AN IN VITRO BLUNT IMPACT EXPERIMENT UPON HUMAN CHINS

In order to investigate the mechanical relationship among the mandible -TMJ-skull during blunt impact, two fresh cadaver heads were impacted at the chins by a blunt body (about 6 kg) with velocity 1.1 m/s and 1.6 m/so The impact forces were in the direction from the chin and vertically to the junction line of both sides of the condyles. It was found that the impact force-time curves were two peaks profiles. After the impact body contacting the chins, the impact forces rose rapidly to the first peaks (100.3/153.2 N, corresponding to 1.1/1.6 m/s of impact velocity) at 7.25/5.57 ms; then they slid for a while and rose again until reached the higher peaks (171.7/321.7 N) at around 21.1/20.1 ms; the total impact durations lasted about 46.1/42.6 ms. This paper obtained the dynamics characteristics of the mandible -TMJ -skull during blunt impact. It should be useful to further study the mechanical role in the genius TMJ disfunction resulted from facial trauma

Stability and Failure Mechanism Analyses of the Zhenggang Landslide in Southwestern China

The Zhenggang landslide is an ancient complex landslide located at southeastern Tibetan Plateau, China. Due to intensive rainfalls in 2008 and heavy snowfalls in 2009, the Zhenggang landslide exhibited a high probability of reactivation once again. In this study, geological structure, matter features, and macrodeformations of the Zhenggang landslide (including Zone I and Zone II) were investigated for uncovering its formation mechanism and evolution tendency first, and then the stability and failure mechanism analyses of the Zhenggang landslide were conducted in detail by a combined limit equilibrium and finite element analysis method. Results of geological investigations indicate that the Zhenggang landslide has undergone sliding several times and is in a metastable state now. The distribution of the activity of the landslide is a retrogressive landslide in Zone I but an advancing landslide in Zone II. Such conclusions are further proved by the numerical stability and failure analyses

Species-specific pharmacology of Trichloro(sulfanyl)ethyl benzamides as transient receptor potential ankyrin 1 (TRPA1) antagonists

Agonists of TRPA1 such as mustard oil and its key component AITC cause pain and neurogenic inflammation in humans and pain behavior in rodents. TRPA1 is activated by numerous reactive compounds making it a sensor for reactive compounds in the body. Failure of AITC, formalin and other reactive compounds to trigger pain behavior in TRPA1 knockout mice, as well as the ability of TRPA1 antisense to alleviate cold hyperalgesia after spinal nerve ligation, suggest that TRPA1 is a potential target for novel analgesic agents. Here, we have characterized CHO cells expressing human and rat TRPA1 driven by an inducible promoter. As reported previously, both human and rat TRPA1 are activated by AITC and inhibited by ruthenium red. We have also characterized noxious cold response of these cell lines and show that noxious cold activates both human and rat TRPA1. Further, we have used CHO cells expressing human TRPA1 to screen a small molecule compound library and discovered that 'trichloro(sulfanyl)ethyl benzamides' (AMG2504, AMG5445, AMG7160 and AMG9090) act as potent antagonists of human TRPA1 activated by AITC and noxious cold. However, trichloro(sulfanyl)ethyl benzamides' (TCEB compounds) displayed differential pharmacology at rat TRPA1. AMG2504 and AMG7160 marginally inhibited rat TRPA1 activation by AITC, whereas AMG5445 and AMG9090 acted as partial agonists. In summary, we conclude that both human and rat TRPA1 channels show similar AITC and noxious cold activation profiles, but TCEB compounds display species-specific differential pharmacology at TRPA1

A multi-target prediction model for dam seepage field

Prediction of dam behavior based on monitoring data is important for dam safety and emergency management. It is crucial to analyze and predict the seepage field. Different from the mechanism-based physical models, machine learning models predict directly from data with high accuracy. However, current prediction models are generally based on environmental variables and single measurement point time series. Sometimes point-by-point modeling is used to obtain multi-point prediction values. In order to improve the prediction accuracy and efficiency of the seepage field, a novel multi-target prediction model (MPM) is proposed in which two deep learning methods are integrated into one frame. The MPM model can capture causal temporal features between environmental variables and target values, as well as latent correlation features between different measurement points at each moment. The features of these two parts are put into fully connected layers to establish the mapping relationship between the comprehensive feature vector and the multi-target outputs. Finally, the model is trained for prediction in the framework of a feed-forward neural network using standard back propagation. The MPM model can not only describe the variation pattern of measurement values with the change of load and time, but also reflect the spatial distribution relationship of measurement values. The effectiveness and accuracy of the MPM model are verified by two cases. The proposed MPM model is commonly applicable in prediction of other types of physical fields in dam safety besides the seepage field

Maize microrna166 inactivation confers plant development and abiotic stress resistance

MicroRNAs are important regulators in plant developmental processes and stress responses. In this study, we generated a series of maize STTM166 transgenic plants. Knock-down of miR166 resulted in various morphological changes, including rolled leaves, enhanced abiotic stress resistance, inferior yield-related traits, vascular pattern and epidermis structures, tassel architecture, as well as abscisic acid (ABA) level elevation and indole acetic acid (IAA) level reduction in maize. To profile miR166 regulated genes, we performed RNA-seq and qRT-PCR analysis. A total of 178 differentially expressed genes (DEGs) were identified, including 118 up-regulated and 60 down-regulated genes. These DEGs were strongly enriched in cell and intercellular components, cell membrane system components, oxidoreductase activity, single organism metabolic process, carbohydrate metabolic process, and oxidation reduction process. These results indicated that miR166 plays important roles in auxin and ABA interaction in monocots, yet the specific mechanism may differ from dicots. The enhanced abiotic stress resistance is partly caused via rolling leaves, high ABA content, modulated vascular structure, and the potential changes of cell membrane structure. The inferior yield-related traits and late flowering are partly controlled by the decreased IAA content, the interplay of miR166 with other miRNAs and AGOs. Taken together, the present study uncovered novel functions of miR166 in maize, and provide insights on applying short tandem target mimics (STTM) technology in plant breeding

Gut dysbiosis in rheumatic diseases: A systematic review and meta-analysis of 92 observational studies

Background: Emerging evidence suggests that dysbiosis in gut microbiota may contribute to the occurrence or development of several rheumatic diseases. Since gut microbiota dysbiosis is potentially modifiable, it has been postulated to be a promising preventive or therapeutic target for rheumatic diseases. However, the current understanding on the potential associations between gut microbiota and rheumatic diseases is still inadequate. Therefore, we aimed to synthesise the accumulating evidence for the relation of gut microbiota to rheumatic diseases. Methods: The PubMed, Embase and Cochrane Library were searched from inception to March 11, 2022 to include observational studies evaluating the associations between gut microbiota and rheumatic diseases. Standardised mean difference (SMD) of α-diversity indices between rheumatic diseases and controls were estimated using random-effects model. β-diversity indices and relative abundance of gut microbes were summarised qualitatively. Findings: Of the included 92 studies (11,998 participants), 68 provided data for α-diversity. Taken together as a whole, decreases in α-diversity indices were consistently found in rheumatic diseases (observed species: SMD = −0.36, [95%CI = −0.63, −0.09]; Chao1: SMD = −0.57, [95%CI = −0.88, −0.26]; Shannon index: SMD = −0.33, [95%CI = −0.48, −0.17]; Simpson index: SMD = −0.32, [95%CI = −0.49, −0.14]). However, when specific rheumatic diseases were examined, decreases were only observed in rheumatoid arthritis (observed species: SMD = −0.51, [95%CI = −0.78, −0.24]; Shannon index: SMD = −0.31, [95%CI = −0.49, −0.13]; Simpson index: SMD = −0.31, [95%CI = −0.54, −0.08]), systemic lupus erythematosus (Chao1: SMD = −1.60, [95%CI = −2.54, −0.66]; Shannon index: SMD = −0.63, [95%CI = −1.08, −0.18]), gout (Simpson index: SMD = −0.64, [95%CI = −1.07, −0.22]) and fibromyalgia (Simpson index: SMD = −0.28, [95%CI = −0.44, −0.11]), whereas an increase was observed in systemic sclerosis (Shannon index: SMD = 1.25, [95%CI = 0.09, 2.41]). Differences with statistical significance in β-diversity were consistently reported in ankylosing spondylitis and IgG4-related diseases. Although little evidence of disease specificity of gut microbes was found, shared alterations of the depletion of anti-inflammatory butyrate-producing microbe (i.e., Faecalibacterium) and the enrichment of pro-inflammatory microbe (i.e., Streptococcus) were observed in rheumatoid arthritis, Sjögren's syndrome and systemic lupus erythematosus. Interpretation: Gut microbiota dysbiosis was associated with rheumatic diseases, principally with potentially non-specific, shared alterations of microbes. Funding: National Natural Science Foundation of China (81930071, 81902265, 82072502 and U21A20352)

Level of physical activity among middle-aged and older Chinese people: evidence from the China health and retirement longitudinal study

Background: With data from different regions accumulated, physical inactivity (PI) was found to be pandemic worldwide. Using China Health and Retirement Longitudinal Study (CHARLS), a nationwide longitudinal survey data, we aimed to delineate the prevalence, incidence and risk factors of physical inactivity (PI) among Chinese people aged 45 years and older. Methods: The CHARLS covered nearly all provinces, autonomous regions, municipalities of mainland China. With data from CHARLS, three cross-sectional analyses and a cohort analysis were conducted. In cross-sectional studies, we used surveys at 2011, 2013 and 2015 to examine the prevalence and its trend of PI. Multivariate generalized linear model was conducted in survey at 2011 to examine the risk factors for prevalent PI. Multiple imputation of missing values was used and results before and after imputation were compared. In cohort analysis, we identified people free of PI at 2011 and followed them up until 2015 to estimate the incidence of PI. Generalized estimating equation was used to examine the risk factors associated with incidence PI. In all analyses, PI was defined as insufficient physical activity according to the International Physical Activity Questionnaire (IPAQ) criterion. Results: 6650, 5946 and 9389 participants were eligible for cross-sectional analyses, and 4525 participants were included for cohort analysis. The weighted prevalence of PI was 22.25% (95% CI: 20.63–23.95%) in 2011, 20.64% (95% CI: 19.22–22.14%) in 2013 and 19.31% (95% CI: 18.28–20.38%) in 2015. In multivariate analysis, PI was associated with older age, higher education, overweight, obesity and difficulties in daily living, and was negatively associated with working and higher level of expenditure. No material change was detected in results after multiple imputation. In cohort analysis, older age, abundant public facilities, difficulties in daily living were identified as risk factors of incidence PI, while urban areas, college and above education, and working were protective factors. Conclusions: PI is pandemic in 45 years and older people in China. People with older age, difficulties in daily living and people who are not working are at higher risk. More efforts should be paid in estimating and promoting leisure-time physical activities

Gut brain interaction theory reveals gut microbiota mediated neurogenesis and traditional Chinese medicine research strategies

Adult neurogenesis is the process of differentiation of neural stem cells (NSCs) into neurons and glial cells in certain areas of the adult brain. Defects in neurogenesis can lead to neurodegenerative diseases, mental disorders, and other maladies. This process is directionally regulated by transcription factors, the Wnt and Notch pathway, the extracellular matrix, and various growth factors. External factors like stress, physical exercise, diet, medications, etc., affect neurogenesis and the gut microbiota. The gut microbiota may affect NSCs through vagal, immune and chemical pathways, and other pathways. Traditional Chinese medicine (TCM) has been proven to affect NSCs proliferation and differentiation and can regulate the abundance and metabolites produced by intestinal microorganisms. However, the underlying mechanisms by which these factors regulate neurogenesis through the gut microbiota are not fully understood. In this review, we describe the recent evidence on the role of the gut microbiota in neurogenesis. Moreover, we hypothesize on the characteristics of the microbiota-gut-brain axis based on bacterial phyla, including microbiota’s metabolites, and neuronal and immune pathways while providing an outlook on TCM’s potential effects on adult neurogenesis by regulating gut microbiota