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Abstract Community detection is an important method to analyze the characteristics and structure of community networks, which can excavate the potential links between nodes and further discover subgroups from complex networks. However, most of the existing methods only unilaterally consider the direct link topology without comprehensively considering the internal and external characteristics of the community as well as the result itself, which fails to maximize the access to the network information, thus affecting the effectiveness of community detection. To compensate for this deficiency, we propose a new community detection method based on multi-constraint non-negative matrix factorization, named orthogonal regular sparse constraint non-negative matrix factorization (ORSNMF). Based on the network topology, the ORSNMF algorithm models the differences of the outside of the community, the similarities of the nodes inside the community, and the sparseness of the community membership matrices at the same time, which together guides the iterative learning process to better reflect the underlying information and inherent attributes of the community structure in order to improve the correct rate of dividing subgroups. An algorithm with convergence guarantee is also proposed to solve the model, and finally a large number of comparative experiments are conducted, and the results show that the algorithm has good results.

Low and unstable digestion performance is a challenging issue for anaerobic digestion, which prompts researchers to develop new strategies. In addition to traditional approaches such as co-digestion, pre-treatment, and recirculation, some emerging strategies, namely additive processes and microaeration, have also been recognized and developed in recent years. Many studies have evaluated the effect of these strategies on digestion performance. However, their comprehensive analysis is lacking, especially regarding the mechanisms of the different strategies. This review presents a comprehensive overview of research progress on these strategies based on the latest research, considering the five main strategies listed above. Through critical thinking, a summary of their mechanism, reactor performance, and availability of these strategies is presented. The results demonstrate that the contribution of microaeration is mainly to balance the composition and activity of hydrolysis, acidogenesis, and methanogenic archaea. Recirculation and co-digestion mainly balance mass and reaction environments. Pre-treatment, such as removing lignin, reducing cellulose crystallinity, and increasing the substrate-specific surface area, makes the characteristics of the substrate more conducive to the digestion of microorganisms. The mechanism of additive strategies varies greatly depending on the type of additive, such as enhancing interspecies electron transfer through conductive materials, resisting adverse digestion conditions through functional microbial additives, and accelerating nutrient absorption by regulating the bioavailability of trace elements. Although these strategies have different mechanisms for promoting digestion performance, their ultimate effect is to allow the parameters of the reactor to reach an ideal status and then achieve a balance among the substance, microorganisms, and water in an anaerobic reactor.

Objective: Bentall procedure used to be standard operation for involved aortic root in acute type A aortic dissection (ATAAD). But aortic root repair for preserving valve is still controversial in ATAAD. This study aimed to evaluate the midterm outcomes of aortic root repair by comparing with Bentall approach. Methods: A retrospective analysis of 1075 ATAAD patients with aortic root involvement was conducted. The patients were divided into aortic root repair group (n = 447) and Bentall group (n = 628). The propensity score matching analysis (PSMA) was used to adjust the baseline. Results: The median follow-up was 44 months (interquartile range, 17–65 months; range, 1–130 months). The 30-day mortality in the repair and replacement groups was 15.0 % and 12.9 % (P = 0.327) respectively; the late overall mortality was 15.9 % and 14.0 % (P = 0.394) respectively. The Kaplan-Meier 10-year survival and free-from-reoperation was 86.0 % and 92.5 % respectively in the repair group. After PSMA, the cumulative survival rate [Hazard Ratio (HR) 0.685; 95 % Confidence Interval (CI) 0.457–1.027; P = 0.747]) and reoperation rate (HR 0.308; 95 % CI 0.070–1.355; P = 0.157) was not significantly higher in the repair group than in the Bentall group. Conclusion: The mid-term outcome of aortic root repair is probably not inferior to Bentall procedure. Therefore, root repair is an alternative approach in ATAAD with the advantage of preserving native valve.

Abstract Human enterovirus A71 (EV‐A71) is a significant etiological agent responsible for epidemics of hand, foot, and mouth disease (HFMD) in Asia‐Pacific regions. There are presently no licensed antivirals against EV‐A71, and the druggable target for EV‐A71 remains very limited. The phenotypic hit 10,10′‐bis(trifluoromethyl) marinopyrrole A derivative, herein termed MPA‐CF3, is a novel potent small‐molecule inhibitor against EV‐A71, but its pharmacological target(s) and antiviral mechanisms are not defined. Here, quantitative chemoproteomics deciphered the antiviral target of MAP‐CF3 as host factor coatomer subunit zeta‐1 (COPZ1). Mechanistically, MPA‐CF3 disrupts the interaction of COPZ1 with the EV‐A71 nonstructural protein 2C by destabilizing COPZ1 upon binding. The destruction of this interaction blocks the coatomer‐mediated transport of 2C to endoplasmic reticulum, and ultimately inhibits EV‐A71 replication. Taken together, our study disclosed that MPA‐CF3 can be a structurally novel host‐targeting anti‐EV‐A71 agent, providing a structural basis for developing the COPZ1‐targeting broad‐spectrum antivirals against enteroviruses. The mechanistic elucidation of MPA‐CF3 against EV‐A71 may offer an alternative COPZ1‐involved therapeutic pathway for enterovirus infection.

Abstract Federated Learning (FL) suffers from the Non-IID problem in practice, which poses a challenge for efficient and accurate model training. To address this challenge, prior research has introduced clustered FL (CFL), which involves clustering clients and training them separately. Despite its potential benefits, CFL can be computationally and communicationally expensive when the data distribution is unknown beforehand. This is because CFL involves the entire neural networks of involved clients in computing the clusters during training, which can become increasingly time-consuming with large-sized models. To tackle this issue, this paper proposes an efficient CFL approach called LayerCFL that employs a Layer-wised clustering technique. In LayerCFL, clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental methods. Our experimental results demonstrate the effectiveness of LayerCFL in mitigating the impact of Non-IID data, improving the accuracy of clustering, and enhancing computational efficiency.

A water-soluble ternary copolymer bearing carboxyl, sulfonic, and amide functional groups was synthesized using ammonium persulfate-catalyzed free radical polymerization in water, resulting in high monomer conversion. This copolymer was then complexed with aluminum sulfate, forming an admixture containing Al(SO4)(OH)·5H2O, which was subsequently combined with silica gel. Characterization revealed that the synthesized copolymer formed a large, thin membrane that covered both the aluminum compounds and the silica gel blocks. The introduction of this complex admixture, combining the copolymer and aluminum sulfate, not only reduced the setting times of the cement paste but also enhanced the mechanical strengths of the mortar compared to using aluminum sulfate alone. The complex admixture led to the formation of katoite, metajennite, and C3A (tricalcium aluminate) in the mortar, demonstrating significant linking effects, whereas pure aluminum sulfate could not completely transform C3S within 24 h. Further addition of silica gel to the complex admixture further shortened the setting times of the paste, slightly reduced compressive strength, but improved flexural strength compared to the initial complex admixture. The silicon components appeared to fill the micropores and mesopores of the mortar, accelerating cement setting and enhancing flexural strength, while slightly decreasing compressive strength. This study contributed to the development of new cementing accelerators with improved hardening properties.

Two water-soluble block copolymers composed of acrylic acid (AA), 2-acrylamido-2-methylpropane sulfonic acid (AMPS), and optionally maleic anhydride (MAH) were synthesized through ammonium persulfate-catalyzed free radical polymerization in water. The introduction of aluminum sulfate (AS) into the resulting mixtures significantly reduced the setting times of the paste and enhanced the mechanical strength of the mortar compared to both the additive-free control and experiments facilitated solely by pure AS. This improvement was primarily attributed to the inhibition of rapid Al3+ hydrolysis, which was achieved through coordination of the synthesized block copolymers, along with the formation of newly identified hydrolytic intermediates. Notably, the ternary copolymer (AA–AMPS–MAH) exhibited superior performance compared to that of the binary copolymer (AA–AMPS). In the early stages of cement setting, clusters of ettringite (AFt) were found to be immobilized over newly detected linkage phases, including unusual calcium silicate hydrate and epistilbite. In contrast to the well-documented role of polymers in retarding cement hydration, this study presents a novel approach by providing both accelerating and hardening agents for cement setting, which has significant implications for the future design of cement additives.

Target detection technology plays a crucial role in fishery ecological monitoring, fishery diversity research, and intelligent aquaculture. Deep learning, with its distinct advantages, provides significant convenience to the fishery industry. However, it still faces various challenges in practical applications, such as significant differences in image species and image blurring. To address these issues, this study proposes a multi-scale, multi-level, and multi-stage cross-domain feature fusion model. In order to train the model more effectively, a new data set called Fish52 (multi-scene fish data set, a data set containing 52 fish species) was constructed, on which the model achieved an mAP (mean average precision is a key measure of model performance) of 82.57%. Furthermore, we compared prevalent one-stage and two-stage detection methods on the Lahatan (single-scene fish data set) and Fish30 data set (a data set containing 30 fish species) and tested them on the F4k (Fish4Knowledge (F4K) is a data set focused on fish detection and identification) and FishNet data set (it is a data set containing 94,532 images from 17,357 aquatic species). The mAP of our proposed model on the Fish30, Lahatan, F4k, and FishNet data sets reaches 91.72%, 98.7%, 88.6%, and 81.5%, respectively, outperforming existing mainstream models. Comprehensive empirical analysis indicates that our model possesses a high generalization ability and reaches advanced performance levels. In this study, the depth of the model backbone is deepened, a novel neck structure is proposed, and a new module is embedded therein. To enhance the fusion ability of the model, a new attention mechanism module is introduced. In addition, in the adaptive decoupling detection head module, introducing classes with independent parameters and regression adapters reduces interaction between different tasks. The proposed model can better monitor fishery resources and enhance aquaculture efficiency. It not only provides an effective approach for fish detection but also has certain reference significance for the identification of similar targets in other environments and offers assistance for the construction of smart fisheries and digital fisheries.

Enhancing the path planning capabilities of ships is crucial for ensuring navigation safety, saving time, and reducing energy consumption in complex maritime environments. Traditional methods, reliant on static algorithms and singular models, are frequently limited by the physical constraints of ships, such as turning radius, and struggle to adapt to the maritime environment’s variability and emergencies. The development of reinforcement learning has introduced new methods and perspectives to path planning by addressing complex environments, achieving multi-objective optimization, and enhancing autonomous learning and adaptability, significantly improving the performance and application scope. In this study, we introduce a two-stage path planning approach for large ships named MAPF–DQN, combining Manipulation-Compliant Artificial Potential Field (MAPF) with Deep Q-Network (DQN). In the first stage, we improve the reward function in DQN by integrating the artificial potential field method and use a time-varying greedy algorithm to search for paths. In the second stage, we use the nonlinear Nomoto model for path smoothing to enhance maneuverability. To validate the performance and effectiveness of the algorithm, we conducted extensive experiments using the model of “Yupeng” ship. Case studies and experimental results demonstrate that the MAPF–DQN algorithm can find paths that closely match the actual trajectory under normal environmental conditions and U-shaped obstacles. In summary, the MAPF–DQN algorithm not only enhances the efficiency of path planning for large ships, but also finds relatively safe and maneuverable routes, which are of great significance for maritime activities.

In the context of Industry 4.0, bearings, as critical components of machinery, play a vital role in ensuring operational reliability. The detection of their health status is thus of paramount importance. Existing predictive models often focus on point predictions of bearing lifespan, lacking the ability to quantify uncertainty and having room for improvement in accuracy. To accurately predict the long-term remaining useful life (RUL) of bearings, a novel time convolutional network model with an attention mechanism-based soft thresholding decision residual structure for quantifying the lifespan interval of bearings, namely TCN-AM-GPR, is proposed. Firstly, a spatio-temporal graph is constructed from the bearing sensor signals as the input to the prediction model. Secondly, a residual structure based on a soft threshold decision with a self-attention mechanism is established to further suppress noise in the collected bearing lifespan signals. Thirdly, the extracted features pass through an interval quantization layer to obtain the RUL and its confidence interval of the bearings. The proposed methodology has been verified using the PHM2012 bearing dataset, and the comparison of simulation experiment results shows that TCN-AM-GPR achieved the best point prediction evaluation index, with a 2.17% improvement in R2 compared to the second-best performance from TCN-GPR. At the same time, it also has the best interval prediction comprehensive evaluation index, with a relative decrease of 16.73% in MWP compared to the second-best performance from TCN-GPR. The research results indicate that TCN-AM-GPR can ensure the accuracy of point estimates, while having superior advantages and practical significance in describing prediction uncertainty.

Flemingia philippinensis, a polyphenol-rich plant, holds potential for improving inflammation, but its mechanisms are not well understood. Therefore, this study employed network pharmacology and molecular docking to explore the mechanism by which Flemingia philippinensis ameliorates inflammation. In this study, 29 kinds of active ingredients were obtained via data mining. Five main active components were screened out for improving inflammation, which were flemichin D, naringenin, chrysophanol, genistein and orobol. In total, 52 core targets were identified, including AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor (TNF), B-cell lymphoma-2 (BCL2), serum albumin (ALB), and estrogen receptor 1 (ESR1). Gene ontology (GO) enrichment analysis identified 2331 entries related to biological processes, 98 entries associated with cellular components, and 203 entries linked to molecular functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis yielded 149 pathways, including those involved in EGFR tyrosine kinase inhibitor resistance, endocrine resistance, and the PI3K–Akt signaling pathway. Molecular docking results showed strong binding effects between the main active components and the core targets, with binding energies less than −5 kcal/mol. In summary, this study preliminarily elucidated the underlying mechanisms by which Flemingia philippinensis, through a multi-component, multi-target, and multi-pathway approach, ameliorates inflammation. This provides a theoretical foundation for the subsequent application of Flemingia philippinensis in inflammation amelioration.

Abstract Human telomeres are linked to genetic instability and a higher risk of developing cancer. Therefore, to improve the dismal prognosis of pancreatic cancer patients, a thorough investigation of the association between telomere-related genes and pancreatic cancer is required. Combat from the R package “SVA” was performed to correct the batch effects between the TCGA-PAAD and GTEx datasets. After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis. Data from the ICGC, GSE62452, GSE71729, and GSE78229 cohorts were used as test cohorts for validating the prognostic signature. The major impact of the signature on the tumor microenvironment and its response to immune checkpoint drugs was also evaluated. Finally, PAAD tissue microarrays were fabricated and immunohistochemistry was performed to explore the expression of this signature in clinical samples. After calculating 502 telomere-associated DEGs, we constructed a three-gene prognostic signature (DSG2, LDHA, and RACGAP1) that can be effectively applied to the prognostic classification of pancreatic cancer patients in multiple datasets, including TCGA, ICGC, GSE62452, GSE71729, and GSE78229 cohorts. In addition, we have screened a variety of tumor-sensitive drugs targeting this signature. Finally, we also found that protein levels of DSG2, LDHA, and RACGAP1 were upregulated in pancreatic cancer tissues compared to normal tissues by immunohistochemistry analysis. We established and validated a telomere gene-related prognostic signature for pancreatic cancer and confirmed the upregulation of DSG2, LDHA, and RACGAP1 expression in clinical samples, which may provide new ideas for individualized immunotherapy.

Abstract Background Metformin as a first-line clinical anti-diabetic agent prolongs the lifespan of model animals and promotes cell proliferation. However, the molecular mechanisms underlying the proliferative phenotype, especially in epigenetics, have rarely been reported. The aim of this study was to investigate the physiological effects of metformin on female germline stem cells (FGSCs) in vivo and in vitro, uncover β-hydroxybutyrylation epigenetic modification roles of metformin and identify the mechanism of histone H2B Lys5 β-hydroxybutyrylation (H2BK5bhb) in Gata-binding protein 2 (Gata2)-mediated proliferation promotion of FGSCs. Methods The physiological effects of metformin were evaluated by intraperitoneal injection and histomorphology. The phenotype and mechanism studies were explored by cell counting, cell viability, cell proliferation assay and protein modification omics, transcriptomics, chromatin immunoprecipitation sequencing in FGSCs in vitro. Results We found that metformin treatment increased the number of FGSCs, promoted follicular development in mouse ovaries and enhanced the proliferative activity of FGSCs in vitro. Quantitative omics analysis of protein modifications revealed that H2BK5bhb was increased after metformin treatment of FGSCs. In combination with H2BK5bhb chromatin immunoprecipitation and transcriptome sequencing, we found that Gata2 might be a target gene for metformin to regulate FGSC development. Subsequent experiments showed that Gata2 promoted FGSC proliferation. Conclusion Our results provide novel mechanistic understanding of metformin in FGSCs by combining histone epigenetics and phenotypic analyses, which highlight the role of the metformin-H2BK5bhb-Gata2 pathway in cell fate determination and regulation.

Abstract Advances in data assimilation (DA) methods and the increasing amount of observations have continuously improved the accuracy of initial fields in numerical weather prediction during the last decades. Meanwhile, in order to effectively utilize the rapidly increasing data, Earth scientists must further improve DA methods. Recent studies have introduced machine learning (ML) methods to assist the DA process. In this paper, we explore the potential of a four‐dimensional variational (4DVar) constrained neural network (NN) method for accurate DA. Our NN is trained to approximate the solution of the variational problem, thereby avoiding the need for expensive online optimization when generating the initial fields. In the context that the full‐field system truths are unavailable, our approach embeds the system's kinetic features described by a series of analysis fields into the NN through a 4DVar‐form loss function. Numerical experiments on the Lorenz96 physical model demonstrate that our method can generate better initial fields than most traditional DA methods at a low computational cost, and is robust when assimilating observations with higher error outside of the distributions where it is trained. Furthermore, our NN‐based DA model is effective against Lorenz96 physical models with larger variable numbers. Our approach exemplifies how ML methods can be leveraged to improve both the efficiency and accuracy of DA techniques.

Amid the expanding green consumer market, companies are increasingly emphasizing their eco-friendly contributions in advertising. This study delves into the value delivery process within green consumption, guided by the service-dominant logic (S-D logic). A survey-based experiment with 589 responses reveals that the information frame in advertising significantly impacts consumer value perceptions and purchase intentions. Notably, the temporal frame of advertising plays a pivotal role, influencing consumers' value perceptions. Future framing enhances emotional value perceptions, while past framing promotes environmental value perceptions. This research enhances our understanding of value delivery mechanisms in green consumption and holds valuable implications for marketers of sustainable products.

In order to solve the problems of strip break and strip bulging in the commissioning process of thin strip continuous casting industrial production line and improve the solidification structure and surface quality of thin strip continuous casting, an unsteady model of the twin roll strip continuous casting process was established based on the finite element software ProCAST. The temperature field and solidification structure of Q195 solidification process were simulated, and the single variable method was used to study the effects of different process parameters, including casting temperature, drawing speed, heat transfer coefficient and molten pool height, on the temperature field and solidification structure of the solidification process. The results show that the optimal parameters in the twin roll strip continuous casting process of Q195 steel under the existing working conditions are as follows: casting temperature of 1 590 ℃, casting speed of 10 m/s, heat transfer coefficient of 2 000 W/(m2·K), and molten pool height of 188 mm, which can effectively prevent strip break and strip bulging, and can achieve the goal of refining grains and improving the quality of thin strip billets. It is of great significance to study the variation of temperature field, solidification structure and stress field in the process of strip solidification for improving the quality of strip and promoting the localization of strip casting and rolling process.

This study evaluated the disparities in growth performance and nutritional composition between two common Chinese mitten crab varieties, “Jianghai 21” and “Changjiang 2”, cultured in Jiangxi Province. Over the breeding period, parameters such as body weight, height, carapace length and width, and lengths of the second and third legs exhibited increases. Growth indices of “Jianghai 21” surpassed those of “Changjiang 2” during the intermediate and late breeding phases. While “Changjiang 2” demonstrated significantly higher crude lipid content than “Jianghai 21”, the latter exhibited markedly higher levels of total amino acids (TAAs), essential amino acids (EAAs), and delectable amino acids. Although overall nutrient composition did not significantly differ, TAA and EAA contents in the hepatopancreas were notably greater in “Jianghai 21” than in “Changjiang 2”. Moreover, a total of 901 differentially expressed genes (DEGs) were identified between the two varieties, with 560 upregulated and 341 downregulated. These DEGs were primarily associated with pathways related to fatty acid degradation, metabolism, and growth regulation. The observed variances in growth performance and nutrient composition between “Jianghai 21” and “Changjiang 2” suggest potential regulation by relevant genes. In summary, the results showed that the growth performance of “Jianghai 21” is greater than that of “Changjiang 2”, offering valuable insights for the selection of aquaculture varieties in the region.

Interplanting is an efficient method of improving nutrient utilization. However, the impact of intraspecific interplanting on rhizosphere microbial nitrogen cycling needs to be studied further. In this study, two corn cultivars were selected as the materials: Zhengdan958 (ZD958, high nitrogen use efficiency) and Denghai3622 (DH3622, low nitrogen use efficiency). Three planting patterns (interplanting, ZD958 monocropping, and DH3622 monocropping) were set up to study the effects of interplanting on crop growth and rhizosphere microbial nitrogen cycle function under two nitrogen levels: low nitrogen (140 kg N ha−1) and normal nitrogen (280 kg N ha−1). The results showed that the grain yield and nitrogen content in interplanting were significantly increased due to an enhanced leaf area index and root dry weight. The nitrogen accumulation and nitrogen use efficiency were enhanced by 8.14% and 19.38% in interplanting, which resulted in reductions in NH4+ and NO3− content in the rhizosphere. Interplanting enhanced rhizosphere nitrogen cycling processes; nitrification, denitrification, and nitrate reduction were increased. This study demonstrated that interplanting promotes corn nitrogen acquisition from the soil and indirectly regulates rhizosphere microbial function. These findings imply that the intraspecific interplanting of crops with appropriate functional traits is a promising approach to establishing diversified, productive, and efficient resource utilization ecosystems.

A study was conducted to evaluate the effects of dietary lentinan (LNT) on growth performance, nonspecific immunity and disease resistance to Aeromonas hydrophila in largemouth bass (Micropterus salmoides). Largemouth bass (initial weight: 10.31 ± 0.25 g) were divided into the CK, L1, L2, L3, L4 and L5 groups in triplicate and fed with diets containing LNT at 0%, 0.05%, 0.1%, 0.2%, 0.3% and 0.4% for eight weeks. Results showed that FBW, WGR, SGR and FE were significantly increased in the L3, L4 and L5 groups compared to the CK group. VSI, HSI, crude protein, crude fat and crude ash were similar among different treatment groups. Trypsin and lipase activities of intestinal tract were elevated in the L4 and L5 groups. The length of intestinal microvilli was increased in all experimental groups (P

Abstract Background Corrected QT (QTc) interval has been reported to be associated with type 2 diabetes. This study aimed to explore the relationship between different glucose tolerance and QTc intervals among middle-aged and older Chinese individuals. Methods We conducted a cross-sectional analysis that included 9898 subjects (3194 men and 6704 women) in a Chinese population. Glucose tolerance was studied during the oral glucose tolerance test (OGTT). Insulin, blood pressure, hemoglobin A1c (HbA1c), serum lipids, hepatic transaminases and waist-to-hip ratio were assessed. The QTc interval was derived from ECG recordings, and the subjects were stratified based on different glucose tolerance. Results QTc interval levels were increased significantly in the subjects with abnormal glucose metabolism compared with the normal glucose regulation group. Multiple regression analyses showed that the QTc interval was significantly associated with fasting plasma glucose, 2-h OGTT plasma glucose and HbA1c. The odds ratio of prolonged QTc was 1.396 for impaired glucose regulation (IFG)/impaired fasting glucose (IGT) (95% CI 0.126–1.730), and 1.342 for type 2 diabetes (95% CI 0.142–1.577) after all potential confounders were adjusted. Conclusions Impaired glucose tolerance (IGR) and diabetes are associated with prolonged QTc intervals among middle-aged and older Chinese individuals. Abnormal glucose regulation can be used to monitor the QTc interval in the population.

Recent works have illustrated that many facial privacy protection methods are effective in specific face recognition algorithms. However, the COVID-19 pandemic has promoted the rapid innovation of face recognition algorithms for face occlusion, especially for the face wearing a mask. It is tricky to avoid being tracked by artificial intelligence only through ordinary props because many facial feature extractors can determine the ID only through a tiny local feature. Therefore, the ubiquitous high-precision camera makes privacy protection worrying. In this paper, we establish an attack method directed against liveness detection. A mask printed with a textured pattern is proposed, which can resist the face extractor optimized for face occlusion. We focus on studying the attack efficiency in adversarial patches mapping from two-dimensional to three-dimensional space. Specifically, we investigate a projection network for the mask structure. It can convert the patches to fit perfectly on the mask. Even if it is deformed, rotated and the lighting changes, it will reduce the recognition ability of the face extractor. The experimental results show that the proposed method can integrate multiple types of face recognition algorithms without significantly reducing the training performance. If we combine it with the static protection method, people can prevent face data from being collected.

Abstract In this study, the passivity‐based robust control and tracking for Hamiltonian systems with unknown perturbations by using the operator‐based robust right coprime factorisation method is concerned. For the system with unknown perturbations, a design scheme is proposed to guarantee the uncertain non‐linear systems to be robustly stable while the equivalent non‐linear systems is passive, meanwhile the asymptotic tracking property of the plant output is discussed. Moreover, the design scheme can be also used into the general Hamiltonian systems while the simulation is used to further demonstrate the effectiveness of the proposed method.

We propose to combine a circularly polarized first-color laser with a linearly polarized second-color laser to control the polarization of THz radiations in the low-frequency region from single-layer graphene. We find that the THz ellipticity can be greatly adjusted by varying the wavelength of second color, and it can be slightly modified by varying the intensity ratio of two colors. We then show that the polarization direction of THz emissions can be dramatically changed by changing the phase difference between two colors. We also identify that the intensity, ellipticity, and polarization direction of THz wave can be changed simultaneously with the time delay between two colors. These can be understood by analyzing the electron currents, intensities of THz emissions in two orthogonal directions, and the phase difference between them. Our proposed scheme can be easily performed in the experiment based on the laser technology nowadays.

In this paper, a novel miniature Joule-Thomson (J-T) cryocooler was proposed and its steady-state cooling characteristics were analyzed via CFD numerical simulation. The performance characteristics were assessed for nitrogen under different inlet pressure and temperature, as well as different orifice widths. The results have outlined the structural advantages of the cooler – its excellent cooling performance, small axial height and simple fabrication. Owing to the structural characteristics of the spiral channel and the centrifugal force and gas viscous force actions, the fluid formed vortexes and secondary flow on the axial shear plane. This improved the heat transfer between the incoming flow and backflow fluid. Therefore, the heat transfer increase will further reduce the cooler stable operating temperature, and the minimum temperature can reach 138.3 K when the inlet pressure is 10 MPa. Meanwhile, there is an optimal throttle orifice width δ value between 0.1 mm and 0.2 mm, the cryocooler can get maximum cooling capacity under minimum temperature. In addition, the inlet pressure increases with the decrease in the throttle orifice width and inlet temperature. The minimum cooler temperature is gradually reduced, extending the cooling duration time. On the other hand, should the minimum temperature and cooling duration time decrease, the mass flow rate will also increase.

Biological yield indicates the potential for increasing yield. Leaf carbon metabolism plays an important role in the biomass accumulation of rapeseed (Brassica napus L.). Field experiments with the hybrid HZ62 (with a conventional plant architecture) grown in 2016–2017, and HZ62 and accession 1301 (with a compact plant architecture) grown in 2017–2018 were conducted to characterize the physiological and proteomic responses of leaf photosynthetic carbon metabolism to density and row spacing configurations. The densities were set at 15×104 ha−1 (D1), 30×104 ha−1 (D2), and 45×104 ha−1 (D3) (main plot), with row spacings of 15 cm (R15), 25 cm (R25), and 35 cm (R35) (subplot). Individual and plant population biomass accumulation was greatest at R25, R15, and R15 for D1, D2, and D3, respectively, for both genotypes. In comparison with D1R25, the individual aboveground biomass of HZ62 decreased by 60.2%, whereas the population biomass increased by 31.9%, and the individual biomass of genotype 1301 decreased by 54.0% and the population biomass increased by 53.9% at D3R15. Leaf carbon metabolic enzymes varied between genotypes at flowering stage. In contrast to D1R25, at D3R15 the activities of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and sucrose phosphate synthase (SPS) and the contents of starch, sucrose and soluble sugars in leaves were significantly decreased in HZ62 and increased in genotype 1301. The activities of fructose-1,6-bisphosphatase (FBPase) decreased, in consistency with the abundance of fructose-bisphosphate aldolase in HZ62. In contrast, sucrose synthase (SuSy) activity appeared to decrease in both genotypes, but a significant increase in abundance of a protein with sucrose synthase was found in the 1301 genotype by proteomic analysis. With increased density and reduced row spacing, the expression of most key proteins involved in carbon metabolism was elevated, and enzyme activity and carbon assimilate content were increased in 1301, whereas HZ62 showed the opposite trend, indicating that the compact plant type can accumulate more population biomass with denser planting.

Abstract Objective To investigate the association of fasting serum fructose concentrations and the incidence of GDM. Research design and methods Five hundred twenty six pregnant women who attended the obstetric clinic of Xinhua Hospital, Chongming Branch were recruited prospectively from September 2019 to November 2020. Fasting serum fructose concentrations were measured by a validated liquid chromatography–tandem mass spectrometry method. GDM was diagnosed according to the criteria of the IADPSG. Independent sample t-test was used to compare the differences between groups. Multiple stepwise regression analysis was used to estimate the associations of serum fructose and other variables. Multivariate logistic regression models were adopted to evaluate the odds ratios (ORs) for GDM. Results Of the 526 pregnant women, 110 were diagnosed with GDM. Fasting fructose concentrations were increased significantly in GDM patients compared to those without GDM (1.30 ug/ml vs 1.16 ug/ml, p

The C-3 modification of 1H-indazole has produced active pharmaceuticals for the treatment of cancer and HIV. But, so far, this transformation has seemed less available, due to the lack of efficient C-C bond formation at the less reactive C-3 position. In this work, a series of silica gel-supported PdO2 nanoparticles of 25–66 nm size were prepared by ball milling silica gel with divalent palladium precursors, and then employed as catalysts for the Suzuki–Miyaura cross-coupling of 1H-indazole derivative with phenylboronic acid. All the synthesized catalysts showed much higher cross-coupling yields than their palladium precursors, and could also be reused three times without losing high activity and selectivity in a toluene/water/ethanol mixed solvent. Although the palladium precursors showed an order of activity of PdCl2(dppf, 1,1′-bis(diphenylphosphino)ferrocene) > PdCl2(dtbpf, 1,1′-bis(di-tert-butylphosphino)ferrocene) > Pd(OAc, acetate)2, the synthesized catalysts showed an order of C1 (from Pd(OAc)2) > C3 (from PdCl2(dtbpf)) > C2 (from PdCl2(dppf)), which conformed to the orders of BET (Brunauer–Emmett–Teller) surface areas and acidities of these catalysts. Notably, the most inexpensive Pd(OAc)2 can be used as a palladium precursor for the synthesis of the best catalyst through simple ball milling. This work provides a highly active and inexpensive series of catalysts for C-3 modification of 1H-indazole, which are significant for the large-scale production of 1H-indazole-based pharmaceuticals.

Advancements in drilling technology are pivotal to optimizing the production and sustainability of oil and gas wells. One of the emerging innovations is the application of a specialized fluid known as “Fuzzy-ball” fluid. This paper comprehensively reviews the historical evolution and advancements in the utilization of fuzzy-ball fluid in drilling and well-repair processes. Fuzzy-ball fluid has been discovered to bolster a formation’s pressure-bearing capabilities and systematically augment resistance against oil, gas, and water flow. These attributes have been instrumental in the phased integration of fuzzy-ball fluid into procedures aiming to enhance production in oil and gas wells. This paper bridges the knowledge gap in the industry regarding the application of fuzzy-ball fluid, thereby circumventing the challenges of inadequate understanding and suboptimal designs. However, the study acknowledges the potential limitation of information loss despite the extensive data collection from diverse sources, such as articles, patents, and reports. As a future direction, this paper emphasizes the need for a more encompassing and critical evaluation of fuzzy-ball fluid’s performance and applications. This will enable a more informed decision-making process, fostering the expanded use and understanding of this fluid’s mechanism within the industry.

The terrestrial gross primary productivity (GPP) has increased over the past two decades. However, the climatic attribution and the physiological and phenological processes that control the trends in the GPP are still unclear. Here, we used remote-sensing-based vegetation GPP and phenology datasets, analyzed the spatial and temporal variation in the GPP, investigated the influence of the growing season length (GSL) and the maximum value of gross primary productivity (GPPmax) on the annual GPP, and quantified the effect of climate variables on the annual GPP. Our results identified a significant increase in the annual GPP (11.97 gC/m2/yr) during 2001–2020 in China’s deciduous forest. The GPPmax trend dominated the trends in the GPP, when compared with the GSL. Moreover, climate warming in summer contributes to the increase in the GPP and the GPPmax, while the extension of the GSL is primarily due to the temperature rise in spring. The annual GPP of the planted forest showed a higher increasing rate than the natural forest, due to the significant enhancement of the GPPmax and the high sensitivity of the GSL to climatic factors in the planted forest. Our findings provide a new perspective on the phenological and physiological causes of the trends in the GPP, and emphasize the importance of capturing the variability in the GPPmax when modeling the GPP.

Higher-accuracy long-term ocean temperature prediction plays a critical role in ocean-related research fields and climate forecasting (e.g., oceanic internal waves and mesoscale eddies). The essential component of traditional physics-based numerical models for ocean temperature prediction is solving partial differential equations (PDEs), which has immense challenges in terms of parameterization, initial values, and boundary conditions setting. Moreover, the existing machine learning models for ocean temperature prediction have “black box” problems, and the influence of external dynamic factors is not considered. Moreover, it is hard to judge whether the model satisfies certain physical laws. In this paper, we propose a physics-guided spatio-temporal data analysis model based on the widely used ConvLSTM model to achieve long-term ocean temperature prediction and adopt two schemes to train the model in vector output and multiple parallel input and multi-step output. Meanwhile, considering the spatio-temporal correlation, physical information such as oceanic stable stratification is introduced to guide the model training. We evaluate our proposed approach on several popular deep learning models in different timesteps and data volumes in the northern coast of the South China Sea, where the frequent occurrence of internal waves leads to an intensity trend of a local transformation of sea temperature. The results show higher prediction accuracy compared with the traditional LSTM, and ConvLSTM models, and the introduction of physical laws can improve data utilization while enhancing the physical consistency of the model.

Deduplication technology can obtain higher space utilization by keeping only one duplicate. But in a distributed storage system, the overall deduplication ratio will be limited due to redundancy elimination across nodes. The traditional deduplication methods usually utilize data similarity and data locality to improve the deduplication ratio. However, higher system overhead is caused by frequent similarity calculations. To deal with this problem, this paper proposes a new Feature-Aware Stateful Routing method (FASR), aiming to reduce the system overhead and keep a high deduplication ratio in the distributed environment. Firstly, we design a feature-aware nodes selection strategy to choose similar nodes by extracting data feature and data distribution characteristics. This strategy will save the similarity calculation with the nodes that are not similar to the data. Then, we present a stateful routing algorithm to determine the target node using super-chunk and handprint technology. Meanwhile, the algorithm maintain load balance of the entire distributed system. Finally, the data is deduplicated locally based on similarity index and fingerprint cache. Extensive experiments demonstrate that FASR can reduce system overhead by around 30% at most and also effectively obtain a higher deduplication ratio.

In recent years, graph neural network is used to process graph data and has been successfully applied to graph node classification task. Due to the complexity of graph structure and the difficulty of obtaining node labels, node classification in datasets with fewer labels becomes a challenge. Existing node classification problems with few-label nodes datasets usually use neighbor aggregation schemes. However, these methods lack the “graph pooling mechanism,” which makes these models impossible to make full use of the global graph information. To solve the problem, we propose a Hierarchical Structure-Feature aware Graph Neural Network (HSFGNN) model. The model learns node features by a hierarchical mechanism that repeatedly utilizes coarsening and refining methods at different levels. Firstly, we use topological information, node characteristics and connectivity of the graph to construct an intuitive and effective coarsening part based on multivariable analysis technology. Secondly, we aggregate the unselected nodes’ features into the top-k nodes, so that the coarser nodes contain more abundant valid graph information. Finally, we propose an additional attention mechanism to obtain more accurate final representations of nodes. Experimental results show that the HSFGNN model achieves outstanding classification accuracy on the dataset with less labeled data.

In systematically considering the advantages and disadvantages of complementarity in high or low milk feeding, novel milk feeding schemes involving altering the volume of supplied milk in different stages of the pre-weaning period but maintaining the total milk feeding volume were tested. Twenty-seven newborn male Holstein calves were selected and randomly assigned to 3 treatments. Calves in the control (CON) group were fed 7 L of milk daily from 4 to 66 d of age. Calves in the low-high (LH) group were fed 6 L of milk daily at the beginning, and then the daily feeding volume was later increased to 7 to 8 L of milk, which served as the early-period low-volume feeding group. The calves in the high-low (HL) group were fed 7 to 8 L daily at the beginning, and then the daily feeding volume was decreased to 6 L of milk, which served as the early-period high-volume feeding group. Then all calves were fed 3 L of milk daily from 67 to 70 d of age, weaned at 70 d of age, and then fed starter feed to 100 d of age. All calves had access to the starter feed from 15 to 100 d of age. The diarrheal condition of calves was recorded daily and the growth performance including the starter feed intake and body weight of calves was recorded at 70 and 100 d of age. Then, five 100-d-old calves from each treatment were sampled for measurement of plasma indices, ruminal morphology, and volatile fatty acids. When compared with the CON and LH groups, calves in the HL group exhibited a significantly increased body weight and lower diarrhoeal rate. When compared with the CON group, calves in the HL group exhibited a significantly increased average daily feed intake, ruminal epithelium papillae length, total volatile fatty acids, and percentages of propionate and butyrate. Moreover, the significantly increased plasma immunoglobulin G (IgG) content and a trend of decreased tumor necrosis factor-α (TNF-α) content (P = 0.083) were also identified in the HL group when compared with the CON group. Overall, the early-period high-volume feeding for calves produced greater body weight gain and a lower incidence of diarrhea.

Introduction: Fate determination of germline stem cells remains poorly understood at the chromatin structure level. Objectives: Our research hopes to develop successful offspring production of ovarian organoids derived from spermatogonial stem cells (SSCs) by defined factors. Methods: The offspring production from oocytes transdifferentiated from mouse SSCs with tracking of transplanted SSCs in vivo, single cell whole exome sequencing, and in 3D cell culture reconstitution of the process of oogenesis derived from SSCs. The defined factors were screened with ovarian organoids. We uncovered extensive chromatin reorganization during SSC conversion into induced germline stem cells (iGSCs) using high throughput chromosome conformation. Results: We demonstrate successful production of offspring from oocytes transdifferentiated from mouse spermatogonial stem cells (SSCs). Furthermore, we demonstrate direct induction of germline stem cells (iGSCs) differentiated into functional oocytes by transduction of H19, Stella, and Zfp57 and inactivation of Plzf in SSCs after screening with ovarian organoids. We uncovered extensive chromatin reorganization during SSC conversion into iGSCs, which was highly similar to female germline stem cells. We observed that although topologically associating domains were stable during SSC conversion, chromatin interactions changed in a striking manner, altering 35% of inactive and active chromosomal compartments throughout the genome. Conclusion: We demonstrate successful offspring production of ovarian organoids derived from SSCs by defined factors with chromatin reorganization. These findings have important implications in various areas including mammalian gametogenesis, genetic and epigenetic reprogramming, biotechnology, and medicine.

Background: Growing evidence suggests that infiltrating neutrophils are key players in hepatocellular carcinoma (HCC) tumor progression. However, a comprehensive analysis of the biological roles of neutrophil infiltration and related genes in clinical outcomes and immunotherapy is lacking.Methods: HCC samples were obtained from the TCGA and GEO databases. The CIBERSORT algorithm was used to reveal the TIME landscape. Gene modules significantly associated with neutrophils were found using weighted gene co-expression network analysis (WGCNA), a “dynamic tree-cut” algorithm, and Pearson correlation analysis. Genes were screened using Cox regression analysis and LASSO and prognostic value validation was performed using Kaplan-Meier curves and receiver operating characteristic (ROC) curves. Risk scores (RS) were calculated and nomograms were constructed incorporating clinical variables. Gene set variation analysis (GSVA) was used to calculate signaling pathway activity. Immunophenoscore (IPS) was used to analyze differences in immunotherapy among samples with different risk scores. Finally, the relationship between RS and drug sensitivity was explored using the pRRophetic algorithm.Results: 10530 genes in 424 samples (50 normal samples, 374 tumor samples) were obtained from the TCGA database. Using WGCNA, the “MEbrown” gene module was most associated with neutrophils. Nine genes with prognostic value in HCC (PDLIM3, KLF2, ROR2, PGF, EFNB1, PDZD4, PLN, PCDH17, DOK5) were finally screened. Prognostic nomograms based on RS, gender, tumor grade, clinical stage, T, N, and M stages were constructed. The nomogram performed well after calibration curve validation. There is an intrinsic link between risk score and TMB and TIME. Samples with different risk scores differed in different signaling pathway activity, immunopharmaceutical treatment and chemotherapy sensitivity.Conclusion: In conclusion, a comprehensive analysis of neutrophil-related prognostic features will help in prognostic prediction and advance individualized treatment.

BackgroundWith advances in early diagnosis and treatment, the number of cancer survivors continues to grow, and more and more cancer survivors face the threat of second primary cancer (SPM). Second primary pancreatic ductal adenocarcinoma (spPDAC) is an important subclass of SPM, but its prognostic characteristics are poorly understood.MethodsA total of 5,439 spPDAC samples and 67,262 primary pancreatic ductal adenocarcinoma (pPDAC) samples were extracted from the SEER database for this study. Survival differences between spPDAC and pPDAC samples were compared using Kaplan–Meier curves and log-rank tests. The Fine and Gray proportional subdistributed hazard method was used to analyze potential associations between clinical variables and pancreatic ductal adenocarcinoma-specific death (PDACSD) and death from other causes. After that, the clinical variables significantly related to PDACSD were screened out to construct a competing risk nomogram, which was used to evaluate the probability of the occurrence of PDACSD. The C-index was used to evaluate the discriminative ability of the model. The area under the curve (AUC) was used to verify the discrimination of the model. The calibration curve was used to verify the calibration of the model. Decision curve analysis (DCA) was used to validate the clinical utility of the model.ResultsCompared with patients with spPDAC, the pPDAC sample had a better prognosis (p = 0.0017). Across all spPDAC samples, the three most common sites of first-present cancer were the prostate, breast, and digestive system. Age (p

Sea surface temperature (SST) prediction has attracted increasing attention, due to its crucial role in understanding the Earth’s climate and ocean system. Existing SST prediction methods are typically based on either physics-based numerical methods or data-driven methods. Physics-based numerical methods rely on marine physics equations and have stable and explicable outputs, while data-driven methods are flexible in adapting to data and are capable of detecting unexpected patterns. We believe that these two types of method are complementary to each other, and their combination can potentially achieve better performances. In this paper, a space-time partial differential equation (PDE) is employed to form a novel physics-based deep learning framework, named the space-time PDE-guided neural network (STPDE-Net), to predict daily SST. Comprehensive experiments for SST prediction were conducted, and the results proved that our method could outperform the traditional finite-difference forecast method and several state-of-the-art deep learning and physics-guided deep learning methods.

When Q215 strip steel is used to coil square tube in iron and steel plant,serious cracking is easy to occur at bending point.In order to reduce the cracking rate of the steel pipe,the causes of the corner cracking of the steel pipe were analyzed from two aspects of the microstructure and the inclusions in the steel pipe.The influence mechanism of the microstructure transformation on the cold deformation properties of medium-carbon and low-carbon steel was discussed.The reasonable hot rolling and cooling processes after rolling were formulated.The properties of the inclusions and microstructure in Q215 steel were analyzed by optical microscope and scanning electron microscope.The transformation point,CCT and TTT curves of Q215 steel were calculated by J Mat Pro software.The results show that the main reason for the cracking of the steel pipe is the existence of ter-tiary cementite precipitated along the ferrite grain boundary in the steel,which belongs to hard brittle phase and causes stress concentration at the grain boundary during bending deformation,and the secondary reason for the cracking is the existence of large-size sulfide inclusions more than 100 μm in the steel.By adjusting the hot rolling and cooling process parameters,the amount of tertiary cementite in the steel is significantly reduced,the amount of pearlite is increased,and the cracking rate of the steel pipe is significantly reduced.The research results provide a reference for preventing the processing cracking of Q215 and similar steel grades.

BackgroundNumerous studies have found that infiltrating M2 macrophages play an important role in the tumor progression of lung adenocarcinoma (LUAD). However, the roles of M2 macrophage infiltration and M2 macrophage-related genes in immunotherapy and clinical outcomes remain obscure.MethodsSample information was extracted from TCGA and GEO databases. The TIME landscape was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to find M2 macrophage-related gene modules. Through univariate Cox regression, lasso regression analysis, and multivariate Cox regression, the genes strongly associated with the prognosis of LUAD were screened out. Risk score (RS) was calculated, and all samples were divided into high-risk group (HRG) and low-risk group (LRG) according to the median RS. External validation of RS was performed using GSE68571 data information. Prognostic nomogram based on risk signatures and other clinical information were constructed and validated with calibration curves. Potential associations of tumor mutational burden (TMB) and risk signatures were analyzed. Finally, the potential association of risk signatures with chemotherapy efficacy was investigated using the pRRophetic algorithm.ResultsBased on 504 samples extracted from TCGA database, 183 core genes were identified using WGCNA. Through a series of screening, two M2 macrophage-related genes (GRIA1 and CLEC3B) strongly correlated with LUAD prognosis were finally selected. RS was calculated, and prognostic risk nomogram including gender, age, T, N, M stage, clinical stage, and RS were constructed. The calibration curve shows that our constructed model has good performance. HRG patients were suitable for new ICI immunotherapy, while LRG was more suitable for CTLA4-immunosuppressive therapy alone. The half-maximal inhibitory concentrations (IC50) of the four chemotherapeutic drugs (metformin, cisplatin, paclitaxel, and gemcitabine) showed significant differences in HRG/LRG.ConclusionsIn conclusion, a comprehensive analysis of the role of M2 macrophages in tumor progression will help predict prognosis and facilitate the advancement of therapeutic techniques.

Background: Numerous studies have shown that infiltrating eosinophils play a key role in the tumor progression of bladder urothelial carcinoma (BLCA). However, the roles of eosinophils and associated hub genes in clinical outcomes and immunotherapy are not well known.Methods: BLCA patient data were extracted from the TCGA database. The tumor immune microenvironment (TIME) was revealed by the CIBERSORT algorithm. Candidate modules and hub genes associated with eosinophils were identified by weighted gene co-expression network analysis (WGCNA). The external GEO database was applied to validate the above results. TIME-related genes with prognostic significance were screened by univariate Cox regression analysis, lasso regression, and multivariate Cox regression analysis. The patient’s risk score (RS) was calculated and divided subjects into high-risk group (HRG) and low-risk group (LRG). The nomogram was developed based on the risk signature. Models were validated via receiver operating characteristic (ROC) curves and calibration curves. Differences between HRG and LRG in clinical features and tumor mutational burden (TMB) were compared. The Immune Phenomenon Score (IPS) was calculated to estimate the immunotherapeutic significance of RS. Half-maximal inhibitory concentrations (IC50s) of chemotherapeutic drugs were predicted by the pRRophetic algorithm.Results: 313 eosinophil-related genes were identified by WGCNA. Subsequently, a risk signature containing 9 eosinophil-related genes (AGXT, B3GALT2, CCDC62, CLEC1B, CLEC2D, CYP19A1, DNM3, SLC5A9, SLC26A8) was finally developed via multiplex analysis and screening. Age (p < 0.001), grade (p < 0.001), and RS (p < 0.001) were independent predictors of survival in BLCA patients. Based on the calibration curve, our risk signature nomogram was confirmed as a good predictor of BLCA patients’ prognosis at 1, 3, and 5 years. The association analysis of RS and immunotherapy indicated that low-risk patients were more credible for novel immune checkpoint inhibitors (ICI) immunotherapy. The chemotherapeutic drug model suggests that RS has an effect on the drug sensitivity of patients.Conclusions: In conclusion, the eosinophil-based RS can be used as a reliable clinical predictor and provide insights into the precise treatment of BLCA.

Abstract Background An association between hemoglobin and metabolic syndrome (MetS) has been reported. However, the relationships between hemoglobin and individual MetS components remain unclear. Therefore, we investigated these associations at baseline and at the 3-year follow-up. Methods We enrolled 9960 middle-aged and elderly subjects (6726 women and 3234 men) and performed a 3-year follow-up cohort study. All subjects completed a questionnaire and underwent anthropometric measurements and laboratory tests. Logistic regression models were developed to assess the association between hemoglobin and MetS and its components. Results MetS was present in 45.1% of women and 41.4% of men at baseline. The hemoglobin concentration was positively correlated with SBP, DBP, TGs, WC, FPG, insulin, HOMA-IR, BMI and uric acid (p

Abstract Background Neck circumference, a proxy for upper-body subcutaneous fat, is a unique and pathogenic fat depot that confers additional metabolic risk. The purpose of present study was to determine whether neck circumference associates with nonalcoholic fatty liver disease (NAFLD) in postmenopausal women with normal body mass index. Methods A cross-sectional survey (n = 2492) and a 3.1-year follow-up investigation (n = 1354) were conducted among Chinese postmenopausal women with normal BMI (18.5 to

Federated learning has been popular for its ability to train centralized models while protecting clients’ data privacy. However, federated learning is highly susceptible to poisoning attacks, which can result in a decrease in model performance or even make it unusable. Most existing defense methods against poisoning attacks cannot achieve a good trade-off between robustness and training efficiency, especially on non-IID data. Therefore, this paper proposes an adaptive model filtering algorithm based on the Grubbs test in federated learning (FedGaf), which can achieve great trade-offs between robustness and efficiency against poisoning attacks. To achieve a trade-off between system robustness and efficiency, multiple child adaptive model filtering algorithms have been designed. Meanwhile, a dynamic decision mechanism based on global model accuracy is proposed to reduce additional computational costs. Finally, a global model weighted aggregation method is incorporated, which improves the convergence speed of the model. Experimental results on both IID and non-IID data show that FedGaf outperforms other Byzantine-robust aggregation rules in defending against various attack methods.

The Baeyer–Villiger oxidation (BVO) of ketone and aldehyde can produce ester and formate, which both have wide applications in many areas. In this work, a series of Sn-containing silicates were prepared through the sol-gel process by using structure-directing and crystallizing agents and post-synthetic coordinated modification of binaphthol. Characterizations revealed that loading of (L)-sodium lactate as the crystallizing agent decreased the crystal size of the synthesized catalyst, and there were SnO2 nanoparticles with sizes of 17–19 nm on the catalyst. Furthermore, quite differently from the 3D mesoporous structure of classical Sn-beta zeolites, the synthesized catalysts had a silt-like mesoporous structure. In the catalysis, when cyclic aliphatic ketones were used as the substrate, only BVO-type products and corresponding ring-opening products were obtained. BVO of aliphatic aldehyde produced both an aerobic oxidation product (carboxylic acid) and a BVO-type product. The presented transformation of aromatic aldehyde (benzaldehyde) only gave an aerobic oxidation product (benzoic acid). The post-synthetic coordinating attachment of (S)-binaphthol to the Sn-containing silicate backbone worsened the BVO of aliphatic ketones but improved the BVO of aliphatic aldehyde and the aerobic oxidation of aromatic aldehyde. In addition, this work also developed two new routes for the synthesis of high-value-added 6-hydroxyhexanoic acid and cyclohexylformate under catalytic BVO conditions.

Network intrusion detection technology is key to cybersecurity regarding the Internet of Things (IoT). The traditional intrusion detection system targeting Binary or Multi-Classification can detect known attacks, but it is difficult to resist unknown attacks (such as zero-day attacks). Unknown attacks require security experts to confirm and retrain the model, but new models do not keep up to date. This paper proposes a Lightweight Intelligent NIDS using a One-Class Bidirectional GRU Autoencoder and Ensemble Learning. It can not only accurately identify normal and abnormal data, but also identify unknown attacks as the type most similar to known attacks. First, a One-Class Classification model based on a Bidirectional GRU Autoencoder is introduced. This model is trained with normal data, and has high prediction accuracy in the case of abnormal data and unknown attack data. Second, a multi-classification recognition method based on ensemble learning is proposed. It uses Soft Voting to evaluate the results of various base classifiers, and identify unknown attacks (novelty data) as the type most similar to known attacks, so that exception classification becomes more accurate. Experiments are conducted on WSN-DS, UNSW-NB15, and KDD CUP99 datasets, and the recognition rates of the proposed models in the three datasets are raised to 97.91%, 98.92%, and 98.23% respectively. The results verify the feasibility, efficiency, and portability of the algorithm proposed in the paper.

The C-3 functionalization of 1H-indazole could produce a lot of highly valuable pharmaceutical precursors, which could be used for the treatment of cancer and many other inflammatory diseases. This work was focused on the C-3 functionalization of 1H-indazole through Suzuki–Miyaura cross-coupling of 3-iodo-1H-indazole with organoboronic acids, catalyzed by various palladium catalysts immobilized over imidazolium ionic liquids, as well as catalyst recycling. A series of reaction parameters, including the substrate, catalyst, and ionic liquid, were fully investigated. It is significant to note that the yields of the present Suzuki–Miyaura cross-coupling were mainly determined by the catalyst and the solvent used, more than the chemical structure of the substrate. Furthermore, ferrocene-based divalent palladium complexes showed better catalytic outputs compared to simple palladium salts. Moreover, using two imidazolium ionic liquids, BMImX (BMIm+ = 1-n-butyl-3-methylimidazolium, X− = BF4−, PF6−) not only improved the yields of cross-coupled products, but also avoided the formation of Pd(0) black, as compared to the non-ionic liquid facilitated reactions, and simultaneously making catalyst recycling more effective. On average, BMImBF4 performed better than BMImPF6. Additionally, scientific calculations revealed that 1,1′-bis(diphenylphosphino)ferrocene-palladium(II)dichloride dichloromethane complex (PdCl2(dppf)) showed a lower energy barrier in the formation of intermediates than [1,1′-bis(di-tert-butylphosphino)ferrocene]dichloropalladium(II) (PdCl2(dtbpf)), leading to higher catalytic outputs. This work may contribute to the development of 1H-indazole-derived new pharmaceuticals.

Determining the attitude of a non-cooperative target in space is an important frontier issue in the aerospace field, and has important application value in the fields of malfunctioning satellite state assessment and non-cooperative target detection in space. This paper proposes a non-cooperative target attitude estimation method based on the deep learning of ground and space access (GSA) scene radar images to solve this problem. In GSA scenes, the observed target satellite can be imaged not only by inverse synthetic-aperture radar (ISAR), but also by space-based optical satellites, with space-based optical images providing more accurate attitude estimates for the target. The spatial orientation of the intersection of the orbital planes of the target and observation satellites can be changed by fine tuning the orbit of the observation satellite. The intersection of the orbital planes is controlled to ensure that it is collinear with the position vector of the target satellite when it is accessible to the radar. Thus, a series of GSA scenes are generated. In these GSA scenes, the high-precision attitude values of the target satellite can be estimated from the space-based optical images obtained by the observation satellite. Thus, the corresponding relationship between a series of ISAR images and the attitude estimation of the target at this moment can be obtained. Because the target attitude can be accurately estimated from the GSA scenes obtained by a space-based optical telescope, these attitude estimation values can be used as training datasets of ISAR images, and deep learning training can be performed on ISAR images of GSA scenes. This paper proposes an instantaneous attitude estimation method based on a deep network, which can achieve robust attitude estimation under different signal-to-noise ratio conditions. First, ISAR observation and imaging models were created, and the theoretical projection relationship from the three-dimensional point cloud to the ISAR imaging plane was constructed based on the radar line of sight. Under the premise that the ISAR imaging plane was fixed, the ISAR imaging results, theoretical projection map, and target attitude were in a one-to-one correspondence, which meant that the mapping relationship could be learned using a deep network. Specifically, in order to suppress noise interference, a UNet++ network with strong feature extraction ability was used to learn the mapping relationship between the ISAR imaging results and the theoretical projection map to achieve ISAR image enhancement. The shifted window (swin) transformer was then used to learn the mapping relationship between the enhanced ISAR images and target attitude to achieve instantaneous attitude estimation. Finally, the effectiveness of the proposed method was verified using electromagnetic simulation data, and it was found that the average attitude estimation error of the proposed method was less than 1°.

Abstract Background The effects of visceral adiposity on decreased lung function have drawn much attention. Recently, the visceral adiposity index (VAI) has been proposed as a visceral fat distribution and dysfunction marker. However, the relationship between the VAI and lung function has not been investigated. The objective of the study was to analyze the association between the VAI and lung function and evaluate the potential of VAI as a predictor of lung function. Methods We collected data from a population-based study of 1786 subjects aged 40 years or older. All subjects completed a questionnaire and underwent anthropometric measurements and laboratory tests. Linear and logistic regression models were developed to assess the association between the VAI and lung function. Results The VAI was inversely related to FVC%predicted in men and negatively associated with both FVC%predicted and FEV1%predicted in women. In the linear regression analysis, the decrease in FVC%predicted associated with each 10% increase in the VAI was 1.127% in men and 1.943% in women; the decrease in FEV1%predicted associated with each 10%increase in the VAI was 0.663% in men and 1.738% in women. Further regression analysis revealed that the VAI was positively correlated with FVC and FEV1 impairment in women. Conclusions We were the first to show a clear correlation between the VAI and lung function impairment in the Chinese population. The VAI could be a simple and reliable approach in daily practice, and individuals, especially women with a high VAI, should receive additional screening and preventive interventions for respiratory disease.

Recognizing the current weather conditions from a single image is of great theoretical significance. It also has potential practical value for daily life and traffic scheduling. To achieve that, typical weather recognition methods focus on learning a general weather description, e.g., sunny, cloudy, foggy, rainy and snowy etc, for the overall weather condition. However, it is far away from being sufficient for many tasks especially traffic management and control. To solve this key problem, this paper proposes a Global-Similarity Local-Salience Network (abbreviated as GSLSNet) for traffic weather recognition. Specifically, a simple but effective Global-Similarity Module (GSM) is proposed to recognize the overall weather condition and a Local-Salience Module (LSM) is presented to restrict the network to focus on road weather details. Besides, this paper also provides a new traffic weather dataset, named TWData, which is the first fine categorized dataset especially for highway weather recognition. Experimental results compared with state-of-the-art methods on both public datasets and TWData demonstrate the superiority of the proposed GSLSNet.

Specialized services and management must understand students' behavioral patterns in a timely and accurate manner. Based on these patterns, we can make targeted rules, especially for unexpected patterns. To perform this type of work, a questionnaire method is usually used to collect data and analyze students' behavioral states. However, the effectiveness of this method is greatly influenced by the timeliness and validity of the feedback data. To address this problem, we propose an unsupervised ensemble clustering framework to use student behavioral data to discover behavioral patterns. Because the behavioral data produced by students on campus are available in real time without intentional bias, clustering analysis can be relatively efficient and reliable. The proposed framework extracts behavior features from the two perspectives of statistics and entropy and then combines density-based spatial clustering of applications with noise (DBSCAN) and k-means algorithms to discover behavioral patterns. To evaluate the performance of the proposed framework, we carry out experiments on six types of behavioral data produced by undergraduates in a university in Beijing and analyze the relationships between different behavioral patterns and students' grade point averages (GPAs). The results show that the framework can not only detect anomalous behavioral patterns but also find mainstream patterns. The findings from this research can assist student-related departments in providing better services and management, such as psychological consulting and academic guidance.