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To date, the human balance assessment is mainly achieved by measuring the center of pressure (COP) trajectory of upright posture. However, COP-based metrics provide inconclusive results under certain circumstances, due to the lack of relevance with the body balance mechanism. This paper developed a novel framework to measure the variability in body sway based on the Poincaré analysis to enhance the balance assessment. It provided nonlinear parameters SD1, SD2, and their ratio SD1/SD2 as quantitative indicators of the local and global balance regulation capacities. 136 healthy adults were categorized into young and elderly groups and received test on a force platform. COP signals were recorded for 30 s under eyes-opened and eyes-closed vision conditions. The results were analyzed using the two-way mixed-design analysis of variance and show that the technology is able to reveal the effect of age and vision on the balance control by SD1, SD2, and SD1/SD2. SD1 mainly responded to vision, and SD2 was more influenced by age. Due to the information about the subtle adjustment during quiet standing, these Poincaré indexes surpassed the conventional COP measurements in demonstrating the balance control behavior. It can be concluded that the supposed method of estimating the local and global variabilities is a valuable approach for the balance assessment. [ABSTRACT FROM AUTHOR]

Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs′ internal body temperature. [ABSTRACT FROM AUTHOR]

Simple Summary: Researching and developing an automated and intelligent method to monitor the pangolin breeding process can effectively help human observation, analysis, breeding and daily behavior studies of pangolins, and has significant implications for the protection and breeding research of pangolin populations. In this paper, a pangolin breeding attention and temporal relative network (PBATn) was used to monitor and identify the breeding and daily behaviors of pangolins. This study demonstrates that the deep learning system can accurately observe pangolin breeding behavior, rendering it useful for analyzing the behavior of these animals. With declining populations in the wild, captive rescue and breeding have become one of the most important ways to protect pangolins from extinction. At present, the success rate of artificial breeding is low, due to the insufficient understanding of the breeding behavior characteristics of pangolins. The automatic recognition method based on machine vision not only monitors for 24 h but also reduces the stress response of pangolins. This paper aimed to establish a temporal relation and attention mechanism network (Pangolin breeding attention and transfer network, PBATn) to monitor and recognize pangolin behaviors, including breeding and daily behavior. There were 11,476 videos including breeding behavior and daily behavior that were divided into training, validation, and test sets. For the training set and validation set, the PBATn network model had an accuracy of 98.95% and 96.11%, and a loss function value of 0.1531 and 0.1852. The model is suitable for a 2.40 m × 2.20 m (length × width) pangolin cage area, with a nest box measuring 40 cm × 30 cm × 30 cm (length × width × height) positioned either on the left or right side inside the cage. A spherical night-vision monitoring camera was installed on the cage wall at a height of 2.50 m above the ground. For the test set, the mean Average Precision (mAP), average accuracy, average recall, average specificity, and average F1 score were found to be higher than SlowFast, X3D, TANet, TSN, etc., with values of 97.50%, 99.17%, 97.55%, 99.53%, and 97.48%, respectively. The recognition accuracies of PBATn were 94.00% and 98.50% for the chasing and mounting breeding behaviors, respectively. The results showed that PBATn outperformed the baseline methods in all aspects. This study shows that the deep learning system can accurately observe pangolin breeding behavior and it will be useful for analyzing the behavior of these animals. [ABSTRACT FROM AUTHOR]

Because of limited treatment options, preeclampsia (PE) is the leading cause of perinatal morbidity and mortality worldwide. Recently, lncRNA TDRG1 is reported to be aberrantly down‐regulated in PE placenta, and the abnormal expression of TDRG1 might play a key or partial role in PE development. In this study, we found that TDRG1 was significantly down‐regulated in PE placenta compared with the normal placenta. The cell proliferation, migration, invasion, and cell cycle were explored by CCK‐8, wound‐healing, transwell, and flow cytometer assay, respectively. Experimental results showed that TDRG1 accelerated the proliferation, migration, and invasion of trophoblast cells. Dual‐luciferase reporter assays confirmed that TDRG1 could bind to miR‐214‐5p. Besides, knockdown of TDRG1 suppressed the cell proliferation, migration, and invasion, while knockdown of miR‐214‐5p reversed the effect. Jagged1 and Notch1 were negatively regulated by miR‐214‐5p while positively modulated by TDRG1. In conclusion, TDRG1 promoted trophoblast cells viability and invasion by negatively regulating miR‐214‐5p expression, contributing to a better understanding of PE pathogenesis and providing new light on TDRG1‐directed diagnosis and treatment. Significance of the study: In this work, we observed that TDRG1 was able to promote cell proliferation, migration, and invasion cells by suppressing the expression of miR‐214‐5p and regulating the Notch signalling pathway in trophoblast cells. As far as we know, the effect of TDRG1/miR‐214‐5p axis on cell viability, migration, and invasion of trophoblast cells was firstly introduced. Our findings provided a better understanding of the mechanism of PE. Moreover, it is reasonable to believe that TDRG1 may be employed as a strategy to treat PE in the future. [ABSTRACT FROM AUTHOR]