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Abstract Ectomycorrhizal fungi (EMF) play pivotal roles in determining temperate forest ecosystem processes. We tracked root EMF community succession across saplings, juveniles, and adults of three temperate broadleaf trees (Acer mono, Betula platyphylla, and Quercus mongolica) in Northeast China. Adult stages showed higher alpha diversity but lower community dissimilarity compared to earlier stages. In particular, the EMF alpha diversity of Quercus mongolica marginally increased along with host developmental stages and ranked as sapling 99%). LEfSe was employed to identify stage- and/or host-specific EMF indicators. This study highlighted the assembly of EMF community during the development of broadleaf trees in temperate forests, thereby advancing understanding of the succession and coevolution of symbiotic relationships.

Antibiotics are effective in treating bacterial infections, but they also disrupt the gut microbiota, thereby causing side effects such as diarrhea, flatulence and indigestion. Probiotics and fecal microbiota transplantation (FMT) have attracted widespread attention due to their ability to restore the balance of the gut microbiota and alleviate various conditions. In this study, the mechanism of action of FMT and the probiotic Lacticaseibacillus paracasei HNU502 in alleviating the side effects of cefotaxime, a common antibiotic, using combination of metagenomics, pathological analysis and gas chromatography. The findings suggested that FMT alleviated antibiotic side effects by restoring the disturbed gut microbiota, while HNU502 worked by reestablishing the balance of the gut microbiota. Both FMT and probiotic supplementation improved gut microbiota composition, but to varying degrees, prevented goblet cell loss, and increased the contents of mucin-2 (MUC-2) and zonula occludens-1 (ZO-1), thus aiding gut mucosal recovery. Moreover, the two approaches ameliorated the metabolic disorders of short-chain fatty acids, inhibited the secretion of pro-inflammatory cytokines such as interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α as well as blood urea nitrogen (BUN), and effectively reduced virulence factor enrichment. This study contributes to a better understanding of the link between human health and gut microbiota, and provides new therapeutic options for mitigating antibiotic side effects, which is of great significance for improving patients’ health status and quality of life.

This paper proposes a novel fast attitude fault-tolerant control (FTC) scheme for over-actuated hypersonic flight vehicles experiencing actuator failures, such as stuck faults and loss of effectiveness faults. The FTC scheme comprises two main components: a systematic algorithm in the control layer and a control allocation method in the reconfiguration layer, working together to achieve fault tolerance. Drawing inspiration from model predictive static programming, a step-by-step algorithm is developed to quickly resolve control torque under fault conditions, ensuring prescribed tracking performance. The vehicle system's coupling and uncertain disturbances are treated as total disturbance and estimated in real-time using an extended state observer. In the reconfiguration layer, a pseudo-inverse-based control allocation method is utilized to distribute the desired control torque across redundant rudder actuators. The proposed FTC scheme is applied to a hypersonic flight vehicle system subject to stuck faults, loss of effectiveness faults, modeling errors, and external disturbances. Simulation results are presented to demonstrate the superiority and effectiveness of the proposed FTC scheme. Owing to its fast computational speed, the scheme holds potential for online implementation, enabling rapid reconstruction of aerodynamic rudder actuators during failures.

Pilots face complex working environments during flight missions, which can easily lead to excessive workload and affect flight safety. Physiological signals are commonly used to evaluate a pilot’s workload because they are objective and can directly reflect physiological mental states. However, existing methods have shortcomings in temporal modeling, making it challenging to fully capture the dynamic characteristics of physiological signals over time. Moreover, fusing features of data from different modalities is also difficult.To address these problems, we proposed a temporal relation modeling and multimodal adversarial alignment network (TRM-MAAN) for pilot workload evaluation. Specifically, a Transformer-based temporal relationship modeling module was used to learn complex temporal relationships for better feature extraction. In addition, an adversarial alignment-based multi-modal fusion module was applied to capture and integrate multi-modal information, reducing distribution shifts between different modalities. The performance of the proposed TRM-MAAN method was evaluated via experiments of classifying three workload states using electroencephalogram (EEG) and electromyography (EMG) recordings of eight healthy pilots.Experimental results showed that the classification accuracy and F1 score of the proposed method were significantly better than the baseline model across different subjects, with an average recognition accuracy of $91.90~\pm ~1.72\%$ and an F1 score of $91.86~\pm ~1.75\%$ .This work provides essential technical support for improving the accuracy and robustness of pilot workload evaluation and introduces a promising way for enhancing flight safety, offering broad application prospects. Clinical and Translational Impact Statement: The proposed scheme provides a promising solution for workload evaluation based on electrophysiological signals, with potential applications in aiding the clinical monitoring of fatigue, mental status, cognitive psychology, and other disorders.

Abstract Background Benign and malignant breast tumors differ in their microvasculature morphology and distribution. Histologic biomarkers of malignant breast tumors are also correlated with the microvasculature. There is a lack of imaging technology for evaluating the microvasculature. Ultrasound localization microscopy (ULM) can provide detailed microvascular architecture at super-resolution. The objective of this trial is to explore the role of ULM in distinguishing benign from malignant breast tumors and to explore the correlations between ULM qualitative and quantitative parameters and histologic biomarkers in malignant breast tumors. Methods/design This prospective and multicenter study will include 83 patients with breast tumors that will undergo ULM. 55 patients will be assigned to the malignant group, and 28 patients will be assigned to the benign group. The primary outcome is the differences in the qualitative parameters (microvasculature morphology, distribution, and flow direction) between benign and malignant breast tumors on ULM. Secondary outcomes include (1) differences in the quantitative parameters (microvasculature density, tortuosity, diameter, and flow velocity) between benign and malignant breast tumors based on ULM; (2) diagnostic performance of the qualitative parameters in distinguishing benign and malignant breast tumors; (3) diagnostic performance of the quantitative parameters in distinguishing benign and malignant breast tumors; (4) relationships between the qualitative parameters and histologic biomarkers in malignant breast tumors; (5) relationships between the quantitative parameters and histologic biomarkers in malignant breast tumors; and (6) the evaluation of inter-reader and intra-reader reproducibility. Discussion Detecting vascularity in breast tumors is of great significance to differentiate benign from malignant tumors and to predict histologic biomarkers. These histologic biomarkers, such as ER, PR, HER2 and Ki67, are closely related to prognosis evaluation. This trial will provide maximum information about the microvasculature of breast tumors and thereby will help with the formulation of subsequent differential diagnosis and the prediction of histologic biomarkers. Trial registration number/date Chinese Clinical Trial Registry ChiCTR2100048361/6th/July/2021. This study is a part of that clinical trial.

Abstract Background The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this setting and limits the dose reduction potential. This study investigated the feasibility of a deep learning iterative reconstruction algorithm (Deep IR) in reducing the radiation dose while improving the image quality for abdominal computed tomography (CT) with low tube voltage and current. Methods Sixty patients (male/female, 36/24; Age, 57.72 ± 10.19 years) undergoing the abdominal portal venous phase CT were randomly divided into groups A (100 kV, automatic exposure control [AEC] with reference tube-current of 213 mAs) and B (80 kV, AEC with reference of 130 mAs). Images were reconstructed via hybrid iterative reconstruction (HIR) and Deep IR (levels 1–5). The mean CT and standard deviation (SD) values of four regions of interest (ROI), i.e. liver, spleen, main portal vein and erector spinae at the porta hepatis level in each image serial were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The image quality was subjectively scored by two radiologists using a 5-point criterion. Results A significant reduction in the radiation dose of 69.94% (5.09 ± 0.91 mSv vs. 1.53 ± 0.37 mSv) was detected in Group B compared with Group A. After application of the Deep IR, there was no significant change in the CT value, but the SD gradually increased. Group B had higher CT values than group A, and the portal vein CT values significantly differed between the groups (P

Technique: Hepatocellular adenoma (HCA) is a benign monoclonal tumour that originates from mature hepatocytes.Liver resection is recommended in case of overt malignant transformation to hepatocellular carcinoma.However, hepatobiliary surgeries are technically challenging in patients with giant HCA (GHCA) owing to the risk of catastrophic intraoperative bleeding and difficulty with its control during laparoscopic treatment. We present a technical note on the utilization of the hepatic vein as anatomical landmarks for laparoscopic removal of giant hepatic glands, without intraoperative ultrasonography and with the aid of an augmented reality navigation system during surgery. Results: This video shows aA 37-year-old man was recommended treatment for a progressively increasing HCA (from 3 to 10 cm in a year) involving the right hepatic vein (RHV), inferior vena cava (IVC) and middle hepatic vein (MHV), resulting in the invisibility of the above intrahepatic anatomic markers in CT. Laparoscopic hepatectomy was performed using the hepatic vein as anatomic markers in a treatment centre specialising in minimally invasive surgeries. The procedure involved fully mobilising the right liver, transecting the parenchyma along the demarcation line in the caudal-to-cranial direction, exposing the involved caudal MHV, isolating and transecting the involved RHV and preserving the integrity of the involved IVC. Conclusions: Laparoscopic hepatectomy for intractable GHCA using the involved intrahepatic anatomic markers is feasible and effective. It reduces pre-operative haemorrhage and open conversion rates while maximising postoperative hepatic function.

Abstract The Suminoe oyster Crassostrea ariakensis (Fujita, 1913) is one of the most important ecological and fishery bivalve mollusks with a worldwide distribution. Here, we reported an improved high-quality chromosomal-level genome assembly of C. ariakensis inhabiting the South China Sea, using Nanopore technology, Illumina sequencing, and high-throughput chromosomal conformation capture analysis. The assembled genome size is 631.73 Mb, with contig N50 length of 5.36 Mb and scaffold N50 length of 61.15 Mb, and is assigned to 10 chromosomes. A total of 29,357 protein-coding genes are predicted, 96.68% of which are functionally annotated. The genome contains 347.11 Mb (54.94%) of repetitive elements and 1130 noncoding RNAs. This improved genome assembly of south C. ariakensis is an important resource for understanding oyster diversity and evolution, and provides insights into genetic improvement, protection and management of oyster resource.

Abstract The Chinese Grouse (Tetrastes sewerzowi), a rare endemic bird in China, belongs to the Galliformes order and Tetraonidae family. Despite extensive research, subspecies classification remains inadequate, especially in Pingwu, Sichuan. This study sequenced the mitochondrial genome of T. sewerzowi in Wanglang Nature Reserve, Sichuan, analyzed its structural features, and reconstructed phylogenetic relationships within Tetraonidae. The results indicate that the high conservation in mitochondrial genome structure and sequence between the T. sewerzowi from Wanglang, Sichuan and Lianhuashan, Gansu indicates that they may belong to the same subspecies. Furthermore, we observed significant differences in the length of the mitochondrial control region between T. sewerzowi and Tetrastes bonasia. Based on the phylogenetic tree constructed using a comprehensive mitochondrial dataset, we propose the reclassification of the Hazel Grouse genus into two independent genera: Tetrastes and Bonasa. This study is the first to sequence the mitochondrial genome of T. sewerzowi from Sichuan and compare it with populations from Gansu, providing important insights into the distribution pattern of T. sewerzowi subspecies and facilitating the formulation of effective conservation strategies.

Abstract Fat accumulation, de novo lipogenesis, and glycolysis are key drivers of hepatocyte reprogramming and the consequent metabolic dysfunction-associated steatotic liver disease (MASLD). Here we report that obesity leads to dysregulated expression of hepatic protein-tyrosine phosphatases (PTPs). PTPRK was found to be increased in steatotic hepatocytes in both humans and mice, and correlates positively with PPARγ-induced lipogenic signaling. High-fat-fed PTPRK knockout male and female mice have lower weight gain and reduced hepatic fat accumulation. Phosphoproteomic analysis in primary hepatocytes and hepatic metabolomics identified fructose-1,6-bisphosphatase 1 and glycolysis as PTPRK targets in metabolic reprogramming. Mechanistically, PTPRK-induced glycolysis enhances PPARγ and lipogenesis in hepatocytes. Silencing PTPRK in liver cancer cell lines reduces colony-forming capacity and high-fat-fed PTPRK knockout mice exposed to a hepatic carcinogen develop smaller tumours. Our study defines the role of PTPRK in the regulation of hepatic glycolysis, lipid metabolism, and tumour development in obesity.

Abstract Loss to follow-up (LTFU) in tuberculosis (TB) management increases morbidity and mortality, challenging effective control strategies. This study aims to develop and evaluate machine learning models to predict loss to follow-up in TB patients, improving treatment adherence and outcomes. Retrospective data encompassing tuberculosis patients who underwent treatment or registration at the National Center for Clinical Medical Research on Infectious Diseases from January 2017 to December 2021 were compiled. Employing machine learning techniques, namely SVM, RF, XGBoost, and logistic regression, the study aimed to prognosticate LTFU. A comprehensive cohort of 24,265 tuberculosis patients underwent scrutiny, revealing a LTFU prevalence of 12.51% (n = 3036). Education level, history of hospitalization, alcohol consumption, outpatient admission, and prior tuberculosis history emerged as precursors for pre-treatment LTFU. Employment status, outpatient admission, presence of chronic hepatitis/cirrhosis, drug adverse reactions, alternative contact availability, and health insurance coverage exerted substantial influence on treatment-phase LTFU. XGBoost consistently surpassed alternative models, boasting superior discriminative ability with an average AUC of 0.921 for pre-treatment LTFU and 0.825 for in-treatment LTFU. Our study demonstrates that the XGBoost model provides superior predictive performance in identifying LTFU risk among tuberculosis patients. The identification of key risk factors highlights the importance of targeted interventions, which could lead to significant improvements in treatment adherence and patient outcomes.

IntroductionBetula tianschanica Rupr. is distributed in regions such as China, Kyrgyzstan, and Tajikistan. Owing to the impacts of climate change, it is increasingly threatened by habitat fragmentation, resulting in a precipitous decline in its population. Currently listed as endangered on the Red List of Trees of Central Asia, this species is predominantly found in the Tianshan Mountains. Examining the influence of climate change on the geographical distribution pattern of Betula tianschanica is crucial for the management and conservation of its wild resources.MethodsThis study employed two models, maximum entropy (MaxEnt) and random forest (RF), combined with 116 distribution points of Betula tianschanica and 27 environmental factor variables, to investigate the environmental determinants of the distribution of Betula tianschanica and project its potential geographical distribution areas.ResultsThe MaxEnt model and the RF model determined the primary environmental factors influencing the potential distribution of Betula tianschanica. The MaxEnt model showed that the percentage of gravel volume in the lower soil layer and elevation are the most significant, while the RF model considered elevation and precipitation of the wettest quarter to be the most crucial. Both models unanimously asserted that elevation is the pivotal environmental element affecting the distribution of Betula tianschanica.The mean area under the curve (AUC) scores for the MaxEnt model and RF were 0.970 and 0.873, respectively, revealing that the MaxEnt model outperformed the RF model in predictive accuracy. Consequently, the present study employed the estimated geographical area for Betula tianschanica modeled by the MaxEnt model as a reference. Following the MaxEnt model’s projected outcomes, Betula tianschanica is mainly located in territories such as the Tianshan Mountains, Ili River Basin, Lake Issyk-Kul, Turpan Basin, Irtysh River, Ulungur River, Bogda Mountains, Kazakh Hills, Lake Balkhash, Amu River, and the middle reaches of the Syr River.Within the MaxEnt model, the total suitable habitat area exhibits growth across all scenarios, with the exception of a decline observed during the 2041–2060 period under the SSP2-4.5 scenario. Remarkably, under the SSP58.5 scenario for the same timeframe, this area expands significantly by 42.7%. In contrast, the RF model demonstrated relatively minor fluctuations in the total suitable habitat area, with the highest recorded increase being 12.81%. This paper recommends establishing protected areas in the Tianshan Mountains, conducting long-term monitoring of its population dynamics, and enhancing international cooperation. In response to future climate change, climate refuges should be established and adaptive management implemented to ensure the survival and reproduction of Betula tianschanica.

Chlorophyll-a (Chl-a) plays a vital role in assessing environmental health and understanding the response of marine ecosystems to physical factors and climate change. In situ sampling, remote sensing, and moored buoys or floats are commonly employed methods for obtaining Chl-a in marine science research. Although in situ sampling, buoys, and floats could provide accurate data, they are limited by the spatial and temporal resolution. Remote sensing offers continuous and broad spatial coverage, while it is often hindered by cloud cover in the South China Sea (SCS). This study discussed the feasibility of a predictive model by linking the physical factors [e.g., wind field, surface currents, sea surface height (SSH), and sea surface temperature (SST)] with surface Chl-a in the SCS based on the ResUnet. The ResUnet architecture performs well in capturing non-linear relationships between variables, with the model achieving a prediction accuracy exceeding 90%. The results indicate that (1) the combination of oceanic dynamical and meteorological data could effectively estimate the Chl-a based on deep learning methods; (2) the combination of meteorological and SST effectively reproduces Chl-a in the northern SCS, while adding surface currents and SSH improves model performance in the southern SCS; (3) With the addition of surface currents and SSH, the model effectively captures the high Chl-a patches induced by eddies. This research presents a viable method for estimating surface Chl-a concentrations in regions where they are highly correlated with dynamic factors, using deep learning and comprehensive oceanic and atmospheric data.

Atrial fibrillation (AF) is the most common tachyarrhythmia and seriously affects human health. Key targets of AF bioinformatics analysis can help to better understand the pathogenesis of AF and develop therapeutic targets. The left atrial appendage tissue of 20 patients with AF and 10 patients with sinus rhythm were collected for sequencing, and the expression data of the atrial tissue were obtained. Based on this, 2578 differentially expressed genes were obtained through differential analysis. Different express genes (DEGs) were functionally enriched on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), mainly focusing on neuroactive ligand-receptor interactions, neuronal cell body pathways, regulation of neurogenesis, and neuronal death, regulation of neuronal death, etc. Secondly, 14 significant module genes were obtained by analyzing the weighted gene co-expression network of DEGs. Next, LASSO and SVM analyzes were performed on the differential genes, and the results were in good agreement with the calibration curve of the nomogram model for predicting AF constructed by the weighted gene co-expression network key genes. The significant module genes obtained by the area under the ROC curve (AUC) analysis were analyzed. Through crossover, two key disease characteristic genes related to AF, HOXA2 and RND2, were screened out. RND2 was selected for further research, and qPCR verified the expression of RND2 in sinus rhythm patients and AF patients. Patients with sinus rhythm were significantly higher than those in AF patients. Our research indicates that RND2 is significantly associated with the onset of AF and can serve as a potential target for studying its pathogenesis.

Currently, ethanol detection in alcoholic beverages typically relies on complicated and costly approaches, including gas chromatography, liquid chromatography, and mass spectrometry, etc. Herein, we developed a non-toxicity, practical, low-cost and portable visual indicator for ethanol detection based on OCDs/I2O4 nanopowders. By color change, the OCDs/I2O4 can visually indicate ethanol concentration ranging from 14 % vol to 56 % vol and the detection limit can be further extended to 2 % vol with a UV-visible spectrum. The OCDs/I2O4 indicator shows good stability and long-term endurance for at least 18 months, and it can be used under different temperatures (at least from 3 °C to 40 °C) and humidity (at least from 10 % to 90 %). A possible mechanism for the color change has been proposed based on the covalent-link hydrogen bonding change between OCDs and I2O4. The proposed OCDs/I2O4 composite offers a convenient and practical solution for ethanol monitoring in various alcoholic beverages.

Secondary metabolites (SM) produced by filamentous fungi have been extensively applied in biomedicine, biological control, food, and other fields. The biosynthesis of most SMs is closely associated with the catalysis of non-ribosome peptide synthetase (NRPS), therefore the expression regulation of NRPS is crucial for SM biosynthesis. In order to further understand the expression regulation patterns of NRPS gene clusters in filamentous fungi, herein, the research progresses on the transcription factors regulating NRPS-mediated SM production are reviewed, of which both pathway-specific transcription factors and global transcription factors are individually descripted. The summarization is hoped to deepen the comprehension of NRPS-mediated SMs production regulated by transcription factors and provide a reference for development of natural products in filamentous fungi in the future.

An in-depth understanding of the bimodal scale particle reinforced composites, including the particle content and size, phase constitution and distribution, and their effects on mechanical properties and deformation mechanisms, is essential to advance the study of high performance high entropy alloys (HEAs). In this study, the bimodal-sized (nano + micron)(N + M)-Al2O3P/CoCrFeMnNi reinforced composites and monomadal-sized micron(M)-Al2O3P/CoCrFeMnNi reinforced composites were successfully fabricated using a two-step ball milling process in conjunction with spark plasma sintering (SPS) technology. Results showed that the Al2O3P/CoCrFeMnNi composites primarily consisted of fcc structure, with Al2O3 and MnAl2O4 as secondary phases. The M-Al2O3P reinforced composites contained obvious heterogeneous structure while (N + M)-Al2O3P reinforced composites exhibited a multi-scale grain structure comprising of double-sized reinforcement and multiple-sized matrix grains. The yield strength (YS) of bimodal-size particle reinforced composites was higher than that of monomadal particle reinforced composites. For example, the YS of (N-5)/(M − 5) and (N-5)/(M − 10) composites was 117% and 133% higher than that of HEA, respectively. Intriguingly, (N + M)-Al2O3P reinforced composites effectively addressed the issue of reduced material strength caused by a high concentration of particles with a monomadal size. Moreover, the composites of (N-5)/(M − 5) and (N-5)/(M − 10) with a high content of lightweight Al2O3P effectively contributed to the reduction in density of HEA, exhibiting a decrease by 11% and 17% compared to pure HEA, respectively.

Abstract Objective To establish a practical risk stratification system (RSS) based on ultrasonography (US) and clinical characteristics for predicting soft tissue masses (STMs) malignancy. Methods This retrospective multicenter study included patients with STMs who underwent US and pathological examinations between April 2018 and April 2023. Chi-square tests and multivariable logistic regression analyses were performed to assess the association of US and clinical characteristics with the malignancy of STMs in the training set. The RSS was constructed based on the scores of risk factors and validated externally. Results The training and validation sets included 1027 STMs (mean age, 50.90 ± 16.64, 442 benign and 585 malignant) and 120 STMs (mean age, 51.93 ± 17.90, 69 benign and 51 malignant), respectively. The RSS was constructed based on three clinical characteristics (age, duration, and history of malignancy) and six US characteristics (size, shape, margin, echogenicity, bone invasion, and vascularity). STMs were assigned to six categories in the RSS, including no abnormal findings, benign, probably benign (fitted probabilities [FP] for malignancy: 0.001–0.008), low suspicion (FP: 0.008–0.365), moderate suspicion (FP: 0.189–0.911), and high suspicion (FP: 0.798–0.999) for malignancy. The RSS displayed good diagnostic performance in the training and validation sets with area under the receiver operating characteristic curve (AUC) values of 0.883 and 0.849, respectively. Conclusion The practical RSS based on US and clinical characteristics could be useful for predicting STM malignancy, thereby providing the benefit of timely treatment strategy management to STM patients. Critical relevance statement With the help of the RSS, better communication between radiologists and clinicians can be realized, thus facilitating tumor management. Key Points There is no recognized grading system for STM management. A stratification system based on US and clinical features was built. The system realized great communication between radiologists and clinicians in tumor management. Graphical Abstract

Abstract Chronic obstructive pulmonary disease (COPD) has been associated with alterations in the brain cortical structure. Nonetheless, the causality between COPD and brain cortical structure has not been determined. In the present study, we used Mendelian randomization (MR) analysis to explore the causal effects of genetic predicated COPD on brain cortical structure, namely cortical surface area (SA) and cortical thickness (TH). Genetic association summary data for COPD were obtained from the FinnGen consortium (N = 358,369; Ncase = 20,066). PRISm summary genetic data were retrieved from a case–control GWAS conducted in the UK Biobank (N = 296,282). Lung function indices, including forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC, were extracted from a meta-analysis of the UK Biobank and SpiroMeta consortium (N = 400,102). Brain cortical structure data were obtained from the ENIGMA consortium (N = 51,665). Inverse-variance weighted (IVW) method was used as the primary analysis, and a series of sensitivity tests were exploited to evaluate the heterogeneity and pleiotropy of our results. The results identified potential causal effects of COPD on several brain cortical specifications, including pars orbitalis, cuneus and inferior parietal gyrus. Furthermore, genetic predicated lung function index (FEV1, FVC and FEV1/FVC), as well as PRISm, also has causal effects on brain cortical structure. According to our results, a total of 15 functional specifications were influenced by lung function index and PRISm. These findings contribute to understanding the causal effects of COPD and lung function to brain cortical structure.

Abstract Osteosarcoma (OS) is a heterogeneous malignant spindle cell tumor that is aggressive and has a poor prognosis. Although combining surgery and chemotherapy has significantly improved patient outcomes, the prognosis for OS patients with metastatic or recurrent OS has remained unsatisfactory. Therefore, it is imperative to gain a fresh perspective on OS development mechanisms and treatment strategies. After studying single-cell RNA sequencing (scRNA-seq) data in public databases, we identified seven OS subclonal types based on intra-tumor heterogeneity. Subsequently, we constructed a prognostic model based on pro-protein synthesis osteosarcoma (PPS-OS)-associated genes. Correlation analysis showed that the prognostic model performs extremely well in predicting OS patient prognosis. We also demonstrated that the independent risk factors for the prognosis of OS patients were tumor primary site, metastatic status, and risk score. Based on these factors, nomograms were constructed for predicting the 3- and 5-year survival rates. Afterward, the investigation of the tumor immune microenvironment (TIME) revealed the vital roles of γδ T-cell and B-cell activation. Drug sensitivity analysis and immune checkpoint analysis identified drugs that have potential application value in OS. Finally, the jumping translocation breakpoint (JTB) gene was selected for experimental validation. JTB silencing suppressed the proliferation, migration, and invasion of OS cells. Therefore, our research suggests that PPS-OS-related genes facilitate the malignant progression of OS and may be employed as prognostic indicators and therapeutic targets in OS.

Abstract Aerosol jet printing has the potential to fabricate fine features on various substrates due to its large stand-off distance. However, the presence of overspray and instability, particularly at high printing resolutions, has limited its widespread application. In this study, we introduce an efficient approach called annular acoustic focusing for aerosol jet printing. By determining the optimal focusing frequency (5.8 MHz) for silver nanoparticles using a particle ejection model, we achieve precise and stable printing. We also propose a modified print nozzle geometry, resulting in ultrafine traces (line width

Graphene and its derivatives are widely used in various fields due to their unique two-dimensional lamellar structure. This study aims to synthesize ultra-large graphene oxide (GO) sheets from natural flake graphite and investigate the factors influencing their size. Using a two-intercalation method based on the modified Hummers' method, we address the challenge of intercalating large-diameter graphene oxide by employing a secondary intercalation technique. Three different approaches were explored to control the size of the produced GO sheets. The results revealed that the completeness of the expansion graphite structure after initial intercalation significantly influenced the final GO sheet size, with more complete expansion leading to larger sheets. Optimal processing conditions were identified, involving soaking natural flake graphite in a mixed solution (H2SO4:H2O2 = 4:1), followed by drying at 60 °C for 24 h. Under these conditions, ultra-large GO sheets were predominantly monolayer with an average size of 220.99 μm and a maximum size of 438 μm. These monolayer GO sheets can be chemically reduced to graphene, making them promising for applications in transparent conductive films, optoelectronic devices, and aligned graphene composites.

Background: There exists a robust correlation between the infiltration of immune cells and the pathogenesis of aortic dissection (AD). Moreover, blood metabolites serve as immunomodulatory agents within the organism, influencing the immune system’s response and potentially playing a role in the development of AD. Nevertheless, the intricate genetic causal nexus between specific immune cells, blood metabolites, and AD remains partially elucidated. Objectives: This study aims to elucidate the causal relationships between specific immune cell types and the risk of developing AD, mediated by blood metabolites, using Mendelian Randomization (MR) methods. Methods: We undertook a comprehensive investigation of 731 immune cell types through the analysis of published genome-wide association studies (GWAS). Our methodology hinged on the application of two-sample Mendelian randomization (MR) and mediator MR analyses, prioritizing blood metabolites as potential intermediary factors and AD as the principal outcome of interest. The primary statistical method employed was inverse variance-weighted estimation, complemented by a variety of sensitivity analyses to reinforce our conclusions. The entirety of our statistical analyses was executed on the R software platform. Results: Our analyses elucidated that three immune cell types exhibited a positive correlation with the incidence of AD, whereas two immune cell types were inversely associated with AD risk. Significantly, our mediation Mendelian randomization (MR) findings identified Benzoate as a pivotal mediator in the influence of CD19 on IgD − CD38br cells on AD, with a mediation proportion of 5.38 %. Additionally, N-acetylproline was determined to mediate the effect of CD24 on IgD- CD38- cells on AD, accounting for a mediation proportion of 13.70 %. Furthermore, Carnitine C5:1 was found to mediate the effect of CD28 on secreting T regulatory (Treg) cells on AD, with a mediation proportion of 17.80 %. Conclusions: These findings offer a nuanced understanding of the pathophysiological mechanisms underlying AD, thereby advancing the precision medicine paradigm in the clinical management of AD.Abbreviations: AD: aortic dissection; AA: aortic aneurysm; GWAS: genome-wide association study; MR: Mendelian randomization; TSMR: two-step Mendelian randomization; Treg: secreting T regulatory cell; VSMC: vascular smooth muscle cell; MMP: matrix metalloproteinase; ROS: reactive oxygen species; IV: instrumental variable; SNP: single-nucleotide polymorphism; IVW: inverse variance weighted; LDSC: linkage disequilibrium score regression; OR: odds ratio; CI: confidence interval; LD: linkage disequilibrium; AC: absolute cell; MFI: median fluorescence intensity; MP: morphological parameter; RC: relative cell; CLSA: Canadian Longitudinal Study of Aging; Lp(a): Lipoprotein a; OxPL: oxidised phospholipid; NMDAR: N-methyl-d-aspartate glutamate receptor; STROBE-MR: Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization.

As a short-rotation woody plant, Salix viminalis has the potential for phytoremediation of cadmium (Cd), but it has poor tolerance to high Cd concentrations. Melatonin (MEL), a candidate bio-promoter, was considered to play an active role in plant responses to Cd. However, the molecular mechanism by which MEL regulates metabolic processes in plants to defend against Cd stress remain unclear. Transcriptomics and global untargeted metabolomic sequencing were used to investigate the rapid response of S. viminalis to high Cd concentrations during initial growth stage after foliar application of MEL. Four treatments were set up in a pot experiment involving foliar application of MEL on the first day, followed by irrigation with a Cd solution the next day. Significant variations in the relevant defence genes and metabolites in leaves exposed to Cd were observed between willows treated with and without MEL. Foliar application of MEL upregulated sulphur metabolism-related genes such as methionine and S-adenosylmethionine synthases in leaves exposed to Cd; glutamine content, which is the key point of nitrogen assimilation, also increased. Additionally, glycolysis and sucrose metabolic genes, including hexokinase, sucrose synthase, invertase, and the inositol phosphate metabolic gene myo-inositol-1-phosphate synthase were also upregulated in leaves. Moreover, MEL also upregulated genes related to the synthesis of flavonoids, anthocyanins, and proanthocyanins in the leaves. These results demonstrated that MEL improved amino acid and saccharide metabolism in the leaves of S. viminalis in response to Cd. It also improved the antioxidant capacity and Cd tolerance in S. viminalis leaves by enhancing synthetic capacity of polyphenol compounds. MEL may be involved in processes of photorespiration, ethylene metabolism, GABA shunt, nitric oxide metabolism, osmotic adjustment, and the synthesis of glutathione and ascorbate in S. viminalis under Cd stress. This series of metabolic changes in S. viminalis occurred within 24 h of the foliar application of MEL, which provided a sufficient substrate for subsequent defence reactions to cope with Cd stress. Our findings will help elucidate the molecular mechanism by which MEL regulates metabolic processes in plants in response to Cd challenges and guide the application of MEL to improve Cd phytoremediation efficiency.

The control incorporation of metals in silica hollow spheres (SHSs) may bring new functions to silica mesoporous structures for applications including catalysis, sensing, molecular delivery, adsorption filtration, and storage. However, the strategies for incorporating metals, whether through pre-loading in the hollow interior or post-encapsulation in the mesoporous shell, still face challenges in achieving quantitative doping of various metals and preventing metal aggregation or channel blockage during usage. In this study, we explored the doping of different metals into silica hollow spheres based on the dissolution-regrowth process of silica. The process may promote the formation of more structural defects and functional silanol groups, which could facilitate the fixation of metals in the silica networks. With this simple and efficient approach, we successfully achieved the integration of ten diverse metal species into silica hollow sphere (SHS). Various single-metal, dual-metal, triple-metal, and quadruple-metal doped SHSs have been prepared, with the doped metals being stable and homogeneously dispersed in the structure. Based on the structural characterizations, we analyzed the influence of metal types on the morphology features of SHSs. The synergistic effects of multi-metals on the catalysis applications were also studied and compared.

Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.

Recent years have witnessed a spurt of progress in China’s tourism industry, contributing greatly to the economy, culture, and ecology. Cultural relics and historic sites boast significant value in terms of tourism, economic, ecological service, cultural, and scientific research value, yet the intentional destruction makes it a serious problem threatening human natural and cultural heritage. So far, there is no scientific and standardized method to assess the value of the damaged cultural relics and historic sites and the corresponding ecological compensation. This study fills up a critical gap in developing a new ecological damage assessment method for the intentional destruction of cultural relics and monuments by taking the graffiti of Taishan Mountain, a World Heritage Site in China, as an example. The method consists of four steps: (1) selecting the assessment indicators based on the characteristics of cultural relics and historic sites, (2) obtaining the evaluation weights of the indicators by conducting a questionnaire survey among experts and the public, (3) determining the spatial distribution of the protected areas and the damage grade of the cultural relics and historic sites by using GIS and remote sensing techniques, and (4) calculating the economic value of the damaged cultural relics and historic sites by using the market price method, the travel cost method, and the contingent valuation method. The results show that the total value of ecological damage in the case was 70,588.07 yuan ($9958.11), which was successfully accepted by both parties involved in the case.

Abstract Finding high‐temperature superconductivity in light‐weight element containing compounds at atmosphere pressure is currently a research hotspot but has not been reached yet. Here it is proposed that hard or superhard materials can be promising candidates to possess the desirable high‐temperature superconductivity. By studying the electronic structures and superconducting properties of H and Li doped c‐BN within the framework of the first‐principles, it is demonstrated that the doped c‐BN are indeed good superconductors at ambient pressure after undergoing the phase transition from the insulating to metallic behavior, though holding different nature of metallization. Li doped c‐BN is predicted to exhibit the superconducting transition temperature of ≈58 K, while H doped c‐BN has stronger electron–phonon interaction and possesses a higher transition temperature of 122 K. These results and findings thus point out a new direction for exploring the ambient‐pressure higher‐temperature superconductivity in hard or superhard materials.

ABSTRACT Background Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary nodules. Methods One thousand and ninety‐eight patients with 6–30 mm pulmonary nodules who received histopathologic diagnosis at 3 centers were included and divided into a primary cohort (PC), an internal test cohort (I‐T), and two external test cohorts (E‐T1, E‐T2). The DLRCN was built by integrating adipose tissue radiomics features, intranodular and perinodular deep learning features, and clinical characteristics for diagnosing malignancy of pulmonary nodules. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. The performance of DLRCN was assessed with respect to its calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Furthermore, we compared it with three radiologists. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and subgroup analysis were also taken into account. Results The incorporation of adipose tissue radiomics features led to significant NRI and IDI (NRI = 1.028, p 0.05). DCA showed that the DLRCN was clinically useful. Under equal specificity, the sensitivity of DLRCN increased by 8.6% compared to radiologist assessments. The subgroup analysis conducted on adipose tissue radiomics features further demonstrated their supplementary value in determining the malignancy of pulmonary nodules. Conclusion The DLRCN demonstrated good performance in predicting the malignancy of pulmonary nodules, which was comparable to radiologist assessments. The adipose tissue radiomics features have notably enhanced the performance of DLRCN.

Differences in main nutritional components in relation to biomarkers of metabolites in purple rice grains at different fillings stages have not been determined previously. This study measured the contents of amino acids, several nutritional indicators, and mineral elements in purple rice grains at five stages following the filling stage. The results revealed that the amino acid, ascorbic acid, total sugar, carotenoid, vitamin B9, cyanidin-3-O-glucoside, peonidin 3-glucoside and seven minerals were highest in the final stage of grain filling. Citric acid, L-isoleucine, trigonelline, and L-glutamate are key metabolites in the metabolic pathway and exhibit strong correlations with various nutritional indicators. Hence, this research preliminarily suggested that trigonelline, L-isoleucine, L-glutamate, and citric acid could be potential biomarkers of nutritional components in purple rice grains during various postfilling stages.