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Porphyromonas gingivalis (P. gingivalis) is closely related to the occurrence and development of periodontitis. It is considered to be one of the important pathogens leading to alveolar bone resorption. At present, research on P. gingivalis mostly adopts standard laboratory strains whose genetic characteristics have been confirmed, are guaranteed and are traceable, such as ATCC 33277. The virulence phenotypes (endotoxin, firmbria, etc.) of clinically extracted isolates are quite different from those of standard strains, and the pathogenic effects and ability of the host are also widely different. In addition, P. gingivalis is considered to have a significant correlation with a variety of systemic diseases, and the virulence characteristics and pathogenic ability of different strains will have different effects on systemic diseases. However, at present, there is a lack of research on clinical strains and standard strains, and there is a lack of systematic comparison between the two sources of bacteria. In this paper, the differences in the virulence phenotypes and pathogenic effects between clinical isolates and standard strains of P. gingivalis in the last 5-10 years are reviewed. The aim is to elucidate the important virulence gene loci in the P. gingivalis gene sequence, which will play an important role in improving therapeutic methods and the development of related drugs.

The purpose of this study was to verify a Chinese version of Community of Inquiry (CoI) instrument with learning presence and explore the causal relationships of the factors in the instrument. This study first examined the reliability and validity of the instrument. All four presences had acceptable levels of reliability (all Cronbach's a > 0.765 or higher). The confirmatory factor modeling approach was used to assess its validity. Then, the study used path analysis and regression analysis to explore the causal relationships of the presences. The key findings showed that teaching and social presences directly influenced the perceptions of learning presence. Learning presence was a partial mediating variable of interactional relationship within CoI constructs.

Cracks exist in the majority of components of ancient Chinese timber structures and have led to serious mechanical property degradation problems, threatening the safety of the whole structures and making the cracks' detection for wooden components a necessity. With the rapid development of intelligent protection technology of cultural buildings, it is important to establish a scientific identification and quantification method for cracks in wooden components to replace traditional manual detection techniques. Deep learning is precisely such an advanced technology. In this study, images of cracked wooden components were first collected from the Yingxian wooden pagoda and the crack characteristics were analyzed. A dataset for crack segmentation was established using a total of 501 images of cracked wooden components, including a training dataset of 450 images and a validation dataset of 51 images. Based on the mathematical principles of deep learning and the fully convolutional neural networks (FCNN), a deep fully convolutional neural network (d‐FCNN) model was constructed based on encoding and decoding methodology. Four model indicators, pixel accuracy (PA), average pixel accuracy (mPA), mean intersection over union (mIoU), and F1‐score were analyzed to train the model and determine the optimal model parameters, including learning rate, batch size, and epoch. It concluded that the optimal initial learning rate takes the value of 10−4, batch size of 6, and epoch of 100, achieving the average accuracy of 78.8%. Further, based on the pixel's accumulation principle, a quantitative calculation method for crack length and maximum width was proposed. Two cracked wooden columns were prepared, and crack image identification and quantification experiments were conducted to verify the correctness of the constructed d‐FCNN model and the proposed crack quantification method. The results show that the model is suitable for crack intelligence detection, identification, and quantification of cracked wooden components. [ABSTRACT FROM AUTHOR]

In remote dynamic hand-gesture recognition, uncertainties in timing and distance of gesture occurrences, coupled with the subtle bodily perturbations induced by arm movements, pose substantial challenges to the accurate extraction of gesture features. In this paper, we propose a lightweight real-time gesture recognition system based on support vector machines. By analyzing the Doppler features of different motion states, a Doppler weighting factor was constructed to suppress bodily micro-motion interference in the range-time spectrum, and achieve foreground extraction of gesture signals concurrently. Furthermore, prior to the extraction of HOG features, we employ Gaussian filtering to suppress abrupt transitions and noise inherent in the gesture signals. This preprocessing significantly enhances the stability of feature extraction. Subsequently, the extracted features are input into an SVM for training and classification. Experimental results demonstrate that, for five distinct gestures exhibited in two different states –– standing and seated –– within a range of 1 to 5 meters, the recognition accuracy reaches 96%. This proves the feasibility of the proposed methodology, and its potential to realize real-time gesture recognition. [ABSTRACT FROM AUTHOR]

The subsurface defects (SSD) of optical elements have a significant impact on the service life and laser damage threshold of the inertial confinement nuclear fusion optical system. Due to the optical diffraction limit, fluorescence microscopy has limited image resolution for detecting SSD in optical elements, making it challenging to meet the requirements for precision detection. A super-resolution method is proposed in this paper for detecting SSD in optical elements based on the fluorescence polarization characteristics of CdSe/ZnS quantum dots fluorescence dichroism. By enhancing the imaging sparsity of adjacent fluorophores through a polarization modulated excitation laser and utilizing the fluorescence polarization modulation algorithm, the resolution is achieved beyond the optical diffraction limit. The results demonstrate that the proposed method in this paper can obtain super-resolution images of SSD in optical elements with at least a two-fold increase in lateral resolution. This approach effectively improves detection accuracy for SSD and holds significant guiding significance. In addition, it also has application value for assessing the quality of high-precision optical elements. [ABSTRACT FROM AUTHOR]

Temporal lobe epilepsy is the most common form of epilepsy. However, for this type of condition, antiseizure medication is not effective for children. As miRNAs are involved in the development of temporal lobe epilepsy in children, they may provide potential therapeutic approaches for treatment. The primary aim of this study was to explore the expression and function of miR-135a-5p in children with temporal lobe epilepsy. Hippocampal slices from either normal (control) children or children with temporal lobe epilepsy were used to detect the expression of miR-135a-5p and its target gene caspase activity and apoptosis inhibitor 1. To further explore the role of miR-135a-5p in the development of temporal lobe epilepsy in children, primary hippocampal neurons from newborn rats were cultured in vitro in a magnesium-free medium to mimic the temporal lobe epilepsy condition in children. The effect of transfection of miR-135a-5p inhibitor into cells was also assessed. Apoptosis and proliferation of hippocampus cells was respectively assessed by flow cytometry or 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The level of miR-135a-5p was significantly increased in both children with temporal lobe epilepsy and the epileptiform discharge model that employed newborn rat hippocampal neurons; whereas, the expression of caspase activity and apoptosis inhibitor 1 was downregulated by overexpression of miR-135a-5p. Moreover, miR-135a-5p mediated the pro-apoptotic effect of temporal lobe epilepsy via repressing caspase activity and apoptosis inhibitor 1 expression. Additionally, miR-135a-5p reduced cell survival in the temporal lobe epilepsy condition. Overexpression of miR-135a-5p induced cell apoptosis through inhibition of caspase activity and apoptosis inhibitor 1 expression and suppressed cell survival in children with temporal lobe epilepsy.

Subsurface damage (SSD) induced during the processing of optical components, which are key parts of large optical systems, can deteriorate optical system performance or damage the complete system. To eliminate the SSD of optical components in subsequent processing, it must be accurately detected. This paper presents a review of the principle, characteristics, research status, and development trends of SSD detection methods for optical components. This review finds that destructive detection methods can damage the components and cannot detect SSD online, but they are reliable and accurate and characterized by facile principles, easy implementation, and inexpensive equipment. Consequently, such methods are widely used for the validation of non-destructive methods and quality control of components in industrial production. In comparison, non-destructive detection methods can detect SSD online without destroying the components, shorten the detection cycle, increase the overall production efficiency, and decrease the cost. Therefore, non-destructive detection methods have promising development prospects. [ABSTRACT FROM AUTHOR]