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Lignocellulosic biomass, including agricultural, forestry, and energy crop waste, is one of Earth’s most abundant renewable resources, accounting for approximately 50% of global renewable resources. It contains cellulose, hemicellulose, and lignin, making it crucial for biofuels and bio-based chemicals. Due to its complex structure, single-pretreatment methods are inefficient, leading to the development of combined pretreatment technologies. These methods enhance cellulose accessibility and conversion efficiency. This paper analyzes the principles, advantages, and disadvantages of various combined pretreatment methods and their practical benefits. It highlights recent research achievements and applications in biofuel, biochemical production, and feed. By integrating multiple pretreatment methods, biomass degradation efficiency can be significantly improved, energy consumption reduced, and chemical reagent use minimized. Future advancements in combined physical, chemical, and biological pretreatment technologies will further enhance biomass utilization efficiency, reduce energy consumption, and protect the environment, providing robust support for sustainable renewable energy development and ecological protection.

The formation of eutectic Al-Mg2Al3 phases along the grain boundaries makes it difficult to improve the mechanical properties of high-magnesium-content aluminum alloys. Here, wire-based friction stir additive manufacturing(W-FSAM) was proposed as a novel solid-state additive manufacturing technology. The shearing, transport, and thermo-plasticisation processes of the deposited materials were achieved by a rotational tool and a stationary barrel. The original Mg2Al3 phases were refined and dissolved into the matrix and formed supersaturated solid solution structures. The refined grains with a diameter of 1.27 ± 0.64 μm were achieved through the dynamic recrystallization process. The components achieved isotropic mechanical properties due to the homogeneous equiaxed fine-grain structure. The ultimate tensile strength reached 415 ± 11 MPa with an elongation of 31.0 ± 2.0%, superior to the commercial 5xxx products. The enhanced strain hardening capacity was achieved by the suppressed emission of dislocations from grain boundaries and the interaction between the supersaturated solid solute atoms and mobile dislocation.

IntroductionPrevious studies have indicated that exposure to heavy metals related to bone health is primarily limited to some common harmful metals, and the impact of lead has not been fully understood. This study aims to explore the relationship between urine lead exposure and bone density.Methods1,310 adults were included from the NHANES database (2015-2018), and through generalized linear regression analysis and constrained cubic spline models, the association between lead levels and total bone density as well as lumbar spine bone density was explored. The study also examined the impact of combined exposure to lead and cadmium on bone density.Results and conclusionsUrinary lead levels were significantly negatively correlated with total bone mineral density (β: −0.015; 95%CI: −0.024, −0.007) and lumbar spine bone mineral density (β: −0.019; 95%CI: −0.031, −0.006). Compared to the lowest three quartiles of lead levels, the adjusted odds ratios for T3 changes in total bone mineral density and lumbar spine bone mineral density were 0.974 (95%CI: 0.959, 0.990) and 0.967 (95%CI: 0.943, 0.991), indicating a significant negative trend. Further analysis with constrained cubic spline models revealed a non-linear decreasing relationship between urinary lead and total bone mineral density as well as lumbar spine bone mineral density. Stratified analyses suggested that the relationship between urinary lead levels and bone mineral density might be significantly influenced by age, while gender showed no significant impact on the relationship. Moreover, combined exposure to lead and cadmium was found to be associated with decreased bone mineral density, emphasizing the potential synergistic effects between lead and cadmium on bone health. However, the specific mechanisms of lead and its effects on different populations require further comprehensive research. This study provides valuable insights for further exploration and development of relevant public health policies.

ObjectivesTo explore the relationship between glymphatic dysfunction and cognitive impairment in unilateral temporal lobe epilepsy (TLE).MethodsThis study retrospectively included 38 patients with unilateral TLE and 26 age- and gender-matched healthy controls (HCs). The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, choroid plexus volume (CPV), and cognitive assessment were obtained for each participant. Neuropsychological test batteries included Montreal Cognitive Assessment (MoCA), Minimum Mental State Examination, Arithmetic Test (AT), Digit Symbol Substitution Test (DSST), Digit Span Test (DST), Boston Naming Test, Block design, Phonological Fluency Test (PFT), and Semantic Verbal Fluency (SVF).ResultsCompared to HCs, TLE patients had lower scores of MoCA, AT, DSST, DST, Block design, PFT and SVF (all p

Fractured soft and low permeability coalbed is commonly developed in China, which restricts the increase of single well gas production and industrial development of coalbed methane (CBM). Indirect fracturing is a kind of fracturing method to communicate with coal seams by creating vertical fractures in the neighboring layers and thus achieve effective coal seam transformation, which can solve disadvantages such as drilling collapse, coal dust production, fracturing fluid filtration loss and thin coal seam thickness. In this paper, physical simulation experiments and extended finite element numerical simulation analysis of indirect fracturing are used to reveal the factors influencing the fracture expansion of indirect fracturing in the coal seam roof and to clarify the fracture expansion mechanism in order to provide guidance for indirect fracturing technology. The indirect fracturing experiments under the influence of direct fracturing of coal seam and different fracture initiation location, vertical stress and construction displacement show that high fracture initiation pressure is more likely to produce long fractures, and the influence of primary fractures is significantly reduced; however, the farther the fracture initiation point is from the coal seam, the more energy is required for fracture initiation, and high fracture pressure will cause crushing damage to the coal seam; under large construction displacement, the fracture initiation pressure is correspondingly increased, the fracture initiation time becomes shorter, and the influence of primary fractures becomes smaller. The degree of impact of primary fracture becomes smaller. Under the condition of model parameter setting in this paper，the numerical simulation results considering the parameters of ground stress, fracture initiation location, rock mechanics and construction displacement show that the maximum horizontal principal stress and vertical stress difference is 3 MPa, and the combination of coal and roof elastic modulus difference

In the face of external shocks from urbanization and the inherent needs of economic development, it is essential for urban and rural heritage to adapt timely to achieve sustainability in development. Employing Historic Urban Landscape (HUL) methodologies for change management holds significant implications for the sustainable preservation and utilization of heritage. This study used Dongxi Ancient Town as a case study, characterized by a distinct evolutionary trajectory and diverse layers of accumulation throughout its historical progression, making it an exemplary instance for change analysis. This paper analyzed the processes and outcomes of historic urban landscape changes through a layered historical approach. Combining historical data translation methods with ArcGIS spatial analysis, we documented and mapped the cultural and natural characteristics of Dongxi Ancient Town. The layered process of the town’s historical landscape was categorized into four stages: the primary formative period from the Western Han to the Ming dynasties, the rapid development during the Qing dynasty, the prosperous period of the Republic of China, and the transitional expansion period following the establishment of the People’s Republic of China. The study analyzed the morphological changes and values of the historical landscape throughout these periods. Based on the analysis results, we suggest three transformation management strategies for historical landscapes oriented towards economic development: (1) converting cultural heritage into cultural assets, (2) implementing moderate and controlled quantitative changes, and (3) enhancing operational feasibility through collaborative efforts among multiple stakeholders. These strategies aim to establish a sustainable model that balances heritage conservation with economic growth.

Background: The increasing prevalence of hepatic steatosis presents a considerable challenge to public health. There is a critical need for the development of novel preventive and screening strategies for this condition. This study evaluated the potential applications of wrist pulse detection technology for the early detection of liver diseases. The pulse time-domain features of a medical exam population with and without hepatic steatosis were assessed to develop a screening model for this disease. Methods: Participants were consecutively recruited from March 2021 to March 2022 in the medical examination centers of the Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and the Shanghai Municipal Hospital of Traditional Chinese Medicine. Clinical data from 255 participants, including general information (sex, age, and body mass index), and data related to glucose and blood lipids (fasting plasma glucose, triglyceride, total cholesterol, high-density lipoprotein, and low-density lipoprotein levels) were collected. Wrist pulse signals were acquired using a pulse detection device, and the pulse time-domain features, including t1, t4, t5, T, w1, w2, h2/h1, h3/h1, and h5/h1 were extracted. Participants were assigned to hepatic steatosis and non-hepatic steatosis groups according to their abdominal ultrasound examination results. Their clinical data and pulse time-domain features were compared using chi-square and parametric or non-parametric statistical methods. Three datasets were used to construct screening models for hepatic steatosis based on the random forest algorithm. The datasets for modeling were defined as Dataset 1, containing blood glucose and lipid data and general information; Dataset 2, containing time-domain features and general information; Dataset 3, containing time-domain features, blood glucose and lipid data, and general information. The evaluation metrics, accuracy, precision, recall, F1-score, and areas under the receiver operating characteristic curve (AUC) were compared for each model. Results: The time-domain features of the two groups differed significantly. The t1, t4, t5, T, h2/h1, h3/h1, w1, and w2 features were higher in the hepatic steatosis group than in the non-hepatic steatosis group (P < 0.05), while the h5/h1 features were lower in the hepatic steatosis group than in the non-hepatic steatosis group (P < 0.05). The screening models for hepatic steatosis based on both time-domain features and blood glucose and lipid data outperformed those based on time-domain features or blood markers alone. The accuracy, precision, recall, F1-score, and AUC of the combined model were 81.18%, 80.56%, 76.32%, 79%, and 87.79%, respectively. These proportions were 1.57%, 1.86%, 1.76%, 2%, and 3.54% higher, respectively, than those of the model based on time-domain features alone and 3.14%, 4.2%, 2.64%, 4%, and 6.47% higher, respectively, than those of the model based on blood glucose and lipid alone. Conclusion: The early screening model for hepatic steatosis using datasets that included pulse time-domain features achieved better performance. The findings suggest that pulse detection technology could be used to inform the development of a mobile medical device or remote home monitoring system to test for hepatitis steatosis.

Paint-free bodywork has become an attractive alternative for rail vehicles, in the direction of easy maintainability and low manufacturing costs. However, conventional resistance spot welding inevitably leaves indentation marks to detrimentally reduce the optical homogeneity of the paint-free bodywork. In light of this, indentation-free resistance spot welding is proposed for joining SUS301L stainless steel sheets in order to achieve superior surficial integrity. A tiny SUS301L steel ball with a diameter of 1.5 mm was chosen as the intermediate filler between two steel sheets to avoid the formation of surficial indentation. The influence of welding current and welding time on the mechanical properties of joints was studied. The optimal parameters of the mechanical properties were obtained when the welding current was 8.0 kA, the welding time was 150 ms, the electrode pressure was 0.35 MPa, and the electrodes were cylindrical planar electrodes, which was determined by comparing the tensile shear test results. The surficial indentation depth was less than 1% of the plate thickness, and no observable indentations were seen on the surface of the optimized welding spots.

The rate of progression of Alzheimer's disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aβ) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79-0.82) to 0.82 (0.81-0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32-0.36) to 0.46 (0.44-0.49). External validation AUROCs ranged from 0.75 (0.70-0.81) to 0.83 (0.82-0.84) and AUPRCs from 0.27 (0.25-0.29) to 0.45 (0.43-0.48). Aβ plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.

Summary: China as a major coal-consuming economy faces the challenge of balancing economic development and carbon neutrality goal. This paper incorporates both efficiency-based and equity-based carbon neutrality policies into a numerical model to quantitatively assess how coal reduction under various carbon-neutral policies affects energy mix, economic growth, and industrial structures by 2060. Results show the nationwide coal intensity will ultimately plunge by over 95% from 2017 to 2060, mainly attributed to the coal-phasing-out in most industries. National Gross Domestic Product losses reaches 4,951 billion USD in efficiency-based scenarios by 2060, and the economic losses are even more severe in less developed provinces, especially provinces in Northern China. Although the equity-based policy can reduce the economic burden for the Northern China, the equity-based policy is accompanied by a significant regional shift in coal across the country: eastern coal-intense industries will be relocated northward, leading to increases in embodied coal consumption.

ABSTRACT Objective To explore the clinical characteristics of mitochondrial encephalomyopathy, lactic acidosis, and stroke‐like episodes (MELAS) caused by mitochondrial DNA‐encoded complex I subunit (mt‐ND) variants. Methods In this retrospective study, the clinical, myopathological and brain MRI features of patients with MELAS caused by mt‐ND variants (MELAS‐mtND) were collected and compared with those of MELAS patients carrying the m.3243A > G variant (MELAS‐A3243G). Result A total of 18 MELAS‐mtND patients (female: 7; median age: 24.5 years) represented 15.9% (n = 113) of all patients with MELAS caused by mtDNA variants in our neuromuscular center from January 2012 to June 2022. In this MELAS‐mtND cohort, the two most common variants were m.10191 T > C (4/18, 22.2%) and m.13513 G > A (3/18, 16.7%). The most frequent symptoms were seizures (14/18, 77.8%) and muscle weakness (11/18, 61.1%). Compared with 87 MELAS‐A3243G patients, MELAS‐mtND patients were significantly more likely to have a variant that was absent in blood cells (40% vs. 1.4%). Furthermore, MELAS‐mtND patients had a significantly lower MDC score (7.8 ± 2.7 vs. 9.8 ± 1.9); less hearing loss (27.8% vs. 54.0%), diabetes (11.1% vs. 37.9%), and migraine (33.3% vs. 62.1%); less short stature (males ≤ 165 cm; females ≤ 155 cm; 23.1% vs. 60.8%) and higher body mass index (20.4 ± 2.5 vs. 17.8 ± 2.7). MELAS‐mtND patients had significantly more normal muscle pathology (31.3% vs. 4.1%) and fewer RRFs/RBFs (62.5% vs. 91.9%), COX‐deficient fibers/blue fibers (25.0% vs. 85.1%) and SSVs (50.0% vs. 81.1%). Moreover, brain MRI evaluated at the first stroke‐like episode showed significantly more small cortical lesions in MELAS‐mtND patients (66.7% vs. 12.2%). Interpretation Our results suggested that MELAS‐mtND patients have distinct clinical, myopathological and brain MRI features compared with MELAS‐A3243G patients.

Abstract The higher prevalence of metabolic syndrome (MetS) in women after menopause is associated with a decrease in circulating 17β-oestradiol. To explore novel treatments for MetS in women with oestrogen deficiency, we studied the effect of exogenous butyrate on diet-induced obesity and metabolic dysfunctions using ovariectomized (OVX) mice as a menopause model. Oral administration of sodium butyrate (NaB) reduced the body fat content and blood lipids, increased whole-body energy expenditure, and improved insulin sensitivity. Additionally, NaB induced oestrogen receptor alpha (ERα) expression, activated the phosphorylation of AMPK and PGC1α, and improved mitochondrial aerobic respiration in cultured skeletal muscle cells. In conclusion, oral NaB improves metabolic parameters in OVX mice with diet-induced obesity. Oral supplementation with NaB might provide a novel therapeutic approach to treating MetS in women with menopause. Graphical Abstract Video Abstract

Introduction: Electromagnetically controlled small-scale robots show great potential in precise diagnosis, targeted delivery, and minimally invasive surgery. The automatic navigation of such robots could reduce human intervention, as well as the risk and difficulty of surgery. However, it is challenging to build a precise kinematics model for automatic robotic control because the controlling process is affected by various delays and complex environments.Method: Here, we propose a learning-based intelligent trajectory planning strategy for automatic navigation of magnetic robots without kinematics modeling. The Long Short-Term Memory (LSTM) neural network is employed to establish a global mapping relationship between the current sequence in the electromagnetic actuation system and the trajectory coordinates.Result: We manually control the robot to move on a curved path 50 times to form the training database to train the LSTM network. The trained LSTM network is validated to output the current sequence for automatically controlling the magnetic robot to move on the same curved path and the tortuous and branched new paths in simulated vascular tracks.Discussion: The proposed trajectory planning strategy is expected to impact the clinical applications of robots.

The importance of accurate prediction of power load variations for ensuring the reliability and rationality of power supply cannot be overstated. The GCES-adRNNe (Grey Relational Analysis Contextually Enhanced ES-adRNNe) is proposed as a load forecasting model that incorporates context augmentation on similar load days. Firstly, the selection of similar load days as representative sequences using the comprehensive grey correlation analysis method is enforced. Furthermore, context information is extracted from the representative sequences, and the individual sequences are dynamically adjusted to accommodate the main track prediction using XGBoost. Moreover, a stacked recurrent neural network (RNN) architecture with multiple layers is used. It incorporates attention-enhanced gated recurrent units (GRU) to capture various dependencies in the time series, such as short-term, long-term, and seasonal dependencies. The GRU also dynamically weight input information. Finally, the prediction results are post-processed. Experimental results indicate that the proposed model yields improvements in terms of RMSE, MAPE, StdPE, and other aspects compared to other load forecasting models. It is indicated that the model, when considering similar load days, is capable of capturing the trends and characteristics of electricity load changes more accurately, thereby providing strong support for ensuring the reliability and rationality of power supply.

Abstract Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold standard for PE diagnoses. Deep learning can enhance the radiologists’workflow by identifying PE using CTPA, which helps to prioritize important cases and hasten the diagnoses for at-risk patients. In this study, we propose a two-phase multitask learning method that can recognize the presence of PE and its properties such as the position, whether acute or chronic, and the corresponding right-to-left ventricle diameter (RV/LV) ratio, thereby reducing false-negative diagnoses. Trained on the RSNA-STR Pulmonary Embolism CT Dataset, our model demonstrates promising PE detection performances on the hold-out test set with the window-level AUROC achieving 0.93 and the sensitivity being 0.86 with a specificity of 0.85, which is competitive with the radiologists’sensitivities ranging from 0.67 to 0.87 with specificities of 0.89–0.99. In addition, our model provides interpretability through attention weight heatmaps and gradient-weighted class activation mapping (Grad-CAM). Our proposed deep learning model could predict PE existence and other properties of existing cases, which could be applied to practical assistance for PE diagnosis.

Quantifying regional health disparities linked to air pollution is essential for enhancing air quality and attaining carbon neutrality objectives. Nonetheless, the efficacy of proactive policies in ensuring equitable health protection in China’s Eastern Coastal Area (ECA) remains uncertain. Here, we employed an integrated assessment model to assess the combined reduction of carbon dioxide (CO _2 ) and atmospheric pollutants and their health repercussions in the ECA of China. Our findings reveal that 273 000 premature deaths are attributable to air pollution in 2060 in the ECA in the absence of mitigation policies. Conversely, carbon reduction policies are poised to curtail 80% of CO _2 emissions, alongside reductions of 76% for NO _x , 79% for SO _2 , 80% for PM _2.5 , 72% for VOCs, and 66% for NH _3 emissions. Air pollution control policies could mitigate premature deaths by 19 600, while carbon reduction policies could potentially lower them by 50 800. The health inequality coefficient among provinces stands at 0.19, primarily attributable to significantly higher mortality rates in Hebei and Shandong. These findings yield valuable insights for crafting synergistic abatement strategies in similarly imbalanced developmental regions grappling with comparable environmental challenges.

This article aims to explore an effective method for reducing vehicle collisions at unsignalized intersections. First, a monocular-binocular vision switching system is built to enable machine vision-based detection of obstacle vehicles in the left and right front directions. Then, the motion state and trajectory of each obstacle vehicle are predicted, and the intersection points of the trajectories of the obstacle vehicle and the ego vehicle are calculated. On this basis, a cross-conflict judgment model based on trajectories and collision times and a safety assessment model based on safety distance are established. Finally, the conflict judgment and safety assessment for the obstacle vehicles are simulated. The results of the simulation demonstrate that the monocular-binocular vision switching system proposed in this article can achieve a detection accuracy of 95%, a ranging accuracy of 96%, and a cross-conflict detection accuracy of 97%, while ensuring a maximum detection area, which can meet the requirements of traffic safety assurance at unsignalized intersections.

ObjectiveIdiopathic inflammatory myopathies (IIMs) are a heterogeneous group of autoimmune diseases with various subtypes, myositis-specific antibodies, and affect multiple systems. The treatment of IIMs remains challenging, especially for refractory myositis. In addition to steroids and traditional immunosuppressants, rituximab (RTX), a B cell-depleting monoclonal antibody, is emerging as an alternative treatment for refractory myositis. However, the therapeutic response to RTX remains controversial. This meta-analysis aimed to systematically evaluate the efficacy and safety of RTX in patients with IIMs, excluding sporadic inclusion body myositis.MethodsPubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, and WanFang Data were searched for relevant studies. The overall effective rate, complete response rate, and partial response rate were calculated to assess the efficacy of RTX. The incidences of adverse events, infection, severe adverse events, severe infection, and infusion reactions were collected to evaluate the safety of RTX. Subgroup analyses were performed using IIM subtypes, affected organs, continents, and countries. We also performed a sensitivity analysis to identify the sources of heterogeneity.ResultsA total of 26 studies were included in the quantitative analysis, which showed that 65% (95% confidence interval [CI]: 54%, 75%) of patients with IIMs responded to RTX, 45% (95% CI: 22%, 70%) of patients achieved a complete response, and 39% (95% CI: 26%, 53%) achieved a partial response. Subgroup analyses indicated that the overall efficacy rates in patients with refractory IIMs, dermatomyositis and polymyositis, as well as anti-synthetase syndrome were 62%, 68%, and 62%, respectively. The overall efficacy rates for muscle, lungs, and skin involvement were 59%, 65%, and 81%, respectively. In addition, studies conducted in Germany and the United States showed that patients with IIMs had an excellent response to RTX, with an effective rate of 90% and 77%, respectively. The incidence of severe adverse events and infections was 8% and 2%, respectively.ConclusionRTX may be an effective and relatively safe treatment choice in patients with IIMs, especially for refractory cases. However, further verification via randomized controlled trials is warranted.

PurposeThe lung is the most common distant metastatic organ in patients with endometrial cancer (EC) but is rarely reported. This study examines the association between clinical characteristics and overall survival (OS) in EC with lung metastasis.MethodsPatients with EC who had accompanying lung metastasis were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Univariate and multivariate Cox regression were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) and assess OS outcomes related to EC with lung metastasis. A Cox proportional hazards nomogram model for OS was constructed and validated. The calibration plot, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the discriminative ability and clinical benefit of the novel nomogram. Kaplan–Meier curves and scatter diagram analysis were used to investigate the risk stratifications of the nomogram.ResultsOverall, 1542 EC patients with lung metastasis between 2010 and 2017 were included and randomly divided into training and validation cohorts. A nomogram model was constructed using the clinical characteristics of tumor grade, histological type, surgery, adjuvant chemotherapy, adjuvant radiation, brain metastasis and liver metastasis. The concordance indexes (C-indexes) were 0.750 (95% CI, 0.732-0.767) and 0.743 (95% CI, 0.719-0.767) for the training cohort and validation cohort, respectively. Calibration plots and DCA showed good clinical applicability of the nomogram. The areas under the curves (AUCs) were 0.803 and 0.766 for 1-year and 3-year OS, respectively, indicating that the nomogram model had a stable discriminative ability. An online calculator of our nomogram is available on the internet at https://endometrialcancer.shinyapps.io/DynNomapp/. Additionally, patients in the high-risk group had a significantly worse OS than those in the low-risk group.ConclusionAn easy-to-use, highly accurate nomogram was developed for predicting the prognosis of EC patients with lung metastasis.

Abstract Objective Given their diverse phenotypes, mitochondrial diseases (MDs) are often difficult to diagnose. Fibroblast growth factor 21 (FGF‐21) and growth differentiation factor 15 (GDF‐15) represent promising biomarkers for MD diagnosis. Herein we conducted a meta‐analysis to compare their diagnostic accuracy for MDs. Methods We comprehensively searched PubMed, EMBASE, MEDLINE, the Web of Science, and Cochrane Library up to 1 January 2020. Data were analyzed by two independent reviewers. We obtained the sensitivity and specificity, positive and negative likelihood ratios (LR+ and LR‐), diagnostic odds ratios (DORs) and summary receiver operating characteristic (SROC) curves of each diagnostic method. Results Eight randomized controlled trials (RCTs) including 1563 participants (five encompassing 718 FGF‐21 assessments; seven encompassing 845 participants for GDF‐15) were included. Pooled sensitivity, specificity, DOR and SROC of FGF‐21 were 0.71 (95% CI 0.53, 0.84), 0.88(95% CI 0.82, 0.93), 18 (95% CI 6, 54), 0.90 (95% CI 0.87, 0.92), respectively, which were lower than GDF‐15 values; 0.83 (95% CI 0.65, 0.92), 0.92 (95% CI 0.84, 0.96), 52 (95% CI 13, 205), 0.94 (95% CI 0.92, 0.96). Interpretation FGF‐21 and GDF‐15 showed acceptable sensitivity and high specificity. Of the biomarkers, GDF‐15 had the highest diagnostic accuracy.

Low earth orbit (LEO) mega-constellations have once again triggered a wave of space-based system construction. On the one hand, LEO communication, LEO navigation, LEO remote sensing constellations and so on are proposed. On the other hand, with the continuous development of software-defined satellite and intelligent satellite technology, space-based systems are developing in the direction of multi-function, integration and cross-domain integration. The whole space-based system is no longer the traditional working mode of a single functional constellation, but a genral cross-domain fusion constellation (CDFC) system for complex tasks. Like the terrestrial global Internet, the space-based system will serve as a global infrastructure for integrating communication, navigation and remote sensing, that is, the intelligent space-based system, to provide services for the global demand. The traditional method of designing constellation for a certain type of function is no longer applicable to this type of constellation design. To solve this problem, this paper proposes a design and optimization method of cross-domain fusion constellation of communication, navigation and remote sensing based on reverse design. The paper optimizes the CDFC through resource coverage. Through experiments, we prove that the number of satellites in the CDFC can be reduced by 30.60% compared with the independent and combined constellations in each domain, and the coverage and service performance of the constellation can be improved. The cost can be reduced by 18.31% compared with the combined constellation. When the same number of satellites is used, the resource coverage of the cross-domain fusion constellation is increased by at least eight times.

Modular design is a widely used strategy that meets diverse customer requirements. Close relationships exist between parts inside a module and loose linkages between modules in the modular products. A change of one part or module may cause changes of other parts or modules, which in turn propagate through a product. This paper aims to present an approach to analyze the associations and change impacts between modules and identify influential modules in modular product design. The proposed framework explores all possible change propagation paths (CPPs), and measures change impact degrees between modules. In this article, a design structure matrix (DSM) is used to express dependence relationships between parts, and change propagation trees of affected parts within module are constructed. The influence of the affected part in the corresponding module is also analyzed, and a reachable matrix is employed to determine reachable parts of change propagation. The parallel breadth-first algorithm is used to search propagation paths. The influential modules are identified according to their comprehensive change impact degrees that are computed by the bat algorithm. Finally, a case study on the grab illustrates the impacts of design change in modular products.

Satellites will play a vital role in the future of the global Internet of Things (IoT); however, the resource shortage is the biggest limiting factor in the regional task of massiveequipment in the IoT for satellite service. Compared with the traditional isolated mode of satellite resources, the current research aims to realize resource sharing through satellite cooperation in satellite edge computing, to solve the problems of limited resources and low service quality of a single satellite. We propose a satellite resource pool architecture-oriented regional task in satellite edge computing. Different from fixed servers in ground systems, the satellite orbital motion brings challenges to the construction of the satellite resource pool. After the capacity planning of the satellite resource pool for regional tasks is given, an algorithm based on search matching is proposed to solve the dynamic satellite selection problem. A ground semi-physical simulation system is built to perform experiments and evaluate the performance of three modes of satellite resource sharing: isolated mode, cooperative mode, and pooled mode. The results show that the pooled mode, compared with the isolated mode, improves the task success rate by 19.52%, and at the same time increases network resources and energy consumption in the same scenario. Compared with the cooperation mode, the performance of task success rate and resource utilization rate is close to that of the pooled mode, but it has more advantages in response time and load balancing of satellite resources. This shows that in the IoT, the resource pool is of great benefit as it improves the task response time and improves the load balance of satellite resources without degrading the performance, which makes sense in task-demanding scenarios.

Neuroimaging studies of hippocampal volumes in patients with amyotrophic lateral sclerosis (ALS) have reported inconsistent results. Our aims were to demonstrate that such discrepancies are largely due to atrophy of different regions of the hippocampus that emerge in different disease stages of ALS and to explore the existence of co-pathology in ALS patients. We used the well-validated King’s clinical staging system for ALS to classify patients into different disease stages. We investigated in vivo hippocampal atrophy patterns across subfields and anterior-posterior segments in different King’s stages using structural MRI in 76 ALS patients and 94 health controls (HCs). The thalamus, corticostriatal tract and perforant path were used as structural controls to compare the sequence of alterations between these structures and the hippocampal subfields. Compared with HCs, ALS patients at King’s stage 1 had lower volumes in the bilateral posterior subiculum and presubiculum; ALS patients at King’s stage 2 exhibited lower volumes in the bilateral posterior subiculum, left anterior presubiculum and left global hippocampus; ALS patients at King’s stage 3 showed significantly lower volumes in the bilateral posterior subiculum, dentate gyrus and global hippocampus. Thalamic atrophy emerged at King’s stage 3. White matter tracts remained normal in a subset of ALS patients. Our study demonstrated that the pattern of hippocampal atrophy in ALS patients varies greatly across King’s stages. Future studies in ALS patients that focus on the hippocampus may help to further clarify possible co-pathologies in ALS.

Addressing climate change and air pollution goals in conjunction would be efficient and cost-effective. Dealing with these two challenges is a common issue for urban clusters pursuing sustainable development. Expected to become the fourth international first-class bay area, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) takes the lead in exploring a green and low-carbon transition path as a key element of being a pioneering economic reform demonstration zone. Based on an integrated modeling framework that couples an energy-economy model (IMED|CGE), decomposition analysis, and regression methods, the potential contribution of achieving the climate mitigation target to air pollutant reduction in the GBA by 2050 was quantified. The results showed that the transport sector has the most significant potential for carbon reduction. Energy intensity and structural transformations are the main contributors to reducing carbon emissions, with the latter becoming increasingly important over time. Climate policy can effectively reduce air pollutant emissions; however, this effect varies for different pollutants and sectors. Based on the assessment of the synergy index and cost of abatement, sectors with priority for synergic governance were identified. The regression results indicated that the carbon shadow price would be significantly more effective in reducing air pollutant emissions in the post-2030 period than before 2030, except for SO _2 and NH _3 , partially because of the existing actions that cause the synergistic effects to decline. In addition, end-of-pipe removal measures still play a relatively significant role in reducing air pollutants, particularly VOC, NH _3, and primary PM _2.5 . Thus, the findings suggest that priority should be given to sectors with huge synergistic benefits, such as transportation and power generation while paying attention to possible trade-offs.

miRNAs are stable and can be extracted from tissues, blood and other body fluid without degradation. miRNAs are abnormally expressed in the presence of a pathological status, including cancer. Therefore, miRNAs are ideal biomarkers for cancer diagnosis and prognosis. Patients with triple negative breast cancer (TNBC) suffer the worst prognosis, although great efforts have been made. Many studies have investigated the role of miRNAs in predicting the outcomes of TNBC patients for better adjustment of treatment. However, results were inconsistent. Thus, we performed a meta-analysis to summarize the published studies for conclusive results.Eligible studies from different database were retrieved from the online databases, and we used STSTA 12.0 to analysis the prognostic role of miRNAs in triple negative breast cancer.Overall high miRNA expression indicated a worse survival with HR value of 1.78 (95% CI: 0.97-3.25). However, subtotal HRs of oncogenic miRNAs and tumor suppressive miRNAs were 2.73 (95% CI: 2.08-3.57; P

Waterlogging is a common abiotic stress in both natural and agricultural systems, and it primarily affects plant growth by the slow oxygen diffusion in water. To sustain root function in the hypoxic environment, a key adaptation for waterlogging tolerant plants is the formation of adventitious roots (ARs). We found that cucumber waterlogging tolerant line Zaoer-N seedlings adapt to waterlogging stress by developing a larger number of ARs in hypocotyls, while almost no AR is generated in sensitive line Pepino. To understand the molecular mechanisms underlying AR emergence, the iTRAQ-based quantitative proteomics approach was employed to map the proteomes of hypocotyls cells of the Zaoer-N and Pepino under control and waterlogging conditions. A total of 5,508 proteins were identified and 146 were differentially regulated proteins (DRPs), of which 47 and 56 DRPs were specific to tolerant and sensitive line, respectively. In the waterlogged Zaoer-N hypocotyls, DRPs related to alcohol dehydrogenases (ADH), 1-aminocyclopropane-1-carboxylicacid oxidases, peroxidases, 60S ribosomal proteins, GSDL esterases/lipases, histone deacetylases and histone H5 and were strongly overrepresented to manage the energy crisis, promote ethylene release, minimize oxidative damage, mobilize storage lipids, and stimulate cell division, differentiation and growth. The evaluations of ethylene production, ADH activity, pyruvate decarboxylase (PDC) activity and ethanol production were in good agreement with the proteomic results. qRT-PCR analysis of the corresponding 146 genes further confirmed the accuracy of the observed protein abundance. These findings shed light on the mechanisms underlying waterlogging triggered cucumber ARs emergence, and provided valuable information for the breeding of cucumber with enhanced tolerance to waterlogging.

Autonomous nanorobots represent an advanced tool for precision therapy to improve therapeutic efficacy. However, current nanorobotic designs primarily rely on inorganic materials with compromised biocompatibility and limited biological functions. Here, we introduce enzyme-powered bacterial outer membrane vesicle (OMV) nanorobots. The immobilized urease on the OMV membrane catalyzes the decomposition of bioavailable urea, generating effective propulsion for nanorobots. This OMV nanorobot preserves the unique features of OMVs, including intrinsic biocompatibility, immunogenicity, versatile surface bioengineering for desired biofunctionalities, capability of cargo loading and protection. We present OMV-based nanorobots designed for effective tumor therapy by leveraging the membrane properties of OMVs. These involve surface bioengineering of robotic body with cell-penetrating peptide for tumor targeting and penetration, which is further enhanced by active propulsion of nanorobots. Additionally, OMV nanorobots can effectively safeguard the loaded gene silencing tool, small interfering RNA (siRNA), from enzymatic degradation. Through systematic in vitro and in vivo studies using a rodent model, we demonstrate that these OMV nanorobots substantially enhanced siRNA delivery and immune stimulation, resulting in the utmost effectiveness in tumor suppression when juxtaposed with static groups, particularly evident in the orthotopic bladder tumor model. This OMV nanorobot opens an inspiring avenue to design advanced medical robots with expanded versatility and adaptability, broadening their operation scope in practical biomedical domains. [ABSTRACT FROM AUTHOR]