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Abstract Background Formolase (FLS) is a computationally designed enzyme that catalyzes the carboligation of two or three C1 formaldehyde molecules into C2 glycolaldehyde or C3 dihydroxyacetone (DHA). FLS lays the foundation for several artificial carbon fixation and valorization pathways, such as the artificial starch anabolic pathway. However, the application of FLS is limited by its low catalytic activity and product promiscuity. Findings FLS, designed and engineered based on benzoylformate decarboxylase from Pseudomonas putida, was selected as a candidate for modification. To evaluate its catalytic activity, 25 residues located within an 8 Å distance from the active center were screened using single-point saturation mutagenesis. A screening approach based on the color reaction of the DHA product was applied to identify the desired FLS variants. After screening approximately 5,000 variants (approximately 200 transformants per site), several amino acid sites that were not identified by directed evolution were found to improve DHA formation. The serine-to-phenylalanine substitution at position 236 improved the activity towards DHA formation by 7.6-fold. Molecular dynamics simulations suggested that the mutation increased local hydrophobicity at the active site, predisposing the cofactor-C2 intermediate to nucleophilic attack by the third formaldehyde molecule for subsequent DHA generation. Conclusions This study provides improved FLS variants and valuable information into the influence of residues adjacent to the active center affecting catalytic efficiency, which can guide the rational engineering or directed evolution of FLS to optimize its performance in artificial carbon fixation and valorization.

Abstract In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model’s utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.

Abstract Background Fibronectin type III domain containing 3B (FNDC3B), a member of the fibronectin type III domain-containing protein family, has been indicated in various malignancies. However, the precise role of FNDC3B in the progression of pancreatic cancer (PC) still remains to be elucidated. Methods In this study, we integrated data from the National Center for Biotechnology Information, the Cancer Genome Atlas, Genotype-Tissue Expression database, and Gene Expression Omnibus datasets to analyze FNDC3B expression and its association with various clinicopathological parameters. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, along with Gene Set Enrichment Analysis (GSEA), single sample Gene Set Enrichment Analysis (ssGSEA) and estimate analysis were recruited to delve into the biological function and immune infiltration based on FNDC3B expression. Additionally, the prognostic estimation was conducted using Cox analysis and Kaplan–Meier analysis. Subsequently, a nomogram was constructed according to the result of Cox analysis to enhance the prognostic ability of FNDC3B. Finally, the preliminary biological function of FNDC3B in PC cells was explored. Results The study demonstrated a significantly higher expression of FNDC3B in tumor tissues compared to normal pancreatic tissues, and this expression was significantly associated with various clinicopathological parameters. GSEA revealed the involvement of FNDC3B in biological processes and signaling pathways related to integrin signaling pathway and cell adhesion. Additionally, ssGSEA analysis indicated a positive correlation between FNDC3B expression and infiltration of Th2 cells and neutrophils, while showing a negative correlation with plasmacytoid dendritic cells and Th17 cells infiltration. Kaplan–Meier analysis further supported that high FNDC3B expression in PC patients was linked to shorter overall survival, disease-specific survival, and progression-free interval. However, although univariate analysis demonstrated a significant correlation between FNDC3B expression and prognosis in PC patients, this association did not hold true in multivariate analysis. Finally, our findings highlight the crucial role of FNDC3B expression in regulating proliferation, migration, and invasion abilities of PC cells. Conclusion Despite limitations, the findings of this study underscored the potential of FNDC3B as a prognostic biomarker and its pivotal role in driving the progression of PC, particularly in orchestrating immune responses.

Abstract Background Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes. Results In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data. Conclusions The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.

Abstract Unmanned aerial vehicles (UAVs) offer a new approach to wireless communication, leveraging their high mobility, flexibility, and visual communication capabilities. Ambient backscatter communication enables Internet of Things devices to transmit data by reflecting and modulating ambient radio waves, eliminating the need for additional wireless channels, and reducing energy consumption and cost for sensors. However, passive ambient backscatter communication has limitations such as limited range and poor communication quality. By utilizing UAVs as communication nodes, these limitations can be overcome, expanding the communication range and improving the quality of communication. Although some research has been conducted on combining UAVs and ambient backscatter, several challenges remain. One key challenge is the strong direct link interference in ambient backscatter under UAV conditions, which significantly affects communication quality. In this paper, we propose an intelligent backward and forward straight link interference cancellation algorithm based on NOMA decoding technique to enhance ambient backscatter communication quality under UAV conditions and extract more ambient energy for UAV energy supply. The paper includes theoretical derivation, algorithm description, and simulation analysis to validate the error bit rate of labeled information bits. The results demonstrate that the forward algorithm reduces the error bit rate by approximately 20% under low signal-to-noise ratio (SNR) conditions, while the backward algorithm reduces the error bit rate under high SNR conditions. The combination of the forward and backward algorithms reduces the error bit rate under both high and low SNR conditions. The proposed method contributes to improving the quality of ambient backscatter communication in UAV settings.

Abstract All-small-molecule organic solar cells with good batch-to-batch reproducibility combined with non-halogen solvent processing show great potential for commercialization. However, non-halogen solvent processing of all-small-molecule organic solar cells are rarely reported and its power conversion efficiencies are very difficult to improve. Herein, we designed and synthesized a small molecule donor BM-ClEH that can take advantage of strong aggregation property induced by intramolecular chlorine-sulfur non-covalent interaction to improve molecular pre-aggregation in tetrahydrofuran and corresponding micromorphology after film formation. Tetrahydrofuran-fabricated all-small-molecule organic solar cells based on BM-ClEH:BO-4Cl achieved high power conversion efficiencies of 15.0% in binary device and 16.1% in ternary device under thermal annealing treatment. In contrast, weakly aggregated BM-HEH without chlorine-sulfur non-covalent bond is almost inefficient under same processing conditions due to poor pre-aggregation induced disordered π-π stacking, indistinct phase separation and exciton dissociation. This work promotes the development of non-halogen solvent processing of all-small-molecule organic solar cells and provides further guidance.

Abstract Organic scintillators have recently gained considerable attentions in X‐ray detection for their potential applications in biomedical radiograph and security inspection. However, the weak X‐ray absorption and/or inefficient exciton utilization have limited the development and commercialization of organic scintillators. Currently, high‐performance X‐ray organic scintillators are scarce and organic scintillators with dual triplet‐harvesting channels have not been explored before. Here, we develop several proof‐of‐concept sulfone‐based organic molecules, C1–C7, using different alkoxy chains to manipulate molecular packing mode. These materials exhibit dual triplet‐harvesting channels of thermally activated delayed fluorescence (TADF) and room‐temperature phosphorescence (RTP) in aggregated state. Inspiringly, these molecules display distinct radioluminescence under the X‐ray stimulation. Among them, C6 behaves the highest light yield of 16,558 photons MeV−1. Moreover, clear X‐ray images are demonstrated in both aggregated state and single‐molecule level. High spatial resolutions of 15.0 and 10.6 line pairs per millimeter (lp mm−1) are achieved for rigid and flexible scintillator screens, exceeding most reported organic and conventional inorganic scintillators. These results highlight the great potential of organic molecules with TADF and RTP nature for efficient X‐ray scintillation and imaging.

Abstract Background LATS1/2 are frequently mutated and down-regulated in endometrial cancer (EC), but the contributions of LATS1/2 in EC progression remains unclear. Impaired antigen presentation due to mutations or downregulation of the major histocompatibility complex class I (MHC-I) has been implicated in tumor immune evasion. Herein, we elucidate the oncogenic role that dysregulation of LATS1/2 in EC leads to immune evasion through the down-regulation of MHC-I. Methods The mutation and expression as well as the clinical significance of LATS1/2 in EC was assessed in the TCGA cohort and our sample cohort. CRISPR-Cas9 was used to construct knockout cell lines of LATS1/2 in EC. Differentially expressed genes were analyzed by RNA-seq. The interaction between LATS1/2 and STAT1 was verified using co-immunoprecipitation and GST pull-down assays. Mass spectrometry, in vitro kinase assays, ChIP-qPCR, flow cytometry, immunohistochemistry, immunofluorescence and confocal microscopy were performed to investigate the regulation of LATS1/2 on MHC-I through interaction with and phosphorylate STAT1. The killing effect of activated PBMCs on EC cells were used to monitor anti-tumor activity. Results Here, we demonstrate that LATS1/2 are frequently mutated and down-regulated in EC. Moreover, LATS1/2 loss was found to be associated with a significant down-regulation of MHC-I, independently of the Hippo-YAP pathway. Instead, LATS1/2 were found to directly interact with and phosphorylate STAT1 at Ser727, a crucial transcription factor for MHC-I upregulation in response to interferon-gamma (IFN-γ) signaling, to promote STAT1 accumulating and moving into the nucleus to enhance the transcriptional activation of IRF1/NLRC5 on MHC-I. Additionally, the loss of LATS1/2 was observed to confer increased resistance of EC cells to immune cell-mediated killing and this resistance could be reversed by over-expression of MHC-I. Conclusion Our findings indicate that dysregulation of LATS1/2 in EC leads to immune evasion through the down-regulation of MHC-I, leading to the suppression of infiltrating activated CD8 + T cells and highlight the importance of LATS1/2 in IFN-γ signaling-mediated tumor immune response, suggesting that LATS1/2 is a promising target for immune checkpoint blockade therapy in EC.

Abstract Background Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. Results Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. Conclusions LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Pathogenic missense mutation of the FGF12 gene is responsible for a variable disease phenotypic spectrum. Disease-specific therapies require precise dissection of the relationship between different mutations and phenotypes. The lack of a proper animal model hinders the investigation of related diseases, such as early-onset epileptic encephalopathy. Here, an FGF12AV52H mouse model was generated using CRISPR/Cas9 technology, which altered the A isoform without affecting the B isoform. The FGF12AV52H mice exhibited seizure susceptibility, while no spontaneous seizures were observed. The increased excitability in dorsal hippocampal CA3 neurons was confirmed by patch-clamp recordings. Furthermore, immunostaining showed that the balance of excitatory/inhibitory neurons in the hippocampus of the FGF12AV52H mice was perturbed. The increases in inhibitory SOM+ neurons and excitatory CaMKII+ neurons were heterogeneous. Moreover, the locomotion, anxiety levels, risk assessment behavior, social behavior, and cognition of the FGF12AV52H mice were investigated by elevated plus maze, open field, three-chamber sociability, and novel object tests, respectively. Cognition deficit, impaired risk assessment, and social behavior with normal social indexes were observed, implying complex consequences of V52H FGF12A in mice. Together, these data suggest that the function of FGF12A in neurons can be immediate or long-term and involves modulation of ion channels and the differentiation and maturation of neurons. The FGF12AV52H mouse model increases the understanding of the function of FGF12A, and it is of great importance for revealing the complex network of the FGF12 gene in physiological and pathological processes.

Abstract Silicon suboxide (SiOx, x ≈ 1) is promising in serving as an anode material for lithium‐ion batteries with high capacity, but it has a low initial Coulombic efficiency (ICE) due to the irreversible formation of lithium silicates during the first cycle. In this work, we modify SiOx by solid‐phase Mg doping reaction using low‐cost Mg powder as a reducing agent. We show that Mg reduces SiO2 in SiOx to Si and forms MgSiO3 or Mg2SiO4. The MgSiO3 or Mg2SiO4 are mainly distributed on the surface of SiOx, which suppresses the irreversible lithium‐ion loss and enhances the ICE of SiOx. However, the formation of MgSiO3 or Mg2SiO4 also sacrifices the capacity of SiOx. Therefore, by controlling the reaction process between Mg and SiOx, we can tune the phase composition, proportion, and morphology of the Mg‐doped SiOx and manipulate the performance. We obtain samples with a capacity of 1226 mAh g–1 and an ICE of 84.12%, which show significant improvement over carbon‐coated SiOx without Mg doping. By the synergistical modification of both Mg doping and prelithiation, the capacity of SiOx is further increased to 1477 mAh g–1 with a minimal compromise in the ICE (83.77%).

Abstract Anoikis resistance, a notable factor in osteosarcoma, plays a significant role in tumor invasion and metastasis. This study seeks to identify a distinct gene signature that is specifically associated with the anoikis subcluster in osteosarcoma. Clinical, single-cell, and transcriptional data from TARGET and GEO datasets were used to develop a gene signature for osteosarcoma based on the anoikis subcluster. Univariate Cox and LASSO regression analyses were employed. The signature's predictive value was evaluated using time-dependent ROC and Kaplan–Meier analyses. Functional enrichment analyses and drug sensitivity analyses were conducted. Validation of three modular genes was performed using RT-qPCR and Western blotting. Signature (ZNF583, CGNL1, CXCL13) was developed to predict overall survival in osteosarcoma patients, targeting the anoikis subcluster. The signature demonstrated good performance in external validation. Stratification based on the signature revealed significantly different prognoses. The signature was an independent prognostic factor. The low-risk group showed enhanced immune cell infiltration and improved immune function. Drug sensitivity analysis indicated efficacy of chemotherapy agents. Prognostic nomograms incorporating the signature provided greater predictive accuracy and clinical utility. Signatures related to the anoikis subcluster play a significant role in osteosarcoma progression. Incorporating these findings into clinical decision-making can improve osteosarcoma treatment and patient outcomes.

Background: Idiopathic pulmonary fibrosis (IPF) is a type of interstitial lung disease characterized by unknown causes and a poor prognosis. Recent research indicates that age-related mechanisms, such as cellular senescence, may play a role in the development of this condition. However, the relationship between cellular senescence and clinical outcomes in IPF remains uncertain. Methods: Data from the GSE70867 database were meticulously analyzed in this study. The research employed differential expression analysis, as well as univariate and multivariate Cox regression analysis, to pinpoint senescence-related genes (SRGs) linked to prognosis and construct a prognostic risk model. The model’s clinical relevance and its connection to potential biological processes were systematically assessed in training and testing datasets. Additionally, the expression location of prognosis-related SRGs was identified through immunohistochemical staining, and the correlation between SRGs and immune cell infiltration was deduced using the GSE28221 dataset. Result: The prognostic risk model was constructed based on five SRGs (cellular communication network factor 1, CYR61, stratifin, SFN, megakaryocyte-associated tyrosine kinase, MATK, C-X-C motif chemokine ligand 1, CXCL1, LIM domain, and actin binding 1, LIMA1). Both Kaplan-Meier (KM) curves (p = 0.005) and time-dependent receiver operating characteristic (ROC) analysis affirmed the predictive accuracy of this model in testing datasets, with respective areas under the ROC curve at 1-, 2-, and 3-years being 0.721, 0.802, and 0.739. Furthermore, qRT-RCR analysis and immunohistochemical staining verify the differential expression of SRGs in IPF samples and controls. Moreover, patients in the high-risk group contained higher infiltration levels of neutrophils, eosinophils, and M1 macrophages in BALF, which appeared to be independent indicators of poor prognosis in IPF patients. Conclusion: Our research reveals the effectiveness of the 5 SRGs model in BALF for risk stratification and prognosis prediction in IPF patients, providing new insights into the immune infiltration of IPF progression.

Online demand prediction plays an important role in transport network services from operations, controls to management, and information provision. However, the online prediction models are impacted by streaming data quality issues with noise measurements and missing data. To address these, we develop a robust prediction method for online network-level demand prediction in public transport. It consists of a PCA method to extract eigen demand images and an optimization-based pattern recognition model to predict the weights of eigen demand images by making use of the partially observed real-time data up to the prediction time in a day. The prediction model is robust to data quality issues given that the eigen demand images are stable and the predicted weights of them are optimized using the network level data (less impacted by local data quality issues). In the case study, we validate the accuracy and transferability of the model by comparing it with benchmark models and evaluate the robustness in tolerating data quality issues of the proposed model. The experimental results demonstrate that the proposed Pattern Recognition Prediction based on PCA (PRP-PCA) consistently outperforms other benchmark models in accuracy and transferability. Moreover, the model shows high robustness in accommodating data quality issues. For example, the PRP-PCA model is robust to missing data up to 50% regardless of the noise level. We also discuss the hidden patterns behind the network level demand. The visualization analysis shows that eigen demand images are significantly connected to the network structure and station activity variabilities. Though the demand changes dramatically before and after the pandemic, the eigen demand images are consistent over time in Stockholm.

The pore architecture of porous scaffolds is a critical factor in osteogenesis, but it is a challenge to precisely configure strut-based scaffolds because of the inevitable filament corner and pore geometry deformation. This study provides a pore architecture tailoring strategy in which a series of Mg-doped wollastonite scaffolds with fully interconnected pore networks and curved pore architectures called triply periodic minimal surfaces (TPMS), which are similar to cancellous bone, are fabricated by a digital light processing technique. The sheet-TPMS pore geometries (s-Diamond, s-Gyroid) contribute to a 3‒4-fold higher initial compressive strength and 20%–40% faster Mg-ion-release rate compared to the other-TPMS scaffolds, including Diamond, Gyroid, and the Schoen's I-graph-Wrapped Package (IWP) in vitro. However, we found that Gyroid and Diamond pore scaffolds can significantly induce osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). Analyses of rabbit experiments in vivo show that the regeneration of bone tissue in the sheet-TPMS pore geometry is delayed; on the other hand, Diamond and Gyroid pore scaffolds show notable neo-bone tissue in the center pore regions during the early stages (3–5 weeks) and the bone tissue uniformly fills the whole porous network after 7 weeks. Collectively, the design methods in this study provide an important perspective for optimizing the pore architecture design of bioceramic scaffolds to accelerate the rate of osteogenesis and promote the clinical translation of bioceramic scaffolds in the repair of bone defects.

Nanocellulose has various outstanding properties and great potential for replacing petrochemical products. The utilization of lignocellulose to produce nanocellulose is of great significance to the sustainable development of the economy and society. However, the direct extraction of nanocellulose from lignocellulose by chemical method is challenged by toxic chemicals utilization, energy and time consumption, and waste water generation. Therefore, this paper addressed the conversion of lignocellulosic biomass into bacterial nanocellulose (BNC) by the biological method. Moreover, this article highlights the recent advances in potentials and challenges of lignocellulosic biomass for BNC production through the bioconversion process, including biomass pretreatment, enzymatic hydrolysis, glucose and xylose fermentation, GA accumulation, and inhibitor tolerant. The development in metabolic and evolutionary engineering to enhance the production capacity of BNC-producing strain is also discussed. It is expected to provide guidance on the effective bioproduction of nanocellulose from lignocellulosic biomass.

The exploration of catalysts for the oxygen evolution reaction (OER) with high activity and acceptable price is essential for water splitting to hydrogen generation. High-entropy materials (HEMs) have aroused increasing interest in the field of electrocatalysis due to their unusual physicochemical properties. In this work, we reported a novel FeCoNiMoZn-OH high entropy hydroxide (HEH)/nickel foam (NF) synthesized by a facile pulsed electrochemical deposition method at room temperature. The FeCoNiMoZn-OH HEH displays a 3D porous nanosheet morphology and polycrystalline structure, which exhibits extraordinary OER activity in alkaline media, including much lower overpotential (248 mV at 10 mA cm−2) and Tafel slope (30 mV dec−1). Furthermore, FeCoNiMoZn-OH HEH demonstrates excellent OER catalytic stability. The enhanced catalytic performance of the FeCoNiMoZn-OH HEH primarily contributed to the porous morphology and the positive synergistic effect between Mo and Zn. This work provides a novel insight into the design of HEMs in catalytic application.

Background: As a dedifferentiated tumor, small cell endometrial neuroendocrine tumors (NETs) are rare and frequently diagnosed at an advanced stage with a poor prognosis. Current treatment recommendations are often extrapolated from histologically similar tumors in other sites or based on retrospective studies. The exploration for diagnostic and therapeutic markers in small cell NETs is of great significance. Methods: In this study, we conducted single-cell RNA sequencing on a specimen obtained from a patient diagnosed with small cell endometrial neuroendocrine carcinoma (SCNEC) based on pathology. We revealed the cell map and intratumoral heterogeneity of the cancer cells through data analysis. Further, we validated the function of ISL LIM Homeobox 1 (ISL1) in vitro in an established neuroendocrine cell line. Finally, we examined the association between ISL1 and tumor staging in small cell lung cancer (SCLC) patient samples. Results: We observed the significant upregulation of ISL1 expression in tumor cells that showed high expression of the neuroepithelial markers. Additionally, in vitro cell function experiments demonstrated that the high ISL1 expression group exhibited markedly higher cell proliferation and migration abilities compared to the low expression group. Finally, we showed that the expression level of ISL1 was correlated with SCLC stages. Conclusions: ISL1 protein in NETs shows promise as a potential biomarker for diagnosis and treatment.

Establishing the relationship between thermal cycling and the induced evolution of microstructures and cladding layer properties is crucial to achieving microstructural and performance control, given the complexity involved in high-speed laser cladding process. We hereby report our new findings from laser cladding of Inconel 625 on Q245R steel substrate using a high-speed laser cladding technique. The microstructure evolution and microhardness variations in the first cladding track with different thermal cycles were examined by XRD, SEM, EBSD, TEM, and microhardness tests in detail. Numerical simulation was also conducted to characteristically illustrate the complex thermal cycles during the laser cladding process. Our work revealed that the first six thermal cycles resulted in significant variations of microstructure in the laser cladding layers. The dislocation density and low angle grain boundaries (LAGBs) of the first cladding track decreased after two thermal cycles due to the high peak temperature (above 1000 °C) of thermal cycles and subsequently increased when subjected to further thermal cycles with low peak temperature (below 500 °C). The microhardness value increased from 220 HV0.2 to 320 HV0.2, mainly caused by the formation of precipitated phase during the first four thermal cycles, and through by the enhancement of dislocations after six thermal cycles.

[Introduction] To achieve the "carbon peak and neutrality" goals, the ways for enterprise energy conservation and emission reduction need to fully explored, and the green and low-carbon policy tools should be fully exploited. Green hydrogen, as a clean energy, has become the key to social low-carbon transition thanks to its full link zero carbon emission. Carbon emission rights trading market is a key policy tool to control and reduce carbon emissions by making use of the market-oriented mechanism. Building a carbon trading center hydrogen energy industry trading mechanism to achieve the coupling of carbon trading and hydrogen energy will greatly promote the achieving of the "carbon peak and neutrality" goals. [Method] Based on this, a coupling mechanism of "green hydrogen market - national carbon trading market - electricity market" was built in this paper. The "green hydrogen market - national carbon trading market - electricity market" was modeled and simulated by further utilizing the system dynamics method, and the interaction of multiple markets was studied. [Result] The results show that: Firstly, the coupling among the green hydrogen market, carbon trading market and electricity market can be realized through green hydrogen certification and carbon quota trading, and it is found that the coupling model is feasible through simulation. Secondly, it is found through the simulation of the basic scenario that the coupling of multiple markets can stimulate the increase of carbon price and control the increase of thermal power generation and green hydrogen production. Finally, increasing the proportion of green hydrogen certification and improving the obsolete unit elimination mechanism and carbon quota auction mechanism will help form a carbon pricing mechanism. [Conclusion] On the one hand, this study diversifies the green hydrogen trading models, promotes the participation of green hydrogen in carbon trading to obtain certain benefits, and reduces the hydrogen energy cost. On the other hand, it makes use of the multi-market linkage mechanism to bring into play the forced effect of carbon price and promote the reduction of the proportion of traditional fossil hydrogen production and thermal power generation.

Ultra-high-speed laser cladding (UHSLC) is an effective way to deposit Inconel 625 coatings on 27SiMn steel to prolong its service life. However, elemental segregation in the Inconel 625 coatings is serious and high-speed laser remelting (HSLR) was investigated in order to tackle the issue. The HSLR treatment was also found to reduce the surface roughness of UHSLC Inconel 625 laser cladding significantly, while enhancing their wear resistance. Furthermore, finite element simulation suggests that a higher remelting power may result in a better melt pool fluidity and a more uniform composition distribution.

Abstract Background Genomic structural variant detection is a significant and challenging issue in genome analysis. The existing long-read based structural variant detection methods still have space for improvement in detecting multi-type structural variants. Results In this paper, we propose a method called cnnLSV to obtain detection results with higher quality by eliminating false positives in the detection results merged from the callsets of existing methods. We design an encoding strategy for four types of structural variants to represent long-read alignment information around structural variants into images, input the images into a constructed convolutional neural network to train a filter model, and load the trained model to remove the false positives to improve the detection performance. We also eliminate mislabeled training samples in the training model phase by using principal component analysis algorithm and unsupervised clustering algorithm k-means. Experimental results on both simulated and real datasets show that our proposed method outperforms existing methods overall in detecting insertions, deletions, inversions, and duplications. The program of cnnLSV is available at https://github.com/mhuidong/cnnLSV . Conclusions The proposed cnnLSV can detect structural variants by using long-read alignment information and convolutional neural network to achieve overall higher performance, and effectively eliminate incorrectly labeled samples by using the principal component analysis and k-means algorithms in training model stage.

Abstract Background Many deep-sea invertebrates largely depend on chemoautotrophic symbionts for energy and nutrition, and some of them have reduced functional digestive tracts. By contrast, deep-sea mussels have a complete digestive system although symbionts in their gills play vital roles in nutrient supply. This digestive system remains functional and can utilise available resources, but the roles and associations among gut microbiomes in these mussels remain unknown. Specifically, how the gut microbiome reacts to environmental change is unclear. Results The meta-pathway analysis showed the nutritional and metabolic roles of the deep-sea mussel gut microbiome. Comparative analyses of the gut microbiomes of original and transplanted mussels subjected to environmental change revealed shifts in bacterial communities. Gammaproteobacteria were enriched, whereas Bacteroidetes were slightly depleted. The functional response for the shifted communities was attributed to the acquisition of carbon sources and adjusting the utilisation of ammonia and sulphide. Self-protection was observed after transplantation. Conclusion This study provides the first metagenomic insights into the community structure and function of the gut microbiome in deep-sea chemosymbiotic mussels and their critical mechanisms for adapting to changing environments and meeting of essential nutrient demand.

IntroductionAnti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a common autoimmune encephalitis, and it is associated with psychosis, dyskinesia, and seizures. Anti-NMDAR encephalitis (NMDARE) in juveniles and adults presents different clinical charactreistics. However, the pathogenesis of juvenile anti-NMDAR encephalitis remains unclear, partly because of a lack of suitable animal models.MethodsWe developed a model of juvenile anti-NMDAR encephalitis using active immunization with an amino terminal domain peptide from the GluN1 subunit (GluN1356 − 385) against NMDARs in 3-week-old female C57BL/6J mice.ResultsImmunofluorescence staining suggested that autoantibody levels in the hippocampus increased, and HEK-293T cells staining identified the target of the autoantibodies as GluN1, suggesting that GluN1-specific immunoglobulin G was successfully induced. Behavior assessment showed that the mice suffered significant cognition impairment and sociability reduction, which is similar to what is observed in patients affected by anti-NMDAR encephalitis. The mice also exhibited impaired long-term potentiation in hippocampal CA1. Pilocarpine-induced epilepsy was more severe and had a longer duration, while no spontaneous seizures were observed.ConclusionThe juvenile mouse model for anti-NMDAR encephalitis is of great importance to investigate the pathological mechanism and therapeutic strategies for the disease, and could accelerate the study of autoimmune encephalitis.

To improve acquisition efficiency and achieve super high-resolution reconstruction, a computational integral imaging reconstruction (CIIR) method based on the generative adversarial network (GAN) network is proposed. Firstly, a sparse camera array is used to generate an elemental image array of the 3D object. Then, the elemental image array is mapped to a low-resolution sparse view image. Finally, a lite GAN super-resolution network is presented to up-sample the low-resolution 3D images to high-resolution 3D images with realistic image quality. By removing batch normalization (BN) layers, reducing basic blocks, and adding intra-block operations, better image details and faster generation of super high-resolution images can be achieved. Experimental results demonstrate that the proposed method can effectively enhance the image quality, with the structural similarity (SSIM) reaching over 0.90, and can also reduce the training time by about 20%.

BackgroundImmunoglobulin A nephropathy (IgAN) is one of the most common kidney diseases leading to renal injury. Of pediatric cases, 25%–30% progress into end-stage kidney disease (ESKD) in 20–25 years. Therefore, predicting and intervening in IgAN at an early stage is crucial. The purpose of this study was to validate the availability of an international predictive tool for childhood IgAN in a cohort of children with IgAN treated at a regional medical centre.MethodsAn external validation cohort of children with IgAN from medical centers in Southwest China was formed to validate the predictive performance of the two full models with and without race differences by comparing four measures: area under the curve (AUC), the regression coefficient of linear prediction (PI), survival analysis curves for different risk groups, and R2D.ResultsA total of 210 Chinese children, including 129 males, with an overall mean age of 9.43 ± 2.71 years, were incorporated from this regional medical center. In total, 11.43% (24/210) of patients achieved an outcome with a GFR decrease of more than 30% or reached ESKD. The AUC of the full model with race was 0.685 (95% CI: 0.570–0.800) and the AUC of the full model without race was 0.640 (95% CI: 0.517–0.764). The PI of the full model with race and without race was 0.816 (SE = 0.006, P

The pore morphology design of bioceramic scaffolds plays a substantial role in the induction of bone regeneration. Specifically, the effects of different scaffold pore geometry designs on angiogenesis and new bone regeneration remain unclear. Therefore, we fabricated Mg/Sr co-doped wollastonite bioceramic (MS-CSi) scaffolds with three different pore geometries (gyroid, cylindrical, and cubic) and compared their effects on osteogenesis and angiogenesis in vitro and in vivo. The MS-CSi scaffolds were fabricated by digital light processing (DLP) printing technology. The pore structure, mechanical properties, and degradation rate of the scaffolds were investigated. Cell proliferation on the scaffolds was evaluated using CCK-8 assays while angiogenesis was assessed using Transwell migration assays, tube formation assays, and immunofluorescence staining. The underlying mechanism was explored by western blotting. Osteogenic ability of scaffolds was evaluated by alkaline phosphatase (ALP) staining, western blotting, and qRT-PCR. Subsequently, a rabbit femoral defect model was prepared to compare differences in the scaffolds in osteogenesis and angiogenesis in vivo. Cell culture experiments showed that the gyroid pore scaffold downregulated YAP/TAZ phosphorylation and enhanced YAP/TAZ nuclear translocation, thereby promoting proliferation, migration, tube formation, and high expression of CD31 in human umbilical vein endothelial cells (HUVECs) while strut-based (cubic and cylindrical pore) scaffolds promoted osteogenic differentiation in bone marrow mesenchymal stem cells and upregulation of osteogenesis-related genes. The gyroid pore scaffolds were observed to facilitate early angiogenesis in the femoral-defect model rabbits while the strut-based scaffolds promoted the formation of new bone tissue. Our study indicated that the pore geometries and pore curvature characteristics of bioceramic scaffolds can be precisely tuned for enhancing both osteogenesis and angiogenesis. These results may provide new ideas for the design of bioceramic scaffolds for bone regeneration.

Virtual synchronous generator control (VSG) is an attractive method for the grid-tied inverter to provide inertia and frequency support. However, it brings some troubles on DC-link voltage control when it is applied to two-stage photovoltaic (PV) power generation. This study proposes a DC-Side synchronous active power Control for two-stage photovoltaic (PV) power generation without energy storage. Synchronous active power Control is applied to DC–DC converters, while the grid-tied inverter still maintains the DC-link voltage constant. The DC–DC converters flexibly regulate active power to provide the simultaneous inertial and primary frequency response like a synchronous generator. A simple sensorless iterative maximum power estimation method is introduced and integrated into the DC-side VSG strategy. It can estimate a rough MAP based on one sample point and converge to an accurate value along with the VSG control. The effectiveness and advantages of the control strategy are validated through case studies on an islanded microgrid that included PV generations with a DC-bus collection system. The results show that the proposed strategy significantly improves the frequency nadir and the steady frequency and maintains DC-link voltage stability during irradiance change conditions.

Abstract A metal electrode is a significant component of a zinc–air battery (ZAB), but the metal material is usually not elastic, which severely restricts the application of flexible and stretchable ZABs in the field of wearable electronic devices. Herein, we report a flexible and stretchable metal‐coated textile prepared by a dynamic stretching–electroplating based on a wavy spandex textile substrate. Benefiting from the unique woven and wavy structure, the metal‐coated textile shows a high stretchability of 100% and stable conductivity. In situ scanning electron microscope observation during stretching showed that the tensile strain of the metal‐coated textile is mainly attributed to the deformation of the microfiber network at the bottom position of the wave structure. In addition, a sodium carboxymethyl cellulose–polyacrylic acid–potassium hydroxide composite hydrogel has been used as the electrolyte. This hydrogel shows excellent ionic conductivity, mechanical properties, and water retention properties, which makes it suitable for the semi‐open system of ZAB. Furthermore, a flexible and stretchable sandwich‐structure ZAB, assembled using the above‐mentioned electrodes and electrolyte, operates stably even under rapid stretching/releasing cycle deformation. Because of its facile preparation and low cost, this flexible and stretchable ZAB is suitable for fabrication of large‐area batteries to obtain higher output current and power to drive wearable electronic devices.

Abstract Alkali metal–carbon dioxide (Li/Na/K‐CO2) batteries are emerging electrochemical energy storage technologies in the context of the energy crisis and the urgent demand for carbon neutrality. Alkali metal–CO2 batteries offer a new strategy for CO2 fixation and utilization, and thus has been receiving considerable attention in recent years. Considerable progress has been achieved since alkali metal–CO2 batteries were invented, especially in terms of development of new electrode materials, and yet, research is lacking on the underlying mechanisms of the systems. This is the first typical review focusing on the electrochemical mechanisms of metal–CO2 batteries that summarizes the current understanding of and provides insights into the thermodynamic reaction pathways, the kinetic characteristics, and the crucial factors determining the reaction mechanisms in alkali metal–CO2 batteries. The review starts with the fundamental concepts of alkali metal–CO2 batteries, followed by a comprehensive discussion of the working mechanisms on cathodes and anodes. Moreover, the operation mechanisms of state‐of‐the‐art electrolytes, including liquid and (quasi‐)solid‐state electrolytes, are also described. Finally, we identify the unsolved problems in current alkali metal–CO2 batteries and propose potential topics for future research.

ABSTRACT Marine sediments are important methane reservoirs. Methane efflux from the seabed is significantly restricted by anaerobic methanotrophic (ANME) archaea through a process known as anaerobic oxidation of methane (AOM). Different clades of ANME archaea occupy distinct niches in methane seeps, but their underlying molecular mechanisms still need to be fully understood. To provide genetic explanations for the niche partitioning of ANME archaea, we applied comparative genomic analysis to ANME archaeal genomes retrieved from global methane seeps. Our results showed that ANME-2 archaea are more prevalent than ANME-1 archaea in shallow sediments because they carry genes that encode a significantly higher number of outer membrane multiheme c-type cytochromes and flagellar proteins. These features make ANME-2 archaea perform direct interspecies electron transfer better and benefit more from electron acceptors in AOM. Besides, ANME-2 archaea carry genes that encode extra peroxidase compared to ANME-1 archaea, which may lead to ANME-2 archaea better tolerating oxygen toxicity. In contrast, ANME-1 archaea are more competitive in deep layers than ANME-2 archaea because they carry extra genes (mtb and mtt) for methylotrophic methanogenesis and a significantly higher number of frh and mvh genes for hydrogenotrophic methanogenesis. Additionally, ANME-1 archaea carry exclusive genes (sqr, TST, and mddA) involved in sulfide detoxification compared to ANME-2 archaea, leading to stronger sulfide tolerance. Overall, this study reveals the genomic mechanisms shaping the niche partitioning among ANME archaea in global methane seeps. IMPORTANCE Anaerobic methanotrophic (ANME) archaea are important methanotrophs in marine sediment, controlling the flux of biologically generated methane, which plays an essential role in the marine carbon cycle and climate change. So far, no strain of this lineage has been isolated in pure culture, which makes metagenomics one of the fundamental approaches to reveal their metabolic potential. Although the niche partitioning of ANME archaea was frequently reported in different studies, whether this pattern was consistent in global methane seeps had yet to be verified, and little was known about the genetic mechanisms underlying it. Here, we reviewed and analyzed the community structure of ANME archaea in global methane seeps and indicated that the niche partitioning of ANME archaea was statistically supported. Our comparative genomic analysis indicated that the capabilities of interspecies electron transfer, methanogenesis, and the resistance of oxygen and hydrogen sulfide could be critical in defining the distribution of ANME archaea in methane seep sediment.

In order to accelerate the construction of a clean, low-carbon, safe and efficient energy system, China set the provincial weight of responsibility for renewable energy power consumption and established a renewable energy power absorption guarantee mechanism in 2019. As a market incentive policy, it has enduring effect on the low-carbon transformation of the power industry. Firstly, the operation mechanism of the renewable energy consumption guarantee mechanism is analyzed. The general framework, core elements and supporting measures are clarified. Secondly, a stock-flow diagram is constructed based on the system dynamics method. It contains the green electricity market sub-module, the green-certificate market sub-module and the excess power absorption market sub-module. Finally, multiple scenarios are set up to simulate the impact of the green-certificate market and excess power absorption market improvements on the installed capacity and tariff of China’s green power market. The renewable energy guarantee mechanism is an effective means to promote the consumption of green electricity in China. In addition, in the short term the cost of electricity for users has increased, but in the long term the cost of electricity shows a fluctuating downward trend. This study provides theoretical references for the formulation of clean and low-carbon policy objectives in the power industry and the optimization of market mechanisms.

Subway vehicle roofs must be inspected when entering and exiting the depot to ensure safe subway vehicle operations. This paper presents an improved method for detecting foreign objects on subway vehicle roofs based on the YOLOv7 algorithm. First, we capture images of foreign objects using a line-scan camera at the depot entrance and exit, creating a dataset of foreign roof objects. Subsequently, we address the shortcomings of the YOLOv7 algorithm by introducing the Ghost module, an improved weighted bidirectional feature pyramid network (WBiFPN), and the Wise intersection over union (WIoU) bounding-box regression loss function. These enhancements are incorporated to build the subway vehicle roof foreign object detection model based on the improved YOLOv7, which we refer to as YOLOv7-GBW. The experimental results demonstrate the practicality and usability of the proposed method. The analysis of the experimental results indicates that the YOLOv7-GBW algorithm achieves a detection accuracy of 90.29% at a speed of 54.3 frames per second (fps) with a parameter count of 15.51 million. The improved YOLOv7 model outperforms mainstream detection algorithms in terms of detection accuracy, speed, and parameter count. This finding confirms that the proposed method meets the requirements for detecting foreign objects on subway vehicle roofs.

The production of long-term landslide maps (LAM) holds crucial importance in estimating landslide activity, vegetation disturbance, and regional stability. However, the availability of LAMs remains limited in many regions, despite the application of various machine-learning methods, deep-learning (DL) models, and ensemble strategies in landslide detection. While transfer learning is considered an effective approach to tackle this challenge, there has been limited exploration and comparison of the temporal transferability of state-of-the-art deep-learning models in the context of LAM production, leaving a significant gap in the research. In this study, an extensive series of tests was conducted to evaluate the temporal transferability of typical semantic segmentation models, specifically U-Net, U-Net 3+, and TransU-Net, using a 10-year landslide-inventory dataset located near the epicenter of the Wenchuan earthquake. The experiment results disclose the feasibility and limitations of implementing transfer-learning methods for LAM production, particularly when leveraging the power of U-Net 3+. Furthermore, following an assessment of the effects of varying data volumes, patch sizes, and time intervals, this study recommends appropriate settings for LAM production, emphasizing the balance between efficiency and production performance. The findings from this study can serve as a valuable reference for devising an efficient and reliable strategy for large-scale LAM production in landslide-prone regions.

Abstract In this work, ultra-low dilution rate Inconel 625 coatings with a thickness of ~534.4 μm were prepared by high-speed laser cladding technique on the Q245R steel. The XRD and TEM results show that the Inconel 625 coatings are mainly composed of the Nb and Mo-enriched laves phase with hexagonal close-packed (HCP) structure and the γ-Ni phase with face-centered-cubic (FCC) structure. The cellular crystal, column crystal, and equiaxed dendritic crystal were observed in the bottom, middle, and top of the Inconel 625 coatings from SEM results, respectively. The wear resistance and corrosion resistance of Q245R steel are significantly improved by high-speed laser cladding Inconel 625 coatings. In addition, the low dilution rate is beneficial for improving the wear and corrosion resistance of high-speed laser cladding Inconel 625 coatings. These findings may provide a method to design the wear and corrosion resistance of coatings on steel's surface.

Abstract Background The pathogen of banana Fusarium oxysporum f. sp. cubense race 4(Foc4) infects almost all banana species, and it is the most destructive. The molecular mechanism of the interactions between Fusarium oxysporum and banana still needs to be further investigated. Methods We use both the interolog and domain-domain method to predict the protein–protein interactions (PPIs) between banana and Foc4. The predicted protein interaction sequences are encoded by the conjoint triad and autocovariance method respectively to obtain continuous and discontinuous information of protein sequences. This information is used as the input data of the neural network model. The Long Short-Term Memory (LSTM) neural network five-fold cross-validation and independent test methods are used to verify the predicted protein interaction sequences. To further confirm the PPIs between banana and Foc4, the GO (Gene Ontology) and KEGG (Kyoto Encylopedia of Genes and Genomics) functional annotation and interaction network analysis are carried out. Results The experimental results show that the PPIs for banana and foc4 predicted by our proposed method may interact with each other in terms of sequence structure, GO and KEGG functional annotation, and Foc4 protein plays a more active role in the process of Foc4 infecting banana. Conclusions This study obtained the PPIs between banana and Foc4 by using computing means for the first time, which will provide data support for molecular biology experiments.

Hepatic ischemia–reperfusion (IR) injury is a serious clinical problem that complicates liver resection and transplantation. Despite recent advances in understanding of the pathophysiology of hepatic IR injury, effective interventions and therapeutics are still lacking. Here, we examined the role of transient receptor potential melastatin 2 (TRPM2), a Ca2+-permeable, non-selective cation channel, in mediating hepatic IR injury. Our data showed that TRPM2 deficiency attenuated IR-induced liver dysfunction, inflammation, and cell death in mice. Moreover, RNA sequencing analysis indicated that TRPM2-induced IR injury occurs via ferroptosis-related pathways. Consistently, as a ferroptosis inducer, (1S,3R)-RSL3 treatment induced mitochondrial dysfunction in hepatocytes and a TRPM2 inhibitor suppressed this. Interestingly, TRPM2-mediated calcium influx caused mitochondrial calcium accumulation via the mitochondrial Ca2+-selective uniporter and increased the expression level of arachidonate 12-lipoxygenase (ALOX12), which results in mitochondrial lipid peroxidation during hepatic IR injury. Furthermore, hepatic IR injury-induced ferroptosis was obviously relieved by a TRPM2 inhibitor or calcium depletion, both in vitro and in vivo. Collectively, these findings demonstrate a crucial role for TRPM2-mediated ferroptosis in hepatic IR injury via increased Ca2+-induced ALOX12 expression, indicating that pharmacological inhibition of TRPM2 may provide an effective therapeutic strategy for hepatic IR injury-related diseases, such as during liver resection and transplantation.

ObjectivesTo evaluate the relationship between systemic family dynamics and adolescent depression.MethodsAn offline survey was distributed to 4,109 students in grades 6–12, with the final analysis including 3,014 students (1,524 boys and 1,490 girls) aged 10–18 years. The questionnaire included the Self-Rating Scale of Systemic Family Dynamics (SSFD), the Self-Rating Depression Scale (SDS), and demographic characteristics.ResultsFamily dynamics were negatively correlated with depressive symptoms, with better family dynamics (high scores) associated with lower levels of depression based on the SDS score. After adjusting for sociodemographic characteristics, an ordinal multiclass logistic regression analysis identified family atmosphere (OR = 0.952, 95% CI: 0.948–0.956, p < 0.001) as the most important protective family dynamic against depression, followed by individuality (OR = 0.964, 95% CI: 0.960–0.968, p < 0.001). Latent class analysis (LCA) created the low family dynamic and high family dynamic groups. There were significant differences in the mean SDS scores between the two groups (45.52 ± 10.57 vs. 53.78 ± 11.88; p < 0.001) that persisted after propensity matching. Family atmosphere and individuation had a favorable diagnostic value for depression, with AUCs of 0.778 (95% CI: 0.760–0.796) and 0.710 (95% CI: 0.690–0.730), respectively. The diagnostic models for depression performed well.ConclusionPoor family dynamics may be responsible for adolescent depression. A variety of early intervention strategies focused on the family may potentially avoid adolescent depression.

Cuproptosis was characterized as a novel type of programmed cell death. Recently, however, the role of cuproptosis-related long noncoding RNAs (CRLs) in tumors has not yet been studied. Identifying a predictive CRL signature in hepatocellular carcinoma (HCC) and investigating its putative molecular function were the goals of this work. Initially, Pearson’s test was used to assess the relationship between lncRNAs and cuproptosis-associated genes obtained from HCC data of The Cancer Genome Atlas (TCGA). By implementing differential expression and univariate Cox analysis, 61 prognostic CRLs were subsequent to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. A prognostic risk score model was then constructed to evaluate its ability to predict patients’ survival when combined with clinicopathological parameters in HCC. The five-lncRNA prognostic signature categorized the HCC patients into high- and low-risk groups. The low-risk group exhibited more sensitivity to elesclomol than the high-risk one. Surprisingly, distinct mitochondrial metabolism pathways connected to cuproptosis and pivotal immune-related pathways were observed between the two groups via gene set enrichment analysis (GSEA). Meanwhile, there were substantial differences between the high-risk group and the low-risk group in terms of tumor-infiltrating immune cells (TIICs). Furthermore, a positive relationship was shown between the risk score and the expression of immune checkpoints. Additionally, differential expression of the five lncRNAs was confirmed in our own HCC samples and cell lines via RT-qPCR. Finally, in vitro assays confirmed that WARS2-AS1 and MKLN1-AS knockdown could sensitize HCC cells to elesclomol-induced cuproptosis. Overall, our predictive signature may predict the prognosis of HCC patients in an independent manner, give a better understanding of how CRLs work in HCC, and offer therapeutic reference for patients with HCC.

Abstract Background The present study aimed to comprehensively investigate the occurrence and risk factors of adverse events (AEs) or adverse drug reactions (ADRs) (especially for thrombocytopenia and bleeding) in Chinese female patients receiving bivalirudin during percutaneous coronary intervention (PCI). Methods A total of 918 female patients from 27 Chinese medical centers took bivalirudin as anticoagulant for PCI were enrolled in this prospective, multi-center, intensive monitoring study. Safety data (AEs, ADRs, thrombocytopenia and bleeding) were collected from admission to 72 h post bivalirudin administration; then, patients were followed up at the 30th day with the safety data collected as well. Results One hundred and twenty (13.1%) patients occurred AEs, among which 7 (0.8%) cases experienced severe AEs, and 2 (0.2%) cases died. Besides, 40 (4.4%) patients occurred bivalirudin-related ADRs, in which 3 (0.3%) cases experienced severe ADRs, but 0 (0.0%) cases died. It was of note that 27 (2.9%) and 13 (1.4%) patients experienced thrombocytopenia and bleeding, respectively. Subsequent multivariate analyses observed that: clinical presentation of spontaneous coronary artery dissection (SCAD) (odds ratio (OR) = 3.191, P = 0.004), CRUSADE high risk (OR = 2.075, P = 0.031), multiple culprit vessel (OR = 2.328, P = 0.019) independently correlated with higher risk of bivalirudin-related ADRs; clinical presentation of SCAD (OR = 4.388, P = 0.002) and multiple culprit vessel (OR = 2.974, P = 0.010) independently linked with raised thrombocytopenia risk; history of diabetes mellitus (OR = 5.227, P = 0.007) and CRUSADE high risk (OR = 4.475, P = 0.016) were independent factor related to elevated bleeding risk. Conclusion Bivalirudin is well tolerated with low ADRs, thrombocytopenia and bleeding incidences in Chinese female patients undergoing PCI.

Abstract Background Long non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute to diagnose, treatment, and prognosis of diseases. However, the identification of LDAs by the biological experiments is time-consuming, costly and inefficient. Therefore, the development of efficient and high-accuracy computational methods for predicting LDAs is of great significance. Results In this paper, we propose a novel computational method (gGATLDA) to predict LDAs based on graph-level graph attention network. Firstly, we extract the enclosing subgraphs of each lncRNA-disease pair. Secondly, we construct the feature vectors by integrating lncRNA similarity and disease similarity as node attributes in subgraphs. Finally, we train a graph neural network (GNN) model by feeding the subgraphs and feature vectors to it, and use the trained GNN model to predict lncRNA-disease potential association scores. The experimental results show that our method can achieve higher area under the receiver operation characteristic curve (AUC), area under the precision recall curve (AUPR), accuracy and F1-Score than the state-of-the-art methods in five fold cross-validation. Case studies show that our method can effectively identify lncRNAs associated with breast cancer, gastric cancer, prostate cancer, and renal cancer. Conclusion The experimental results indicate that our method is a useful approach for predicting potential LDAs.

The structural stability of γ-boron is investigated using Raman spectra and DFT calculations under high pressures, up to 126 GPa, at ambient temperature. The pressure dependence of all the Raman-active modes of the γ-boron is reported. We also observe amusing changes within the B3g and B1g Raman-active vibrational modes, which result in the phenomenon of first merging and then separating the Raman peaks. In addition to the Raman measurements, the changes in crystal structure and force constants are calculated to reasonably explain the discrepancy between the two Raman modes in response to pressure. The results of the continuous shifts for all Raman modes and the unit-cell parameters, as well as volume with increasing pressure, indicate that there is no structural transformation of γ-boron below this pressure value, with no changes in either symmetry or structure.

A control strategy for a certain class of hypersonic flight aircraft dynamic models with unknown parameters is proposed in this article. The strategy is adaptive dynamic surface input quantization control. To address the issues in conventional inversion control, a first-order low-pass filter and an adaptive parameter minimum learning law are introduced in the control system design process. This method has the following features: (1) it solves the problem of repeated differentiation of the virtual control law in the conventional back-stepping method, greatly simplifying the control law structure; (2) by using the norm of the neural network weight vector as the adaptive adjustment parameter instead of updating each element online, the number of adaptive adjustment parameters is significantly reduced, improving the execution efficiency of the controller; (3) the introduced hysteresis quantizer overcomes the disadvantage of the quantization accuracy deterioration when the input value is too low in the logarithm quantizer, improving the accuracy of the quantizer. Stability analysis has shown that all signals in the closed-loop system are semi-globally uniformly bounded, and simulation results have verified the effectiveness of the proposed adaptive quantized control scheme.

The preparation of tetragonal barium titanate (BT) powders with uniform and suitable particle sizes is a significant prerequisite for ultra-thin and highly integrated multilayer ceramic capacitors (MLCCs). However, the balance of high tetragonality and controllable particle size remains a challenge, which limits the practical application of BT powders. Herein, the effects of different proportions of hydrothermal medium composition on the hydroxylation process are explored to obtain high tetragonality. The high tetragonality of BT powders under the optimal solvent condition of water:ethanol:ammonia solution of 2:2:1 is around 1.009 and increases with the particle size. Meanwhile, the good uniformity and dispersion of BT powders with particle sizes of 160, 190, 220, and 250 nm benefit from the inhibition of ethanol on the interfacial activity of BT particles (BTPs). The core–shell structure of BTPs is revealed by different lattice fringe spacings of the core and edge and the crystal structure by reconstructed atomic arrangement, which reasonably explains the trend between tetragonality and average particle size. These findings are instructive for the related research on the hydrothermal process of BT powders.

Abstract Background Although electrical stimulation of the peripheral and central nervous systems has attracted much attention owing to its potential therapeutic effects on neuropsychiatric diseases, its non-cell-type-specific activation characteristics may hinder its wide clinical application. Unlike electrical methodologies, optogenetics has more recently been applied as a cell-specific approach for precise modulation of neural functions in vivo, for instance on the vagus nerve. The commonly used implantable optical waveguides are silica optical fibers, which for brain optogenetic stimulation (BOS) are usually fixed on the skull bone. However, due to the huge mismatch of mechanical properties between the stiff optical implants and deformable vagal tissues, vagus nerve optogenetic stimulation (VNOS) in free-behaving animals continues to be a great challenge. Results To resolve this issue, we developed a simplified method for the fabrication of flexible and stretchable polymer optical fibers (POFs), which show significantly improved characteristics for in vivo optogenetic applications, specifically a low Young’s modulus, high stretchability, improved biocompatibility, and long-term stability. We implanted the POFs into the primary motor cortex of C57 mice after the expression of CaMKIIα-ChR2-mCherry detected frequency-dependent neuronal activity and the behavioral changes during light delivery. The viability of POFs as implantable waveguides for VNOS was verified by the increased firing rate of the fast-spiking GABAergic interneurons recorded in the left vagus nerve of VGAT-ChR2 transgenic mice. Furthermore, VNOS was carried out in free-moving rodents via chronically implanted POFs, and an inhibitory influence on the cardiac system and an anxiolytic effect on behaviors was shown. Conclusion Our results demonstrate the feasibility and advantages of the use of POFs in chronic optogenetic modulations in both of the central and peripheral nervous systems, providing new information for the development of novel therapeutic strategies for the treatment of neuropsychiatric disorders.

BackgroundPostoperative non-union of femoral neck fracture often needs secondary operation. We report a case of a postoperative non-union of femoral neck fracture treated with teriparatide.Case presentationA young male patient with Garden IV femoral neck fracture who showed no obvious signs of healing 3 months after percutaneous hollow nail fixation in which the fracture line was enlarged and the hollow nail was withdrawn. Bone non-union healed after 6 months of continuous subcutaneous injection of teriparatide at a dosage of 20 mg/day after the patient refused a secondary surgery. As far as we know, there have been no relevant reports on this type of fracture yet.ConclusionsTeriparatide is expected to be beneficial in treating young patients with a displaced femoral neck fracture who have difficulty in healing from non-union and who are keen on avoiding secondary surgery.

During the COVID-19 pandemic, online learning has experienced increasing utilization and poses new challenges for schoolteachers to engage students. Project-based Learning (PBL) is widely acknowledged as an effective pedagogy for motivating and involving students. However, few studies have examined scaffolds that facilitate student engagement in the context of distance PBL. This mixed-method study was conducted with grade 7 teachers and students in a junior middle school in East China from March 2020 to April 2020. Qualitative analysis was employed in interviews with 2 teachers and 21 students. Quantitative analysis was used to visualize the self-reflection reports of 39 students. The findings suggest that the scaffolds of teacher direction, technology support, peer collaboration, and parental assistance play a significant role. In addition, specific scaffolding within the above categories was revealed. The results highlight the problem-oriented, methodological, and synthesized application of various scaffold(ing)s in engaging students and emphasize that scaffolding students emotionally is the core issue to support engagement for remote learning.