Optical-Propulsion Metastructures.

Pulsed laser micropropulsion (PLMP) offers a promising avenue for miniature space craft, yet conventional propellants face challenges in balancing efficiency and stability. An optical-propulsion metastructure strategy using metal-organic frameworks (MOFs) is presented to generate graphene-metal metastructures (GMM), specifically GMM-(HKUST-1), which significantly enhances PLMP performance. This novel approach leverages the unique interaction between pulsed lasers and the precisely engineered GMMs-comprising optimized metal nanoparticle size, graphene layers, and inter-particle gaps-to boost both propulsion efficiency and stability. Experimental and numerical analyses reveal that GMM-(HKUST-1) achieves aspecific impulse of 1072.94 s, ablation efficiency of 51.22%, and impulse thrust per mass of 105.15 µN µg-1, surpassing traditional propellants. With an average particle size of ≈12 nm and a density of 0.958 g cm-3, these metastructures exhibit 99% light absorption efficiency and maintain stability under atmospheric and humid conditions. The graphene nanolayer efficiently absorbs and converts laser energy, while the metal nanostructures enhance light-matter interactions, promoting energy transfer and material stability. These findings suggest that this GMM-based optical-propulsion strategy can revolutionize microspacecraft propulsion and energy systems, offering significant advancements across various domains.

Highly Efficient Laser Bidirectional Graphene Printing: Integration of Synthesis, Transfer and Patterning.

Graphene has tremendous potential in future electronics due to its superior force, electrical, and thermal properties. However, the development of graphene devices is limited by its complex, high-cost, and low-efficiency preparation process. This study proposes a novel laser bidirectional graphene printing (LBGP) process for the large-scale preparation of patterned graphene films. In LBGP, a sandwich sample composed of a thermoplastic elastomer (TPE) substrate, carbon precursor powder, and a glass cover is irradiated by a nanosecond pulsed laser. The laser photothermal effect converts the carbon precursor into graphene, with partial graphene sheets deposited directly on the TPE substrate and the remaining transferred to the glass cover via a laser-induced plasma plume. This method simultaneously prepares two face-to-face graphene films in a single laser irradiation, integrating synthesis, transfer, and patterning. The resulting graphene patterns demonstrate good performance in flexible pressure sensing and Joule heating, showcasing high sensitivity (7.7 kPa-1), fast response (37 ms), and good cycling stability (2000 cycles) for sensors, and high heating rate (1 °C s-1) and long-term stability (3000 s) for heaters. It is believed that the simple, low-cost, and efficient LBGP process can promote the development of graphene electronics and laser manufacturing processes.

Spatom: a graph neural network for structure-based protein-protein interaction site prediction.

Accurate identification of protein-protein interaction (PPI) sites remains a computational challenge. We propose Spatom, a novel framework for PPI site prediction. This framework first defines a weighted digraph for a protein structure to precisely characterize the spatial contacts of residues, then performs a weighted digraph convolution to aggregate both spatial local and global information and finally adds an improved graph attention layer to drive the predicted sites to form more continuous region(s). Spatom was tested on a diverse set of challenging protein-protein complexes and demonstrated the best performance among all the compared methods. Furthermore, when tested on multiple popular proteins in a case study, Spatom clearly identifies the interaction interfaces and captures the majority of hotspots. Spatom is expected to contribute to the understanding of protein interactions and drug designs targeting protein binding.

Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network.

Here, we describe the use of TriNet to predict peptides with anticancer and antimicrobial properties by a tri-fusion neural network. We detail the use of TriNet for both the offline Python script version and the online service, thereby demonstrating its convenience for users. In addition, we provide a detailed explanation of the training process of TriNet to enhance the understanding of researchers seeking to leverage deep learning techniques for peptide classification. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Epidermal Patch with Biomimetic Multistructural Microfluidic Channels for Timeliness Monitoring of Sweat.

Wearable sweat sensors have been developed rapidly in recent years due to the great potential in health monitoring. Developing a convenient manufacturing process and a novel structure to realize timeliness and continuous monitoring of sweat is crucial for the practical application of sweat sensors. Herein, inspired by the striped grooves and granular structures of bamboo leaves, we realized an epidermal patch with biomimetic multilevel structural microfluidic channels for timeliness monitoring of sweat via 3D printing and femtosecond laser processing. The striped grooves and ridges are alternately arranged at the bottom of the microfluidic channels, and the surface of the ridges has rough granular structures. The striped grooves improve the capillary effect in the microchannels by dividing the microchannels, and the granular structures enhance the slip effect of sweat by increasing surface hydrophobicity. The experimental results show that compared with the conventional microfluidic channels, the water collecting rate of the biomimetic microchannels increased by about 60%, which is consistent with the theoretical analysis. The superior sweat-collecting efficiency in the epidermal patch with the biomimetic multistructure enables sensitive, continuous, and stable monitoring of sweat physiological signals. Besides, this work provides new design and manufacturing approaches for other microfluidic applications.

High-Efficiency Copper Removal by Nitrogen Plasma-Assisted Picosecond Laser Processing.

Copper (Cu) removal efficiency is a key parameter in the processing of Cu-based electronic devices. Herein, a nitrogen plasma-assisted picosecond (ps) laser process for Cu removal is presented. Based on the cleaning and activation effect of nitrogen plasma on the surface of Cu film in ps-laser ablation, the removal efficiency can be significantly improved. Theoretically, the interaction mechanism between Cu and the ps-laser under the action of the plasma flow field is investigated by the dual temperature model (TTM) and finite element analysis (FEA). Meanwhile, the experimental results show that the angle of the plasma flow significantly affects the laser ablation of Cu. Small-angle plasma helps to improve the ps-laser processing precision of Cu, while large-angle plasma can effectively improve the ps-laser processing efficiency of Cu. Under the laser fluence of 2.69 J/cm2, the removal depth of the Cu film by a 30° plasma-assisted ps-laser is 148% higher than that by the non-plasma-assisted ps-laser, which indicates the application potential of nitrogen plasma in improving the laser ablation process.

Discontinuous Track Recognition System Based on PolyLaneNet for Darwin-op2 Robot.

This paper proposes and demonstrates a single-line discontinuous track recognition system by associating the track recognition problem of a humanoid robot with the lane detection problem. The proposal enables the robot to achieve stable running on the single-line discontinuous track. The system consists of two parts: the robot end and the graphics computing end. The robot end is responsible for collecting track information and the graphics computing end is responsible for high-performance computing. These two parts use the TCP for communication. The graphics computing side uses PolyLaneNet lane detection algorithm to train the track image captured from the first perspective of the darwin-op2 robot as the data set. In the inference, the robot end sends the collected tracking images to the graphics calculation end and uses the graphics processor to accelerate the calculation. After obtaining the motion vector, it is transmitted back to the robot end. The robot end parses the motion vector to obtain the motion information of the robot so that the robot can achieve stable running on the single-line discontinuous track. The proposed system realizes the direct recognition of the first perspective image of the robot and avoids the problems of poor stability, inability of identifying curves and discontinuous lines, and other problems in the traditional line detection method. At the same time, this system adopts the method of cooperative work between the PC side and the robot by deploying the algorithm with high computational requirements on the PC side. The data transmission is carried out by stable TCP communication, which makes it possible for the robot equipped with weak computational controllers to use deep-learning-related algorithms. It also provides ideas and solutions for deploying deep-learning-related algorithms on similar low computational robots.

Nanoscale-Precision Removal of Copper in Integrated Circuits Based on a Hybrid Process of Plasma Oxidation and Femtosecond Laser Ablation.

Copper (Cu) is the main interconnect conductor for integrated circuits (IC), and its processing quality is very important to device performance. Herein, a hybrid process of plasma oxidation and femtosecond laser (fs-laser) ablation was proposed for the nanoscale precision removal of Cu in integrated circuits. In this hybrid process, the surface layer of Cu was oxidized to the copper oxide by plasma oxidation, and then the fs-laser with a laser fluence lower than the Cu ablation threshold was used to remove the copper oxide without damaging the underlying Cu. Theoretically, the surface temperature evolutions of Cu and copper oxide under the femtosecond laser were studied by the two-temperature model, and it was revealed that the ablation threshold of copper oxide is much lower than that of Cu. The experimental results showed that the ablation threshold of copper oxide is lower than that of Cu, which is consistent with the theoretical analysis. Using the hybrid process, a surface roughness of 3 nm and a removal accuracy of 4 nm were obtained in the process of Cu film processing, which were better than those obtained by fs-laser ablation. This demonstrated that the hybrid process has good application potential in the field of copper micromachining.

Cyclin-Dependent Kinase 1 (CDK1) is Co-Expressed with CDCA5: Their Functions in Gastric Cancer Cell Line MGC-803.

BACKGROUND Gastric cancer (GC) is a worldwide malignancy and the molecular mechanism of the GC carcinogenesis has not been fully elucidated. Our previous study suggested CDCA5 played a role in GC development via regulating cell proliferation, migration, and apoptosis in GC cells. MATERIAL AND METHODS Here, we first carried out bioinformatics analysis and found cyclin-dependent kinase 1 (CDK1) was possibly associated with CDCA5 using STRING. Then, the expression levels of CDK1 and CDCA5 in cancer tissues were estimated through Oncomine and The Cancer Genome Atlas (TCGA) database. After that, functional experiments were exerted to detect the association of CDK1 and CDCA5. Finally, cell proliferation assay, colon formation assay, cell scratch assay, transwell migration and invasion assays were applied to explore the roles of CDK1 and CDCA5 in GC cells MGC-803. RESULTS CDK1 and CDCA5 were both upregulated and co-expressed in GC tissues. The expression of CDK1 and CDCA5 in MGC-803 was positively related. CDK1 or CDCA5 inhibition can suppress the proliferation, colon formation, migration, and invasion abilities of GC cells. CONCLUSIONS Co-expression of CDK1 and CDCA5 might confer cell proliferation, migration, and invasion abilities in GC cells, and this can provide some clues for further therapies of gastric tumors.

Association between potentially functional polymorphisms of chemokine family members and the survival of esophageal cancer patients in a Chinese population.

Background: The chemokine family plays an important role in the growth, invasion, and metastasis of tumors. However, most studies have only focused on a few genes or a few gene loci, and thus could not reveal the associations between functional polymorphisms of chemokine family members and tumor progression. This study aimed to determine the associations between single nucleotide polymorphisms (SNPs) of chemokine family members and the prognosis of esophageal cancer (EC). Methods: The Cox risk proportional model and log-rank test were used to analyze the associations of 16 potentially functional SNPs in 13 genes from the chemokine family with the survival of 729 Chinese patients with EC. Results: Prognostic analysis on the 16 SNPs showed that different genotypes of 5 SNPs were associated with patients' survival and the risk of death. Multivariate Cox regression analysis showed that the risk of death was higher in CCL26rs2302009 genotype A/C carriers than in A/A carriers and it was also higher in CX3CL1rs2239352 genotype T/T carriers than in C/C carriers. Stepwise Cox regression analysis showed that CCL26rs2302009 genotype A/C was an independent prognostic factor of EC, and its association with increased risk of death was stronger in patients who were ≤60 years old, female, with tumors located in the middle part of esophagus, with undifferentiated or poorly differentiated tumors, with early-stage pathologic type disease, with the longest diameter of tumor ≤5cm than in their counterparts. Conclusion: These findings suggest that CCL26rs2302009 may be a candidate biomarker for EC and its effect on death risk are associated with the histological grade, pathologic type, and the longest diameter of tumor.

Gene expression profile analysis of ENO1 knockdown in gastric cancer cell line MGC-803.

Gastric cancer (GC) is the third leading cause of cancer-associated mortality. In a previous study, we identified that α-enolase (ENO1) promoted cell migration in GC, but the underlying molecular mechanisms remain to be fully elucidated. In the present study, small interfering RNAs were identified to interfere with ENO1 expression. The cDNA expression profiling was performed using an Affymetrix mRNA array platform to identify genes that may be associated with ENO1 in human GC cell line MGC-803. The differentially expressed genes (DEGs) were identified using the reverse transcription-quantitative polymerase chain reaction, followed by a series of bioinformatic analyses. As a result, there were 448 DEGs, among which 183 (40.85%) were downregulated. The most significant functional terms for the DEGs were the nuclear lumen for cell components (P=2.83×10-4), transcription for biological processes (P=3.7×10-7) and transcription factor activity for molecular functions (P=1.16×104). In total, six significant pathways were enriched, including the most common cancer-associated forkhead box O signaling pathway (P=0.0077), microRNAs in cancer (P=0.0183) and the cAMP signaling pathway (P=0.0415). Furthermore, a network analysis identified three hub genes (HUWE1, PPP1CB and HSPA4), which were all involved in tumor metastasis. Taken together, the DEGs, significant pathways and hub genes identified in the present study shed some light on the molecular mechanisms of ENO1 involved in the pathogenesis of GC.