Implantable Tumor Treating Fields (ITF) for glioblastoma is an emerging research direction and has increasingly garnered significant attention. The intricate physical environment of brain tissue and inter-patient heterogeneity present a substantial challenge in defining optimal treating parameters, severely hindering the clinical feasibility of this technology. Therefore, achieving automated personalized parameter configuration is of great importance. This study trained a deep-learning model for automatic brain tumor segmentation with BraTS dataset. The segmentation results were subsequently imported into 3D Slicer software for three-dimensional (3D) reconstruction, and the least squares method was used to fit the irregular tumor morphology parameters. The reconstructed 3D model was then imported into the COMSOL platform for finite element analysis. Through the COMSOL Livelink with Matlab interface, a genetic algorithm was utilized to optimize the objective function based on feedback from finite element simulation results, thereby generating personalized treatment parameter recommendations and achieving conformal therapy. This study integrated medical image segmentation and 3D reconstruction technologies, proposing a comprehensive workflow for personalized brain tumor 3D model reconstruction and parameter planning based on brain MRI images. It proposed the genetic optimization algorithm coupled with finite element simulation, aiming to provide individualized conformal treatment recommendations. This methodology achieved fully automated planning from MRI images to personalized parameter configuration, offering a new strategy for the precise treatment of brain tumors using ITF.

Blue light irradiation can induce skin pigmentation by causing oxidative stress in melanocytes. Researches have demonstrated that Nicotinamide Nucleotide Transhydrogenase (NNT) located in mitochondrial inner membrane regulates pigmentation, which is also related to redox homeostasis. In order to investigate the potential relationship between these two pathways, this study utilized mouse melanoma cell B16-F10 and human melanoma cell MNT1 to establish models of two pigmentation models of Nnt (NNT) knockdown and blue light irradiation. The effects of blue light irradiation on Nnt (NNT) expression and distribution were assessed, and initially explored the effects of both induction conditions on mitochondrial function and morphology. Moreover, the colocalization of mitochondria and melanosomes under blue light induced model was observed. The results indicated that both blue light irradiation and Nnt (NNT) knockdown led to an increase of melanin in melanocytes. And blue light irradiation down-regulated the expression of Nnt (NNT) and the protein was localized to the nucleus. Assessment of mitochondrial membrane potential revealed that both blue light exposure and Nnt (NNT) knockdown could reduce mitochondrial membrane potential. Furthermore, analysis of mitochondrial dynamics-related proteins demonstrated that blue light exposure and Nnt knockdown slightly elevated the expression of certain mitochondrial fusion-related proteins (MFN1, MFN2, OPA1) and down-regulated the expression of the mitochondrial fission-related protein DRP1 in B16-F10 cells. Co-localization staining of mitochondria and melanosomes showed an extensive distribution, with increased co-localization in the perinuclear region after blue light irradiation. This study provides initial insights into the potential relationship between mitochondria and melanin synthesis, offering a novel perspective for exploring the regulatory mechanisms of pigmentation.

Giant viruses have garnered significant attention in virological research due to their large genomes and unique gene content. As a member of the giant virus family, Marseillevirus contains several genes related to protein translation in its genome. This study aims to investigate the function of MarSH~~314 of Marseillevirus Shanghai strain (Mar-SH2016), which was annotated to be the eukaryotic translation initiation factor eIF2/eIF5. We constructed a strain of Acanthamoeba castellanii overexpressing MarSH~~314, and identified the interacting proteins of MarSH~~314 through co-immunoprecipitation (Co-IP) and mass spectrometry analysis. Mass spectrometry and following biochemical results suggested a potential interaction between MarSH~~314 and the host transcriptional repressor Tup1. When interfering with the expression of Tup1 using siRNA, viral replication was significantly inhibited. The result did not support MarSH~~314 as a translation factor, instead, it suggested that MarSH~~314 has an important role in virus replication through interacting with Tup1.

Fluorescent proteins are essential and widely used molecular tools in biological research. T-Sapphire is a commonly employed green fluorescent protein with a large stokes shift (LSS), significantly reducing the absorption peak near 475 nm of the original GFP and retaining a peak at 389 nm. However, T-Sapphire still exhibits residual absorption between 475 nm and 500 nm, and its stability under acidic conditions requires improvement. We designed a fluorescent protein molecule—mJade based on superfolder GFP (sfGFP) gene sequence. Recombinant mJade was expressed, purified, and crystallized, and its crystal structure was resolved. We discovered that a unique hydrogen bond network near the chromophore of mJade leads to its large Stokes shift. By comparing the excitation spectra of mJade and T-Sapphire, we found that mJade significantly reduces absorption of light in the 475 500 nm range. Fluorescence intensity measurements of mJade under different pH conditions revealed excellent stability under alkaline conditions and significantly higher stability under acidic conditions compared to sfGFP and T-Sapphire. Bio-Layer Interferometry (BLI) experiments demonstrated that mJade maintains a high affinity for binding with the green fluorescent protein nanobody GBP1, which indicate that mJade can be used for simultaneous labeling of different molecules with EGFP and other fluorescent proteins to achieve multicolor tracking imaging.

Silver carp (Hypophthalmichthys molitrixis) is one of the primary freshwater aquaculture fish species in China. Its filter-feeding habits on phytoplankton and its nutritional characteristics, such as rich protein content and high unsaturated fatty acid levels, contribute to its significant ecological and production value. Despite the long-standing use of feed coeffecient (FC) for evaluating silver carp productivity in natural water bodies like lakes and reservoirs, as well as assessing economic losses caused by sewage discharge in China, there is still no universally accepted standard value. Different scholars and official normative documents continue to present varying FC values, ranging from 20-50 to even 80 or 100. This paper aims to systematically review the historical origin and application progress of FC values adopted by the Chinese academic community while re-evaluating FC based on the concept of primary production required (PPR), aiming to identify reasons behind these discrepancies. The findings support using FC values between 20 and 50 but do not endorse values exceeding 80.

Blister wounds are featured with over-generated wound exudate and extensive skin peeling, call for breathable temporary skin with effective exudate management, and function as an extracellular matrix to accelerate regeneration of wound skin. Traditional extracellular matrix (ECM) mimicked nanofibrous 3D scaffold and corresponding hydrogel composites suffer from poor mechanical strength, and the wound exudate management behavior is seldom studied. Herein, we proposed the strategy to enhance the mechanical properties of a 3D nanofiber scaffold via constructing a long nanofiber (NF) and sodium alginate (SA) aerogel interpenetrated architecture (NF/SA). The as-prepared scaffold was then evaluated as temporary skin for a full-thickness defect wound. After absorption of blister fluid, the aerogel transferred into a hydrogel and imparted a wet wound care environment with a water-vapor transmission rate of (6 001.90±522.04) g/(m2·24 h), and Young's modulus of (2.97±0.38) MPa. The exudate was continuously refreshed by a directed and dynamic pump, followed by volatilization driven by Brownian motion. Meanwhile, the NF/SA scaffold exhibited decent compatibility with blister fluid. The basic fibroblast growth factor (bFGF)-loaded NF/SA improved the wound healing rate by 36.46% on Day 3 and 15.34% on Day 7 in the full-thickness defect wound model.

The NH4V4O10(NVO) cathode for aqueous zinc-ion batteries exhibits a high specific capacity due to its large interlayer spacing, but poor structural stability during charge and discharge processes leads to rapid capacity decay during cycling. Herein, K+-doped NH4V4O10(KNVO) cathode materials with 3D flower-like micromorphology are synthesized via a facile one-step hydrothermal method. The 3D flower-like micromorphology facilitates the electrolyte infiltration and Zn2+ diffusion. K+ doping partially replaces NH4+, alleviating irreversible ammonium loss during the first cycle, thus enhancing material cyclic stability. Furthermore, the oxygen defects introduced by K+ doping facilitate electron and ion transfer, which enhances the rate performance of materials. The optimized K+ doping level of KNVO-2.5 cathode exhibits a high specific capacity (462.4 mAh·g-1 at 0.5 A·g-1), long cyclic life (with a specific capacity of 200.8 mAh·g-1 and a capacity retention of 65.1% after 10 000 cycles at 15 A·g-1), and impressive rate ability (156.6 mAh·g-1 at 30 A·g-1).

Carbon Fiber Reinforced Plastics (CFRP) are lightweight and high strength composites that are widely used in aerospace and other fields. The 2D-T800 carbon fiber reinforced composites were prepared based on Vacuum Impregnation Hot-Pressing System (VIHPS), and 5 000 cyclic loading experiments were carried out under the conditions of 500 MPa load, 15 Hz sinusoidal loading waveform, and stress ratio of 0.1, and the mechanical behaviors of the composites with Graphene Oxide (GO) mass fraction (w(GO)) of 0%, 0.25%, 0.50%, 0.75%, and 1.00% respectively were investigated. The microstructures were characterized by scanning electron microscopy, and the fracture morphology was observed to investigate the effect of GO content on the fatigue tensile properties of the composites. The results showed that the fatigue tensile strength of CFRP showed a tendency of increasing and then decreasing with the increase of GO content, and the optimum fatigue tensile performance was achieved when the content of GO was 0.50%, with the fatigue tensile strength of 1 123.34 MPa and the elastic modulus of 77.92 GPa. The fatigue tensile strength was reduced by 25.39% when the w(GO) was 1.00%. When the w(GO) was 0.50%, GO was uniformly coated on the carbon fiber filaments in a scale-like manner, which increased the stress transfer pathway, reduced the porosity, and the matrix was sufficiently infiltrated, thus significantly improving the fatigue tensile strength of CFRP. When the w(GO) is increased to 1.00%, significant agglomeration of high content of GO occurs on carbon fiber filaments, which leads to a serious impact on the matrix infiltration effect and a decrease in fatigue strength.

In order to obtain the effect of layup method on the properties and microstructure of graphene-carbon fiber/basalt fiber (GO-CF/BF) hybrid reinforced composites, the finite element simulation was used in this study, and four kinds of GO-CF/BF hybrid reinforced composites with different layup methods, namely C6, B2C4, B4C2 and B6, were respectively investigated from macroscopic and microscopic perspectives. The matrix infiltration and three-point bending were simulated to obtain the matrix infiltration effect and bending strength under different layup methods. Finally, the simulation results were compared with the microstructure and test results, and it can be seen that when the layup method is C6, the volume content of matrix in the materials is the highest, and the bending strength is also the highest, up to 525.31 MPa. Basalt fiber has good fracture toughness and low modulus strength, and because of the low bonding rate between basalt fiber and resin, the matrix infiltration effect of GO-CF/BF hybrid reinforced composite is poor and there are pore defects when the layup method is B6. In addition, when the carbon fiber is brittle fracture failure, the load is gradually transferred to the basalt fiber with lower modulus and stiffness, thus affecting the interface bonding ability between the fiber and the matrix. Based on the above reasons, the bending s trength simultion value of the composite materials with a layup method of B6 is reduced to 115.16 MPa. Combined with the actual test results, it is shown that the finite element simulation is reasonable to predict the infiltration effect and bending strength of GO-CF/BF hybrid reinforced composites.

This paper explores the construction of a distributed energy trading system based on Hyperledger Fabric blockchain technology and designs a smart contract mechanism for the distributed energy trading system. It first simulates and evaluates the energy production in different regions using photovoltaic power generation systems. Subsequently, the CRITIC (Criteria Importance Through Intercriteria Correlation) weight method is employed to determine the importance weights of each evaluation indicator. Finally, the paper adopts a dual-layer game mechanism to determine the final energy pricing for energy sellers. Through the interaction between the system and energy buyers and sellers, the energy selling price is dynamically adjusted. The balance requirements of the energy trading market are met by observing the difference between the actual and expected values of energy supply and demand. The blockchain system based on Hyperledger Fabric is implemented on the Ubuntu system. Compared with the average price mechanism, simulation results indicate that the proposed model achieves higher system profit performance. Moreover, the profit records from each transaction demonstrate that the model more accurately reflects the supply and demand relationship in the market. Therefore, the blockchain platform proposed in this paper can be utilized to construct a P2P trading network with higher trading efficiency and more comprehensive trading rules for buyers and sellers.

Aiming at the problems of boundary effect and filter degradation in the correlation filter tracking algorithm in the high-speed scene of Unmanned Aerial Vehicle (UAV), this stady proposes an adaptive spatial-temporal regularization correlation filter algorithm for UAV tracking based on camera motion compensation. Firstly, an adaptive spatial regularization method is designed in the correlation filter to alleviate the boundary effect. Secondly, in order to prevent the degradation of the filter, a time regularization method and an optimization update strategy based on high-confidence samples are designed. At the same time, the ADMM optimization objective function is used to ensure the efficiency of the algorithm. Then, a fast scale filter is designed to achieve fast estimation of the target scale. Finally, a robust tracking uncertainty rule and a re-detector based on camera motion compensation are proposed, so that the target can be recovered in time after loss. The experimental results show that the precision and success rate of the proposed algorithm on the UAV dataset can reach 0.739 and 0.634, respectively, which are 1.65% and 6.9% higher than those of the ECO~~HC algorithm. Compared with similar mainstream algorithms, it effectively improves the precision and success rate of tracking, and also shows good tracking performance when experiencing fast motion, target occlusion, etc.

This study proposes a model of musical gene expression programming to realize the automatic generation of music of a specific musical style. A music score material library of specific styles of music is established to be used as evolutionary material and neural network training data. In the stage of music evolution, gene expression programming technology is migrated to music composition to form music gene expression programming operations. A mapping mechanism between 16-bit octal numbers and musical scores is proposed to realize the evolution of musical scores in units of measures. In the music evaluation stage, the convolutional neural network evaluation model is trained in a semi-supervised learning manner to evaluate the fitness value of the songs produced by evolution and provide feedback for music evolution. Compared with the music pairs generated by other current studies, the music generated by this research method has obvious characteristics of specific music styles.