The role of autonomy, status conflict and techno-complexity in exhaustion and self-efficacy: a study with chinese university teachers

Since the transformation of higher educational system, university teachers are confronting lots of challenges and stress at workplace. Previous studies have explored job demands and job resources played a significant role in explaining exhaustion of employees. In this study, 172 Chinese university teachers participated in the questionnaire survey. This cross-sectional study aimed to analyze how autonomy, status conflict, and techno-complexity could impact emotional exhaustion according to the JD-R model and how self-efficacy, as a moderator, could moderate these relationships. Results showed that autonomy negatively related to exhaustion, however, status conflict and techno-complexity were positively associated with exhaustion. The results revealed that the interaction of autonomy and higher-level self-efficacy was significant in predicting exhaustion. Additionally, the interaction of lower self-efficacy and status conflict was significant in predicting exhaustion. Furthermore, the interaction between techno-complexity and both levels of self-efficacy were statistically significant in predicting exhaustion. These results revealed that higher level of status conflict and higher level of techno-complexity were associated with exhaustion when self-efficacy was low. The practical implications of the results, limitation, and some suggestions for future research are presented at the end of the study.Com a transformação do sistema de ensino superior, os professores universitários estão enfrentando muitos desafios e estresse no local de trabalho. Estudos anteriores concluíram que as exigências e recursos do trabalho desempenham um papel importante na explicação da exaustão nos trabalhadores. Neste estudo, 172 professores universitários chineses participaram através da resposta a um questionário. Este estudo transversal teve como objetivo analisar como a autonomia, o conflito de estatuto e a complexidade tecnológica podem ter impacto na exaustão emocional utilizando o modelo JD-R e verificar de que modo a autoeficácia pode moderar estas relações. Os resultados mostraram que a autonomia estava negativamente associada à exaustão, porém, o conflito de estatuto e a complexidade tecnológica estavam positivamente relacionadas com a exaustão. Os resultados revelaram também um impacto significativo da interação entre autonomia e a autoeficácia elevada na exaustão. Adicionalmente, a interação entre a autoeficácia baixa e o conflito de estatuto foi significativa na previsão de exaustão. Além disso, a interação entre a complexidade tecnológica e os dois níveis de autoeficácia foram estatisticamente significativos na previsão da exaustão. Estes resultados revelaram que níveis mais elevados de conflito de estatuto e de complexidade tecnológica estavam associados à exaustão quando a autoeficácia era baixa. No final do estudo apresentam-se as implicações práticas dos resultados, as limitações e algumas sugestões para pesquisas futuras

Exploiting Spatial-temporal Correlations for Video Anomaly Detection

Video anomaly detection (VAD) remains a challenging task in the pattern recognition community due to the ambiguity and diversity of abnormal events. Existing deep learning-based VAD methods usually leverage proxy tasks to learn the normal patterns and discriminate the instances that deviate from such patterns as abnormal. However, most of them do not take full advantage of spatial-temporal correlations among video frames, which is critical for understanding normal patterns. In this paper, we address unsupervised VAD by learning the evolution regularity of appearance and motion in the long and short-term and exploit the spatial-temporal correlations among consecutive frames in normal videos more adequately. Specifically, we proposed to utilize the spatiotemporal long short-term memory (ST-LSTM) to extract and memorize spatial appearances and temporal variations in a unified memory cell. In addition, inspired by the generative adversarial network, we introduce a discriminator to perform adversarial learning with the ST-LSTM to enhance the learning capability. Experimental results on standard benchmarks demonstrate the effectiveness of spatial-temporal correlations for unsupervised VAD. Our method achieves competitive performance compared to the state-of-the-art methods with AUCs of 96.7%, 87.8%, and 73.1% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech, respectively.Comment: This paper is accepted at IEEE 26TH International Conference on Pattern Recognition (ICPR) 202

SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation

In this paper, we propose a scribble-based video colorization network with temporal aggregation called SVCNet. It can colorize monochrome videos based on different user-given color scribbles. It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding. To improve the colorization quality and strengthen the temporal consistency, we adopt two sequential sub-networks in SVCNet for precise colorization and temporal smoothing, respectively. The first stage includes a pyramid feature encoder to incorporate color scribbles with a grayscale frame, and a semantic feature encoder to extract semantics. The second stage finetunes the output from the first stage by aggregating the information of neighboring colorized frames (as short-range connections) and the first colorized frame (as a long-range connection). To alleviate the color bleeding artifacts, we learn video colorization and segmentation simultaneously. Furthermore, we set the majority of operations on a fixed small image resolution and use a Super-resolution Module at the tail of SVCNet to recover original sizes. It allows the SVCNet to fit different image resolutions at the inference. Finally, we evaluate the proposed SVCNet on DAVIS and Videvo benchmarks. The experimental results demonstrate that SVCNet produces both higher-quality and more temporally consistent videos than other well-known video colorization approaches. The codes and models can be found at https://github.com/zhaoyuzhi/SVCNet.Comment: accepted by IEEE Transactions on Image Processing (TIP

VCGAN: Video Colorization with Hybrid Generative Adversarial Network

We propose a hybrid recurrent Video Colorization with Hybrid Generative Adversarial Network (VCGAN), an improved approach to video colorization using end-to-end learning. The VCGAN addresses two prevalent issues in the video colorization domain: Temporal consistency and unification of colorization network and refinement network into a single architecture. To enhance colorization quality and spatiotemporal consistency, the mainstream of generator in VCGAN is assisted by two additional networks, i.e., global feature extractor and placeholder feature extractor, respectively. The global feature extractor encodes the global semantics of grayscale input to enhance colorization quality, whereas the placeholder feature extractor acts as a feedback connection to encode the semantics of the previous colorized frame in order to maintain spatiotemporal consistency. If changing the input for placeholder feature extractor as grayscale input, the hybrid VCGAN also has the potential to perform image colorization. To improve the consistency of far frames, we propose a dense long-term loss that smooths the temporal disparity of every two remote frames. Trained with colorization and temporal losses jointly, VCGAN strikes a good balance between color vividness and video continuity. Experimental results demonstrate that VCGAN produces higher-quality and temporally more consistent colorful videos than existing approaches.Comment: Submitted Major Revision Manuscript of IEEE Transactions on Multimedia (TMM

NaoXinTong Inhibits the Development of Diabetic Retinopathy in d

Buchang NaoXinTong capsule (NXT) is a Chinese Materia Medica standardized product extracted from 16 Chinese traditional medical herbs and widely used for treatment of patients with cerebrovascular and cardiovascular diseases in China. Formation of microaneurysms plays an important role in the development of diabetic retinopathy. In this study, we investigated if  NXT can protect diabetic mice against the development of diabetic retinopathy. The db/db mice (~6 weeks old), a diabetic animal model, were divided into two groups and fed normal chow or plus NXT for 14 weeks. During the treatment, fasting blood glucose levels were monthly determined. After treatment, retinas were collected to determine retinal thickness, accumulation of carbohydrate macromolecules, and caspase-3 (CAS-3) expression. Our results demonstrate that administration of NXT decreased fasting blood glucose levels. Associated with the decreased glucose levels, NXT blocked the diabetes-induced shrink of multiple layers, such as photoreceptor layer and outer nuclear/plexiform layers, in the retina. NXT also inhibited the diabetes-induced expression of CAS-3 protein and mRNA, MMP-2/9 and TNFα mRNA, accumulation of carbohydrate macromolecules, and formation of acellular capillaries in the retina. Taken together, our study shows that NXT can inhibit the development of diabetic retinopathy and suggests a new potential application of NXT in clinic

Cardiolipin externalization mediates prion protein (PrP) peptide 106–126-associated mitophagy and mitochondrial dysfunction

Proper mitochondrial performance is imperative for the maintenance of normal neuronal function to prevent the development of neurodegenerative diseases. Persistent accumulation of damaged mitochondria plays a role in prion disease pathogenesis, which involves a chain of events that culminate in the generation of reactive oxygen species and neuronal death. Our previous studies have demonstrated that PINK1/Parkin-mediated mitophagy induced by PrP106−126 is defective and leads to an accumulation of damaged mitochondria after PrP106−126 treatment. Externalized cardiolipin (CL), a mitochondria-specific phospholipid, has been reported to play a role in mitophagy by directly interacting with LC3II at the outer mitochondrial membrane. The involvement of CL externalization in PrP106−126-induced mitophagy and its significance in other physiological processes of N2a cells treated with PrP106−126 remain unknown. We demonstrate that the PrP106−126 peptide caused a temporal course of mitophagy in N2a cells, which gradually increased and subsequently decreased. A similar trend in CL externalization to the mitochondrial surface was seen, resulting in a gradual decrease in CL content at the cellular level. Inhibition of CL externalization by knockdown of CL synthase, responsible for de novo synthesis of CL, or phospholipid scramblase-3 and NDPK-D, responsible for CL translocation to the mitochondrial surface, significantly decreased PrP106−126-induced mitophagy in N2a cells. Meanwhile, the inhibition of CL redistribution significantly decreased PINK1 and DRP1 recruitment in PrP106−126 treatment but had no significant decrease in Parkin recruitment. Furthermore, the inhibition of CL externalization resulted in impaired oxidative phosphorylation and severe oxidative stress, which led to mitochondrial dysfunction. Our results indicate that CL externalization induced by PrP106−126 on N2a cells plays a positive role in the initiation of mitophagy, leading to the stabilization of mitochondrial function

Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection.

MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here, we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 patients with COVID-19 using the high-throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the patients with COVID-19, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection