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Background: In recent years, mRNA-based vaccines with promising safety and functional characteristics have gained significant momentum in cancer immunotherapy. However, stable immunological molecular subtypes of lung adenocarcinoma (LUAD) and novel tumor antigens for LUAD mRNA vaccine development remain elusive. Therefore, a novel approach is urgently needed to identify suitable LUAD subtypes and potential tumor antigens. Methods: The Cancer Genome Atlas (TCGA), the Genotype Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases were utilized to retrieve gene expression data. The LUAD Immunological Multi-Omics Classification (LIMOC) system was developed using seven machine learning (ML) algorithms by performing integrative immunogenomic analysis of single-cell and bulk tissue transcriptome profiling. Subsequently, a panel of approaches was applied to identify novel tumor antigens. Results: First, the LIMOC system was construct to identify three subtypes: LIMOC1, LIMOC2, and LIMOC3. Second, we identified CHIT1, LILRA4, and MEP1A as novel tumor antigens in LUAD; these genes were up-regulated, amplified, and mutated, and showed a positive association with APC infiltration, making them promising candidates for designing mRNA vaccines. Notably, the LIMOC2 subtype had the worst prognosis and could benefit most from mRNA immunization. Furthermore, we performed a comprehensive in silico screening of approximately 2000 compounds and identified Sorafenib and Azacitidine as potential subtype-specific therapeutic agents. Conclusions: Overall, our study established a robust LIMOC system and identified CHIT1, LILRA4, and MEP1A as promising tumor antigen candidates for development of anti-LUAD mRNA vaccines, particularly for the LIMOC2 subtype.

Abstract Background Plant compactness (PC) is a crucial agronomic trait that affects plant density in wheat, which in turn influences biomass and grain yield potential. The canopy of high-yielding wheat varieties should exhibit appropriate aboveground plant architecture. In this study, three recombinant inbred line (RIL) populations were generated to identify and validate quantitative trait loci (QTL) related to plant compactness. The erect and compact tillering genotype SN05525 was used as a common parent. A total of 193 F8 RILs from the cross SN05525/SN22 were genotyped using the high-density Illumina iSelect 90 K single nucleotide polymorphism (SNP) assay. Results A linkage map with 7180 SNP loci was constructed, revealing six QTL on chromosomes 3B (Qpc.sdau-3B.1, Qpc.sdau-3B.2),5B (Qpc.sdau-5B), 5D (Qpc.sdau-5D), 2 A (Qpc.sdau-2 A), and 7 A (Qpc.sdau-7 A) that control compact tillers. Qpc.sdau-3B.1, Qpc.sdau-5D, and Qpc.sdau-5B accounted for up to 16.70%, 16.89%, and 14.56% of the phenotypic variance, respectively. Kompetitive allele-specific PCR (KASP) markers were developed for these QTL, and their effects were validated in two additional RIL populations, SN05525/Luyuan 502 and SN05525/Xinong 511. Significant effects of Qpc.sdau-3B.1 and Qpc.sdau-5D on compactness were observed in the validation populations. Conclusion Three major QTL loci closely related to plant compactness in wheat were successfully identified, and their effects were validated in two additional RIL populations across multi environments. Plant architecture plays a crucial role in enhancing yield and economic value in wheat. In the process of molecular marker-assisted selection breeding, the closely linked KASP markers could potentially be utilized in molecular marker-assisted selection for adjusting plant compactness and for further characterization of the underlying gene(s).

Abstract Liver-expressed antimicrobial peptide 2 (LEAP2) is a peptide that counteracts the hunger hormone ghrelin-induced functions. Recently, we showed that vertical sleeve gastrectomy (VSG) did not alter the serum LEAP2 concentration in individuals with obesity. Here, we investigated the effects of VSG in both chow diet (CD)-fed and high-fat diet (HFD)-fed mice. In CD-fed mice, VSG increased plasma LEAP2 levels and hepatic Leap2 mRNA levels while decreasing body weight, blood glucose levels, and ghrelin levels. Intraperitoneal (ip) administration of ghrelin reversed these changes. These effects were found in both male and female mice. In contrast, VSG or weight loss in HFD-induced obese mice decreased LEAP2 levels. After fasting, the plasma LEAP2 concentration was in the following order: hepatic vein > abdominal aorta > portal vein. A high glucose concentration robustly increased the plasma LEAP2 concentration in the hepatic vein and abdominal aorta but not in the portal vein. In addition, corn oil or palmitate increased LEAP2 expression and secretion. The increase in LEAP2 levels after the meal tolerance test was delayed in the human subjects with diabetes. Our data suggest that various factors (metabolic, hormonal, and nutritional) regulate LEAP2, and the liver is the predominant site for the production and secretion of LEAP2. Furthermore, the interaction between ghrelin and LEAP2 is involved in the pathogenesis of obesity and diabetes.

Programmed cell death ligand-1 (PD-L1) is a T cell inhibitory immune checkpoint molecule that interacts with programmed cell death-1 (PD-1) to promote immune escape of tumor cells. Compared with antibody therapies, small molecule drugs show better prospects due to their advantages such as higher bioavailability, better tissue penetration, and reduced risk of immunogenicity. Here, we found that the small molecule demethylzeylasteral (Dem) can significantly downregulate the expression of PD-L1 in colorectal cancer cells and enhance the killing effect of T cells on tumor cells. Mechanistically, Dem binds to the deubiquitinating enzyme USP22 and promotes its degradation, resulting in increased ubiquitination and degradation of PD-L1 through the proteasome pathway. In addition, Dem increased the activity of cytotoxic T cells and reduced the number of myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs) in tumor-infiltrating lymphocytes (TILs), thereby activating the tumor immune microenvironment and inhibiting the growth of subcutaneous MC38 tumors in C57BL/6 mice. Moreover, we also found that the combination of Dem and CTLA4 antibodies can further improve the efficacy of antitumor therapy. Our study reveals the mechanism by which Dem promotes PD-L1 degradation and suggests that the combination of Dem and CTLA4 antibodies may improve the efficacy of immunotherapy.

Both β-catenin and STAT3 drive colorectal cancer (CRC) growth, progression, and immune evasion, and their co-overexpression is strongly associated with a poor prognosis. However, current small molecule inhibitors have limited efficacy due to the reciprocal feedback activation between STAT3 and β-catenin. Inspired by the PROteolysis TArgeting Chimera (PROTAC), a promising pharmacological modality for the selective degradation of proteins, we developed a strategy of nanoengineered peptide PROTACs (NP-PROTACs) to degrade both β-catenin and STAT3 effectively. The NP-PROTACs were engineered by coupling the peptide PROTACs with DSPE-PEG via disulfide bonds and self-assembled into nanoparticles. Notably, the dual degradation of β-catenin and STAT3 mediated by NP-PROTACs led to a synergistic antitumor effect compared to single-target treatment. Moreover, NP-PROTACs treatment enhanced CD103+ dendritic cell infiltration and T-cell cytotoxicity, alleviating the immunosuppressive microenvironment induced by β-catenin/STAT3 in CRC. These results highlight the potential of NP-PROTACs in facilitating the simultaneous degradation of two pathogenic proteins, thereby providing a novel avenue for cancer therapy.

To enhance the tribocorrosion resistance of 7075-T6 aluminum alloy in deep sea environments, TiB2–Ni coatings with varying La2O3 content were deposited on 7075-T6 aluminum alloy substrates using plasma spray technology. The effects of different La2O3 contents on the organization, structure, and properties of the coatings were analyzed. The results indicated that La2O3 incorporation suppressed brittle phase formation such as Ni3B; 1.0 wt% La2O3 addition reduced Ni3B by 16.9%. The coating's corrosion resistance significantly improved with La2O3 doping - the self-corrosion current density of the 1.0 wt% La2O3-doped coating decreased from 71.2 nA/cm2 to 30.2 nA/cm2.

Immunotherapies hold immense potential for achieving durable potency and long-term survival opportunities in cancer therapy. As vital biological mediators, peptides with high tissue penetration and superior selectivity offer significant promise for enhancing cancer immunotherapies (CITs). However, physicochemical peptide features such as conformation and stability pose challenges to their on-target efficacy. This review provides a comprehensive overview of recent advancements in therapeutic peptides targeting key steps of the cancer-immunity cycle (CIC), including tumor antigen presentation, immune cell regulation, and immune checkpoint signaling. Particular attention is given to the opportunities and challenges associated with these peptides in boosting CIC within the context of clinical progress. Furthermore, possible future developments in this field are also discussed to provide insights into emerging CITs with robust efficacy and safety profiles.

Abstract Coal pitch, an important by-product in the coal coking industry with a high output, is a low-cost and high-carbon yield precursor for the manufacturing of high-value carbon materials. Herein, N/O co-doped carbon fiber (CFCP), fabricated by electrospinning using pre-oxidized coal pitch as the precursor, was employed as the sulfur host for Li-S batteries. The presence of more pyrrolic N and graphic N in CFCP than carbon fiber made from polyacrylonitrile benefits the adsorption of lithium polysulfide and the battery’s life. Sulphur-CFCP cathode (S@CFCP) exhibited excellent specific capacity and cyclability, with a specific capacity of 701.1 mAh/g and a low capacity decay rate of 0.088% per cycle over 200 cycles at 2.0 C, respectively. The high ion diffusion rate, low charge transfer resistance, and effective conversion of lithium polysulfides enable the high electrochemical performance of S@CFCP.

Introduction Hypertension and rising serum uric acid (sUA) played a pivotal role in the development of Chronic Kidney Disease (CKD). This study investigates the interactive effect of sUA and hypertension on CKD and identifies the optimal threshold of sUA among individuals with and without hypertension in the Chinese community population.Materials and methods The study included 4180 individuals aged 45–85 years, derived from the China Health and Retirement Longitudinal Study (CHARLS) between 2011 and 2015. Additionally, a hospital-based study enrolled subjects in the Department of Nephrology at Zhongshan Hospital, China from January 1, 2019, to December 31, 2021. The interaction effect analysis were used to assess the impact of sUA and hypertension on CKD. We also compared the distribution of sUA and the CKD risk in community populations, distinguishing between those with and without hypertension. For the hospital-based population, kidney injury was marked by a KIM-1 positive area.Results Our results indicate a higher prevalence of CKD in the community population with hypertension (10.2% vs. 3.9%, p

The level of detail (LOD) modelling of vector and terrain data is individual, resulting in geometric and topological inconsistencies in simplification processes. The three dimension Douglas Peucker (3D_DP)algorithm can realize gradual discrete point selection through threshold, which is mainly used in DEM synthesis, and its simplified process is very suitable for the dynamic establishment of massive data sets. A new LOD modeling method based on 3D_DP algorithm is proposed to simplify the consistency of river network vector elements and DEM in this paper. The specific steps are as follows: Firstly, the “Bending Adjustment Index (BAI)” is introduced to improve the 3D_DP algorithm, called the improved 3D_DP algorithm; Secondly, the DEM data is extracted into a 3D discrete point dataset, and the river line vector data is also converted into a discrete point dataset, assigned with elevation attributes, and merges with the DEM’s 3D discrete points. The merged point datasets are equenced based on the importance of each point, which are computed by the improved 3D_DP algorithm. The order of deleting points is determined by the sequence and the corresponding model spatial errors are calculated with the deletion of points. Then, the DEM’s 3D discrete points are constructed a Delaunay network with the river line as a mandatory constraint condition. The required triangulation is called in real time with the change of sight distance depending on the simple correspondence between screen projection error and model space error, and the unified LOD model for river line vector and DEM is established. The results show that the river’s overall shape and the terrain’s main features can be reserved under the same simplified factor based on the improved 3D_DP algorithm. The unified LOD model for the river network and DEM is feasible under the importance sequence of merged point datasets by the improved 3D_DP algorithm. Under the proper operation of data blocking, the rendering frame rate can meet practical application requirements.

BackgroundPapillary thyroid cancer (PTC) with lymph node metastasis (LNM) is associated with an increased risk of postoperative recurrence. Understanding the clinical characteristics of PTC patients can help surgeons assess the likelihood of LNM. This study aims to identify risk factors for LNM in PTC patients.MethodsWe retrospectively analyzed clinical data from 2,428 patients diagnosed with PTC who underwent initial thyroid surgery in our single center. Risk factors related to central lymph node metastasis (CLNM), lateral lymph node metastasis (LLNM), and skip metastasis were investigated by univariate and multivariate binary logistic regression analyses.ResultsUnivariate analysis revealed that the following factors were associated with an increased likelihood of CLNM (P 1 cm, body mass index (BMI) ≥ 28 kg/m² and multifocality. Univariate analysis also revealed that patients with age < 45 years, MAD > 1 cm, BMI ≥ 28 kg/m², multifocality, and CLNM demonstrated a higher propensity for LLNM (P 1 cm, multifocality, and CLNM can help predict LNM. In specific cases, selective lymphadenectomy in the central or lateral neck area may be warranted.

Abstract To analyze the clinical characteristics and to improve clinicians’ understanding of multiple pulmonary sclerosing pneumocytoma (PSP) patients. A total of 36 PSP patients with multiple tumor characteristics were identified from the literature search. They were compared with 43 solitary PSP patients diagnosed and treated in our hospital in the past 5 years. Thus, the pathogenesis, clinical symptoms, diagnosis methods, treatment strategies, and prognosis of pulmonary sclerosing pneumocytoma (PSP) patients with multiple tumors were explored. Patients with multiple PSP are mostly distributed in Asia (88.89%) and are females (83.33%). PSP can be located in any one lobe (19.44%), or grow across ipsilateral lobes (44.44%), or even, bilateral lobes (36.11%). It can be accompanied by metastasis (9.09%) and is prone to misdiagnosis (27.78%). Compared with solitary PSP, the occurrence age of multiple PSP was younger (mean ± standard deviation [SD]: 40.36 ± 18.12: 51.28 ± 12.74 years), but there was no significant difference in sex, tumor size (mean ± SD: 43.54 ± 46.18: 30.56 ± 17.62 mm), or symptoms. Individualized surgical resection is required for treatment, including pneumonectomy (17.65%), lobectomy (23.53%), subpulmonary lobectomy (38.24%), or combined lobectomy (5.88%). Multiple PSP is relatively rare. Surgical resection within a limited time should be the main treatment for such patients. The prognosis of patients with multiple PSP is generally good, but inappropriate diagnosis and treatment plans may lead to poor prognosis.

Abstract Background Elevated interstitial fluid pressure within tumors, resulting from impaired lymphatic drainage, constitutes a critical barrier to effective drug penetration and therapeutic outcomes. Results In this study, based on the photosynthetic characteristics of algae, an active drug carrier (CP@ICG) derived from Chlorella pyrenoidosa (CP) was designed and constructed. Leveraging the hypoxia tropism and phototropism exhibited by CP, we achieved targeted transport of the carrier to tumor sites. Additionally, dual near-infrared (NIR) irradiation at the tumor site facilitated photosynthesis in CP, enabling the breakdown of excessive intratumoral interstitial fluid by generating oxygen from water decomposition. This process effectively reduced the interstitial pressure, thereby promoting enhanced perfusion of blood into the tumor, significantly improving deep-seated penetration of chemotherapeutic agents, and alleviating tumor hypoxia. Conclusions CP@ICG demonstrated a combined effect of photothermal/photodynamic/starvation therapy, exhibiting excellent in vitro/in vivo anti-tumor efficacy and favorable biocompatibility. This work provides a scientific foundation for the application of microbial-enhanced intratumoral drug delivery and tumor therapy. Graphical Abstract

PLCD3 belongs to the phospholipase C delta group and is involved in numerous biological functions, including cell growth, programmed cell death, and specialization. However, the role of PLCD3 in lung cancer still needs further investigation. This research aimed to investigate if PLCD3 influences glycolytic reprogramming and lung cancer development through the PKC-dependent Rap1 signaling pathway. This study found that PLCD3 was increased in lung cancer tissues. PLCD3 promotes the proliferation and invasion of lung cancer cells by activating the PKC-dependent Rap1 pathway. The detailed process involves PLCD3 triggering PKC, which subsequently stimulates the Rap1 pathway, leading to glycolytic reprogramming that supplies adequate energy and metabolic substrates necessary for the growth and spread of lung cancer cells. Moreover, PLCD3 can also promote the metastasis and invasion of lung cancer cells by activating the Rap1 pathway. This study reveals the mechanism of PLCD3 in lung cancer and provides new ideas for the treatment of lung cancer. Inhibiting PLCD3, PKC, and the Rap1 pathway may be an effective strategy for treating lung cancer.

Abstract Integrated multimodal imaging in theranostics nanomaterials offers extensive prospects for precise and noninvasive cancer treatment. Precisely controlling the structural evolution of plasmonic nanoparticles is crucial in the development of photothermal agents. However, previous successes have been limited to static assemblies and single‐component structures. Here, an activatable plasmonic theranostics system utilizing self‐assembled 1D silver‐coated gold nanochains (1D nanochains) is presented for precise tumor diagnosis and effective treatment. The absorbance of the adaptable core–shell chain structure can shift from visible to near‐infrared (NIR) regions due to the fusion between nearby Au@Ag nanoparticles induced by elevated H2O2 levels in the tumor microenvironment (TME), resulting in the creation of a novel 3D aggregates with strong NIR absorption. With a high photothermal conversion efficiency of 60.2% at 808 nm, nanochains utilizing the TME‐activated characteristics show remarkable qualities for photoacoustic imaging and significantly limit tumor growth in vivo. This study may pave the way for precise tumor diagnosis and treatment through customizable, optically tunable adaptive plasmonic nanostructures.

Abstract Targeting the programmed cell death 1/programmed cell death ligand 1 (PD‐1/PD‐L1) pathway has been identified as a successful approach for tumor immunotherapy. Here, we identified that the small molecule 5,7,4′‐trimethoxyflavone (TF) from Kaempferia parviflora Wall reduces PD‐L1 expression in colorectal cancer cells and enhances the killing of tumor cells by T cells. Mechanistically, TF targets and stabilizes the ubiquitin ligase HMG‐CoA reductase degradation protein 1 (HRD1), thereby increasing the ubiquitination of PD‐L1 and promoting its degradation through the proteasome pathway. In mouse MC38 xenograft tumors, TF can activate tumor‐infiltrating T‐cell immunity and reduce the immunosuppressive infiltration of myeloid‐derived suppressor cells and regulatory T cells, thus exerting antitumor effects. Moreover, TF synergistically exerts antitumor immunity with CTLA‐4 antibody. This study provides new insights into the antitumor mechanism of TF and suggests that it may be a promising small molecule immune checkpoint modulator for cancer therapy.

Abstract The binding of peroxisome proliferator‐activated receptor γ (PPARγ) to the orphan nuclear receptor Nur77 facilitates the ubiquitination and degradation of Nur77, and leads to aberrant fatty acid uptake for breast cancer progression. Because of its crucial role in clinical prognosis, the interaction between Nur77 and PPARγ is an attractive target for anti‐breast‐cancer therapy. However, developing an inhibitor of the Nur77‐PPARγ interaction poses a technical challenge due to the absence of the crystal structure of PPARγ and its corresponding interactive model with Nur77. Here, ST‐CY14, a stapled peptide, is identified as a potent modulator of Nur77 with a KD value of 3.247 × 10−8 M by in silico analysis, rational design, and structural modification. ST‐CY14 effectively increases Nur77 protein levels by blocking the Nur77‐PPARγ interaction, thereby inhibiting lipid metabolism in breast tumor cells. Notably, ST‐CY14 significantly suppresses breast cancer growth and bone metastasis in mice. The findings demonstrate the feasibility of exploiting directly Nur77‐PPARγ interaction in breast cancer, and generate what to the best knowledge is the first direct inhibitor of the Nur77‐PPARγ interaction available for impeding fatty acid uptake and therapeutic development.

NUAK2 is a member of the AMP-activated protein kinase (AMPK) family, which plays an essential role in cellular processes such as apoptosis, proliferation, and cell fate. Recent studies have already shown that silencing of NUAK2 blocks proliferation and promotes apoptosis of human melanoma cells and liver cancer cells. In addition, NUAK2 is involved in the development of glioblastoma via regulating the expression of cancer stem cell-related genes, and it promotes the cell cycle entry in the glioblastoma cells. However, the expression and the role of NUAK2 in the progress of peripheral nerve regeneration after injury are yet to be elucidated. We observed that NUAK2 was upregulated following distal sciatic nerve crush (SNC). Interestingly, we discovered that NUAK2 showed co-localization with S100 (Schwann cell marker). Furthermore, we found that the NUAK2 had a spatiotemporal protein expression, which was consistent with proliferating cell nuclear-antigen (PCNA). The protein level of NUAK2 and YAP was upregulated in the model of TNF-α-induced Schwann cell (SC) proliferation. Furthermore, flow cytometry analysis, CCK-8, transwell assays, and wound healing assays were all performed with the purpose of exploring the role of NUAK2 in the regulation of SC proliferation and migration. More importantly, we found that NUAK2-deficient SCs showed significantly reduced expression of Yes-associated protein (YAP). Bioinformatic analysis identified upstream regulators of NUAK2 and NUAK2-associated genes (e.g., YAP1). Finally, we investigated the recovery changes during regeneration progress through the walking track analysis. Thus, we speculated that NUAK2 was involved in biochemical and physiological responses of SCs after SNC via YAP-driven proliferation and migration, and this study determined the importance of NUAK2 as a potential target in peripheral nerve regeneration.

In the present study, pre-deformation at liquid nitrogen temperature (77 K) was applied to introduce twins into Ti–Mo alloys with different contents of molybdenum. The microstructural evolution and mechanical properties with different cryogenic pre-deformation amounts (from 0 to 9 %) were characterized and analyzed. The results show that the twinning activities and the evolution of twinning systems are mainly influenced by β phase stability and cryogenic pre-deformation amount. After cryogenic pre-deformation, the primary and secondary twinning of {332}twins are observed in Ti–12Mo and Ti–15Mo alloys but absent in Ti–20Mo alloy. With the cryogenic pre-deformation amounts increasing, the width and quantity of twins increase constantly. Moreover, {112}twinning is activated in Ti–15Mo alloy pre-deformed by 6 % and 9 %. Twins and high-density dislocations induced by cryogenic pre-deformation cause a significant enhancement in strength of all these three alloys. However, the effect of cryogenic pre-deformation on the elongation of alloys is complicated. For Ti–12Mo alloys, the yield strength increases monotonically (from 527 to 778 MPa) with the increase of cryogenic pre-deformation reduction, while the elongation is completely opposite (from 47 to 20 %). For Ti–15Mo alloys, the optimal matching of strength and ductility is achieved when the amount of cryogenic pre-deformation is 6 %, with a tensile strength of 815 MPa and an elongation of 24 %. Compared with the other two alloys, Ti–20Mo alloy gets a smaller increase in strength and a slight decrease in elongation with the amount of cryogenic pre-deformation increasing.

ObjectiveThis paper develops a multi-stage path planning algorithm for the auto docking operation of an underactuated surface vehicle (USV). As one of the most difficult tasks, docking requires advanced maneuvering skills. Therefore, it is necessary to generate a smooth trajectory in the berthing environment while maintaining control accuracy. MethodThe proposed method introduces a hybrid A* search algorithm in the pre-docking stage in which the well-known A* search algorithm is combined with the kinematic state space of the USV using the Reeds–Shepp curves, enabling it to produce sub-optimal paths that guarantee kinematic feasibility and low collision risk. For the docking stage, a cubic Bezier curve is introduced to represent the target path. The generated trajectories are then tested in numerical simulations wherein a mathematical model of a ship with two fixed propellers is established and verified. ResultsThe simulation and experimental results show that the path planning and tracking performance of the method are satisfactory.ConclusionThe proposed path planning method can contribute to automatic ship docking and the development of smart ships.

With the increase of organic matter content in global water bodies, timely and accurate identification of algae objects in water bodies is of great significance for rapid treatment of water eutrophication. To improve the performance of seaweed microscopic image recognition model, an improved concave matching algorithm was designed and applied to the segmentation of original microscopic images. In addition, an IResNet algorithm with self-adjusting pooling layer is designed. In this study, an improved concave matching algorithm and an improved ResNet algorithm were used to construct a microscopic algae image recognition model. The average Accuracy and Precision of the proposed algorithm in the data set are 88.6% and 90.3%, which is significantly higher than all tr4aditional and mainstream image segmentation algorithms. Among the five modified ResNet algorithms with self-adjusting pooling layers designed in this study, the fifth modified scheme has the best performance on the training set. The model’s recognition accuracy and accuracy corresponding to the improved ResNet algorithm on the whole test set are 84.1% and 82.8%, respectively. The experimental data on the real data set show that the algae microscopic image recognition model has certain practical potential.

Abstract Background The poor targeting delivery efficiency and limited efficacy of single therapeutic approach have consistently posed significant challenges in tumor management. Results In this research, we have conceived and synthesized a dual-targeting nanodrug delivery system denoted as PDA-DEM-Fe3O4@M, which incorporates a polydopamine nanoparticle (PDA) with photothermal properties, diethyl maleate (DEM) as a chemotherapy agent accelerating tumor apoptosis, iron oxide nanoparticles (Fe3O4) eliciting magnetic targeting effects, and tumor cell membranes (M) contributing to homologous targeting capabilities. The synergistic effect of PDA-induced photothermal therapy and DEM-mediated chemotherapy has been demonstrated in this study to exert a robust inhibitory and cytotoxic influence on tumor cells. Additionally, the biocompatibility of this system has also been demonstrated. Conclusions Through the synergistic effects of PDA’s photothermal therapy and DEM’s chemotherapy, this system demonstrated excellent inhibition and killing effects on tumor cells. Furthermore, we established its excellent biological safety profile. This study demonstrated the potential of this nanomaterial for clinical application in tumor therapy.

With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance.

Abstract Background Mucinous colorectal adenocarcinoma (MCA) is a distinct subtype of colorectal cancer (CRC) with the most aggressive pattern, but effective treatment of MCA remains a challenge due to its vague pathological characteristics. An in‐depth understanding of transcriptional dynamics at the cellular level is critical for developing specialised MCA treatment strategies. Methods We integrated single‐cell RNA sequencing and spatial transcriptomics data to systematically profile the MCA tumor microenvironment (TME), particularly the interactome of stromal and immune cells. In addition, a three‐dimensional bioprinting technique, canonical ex vivo co‐culture system, and immunofluorescence staining were further applied to validate the cellular communication networks within the TME. Results This study identified the crucial intercellular interactions that engaged in MCA pathogenesis. We found the increased infiltration of FGF7+/THBS1+ myofibroblasts in MCA tissues with decreased expression of genes associated with leukocyte‐mediated immunity and T cell activation, suggesting a crucial role of these cells in regulating the immunosuppressive TME. In addition, MS4A4A+ macrophages that exhibit M2‐phenotype were enriched in the tumoral niche and high expression of MS4A4A+ was associated with poor prognosis in the cohort data. The ligand‐receptor‐based intercellular communication analysis revealed the tight interaction of MUC1+ malignant cells and ZEB1+ endothelial cells, providing mechanistic information for MCA angiogenesis and molecular targets for subsequent translational applications. Conclusions Our study provides novel insights into communications among tumour cells with stromal and immune cells that are significantly enriched in the TME during MCA progression, presenting potential prognostic biomarkers and therapeutic strategies for MCA. Key points Tumour microenvironment profiling of MCA is developed. MUC1+ tumour cells interplay with FGF7+/THBS1+ myofibroblasts to promote MCA development. MS4A4A+ macrophages exhibit M2 phenotype in MCA. ZEB1+ endotheliocytes engage in EndMT process in MCA.

Abstract Gastric cancer (GC) presents a formidable global health challenge, and conventional therapies face efficacy limitations. Ubiquitin‐specific protease 7 (USP7) plays pivotal roles in GC development, immune response, and chemo‐resistance, making it a promising target. Various USP7 inhibitors have shown selectivity and efficacy in preclinical studies. However, the mechanistic role of USP7 has not been fully elucidated, and currently, no USP7 inhibitors have been approved for clinical use. In this study, DHPO is identified as a potent USP7 inhibitor for GC treatment through in silico screening. DHPO demonstrates significant anti‐tumor activity in vitro, inhibiting cell viability and clonogenic ability, and preventing tumor migration and invasion. In vivo studies using orthotopic gastric tumor mouse models validate DHPO's efficacy in suppressing tumor growth and metastasis without significant toxicity. Mechanistically, DHPO inhibition triggers ferroptosis, evidenced by mitochondrial alterations, lipid Reactive Oxygen Species (ROS), Malondialdehyde (MDA) accumulation, and iron overload. Further investigations unveil USP7's regulation of Stearoyl‐CoA Desaturase (SCD) through deubiquitination, linking USP7 inhibition to SCD degradation and ferroptosis induction. Overall, this study identifies USP7 as a key player in ferroptosis of GC, elucidates DHPO's inhibitory mechanisms, and highlights its potential for GC treatment by inducing ferroptosis through SCD regulation.

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.

Abstract Insulin secretion from pancreatic β cells is regulated by multiple stimuli, including nutrients, hormones, neuronal inputs, and local signalling. Amino acids modulate insulin secretion via amino acid transporters expressed on β cells. The granin protein VGF has dual roles in β cells: regulating secretory granule formation and functioning as a multiple peptide precursor. A VGF-derived peptide, neuroendocrine regulatory peptide-4 (NERP-4), increases Ca2+ influx in the pancreata of transgenic mice expressing apoaequorin, a Ca2+-induced bioluminescent protein complex. NERP-4 enhances glucose-stimulated insulin secretion from isolated human and mouse islets and β-cell–derived MIN6-K8 cells. NERP-4 administration reverses the impairment of β-cell maintenance and function in db/db mice by enhancing mitochondrial function and reducing metabolic stress. NERP-4 acts on sodium-coupled neutral amino acid transporter 2 (SNAT2), thereby increasing glutamine, alanine, and proline uptake into β cells and stimulating insulin secretion. SNAT2 deletion and inhibition abolish the protective effects of NERP-4 on β-cell maintenance. These findings demonstrate a novel autocrine mechanism of β-cell maintenance and function that is mediated by the peptide–amino acid transporter axis.

Abstract Background The gut mycobiome of patients with lung adenocarcinoma (LUAD) remains unexplored. This study aimed to characterize the gut mycobiome in patients with LUAD and evaluate the potential of gut fungi as non-invasive biomarkers for early diagnosis. Methods In total, 299 fecal samples from Beijing, Suzhou, and Hainan were collected prospectively. Using internal transcribed spacer 2 sequencing, we profiled the gut mycobiome. Five supervised machine learning algorithms were trained on fungal signatures to build an optimized prediction model for LUAD in a discovery cohort comprising 105 patients with LUAD and 61 healthy controls (HCs) from Beijing. Validation cohorts from Beijing, Suzhou, and Hainan comprising 44, 17, and 15 patients with LUAD and 26, 19, and 12 HCs, respectively, were used to evaluate efficacy. Results Fungal biodiversity and richness increased in patients with LUAD. At the phylum level, the abundance of Ascomycota decreased, while that of Basidiomycota increased in patients with LUAD. Candida and Saccharomyces were the dominant genera, with a reduction in Candida and an increase in Saccharomyces, Aspergillus, and Apiotrichum in patients with LUAD. Nineteen operational taxonomic unit markers were selected, and excellent performance in predicting LUAD was achieved (area under the curve (AUC) = 0.9350) using a random forest model with outcomes superior to those of four other algorithms. The AUCs of the Beijing, Suzhou, and Hainan validation cohorts were 0.9538, 0.9628, and 0.8833, respectively. Conclusions For the first time, the gut fungal profiles of patients with LUAD were shown to represent potential non-invasive biomarkers for early-stage diagnosis.

Abstract Osteoarthritis (OA) is a degenerative disease. It is common in middle-aged and elderly people and is one of the main causes of disability. Currently, the etiology of OA is unclear, and no specific biomarkers for the diagnosis of OA have been identified. Therefore, finding a highly sensitive biomarker is essential for a proper diagnosis. TRNA-derived fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs) are newly discovered classes of noncoding RNAs. tRF has been proven in several studies to have significant associations with tumor diagnosis, making it a promising biomarker in cancer research. However, the diagnostic utility of tRF in OA patients and the correlation between OA progression and trf differential expression have yet to be elaborated. The purpose of this research was to identify tRFs with differential expression in OA to assess their potential as OA biomarkers. To determine the tRF-5022B expression level in this research, real-time fluorescence quantitative PCR has been employed. Agarose gel electrophoresis, Sanger sequencing, and other investigations have been employed for evaluating tRF-5022B's molecular properties. Receiver operating characteristic curve analysis has been utilized for assessing the diagnostic effectiveness of the tRF-5022B. The findings demonstrated that tRF-5022B expression was considerably lower in OA serum. The Kellgren–Lawrence grading scale was shown to correspond with serum expression levels. The ROC curve confirmed that tRF-5022B serum expression levels might differentiate OA cases from healthy individuals and RA patients. According to the aforementioned findings, tRF-5022B may be employed as a novel biomarker for OA diagnosis due to its excellent diagnostic value.

Abstract Reversing ventricular remodeling represents a promising treatment for the post-myocardial infarction (MI) heart failure (HF). Here, we report a novel small molecule HHQ16, an optimized derivative of astragaloside IV, which effectively reversed infarction-induced myocardial remodeling and improved cardiac function by directly acting on the cardiomyocyte to reverse hypertrophy. The effect of HHQ16 was associated with a strong inhibition of a newly discovered Egr2-affiliated transcript lnc9456 in the heart. While minimally expressed in normal mouse heart, lnc9456 was dramatically upregulated in the heart subjected to left anterior descending coronary artery ligation (LADL) and in cardiomyocytes subjected to hypertrophic stimulation. The critical role of lnc9456 in cardiomyocyte hypertrophy was confirmed by specific overexpression and knockout in vitro. A physical interaction between lnc9456 and G3BP2 increased NF-κB nuclear translocation, triggering hypertrophy-related cascades. HHQ16 physically bound to lnc9456 with a high-affinity and induced its degradation. Cardiomyocyte-specific lnc9456 overexpression induced, but knockout prevented LADL-induced, cardiac hypertrophy and dysfunction. HHQ16 reversed the effect of lnc9456 overexpression while lost its protective role when lnc9456 was deleted, further confirming lnc9456 as the bona fide target of HHQ16. We further identified the human ortholog of lnc9456, also an Egr2-affiliated transcript, lnc4012. Similarly, lnc4012 was significantly upregulated in hypertrophied failing hearts of patients with dilated cardiomyopathy. HHQ16 also specifically bound to lnc4012 and caused its degradation and antagonized its hypertrophic effects. Targeted degradation of pathological increased lnc4012/lnc9456 by small molecules might serve as a novel promising strategy to regress infarction-induced cardiac hypertrophy and HF.

With the rapid development of the unmanned aerial vehicles (UAVs) industry, there is increasing demand for UAV surveillance technology. Automatic Dependent Surveillance-Broadcast (ADS-B) provides accurate monitoring of UAVs. However, the system cannot encrypt messages or verify identity. To address the issue of identity spoofing, radio frequency fingerprinting identification (RFFI) is applied for ADS-B transmitters to determine the true identities of UAVs through physical layer security technology. This paper develops an ensemble learning ADS-B radio signal recognition framework. Firstly, the research analyzes the data content characteristics of the ADS-B signal and conducts segment processing to eliminate the possible effects of the signal content. To extract features from different signal segments, a method merging end-to-end and non-end-to-end data processing is approached in a convolutional neural network. Subsequently, these features are fused through EL to enhance the robustness and generalizability of the identification system. Finally, the proposed framework’s effectiveness is evaluated using collected ADS-B data. The experimental results indicate that the recognition accuracy of the proposed ELWAM-CNN method can reach up to 97.43% and have better performance at different signal-to-noise ratios compared to existing methods using machine learning.

Abstract Background Adjuvant therapy for stage IB non‐small cell lung cancer remains debatable. In this real‐world study, we evaluate the efficacy and safety of adjuvant epidermal growth factor receptor tyrosine kinase inhibitors (EGFR‐TKIs) for resected stage IB lung adenocarcinoma. Methods This real‐world study recruited 249 patients diagnosed with stage IB disease after surgical resection between January 2013 and September 2021. Sixty‐six (26.5%) patients received adjuvant targeted therapy (TKIs group), and 183 (73.5%) were enrolled in the clinical observation (CO) group. Propensity scores were matched to minimize the observed confounder effects between the two groups, and 59 patient pairs were matched. The primary endpoint was disease‐free survival (DFS). Results In the TKI group, 38 (64.4%) patients chose to receive icotinib, 27.1% (16/59) received gefitinib, and 5 patients (8.5%) chose osimertinib. The median follow‐up time was 30.8 months (range: 7–107 months). Two (3.4%) patients in the TKI group and 10 (16.9%) in the CO group experienced disease relapse. The 3‐year DFS rates were 98.3% in the TKI group and 83.0% in the CO group (HR: 0.10; 95% CI: 0.01–0.78; p = 0.008). DFS differences were found in the entire cohort (p = 0.005) and the matched cohort (p = 0.024) between the two groups. Multivariate analysis showed that adjuvant EGFR‐TKIs was an independent factor for DFS (HR: 0.211; 95% CI: 0.045–0.979; p = 0.047), along with poor cell differentiation (HR: 5.256; 95% CI: 1.648–16.769; p = 0.005), and spread through air spaces (HR: 5.612; 95% CI: 1.137–27.700; p = 0.034). None of the patients discontinued EGFR‐TKIs owing to the low occurrence rate of treatment‐related serious adverse events. Conclusion Adjuvant EGFR‐TKIs could significantly improve DFS among patients with stage IB lung adenocarcinoma compared with CO, with a safe and tolerable profile.

The first pilot test of polyacrylamide microsphere alternate surfactant flood (PMAS) with mixtures of anionic-cationic surfactants (Sa/c) was carried out for a high-temperature, high-salinity, and high-hardness sandstone reservoir to demonstrate the potential of this novel technique to improve oil recovery. A critical micelle concentration (CMC) of 4.82 mg/L, an ultralow interfacial tension (IFT) of 8 × 10−4 mN/m, and a high oil solubilization of 22 were obtained. Static and dynamic adsorptions of Sa/c on natural core containing 15 wt% clay were reduced to about 2.20 and 0.30 mg/g-core, respectively, with the addition of adsorption inhibitor (AI). Since June 2014, the pilot test of PMAS was carried out in a Sinopec reservoir with a temperature of 87 °C, a salinity of 260,393 mg/L, and a hardness of 6,401 mg/L. Twelve cycles of alternative injection of 0.0125 PV Sa/c with a concentration of 0.1% and 0.0125 PV polyacrylamide microsphere with a concentration of 0.2% were conducted at an injection rate of 0.1 PV/yr, for a total of 0.3 PV chemical injection. As a result, the net daily oil production increased from 0 t to 6.5 t, and the water cut decreased from 96.3% to 93.8%, leading to an ultimate improved oil recovery of 6.3% original oil-in-place.

Background: Prediction of early recurrence (ER) of HCC after radical treatment is of great significance for follow-up and subsequent treatment, and there is a lot of unmet needs. Here, our goal is to develop and validate a radiomics nomogram that can predict ER after curative ablation. Objective: The aim of this study was to evaluate the efficacy and safety of regorafenib after disease progression with sorafenib in Chinese patients with advanced HCC through this retrospective analysis. Methods: 149 HCC patients treated between November 2008 and February 2018 were enrolled and randomly divided into training cohort (n = 105) and validation cohort (n = 44). The survival endpoint was recurrence-free survival (RFS). A total of 16908 radiomics features were extracted from the contrast-enhanced MR images of each patient. The minimum redundancy maximum relevance algorithm (mRMR) and random survival forest (RSF) were used for feature selection. Twelve kinds of support vector machine (SVM) models, a Cox regression model (Cox PH), a random survival forest (RSF) model and a gradient boosting model (GBoost) were used to build a radiomics signature. These models were trained after adjusting the model parameters using 5-fold cross-validation. The best models were selected according to the C-index. Results: Using the machine learning (ML) framework, 40 features were identified that demonstrated good prediction of HCC recurrence across all cohorts. The random survival forest (RSF) model showed higher prognostic value, with a C-index of 0.733–0.801 and an integrated Brier score of 0.147–0.165, compared with other SVM models, Cox regression models, etc. (all P

Abstract Highly diverse exoenzymes mediate the energy flow from substrates to the multitrophic microbiota within the soil decomposer micro‐food web. Here, we used a “soil enzyme profile analysis” approach to establish a series of enzyme profile indices; those indices were hypothesized to reflect micro‐food web features. We systematically evaluated the shifts in enzyme profile indices in relation to the micro‐food web features in the restoration of an abandoned cropland to a natural area. We found that enzymatic C:N stoichiometry and decomposability index were significantly associated with substrate availability. Furthermore, the higher Shannon diversity index in the exoenzyme profile, especially for the C‐degrading hydrolase, corresponded to a greater microbiota community diversity. The increased complexity and stability of the exoenzyme network reflected similar changes with the micro‐food web networks. In addition, the gross activity of the enzyme profile as a parameter for soil multifunctionality, effectively predicted the substrate content, microbiota community size, diversity, and network complexity. Ultimately, the proposed enzymic channel index was closely associated with the traditional decomposition channel indices derived from microorganisms and nematodes. Our results showed that soil enzyme profile analysis reflected very well the decomposer food web features. Our study has important implications for projecting future climate change or anthropogenic disturbance impacts on soil decomposer micro‐food web features by using soil enzyme profile analysis.

Background: As commonly used harvest residue management practices in subtropical plantations, stem only harvesting (SOH) and whole tree harvesting (WTH) are expected to affect soil organic carbon (SOC) content. However, knowledge on how SOC and its fractions (POC: particulate organic carbon; MAOC: mineral-associated organic carbon) respond to different harvest residue managements is limited. Methods: In this study, a randomized block experiment containing SOH and WTH was conducted in a Chinese fir (Cunninghamia lanceolata) plantation. The effect of harvest residue management on SOC and its fractions in topsoil (0–10 ​cm) and subsoil (20–40 ​cm) was determined. Plant inputs (harvest residue retaining mass and fine root biomass) and microbial and mineral properties were also measured. Results: The responses of SOC and its fractions to different harvest residue managements varied with soil depth. Specifically, SOH enhanced the content of SOC and POC in topsoil with increases of 15.9% and 29.8%, respectively, compared with WTH. However, SOH had no significant effects on MAOC in topsoil and SOC and its fractions in subsoil. These results indicated that the increase in POC induced by the retention of harvest residue was the primary contributor to SOC accumulation, especially in topsoil. The harvest residue managements affected SOC and its fractions through different pathways in topsoil and subsoil. The plant inputs (the increase in fine root biomass induced by SOH) exerted a principal role in the SOC accumulation in topsoil, whereas mineral and microbial properties played a more important role in regulating SOC dynamics than plants inputs in subsoil. Conclusion: The retention of harvest residues can promote SOC accumulation by increasing POC, and is thus suggested as an effective technology to enhance the soil carbon sink for mitigating climate change in plantation management.

Existing methods are unable to achieve high detection rates and low false alarm rates of satellite-based Automatic Dependent Surveillance-Broadcast (ADS-B) signal preambles at extremely low signal-to-noise ratios (SNRs) using limited on-star resources. In this paper, a dual-hierarchy synchronization method is proposed, including a first-level coarse synchronization and a second-level fine synchronization. The coarse synchronization process involves three steps: (1) detection of unknown signals, (2) soft decision, and (3) adaptive interval output. The first step introduces the threshold (TMSED) of the minimum signal energy to be detected to guarantee a high detection rate. In the soft decision step, a value (SV) designed to improve the robustness of the system curbs false detection caused by noise interference. In the last step, the coarse synchronization interval radius (r) is mapped out according to the SNR to reduce resource consumption. The fine synchronization process is based on the coarse synchronization output, and the correlation peak is calculated to complete the synchronization of the signal preambles. The results show that the proposed method achieves a high detection rate of 96% at an extremely low SNR using a low sampling frequency of 10 MHz. Furthermore, the adjustment of TMSED allows this method to be applied to ADS-B receivers with different sensitivities. The comprehensive performance of this method to achieve high detection rates and acceptable false alarm rates at extremely low SNRs with limited on-star resources is verified by final simulations to be superior to other methods.

The distribution of mining-induced stress and the resulting rock fractures are two crucial factors affecting mineral extraction in protective layer mining. This research establishes a correlation between the vertical fracture aperture and the second derivative of the rock layer’s subsidence curve equation. The article explores the span requirement for a simply supported beam to fracture. This condition is relevant to understanding the dynamic evolution of rock movement and stress fractures during repeated mining of close-distance coal seams. Our study investigates alterations in rock stress and fractures resulting from repeated upward mining of coal seams, using the nearby coal seam cluster in Jincheng Mine as a case study. The research findings indicate that during the mining of the upper coal seam, the roof experiences significant but brief periodic loading intervals, as well as severe and moderate periodic loading. As mining progresses to the lower coal seam, pressure relief of the upper coal seam gradually increases in both degree and range. In the upper coal seam, the vertical stress distribution follows a sequence of “V,” “U,” and “W” forms. The upper coal seam undergoes five stages of expansion deformation: compression, expansion, increased expansion, decreased expansion, and stable expansion.

Toad skin (TS) and toad venom (TV) are two different traditional Chinese medicine (TCM) prepared from Bufo bufo gargarizans Cantor and Bufo melanostictus Schneider. Both of them were used in the treatment of ulcers, and now they are regarded as animal drugs with anti-tumor activity. However, the comparison analysis between their compositions and anti-tumor active ingredients has not been elucidated clearly. In this study, technology of ultra-performance liquid chromatography coupled with tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and bioactivity-based molecular networking were applied to investigate the anti-tumor active ingredients of TS and TV. A method of collision energies-relied mass spectrometry combined with molecular networking was initially developed for identifying their chemical components. A combination of Cytoscape and molecular networking was then employed to analyze the prototype constitutes of TS absorbed in rat serum. In addition, cytotoxicity test was applied to evaluate their anti-proliferative activities against human gastric cancer (GC) cells. As a result, a total of 162 compounds were tentatively characterized, and four structural types existed in both TS and TV, including bufogenins, bufotoxins, indolealkylamines (IAAs), and acyl-arginine. Cyclic dipeptides could only be detected in TS, and the total number of bufogenins and bufotoxins in TV was higher than that of TS. Both of them significantly inhibited the viability of SGC7901 cells, and 70% methanol eluate of TS was the most potential active fraction. 44 prototype compounds in TS were identified, from which three types of potential tumor cytotoxic components, including bufogenins, IAAs, and cyclic dipeptides, were predicted. In human GC cells SGC7901, a mixture of nine bufogenins, including bufotalin, cinobufotalin, gamabufotalin, telocinobufagin, cinobufagin, bufarenogin, arenobufagin, hellebrigenin, and desacetylcinobufotalin, was conducted as bioactive components, which exerted a significant inhibitory effect on the cell viability of SGC7901 cells with a half maximal inhibitory concentration (IC50) value of 0.23 (0.19–0.27, 95%CI) μg/mL. Comparison of their chemical composition and anti-tumor activity reveals that TS might be able to partially replace TV, whilst bufogenins are the potential active ingredients.

Abstract Background Male factors-caused decline in total fertility has raised significant concern worldwide. LncRNAs have been identified to play various roles in biological systems, including spermatogenesis. This study aimed to explore the role of lncRNA5251 in mouse spermatogenesis. Methods The expression of lncRNA5251 was modulated in mouse testes in vivo or spermatogonial stem cells (C18-4 cells) in vitro by shRNA. Results The sperm motility in two generations mice after modulation of lncRNA5251 (muF0 and muF1) was decreased significantly after overexpression of lncRNA5251. GO enrichment analysis found that knockdown lncRNA5251 increased the expression of genes related to cell junctions, and genes important for spermatogenesis in mouse testes. Meanwhile, overexpressing lncRNA5251 decreased the gene and/or protein expression of important genes for spermatogenesis and immune pathways in mouse testes. In vitro, knockdown lncRNA5251 increased the expression of genes for cell junction, and the protein levels of some cell junction proteins such as CX37, OCLN, JAM1, VCAM1 and CADM2 in C18-4 cells. LncRNA5251 is involved in spermatogenesis by modulation of cell junctions. Conclusion This will provide a theoretical basis for improving male reproductive ability via lncRNA.

Titanium matrix composites (TMCs) reinforced with 2–8.3 wt.% NiCoCr medium-entropy alloy particles (NiCoCrp/Ti) were synthesized through an in-situ reaction between NiCoCr and Ti powders during a spark plasma sintering process at 850 °C and 1000 °C. At 850 °C, there are obvious interdiffusion layers with Ti3(Ni, Co) intermetallic around NiCoCr particles. At 1000 °C, the sufficient diffusion of Ni, Co and Cr away from the prior NiCoCr particles results in clusters of granular intermetallic (Ti3(Ni, Co) and Ti2(Ni, Co)) in the Ti matrix. Besides, the addition of NiCoCr particles leads to a significant improvement in tensile strength and hardness. Ti-2wt.%NiCoCr sintered at 1000 °C has a combination of high tensile strength (788 MPa) and good ductility (23.4%). Furthermore, the mechanical properties are mainly correlated to the amount and distribution of the intermetallic compounds in TMCs.

In this article we revisit the global existence result of the wave-Klein-Gordon model of the system of the self-gravitating massive field. Our new observation is that, by applying the conformal energy estimates on hyperboloids, we obtain mildly increasing energy estimate up to the top order for the Klein-Gordon component, which clarify the question on the hierarchy of the energy bounds of the Klein-Gordon component in our previous work. Furthermore, a uniform-in-time energy estimate is established for the wave component up to the top order, as well as a scattering result. These improvements indicate that the partial conformal symmetry of the Einstein-massive scalar system will play an important role in the global analysis.

Although deep learning has been proven to significantly outperform most traditional methods in the classification of large-scale balanced image datasets, collecting enough samples for defect classification is extremely time-consuming and costly. In this paper, we propose a lightweight defect classification method based on few-shot image generation and self-attention fused convolution features. We constructed a 4-class dataset using welding seam images collected from a solar cell module packaging production line. To address the issue of limited defect samples, especially for classes with less than 10 images, we implemented two strategies. Geometric enhancement techniques were first used to extend the defective images. Secondly, multi-scale feature fusion Generative Adversarial Networks (GANs) were utilized to further enhance the dataset. We then performed the feature-level fusion of convolution neural networks and self-attention networks, achieving a classification accuracy of 98.19%. Our experimental results demonstrate that the proposed model performs well in small sample defect classification tasks. And, it can be effectively applied to product quality inspection tasks in industrial production lines.

Ship berthing is one of the most challenging operations for crews, involving optimal trajectory generation and intricate harbor maneuvering at low speed. In this paper, we present a practical path-planning method that generates smooth trajectories for an underactuated surface vehicle (USV) traveling in a confined harbor environment. Our approach introduces a Generalized Voronoi Diagram (GVD)-based path planner to handle the unberthing phase. The hybrid A* search-based path finding method is used for the transportation phase. A simple planner based on a Bézier curve is proposed for the berthing phase. To track the target path, an adaptive pure pursuit method and proportional-derivative (PD) controller is used. The performance of the given method is tested numerically and experimentally on a catamaran with a pair of non-steerable thrusters. The results demonstrate that the proposed algorithm can achieve a successful berthing operation through static obstacle handling and smooth trajectory generation.

The Snake Optimizer (SO) is an advanced metaheuristic algorithm for solving complicated real-world optimization problems. However, despite its advantages, the SO faces certain challenges, such as susceptibility to local optima and suboptimal convergence performance in cases involving discretized, high-dimensional, and multi-constraint problems. To address these problems, this paper presents an improved version of the SO, known as the Snake Optimizer using Sobol sequential nonlinear factors and different learning strategies (SNDSO). Firstly, using Sobol sequences to generate better distributed initial populations helps to locate the global optimum solution faster. Secondly, the use of nonlinear factors based on the inverse tangent function to control the exploration and exploitation phases effectively improves the exploitation capability of the algorithm. Finally, introducing learning strategies improves the population diversity and reduces the probability of the algorithm falling into the local optimum trap. The effectiveness of the proposed SNDSO in solving discretized, high-dimensional, and multi-constraint problems is validated through a series of experiments. The performance of the SNDSO in tackling high-dimensional numerical optimization problems is first confirmed by using the Congress on Evolutionary Computation (CEC) 2015 and CEC2017 test sets. Then, twelve feature selection problems are used to evaluate the effectiveness of the SNDSO in discretized scenarios. Finally, five real-world technical multi-constraint optimization problems are employed to evaluate the performance of the SNDSO in high-dimensional and multi-constraint domains. The experiments show that the SNDSO effectively overcomes the challenges of discretization, high dimensionality, and multi-constraint problems and outperforms superior algorithms.