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Abstract Background Clear cell renal cell carcinoma (ccRCC) is one of the most common urological diseases and the most common subtype of renal cell carcinoma. Nuclear mitochondria‐related genes (MTRGs) play an essential role in cancer, but their effect on ccRCC has not been clarified. This work aimed to investigate the role of nuclear MTRGs in ccRCC. Methods We collected nuclear MTRGs from the MITOMAP database and obtained the ccRCC profile from the TCGA database. Gene expression validation came from the GEO database. The ccRCC subtypes were determined by unsupervised clustering analysis based on the nuclear MTRGs. Immune scores were computed using the EPIC algorithm. The drug sensitivity scores were calculated using GDSC resources. A nomogram explored the diagnostic value of the nuclear MTRGs. Results In total, 11 nuclear MTRGs were identified as both related to prognosis and differentially expressed in ccRCC, showing a significant positive correlation with CD274 expression. We determined two subtypes of ccRCC based on these genes and found remarkable differences in survival status, immune infiltration, mutation landscape, and drug sensitivity between the two subtypes. The high‐MTRG group had a better prognosis and a lower tumor stage than the low‐MTRG group. Immune checkpoint blockade therapy was more effective for the high‐MTRG group. A nomogram based on the nuclear MTRGs concluded that patients with a higher score had poorer survival. SUCLA2 (succinate‐CoA ligase ADP‐forming subunit beta) was identified as the hub gene linked to ccRCC. High SUCLA2 expression showed a correlation with better survival and a negative correlation with the tumor mutation burden in ccRCC. Pan‐cancer analysis revealed wide‐ranging roles for SUCLA2 across human tumors. Conclusions Nuclear MTRGs play vital roles in determining the molecular features, immune infiltration, and drug sensitivity of ccRCC. High levels of nuclear MTRGs may indicate a better prognosis for patients with ccRCC. SUCLA2 is a representative nuclear MTRG and may serve as a protective biomarker in ccRCC. Our study provides therapeutic guidance and potential biomarkers for ccRCC patients, and contributes to the advancement of precision medicine.

Abstract The oxide and halide perovskite materials with a ABX3 structure exhibit a number of excellent properties, including a high dielectric constant, electrochemical properties, a wide band gap, and a large absorption coefficient. These properties have led to a range of applications, including renewable energy and optoelectronics, where high-performance catalysts are needed. However, it is difficult for a single structure of perovskite alone to simultaneously fulfill the diverse needs of multiple applications, such as high performance and good stability at the same time. Consequently, perovskite nanocomposites have been developed to address the current limitations and enhance their functionality by combining perovskite with two or more materials to create complementary materials. This review paper categorizes perovskite nanocomposites according to their structural composition and outlines their synthesis methodologies, as well as their applications in various fields. These include fuel cells, electrochemical water splitting, CO2 mitigation, supercapacitors, and optoelectronic devices. Additionally, the review presents a summary of their research status, practical challenges, and future prospects in the fields of renewable energy and electronics. Graphical abstract

Abstract Profit distribution in sequential innovation is a crucial yet relatively unexplored area of empirical research. With novel cross-section data from the game live-streaming industry, we are the first to assess the contribution shared by initial and follow-on innovators empirically. Unlike the complex innovation paths of patents, the copyright structure within the game live-streaming industry is clear and concise, enabling direct measurement of innovation value. At the industry’s average level, the share stands at 1:2 between game producers and streamers. This measurement remains robust even after controlling for income levels and distinguishing between professionals and amateurs. Nevertheless, significant heterogeneity exists across different game genres. We suggest that a balanced distribution scheme between initial and follow-on innovators should seriously consider their contribution shares.

Pharmacogenetic testing technology can effectively determine the individual differences among patients and scientifically assist doctors to select the most suitable drugs for each patient. In this study, an electrochemical biosensor based on magnetic α-Fe2O3/Fe3O4 heterogeneous nanosheets was constructed to detect Vitamin K epoxide reductase complex 1 type AA (VKORC1*2). Firstly, we fabricated magnetic α-Fe2O3/Fe3O4 heterogeneous nanosheets as the substrate material for electrochemical biosensors. Subsequently, the material surface was modified with Au nanoparticles to facilitate the connection of sensing probes and enhance current signal amplification. The electrochemical biosensor showed a negative linear relationship with the concentrations of target DNA (tDNA, VKORC1*2 gene) within 1 pM − 1 μM, a limit of detection was (LOD) = 0.36 pM, and a limit of quantification was (LOQ) = 1.19 pM. The biosensor demonstrated exceptional specificity, reproducibility, and stability. In real sample analysis, it exhibited a recovery range of 96.63 % − 110.57 % (RSD ≤ 3.07 %) for various tDNA concentrations, thereby indicating its promising potential in clinical diagnostics.

Abstract NLRP inflammasomes are a group of cytosolic multiprotein oligomer pattern recognition receptors (PRRs) involved in the recognition of pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs) produced by infected cells. They regulate innate immunity by triggering a protective inflammatory response. However, despite their protective role, aberrant NLPR inflammasome activation and gain-of-function mutations in NLRP sensor proteins are involved in occurrence and enhancement of non-communicating autoimmune, auto-inflammatory, and neurodegenerative diseases. In the last few years, significant advances have been achieved in the understanding of the NLRP inflammasome physiological functions and their molecular mechanisms of activation, as well as therapeutics that target NLRP inflammasome activity in inflammatory diseases. Here, we provide the latest research progress on NLRP inflammasomes, including NLRP1, CARD8, NLRP3, NLRP6, NLRP7, NLRP2, NLRP9, NLRP10, and NLRP12 regarding their structural and assembling features, signaling transduction and molecular activation mechanisms. Importantly, we highlight the mechanisms associated with NLRP inflammasome dysregulation involved in numerous human auto-inflammatory, autoimmune, and neurodegenerative diseases. Overall, we summarize the latest discoveries in NLRP biology, their forming inflammasomes, and their role in health and diseases, and provide therapeutic strategies and perspectives for future studies about NLRP inflammasomes.

Abstract Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls, hindering them from the sustainable performance improvement and iterative multi-task learning. Referring to another modality of light, photonic computing has been progressively applied in high-efficient neuromorphic systems. Here, we innovate a reconfigurable lifelong-learning optical neural network (L2ONN), for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware co-design. Benefiting from the inherent sparsity and parallelism in massive photonic connections, L2ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field, while incrementally acquiring expertise on various tasks by gradually enlarging the activation. The multi-task optical features are parallelly processed by multi-spectrum representations allocated with different wavelengths. Extensive evaluations on free-space and on-chip architectures confirm that for the first time, L2ONN avoided the catastrophic forgetting issue of photonic computing, owning versatile skills on challenging tens-of-tasks (vision classification, voice recognition, medical diagnosis, etc.) with a single model. Particularly, L2ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks, and 14× larger capacity than existing optical neural networks while maintaining competitive performance on each individual task. The proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme, permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.

Abstract Optoelectronic neural networks (ONN) are a promising avenue in AI computing due to their potential for parallelization, power efficiency, and speed. Diffractive neural networks, which process information by propagating encoded light through trained optical elements, have garnered interest. However, training large-scale diffractive networks faces challenges due to the computational and memory costs of optical diffraction modeling. Here, we present DANTE, a dual-neuron optical-artificial learning architecture. Optical neurons model the optical diffraction, while artificial neurons approximate the intensive optical-diffraction computations with lightweight functions. DANTE also improves convergence by employing iterative global artificial-learning steps and local optical-learning steps. In simulation experiments, DANTE successfully trains large-scale ONNs with 150 million neurons on ImageNet, previously unattainable, and accelerates training speeds significantly on the CIFAR-10 benchmark compared to single-neuron learning. In physical experiments, we develop a two-layer ONN system based on DANTE, which can effectively extract features to improve the classification of natural images.

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.

Organic-inorganic metal halide perovskites solar cells (PSCs) have been emerging as a counterpart or a supplement of silicon-based solar cells. They have shown various interesting optoelectronic properties and impressive power conversion efficiencies, even outperforming the theoretical limits in tandem configurations. However, challenges such as long-term stability and scalable manufacturing remain significant obstacles to commercialization. Key factors like material composition and crystal quality are essential for the reliability and performance of PSCs. Traditional solution-based processes face challenges in scalability and reproducibility. This has drawn attention to vacuum processes, which have been successfully employed in the commercial mass production of optoelectronic devices like displays. Also, recent innovations in automated deposition systems aided by machine learning offer promising solutions. These technological advancements enable rapid optimization of material combinations and manufacturing processes, facilitating a transition from lab-scale prototypes to industrial applications. This review highlights the converging efforts from multiple disciplines—materials science, process engineering and machine learning—that are essential for the transition from experimental validation to commercial, sustainable energy solutions. In summary, the work sets a path forward, where collective expertize can address lingering challenges, making clean, accessible, and affordable energy an attainable goal.

Water-related ecosystem services (WES) critical to anthropogenic-related water security are being severely degraded, primarily due to climate and land-use change. Clarifying future trends in WES (i.e., water yield and water purification services) will contribute to water resource sustainability. However, most existing studies are single case studies and rarely consider the combined impacts of climate change and land-use change on ecosystem services, resulting in patterns that cannot be adequately summarized. In this study, the “Annual Water Yield” module of the InVEST model was used to estimate water yield and the “Nutrient Delivery Ratio” module was used to analyze water purification services. In order to compare the impacts of different natural conditions and geographic locations on water ecosystem services, 17 typical watersheds throughout China were selected as study areas. Coupled scenarios were also considered when setting climate and land-use scenarios to predict prospective (2020–2100) evolutionary trends in WES. The response of WES under different scenarios was also assessed. Results show that climate change is more likely to affect water yield services than water purification services. The impact of land use on water purification services is more pronounced than its impact on water yield services. Climate change contributed > 90 % to water yields in 15 out of the 17 watersheds, contributed 79.4 % in the Hei River watershed, while only contributing 11.2 % in the Yarkant River watershed. The influence of climate change on total nitrogen (TN) output in eight of the watersheds was greater than that of land-use change, especially in the Min River and Mintuo River watersheds, for which the contribution was > 95 %. Furthermore, water purification services in nine of the watersheds were more affected by land-use change, especially in the Dongting Lake and Hei River watersheds, for which the contribution was > 95 %. The aim of this study is to assist in the planning of sound water resource measures while developing sustainable management strategies to help relevant water resource sectors cope with prospective risks and challenges.

Tourism in urban forests is rapidly becoming an increasing trend; however, rather few studies have used quantitative measurement to describe the relationship between tourism intensity and ecological functions. This study provides a practical framework that integrates ecosystem service value (ESV) assessment, Internet big data mining and spatial regression analysis to identify the spatial response of ESV and land use/land cover change to tourism activities from 2009 to 2019 in the Siming Mountain Region (SMR), a famous tourist resort located in the eastern coastal China. Results showed that between 2009 and 2019 total ESV increased by 7.1%. Nevertheless, there have been drastic transitions in land use types with function adjustments from traditional agricultural production to diversified tourism-oriented services. Significant spatial autocorrelation was identified for the patterns of ESV changes. GWR further highlighted that the relationship between ESV change and rural tourism indicators varied in space. ESV change in the core zone was negatively correlated with changes in catering service spots and recreational venues, whereas it was positively correlated with local lodgings. Ultimately, targeted recommendations and countermeasures for spatial planning and sustainable tourism development of urban forests under new circumstances were discussed.

ABSTRACT: To study the effects of mineral fulvic acid (FuA) on broiler performance, slaughter performance, blood biochemistry index, antioxidant function, immune performance, and intestinal microflora, 360 Arbor Acres (AA) broiler chickens with similar body weights were randomly divided into 5 groups with 6 replicates in each group and 12 chickens in each replicate in the current study. Chickens in the control group (C) were fed with the basal diet, and chickens in the test groups (I, II, III, and IV) were fed with the diet supplemented with 0.05%, 0.1%, 0.2%, and 0.3% mineral FuA, respectively. The indicators were measured on the hatching day, d 21 and d 35. From the whole experimental period, FuA supplement significantly increased average body weight (ABW) (P < 0.05), average daily gain (ADG) of broilers (P < 0.05), and thymus weight (P < 0.05) in II and IV groups, but bascially reduced the pH value of thigh meat. FuA supplement significantly improved aspartate aminotransferase (AST) activity in the group III on d 35 (P < 0.05) and the serum levels of IgA and IgG on d 21 and d 35 (P < 0.05), but reduced glutathione peroxidase (GSH-Px) level on d 21 (P < 0.05) and malondialdehyde (MDA) level in serum on d 35 (P < 0.05). FuA supplement significantly affected the abundance of Barnesiella, Lachnospiraceae, Alistipes, Lactobacillus, and Christensenellaceae on genus level. Differences between group III and other groups were significant in the genera microflora composition on d 21 and d 35. Functional analysis showed that the cecum microbiota were mainly enriched in carbohydrate metabolism, amino acid metabolism, and energy metabolism. In conclusion, FuA may potentially have significant positive effects on the growth performance and immune function of AA chickens through the modulation of the gut microbiota, and the 0.1% FuA was the best in broiler diet based on the present study.

Wildfires cause great losses to the ecological environment, economy, and people’s safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as meteorological and topographical data, can effectively predict and evaluate wildfire susceptibility. Accordingly, this paper converts meteorological and topographical data into fire-influencing factor raster maps for wildfire susceptibility prediction. The continuous convolutional neural network (CCNN for short) based on coordinate attention (CA for short) can aggregate different location information into channels of the network so as to enhance the feature expression ability; moreover, for different patches with different resolutions, the improved CCNN model does not need to change the structural parameters of the network, which improves the flexibility of the network application in different forest areas. In order to reduce the annotation of training samples, we adopt an active learning method to learn positive features by selecting high-confidence samples, which contributes to enhancing the discriminative ability of the network. We use fire probabilities output from the model to evaluate fire risk levels and generate the fire susceptibility map. Taking Chongqing Municipality in China as an example, the experimental results show that the CA-based CCNN model has a better classification performance; the accuracy reaches 91.7%, and AUC reaches 0.9487, which is 5.1% and 2.09% higher than the optimal comparative method, respectively. Furthermore, if an accuracy of about 86% is desired, our method only requires 50% of labeled samples and thus saves about 20% and 40% of the labeling efforts compared to the other two methods, respectively. Ultimately, the proposed model achieves the balance of high prediction accuracy and low annotation cost and is more helpful in classifying fire high warning zones and fire-free zones.

Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation.

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather.

Abstract A monolithic perovskite/silicon tandem solar cell architecture is employed to surpass the single‐junction efficiency limit. Recently, there is an increasing need for the double‐sided textures in the Si bottom cell to be compatible with the solution‐processed perovskite top cell from an industrial perspective. Herein, a silver‐assisted alkaline etching method is applied to fabricate nanoscale Si pyramid textures, and the influence of varying pyramid size (400–900 nm) on the interface morphology and the performance of perovskite/Si tandem cells is investigated. It is demonstrated that electrical shunting starts to increase, and the open‐circuit voltage (VOC) decreases when the texture size exceeds the perovskite thickness (~500nm) due to the non‐uniform top‐cell formation on a rough Si surface. However, when the texture size is reduced to 400–500 nm, all spin‐coated perovskite top‐cell component layers exhibit an even form over the nanopyramid Si, resulting in a high VOC and an enhanced Si bottom cell current (≈1.0 mA cm−2) due to the suppressed reflectance at the top/bottom cell interface without using optical couplers. The double‐sided nanopyramid Si texture offers opportunities to increase tandem cell efficiency while reducing its production cost compared with the commonly used single‐sided textured Si.

The observation and analysis of sediment transport in oceans is an important means for the protection of the marine environment, resource development, construction engineering, and element cycling. However, traditional methods of observing sediment transport are either limited by the range of the instruments used or their own observational attributes, such that they cannot be used to accurately detect and analyze the process of transport of marine sediment. A 3D sediment trap has been proposed to compensate for the shortcomings of the various monitoring tools in our team, but no mature method for the analytical inversion of the data obtained from this device has been developed to date. In this paper, we developed analytical methods to invert sediment transport processes using corrected capture efficiency, sample inversion, and transport flux analysis. Through an annular flume test, we measured the turbidity, pressure, and particle size of the water stream and substituted them into the proposed analytical equations, thus verifying the applicability of the analytical methods. We used the slice experiment of the time series of the sediment samples, to determine the validity of the sample inversion, and establish the relationship between the particle size and concentration of the captured samples. We performed restoration tests on the process of sediment transport to establish a set of methods of flux analysis based on the velocity and turbidity of flow. And finally corrected for capture efficiency by particle size. The combination of analytical methods and 3D sediment trap could provide technological support for investigating the evolution of the sea, ecological cycle, and marine engineering.

Irregularly shaped microplastics (MPs) released from infant feeding bottles (PP-IFBs) may exhibit increased cytotoxicity, in contrast to the commonly studied spherical MPs. This study presents an initial analysis of the thermal-oxidative aging process of plastic shedding from feeding bottles, and investigates the inflammatory response induced by these atypical MPs in human intestinal cells (Caco-2). The PP-IFBs' surface displayed non-uniform white patches and increased roughness, revealing substantial structural alteration and shedding, especially during actions such as shaking, boiling water disinfection, and microwave heating. FT-IR and 2D-COS analyses revealed that oxygen targeted the C–H and C–C bonds of polypropylene molecular chain, producing RO· and ·OH, thereby hastening polypropylene degradation. When human intestinal cells were exposed to MPs from PP-IFBs, oxidative stress was triggered, resulting in lowered glutathione levels, augmented reactive oxygen species (ROS), and heightened lipid peroxidation. Elevated levels of pro-inflammatory cytokines (IL-6 and TNFα) signified an active inflammatory process. The inflammatory response was notably more intense when exposed to MPs released through boiling water disinfection and microwave heating treatments, primarily due to the larger quantity of MPs released and their higher proportion of smaller particles. Furthermore, the NLRP3 inflammasome was identified as critical in initiating this inflammatory chain reaction due to the mitochondrial ROS surge caused by MPs exposure. This was further validated by inhibitor studies, emphasizing the role of the ROS/NLRP3/Caspase-1/IL-1β signaling pathway in in promoting intestinal inflammation. Therefore, swift actions are recommended to protect infants against the potential health effects of MPs exposure.

Abstract Epitaxy technology produces high-quality material building blocks that underpin various fields of applications. However, fundamental limitations exist for conventional epitaxy, such as the lattice matching constraints that have greatly narrowed down the choices of available epitaxial material combinations. Recent emerging epitaxy techniques such as remote and van der Waals epitaxy have shown exciting perspectives to overcome these limitations and provide freestanding nanomembranes for massive novel applications. Here, we review the mechanism and fundamentals for van der Waals and remote epitaxy to produce freestanding nanomembranes. Key benefits that are exclusive to these two growth strategies are comprehensively summarized. A number of original applications have also been discussed, highlighting the advantages of these freestanding films-based designs. Finally, we discuss the current limitations with possible solutions and potential future directions towards nanomembranes-based advanced heterogeneous integration. Graphical Abstract

Sediment deposition significantly impacts soil erosion processes, consequently influencing the geographical morphology and surrounding environments of reservoirs and estuaries. Given the intricate nature of sediment deposition, it is imperative to consolidate and analyze existing research findings. Presently, studies on sediment settling velocity primarily employ theoretical, laboratory, and field experimentation methods. Theoretical approaches, rooted in mechanics, examine the various forces acting on sediment particles in water to derive settling velocity equations. However, they often overlook external factors like temperature, salinity, organic matter, and pH. Although laboratory experiments scrutinize the influence of these external factors on sedimentation velocity, sediment settling is not solely influenced by individual factors but rather by their collective interplay. Field observations offer the most accurate depiction of sediment deposition rates. However, the equipment used in such experiments may disrupt the natural sedimentation process and damage flocs. Moreover, measurements of sediment particle size from different instruments yield varied results. Additionally, this paper synthesizes the impact of suspended sediment concentration, particle size, shape, temperature, salinity, and organic matter on sediment settling velocity. Future research should focus on innovating new laboratory observation methods for sediment settling velocity and utilizing advanced scientific and technological tools for on-site measurements to provide valuable insights for further investigation into sediment settling velocity.

ABSTRACT In order to produce therapeutic glycoproteins modified with human-like N-glycan structures, much progress has been achieved toward the humanization of N-glycosylation pathways in yeasts. In this study, a Pichia pastoris mutant Glyco4, which has a humanized N-glycosylation pathway and could successfully generate the human-like N-glycans, was carefully characterized. Glyco4 displays a significant growth delay and cell wall defects. Comparative transcriptomics reveals that manipulating the N-glycosylation pathway could notably affect the expression pattern of numerous biological pathways in Glyco4. Among the differentially expressed genes, the down-regulation of PpSPI1 was proven to be the main cause of the cell wall defects in Glyco4. Deletion of PpSPI1 in P. pastoris GS115 strain presented growth delay and weaker resistance to cell wall/membrane perturbing agents. PpSpi1 was shown to be a glycosylphosphatidylinositol-anchored cell wall glycoprotein and involved in the formation of the mannoprotein layer on the outer surface of cell wall. Overexpressing PpSPI1 in Glyco4 could partially recover the cell wall defects and also improve its resistance to cell wall perturbing agents and osmotic stress. Thus, overexpression of PpSpi1 is a useful strategy to facilitate P. pastoris industrial applications in the manufacture of human glycoproteins. IMPORTANCE Engineering of biological pathways in various microorganisms is a promising direction for biotechnology. Since the existing microbial cells have evolved over a long period of time, any artificial engineering may cause some unexpected and harmful effects on them. Systematically studying and evaluating these engineered strains are very important and necessary. In order to produce therapeutic proteins with human-like N-glycan structures, much progress has been achieved toward the humanization of N-glycosylation pathways in yeasts. The properties of a P. pastoris strain with humanized N-glycosylation machinery were carefully evaluated in this study. Our work has identified a key glycoprotein (PpSpi1) responsible for the poor growth and morphological defects of this glycoengineered strain. Overexpression of PpSpi1 could significantly rescue the growth defect of the glycoengineered P. pastoris and facilitate its future industrial applications.

Background: Glioma is the most frequent malignant primary brain tumor, and mitochondria may influence the progression of glioma. The aim of this study was to analyze the role of nuclear mitochondria related genes (MTRGs) in glioma, identify subtypes and construct a prognostic model based on nuclear MTRGs and machine learning algorithms. Methods: Samples containing both gene expression profiles and clinical information were retrieved from the TCGA database, CGGA database, and GEO database. We selected 16 nuclear MTRGs and identified two clusters of glioma. Prognostic features, microenvironment, mutation landscape, and drug sensitivity were compared between the clusters. A prognostic model based on multiple machine learning algorithms was then constructed and validated by multiple datasets. Results: We observed significant discrepancies between the two clusters. Cluster One had higher nuclear MTRG expression, a lower survival rate, and higher immune infiltration than Cluster Two. For the two clusters, we found distinct predictive drug sensitivities and responses to immune therapy, and the infiltration of immune cells was significantly different. Among the 22 combinations of machine learning algorithms we tested, LASSO was the most effective in constructing the prognostic model. The model's accuracy was further verified in three independent glioma datasets. We identified MGME1 as a vital gene associated with infiltrating immune cells in multiple types of tumors. Conclusion: In short, our research identified two clusters of glioma and developed a dependable prognostic model based on machine learning methods. MGME1 was identified as a potential biomarker for multiple tumors. Our results will contribute to precise medicine and glioma management.

Climate change and intensive anthropogenic activities have severely challenged the water quality of China’s Pearl River Estuary (PRE). Further investigations into long-term water quality variation and associated driving mechanisms are therefore necessary to support the sustainable development of the PRE’s Greater Bay Area (GBA). This study used remote sensing retrieval to address long-term spatiotemporal chlorophyll-a (Chl-a) variation characteristics in the PRE and the relationship between Chl-a concentrations and socioeconomic/environmental indicators. Three decades of Landsat satellite images and measured data were collected, and a two-band global algorithm was used to retrieve Chl-a concentration data. Results reveal significant spatiotemporal variability in Chl-a concentrations. The space-averaged Chl-a concentration exhibited a slight downward trend during the past three decades, and the multi-year mean value was 5.20 mg/L. Changes to environmental protection policies in recent years have improved overall PRE water quality. The western section of the PRE had the highest Chl-a concentration (i.e., 5.92 mg/L average) while the eastern section had the lowest (i.e., 3.98 mg/L average). This discrepancy was likely caused by the western section’s more intensive industrial activities, resulting in a higher overall wastewater discharge volume. Affected by climatic conditions, winter Chl-a concentrations were evenly distributed while summer concentrations were significantly higher. Additionally, Chl-a concentrations significantly and positively correlated with total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), and the biotic oxygen demand (BOD5). Chl-a concentrations also correlated with external factors (i.e., climate and anthropogenic activities). Among these factors, industrial wastewater discharge and the proportion of primary industries in coastal cities significantly and positively correlated with water quality. This study is intended to help direct water quality improvement management and urban sustainable development in the GBA.

ObjectiveTo address a novel lower-dose rituximab (RTX) therapy strategy based on our clinical experience and assess its efficacy and safety in neuromyelitis optica spectrum disorder (NMOSD).MethodsA multicenter, open-label, self-controlled, prospective follow-up study. Totally, 108 NMOSD patients were enrolled and a lower-dose RTX strategy was applied including 100 mg weekly for 3 weeks and then reinfusions every 6 months. Annualized relapse rate (ARR), the expanded disability status scale (EDSS) score and length of spinal cord lesions were included to evaluate the efficacy. Side effects were recorded to assess the safety profile.ResultsOf 108 patients, 80 (74.1%) initiated low-dose RTX therapy immediately after acute attack treatment and 33 (30.6%) initiated it after the first attack. During a median treatment period of 35.5 (22.0–48.8) months, significant decreases were observed in median ARR (1.1 [0.8–2.0] versus 0 [0–0.2], p < 0.001), EDSS score (3.5 [2.5–4.0] versus 2.0 [1.0–3.0], p < 0.001) and spinal cord lesion segments (5.0 [4.0–8.0] versus 3.0 [1.0–6.0], p < 0.001). The cumulative risk of relapses significantly decreased during the post- versus pre-RTX period (HR 0.238, 95%CI 0.160–0.356, p < 0.001) and on early therapy initiated within 24 months after disease onset versus delayed therapy (HR 0.506, 95%CI 0.258–0.994, p = 0.041). No serious side effects were recorded and all the subjects did not discontinue treatment due to RTX-related side effects.ConclusionOur research provided evidence supporting the lower-dose RTX strategy in treating NMOSD and reopened the issues of optimal dosage and therapy initiation timing.

To improve nutritive value and quality of chiffon cake, Haematococcus pluvialis powder was added to chiffon cake in this study. Nutrients of H. pluvialis was determined by HPLC and GC-MS. Using automatic color difference meter and texture analyser to explore the color and texture of chiffon cake with different levels of H. pluvialis. Antioxidant activity of chiffon cake and the effect of baking on astaxanthin were also investigated. The results showed that the content of astaxanthin, carbohydrate, protein and fatty acid was 20.93±0.41 mg/g DCW, 25.56%±0.94%, 19.97%±0.13% and 27.11%±0.25%. The color of chiffon cake turned orange red gradually, the hardness, elasticity and chewiness of chiffon cake were gradually increased, and antioxidant activity was improved with the increase of H. pluvialis addition. Specific volume was highest with the addition of 2.0% H. pluvialis. Scavengingrate of DPPH and hydroxyl radical increased by 119.92% and 9.17% with the addition of 4.0% H. pluvialis compared with the control group. The content of astaxanthin decreased sharply with the loss rate of 49.37%~62.17% after baking. Besides, algal fishy smell emitted with the addition of H. pluvialis increased, which affected the flavor of chiffon cake. Therefore, 1.0%~2.0% of H. pluvialis was appropriate for chiffon cake to improve its quality and nutritive value.

We develop Monet: a multichannel optical neural network architecture for a universal multiple-input multiple-channel optical computing based on a novel projection-interference-prediction framework, solving real-world advanced machine vision tasks optically.

With the increasing demand for reliable power supply and the widespread integration of distributed energy sources, the topology of distribution networks is subject to frequent changes. Consequently, the dynamic alterations in the connection relationships between distribution transformers and feeders occur frequently, and these changes are not accurately monitored by grid companies in real-time. In this paper, we present a data-driven machine learning approach for identifying the feeder-transformer relationship in distribution networks. Initially, we preprocess the collected three-phase voltage magnitude data of distribution transformers, addressing data quality and enhancing usability through three-phase voltage normalization. Subsequently, we derive the correlation coefficient calculations between distribution transformers, as well as between distribution transformers and feeders. To tackle the challenging task of determining the correlation coefficient threshold, we propose a multi-feature fusion approach. We extracted additional features from the feeders and combined them with the correlation coefficients to create a feature matrix. Machine learning algorithms were then applied to calculate the results. Through experimentation on a real distribution network in Jiangxi province, we demonstrated the effectiveness of the proposed method. When compared to other approaches, our method achieved outstanding results with an F1 score of 0.977, indicating high precision and recall. The precision value was 0.973 and the recall value was 0.981. Importantly, our method eliminates the need for additional measurement installations, as the required data can be obtained using existing collection devices. This significantly reduces the application cost associated with implementing our approach.

We investigate the influence of quasiperiodic modulations on one-dimensional non-Hermitian diamond lattices with an artificial magnetic flux θ that possess flat bands. Our study shows that the symmetry of these modulations and the magnetic flux θ play a pivotal role in shaping the localization properties of the system. When θ = 0, the non-Hermitian lattice exhibits a single flat band in the crystalline case, and symmetric as well as antisymmetric modulations can induce accurate mobility edges. In contrast, when $\theta = \pi$ , the clean diamond lattice manifests three dispersionless bands referred to as an ‘all-band-flat’ (ABF) structure, irrespective of the non-Hermitian parameter. The ABF structure restricts the transition from delocalized to localized states, as all states remain localized for any finite symmetric modulation. Our numerical calculations further unveil that the ABF system subjected to antisymmetric modulations exhibits multifractal-to-localized edges. Multifractal states are predominantly concentrated in the internal region of the spectrum. Additionally, we explore the case where θ lies within the range of $(0, \pi)$ , revealing a diverse array of complex localization features. Finally, we propose a classical electrical circuit scheme to realize the non-Hermitian flat-band chain with quasiperiodic modulations.

Sediment transport serves as a link for material exchange between land and sea. Using sediment traps, we can observe the capture and transport processes of sediments. Based on the sediment particle size distribution characteristics in Jiaozhou Bay, this paper analyzes the influence of a newly designed 3D sediment trap on the water–sand two-phase flow process inside and outside a trap device during its operation. Meanwhile, under a certain concentration condition, a numerical formula model is researched and proposed to evaluate the impact of the device’s structure, the environmental flow speed, and the particle size on particle capture efficiency. This model is based on the CFD-DPM coupling in Fluent 2021R1 software, and the particle filtration process is solved using a combination of porous media and UDF functions. Finally, by analyzing the distribution of sediment movement in the fluid domain, two concepts, namely the percentage of particles entering the tube and the effective capture rate, are proposed. Suggestions for optimizing the structure of the trap are put forward to achieve optimal capture effects.

In an increasingly complex and changing competitive environment, organizations inevitably face various conflicting demands, such as the contradiction between the psychological needs of employees and the organization’s performance requirements. Paradoxical leadership could focus on these competing needs of the organization and employees in multiple ways simultaneously. According to the trickle-down effect of social learning theory, we investigated whether and how paradoxical leadership may increase team adaptation and team performance. The study had a time-lagged survey design and included 254 team members and 60 leaders in 60 work teams in mainland China. The results of the structural equation modeling analysis indicated that paradoxical leadership is an essential predictor of team adaptation and performance, and that inclusive climate is mediating in this relationship. Our findings reveal a mechanism underlying the benefits of paradoxical leadership on team adaptation and team performance from a team-level perspective.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease (ILD) without an identifiable cause. If not treated after diagnosis, the average life expectancy is 3–5 years. Currently approved drugs for the treatment of IPF are Pirfenidone and Nintedanib, as antifibrotic drugs, which can reduce the decline rate of forced vital capacity (FVC) and reduce the risk of acute exacerbation of IPF. However these drugs can not relieve the symptoms associated with IPF, nor improve the overall survival rate of IPF patients. We need to develop new, safe and effective drugs to treat pulmonary fibrosis. Previous studies have shown that cyclic nucleotides participate in the pathway and play an essential role in the process of pulmonary fibrosis. Phosphodiesterase (PDEs) is involved in cyclic nucleotide metabolism, so PDE inhibitors are candidates for pulmonary fibrosis. This paper reviews the research progress of PDE inhibitors related to pulmonary fibrosis, so as to provide ideas for the development of anti-pulmonary fibrosis drugs.

Study region: Seventeen watersheds distributed throughout China Study focus: Climate and land-use changes result in significant impacts on watershed water yields. The water yield response to climate and land-use changes differs due to variations in watershed geographical, climatic, and socioeconomic characteristics. Effects of watershed characteristics on water yield response remain unclear. In this study, the InVEST model is used to simultaneously assess water yields of 17 typical watersheds distributed throughout China, with widely differing characteristics, and specific scenario simulations are conducted to compare similarities and differences among different watersheds in the water yield response to climate and land-use changes. New hydrological insights for the region: Results show that about three quarters of the investigated watersheds are characterized by increasing water yields from 1990 to 2020. The impact of climate change on water yield is similar in these watersheds. However, water yield response to land-use changes exhibits significant spatial differences, which is affected by watershed geographical and climatic characteristics. The mean annual water yield of Hai River watershed with two megacities (Beijing and Tianjin) is lowest among investigated watersheds, which is only 0.14 % of that in the Min River watershed in southeastern China. This implies that the mismatch of regional socioeconomic development and water availability remains a big challenge in China and requires rational cross-watershed management strategies.

Abstract Predicting the nature of each urban functional region based on the transfer rate of empty cars plays a crucial role in constructing smart cities and urban planning. The transfer rate of empty cars describes the probability of a taxi driving from one region to another without any passengers. It can reflect the main driving directions of taxies and the main flow directions of people among different urban functional regions. Although current researches have focused on the functional regions divided by remote sensing satellite images, there is almost no discussion on determining the nature of the region through taxi behaviour. The authors consider using taxi behaviour to classify urban functional regions. Besides, the attentional spatio‐temporal model (Attentional Gated Recurrent Unit, AGRU) is introduced in the work. The AGRU consists of three modules, which are the spatial feature extraction module, the temporal feature extraction module, and the attentional pooling mechanism. The model has been evaluated on the data set provided by Didi Chuxing and it has been compared with some typical models. The experimental results show that the AGRU can reflect spatio‐temporal information, and its attentional pooling mechanism can distinguish whether a region is the place of departure or the destination.

In this study, quasi-two-dimensional BixSn1−xO2 (BTO) thin films were fabricated using a liquid metal transfer method. The ultraviolet (UV) photodetector based on BTO thin films was constructed, and the ultrahigh responsivity of 589 A/W was observed at 300 nm UV light illumination. Interestingly, by dropping ethanol during light-off period, the recovery time induced by the persistent photoconductivity (PPC) effect is reduced from 1.65 × 103 s to 5.71 s. Furthermore, the recovery time can also be reduced by dropping methanol, propylene glycol, NaNO2, and Na2SO3 after light termination. The working mechanisms are attributed to the rapid consumption of holes stored in BTO thin films by reaction with those solutions. This work demonstrates that the BTO thin films have potential applications in high-performance UV detectors and present an innovation route to weaken the PPC effects in semiconductors by introducing chemical liquids on their surface.

Coastlines and estuaries as one of the most dynamic systems in the marine environment are influenced by not only natural factors but also various anthropogenic activities, but there have been limited comprehensive studies on the relationship between coastline variation and anthropogenic activities at the watershed and local scales. The Pearl River Estuary (PRE) in China has experienced remarkable coastline variation along with rapid economic development and urbanization. In this study, the coastline of the PRE was extracted using Landsat images during 1987-2017, and the impacts of multiple factors on coastline variation were explored with correlation analysis and regression analysis. Results suggested coastlines of the PRE demonstrated a clear expanding trend over recent decades, and the coastline length had increased by 74.81 km and the land area had increased by 679.25 km2 by 2017, and results also showed rapid coastline variation during 1987-2004 and relatively slow variation during 2004-2017. Artificial coastlines accounted for 80.90% of the total coastline length in 2017, while natural coastlines accounted for only 19.10%. In addition, reduction in the sand content caused by construction of reservoirs and river dredging in upstream areas played a dominant role in affecting natural coastline variation in the PRE, especially on the west coast. Moreover, the artificial coastline in the study area expanded quickly due to the influence of various human activities, among which the urbanization and land reclamation for aquaculture played a dominant role in affecting the artificial coastline variation on the east and west coasts, respectively. Results of this study have important value for understanding the individual and joint effects of various human activities on coastline variation and can provide important reference information for the sustainable management of the PRE as well as other estuaries.

The NLRP1 inflammasome functions as canonical cytosolic sensor in response to intracellular infections and is implicated in auto-inflammatory diseases. But the regulation and signal transduction mechanisms of NLRP1 are incompletely understood. Here, we show that the T60 variant of CARD8, but not the canonical T48 isoform, negatively regulates the NLRP1 inflammasome activation by directly interacting with the receptor molecule NLRP1 and inhibiting inflammasome assembly. Furthermore, our results suggest that different ASC preference in three types of inflammasomes, namely the ASC-indispensable NLRP1 inflammasome, ASC-dispensable mNLRP1b inflammasome and ASC-independent CARD8 inflammasome, is mainly caused by the CARD domain, not the UPA subdomain. Based on the systematic site-directed mutagenesis and structural analysis, we find that signal transduction of the NLRP1 inflammasome relies on multiple interaction surfaces at its CARD domain. Finally, our results partly explain how mutations in NLRP1 lead to its constitutive activation in auto-inflammatory diseases. In conclusion, our study not only reveals how CARD8 downregulates the NLRP1 inflammasome activation, but also provides insights into the assembly mechanisms of CARD-containing inflammasomes.

NUCLEAR FACTOR Y subunit alpha (NF-YA), together with NF-YB and NF-YC, regulates plant growth and development, as well as plant responses to biotic and abiotic stresses. Although extensive studies have examined the functions of NF-YAs in Arabidopsis thaliana, the roles of NF- YAs in Glycinme max are poorly understood. In this study, we identified a phosphorus (P) starvation-responsive NF-YA8 in soybean. The expression of GmNF-YA8 is induced by low P or low nitrogen in leaves, but not by potassium or iron starvation, respectively. GmNF-YA8 is localized in the nucleus and plasma membrane. Ectopic expression of GmNF-YA8 inhibits plant growth and delayed flowering in Arabidopsis. Exogenous application of gibberellic acid (GA) rescues the delayed flowering phenotype in Arabidopsis overexpressing GmNF-YA8 lines GmNF-YA8OE-05 and GmNF-YA8OE-20. Moreover, quantitative real time PCR (qRT-PCR) verified that overexpression of GmNF-YA8 downregulates GA20ox2 and GA3ox2 expression, but upregulates GA2ox2 and GA2ox3 that encode enzymes, which inactive bioactive GAs. Consistent with the late flowering phenotype of Arabidopsis trangenic lines that overexpress GmNF-YA8, the transcript levels of flowering-promoting genes AP1, CO, LFY, and SOC1 are reduced. In addition, overexpression of GmNF-YA8 promotes the emergence of lateral root (LR) primordium from epidermis rather than the initiation of LR in low P, and increases the LR density in low nitrogen. Our results provide insights into the roles of GmNF-YA8.

Hydrologic (water temperature) and hydrodynamic (water depth, flow velocity, and Froude number) factors affect fish spawning activities, and spawning grounds provide suitable hydrologic and hydrodynamic conditions for fish spawning to occur. However, locating fish spawning grounds is encumbered by uncertainty, particularly for pelagic spawners. This may be because such fish species have unique hydrologic and hydrodynamic requirements during their spawning periods, resulting in the wide-ranging approaches used to locate their spawning grounds. Accordingly, this study was designed to accurately locate fish spawning grounds through means of spatial suitability evaluation. For this experiment, the four major “Asian carp” target species were selected in the Dongta spawning reach, a tributary of the Pearl River. First, we investigated the historical information on the location of the spawning reaches. An acoustic doppler current profiler (ADCP) was used to measure topographic and hydrodynamic data of the spawning reaches during the spawning period. Then, based on the spatial clustering method, cluster analysis on spatial attributes (water depth, flow velocity, water temperature, and Froude number) was conducted on potential spawning grounds. The cluster analysis method uses k-means clustering; a method often employed to classify large amounts of data. Finally, we analyzed and evaluated the spatial suitability of spawning reaches by combining fish spawning suitability curves to obtain spatial preferences associated with fish spawning activities. Proportionally, results revealed a high suitability (>0.4) area (60.86%). Moreover, spawning suitability in curved river sections and deep pools in straight river sections were significantly higher than bifurcated sections. Furthermore, areas near the riverbanks were more suitable than mid-course sections of the river. Finally, the locations of six potential Asian carp spawning grounds were determined according to their spatial suitability. This study provides technical support to accurately locate spawning grounds for the fish that produce drifting eggs.

In a modern power system, to realize the safe operation of units and maintain the frequency stability of the power network, various means of frequency modulation can be adopted. However, there are some deficiencies in the real-time monitoring and assessment management of primary frequency modulation (PFM). To solve these problems, a PFM online monitoring and management method based on the “master–sub” control station model in a wide area measurement system (WAMS) is proposed as a control strategy to support frequency modulation of units and assist power network assessment. In the hybrid monitoring system strategy of the master control station and sub control station, the sub control station is built locally in the power plant to monitor the operational status of the unit in real-time and calculate the PFM assessment index. The master control station is built in the dispatching center, receiving the assessment data uploaded by the sub control station and sending the PFM simulation test instruction. This strategy can effectively improve the real-time performance of dynamic monitoring and parameter calculation, and reduce the probability of frame loss and communication instability in long-distance data transmission. The experimental results show that the PFM online monitoring management control strategy based on the “master–sub” control station model can effectively monitor the unit and provide data support for PFM assessment.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease, and its occurrence and development are mediated by cellular senescence. Drugs targeting senescent cells seem like a promising and efficacious strategy for IPF treatment. Previous studies have illustrated that pentoxifylline (PTX) may play a certain role in inhibiting pulmonary fibrosis and combating cellular senescence. In this study, we demonstrated that PTX administration inhibits pulmonary fibrosis development and cellular senescence in the bleomycin (BLM)-induced IPF mice model. Moreover, the expression levels of fibrosis-related genes and senescence-related genes in mice lung tissue and primary pulmonary fibroblasts illustrated lung fibroblasts’ vital role in these two processes. And the curative effect of PTX was completed mainly by acting on lung fibroblasts. Besides, during the whole treatment, delayed initiation or advanced halt of PTX administration would influence its effectiveness in reducing fibrotic and senescent traits in various degrees, and the latter influenced more. We further determined that a long period of PTX administration could bring noticeable benefits to mice in recovering BLM-induced lung fibrosis and suppressing age-associated cellular senescence. Moreover, it was still effective when PTX administration was used to treat senescent human fibroblasts. Thus, our findings manifested that PTX therapy is an efficient remedy for pulmonary fibrosis by suppressing cellular senescence.

Abstract Cytosolic inflammasomes are supramolecular complexes that are formed in response to intracellular pathogens and danger signals. However, as to date, the detailed description of a homotypic caspase recruitment domain (CARD) interaction between NLRP1 and ASC has not been presented. We found the CARD–CARD interaction between purified NLRP1CARD and ASCCARD experimentally and the filamentous supramolecular complex formation in an in vitro proteins solution. Moreover, we determined a high-resolution crystal structure of the death domain fold of the human ASCCARD. Mutational and structural analysis revealed three conserved interfaces of the death domain superfamily (Type I, II, and III), which mediate the assembly of the NLRP1CARD/ASCCARD complex. In addition, we validated the role of the three major interfaces of CARDs in assembly and activation of NLRP1 inflammasome in vitro. Our findings suggest a Mosaic model of homotypic CARD interactions for the activation of NLRP1 inflammasome. The Mosaic model provides insights into the mechanisms of inflammasome assembly and signal transduction amplification.

The high-tech industry plays a crucial role in reducing carbon emission and achieving green economic development. This research uses Meta-Frontier data envelopment analysis to measure the innovation efficiency level of the high-tech industry in China’s provinces from 1999 to 2018, compares the difference in this industry’s innovation efficiency under the regional Frontier and common Frontier, and inspects the convergence condition of its innovation efficiency in the three major areas of eastern, central, and western China. The results show under the regional Frontier that the difference in innovation efficiency of the western region’s high-tech industry is the biggest, while the difference in the central region is the smallest, and under the national common Frontier the innovation efficiency level of the eastern region’s high-tech industry is the highest, while that of the western region is the lowest. The regional pattern of innovation efficiency in the high-tech industry is consistent with the development trend of the regional economy. Moreover, by using the ratio of the technology gap ratio, we find that the eastern region has the potential optimal technology in China, whereas the central and western regions have large room for improvement. Lastly, the stochastic convergence test shows that the innovation efficiency of the central region’s high-tech industry presents a convergence trend, but the same trend does not occur in the western and eastern regions as well as for the whole country.

Abstract Background Transbronchial needle aspiration (TBNA) is a minimally invasive procedure performed to diagnose lymph node (LN) adenopathy. TBNA with and without endobronchial ultrasound (EBUS) guidance has a high diagnostic yield for malignant LN enlargement, but the value for diagnosing benign LN enlargement has been less thoroughly investigated. Methods We retrospectively evaluated 3540 patients with mediastinal LN enlargement who received TBNA. One hundred sixty-six patients with benign mediastinal lymphadenopathy were included and 293 LNs were biopsied. A positive result was defined as a specific histological abnormality. Conventional TBNA (cTBNA) and EBUS-TBNA, as well as cTBNA and transbronchial forceps biopsy (TBFB), were compared. The subgroup analysis was stratified by disease type and LN size. Results A diagnosis was made in 76.84% of the EBUS-TBNA and 61.31% of the cTBNA (P 20 mm the difference was marginal. These findings were confirmed in a group of independent patients who received cTBNA plus EBUS-TBNA. The diagnostic yield did not differ between cTBNA and TBFB, but significantly increased to 76.67% when both modalities were employed. Conclusions EBUS-TBNA is the preferred minimally invasive diagnostic method for benign mediastinal LN disease. Combined cTBNA and TBFB is a safe and feasible alternative when EBUS is unavailable.

The amylose content (AC) of rice endosperm starch varies from 0 to 35%, and is associated with rice cooking and eating quality. Soft rice has low AC, generally between 6% and 15%, and its eating quality is high whether it is consumed hot or cold. However, the appearance quality of current soft rice cultivars needs to be improved, especially opaque endosperm. Conventional genetic engineering has improved some agronomic traits of soft rice varieties, but not endosperm appearance. In the present study, a RNAi construct of the soluble starch synthase II-2 (SSSII-2) and the hygromycin phosphotransferase (HPT) gene were introduced into an elite japonica rice variety, Kangtiaowuyunjing (KWY8) by co-transformation. Several selectable marker-free (SMF) transgenic lines were obtained, and SSSII-2 expression was significantly downregulated in selected transgenic lines, resulting in lower AC of the endosperm. The physicochemical properties of the transgenic rice kernels, including gel consistency (GC) and rapid visco analyzer (RVA) profile, differed significantly from those of wild-type rice and were similar to those of a soft rice variety, Nanjing 46 (NJ46). These findings indicate that the cooking, eating, and processing qualities of transgenic rice are comparable to those of NJ46. However, the transgenic rice endosperm retained a transparent appearance under low-moisture conditions. Thus, SMF SSSII-2 RNAi rice provides a resource for breeding soft rice with transparent endosperm. Keywords: Rice (Oryza sativa L.), SSSII-2, RNAi, Starch quality, SMF transgenic lines

Dual robotic manipulators are robotic systems that are developed to imitate human arms, which shows great potential in performing complex tasks. Collision-free motion planning in real time is still a challenging problem for controlling a dual robotic manipulator because of the overlap workspace. In this paper, a novel planning strategy under physical constraints of dual manipulators using dynamic neural networks is proposed, which can satisfy the collision avoidance and trajectory tracking. Particularly, the problem of collision avoidance is first formulated into a set of inequality formulas, whereas the robotic trajectory is then transformed into an equality constraint by introducing negative feedback in outer loop. The planning problem subsequently becomes a Quadratic Programming (QP) problem by considering the redundancy, the boundaries of joint angles and velocities of the system. The QP is solved using a convergent provable recurrent neural network that without calculating the pseudo-inversion of the Jacobian. Consequently, numerical experiments on 8-DoF modular robot and 14-DoF Baxter robot are conducted to show the superiority of the proposed strategy.

Robotic bin-picking is a common process in modern manufacturing, logistics, and warehousing that aims to pick-up known or unknown objects with random poses out of a bin by using a robot-camera system. Rapid and accurate object pose estimation pipelines have become an escalating issue for robot picking in recent years. In this paper, a fast 6-DoF (degrees of freedom) pose estimation pipeline for random bin-picking is proposed in which the pipeline is capable of recognizing different types of objects in various cluttered scenarios and uses an adaptive threshold segment strategy to accelerate estimation and matching for the robot picking task. Particularly, our proposed method can be effectively trained with fewer samples by introducing the geometric properties of objects such as contour, normal distribution, and curvature. An experimental setup is designed with a Kinova 6-Dof robot and an Ensenso industrial 3D camera for evaluating our proposed methods with respect to four different objects. The results indicate that our proposed method achieves a 91.25% average success rate and a 0.265s average estimation time, which sufficiently demonstrates that our approach provides competitive results for fast objects pose estimation and can be applied to robotic random bin-picking tasks.

In this study, we investigate the problem of cooperative kinematic control for multiple redundant manipulators under partially known information using recurrent neural network (RNN). The communication among manipulators is modeled as a graph topology network with the information exchange that only occurs at the neighbouring robot nodes. Under partially known information, four objectives are simultaneously achieved, i.e, global cooperation and synchronization among manipulators, joint physical limits compliance, neighbor-to-neighbor communication among robots, and optimality of cost function. We develop a velocity observer for each individual manipulator to help them to obtain the desired motion velocity information. Moreover, a negative feedback term is introduced with a higher tracking precision. Minimizing the joint velocity norm as cost function, the considered cooperative kinematic control is built as a quadratic programming (QP) optimization problem integrating with both joint angle and joint speed limitations, and is solved online by constructing a dynamic RNN. Moreover, global convergence of the developed velocity observer, RNN controller and cooperative tracking error are theoretically derived. Finally, under a fixed and variable communication topology, respectively, application in using a group of iiwa R800 redundant manipulators to transport a payload and comparison with the existing method are conducted. Among the simulative results, the robot group synchronously achieves the desired circle and rhodonea trajectory tracking, with higher tracking precision reaching to zero. When joint angles and joint velocities tend to exceed the setting constraints, respectively, they are constrained into the upper and lower bounds owing to the designed RNN controller.

When terahertz imaging technology is used for the nondestructive testing of composite materials, the signal is often affected by the experimental environment and internal noise of the system, as well as the absorption and scattering effect of the tested materials. The obtained image has degradation phenomena such as low contrast, poor resolution of small targets and blurred details. In order to improve the image quality, this paper proposes a novel method for the enhancement of composite materials’ terahertz image by using unsharp masking and guided filtering technology. The method includes the processing steps of hard threshold shrinkage denoising based on discrete wavelet transform, amplitude imaging, unsharp masking, guided filtering, contrast stretching, and pseudo-color mapping. In this paper, these steps are reasonably combined and optimized to obtain the final resulting image. To verify the effectiveness of the proposed method, a 150–220 GHz high frequency terahertz frequency modulated radar imaging system was used to image three commonly used sandwich structure composites, and the enhancement processing were carried out. The resulting images with significantly enhanced contrast, detail resolution and edge information were obtained, and the prefabricated defects were all detected; Five objective evaluation indexes including standard deviation, mean gradient, information entropy, energy gradient and local contrast were used to compare and analyze the processing results of different image enhancement methods. The subjective and objective evaluation results showed that the proposed method can effectively suppress the noise in terahertz detection signals, enhance the ability of defect detection and positioning, and improve the accuracy of detection. The proposed method in this paper is expected to play a positive role in improving the practicability of terahertz imaging detection technology and expanding its application fields.

Ga2O3 has emerged as a promising ultrawide bandgap semiconductor for numerous device applications owing to its excellent material properties. In this paper, we present a comprehensive review on major advances achieved over the past thirty years in the field of Ga2O3-based gas sensors. We begin with a brief introduction of the polymorphs and basic electric properties of Ga2O3. Next, we provide an overview of the typical preparation methods for the fabrication of Ga2O3-sensing material developed so far. Then, we will concentrate our discussion on the state-of-the-art Ga2O3-based gas sensor devices and put an emphasis on seven sophisticated strategies to improve their gas-sensing performance in terms of material engineering and device optimization. Finally, we give some concluding remarks and put forward some suggestions, including (i) construction of hybrid structures with two-dimensional materials and organic polymers, (ii) combination with density functional theoretical calculations and machine learning, and (iii) development of optical sensors using the characteristic optical spectra for the future development of novel Ga2O3-based gas sensors.