Model Prediction and Experimental Validation of Transverse Permeability of Large-Tow Carbon Fiber Composites.

Large-tow carbon fiber (LCF) meets the low-cost requirements of modern industry. However, due to the large and dense number of monofilaments, there are problems with uneven and insufficient infiltration during material preparation. The permeability of large-tow carbon fibers can be used as a two-scale expression of resin flow during infiltration, making it an important factor to consider. This paper provides support for the study of pore formation. A two-dimensional model of randomly bundled large-filament carbon fibers is generated based on scanning electron microscope (SEM) maps. Microstructure size parameters are obtained, and a semi-analytical model of the transverse permeability of large-filament-bundled carbon fibers is established. Permeability values are then obtained. The analysis shows that the monofilaments in the tow are arranged randomly, and their periodic arrangement cannot be used to calculate permeability. Additionally, the number of monofilaments in a carbon fiber tow of the same volume fraction affects the permeability of the tow. Therefore, the permeability model of large-tow carbon fibers is reliable.

CenterNet-Saccade: Enhancing Sonar Object Detection with Lightweight Global Feature Extraction.

Sonar imaging technology is widely used in the field of marine and underwater monitoring because sound waves can be transmitted in elastic media, such as the atmosphere and seawater, without much interference. In underwater object detection, due to the unique characteristics of the monitored sonar image, and since the target in an image is often accompanied by its own shadow, we can use the relative relationship between the shadow and the target for detection. To make use of shadow-information-aided detection and realize accurate real-time detection in sonar images, we put forward a network based on a lightweight module. By using the attention mechanism with a global receptive field, the network can make the target pay attention to the shadow information in the global environment, and because of its exquisite design, the computational time of the network is greatly reduced. Specifically, we design a ShuffleBlock model adapted to Hourglass to make the backbone network lighter. The concept of CNN dimension reduction is applied to MHSA to make it more efficient while paying attention to global features. Finally, CenterNet's unreasonable distribution method of positive and negative samples is improved. Simulation experiments were carried out using the proposed sonar object detection dataset. The experimental results further verify that our improved model has obvious advantages over many existing conventional deep learning models. Moreover, the real-time monitoring performance of our proposed model is more conducive to the implementation in the field of ocean monitoring.

Interaction of PALE CRESS with PAP2/pTAC2 and PAP3/pTAC10 affects the accumulation of plastid-encoded RNA polymerase complexes in Arabidopsis.

The transcription of photosynthesis genes in chloroplasts is largely mediated by the plastid-encoded RNA polymerase (PEP), which resembles prokaryotic-type RNA polymerases, but with plant-specific accessory subunits known as plastid transcriptionally active chromosome proteins (pTACs) or PEP-associated proteins (PAPs). However, whether additional factors are involved in the biogenesis of PEP complexes remains unknown. Here, we investigated the function of an essential gene, PALE CRESS (PAC), in the accumulation of PEP complexes in chloroplasts. We established that an Arabidopsis leaf variegation mutant, variegated 6-1 (var6-1), is a hypomorphic allele of PAC. Unexpectedly, we revealed that a fraction of VAR6/PAC is associated with thylakoid membranes, where it interacts with PEP complexes. The accumulation of PEP complexes is defective in both var6-1 and the null allele var6-2. Further protein interaction assays confirmed that VAR6/PAC interacts directly with the PAP2/pTAC2 and PAP3/pTAC10 subunits of PEP complexes. Moreover, we generated viable hypomorphic alleles of the essential gene PAP2/pTAC2, and revealed a genetic interaction between PAC and PAP2/pTAC2 in photosynthesis gene expression and PEP complex accumulation. Our findings establish that VAR6/PAC affects PEP complex accumulation through interactions with PAP2/pTAC2 and PAP3/pTAC10, and provide new insights into the accumulation of PEP and chloroplast development.

Metagenomic evidence for antibiotics-driven co-evolution of microbial community, resistome and mobilome in hospital sewage.

Overconsumption of antibiotics is an immediate cause for the emergence of antimicrobial resistance (AMR) and antibiotic resistant bacteria (ARB), though its environmental impact remains inadequately clarified. There is an urgent need to dissect the complex links underpinning the dynamic co-evolution of ARB and their resistome and mobilome in hospital sewage. Metagenomic and bioinformatic methods were employed to analyze the microbial community, resistome and mobilome in hospital sewage, in relation to data on clinical antibiotic use collected from a tertiary-care hospital. In this study, resistome (1,568 antibiotic resistance genes, ARGs, corresponding to 29 antibiotic types/subtypes) and mobilome (247 types of mobile genetic elements, MGEs) were identified. Networks connecting co-occurring ARGs with MGEs encompass 176 nodes and 578 edges, in which over 19 types of ARGs had significant correlations with MGEs. Prescribed dosage and time-dependent antibiotic consumption were associated with the abundance and distributions of ARGs, and conjugative transfer of ARGs via MGEs. Variation partitioning analyses show that effects of conjugative transfer were most likely the main contributors to transient propagation and persistence of AMR. We have presented the first evidence supporting idea that use of clinical antibiotics is a potent driving force for the development of co-evolving resistome and mobilome, which in turn supports the growth and evolution of ARB in hospital sewage. The use of clinical antibiotics calls for greater attention in antibiotic stewardship and management.

Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys.

Compared with traditional alloys, high-entropy alloys have better mechanical properties and corrosion resistance. However, their mechanical properties and microstructural evolution behavior are unclear due to their complex composition. Machine learning has powerful data processing and analysis capabilities, that provides technical advantages for in-depth study of the mechanical properties of high-entropy alloys. Thus, we combined machine learning and molecular dynamics to predict the mechanical properties of FeNiCrCoCu high-entropy alloys. The optimal multiple linear regression machine learning algorithm predicts that the optimal composition is Fe33Ni32Cr11Co11Cu13 high-entropy alloy, with a tensile strength of 28.25 GPa. Furthermore, molecular dynamics is used to verify the predicted mechanical properties of high-entropy alloys, and it is found that the error between the tensile strength predicted by machine learning and the tensile strength obtained by molecular dynamics simulation is within 0.5%. Moreover, the tensile-compression asymmetry of Fe33Ni32Cr11Co11Cu13 high-entropy alloy increased with the increase of temperature and Cu content and the decrease of Fe content. This is due to the increase in stress caused by twinning during compression and the decrease in stress due to dislocation slip during stretching. Interestingly, high-entropy alloy coatings reduce the tensile-compression asymmetry of nickel; this is attributed to the reduced influence of dislocations and twinning at the interface between the high-entropy alloy and the nickel matrix.

Clinical Significance of Fusobacterium nucleatum and Microsatellite Instability in Evaluating Colorectal Cancer Prognosis.

Objective: Both genetic and microbial factors play important roles in colorectal cancer (CRC) development. The effects of Fusobacterium nucleatum (F. nucleatum) and microsatellite instability (MSI) on CRC prognosis require more clinical evidence. We aimed to investigate the role of F. nucleatum and MSI as biomarkers in predicting the prognosis of CRC. Methods: CRC patients in various TNM stages were enrolled. MSI status and F. nucleatum were detected by immunohistochemical staining of formalin-fixed paraffin-embedded (FFPE) specimens. The associations between MSI status and F. nucleatum and clinical parameters were analyzed. Results: MSI tumors were more frequently observed in the colon than in the rectum. Cancerous tissues had higher levels of F. nucleatum than adjacent noncancerous tissues. There were no significant differences in F. nucleatum abundance in different age, sex, tumor stage, location, and tumor marker groups. MSI status was associated with tumor location and stage. Survival analyses revealed that disease-free survival (DFS) was significantly longer in the F. nucleatum-negative, younger age, and TNM stage I-II groups (p< 0.05), and age, advanced TNM stage (III and IV), and F. nucleatum status were independent factors for poor prognosis. Multivariate Cox regression and receiver operating characteristic (ROC) curve analyses showed that conventional tumor biomarkers of CRC had more prognostic value than F. nucleatum and MSI. Conclusion: Age, advanced TNM stage, and F. nucleatum positivity were independent factors of poor prognosis, suggesting that F. nucleatum and MSI may contribute to the identification of new strategies for the prevention and treatment of CRC.

Effect of Hydrophobic Nano-SiO2 Particle Concentration on Wetting Properties of Superhydrophobic Surfaces.

As a unique surface wettability, superhydrophobicity has great application value. A variety of preparation methods for superhydrophobic surfaces have been reported, which have the disadvantages of high cost and complicated process. In order to design a method that is easy to operate, low-cost, and suitable for large-scale preparation of superhydrophobic surfaces, in this paper, hydrophobic nano-SiO2 particles are used as spray fillers, and superhydrophobic surfaces are successfully obtained by the spraying process. According to the classical Cassie and Wenzel theory, the influence of the concentration change of hydrophobic nano-SiO2 particles on their wettability is explained, and the appropriate spray concentration parameters are obtained. The results show that the proportion of hydrophobic nano-SiO2 particles is lower than 0.05 g/mL, which will lead to insufficient microstructure on the surface of the coating, and cannot support the droplets to form the air bottom layer. However, an excessively high proportion of hydrophobic nano-SiO2 particles will reduce the connection effect of the silicone resin and affect the durability of the surface. Through theoretical analysis, there are Wenzel state, tiled Cassie state, and stacked Cassie state in the spraying process. When the substrate surface enters the Cassie state, the lower limit of the contact angle is 149°. This study has far-reaching implications for advancing the practical application of superhydrophobic surfaces.

Metallization on Sapphire and Low-Temperature Joining with Metal Substrates.

To meet the packaging requirements of sapphire in special electronic components, there is an urgent need for a joining process that can realize a good connection between sapphire and dissimilar metals at a low temperature. In this work, the surface of a sapphire substrate was successfully catalytically activated and metallized by an electroless nickel plating process. Moreover, the solderability and interconnection of metallized sapphire with Sn-based solders were evaluated and investigated at 250 °C, and the wetting angle of the Sn-based solders on sapphire on sapphire without and with metallization was 125° and 51°, respectively. The interfacial microscopic morphology and element distribution in the Cu/Sn-Ag solder/sapphire solder joints were analyzed. It was found that the middle solder layer has diffused during the reflow process, inferring good adhesion between sapphire and Cu substrate with the aid of the Ni-P deposition. Thus, a sapphire welding method with a simple process suitable for practical applications is demonstrated.

Hybridly double-crosslinked carbon nanotube networks with combined strength and toughness via cooperative energy dissipation.

Although chemical crosslinking has been extensively explored to enhance the mechanical properties of network-type materials for structural and energy (electrochemical, thermal, etc.) applications, loading-induced energy dissipations usually occur through a single channel that either leads to network brittleness or low strength/stiffness. In this work, we apply coarse-grained molecular dynamics simulations to explore the potential of hybridly double-crosslinked carbon nanotube (CNT) networks as a light weight functional material with combined strength and toughness. While increasing the crosslinking density or strong crosslink composition may, in general, enhance the strength and toughness, further increasing the two parameters would surprisingly lead to deteriorated strength and toughness. We find that double-crosslinked networks can nicely achieve cooperative energy dissipation with minimal structural damage. In particular, the weak crosslinks serve as "sacrificial bonds" to dissipate elastic energies from external loading, while the strong crosslinks act as "structure holders" and break at a much later stage during the tensile test. Therefore, the combination of more than one type of crosslinking with hybrid potential energy landscapes and breaking time scales can prevent premature simultaneous breaking of multiple strong crosslinks. By deploying intermediate amounts of weak and strong crosslinks, we observe an outstanding density-normalized strength of 227-2130 kPa m3 kg-1 as compared to many structural materials and advanced nanocomposites. The crosslinking strategies developed here would pave new avenues for the rational design of functional network materials beyond CNTs, such as hydrogels, nanofibers, and nanocomposites.