on Social Media.

Leveraging social media for stress detection has been growing attention in recent years. Most relevant studies so far concentrated on training a stress detection model on the entire data in a closed environment, and did not continuously incorporate new information into the already established models but instead regularly reconstruct a new model from scratch. In this study, we formulate a social media based continuous stress detection task with two particular questions to be addressed: (1) when to adapt a learned stress detection model? and (2) how to adapt a learned stress detection model? We design a protocol to quantify the conditions that trigger model's adaptation, and develop a layer-inheritance based knowledge distillation method to continually adapt the learned stress detection model to incoming data, while retaining the knowledge gained previously. The experimental results on a constructed dataset containing 69 users on Tencent Weibo validate the effectiveness of the proposed adaptive layer-inheritance based knowledge distillation method, achieving 86.32% and 91.56% of accuracy in 3-label and 2-label continuous stress detection. Implications and further possible improvements are also discussed at the end of the article.

How do the Growth and Metabolic Activity of Aquatic fungi Geotrichum Candidum and Aspergillus Niger Respond to Nanoplastics?

In this study, exposure experiments were conducted to assess the effects of polystyrene nanoparticles (PS) and amine-modified polystyrene nanoparticles (APS) at environmental concentrations (1, 10, and 100 µg L- 1) on two fungal species (Geotrichum candidum and Aspergillus niger), isolated from leaf litter in streams, concerning their growth and metabolic activity. Results showed that PS at 1 and 10 µg L- 1 have hormesis effects on G. candidum growth. Compared with G. candidum, A. niger had higher sensitivity to nanoplastic exposure. Besides, the peroxidase and cellobiohydrolase activities of A. niger were significantly inhibited by nanoplastics (except 1 µg L- 1 PS), which would weaken its metabolic activity in carbon cycling. These results provided a new thought on how the growth and functions of aquatic fungi cope with the stress induced by nanoplastics. Overall, the study provided evidence for the different responses of aquatic fungi to nanoplastics in streams.

Category-Aware Chronic Stress Detection on Microblogs.

People today live a stressful life. Compared with acute stress, long-term chronic stress is more harmful, and may cause or exacerbate many serious health problems, including high blood pressure, heart disease, chronic pain, and mental diseases. With social media becoming an integral part of our daily lives for information sharing and self-expression, detecting category-aware long-standing chronic stress from a large volume of historic open posts made by social media users is possible. In this study, we construct a data set containing 971 chronically stressed users with totally 54 546 open posts on Sina microblog from July 5, 2018 to December 1, 2019, and design two techniques for category-aware chronic stress detection: (1) a stress-oriented word embedding on the basis of an existing pre-trained word embedding, aiming to strengthen the sensibility of stress-related expressions for linguistic post analysis; (2) a multi-attention model with three layers (i.e., category-attention layer, posts self-attention layer, and category-specific post attention layer), aiming to capture inter-relevance from a sequence of posts and infer long-term stress categories and stress levels. The experimental results show that the proposed multi-attention model equipped with the stress-oriented word embedding can achieve 80.65% accuracy in detecting category-aware stress levels, 86.49% accuracy in detecting chronic stress levels only, and 93.07% accuracy in detecting chronic stress categories only. Limitations and implications of the study are also discussed at the end of the paper.

Video-Based Stress Detection through Deep Learning.

Stress has become an increasingly serious problem in the current society, threatening mankind's well-beings. With the ubiquitous deployment of video cameras in surroundings, detecting stress based on the contact-free camera sensors becomes a cost-effective and mass-reaching way without interference of artificial traits and factors. In this study, we leverage users' facial expressions and action motions in the video and present a two-leveled stress detection network (TSDNet). TSDNet firstly learns face- and action-level representations separately, and then fuses the results through a stream weighted integrator with local and global attention for stress identification. To evaluate the performance of TSDNet, we constructed a video dataset containing 2092 labeled video clips, and the experimental results on the built dataset show that: (1) TSDNet outperformed the hand-crafted feature engineering approaches with detection accuracy 85.42% and F1-Score 85.28%, demonstrating the feasibility and effectiveness of using deep learning to analyze one's face and action motions; and (2) considering both facial expressions and action motions could improve detection accuracy and F1-Score of that considering only face or action method by over 7%.

Chronic impacts of TiO2 nanoparticles on Populus nigra L. leaf decomposition in freshwater ecosystem.

Titanium dioxide (TiO2) nanoparticles have been applied in diverse commercial products, which could lead to toxic effects on aquatic microbes and would inhibit some important ecosystem processes. The study aimed to investigate the chronic impacts of TiO2 nanoparticles with different concentrations (5, 50, and 500 mg L-1) on Populus nigra L. leaf decomposition in the freshwater ecosystem. After 50 d of decomposing, a significant decrease in decomposition rates was observed with higher concentrations of TiO2 nanoparticles. During the period of litter decomposition, exposure of TiO2 nanoparticles led to decreases in extracellular enzyme activities, which was caused by the reduction of microbial especially fungal biomass. In addition, the diversity and composition of the fungal community associated with litter decomposition were strongly affected by the concentrations of TiO2 nanoparticles. The diversity and composition of the fungal community associated with litter decomposition was strongly affected. The abundance of Tricladium chaetocladium decreased with the increasing concentrations of TiO2 nanoparticles, indicating the little contribution of the species to the litter decomposition. In conclusion, this study provided the evidence for the chronic exposure effects of TiO2 nanoparticles on the litter decomposition and further the functions of freshwater ecosystems.