Research Advances in the Role of the Tropomyosin Family in Cancer.

Cancer is one of the most difficult diseases for human beings to overcome. Its development is closely related to a variety of factors, and its specific mechanisms have been a hot research topic in the field of scientific research. The tropomyosin family (Tpm) is a group of proteins closely related to the cytoskeleton and actin, and recent studies have shown that they play an important role in various cancers, participating in a variety of biological activities, including cell proliferation, invasion, and migration, and have been used as biomarkers for various cancers. The purpose of this review is to explore the research progress of the Tpm family in tumorigenesis development, focusing on the molecular pathways associated with them and their relevant activities involved in tumors. PubMed and Web of Science databases were searched for relevant studies on the role of Tpms in tumorigenesis and development and the activities of Tpms involved in tumors. Data from the literature suggest that the Tpm family is involved in tumor cell proliferation and growth, tumor cell invasion and migration, tumor angiogenesis, tumor cell apoptosis, and immune infiltration of the tumor microenvironment, among other correlations. It can be used as a potential biomarker for early diagnosis, follow-up, and therapeutic response of some tumors. The Tpm family is involved in cancer in a close relationship with miRNAs and LncRNAs. Tpms are involved in tumor tissue invasion and migration as a key link. On this basis, TPM is frequently used as a biomarker for various cancers. However, the specific molecular mechanism of its involvement in cancer progression has not been explained clearly, which remains an important direction for future research.

Experimental Study of Dynamic Tensile Strength of Steel-Fiber-Reinforced Self-Compacting Concrete Using Modified Hopkinson Bar.

As a typical brittle material, the tensile strength of concrete is much lower than its compressive strength. The main failure mode of concrete buildings under explosive and impact loading is spalling, so it is crucial to understand the dynamic tensile performance of concrete. This paper presents an experimental study on the dynamic tensile strength of steel-fiber-reinforced self-compacting concrete (SFRSCC). Specimens of two different self-compacting concrete (SCC) mixes (C40 and C60) and four different fiber volume fractions (0.5%, 1.0%, 1.5%, and 2.0%) are fabricated. Dynamic tensile strengths of SFRSCC are obtained using a modified Hopkinson bar system. The relationships between the dynamic tensile strength of the corresponding SCC mix, the quasi-static compressive strength, and the fiber volume fraction are discussed. An empirical equation is proposed. It is shown that SFRSCC with high compressive strength has higher dynamic tensile strength than low-strength SFRSCC for the same fiber content, and the dynamic tensile strength of SFRSCC possesses an approximately linear relation with the fiber volume fraction. The mechanism underlying this fiber-reinforcement effect is investigated.

Accuracy and eligibility of Bonwill⁃Hawley arch form established by CBCT image for dental crowding measurement: a comparative study with the conventional brass wire and caliper methods.

OBJECTIVES: The purpose of this study was to develop a novel Bonwill⁃Hawley method (Bonwill⁃Hawley arch form based on CBCT image) for the assessment of dental crowding, and to investigate and compare the accuracy and eligibility with the conventional brass wire and caliper methods under different crowding conditions. MATERIAL AND METHODS: Sixty patients with the pair of plaster casts and CBCT data were collected. All the casts were marked and transformed into digital models using iTero scanner, and imported into OrthoCAD software to measure the required space. Using the conventional brass wire (M1) and caliper methods (M2), the available space and dental crowding were measured and calculated basing on digital models, respectively. Correspondingly, the axial planes in the level of dental arches were oriented and captured from the CBCT images to draw the Bonwill⁃Hawley arch forms (M3), which were used to measure and calculate the available space and dental crowding. For each method, intra and inter-examiner reliabilities were evaluated with intra-class correlation coefficients (ICCs). Wilcoxon test and Kruskal-Wallis test were performed for statistically analyzing the discrepancy among different groups. RESULTS: Both intra- and inter-examiner reliability were generally excellent for all parameters obtained by the three methods, except for the dental crowding measured using M1(ICC: 0.473/0.261). The dental crowding measured using M2 were significantly increased in mild, moderate and severe-crowding groups compared with M1. However, no significant difference was detected between M1 and M3 in severe-crowding group (maxilla, p = 0.108 > 0.05; mandible, p = 0.074 > 0.05). With the deterioration of crowding condition, the discrepancy of dental crowding between M1 and M2, or M1 and M3 were significantly decreased (maxilla, M2-M1, mild VS serve, p = 0.003 < 0.05; maxilla, M3-M1, mild VS serve, p = 0.003 < 0.05; mandible, M2-M1, mild VS serve, p = 0.000 < 0.001; mandible, M3-M1, mild VS serve, p = 0.043 < 0.05). CONCLUSION: Dental crowding measured using the novel Bonwill⁃Hawley method was relatively greater than the caliper method, but not exceeding the brass wire method, which wound gradually come close to the brass wire method with the deterioration of crowding condition. CLINICAL RELEVANCE: The Bonwill⁃Hawley method basing on CBCT image proved to be a reliable and acceptable choice for orthodontists to analyze the dental crowding.

Bearing Fault Diagnosis Based on Randomized Fisher Discriminant Analysis.

In this paper, a novel randomized Fisher discriminant analysis (RFDA) based bearing fault diagnosis method is proposed. First, several representative time-domain features are extracted from the raw vibration signals. Second, linear Fisher discriminant analysis (FDA) is extended to nonlinear FDA named RFDA by introducing the random feature map to deal with the non-linearity issue. Specifically, the extracted time-domain features data are mapped onto a high-dimensional space using the random feature map function rather than kernel functions. Third, the time-domain features are fed into the built RFDA model to extract the discriminant features for diagnosis. Moreover, a Bayesian inference is employed to identify the class of the collected vibration signals to diagnose the bearing status. The proposed method uses random Fourier features to approximate the kernel matrix in the kernel Fisher discriminant analysis. Through employing randomized Fisher discriminant analysis, the nonlinearity issue is dealt with, and the computational burden is remarkably reduced compared to the kernel Fisher discriminant analysis (KFDA). To illustrate the superior performance of the proposed RFDA-based bearing fault diagnosis method, comparative experiments are conducted on two widely used datasets, the Case Western Reserve University (CWRU) bearing dataset and the Paderborn University (PU) bearing dataset. For the CWRU dataset, the computation time of RFDA is much shorter than KFDA, while the accuracy rate reaches the same level of KFDA. For the PU dataset, the accuracy rate of RFDA is slightly higher than KFDA, and the computation time is only 44.14% of KFDA.