Comparative analysis of the expression patterns of TM9SF family members in mice.

Transmembrane 9 superfamily proteins (TM9SFs) define a highly conserved protein family, each member of which is characterized by a variable extracellular domain and presumably nine transmembrane domains. Although previous studies have delineated the potential cytological roles of TM9SFs like autophagy and secretory pathway, their functions during development are largely unknown. To establish the basis for dissecting the functions of TM9SFs in vivo, we employed the open-source database, structure prediction, immunofluorescence and Western blot to describe the gene and protein expression patterns of TM9SFs in human and mouse. While TM9SFs are ubiquitously and homogeneously expressed in all tissues in human with RNA sequencing and proteomics analysis, we found that all mice Tm9sf proteins are preferentially expressed in lung except Tm9sf1 which is enriched in brain although they all distributed in various tissues we examined. In addition, we further explored their expression patterns in the mice central nervous system (CNS) and its extension tissue retina. Interestingly, we could show that Tm9sf1is developmentally up-regulated in brain. In addition, we also detected all Tm9sf proteins are located in neurons and microglia instead of astrocytes. Importantly, Tm9sf3 is localized in the nuclei which is distinct from the other members that are dominantly targeted to the plasma membrane/cytoplasm as expected. Finally, we also found that Tm9sf family members are broadly expressed in the layers of INL, OPL, and GCL of retina and likely targeted to the plasma membrane of retinal cells. Thus, our data provided a comprehensive overview of TM9SFs expression patterns, illustrating their ubiquitous roles in different organs, implying the possible roles of Tm9sf2/3/4 in lung functions and Tm9sf1 in neurodevelopment, and highlighting a unique cell biological functions of TM9SF3 in neuronal and microglia.

Erratum: Upper limit of the transition temperature of superconducting materials.

[This corrects the article DOI: 10.1016/j.patter.2022.100609.].

Upper limit of the transition temperature of superconducting materials.

Why are the transition temperatures (T c) of superconducting materials so different? The answer to this question is not only of great significance in revealing the mechanism of high-T c superconductivity but also can be used as a guide for the design of new superconductors. However, so far, it is still challenging to identify the governing factors affecting the T c. In this work, with the aid of machine learning and first-principles calculations, we found a close relevance between the upper limit of the T c and the energy-level distribution of valence electrons. It implies that some additional inter-orbital electron-electron interaction should be considered in the interpretation of high-T c superconductivity.

The distribution and risk of microplastics discharged from sewage treatment plants in terrestrial and aquatic compartment.

Many microplastics (MPs) were produced in daily life, which would enter sewage treatment plants (STPs) with the wastewater. Although the STPs has a good interception effect on these MPs, there will still be a part of MPs entering the environment with the effluent and sludge treatment, causing a certain ecological risk. This study investigated the abundance, characteristics and retention of MPs in different STPs, as well as the ecological risks caused by MPs entering the environment. The abundance of MPs in influent and effluent was ranged from 2.02 to 2.50 items L-1 and 0.27-0.48 items L-1, respectively. The abundance of MPs in dewatered sludge and sediment of Lake Dianchi was ranged from 3.719-6.949 × 103 items (kg Ds)-1 and 1.84-5.23 × 103 items (kg Ds)-1, respectively. So roughly 80% of the MPs were trapped and transferred into the dewatered sludge. The observed colors of MPs were transparent, black, blue, red, pale brown, green and gray, and their main species were polypropylene (PP) and polyethylene (PE). To further evaluate the ecological risks of MPs, the oyster mushroom was cultivated in a medium supplemented with MPs. It was found that MPs could be absorbed by oyster mushrooms with a 7-11% of absorption rate, the fibers were widely distributed in the stipes and the pileus. This study had theoretical significance for exploring the distribution of MPs in STPs and clarifying the ecological risk posed by MPs in the environment.

Effect of Alloying Elements on the Stacking Fault Energy and Ductility in Mg2Si Intermetallic Compounds.

Alloying elements can pronouncedly change the mechanical properties of intermetallic compounds. However, the effect mechanism of this in Mg2Si alloys is not clear yet. In this paper, systematic first-principles calculations were performed to investigate the effect of alloying elements on the ductility of Mg-Si alloys. It was found that some alloying elements such as In, Cu, Pd, etc. could improve the ductility of Mg2Si alloys. Moreover, the interatomic bonding mechanisms were analyzed through the electron localization functional. Simultaneously, the machine-learning method was employed to help identify the most important features associated with the toughening mechanisms. It shows that the ground state atomic volume (V GS) is strongly related to the stacking fault energy (γus) of Mg2Si alloys. Interestingly, the alloying elements with appropriate V GS and higher Allred-Rochow electronegativity (En) would reduce the γus in the Mg-Si-X system and yield a better ductility. This work demonstrates how a fundamental theoretical understanding at the atomic and electronic levels can rationalize the mechanical properties of Mg2Si alloys at a macroscopic scale.

Deep learning-based automatic inpainting for material microscopic images.

The microscopic image is important data for recording the microstructure information of materials. Researchers usually use image-processing algorithms to extract material features from that and then characterise the material microstructure. However, the microscopic images obtained by a microscope often have random damaged regions, which will cause the loss of information and thus inevitably influence the accuracy of microstructural characterisation, even lead to a wrong result. To handle this problem, we provide a deep learning-based fully automatic method for detecting and inpainting damaged regions in material microscopic images, which can automatically inpaint damaged regions with different positions and shapes, as well as we also use a data augmentation method to improve the performance of inpainting model. We evaluate our method on Al-La alloy microscopic images, which indicates that our method can achieve promising performance on inpainted and material microstructure characterisation results compared to other image inpainting software for both accuracy and time consumption. LAY DESCRIPTION: A basic goal of materials data analysis is to extract useful information from materials datasets that can in turn be used to establish connections along the composition-processing-structure-properties chain. The microscopic images obtained by a microscope is the key carrier of material microstructural information. Researchers usually use image analysis algorithms to extract regions of interest or useful features from microscopic images, aiming to analyse material microstructure, organ tissues or device quality etc. Therefore, the integrity and clarity of the microscopic image are the most important attributes for image feature extraction. Scientists and engineers have been trying to develop various technologies to obtain perfect microscopic images. However, in practice, some extrinsic defects are often introduced during the preparation and/or shooting processes, and the elimination of these defects often requires mass efforts and cost, or even is impossible at present. Take the microstructure image of metallic material for example, samples prepared to microstructure characterisation often need to go through several steps such as cutting, grinding with sandpaper, polishing, etching, and cleaning. During the grinding and polishing process, defects such as scratches could be introduced. During the etching and cleaning process, some defects such as rust caused by substandard etching, stains etc. may arise and be persisted. These defects can be treated as damaged regions with nonfixed positions, different sizes, and random shapes, resulting in the loss of information, which seriously affects subsequent visual observation and microstructural feature extraction. To handle this problem, we provide a deep learning-based fully automatic method for detecting and inpainting damaged regions in material microscopic images, which can automatically inpaint damaged regions with different positions and shapes, as well as we also use a data augmentation method to improve the performance of inpainting model. We evaluate our method on Al-La alloy microscopic images, which indicates that our method can achieve promising performance on inpainted and material microstructure characterisation results compared to other image inpainting software for both accuracy and time consumption.

Abundance and distribution characteristics of microplastic in plateau cultivated land of Yunnan Province, China.

Microplastic pollution in cultivated soil has received increasing attention recently. There may be more serious microplastic abundance but little research has been done in cultivated soil in plateau areas. To survey the pollution characteristics of microplastics in inland cultivated soil, 100 soil samples collected from 10 counties of Yunnan Province were investigated through density separation and microscopic examination. The research results showed that microplastic abundance was in the range of 0.9 × 103 to 40.8 × 103 particles (kg Ds)-1 with average abundance of 9.8 × 103 particles (kg Ds)-1. Moreover, compared with other studies on sediments, it was found that microplastic abundance in inland soil was one order of magnitude higher than that in offshore sediments. The use of plastic mulch and its long-term residue in cultivated soil was an important reason for microplastic pollution. In this survey, various morphologies of microplastics existed, including fragment (78.3%), transparent/translucency (49.7%), and micro-size microplastics (< 500 µm) (89.3%). And the microplastic morphologies occurred in different degrees of aging phenomenon under the influence of the environment factors such as ultraviolet radiation. The findings provided the pollution status of microplastics in cultivated soil, and more attention should be paid to inland soil microplastic pollution. Grapical abstract.

Two-Way Shape Memory Effect Induced by Tensile Deformation in Columnar-Grained Cu71.7Al18.1Mn10.2 Alloy.

Columnar-grained Cu71.7Al18.1Mn10.2 shape memory alloy (SMA) was prepared by a directional solidification method and exhibited a high superelasticity of 8.18% and excellent ductility at room temperature, which provided the possibility of obtaining high shape memory. However, proper pre-deformation is an essential part of repeatedly obtaining large and stable shape change. In this paper, one-time uniaxial tensile pre-deformation was carried out at the temperature range -70⁻-80 °C. Then, the two-way shape memory effect (TWSME) of the alloy was evaluated by the martensitic transformation strain (εM) which was measured by a thermal expansion test to investigate the relationship between the pre-deformation strain (εT) and the TWSME. The results showed that εM of the columnar-grained Cu71.7Al18.1Mn10.2 alloy increased at first and then decreased with the increase of εT. The maximum value 2.91% of the εM could be reached when εT was 6%. The effects of the εT on transformation temperatures were also measured by differential scanning calorimetry. Based on the variations of transformation temperatures, the relationship between the internal stress induced by the pre-deformation process and the εM, and the influence mechanism of the pre-deformation strain on the TWSME in columnar-grained Cu71.7Al18.1Mn10.2 alloy, were discussed. The results obtained from this work may provide reference for potential applications of Cu-based SMAs, such as self-control components, fasteners, etc.