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The vigorous development of wind power generation technology is the core path for the country to promote the low-carbon energy transformation and realize the "dual-carbon" (carbon peaking and carbon neutrality) goals. With the gradual saturation of the development of onshore and offshore wind power resources, wind power development from the near-shore to the distant deep sea is the inevitable trend of the future development of the wind power industry. The research and development and optimization of floating offshore wind turbine (FOWT) platforms is the key to promoting the development of offshore wind power in distant deep sea areas. Based on an analysis of the current development status of FOWT platforms at home and abroad, this paper further analyzes the current research status of aerodynamic loads, hydrodynamic loads, mooring systems, coupling analysis methods and commonly used numerical simulation tools in the design of FOWT platforms, and focuses on sorting out the key technological ideas and constitutive features of FOWT platforms that are driven by the enhancement of structural stability and the reduction of costs. By summarizing the current research status of FOWT platform optimization, we can forecast the problems and challenges in the design of FOWT platforms with a view to providing technical references for researchers in related fields.

Abstract Adenoid cystic carcinoma (ACC) is a rare, usually slow-growing yet aggressive head and neck malignancy. Despite its clinical significance, our understanding of the cellular evolution and microenvironment in ACC remains limited. We investigated the intratumoral microbiomes of 50 ACC tumor tissues and 33 adjacent normal tissues using 16S rRNA gene sequencing. This allowed us to characterize the bacterial communities within the ACC and explore potential associations between the bacterial community structure, patient clinical characteristics, and tumor molecular features obtained through RNA sequencing. The bacterial composition in the ACC was significantly different from that in adjacent normal salivary tissue, and the ACC exhibited diverse levels of species richness. We identified two main microbial subtypes within the ACC: oral-like and gut-like. Oral-like microbiomes, characterized by increased diversity and abundance of Neisseria, Leptotrichia, Actinomyces, Streptococcus, Rothia, and Veillonella (commonly found in healthy oral cavities), were associated with a less aggressive ACC-II molecular subtype and improved patient outcomes. Notably, we identified the same oral genera in oral cancer and head and neck squamous cell carcinomas. In both cancers, they were part of shared oral communities associated with a more diverse microbiome, less aggressive tumor phenotype, and better survival that reveal the genera as potential pancancer biomarkers for favorable microbiomes in ACC and other head and neck cancers. Conversely, gut-like intratumoral microbiomes, which feature low diversity and colonization by gut mucus layer-degrading species, such as Bacteroides, Akkermansia, Blautia, Bifidobacterium, and Enterococcus, were associated with poorer outcomes. Elevated levels of Bacteroides thetaiotaomicron were independently associated with significantly worse survival and positively correlated with tumor cell biosynthesis of glycan-based cell membrane components.

Abstract Background The Bric-a-Brac/Tramtrack/Broad Complex (BTB) gene family plays essential roles in various biological processes in plants. These genes encode proteins that contain a conserved BTB domain, which is involved in protein-protein interactions and regulation of gene expression. However, there is no systematic reports on the BTB gene family in G.max. Results In total, 122 soybean BTB genes were identified, which were classified into four groups based on the phylogenetic analysis. Gene structures analysis indicated that the number of exon–intron in GmBTBs ranges from 0 to18. Cis-element analysis revealed that most GmBTB genes contained cis-elements related to an abiotic stress response. In addition, qRT-PCR analyses indicated that most GmBTBs are significantly up-regulated under salinity, drought, and nitrate stresses. They suggested their potential for targeted improvement of soybean response to multiple abiotic stresses and nitrate availability. Conclusion These results provide valuable information for identifying the members of the GmBTB gene family in soybean and could provide a functional characterization of GmBTB genes in further research.

Abstract Light-emitting diodes (LEDs) based on perovskite semiconductor materials with tunable emission wavelength in visible light range as well as narrow linewidth are potential competitors among current light-emitting display technologies, but still suffer from severe instability driven by electric field. Here, we develop a stable, efficient and high-color purity hybrid LED with a tandem structure by combining the perovskite LED and the commercial organic LED technologies to accelerate the practical application of perovskites. Perovskite LED and organic LED with close photoluminescence peak are selected to maximize photon emission without photon reabsorption and to achieve the narrowed emission spectra. By designing an efficient interconnecting layer with p-type interface doping that provides good opto-electric coupling and reduces Joule heating, the resulting green emitting hybrid LED shows a narrow linewidth of around 30 nm, a peak luminance of over 176,000 cd m−2, a maximum external quantum efficiency of over 40%, and an operational half-lifetime of over 42,000 h.

Abstract N 6-methyladenosine (m6A) plays critical roles in regulating mRNA metabolism. However, comprehensive m6A methylomes in different plant tissues with single-base precision have yet to be reported. Here, we present transcriptome-wide m6A maps at single-base resolution in different tissues of rice and Arabidopsis using m6A-SAC-seq. Our analysis uncovers a total of 205,691 m6A sites distributed across 22,574 genes in rice, and 188,282 m6A sites across 19,984 genes in Arabidopsis. The evolutionarily conserved m6A sites in rice and Arabidopsis ortholog gene pairs are involved in controlling tissue development, photosynthesis and stress response. We observe an overall mRNA stabilization effect by 3’ UTR m6A sites in certain plant tissues. Like in mammals, a positive correlation between the m6A level and the length of internal exons is also observed in plant mRNA, except for the last exon. Our data suggest an active m6A deposition process occurring near the stop codon in plant mRNA. In addition, the MTA-installed plant mRNA m6A sites correlate with both translation promotion and translation suppression, depicting a more complicated regulatory picture. Our results therefore provide in-depth resources for relating single-base resolution m6A sites with functions in plants and uncover a suppression-activation model controlling m6A biogenesis across species.

Abstract Background Pleural fluid is one of the common complications of thoracic diseases, and tuberculous pleural effusion (TPE) is the most common cause of pleural effusion in TB-endemic areas and the most common type of exudative pleural effusion in China. In clinical practice, distinguishing TPE from pleural effusion caused by other reasons remains a relatively challenging issue. The objective of present study was to explore the clinical significance of the pleural fluid lactate dehydrogenase/adenosine deaminase ratio (pfLDH/pfADA) in the diagnosis of TPE. Methods The clinical data of 618 patients with pleural effusion were retrospectively collected, and the patients were divided into 3 groups: the TPE group (412 patients), the parapneumonic pleural effusion (PPE) group (106 patients), and the malignant pleural effusion (MPE) group (100 patients). The differences in the ratios of pleural effusion-related and serology-related indicators were compared among the three groups, and receiver operating characteristic curves were drawn to analyze the sensitivity and specificity of the parameter ratios of different indicators for the diagnosis of TPE. Results The median serum ADA level was higher in the TPE group (13 U/L) than in the PPE group (10 U/L, P

Abstract Neoantigens derived from somatic mutations in Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), the most frequently mutated oncogene, represent promising targets for cancer immunotherapy. Recent research highlights the potential role of human leukocyte antigen (HLA) allele A*11:01 in presenting these altered KRAS variants to the immune system. In this study, we successfully generate and identify murine T-cell receptors (TCRs) that specifically recognize KRAS8–16 G12V from three predicted high affinity peptides. By determining the structure of the tumor-specific 4TCR2 bound to KRASG12V-HLA-A*11:01, we conduct structure-based design to create and evaluate TCR variants with markedly enhanced affinity, up to 15.8-fold. This high-affinity TCR mutant, which involved only two amino acid substitutions, display minimal conformational alterations while maintaining a high degree of specificity for the KRASG12V peptide. Our research unveils the molecular mechanisms governing TCR recognition towards KRASG12V neoantigen and yields a range of affinity-enhanced TCR mutants with significant potential for immunotherapy strategies targeting tumors harboring the KRASG12V mutation.

Abstract Background The COVID-19 pandemic have impacts on the prevalence of other pathogens and people’s social lifestyle. This study aimed to compare the pathogen, allergen and micronutrient characteristics of pediatric inpatients with pneumonia prior to and during the COVID-19 pandemic in a large tertiary hospital in Shanghai, China. Methods Patients with pneumonia admitted to the Department of Pediatric Pulmonology of Xinhua Hospital between March-August 2019 and March-August 2020 were recruited. And clinical characteristics of the patients in 2019 were compared with those in 2020. Results Hospitalizations for pneumonia decreased by 74% after the COVID-19 pandemic. For pathogens, virus, mycoplasma pneumoniae (MP) and mixed infection rates were all much lower in 2020 than those in 2019 (P

Abstract Background Hamartoma is a common benign tumor that usually occurs in the kidney, liver, lung, and pancreas. Large renal hamartomas may spontaneously rupture and hemorrhage, which is potentially life-threatening. Case presentation This report describes a 46-year-old Han Chinese female patient with multiple renal and hepatic hamartomas with rupture and hemorrhage of giant hamartoma in the left kidney. She underwent arterial embolization three times successively, and her condition was stable during the 2-year follow-up. This report includes a review of the relevant literature Conclusions the findings in this report and previous literature suggest that arterial embolization can not only rapidly treat hamartoma hemorrhage in the acute phase but can also effectively control multiple lesions in the long term after repeated multisite arterial embolization.

Abstract While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

Spikelet degeneration is a critical physiological issue that limits grain yield in rice (Oryza sativa L.), influenced by soil moisture conditions during meiosis. The study aimed to investigate the role and mechanism of moderate soil drying in spikelet degeneration and grain yield, as well as to establish a strategy and irrigation regime for suppressing spikelet degeneration to increase grain yield in rice. Field experiments were conducted involving two irrigation regimes: conventional well-watered (C-WW) and moderate soil drying (M-SD) during meiosis. Transgenic rice lines and chemical regulators were employed to elucidate the underlying partial biological mechanisms of this process. The results showed that M-SD regime effectively reduced spikelet degeneration rate and increased grain yield compared to C-WW. This improvement under M-SD regime was primarily attributed to the enhanced proline and aquaporin-mediated osmotic balance and redox homeostasis in young rice panicles, as well as the increased root activity during meiosis. The increased levels of brassinosteroids (BRs) and decreased levels of ethylene (ETH) in young panicles under the M-SD were closely associated with the enhanced proline and aquaporin-mediated osmotic balance and redox homeostasis, decreased oxidative damage, and reduced spikelet degeneration rate. The intrinsic relationship among key aquaporin genes expression and proline levels, osmotic balance and redox homeostasis, spikelet degeneration rate, as well as BRs and ETH levels, was further confirmed through the use of transgenic rice lines and chemical regulators. Collectively, an M-SD regime during meiosis can effectively suppress spikelet degeneration and thereby enhance grain yield, primarily through well-maintained osmotic balance and redox homeostasis in rice.

This paper establishes a high-fidelity and efficient Computational Fluid Dynamics (CFD) numerical method (AL-LDS-Ωnew) for wind turbine wake by combining the actuator line (AL), the localized dynamic Smagorinsky (LDS) sub-grid scale (SGS), and the new generation Ωnew vortex identification method under the framework of large eddy simulation. The model advantages are encouraging: 1) In terms of turbine modeling, the AL model is adopted to replace the traditional three-dimensional solid model, which avoids solving the boundary layer on the blade surface and improves computational efficiency; 2) In terms of wake simulation, the LDS SGS model is used to model turbulence, reducing vortex dissipation and further improving the refinement of turbine wake; 3) In terms of vortex identification, the new generation Ωnew vortex identification method avoids the difficult threshold selection in previous vortex identification and captures more refined vortex structures. The accuracy of the model is validated against published data of a NREL 5 MW wind turbine, and then extended to simulate the wake interference of tandem twin-rotor turbines by changing the pitch angle of the upstream wind turbine (WT1). The influence mechanisms between array wake interference and energy conversion efficiency under the pitch strategy are explored, demonstrating the AL-LDS-Ωnew coupling method is computationally accurate and efficient for simulating the complex wake interference. From analyses, the pitch strategy can effectively suppress the wake effect of the upstream turbine (WT1) and increase the power output of the downstream turbine (WT2), thus improving the overall output power of the array farm. Compared with the non-pitch condition (0 pitch angle), a pitch angle of (2°) maximizes the global energy conversion efficiency of the twin-rotor array: power augmentation by 0.29%, and thrust reduction by 5%. This optimal state reduces the fatigue load of the turbine and is more conducive to long-term operation. The findings, whilst preliminary, encourage the use of turbine pitch strategies in the wind farm planning and operation.

The response of Hypoxia Inducible Gene Domain (HIGD) proteins to hypoxia plays a crucial role in plant development. However, the research on this gene family in soybean has been lacking. In this study, we aimed to identify and comprehensively analyze soybean HIGD genes using the Glycine max genome database. As a result, six GmHIGD genes were successfully identified, and their phylogeny, gene structures, and putative conserved motifs were analyzed in comparison to Arabidopsis and rice. Collinearity analysis indicated that the HIGD gene family in soybean has expanded to some extent when compared to Arabidopsis. Additionally, the cis-elements in the promoter regions of GmHIGD and the transcription factors potentially binding to these regions were identified. All GmHIGD genes showed specific responsiveness to submergence and hypoxic stresses. Expression profiling through quantitative real-time PCR revealed that these genes were significantly induced by PEG treatment in root tissue. Co-expressed genes of GmHIGD were primarily associated with oxidoreductase and dioxygenase activities, as well as peroxisome function. Notably, one of GmHIGD genes, GmHIGD3 was found to be predominantly localized in mitochondria, and its overexpression in Arabidopsis led to a significantly reduction in catalase activity compared to wild-type plants. These results bring new insights into the functional role of GmHIGD in terms of subcellular localization and the regulation of oxidoreductase activity.

[Introduction] Under the background of "carbon peak and neutrality" target and "ocean power" strategy, the integration of ocean renewable energy represented by offshore wind power and ocean resources represented by marine ranch is the new direction, new situation and new opportunity for the future development and utilization of ocean resources. The article reviews and analyzes the integrated development mode and key technical problems of offshore wind power and marine pasture, in order to provide technical references to the relevant research and engineering projects in China. [Method] Taking the integration of China's marine resources development needs as the guide, the global development trend of offshore wind power and marine ranch integration was analyzed, and the current situation and shortcomings of China's wind-fishery integration development were clarified. On this basis, the integration mode, equipment design, engineering cases and key technical problems of the integration development of offshore wind power and ocean ranch were analyzed in detail. Finally, a future outlook on the integration development of offshore wind power and ocean ranch in China was discussed. [Result] Through the analysis of the integrated development mode and key technical problems of the integration of offshore wind power and marine ranch development, the bottlenecks and technical challenges are clarified, and the key breakthrough directions are pointed out, providing scientific guidance and reference basis for the comprehensive development and utilization of offshore resources in China. [Conclusion] The article illustrates the necessity and feasibility of integrating the development of offshore wind power and marine ranch, and proposes a feasible way to integrate offshore wind power and marine ranch in China.

Jasmonic acid is a crucial phytohormone that plays a pivotal role, serving as a regulator to balancing plant development and resistance. However, there are analogous and distinctive characteristics exhibited in JA biosynthesis, perception, and signal transduction pathways in both herbaceous and woody plants. Moreover, the majority of research subjects have predominantly focused on the function of JA in model or herbaceous plants. Consequently, there is a significant paucity of studies investigating JA regulation networks in woody plants, particularly concerning post-transcriptional regulatory events such as alternative splicing (AS). This review article aims to conduct a comprehensive summary of advancements that JA signals regulate plant development across various woody species, comparing the analogous features and regulatory differences to herbaceous counterparts. In addition, we summarized the involvement of AS events including splicing factor (SF) and transcripts in the JA regulatory network, highlighting the effectiveness of high-throughput proteogenomic methods. A better understanding of the JA signaling pathway in woody plants has pivotal implications for forestry production, including optimizing plant management and enhancing secondary metabolite production.

Abstract Noncontact humidity sensor overcomes the limitations of its contact sensing counterparts, including mechanical wear and cross infection, which becomes a promising candidate in healthcare and human-machine interface application. However, current humidity sensors still suffer the ubiquitous issue of uncomfortable wear and skin irritation hindering the long-term use. In this study, we report a skin-conformal and breathable humidity sensor assembled by anchoring MXenes-based composite into electrospun elastomer nanofibers coated with a patterned electrode. This composite is highly sensitive to the water molecules due to its large specific surface area and abundant water-absorbing hydroxyl groups, while the elastomeric nanofibers provide an ultrathin, highly flexible, and permeable substrate to support the functional materials and electrodes. This sensor presents not only excellent air permeability (0.078 g cm−2 d−1), high sensitivity (S = 704), and fast response/recovery (0.9 s/0.9 s), but also high skin conformability and biocompatibility. Furthermore, this humidity sensor is confirmed to realize the recognition of motional states and emotional modes, which provides a way for the advanced noncontact human-machine interface.

Abstract Objective Rheumatoid arthritis (RA) is a chronic, progressive autoimmune disease with a complex pathogenesis that has not yet been fully elucidated, and T-cell pyroptosis is an important pathogenetic factor in RA. This study aimed to investigate the role of endoplasmic reticulum aminopeptidase 2 (ERAP2) in the pyroptosis of CD4+ T cells in RA and the specific molecular mechanism. Methods Peripheral venous blood was collected from human subjects, and CD4+ T cells were isolated and activated to measure the level of pyroptosis and ERAP2 expression. Pyroptosis levels were assessed using immunofluorescence, flow cytometry, qRT-PCR, and Western blotting. Changes in pyroptosis levels were observed upon knockdown or overexpression of ERAP2. To detect activated Caspase-1 in tissues, chimeric mice were engrafted with human synovial tissue and reconstituted with human CD4+ T cells. CD4 + T cells were treated with GLI1 antagonists and SMO receptor agonists to detect changes in pyroptosis levels. Results CD4+ T cell levels undergoing pyroptosis were found to be elevated in the blood and synovium of RA patients. The gene and protein expression of ERAP2 were significantly higher in CD4+ T cells from RA patients. Deletion of ERAP2 suppressed pyroptosis of these cells, attenuated the activation of Caspase-1 in tissue T cells, and reduced tissue inflammatory responses. Reciprocally, overexpression of ERAP2 triggered inflammasome assembly, activated Caspase-1, and induced pyroptosis in CD4+ T cells. Mechanistically, ERAP2 inhibits the Hedgehog signaling pathway and upregulates the expression of nucleotide-binding oligomerization segment-like receptor family 3(NLRP3), cleaved Caspase-1, and Gasdermin D to promote pyroptosis in CD4+ T cells. Conclusions Taken together, our results identify a novel mechanism by which ERAP2 regulates RA development and document the effect of the ERAP2/Hedgehog signaling axis on pyroptosis of CD4+ T cells from RA patients.

This paper reports a performance optimized operational amplifier (OPAMP) using transconductance enhancement topology based on the amorphous indium- gallium-zinc-oxide (a-IGZO) thin-film transistors (TFTs). The performance of TFTs is enhanced by N2O plasma treatment that presents electrical characteristics suitable for accomplishing an OPAMP. The circuit consists of 19 TFTs with measured phase margin (PM) and unity-gain frequency (UGF) of 35.8° and 200 kHz, respectively. The DC power consumption (PDC) is 0.68 mW. Notably, it exhibits a high voltage gain (Av) of 32.67 dB and bandwidth (BW) of 15 kHz with 15 V DC supply voltage. Scarcely any work was reported with such a high gain while having a sufficient BW. The OPAMP demonstrates excellent performance among all a-IGZO literature and provides substantial support for the future development of TFT-based integrated circuits (ICs).

Al2O3, as one of the gate dielectric materials for thin film transistors (TFTs), has been extensively investigated because of its large bandgap, high breakdown field, and good thermal stability. However, the further development of Al2O3 thin films is limited by the presence of defects such as oxygen vacancies, self-interstitial atoms, or impurity elements. To overcome this obstacle, we have developed a novel method for fabricating Al2O3 thin films by using the atomic layer deposition (ALD) technique. This method replaces the conventional Trimethylaluminium (TMA)/H2O cycles with TMA/TMA/H2O/H2O cycles (referred to as ‘double cycles’), to deposit the Al2O3. The 5-nm ultrathin Al2O3 film showed a high areal capacitance of 660 nF/cm2 at 20 Hz, and a relatively low current density of 10−8 A/cm2 at 1 MV/cm. InGaZnO (IGZO) TFTs with ultrathin Al2O3 gate dielectric grown by double cycles exhibited outstanding performances, such as a near theoretical limit subthreshold swing (SS) of 70 mV/decade, a higher on/off current ratio $(I_{\mathrm{ on}} / I_{\mathrm{ off}}$ ) of 106, an increased field-effect mobility ( $\mu $ ) of 6.5 cm2/Vs, a lower threshold voltage $(V_{\mathrm{ th}})$ of 0.2 V, and a low operating voltage of 3 V. These results are superior to the IGZO TFTs with Al2O3 dielectrics deposited using the single TMA/H2O cycle. Therefore, the implementation of ‘double cycles’ in the fabrication of dielectrics through ALD demonstrates considerable potential for future application in low-power electronic devices.

The 6 GHz band plays a crucial role in the development of the 6G. A profound comprehension of channel reciprocity is essential for designing time division duplexing/frequency division duplexing (TDD/FDD) systems within this band. Firstly, in an indoor corridor scenario, precise and impartial measurements are conducted for both the uplink (UL) and downlink (DL) channels in the 6 GHz band; A denoising algorithm is proposed to extract multipath components (MPCs) from the measurement data, enabling a more equitable assessment of channel reciprocity; Then, a comprehensive analysis of channel reciprocity has been conducted, focusing on four aspects: path loss, delay spread, cluster-based correlation coefficient (CBCC), and multipath power dissimilarity (MPD). The findings indicate that TDD systems demonstrate nearly perfect reciprocity, whereas FDD systems exhibit partial reciprocity in indoor scenarios. Specifically, in TDD systems, the CBCCs between UL and DL exceed 95%, while in FDD systems, they fluctuate between 80% and 90%. Additionally, a model has been provided to depict the relationship between MPD and center frequency, as well as frequency interval; Finally, a comparative analysis of measured and ray-tracing simulated results reveals the presence of numerous public MPCs, which share the same propagation delay and spatial angle between the UL and DL in FDD systems, as well as private MPCs that exist exclusively in either the UL or DL. They collectively influence the channel reciprocity.

Background: Pediatric pneumonia presents a significant global health challenge, particularly in low- and middle-income countries. This study aimed to investigate the incidence of pneumonia in preschool children in Urumqi and its association with indoor environmental factors. Methods: This case-control study collected data from December 2018 to December 2019 on 1522 preschool children in Urumqi (779 boys and 743 girls) who were diagnosed with pneumonia by a physician. A control group of children who had never had pneumonia was matched in a 1:1 ratio based on gender, age, and ethnicity. Using questionnaires, data were collected on children's general characteristics, passive smoking, types of housing, flooring materials, and indoor dampness, analyzing potential factors associated with the incidence of pediatric pneumonia. Results: Multivariate analysis revealed that cesarean birth (odds ratio [OR] = 1.27; 95 % confidence interval [95%CI] = 1.08–1.48), being an only child (OR = 1.32; 95%CI = 1.13–1.55), antibiotic treatment during the first year of life (OR = 2.51; 95%CI = 1.98–3.19), passive smoking during the mother's pregnancy (OR = 1.62; 95%CI = 1.24–2.13), living in multi-family apartment housing (OR = 1.64; 95%CI = 1.28–2.10) and other types of housing (OR = 1.47; 95%CI = 1.09–1.99), laminate flooring (OR = 1.31; 95%CI = 1.01–1.72), and tile/stone/cement flooring flooring (OR = 1.31; 95%CI = 1.06–1.61), and dampness in dwelling (during first year of mother's pregnancy) (OR = 1.30; 95%CI = 1.04–1.63) were risk factors for pediatric pneumonia. The use of fresh air filtration systems in children's residences (OR = 0.66; 95%CI = 0.50–0.86) was identified as a protective factor. Conclusion: This study underscores the importance of indoor environmental factors in the prevention of pediatric pneumonia. Public health strategies should consider these factors to reduce the incidence of pneumonia in children. Future research needs to be conducted over a broader geographical range and consider a more comprehensive range of factors influencing pediatric pneumonia.

Abstract Background Lung cancer remains the foremost reason of cancer‐related mortality, with invasion and metastasis profoundly influencing patient prognosis. N‐acetyltransferase 10 (NAT10) catalyzes the exclusive N (4)‐acetylcytidine (ac4C) modification in eukaryotic RNA. NAT10 dysregulation is linked to various diseases, yet its role in non‐small cell lung cancer (NSCLC) invasion and metastasis remains unclear. Our study delves into the clinical significance and functional aspects of NAT10 in NSCLC. Methods We investigated NAT10's clinical relevance using The Cancer Genome Atlas (TCGA) and a group of 98 NSCLC patients. Employing WB, qRT‐PCR, and IHC analyses, we assessed NAT10 expression in NSCLC tissues, bronchial epithelial cells (BECs), NSCLC cell lines, and mouse xenografts. Further, knockdown and overexpression techniques (siRNA, shRNA, and plasmid) were employed to evaluate NAT10's effects. A series of assays were carried out, including CCK‐8, colony formation, wound healing, and transwell assays, to elucidate NAT10's role in proliferation, invasion, and metastasis. Additionally, we utilized lung cancer patient‐derived 3D organoids, mouse xenograft models, and Remodelin (NAT10 inhibitor) to corroborate these findings. Results Our investigations revealed high NAT10 expression in NSCLC tissues, cell lines and mouse xenograft models. High NAT10 level correlated with advanced T stage, lymph node metastasis and poor overall survive. NAT10 knockdown curtailed proliferation, invasion, and migration, whereas NAT10 overexpression yielded contrary effects. Furthermore, diminished NAT10 levels correlated with increased E‐cadherin level whereas decreased N‐cadherin and vimentin expressions, while heightened NAT10 expression displayed contrasting results. Notably, Remodelin efficiently attenuated NSCLC proliferation, invasion, and migration by inhibiting NAT10 through the epithelial‐mesenchymal transition (EMT) pathway. Conclusions Our data underscore NAT10 as a potential therapeutic target for NSCLC, presenting avenues for targeted intervention against lung cancer through NAT10 inhibition.

Cotton, a vital textile raw material, is intricately linked to people’s livelihoods. Throughout the cotton cultivation process, various diseases threaten cotton crops, significantly impacting both cotton quality and yield. Deep learning has emerged as a crucial tool for detecting these diseases. However, deep learning models with high accuracy often come with redundant parameters, making them challenging to deploy on resource-constrained devices. Existing detection models struggle to strike the right balance between accuracy and speed, limiting their utility in this context. This study introduces the CDDLite-YOLO model, an innovation based on the YOLOv8 model, designed for detecting cotton diseases in natural field conditions. The C2f-Faster module replaces the Bottleneck structure in the C2f module within the backbone network, using partial convolution. The neck network adopts Slim-neck structure by replacing the C2f module with the GSConv and VoVGSCSP modules, based on GSConv. In the head, we introduce the MPDIoU loss function, addressing limitations in existing loss functions. Additionally, we designed the PCDetect detection head, integrating the PCD module and replacing some CBS modules with PCDetect. Our experimental results demonstrate the effectiveness of the CDDLite-YOLO model, achieving a remarkable mean average precision (mAP) of 90.6%. With a mere 1.8M parameters, 3.6G FLOPS, and a rapid detection speed of 222.22 FPS, it outperforms other models, showcasing its superiority. It successfully strikes a harmonious balance between detection speed, accuracy, and model size, positioning it as a promising candidate for deployment on an embedded GPU chip without sacrificing performance. Our model serves as a pivotal technical advancement, facilitating timely cotton disease detection and providing valuable insights for the design of detection models for agricultural inspection robots and other resource-constrained agricultural devices.

BackgroundThe interplay between gut microbiota and metabolites in the early stages of sepsis-induced acute kidney injury (SA-AKI) is not yet clearly understood. This study explores the characteristics and interactions of gut microbiota, and blood and urinary metabolites in patients with SA-AKI.MethodsUtilizing a prospective observational approach, we conducted comparative analyses of gut microbiota and metabolites via metabolomics and metagenomics in individuals diagnosed with SA-AKI compared to those without AKI (NCT06197828). Pearson correlations were used to identify associations between microbiota, metabolites, and clinical indicators. The Comprehensive Antibiotic Resistance Database was employed to detect antibiotic resistance genes (ARGs), while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways informed on metabolic processes and microbial resistance patterns.ResultsOur study included analysis of four patients with SA-AKI and five without AKI. Significant disparities in bacterial composition were observed, illustrated by diversity indices (Shannon index: 2.0 ± 0.4 vs. 1.4 ± 0.6, P = 0.230; Simpson index: 0.8 ± 0.1 vs. 0.6 ± 0.2, P = 0.494) between the SA-AKI group and the non-AKI group. N6, N6, N6-Trimethyl-L-lysine was detected in both blood and urine metabolites, and also showed significant correlations with specific gut microbiota (Campylobacter hominis and Bacteroides caccae, R > 0, P < 0.05). Both blood and urine metabolites were enriched in the lysine degradation pathway. We also identified the citrate cycle (TCA cycle) as a KEGG pathway enriched in sets of differentially expressed ARGs in the gut microbiota, which exhibits an association with lysine degradation.ConclusionsSignificant differences in gut microbiota and metabolites were observed between the SA-AKI and non-AKI groups, uncovering potential biomarkers and metabolic changes linked to SA-AKI. The lysine degradation pathway may serve as a crucial link connecting gut microbiota and metabolites.

Compute‐in‐memory (CIM) is a pioneering approach using parallel data processing to eliminate traditional data transmission bottlenecks for faster, energy‐efficient data handling. Crossbar arrays with two‐terminal devices such as memristors and phase‐change memory are commonly employed in CIM, but they encounter challenges such as leakage current and increased power usage. Three‐terminal transistor arrays have potential solutions, yet large‐scale electrolyte‐gated transistors (EGTs) demonstrations are uncommon due to compatibility issues with existing photolithography processes. Herein, a 20 × 20 EGTs array is designed using indium‐gallium‐zinc‐oxide as the semiconductor channel and polyacrylonitrile (PAN) doped with C2F6LiNO4S2 as the electrolyte. Each transistor unit in the array can serve as a synapse, exhibiting a large conductance range, low energy consumption (6.984 fJ) for read–write operations, excellent repeatability, and quasilinear update characteristics. It has been confirmed that the EGTs array not only enables precise device programming but also virtually eliminates signal interference between neighboring devices during the programming process. Using 54 transistors in the EGTs array, unsupervised learning with a winner‐takes‐all neural network is successfully demonstrated. After 50 training iterations, the neural network achieves perfect 100% accuracy in classifying test‐set letters. The work demonstrates the potential of EGTs for constructing large‐scale integration synaptic array toward efficient computing architectures.

Nitrogen (N) is the major nutrient limiting rice (Oryza sativa L.) production. Avoiding excessive N use without compromising yields has long been a priority for both research and government policy in China. Nitrogen use efficiency (NUE) reflects the relationship between dry matter production or economic yield and the quantity of N applied. As an important organ for nutrient absorption, the root system of rice is closely related to NUE. We hypothesized that NUE could be improved by optimizing the root system. Here, we described and compared the evaluation methods of NUE in rice and discussed the relationship of rice root morphological (quantity, weight, length, surface area, number, diameter, volume, distribution, and structure) and physiological (root oxidation activity, active absorbing area or rate, hormones, composition and concentration of root bleeding and root exudates, nutrition concentration, and NH4+/NH3− flux) characteristics with N absorption and utilization. The key agronomic practices were proposed to improve NUE from several aspects, including adoption of N-efficient varieties and improvements in N and water management, farming systems, and soil properties. Further research is needed to clarify the root-soil interface principle based on efficient N uptake, reveal the root-shoot interaction mechanism based on efficient N utilization, and explore the integrative management strategy of high yield and efficient N utilization through maximizing the root system of rice. Our objective in this review is to provide ideas to further exploit potentialities of the root system through variety selection and cultivation management to improve NUE.

Abstract The development of advanced sustainable biomedical materials with superior biosafety and bioactivity for clinical applications is highly desirable. In the present investigation, biomass-based nanoparticles (NPs) were assembled through the Mannich reaction between the plant polyphenols and the broad-spectrum antibiotic tigecycline (TG). The fabricated NPs with uniform size demonstrated excellent oxidative balance effects, pH-responsive release properties and antibacterial performances. Furthermore, the intracellular and in vivo studies confirmed that the NPs are capable of reducing oxidative damage to cells and significantly repairing tissue injury in mice with peritonitis. This work presents an effective method and idea for constructing biomass-based materials for the treatment of infection-induced diseases. Graphical Abstract

Abstract Mitochondria morphology and function, and their quality control by mitophagy, are essential for heart function. We investigated whether these are influenced by time of the day (TOD), sex, and fed or fasting status, using transmission electron microscopy (EM), mitochondrial electron transport chain (ETC) activity, and mito-QC reporter mice. We observed peak mitochondrial number at ZT8 in the fed state, which was dependent on the intrinsic cardiac circadian clock, as hearts from cardiomyocyte-specific BMAL1 knockout (CBK) mice exhibit different TOD responses. In contrast to mitochondrial number, mitochondrial ETC activities do not fluctuate across TOD, but decrease immediately and significantly in response to fasting. Concurrent with the loss of ETC activities, ETC proteins were decreased with fasting, simultaneous with significant increases of mitophagy, mitochondrial antioxidant protein SOD2, and the fission protein DRP1. Fasting-induced mitophagy was lost in CBK mice, indicating a direct role of BMAL1 in regulating mitophagy. This is the first of its kind report to demonstrate the interactions between sex, fasting, and TOD on cardiac mitochondrial structure, function and mitophagy. These studies provide a foundation for future investigations of mitochondrial functional perturbation in aging and heart diseases.

Abstract Background To study whether the four locus gene model consisting of ADRB2 rs1042713, IL4 rs2243250, FCER1B rs569108 and L13 rs20541 can predict asthma of the Kazak children in Xinjiang, China. Methods Four single nucleotide polymorphisms about the 4 genes were genotyped in asthma group and control group of Han children and Kazak children respectively. The frequencies of different genotypes and alleles were compared between the asthma group and the control group in the two nationalities. Different risk genotypes for asthma were evaluated in the two nationalities. Results The differences about frequencies of genotypes in ADRB2 rs1042713 and IL4 rs2243250 and IL13 rs20541 between asthma group and control group were statistically significant in Han children, as were the frequencies of alleles in the 3 single nucleotide polymorphisms, but there were no statistical differences in FCER1B rs569108(P > 0.05). For the Kazak children, no differences were existed among all the genotypes and alleles in asthma group and control group. For the Han children, more children were asthma high risk genotype in the asthma group than those in the control group and no difference was found in the Kazak children. Conclusions The four locus gene model consisting of ADRB2 rs1042713, IL4 rs2243250, FCER1B rs569108 and L13 rs20541 can predict asthma of Han children but not for the Kazak children in Xinjiang, which illustrating that the difference of asthma prevalence between different races is closely related to the genetic background.

Abstract Background Sestrin2 is a highly conserved stress-inducible protein with neuroprotective properties. Herein, we investigated the prognostic significance of serum sestrin2 in human intracerebral hemorrhage (ICH). Methods In this prospective observational longitudinal study, we enrolled 126 patients with supratentorial ICH as cases together with 126 healthy individuals as controls. Severity indicators were National Institutes of Health Stroke Scale (NIHSS) and hematoma volume. Prognostic parameters were early neurologic deterioration (END) and post-stroke 6-month poor prognosis [modified Rankin Scale (mRS) scores of 3–6]. Multivariate analysis was performed to assess relations of serum sestrin2 levels to severity and prognosis. Results Patients had statistically significantly higher serum sestrin2 levels than controls. Serum sestrin2 levels of patients were independently correlated with NIHSS scores and hematoma volume, as well as were substantially elevated in order of mRS scores from 0 to 6. Serum sestrin2 was identified as an independent predictor of END and poor prognosis. Based on the receiver operating characteristic curve, serum sestrin2 had a similar predictive ability for END and poor prognosis, as compared to NIHSS scores and hematoma volume. Prediction models of END and poor prognosis, in which serum sestrin2, NIHSS scores and hematoma volume were integrated, were visually described via nomogram, were reliable and stable under calibration curve and were of clinical benefit using decision curve analysis. Also, prediction model of poor prognosis showed dramatically higher discriminatory efficiency than any of NIHSS scores, hematoma volume and serum sestrin2. Conclusion Serum sestrin2 levels, which are obviously increased following acute ICH, are independently related to illness severity and poor clinical outcomes, substantializing serum sestrin2 as a clinically valuable prognostic biomarker of ICH.

Objective: To evaluate the efficacy and safety of Baimai ointment in patients with lumbar disc herniation (LDH). Methods: This study presents a prospective randomized, double-blind, placebo-controlled, multicenter clinical trial protocol. A total of 194 cases will be employed, with a 1:1 allocation ratio for each group. Patients will receive either Baimai ointment or placebo over a 14-day treatment period, which will be followed by a 1-week follow-up period. Visual analogue scale scores will be used for the level of pain, the Japanese Orthopedic Association score will be measured primarily to determine the functional status, the Likert scale will be graded for the level of numbness, and rescue therapy use and compliance with health education will be recorded. Laboratory tests and adverse event records will also be applied for safety assessments. Discussion: In summary, this trial will assess the clinical efficacy and safety of Baimai ointment for LDH for the first time. The use of a placebo has the advantage of precluding anticipatory biases resulting from inadequate blinding. Outcome assessors, data managers, statisticians, and all related study staff will be blinded to avoid any bias caused by subjective factors in the study subjects and investigators. Valuable information for clinical LDH treatment and future research on Baimai ointment will be generated from the findings.

Abstract Current approaches in myocardial infarction treatment are limited by low cellular oxidative stress resistance, reducing the long-term survival of therapeutic cells. Here we develop a liquid-crystal substrate with unique surface properties and mechanical responsiveness to produce size-controllable cardiospheres that undergo pyroptosis to improve cellular bioactivities and resistance to oxidative stress. We perform RNA sequencing and study cell metabolism to reveal increased metabolic levels and improved mitochondrial function in the preconditioned cardiospheres. We test therapeutic outcomes in a rat model of myocardial infarction to show that cardiospheres improve long-term cardiac function, promote angiogenesis and reduce cardiac remodeling during the 3-month observation. Overall, this study presents a promising and effective system for preparing a large quantity of functional cardiospheres, showcasing potential for clinical application.

The lysosome is responsible for protein and organelle degradation and homeostasis and the cathepsins play a key role in maintaining protein quality control. Cathepsin D (CTSD), is one such lysosomal protease, which when deficient in humans lead to neurolipofuscinosis (NCL) and is important in removing toxic protein aggregates. Prior studies demonstrated that CTSD germ-line knockout-CtsdKO (CDKO) resulted in accumulation of protein aggregates, decreased proteasomal activities, and postnatal lethality on Day 26 ± 1. Overexpression of wildtype CTSD, but not cathepsin B, L or mutant CTSD, decreased α-synuclein toxicity in worms and mammalian cells. In this study we generated a mouse line expressing human CTSD with a floxed STOP cassette between the ubiquitous CAG promoter and the cDNA. After crossing with Nestin-cre, the STOP cassette is deleted in NESTIN + cells to allow CTSD overexpression-CTSDtg (CDtg). The CDtg mice exhibited normal behavior and similar sensitivity to sub-chronic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced neurodegeneration. By breeding CDtg mice with CDKO mice, we found that over-expression of CTSD extended the lifespan of the CDKO mice, partially rescued proteasomal deficits and the accumulation of Aβ42 in the CDKO. This new transgenic mouse provides supports for the key role of CTSD in protecting against proteotoxicity and offers a new model to study the role of CTSD enhancement in vivo.

Due to a wide band gap and large exciton binding energy, zinc oxide (ZnO) is currently receiving much attention in various areas, and can be prepared in various forms including nanorods, nanowires, nanoflowers, and so on. The reliability of ZnO produced by a single dopant is unstable, which in turn promotes the development of co-doping techniques. Co-doping is a very promising technique to effectively modulate the optical, electrical, magnetic, and photocatalytic properties of ZnO, as well as the ability to form various structures. In this paper, the important advances in co-doped ZnO nanomaterials are summarized, as well as the preparation of co-doped ZnO nanomaterials by using different methods, including hydrothermal, solvothermal, sol-gel, and acoustic chemistry. In addition, the wide range of applications of co-doped ZnO nanomaterials in photocatalysis, solar cells, gas sensors, and biomedicine are discussed. Finally, the challenges and future prospects in the field of co-doped ZnO nanomaterials are also elucidated.

In this work, zinc oxide (ZnO)/granular activated carbon (GAC) composites at different ZnO concentrations (0.25M-ZnO@GAC, 0.5M-ZnO@GAC, and 0.75M-ZnO@GAC) were prepared by an in-situ hydrothermal method and demonstrated synergistic photocatalytic degradation and adsorption of rhodamine B (RhB). The thermal stability, morphological structure, elemental composition, crystallographic structure, and textural properties of developed catalysts were characterized by thermal gravimetric analysis (TGA/DTG), scanning electron microscopy equipped with energy dispersive-x-ray (SEM-EDS), X-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) analysis. The successful loading of ZnO onto GAC was confirmed by SEM-EDS and XRD analysis. The BET surface areas of GAC, 0.25M-ZnO@GAC, 0.5M-ZnO@GAC, and 0.75M-ZnO@GAC were 474 m2/g, 450 m2/g, 453 m2/g, and 421 m2/g, respectively. The decrease in GAC could be attributed to the successful loading of ZnO on the GAC surface. Notably, 0.5M-ZnO@GAC exhibited the best photocatalytic degradation efficiency of 82% and 97% under UV-A and UV-C light over 120 min, attributed to improved crystallinity and visible light absorption. The photocatalytic degradation parameters revealed that lowering the RhB concentration and raising the catalyst dosage and pH beyond the point of zero charge (PZC) would favor the RhB degradation. Photocatalytic reusability was demonstrated over five cycles. Scavenger tests revealed that the hydroxyl radicals (•OH), superoxide radicals (O2−•), and photoinduced hole (h+) radicals play a major role during the RhB degradation process. Based on the TOC results, the RhB mineralization efficiency of 79.1% was achieved by 0.5M-ZnO@GAC. Additionally, GAC exhibited a strong adsorptive performance towards RhB, with adsorption capacity and the RhB removal of 487.1 mg/g and 99.5% achieved within 90 min of equilibrium time. The adsorption characteristics were best described by pseudo-second-order kinetics, suggesting chemical adsorption. This research offers a new strategy for the development of effective photocatalyst materials with potential for wider wastewater treatment applications.

In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true label data for major crops in China, and the visual interpretation process is usually time-consuming and laborious. The sample size also makes it difficult to support the model to learn enough ground features, resulting in poor generalisation ability of the model, which in turn makes the model difficult to apply in fine extraction tasks of large-area crops. In this study, a method to establish a pseudo-label sample set based on the random forest algorithm to train a semantic segmentation model (U-Net) was proposed to perform winter wheat extraction. With the help of the GEE platform, Winter Wheat Canopy Index (WCI) indicators were employed in this method to initially extract winter wheat, and training samples (i.e., pseudo labels) were built for the semantic segmentation model through the iterative process of “generating random sample points—random forest model training—winter wheat extraction”; on this basis, the U-net model was trained with multi-time series remote sensing images; finally, the U-Net model was employed to obtain the spatial distribution map of winter wheat in Henan Province in 2022. The results illustrated that: (1) Pseudo-label data were constructed using the random forest model in typical regions, achieving an overall accuracy of 97.53% under validation with manual samples, proving that its accuracy meets the requirements for U-Net model training. (2) Utilizing the U-Net model, U-Net++ model, and random forest model constructed based on pseudo-label data for 2022, winter wheat mapping was conducted in Henan Province. The extraction accuracy of the three models is in the order of U-Net model > U-Net++ model > random forest model. (3) Using the U-Net model to predict the winter wheat planting areas in Henan Province in 2019, although the extraction accuracy decreased compared to 2022, it still exceeded that of the random forest model. Additionally, the U-Net++ model did not achieve higher classification accuracy. (4) Experimental results demonstrate that deep learning models constructed based on pseudo-labels exhibit higher classification accuracy. Compared to traditional machine learning models like random forest, they have higher spatiotemporal adaptability and robustness, further validating the scientific and practical feasibility of pseudo-labels and their generation strategies, which are expected to provide a feasible technical pathway for intelligent extraction of winter wheat spatial distribution information in the future.

Abstract Industrial defect detection is an important part of intelligent manufacturing, and Internet of things (IoT)‐based defect detection is receiving more and more attention. Although deep learning (DL) can help defect detection reduce the cost and improve the accuracy of traditional manual quality inspection, DL requires huge computational resources and is difficult to be simply deployed on IoT devices with limited computational power and memory resources. Digital signal processor (DSP) is an important IoT device with small size, high performance and low energy consumption, which has been widely used in intelligent manufacturing. In order to perform accurate defect detection on DSP, the authors proposed various optimisation strategies and then used a parallel scheme to scale the model to execute on multiple cores. The authors’ method evaluated it on Northeastern University Surface Defect Dataset, Magnetic Tile Defect Dataset, Rail Surface Defect Dataset and Silk Cylinder Defect Dataset, and the experimental results showed that the authors’ method obtains faster speeds without accuracy loss compared to running the same Convolutional Neural Networks model on a mainstream desktop CPU. This means that the authors’ method can realise efficient and accurate defect detection on IoT devices with limited computational power and memory resources, which opens up new possibilities for future development in the field of smart manufacturing.

Background The prevention and prediction of sudden cardiac death (SCD) present persistent challenges, prompting exploration into common genetic variations for potential insights. T-box 5 (TBX5), a critical cardiac transcription factor, plays a pivotal role in cardiovascular development and function. This study systematically examined variants within the 500-bp region downstream of the TBX5 gene, focusing on their potential impact on susceptibility to SCD associated with coronary artery disease (SCD-CAD) in four different Chinese Han populations. Methods In a comprehensive case-control analysis, we explored the association between rs11278315 and SCD-CAD susceptibility using a cohort of 553 controls and 201 SCD-CAD cases. Dual luciferase reporter assays and genotype-phenotype correlation studies using human cardiac tissue samples as well as integrated in silicon analysis were applied to explore the underlining mechanism. Result Binary logistic regression results underscored a significantly reduced risk of SCD-CAD in individuals harboring the deletion allele (odds ratio = 0.70, 95% CI [0.55–0.88], p = 0.0019). Consistent with the lower transcriptional activity of the deletion allele observed in dual luciferase reporter assays, genotype-phenotype correlation studies on human cardiac tissue samples affirmed lower expression levels associated with the deletion allele at both mRNA and protein levels. Furthermore, our investigation revealed intriguing insights into the role of rs11278315 in TBX5 alternative splicing, which may contribute to alterations in its ultimate functional effects, as suggested by sQTL analysis. Gene ontology analysis and functional annotation further underscored the potential involvement of TBX5 in alternative splicing and cardiac-related transcriptional regulation. Conclusions In summary, our current dataset points to a plausible correlation between rs11278315 and susceptibility to SCD-CAD, emphasizing the potential of rs11278315 as a genetic risk marker for aiding in molecular diagnosis and risk stratification of SCD-CAD.

Background: Environmental factors serve as one of the important pathogenic factors for gliomas. Yet people focus only on the effect of electromagnetic radiation on its pathogenicity, while metals in the environment are neglected. This study aimed to investigate the relationship between metal ion stimulation and the clinical characteristics and immune status of GM patients. Methods: Firstly, mRNA expression profiles of GM patients and normal subjects were obtained from Chinese GM Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) to identify differentially expressed metal ion stimulation-related genes(DEMISGs). Secondly, two molecular subtypes were identified and validated based on these DEMISGs using consensus clustering. Diagnostic and prognostic models for GM were constructed after screening these features based on machine learning. Finally, supervised classification and unsupervised clustering were combined to classify and predict the grade of GM based on SHAP values. Results: GM patients are divided into two different response states to metal ion stimulation, M1 and M2, which are related to the grade and IDH status of the GM. Six genes with diagnostic value were obtained: SLC30A3, CRHBP, SYT13, DLG2, CDK1, and WNT5A. The AUC in the external validation set was higher than 0.90. The SHAP value improves the performance of classification prediction. Conclusion: The gene features associated with metal ion stimulation are related to the clinical and immune characteristics of transgenic patients. XGboost/LightGBM Kmeans has a higher classification prediction accuracy in predicting glioma grades compared to using purely supervised classification techniques.

The remarkable skill of changing its grasp status and relocating its fingers to perform continuous in-hand manipulation is essential for a multifingered anthropomorphic hand. A commonly utilized method of manipulation involves a series of basic movements executed by a high-level controller. However, it remains unclear how these primitives evolve into sophisticated finger gaits during manipulation. Here, we propose an adaptive finger gait-based manipulation method that offers real-time regulation by dynamically changing the primitive interval to ensure the force/moment balance of the object. Successful manipulation relies on contact events that act as triggers for real-time online replanning of multifinger manipulation. We identify four basic motion primitives of finger gaits and create a heuristic finger gait that enables the continuous object rotation of a round cup. Our experimental results verify the effectiveness of the proposed method. Despite the constant breaking and reengaging of contact between the fingers and the object during manipulation, the robotic hand can reliably manipulate the object without failure. Even when the object is subjected to interfering forces, the proposed method demonstrates robustness in managing interference. This work has great potential for application to the dexterous operation of anthropomorphic multifingered hands.

Human organotypic bone models are an emerging technology that replicate bone physiology and mechanobiology for comprehensive in vitro experimentation over prolonged periods of time. Recently, we introduced a mineralized bone model based on 3D bioprinted cell-laden alginate-gelatin-graphene oxide hydrogels cultured under dynamic loading using commercially available human mesenchymal stem cells. In the present study, we created cell-laden scaffolds from primary human osteoblasts isolated from surgical waste material and investigated the effects of a previously reported optimal cell printing density (5 × 106 cells/mL bioink) vs. a higher physiological cell density (10 × 106 cells/mL bioink). We studied mineral formation, scaffold stiffness, and cell morphology over a 10-week period to determine culture conditions for primary human bone cells in this microenvironment. For analysis, the human bone-derived cell-laden scaffolds underwent multiscale assessment at specific timepoints. High cell viability was observed in both groups after bioprinting (>90%) and after 2 weeks of daily mechanical loading (>85%). Bioprinting at a higher cell density resulted in faster mineral formation rates, higher mineral densities and remarkably a 10-fold increase in stiffness compared to a modest 2-fold increase in the lower printing density group. In addition, physiological cell bioprinting densities positively impacted cell spreading and formation of dendritic interconnections. We conclude that our methodology of processing patient-specific human bone cells, subsequent biofabrication and dynamic culturing reliably affords mineralized cell-laden scaffolds. In the future, in vitro systems based on patient-derived cells could be applied to study the individual phenotype of bone disorders such as osteogenesis imperfecta and aid clinical decision making.

Diseases cause crop yield reduction and quality decline, which has a great impact on agricultural production. Plant disease recognition based on computer vision can help farmers quickly and accurately recognize diseases. However, the occurrence of diseases is random and the collection cost is very high. In many cases, the number of disease samples that can be used to train the disease classifier is small. To address this problem, we propose a few-shot disease recognition algorithm that uses supervised contrastive learning. Our algorithm is divided into two phases: supervised contrastive learning and meta-learning. In the first phase, we use a supervised contrastive learning algorithm to train an encoder with strong generalization capabilities using a large number of samples. In the second phase, we treat this encoder as an extractor of plant disease features and adopt the meta-learning training mechanism to accomplish the few-shot disease recognition tasks by training a nearest-centroid classifier based on distance metrics. The experimental results indicate that the proposed method outperforms the other nine popular few-shot learning algorithms as a comparison in the disease recognition accuracy over the public plant disease dataset PlantVillage. In few-shot potato leaf disease recognition tasks in natural scenarios, the accuracy of the model reaches the accuracy of 79.51% with only 30 training images. The experiment also revealed that, in the contrastive learning phase, the combination of different image augmentation operations has a greater impact on model. Furthermore, the introduction of label information in supervised contrastive learning enables our algorithm to still obtain high accuracy in few-shot disease recognition tasks with smaller batch size, thus allowing us to complete the training with less GPU resource compared to traditional contrastive learning.

Pulmonary embolism and massive hemoptysis caused by intravascular foreign bodies have rarely been reported. We report a case of an end-stage renal disease patient in which the tip of the angiographic catheter fell off into the pulmonary artery during endovascular interventional opening when the patient underwent vascular access occlusion for dialysis. During the operation, the foreign body was displaced repeatedly and finally anchored to the posterior basal segment branch of the right lower pulmonary artery. A pulmonary embolism occurred during the operation, and massive hemoptysis and hemorrhagic shock occurred after anticoagulation and thrombolytic therapy. After receiving anti-shock and symptomatic treatment, the patient gradually recovered. After six months of follow-up, no pulmonary embolism or pulmonary infarction occurred. Our case report presents an alternative approach to extracting a foreign object from the pulmonary artery by locating the foreign object within the vascular terminations, without resorting to forceful removal. This method mitigates the potential risks of pulmonary embolism and bleeding associated with forceful extraction.

This research aimed to investigate the mediating function of Green Employee Empowerment (GEE) in the relationship between Green Human Resource Management practices (GHRM) and the environmental performance of small and medium-sized enterprises (SMEs) in Ghana, drawing on the Ability-Motivation-Opportunity (AMO) theory. This study assessed the hypotheses in the established research model using structural equation modeling based on data collected from 320 participants from small and medium-sized firms in Ghana. The study's results revealed that GHRM practices were significantly correlated with the firm's environmental performance. The study found significant GHRM's indirect consequences on environmental performance through GEE in all models examined. These findings suggest that GEE plays a crucial role in translating the impact of GHRM practices into improved environmental performance. The study overlooked other potential mediators or moderators in the relationship between GHRM practices and environmental performance, focusing on GEE. To better understand the complex dynamics behind GHRM techniques' environmental performance, future research might examine business culture, leadership style, and employee sustainability attitudes.

Seed germination rate is one of the important indicators in measuring seed quality and seed germination ability, and it is also an important basis for evaluating the growth potential and planting effect of seeds. In order to detect seed germination rates more efficiently and achieve automated detection, this study focuses on wild rice as the research subject. A novel method for detecting wild rice germination rates is introduced, leveraging the SGR-YOLO model through deep learning techniques. The SGR-YOLO model incorporates the convolutional block attention module (efficient channel attention (ECA)) in the Backbone, adopts the structure of bi-directional feature pyramid network (BiFPN) in the Neck part, adopts the generalized intersection over union (GIOU) function as the loss function in the Prediction part, and adopts the GIOU function as the loss function by setting the weighting coefficient to accelerate the detection of the seed germination rate. In the Prediction part, the GIOU function is used as the loss function to accelerate the learning of high-confidence targets by setting the weight coefficients to further improve the detection accuracy of seed germination rate. The results showed that the accuracy of the SGR-YOLO model for wild rice seed germination discrimination was 94% for the hydroponic box and 98.2% for the Petri dish. The errors of germination potential, germination index, and average germination days detected by SGR-YOLO using the manual statistics were 0.4%, 2.2, and 0.9 days, respectively, in the hydroponic box and 0.5%, 0.5, and 0.24 days, respectively, in the Petri dish. The above results showed that the SGR-YOLO model can realize the rapid detection of germination rate, germination potential, germination index, and average germination days of wild rice seeds, which can provide a reference for the rapid detection of crop seed germination rate.

Abstract The artificial synapse array with an electrolyte‐gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo‐lithography. This study presents a scheme based on a lateral‐gate structure to realize high‐density integration of EGTs and proposes the integration of 100 × 100 EGTs into a 2.5 × 2.5 cm2 glass, with a unit density of up to 1600 devices cm−2. Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 × 10−3 S cm−1 owing to the porosity of zeolitic imidazolate frameworks‐67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.

KASP marker technology has been used in molecular marker-assisted breeding because of its high efficiency and flexibility, and an intelligent evaluation model of KASP marker primer typing results is essential to improve the efficiency of marker development on a large scale. To this end, this paper proposes a gene population delineation method based on NTC identification module and data distribution judgment module to improve the accuracy of K-Means clustering, and introduces a decision tree to construct the KASP-IEva primer typing evaluation model. The model firstly designs the NTC identification module and data distribution judgment module to extract four types of data, grouping and categorizing to achieve the improvement of the distinguishability of amplification product signals; secondly, the K-Means algorithm is used to aggregate and classify the data, to visualize the five aggregated clusters and to obtain the morphology location eigenvalues; lastly, the evaluation criteria for the typing effect level are constructed, and the logical decision tree is used to make conditional discrimination on the eigenvalues in order to realize the score prediction. The performance of the model was tested by the KASP marker typing test results of 2519 groups of cotton varieties, and the following conclusions were obtained: the model is able to visualize the aggregation and classification effects of the amplification products of NTC, pure genotypes, heterozygous genotypes, and untyped genotypes, enabling rapid and accurate KASP marker typing evaluation. Comparing and analyzing the model evaluation results with the expert evaluation results, the average accuracy rate of the four grades evaluated by the model was 87%, and the overall evaluation results showed an uneven distribution of the grades with significant differential characteristics. When evaluating 2519 KASP fractal maps, the expert evaluation consumes 15 hours, and the model evaluation only uses 8min27.45s, which makes the model intelligent evaluation significantly better than the expert evaluation from the perspective of time. The establishment of the model will further enhance the application of KASP markers in molecular marker-assisted breeding and provide technical support for the large-scale screening and identification of excellent genotypes.