High Expression of Testes-Specific Protease 50 Is Associated with Poor Prognosis in Colorectal Carcinoma

Testes-specific protease 50 (TSP50) is normally expressed in testes and abnormally expressed in breast cancer, but whether TSP50 is expressed in colorectal carcinoma (CRC) and its clinical significance is unclear. We aimed to detect TSP50 expression in CRC, correlate it with clinicopathological factors, and assess its potential diagnostic and prognostic value. = 0.009).Our data demonstrate that TSP50 is a potential effective indicator of poor survival for CRC patients, especially for those with early-stage tumors

Grain Knowledge Graph Representation Learning: A New Paradigm for Microstructure-Property Prediction

The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been used to predict the properties of polycrystalline materials. However, they cannot capture the complex interactive relationship between the grains in the microstructure, which is a crucial factor affecting the material’s macroscopic properties. Here, we propose a grain knowledge graph representation learning method. First, based on the polycrystalline structure, an advanced digital representation of the knowledge graph is constructed, embedding ingenious knowledge while completely restoring the polycrystalline structure. Then, a heterogeneous grain graph attention model (HGGAT) is proposed to realize the effective high-order feature embedding of the microstructure and to mine the relationship between the structure and the material properties. Through benchmarking with other machine learning methods on magnesium alloy datasets, HGGAT consistently demonstrates superior accuracy on different performance labels. The experiment shows the rationality and validity of the grain knowledge graph representation and the feasibility of this work to predict the material’s structural characteristics

Grain Knowledge Graph Representation Learning: A New Paradigm for Microstructure-Property Prediction

The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been used to predict the properties of polycrystalline materials. However, they cannot capture the complex interactive relationship between the grains in the microstructure, which is a crucial factor affecting the material’s macroscopic properties. Here, we propose a grain knowledge graph representation learning method. First, based on the polycrystalline structure, an advanced digital representation of the knowledge graph is constructed, embedding ingenious knowledge while completely restoring the polycrystalline structure. Then, a heterogeneous grain graph attention model (HGGAT) is proposed to realize the effective high-order feature embedding of the microstructure and to mine the relationship between the structure and the material properties. Through benchmarking with other machine learning methods on magnesium alloy datasets, HGGAT consistently demonstrates superior accuracy on different performance labels. The experiment shows the rationality and validity of the grain knowledge graph representation and the feasibility of this work to predict the material’s structural characteristics

2D-DOA and Mutual Coupling Estimation in Vehicle Communication System via Conformal Array

Many direction-of-arrival (DOA) estimation algorithms have been proposed recently. However, the effect of mutual coupling among antenna elements has not been taken into consideration. In this paper, a novel DOA and mutual coupling coefficient estimation algorithm is proposed in intelligent transportation systems (ITS) via conformal array. By constructing the spectial mutual coupling matrix (MCM), the effect of mutual coupling can be eliminated via instrumental element method. Then the DOA of incident signals can be estimated based on parallel factor (PARAFAC) theory. The PARAFAC model is constructed in cumulant domain using covariance matrices. The mutual coupling coefficients are estimated based on the former DOA estimation and the matrix transformation between MCM and the steering vector. Finally, due to the drawback of the parameter pairing method in Wan et al., 2014, a novel method is given to improve the performance of parameter pairing. The computer simulation verifies the effectiveness of the proposed algorithm

A push-based probabilistic method for source location privacy protection in underwater acoustic sensor networks

As the research topics in ocean emerge, Underwater Acoustic Sensor Networks (UASNs) have become ever more relevant. Consequently, challenges arise with the security and privacy of the UASNs. Compared to the active attacks, the characteristics of passive attacks are more difficult to discriminate. Thus, the focus of this study is on the passive attacks in UASNs, where a Push-based Probabilistic method for Source Location Privacy Protection (PP-SLPP) is proposed. The fake packet technology and the multi-path technology are utilized in the PP-SLPP scheme to counter the passive attacks, so as to protect the source location privacy in UASNs. Moreover, the Ekman drift current model is employed to simulate the underwater environment. And the mean shift algorithm and the k-means algorithm are adopted in the dynamic layer and static layer of the Ekman drift current model, respectively, to increase the stability of the clusters. Finally, an Autonomous Underwater Vehicle (AUV) swarm is implemented to collect data in clusters. Through the comparison with existing data collection schemes in UASNs, the simulation results have demonstrated that the PP-SLPP scheme can achieve a longer safety period, with a minor compromise of energy consumption and delay

Pyrite Morphology as an Indicator of Paleoredox Conditions and Shale Gas Content of the Longmaxi and Wufeng Shales in the Middle Yangtze Area, South China

Pyrite is the most common authigenic mineral preserved in many ancient sedimentary rocks. Pyrite also widely exists in the Longmaxi and Wufeng marine shales in the middle Yangtze area in South China. The Longmaxi and Wufeng shales were mainly discovered with 3 types of pyrites: pyrite framboids, euhedral pyrites and infilled framboids. Euhedral pyrites (Py4) and infilled framboids (Py5) belong to the diagenetic pyrites. Based on the formation mechanism of pyrites, the pyrites could be divided into syngenetic pyrites, early diagenetic pyrites, and late diagenetic pyrites. Under a scanning electron microscope (SEM), the syngenetic pyrites are mostly small framboids composed of small microcrystals, but the diagenetic pyrites are variable in shapes and the diagenetic framboids are variable in sizes with large microcrystals. Due to the deep burial stage, the pore space in the sediment was sharply reduced and the diameter of the late diagenetic framboids that formed in the pore space is similar to the diameter of the syngenetic framboids. However, the diameter of the syngenetic framboid microcrystals is suggested to range mainly from 0.3 µm to 0.4 µm, and that of the diagenetic framboid microcrystals is larger than 0.4 µm in the study area. According to the diameter of the pyrite framboids (D) and the diameter of the framboid microcrystals (d), the pyrite framboids could be divided into 3 sizes: syngenetic framboids (Py1, D < 5 µm, d ≤ 0.4 µm), early diagenetic framboids (Py2, D > 5 µm, d > 0.4 µm) and late diagenetic framboids (Py3, D < 5 µm, d > 0.4 µm). Additionally, the mean size and standard deviation/skewness values of the populations of pyrite framboids were used to distinguish the paleoredox conditions during the sedimentary stage. In the study area, most of the pyrite framboids are smaller than 5 µm, indicating the sedimentary water body was a euxinic environment. However, pyrite framboids larger than 5 µm in the shales indicated that the sedimentary water body transformed to an oxic-dysoxic environment with relatively low total organic carbon (TOC: 0.4–0.99%). Furthermore, the size of the framboid microcrystals could be used to estimate the gas content due to thermochemical sulfate reduction (TSR). The process of TSR occurs with oxidation of organic matter (OM) and depletes the H bond of the OM, which will influence the amount of alkane gas produced from the organic matter during the thermal evolution. Thus, syngenetic pyrites (d ranges from 0.35 µm to 0.37 µm) occupy the main proportion of pyrites in the Wufeng shales with high gas content (1.30–2.30 m3/t), but the Longmaxi shales (d ranges from 0.35 µm to 0.72 µm) with a relatively low gas content (0.07–0.93 m3/t) contain diagenetic pyrites. Because of TSR, the increasing size of the microcrystals may result in an increase in the value of δ13C1 and a decrease in the value of δ13C1-δ13C2. Consequently, the size of pyrite framboids and microcrystals could be widely used for rapid evaluation of the paleoredox conditions and the gas content in shales

One-day thermal regime extends the lifespan in Caenorhabditis elegans

Environmental factors, including temperature, can modulate an animal’s lifespan. However, their underlying mechanisms remain largely undefined. We observed a profound effect of temperature on the aging of Caenorhabditis elegans (C. elegans) by performing proteomic analysis at different time points (young adult, middle age, and old age) and temperature conditions (20 °C and 25 °C). Importantly, although at the higher temperature, animals had short life spans, the shift from 20 °C to 25 °C for one day during early adulthood was beneficial for protein homeostasis since; it decreased protein synthesis and increased degradation. Consistent with our findings, animals who lived longer in the 25 °C shift were also more resistant to high temperatures along with oxidative and UV stresses. Furthermore, the lifespan extension by the 25 °C shift was mediated by three important transcription factors, namely FOXO/DAF-16, HSF-1, and HIF-1. We revealed an unexpected and complicated mechanism underlying the effects of temperature on aging, which could potentially aid in developing strategies to treat age-related diseases. Our data are available in ProteomeXchange with the identifier PXD024916