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Background: Early intervention and diagnosis of Metabolic Syndrome (MetS) are crucial for preventing adult cardiovascular disease. However, the optimal indicator for identifying MetS in adolescent remains controversial. Methods: In total,1408 Chinese adolescents and 3550 American adolescents aged 12–17 years were included. MetS was defined according to the modified version for adolescents based on Adult Treatment Panel III (NCEP-ATP III) criteria. Areas under the curve (AUC) and corresponding 95% confidence interval (95% CI) of 8 anthropometric/metabolic indexes, such as waist circumference (WC), body mass index (BMI), a body shape index (ABSI), waist triglyceride index (WTI), were calculated to illustrate their ability to differentiate MetS. Sensitivity analysis using the other MetS criteria was performed. Results: Under the modified NCEP-ATP III criteria, WTI had the best discriminating ability in overall adolescents, with AUC of 0.922 (95% CI: 0.900–0.945) in Chinese and 0.959 (95% CI: 0.949–0.969) in American. In contrast, ABSI had the lowest AUCs. Results of sensitivity analysis were generally consistent for the whole Chinese and American population, with the AUC for WC being the highest under some criteria, but it was not statistically different from that of WTI. Conclusions: WTI had relatively high discriminatory power for MetS detection in Chinese and American adolescents, but the performance of ABSI was poor. Impact:

While many studies have compared the discriminatory power of some anthropometric indicators for MetS, there are few focused on pediatrics.

The current study is the first to compare the discriminating ability of anthropometric/metabolic indicators (WC, BMI, TMI, ABSI, WHtR, VAI, WTI, and TyG) for MetS in adolescents.

WTI remains the optimal indicator in screening for MetS in adolescents.

WC was also a simple and reliable indicator when screening for MetS in adolescents, but the performance of ABSI was poor.

This study provides a theoretical basis for the early identification of MetS in adolescents by adopting effective indicators.

Long-chain free fatty acids (FFAs) are associated with risk of incident diabetes. However, a comprehensive assessment of the associations in normoglycemic populations is lacking.

•Drug-resistant Chryseobacterium indoltheticumPOL15 was found in farm wastewater.•A novel integrative and conjugative element ICECiPOL15 was identified in strain POL15.•ICECiPOL15 carries a β-lactamase gene and mediates β-lactam antibiotic resistance.•ICECiPOL15 might have evolved from other Chryseobacteriumspecies.•ICECiPOL15 is capable of horizontal transfer to Escherichia Coli.

ABSTRACT The HDHS (high dense and high strength) cement-based paste and mortar were designed based on the densest packing model determined by Dinger-Funk equation. The HDHS cement-based paste was modified [...]

In industry, many systems exhibit load-sharing characteristics. In a load-sharing system, failure of an asset, in addition to affect system reliability, increases the workloads of remaining surviving assets and so their failure rates. When managing such the assets in a system, it is important for decision makers to ensure overall performance of the system, by determining redundancy of assets and a preventive maintenance plan with consideration of load sharing and uncertain environmental conditions. This article proposes an approach for synthetically optimizing redundancy design and age-based preventive maintenance for a load-sharing system with identical assets. A two-stage stochastic programming model with recourse is established, which incorporates risk-aversion preference of decision makers. A decomposition algorithm is developed to solve the joint optimization model, incorporating analytical properties of system failure rate functions and models. A comparative study with deterministic optimization and robust optimization is conducted to demonstrate the advantages of the proposed risk-averse stochastic programming approach. Finally, a numerical study on an effluent treatment system is conducted to analyze the optimal redundancy design and maintenance plan and practical insights.

Network digital twin, as a fundamental enabling technology in emerging network scenarios and applications, establishes a connection between the physical and virtual networks through real-time representation and synchronization of physical entities. The value of network digital twins lies in their ability to accurately model future network states and behaviors, facilitating numerous downstream network tasks such as network optimization and management. However, the importance of network traffic prediction as an essential prerequisite module for these network tasks has been relatively overlooked. Limited surveys provide insights into the network traffic characteristics and advanced prediction models, resulting in performance degradation and unnecessary resource consumption. Hence, it deserves to explore an efficient data-driven traffic prediction framework that can adapt to different network tasks. This article first examines the general forms and characteristics of various network traffic based on specific datasets. Subsequently, we introduce a multi-module data-driven framework for network traffic prediction to effectively cater to diverse data types and downstream task requirements. A case study of cellular traffic prediction based on traffic characteristics and requirements is proposed to demonstrate the practicability of our framework. Finally, we outline several open challenges toward future traffic prediction in network digital twins.

The metaverse is at the forefront of the next-generation internet application, where billions of users seamlessly immerse themselves in a hybrid reality of physical-virtual worlds and switch between virtual environments thanks to reliable resource allocation and synchronization. However, the exponential growth of users and computationally intensive applications make joint optimization of multiple indicators challenging. Therefore, predicting user behavior is pivotal in assisting the optimization process. Although graph neural networks have demonstrated remarkable performance in traffic prediction, most existing schemes link nodes based on their distances and require significant computational resources, limiting their generalization and deployment in the metaverse. To solve this problem, we propose an efficient Spatio-temporal Identity Multi-graph convolutional network Framework (SIMF) for application-level traffic prediction in the metaverse. In the SIMF, we design a spatio-temporal embedding layer and multi-graph convolutional module to jointly capture spatio-temporal correlations among nodes (avatars) and reduce the dependence on topology information, which is more consistent with the real relationship between avatars in the metaverse. We conduct extensive experiments to evaluate the SIMF, which show that our proposed framework achieves superior accuracy even without graph information while maintaining low time complexity, making it suitable for traffic prediction in the metaverse.

Consider a system consisting of multistate components that perform the same function, and each component occupies a location in the system. The deterioration processes of components differ due to different workloads, usage rates, or environmental stresses that are associated with the locations. This article proposes a condition-based maintenance policy, in which the component reallocation (CR) with distinct assignments of components to locations and the preventive replacement of system are dynamically implemented based on the system state. A Markov decision process (MDP) is formulated to optimize the proposed condition-based multi-CR maintenance policy by determining the actions for each system state that minimize the expected long-run system maintenance cost. In the current studies on the MDP for maintenance optimization, the actions mainly include component replacement, imperfect repair, and system replacement. In this article, including CRs of distinct assignments as actions and considering multiple times of CRs increase the action space of the MDP significantly. An enumeration-based value iteration algorithm and a genetic-algorithm-based value iteration algorithm are proposed. Numerical experiments on $k$-out-of-$n$:G systems and Monte Carlo simulation tests show the effectiveness of CRs on reducing the system maintenance cost and extending system lifetime and provide structural insights on the optimal maintenance policy.

Maintenance and inventory control of spare parts are of great importance for efficiently managing onshore wind farms. Considering the economic and graphical dependencies among wind turbines and the stochastic degradations of the components in the turbines, this article studies the joint optimization of opportunistic condition-based maintenance and spare parts supply for onshore wind farms to minimize the operation and maintenance cost rate. For each field maintenance, the specific turbines, with failed components, are selected for replacement using a clustering method, and the shortest route for visiting the turbines with failed components is scheduled. The maintenance team departs from a maintenance center with spare parts, maintains the selected turbines with failed or degraded components, and then returns to the center. This complicated and practical problem has never been considered as a whole in the literature. An optimization model is established, and a simulation-based approach is developed to solve it. A special case, for which a solution can be obtained by directly solving a nonlinear programming model, is provided to verify the results obtained by the simulation-based approach. Examples for various scales of wind farms are provided, and an analysis is performed for determining the length of a recommended maintenance interval.

A series of bifluorenylidene derivatives has been facilely synthesized via a palladium-catalyzed tandem multistep Suzuki cross-coupling and Heck cyclization in one pot. The tandem reaction involves two sequential Suzuki couplings and a subsequent Heck cyclization. The target products are obtained in good yields up to 94%. Even in an extended conjugated substrate, the anticipated products can be prepared.

Dissimilatory iron reduction (DIR) coupled with carbon cycling is increasingly being recognized as an influential process in freshwater wetland soils and sediments. The role of DIR in organic matter (OM) mineralization, however, is still largely unknown in lake sediment environments. In this study, we clarified rates and pathways of OM mineralization in two shallow lakes with seasonal hydrological connectivity and different eutrophic situations. We found that in comparison with the domination of DIR (55%) for OM mineralization in Lake Xiaoxingkai, the contribution of methanogenesis was much higher (68%) in its connected lake (Lake Xingkai). The differences in rates and pathways of sediment OM mineralization between the two lakes were attributed to higher concentrations of carbonate associated iron oxides (Fecarb) in Lake Xiaoxingkai compared to Lake Xingkai (P = 0.002), due to better deposition mixing, more contributions of terrigenous detrital materials, and higher OM content in Lake Xiaoxingkai. Results of structural equation modeling showed that Fecarband total iron content (TFe) regulated 25% of DIR in Lake Xiaoxingkai and 76% in Lake Xingkai, accompanied by a negative effect of TFe on methanogenesis in Lake Xingkai. The relative abundance and diversity of Fe-reducing bacteria were significantly different between the two lakes, and showed a weak effect on sediment OM mineralization. Our findings emphasize the role of iron minerals and geochemical characterizations in regulating rates and pathways of OM mineralization, and deepen the understanding of carbon cycling in lake sediments.

The continuously booming of information technology has shed light on developing a variety of communication networks, multimedia, social networks and Internet of Things applications. However, users inevitably suffer from the intrusion of malicious users. Some studies focus on static characteristics of malicious users, which is easy to be bypassed by camouflaged malicious users. In this paper, we present a malicious user detection method based on ensemble feature selection and adversarial training. Firstly, the feature selection alleviates the dimension disaster problem and achieves more accurate classification performance. Secondly, we embed features into the multidimensional space and aggregate it into a feature map to encode the explicit content preference and implicit interaction preference. Thirdly, we use an effective ensemble learning which could avoid over-fitting and has good noise resistance. Finally, we propose a datadriven neural network detection model with the regularization technique adversarial training to deeply analyze the characteristics. It simplifies the parameters, obtaining more robust interaction features and pattern features. We demonstrate the effectiveness of our approach with numerical simulation results for malicious user detection, where the robustness issues are notable concerns.

Ensemble learning has been shown to be an effective approach to solve multi-label classification problem. However, most existing ensemble learning methods do not consider the difference between unseen instances, and existing methods that consider such difference fail to effectively explore label correlation, which limits their performance. To address these issues, we propose a novel method named MLDE (Multi-Label classification with Dynamic Ensemble learning). In MLDE, the most competent ensemble of base classifiers is selected and combined to predict each unseen instance. To make dynamic selection specific to multi-label problem and achieve better performance, we combine classification accuracy and ranking loss to serve as the competence measurement for the base classifiers. Specifically, classification accuracy is decomposable to multiple labels and distinguishes the ability difference of a classifier with respect to different labels, while ranking loss focuses on the overall performance of a classifier on the label set and thus fully considers the correlation between multiple labels. Extensive experiments on 24 publicly available datasets demonstrate that MLDE outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems. However, previous works mainly focus on showing and evaluating the conversational performance of the released dialogue model, ignoring the discussion of some key factors towards a powerful human-like chatbot, especially in Chinese scenarios. In this paper, we conduct extensive experiments to investigate these under-explored factors, including data quality control, model architecture designs, training approaches, and decoding strategies. We propose EVA2.0, a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters, and will make our models and codes publicly available. Automatic and human evaluations show that EVA2.0 significantly outperforms other open-source counterparts. We also discuss the limitations of this work by presenting some failure cases and pose some future research directions on large-scale Chinese open-domain dialogue systems.

Feed efficiency is one of the most important issues for sustainable pig production. Daily-phase feeding (DPF) is a form of precision feeding that could improve feed efficiency in pigs. Gut microbiota can regulate host nutrient digestion, absorption, and metabolism. However, which key microbes may play a vital role in improving the feed efficiency during DPF remains unclear. In the present study, we used a DPF program compared to a three-phase feeding (TPF) program in growing-finishing pigs to investigate the effects of gut microbiota on feed efficiency. A total of 204 Landrace × Yorkshire pigs (75 d) were randomly assigned into 2 treatments. Each treatment was replicated 8 times with 13 to 15 pigs per replicate pen. Pigs in the TPF group were fed with a commercial feeding program that supplied fixed feed for phases I, II, and III, starting at 81, 101, and 132 d of age, respectively, and pigs in the DPF group were fed a blend of adjacent phase feed from 81 to 155 d at a gradual daily ratio and phase III feed from 155 to 180 d of age. Daily feed intake and body weight were recorded by a computerized device in the feeders. Feces and blood samples were collected from 1 pig per replicate at 155 and 180 d of age. The results showed that the DPF program remarkably improved the feed efficiency at 155 d (P < 0.001) and 180 d of age (P < 0.001), with a significant reduction of the intake of crude protein (P < 0.01), net energy (P < 0.001), crude fiber (P < 0.001), ether extract (P < 0.01), and ash (P < 0.001). The daily-phase feeding program increased the abundance of Prevotella copri(P < 0.05) and Paraprevotella clara(P < 0.05), while it decreased the abundance of Ocilibacter(P < 0.05) at 155 d of age. The results of correlation analysis indicated that the differentially abundant microbiota communities were closely associated with 20 metabolites which enriched amino acid and phenylalanine metabolism. Our results suggest that 2 key microbes may contribute to feed efficiency during daily-phase feeding strategies in pigs.

Lungs are structurally and functionally complex organs consisting of diverse cell types from the proximal to distal axis. They have direct contact with the external environment and are constantly at risk of various injuries. Capable to proliferate and differentiate, pulmonary endogenous progenitor stem cells contribute to the maintenance of lung structure and function both under homeostasis and following injuries. Discovering candidate pulmonary endogenous progenitor stem cell types and underlying regenerative mechanisms provide insights into therapeutic strategy development for lung diseases. In this review, we reveal their compositions, roles in lung disease pathogenesis and injury repair, and the underlying mechanisms. We further underline the advanced progress in research approach and potential therapy for lung regeneration. We also demonstrate the feasibility and prospects of pulmonary endogenous stem cell transplantation for lung disease treatment.

Time series of image data on the surfaces or interior structures of units can be used for remaining useful life (RUL) prediction. However, only a few studies have used this type of imaging, and even then those studies have not dealt with situations in which only a limited number of images or images taken at unequal time intervals are available. This article proposes an image-driven classification-based framework (IDCBF) for predicting RUL using a limited number of images. Multiple methods of numerical interpolations are applied to approximate expected degradation paths that are unknown, contributing to the IDCBF’s flexibility. The proposed approach applies and extends the classification of images to the RUL prediction. The IDCBF framework augments the image streams, extends the trajectories of in-field units, judges the working-failure states of units using a classifier, and infers the RUL based on the classification results. Numerical experiments on a simulated image dataset and a case study on a real image dataset demonstrate the effectiveness of the proposed methodology.

With the development of materials science, photodynamic-based treatments have gradually entered clinics. Photodynamic therapy is ideal for cancer treatment due to its non-invasive and spatiotemporal properties and is the first to be widely promoted in clinical practice. However, the shortcomings resulting from the gap between technical and clinical demands, such as phototoxicity, low tissue permeability, and tissue hypoxia, limit its wide applications. This article reviews the available data regarding the pharmacological and clinical factors affecting the efficacy of photodynamic therapy, such as photosensitizers and oxygen supply, disease diagnosis, and other aspects of photodynamic therapy. In addition, the synergistic treatment of photodynamic therapy with surgery and nanotechnology is also discussed, which is expected to provide inspiration for the design of photodynamic therapy strategies.

ABSTRACTAmino acids play crucial roles in the MTOR (mechanistic target of rapamycin kinase) complex 1 (MTORC1) pathway. However, the underlying mechanisms are not fully understood. Here, we establish a cell-free system to mimic the activation of MTORC1, by which we identify CANX (calnexin) as an essential regulator for leucine-stimulated MTORC1 pathway. CANX translocates to lysosomes after leucine deprivation, and its loss of function renders either the MTORC1 activity or the lysosomal translocation of MTOR insensitive to leucine deprivation. We further find that CANX binds to LAMP2 (lysosomal associated membrane protein 2), and LAMP2 is required for leucine deprivation-induced CANX interaction with the Ragulator to inhibit Ragulator activity toward RRAG GTPases. Moreover, leucine deprivation promotes the lysine (K) 525 crotonylation of CANX, which is another essential condition for the lysosomal translocation of CANX. Finally, we find that KAT7 (lysine acetyltransferase 7) mediates the K525 crotonylation of CANX. Loss of KAT7 renders the MTORC1 insensitivity to leucine deprivation. Our findings provide new insights for the regulatory mechanism of the leucine-stimulated MTORC1 pathway.Abbreviations:CALR: calreticulin; CANX: calnexin; CLF: crude lysosome fraction; EIF4EBP1: eukaryotic translation initiation factor 4E binding protein 1; ER: endoplasmic reticulum; GST: glutathione S-transferase; HA: hemagglutinin; HEK293T: human embryonic kidney-293T; KAT7: lysine acetyltransferase 7; Kcr; lysine crotonylation; KO: knockout; LAMP2: lysosomal associated membrane protein 2; LAMTOR/Ragulator: late endosomal/lysosomal adaptor: MAPK and MTOR activator; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; MTOR: mechanistic target of rapamycin kinase; PDI: protein disulfide isomerase; PTM: post-translational modification; RPS6KB1/p70S6 kinase 1: ribosomal protein S6 kinase B1; RPTOR: regulatory associated protein of MTOR complex 1; SESN2: sestrin 2; TMEM192: transmembrane protein 192; ULK1: unc-51 like autophagy activating kinase 1.

The current study was carried out to detect the effect of dietary guanidinoacetic acid (GAA) supplementation on carcass characteristics and meat quality in finishing pigs fed different dietary crude protein (CP) levels. Sixty-four barrows with an initial body weight of 73.05 ± 2.34 kg were randomly allocated into 1 of 4 dietary treatments in a 2 (100% vs. 125% NRC CP level) × 2 (0 vs. 300 mg/kg GAA) factorial arrangement (n = 7). The feeding trial lasted for 49 d. GAA supplementation significantly reduced drip loss (P = 0.01), free water distribution (T23peak area ratio) (P = 0.05) and the concentrations of free alanine, threonine, methionine and isoleucine (P < 0.05); but increased total glycine content (P = 0.03) in the longissimus dorsi muscle of finishing pigs regardless of the dietary CP levels. Furthermore, primary myogenic cell differentiation system was employed to investigate the influence of inclusion of GAA on free amino acid concentrations in myotubes (n = 4) and validate the finding in the animal feeding trial. We found that GAA inclusion in culture medium also decreased intracellular concentrations of free alanine, threonine, methionine, isoleucine, valine and proline in differentiated primary myogenic cells in vitro (P < 0.05). Meanwhile, relative to diets with 100% NRC CP level, the intake of diets with 125% NRC CP level improved sarcoplasmic protein solubility, increased the contents of carnosine and total free amino acids as well as flavor amino acids in the longissimus dorsi muscle and decreased backfat thickness at the 6–7th ribs in pigs (P < 0.05). In addition, we observed that the impact of dietary GAA supplementation on the last rib fat thickness, shear force, and free lysine content in the longissimus dorsi muscle was dependent on dietary CP levels (P < 0.05). Collectively, dietary GAA supplementation can reduce drip loss, decrease the concentrations of free amino acids and flavor amino acids of fresh meat independent of dietary CP levels.

For new ladder-type oligomers and polymers with versatile and robust synthetic strategies, in this study, four fully conjugated ladder-type overcrowded 9,9′-bifluorenylidene-based compounds and oligomers (BFY1, BFY2, BFY3, and BFY4) were synthesized via a Pd-catalyzed tandem Suzuki coupling/Heck cyclization reaction. By monomer screening and route optimization, the target products were obtained in high yields and characterized by 1H and 13C NMR spectroscopy and high resolution mass spectroscopy.

Nowadays, the fifth-generation (5G) mobile communication system has obtained prosperous development and deployment, reshaping our daily lives. However, anomalies of cell outages and congestion in 5G critically influence the quality of experience and significantly increase operational expenditures. Although several big data and artificial intelligence-based anomaly detection methods have been proposed for wireless cellular systems, they change distributions of the data and ignore the relevance among user activities, causing anomaly detection ineffective for some cells. In this paper, we propose a highly effective and accurate anomaly detection framework by utilizing generative adversarial networks (GAN) and long short-term memory (LSTM) neural networks. The framework expands the original dataset while simultaneously keeping the distribution of data unchanged, and explores the relevance among user activities to further improve the system performance. The results demonstrate that our framework can achieve 97.16% accuracy and 2.30% false positive rate by utilizing the correlation of user activities and data expansion.

Due to the difficulty of annotation, multi-label learning sometimes obtains a small amount of labeled data and a large amount of unlabeled data as supplements. To make up this issue, many algorithms extended the existing semi-supervised strategies in single-label patterns to multi-label applications, but failed to effectively consider the characteristics of semi-supervised multi-label learning. In this paper, a novel method named SCTML (Stacked Co-Training for Multi-Label learning) is proposed for semi-supervised multi-label learning. Through a two-layer stacking framework, SCTML learns label correlation in both base learners and meta learner, and effectively incorporates the semi-supervised assumptions of co-training, clustering and manifold. Extensive experiments demonstrate that the combination of multiple semi-supervised learning strategies effectively solves the semi-supervised multi-label learning problem. [ABSTRACT FROM AUTHOR]

Objective: Rhinosinusitis is a global health problem affecting millions of people around the world. Baicalin is a bioactive compound isolated from medicinal plant Scutellaria baicalensisGeorgi. The present study aims to investigate potential effects of baicalin on clinicopathological changes in nasal/sinus mucosa in a mouse model.Methods: A mouse model of sinonasal inflammation induced by high dose of ovalbumin was applied to evaluate effects of baicalin. Rhinosinusitis symptoms, histopathological features, levels of histamine, immunoglobulin E (IgE), IL-17A, IL-10, and balance of regulatory T cell (Treg)/T-helper 17 (Th17) responses were examined.Results: Baicalin significantly relieved rhinosinusitis symptoms in mice, reduced histopathological changes, and suppressed serum levels of histamine and IgE in a dose-dependent manner. In lymphocytes of mice, baicalin modulated balance of Treg/Th17 proportions by attenuating Th17 cells and enhancing Treg cells, respectively. The serum IL-17A was decreased and IL-10 was increased in mice treated by baicalin. In addition, baicalin promoted levels of Smad protein 3 (p-Smad3) and forkhead box P3 (FOXP3) to promote Treg cells while suppressed levels of p-Stat3 and retineic-acid-receptor-related orphan nuclear receptor γt (RORγt) to inhibit Th17 cells.Conclusions: These data demonstrate that baicalin effectively ameliorates sinonasal inflammation in a mouse model by recovering the immunological balance of Treg/Th17 responses. Our finding highlights the potential value of baicalin for the treatment of rhinosinusitis.

Considering the large-scale networks that can represent construction of components in a unit, a transportation system, a supply chain, a social network system, and so on, some nodes have similar topological structures and thus play similar roles in the network and system analysis, usually complicating the analysis and resulting in considerable duplicated computations. In this paper, we present a graph learning approach to define and identify structural similarity between the nodes in a network or the components in a network system. Based on the structural similarity, we investigate component clustering at various significance levels that represent different extents of similarity. We further specify a spectral-graph-wavelet based graph learning method to measure the structural similarity and present its application in easing computation load of evaluating system survival signature and system reliability. The numerical examples and the application show the insights of structural similarity and effectiveness of the graph learning approach. Finally, we discuss potential applications of the graph-learning based structural similarity and conclude that the proposed structural similarity, component clustering, and graph learning approach are effective in simplifying the complexity of the network systems and reducing the computational cost for complex network analysis.

Water resources are a crucial factor that determines the health of ecosystems and socio-economic development; however, they are under threat due to climate change and human activities. The quantitative assessment of water resources using the concept of blue water and green water can improve regional water resources management. In this study, spatiotemporal distributions of blue water and green water were simulated and analyzed under scenarios of climate change and land-use changes using the Soil and Water Assessment Tool (SWAT) in Ningxia Hui Autonomous Region, Northwest China, between 2009 and 2014. Green water, a leading component of water resources, accounted for more than 69.00% of the total water resources in Ningxia. Blue water and green water showed a single peak trend on the monthly and annual scales during the study period. On the spatial scale, the southern region of Ningxia showed higher blue water and green water resources than the northern region. The spatiotemporal distribution features of blue water, green water, and green water flow had strong correlations with precipitation. Furthermore, the simulation identified the climate change in Ningxia to be more influential on blue water and green water than land-use changes. This study provides a specific scientific foundation to manage water resources in Ningxia when encountered with climate change together with human activities.

Nowadays, resorcinol (RC) has been widely applied in the chemical and pharmaceutical industries. However, the electrochemical detection technique of RC still features some significant drawbacks, for instance, a low sensitivity. Hence, in the present work, a glass carbon electrode was developed for the electrochemical detection of RC with good specificity and stability, through modifying the glass carbon electrode (GCE) by polyphenol oxidase (PPO), an NH2- SBA-15 mesoporous material (NIL-SBA-15), l-tyrosine (l-Tyr) and gold nano-particles (AuNPs). After being successively modified by AuNPs, l-Tyr, NH2-SBA-15 and PPO, the constructed PPO/NH2-SBA-15/l-Tyr/AuNPs/GCE was used to discriminate RC from ions and other common micromolecules, which showed a fairly good specificity and stability. The proposed electrochemical detection method features a linear range of from 0.5 to 21.0 μM with a LOD down to 0.15 µM, revealing a better sensitivity than the existing methods. It is worth mentioning that the proposed PPO/NH2-SBA-15/l-Tyr/ AuNPs/GCE has been successfully used as an electrochemical probe for the RC assay in domestic sewage.

Untethered robots with smart human–machine interactions can execute complex activities such as target cargo delivery or assembly of functional scaffolds. However, it remains challenging for fabricating microscale hollow hydrogel robots that can go with autonomous transformation of their geometric formations to adapt to unstructured environments. We herein report hydrogel-based microscopic hollow swarming spheres (HSSs) with anisotropic/isotropic alignments of Fe3O4particles in the porous wall that can navigate under complex topography conditions by altering their geometric formation, including passing around or jumping over obstacles, assembling into various formation patterns, and swimming in a high-viscosity system. We introduce HSSs into a catalytic reaction model, in which HSSs as a catalyst can shift between water and oil phases to initiate or terminate the decomposition reaction of H2O2. This dynamic catalysis is expected to construct free-radical “living” polymerization for controlling the reaction rate and polymer dispersity index in the future.

Deep speaker embedding has demonstrated state-of-the-art performance in speaker recognition tasks. However, one potential issue with this approach is that the speaker vectors derived from deep embedding models tend to be non-Gaussian for each individual speaker, and non-homogeneous for distributions of different speakers. These irregular distributions can seriously impact speaker recognition performance, especially with the popular PLDA scoring method, which assumes homogeneous Gaussian distribution. In this article, we argue that deep speaker vectors require deep normalization, and propose a deep normalization approach based on a novel discriminative normalization flow (DNF) model. We demonstrate the effectiveness of the proposed approach with experiments using the widely used SITW and CNCeleb corpora. In these experiments, the DNF-based normalization delivered substantial performance gains and also showed strong generalization capability in out-of-domain tests.

1,8-Diazabicyclo[5.4.0]-undec-7-ene (DBU)-glycerol solution is employed as a promising CO2absorbent. The regeneration of the CO2-loaded solution is of vital importance for its commercialization. It was investigated and compared with 30 wt % monoethanolamine (MEA). Variables affecting the absorption and desorption processes were studied, including the solvent composition, reaction temperature, and agitation. It shows that the absorption capacity for DBU-glycerol is comparable with 30 wt % MEA, and the desorption ratio for CO2-loaded DBU-glycerol mixture can reach as high as 95% in 60 min, 363 K at the 3:1 molar ratio of DBU to glycerol, while it is only 68% even after 165 min for CO2-saturated 30 wt % MEA. DBU-glycerol solution has higher cycling CO2loading than 30 wt % MEA. Its cyclic capacity could keep above 90% after 10 cycles of absorption–desorption experiments. The desorption reaction is irreversible at the initial stage, and the reaction rate is expected as a first-order reaction from 349 to 377 K, and the apparent activation energy is 68.94 kJ/mol. Moreover, the heat duty of the reboiler during regeneration is estimated to be reduced by about 27% when compared with 30 wt % MEA.

In spite of the great advancements of machine reading comprehension (RC), existing RC models are still vulnerable and not robust to different types of adversarial examples. Neural models over-confidently predict wrong answers to semantic different adversarial examples, while over-sensitively predict wrong answers to semantic equivalent adversarial examples. Existing methods which improve the robustness of such neural models merely mitigate one of the two issues but ignore the other. In this article, we address the over-confidence issue and the over-sensitivity issue existing in current RC models simultaneously with the help of external linguistic knowledge. We first incorporate external knowledge to impose different linguistic constraints (entity constraint, lexical constraint, and predicate constraint), and then regularize RC models through posterior regularization. Linguistic constraints induce more reasonable predictions for both semantic different and semantic equivalent adversarial examples, and posterior regularization provides an effective mechanism to incorporate these constraints. Our method can be applied to any existing neural RC models including state-of-the-art BERT models. Extensive experiments show that our method remarkably improves the robustness of base RC models, and is better to cope with these two issues simultaneously.

This work aims to investigate the minimum dosage required to achieve optimal removal of carbon, nitrogen, and phosphorus in the Chemical Enhanced Primary Treatment (CEPT) process using magnetic coagulation technology. A response surface methodology was adopted to generate multiple regression models to optimize the dosages of three types of coagulation, i.e., polyaluminum chloride (PAC), polyacrylamide (PAM), and magnetic medium Fe3O4particles (MP), based on the removal rates of chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total phosphorus (TP). The results showed that the regression models could predict results well with actual engineering results. Compared to the traditional coagulation process, the synergistic enhancement of PAC+PAM+MP in the CEPT process significantly improved the treatment effects. Based on the COD removal rates, PAC performed better than MP, followed by PAM; likewise, for the NH3-N and TP removal rates, PAC outperformed PAM, followed by MP. The optimized PAC, PAM, and MP dosages were 135.87, 1.51, and 108.36mg/L, respectively, reaching 92.27% COD, 71.98% NH3-N, and 91.33% TP removal rates with less than 5% errors compared to the experimental verification results. These optimization models offer a practical and cost-saving reference for enhancing the primary treatment effect on carbon, nitrogen, and phosphorus removal using PAC+PAM+MP.

Reverse chemical ecology has been widely applied for the functional characterization of olfactory proteins in various arthropods, but few related studies have focused on parasitic wasps. Here, the odorant carrier Niemann–Pick C2 protein of Baryscapus dioryctriae(BdioNPC2b) was studied in vitro and in vivo. Ligand binding analysis revealed that BdioNPC2b most strongly bound to 2-butyl-2-octenal and which compound could elicit an EAG response and attracted B. dioryctriaeadults. Moreover, this odorant attractant significantly improved the reproductive efficiency of B. dioryctriaecompared to that of the control. Then, the relationship between BdioNPC2b and 2-butyl-2-octenal was validated by RNAi, and site-directed mutagenesis revealed the involvement of three key residues of BdioNPC2b in binding to 2-butyl-2-octenal through hydrogen bonding. Our findings provide not only a deeper understanding of the olfactory function of NPC2 in wasps but also useful information for improving the performance of the parasitoid B. dioryctriaeas a biological control agent.

ABSTRACTBiliary tract cancer (BTC) is an aggressive malignancy with few options for advanced-stage treatment. The combination of PD-1/PD-L1 inhibitors with famitinib, a receptor tyrosine kinase (RTK) inhibitor, has demonstrated improved clinical outcomes in several clinical trials. We herein reported a case of a gallbladder cancer (GBC) patient with liver metastases, previously resistant to traditional chemotherapy. Remarkably, the patient achieved a complete response (CR) with a long-lasting survival benefit exceeding 3 years. This was achieved using a novel regimen combining SHR-1701, an anti-PD-L1/TGF-βR fusion protein, and famitinib, even though the patient had proficient mismatch repair (pMMR) and tested negative for PD-L1. Adverse events were limited and manageable. This is the first report of such a treatment regimen being applied in a clinical setting, suggesting that the SHR-1701 and famitinib combination may be a promising immunotherapeutic approach for patients with refractory advanced GBC.

A chromosome segment substitution line (CSSL) is a powerful tool for combining quantitative trait locus (QTL) mapping with the pyramiding of desirable alleles. The rice CSSL Z1364 with increased kernel number was identified in a BC3F8population derived from a cross of Nipponbare as the recipient with Xihui 18 as the donor parent. Z1364 carried three substitution segments distributed on chromosomes 1, 6, and 8. The mean substitution length was 1.19 Mb. Of 17 QTL identified on the substitution segments, qSP1for spikelets per panicle, qSSD1for seed-set density, and qNSB1for number of secondary branches explained respectively 57.34%, 87.7%, and 49.44% of the corresponding phenotypic variance and were all linked to RM6777. Chi-square analysis showed that the increased kernel number in Z1364 was inherited recessively by a single gene. By fine mapping, qSP1was delimited to a 50-kb region on the short arm of chromosome 1. Based on DNA sequence, a previously uncharacterized rice homolog of Arabidopsis thaliana AT4G32551was identified as a candidate gene for qSP1in which mutation increases the number of spikelets and kernels in Z1364. qSP1was expressed in all tissues, but particularly in 1-cm panicles. The expression levels of OsMADS22, GN1A, and DSTwere upregulated and those of LAX2, GNP1, and GHD7were downregulated in Nipponbare. These results provide a foundation for functional research on qSP1.

Consider non-homogeneous Markov-dependent components in an m-consecutive-k-out-of-n:F (G) system with sparse d, which consists of nlinearly ordered components. Two failed components are consecutive with sparse dif and if there are at most dworking components between the two failed components, and the m-consecutive-k-out-of-n:F system with sparse dfails if and if there exist at least mnon-overlapping runs of kconsecutive failed components with sparse dfor 1⩽d⩽n−k. We use conditional probability generating function method to derive uniform closed-form formulas for system reliability, marginal reliability importance measure, and joint reliability importance measure for such the F system and the corresponding G system. We present numerical examples to demonstrate the use of the formulas. Along with the work in this article, we summarize the work on consecutive-ksystems of Markov-dependent components in terms of system reliability, marginal reliability importance, and joint reliability importance.

Over the years, numerous researchers have explored the relationship between ischemic stroke (IS) and interleukin-18 (IL-18) gene polymorphisms. However, those studies reported conflicting and ambiguous results. The effects of IL-18(137G/C and 607C/A) genetic variants on IS were investigated in this article. We performed a systematic search that was comprehensively executed in online databases for studies published up to 30 April 2018. Calculation of pooled odds ratios (ORs) and 95% confidence intervals was applied to assess the intensity of correlation using Stata.12.0. The overall outcome showed that 137G allele increased the risk of IS under the homozygous model (OR=1.36, P=0.027). Nevertheless, on the basis of ethnicity for the subgroup analysis (Asian and Egyptian), it was disclosed that the association was only found in the Egyptian population under the allelic model (OR=2.72, P=0.001) and recessive model (OR=5.04, P=0.000). In the overall analysis, 607C allele increased the risk of IS under all hereditary models (C vs. A: OR=1.26, P=0.002; CC vs. AA: OR=1.67, P=0.002; CA vs. AA: OR=1.30, P=0.001; CC+CA vs. AA: OR=1.41, P=0.000; CC vs. AA+CA: OR=1.48, P=0.000); a similar trend was observed in the Asian population. However, 607C allele was linked to decreased IS risk in the Egyptian population under all genetic models except the heterozygous model (C vs. A: OR=0.48, P=0.006; CC vs. AA: OR=0.19, P=0.007; CA vs. AA: OR=0.47, P=0.078; CC+CA vs. AA: OR=0.39, P=0.020; CC vs. AA+CA:OR=0.30, P=0.030). Although two polymorphisms were associated with IS, the association varied significantly in different countries. Large epidemiological studies will be required to verify these findings in the future.

HSP40’s molecular features meant for transient interactions in chaperoning may enable its trafficking with the presumptive client, concerted refolding with the client, and remaining with the refolded client to bolster structural stability crucial for rhythmic beating.

The ability of story comprehension is a strong indicator of natural language understanding. Recently, Story Cloze Test has been introduced as a new task of machine reading comprehension, i.e., selecting a correct ending from two candidate endings given a four-sentence story context. Most existing methods for Story Cloze Test are essentially matching-based that operate by comparing an individual ending with a given context, therefore suffering from the evidence bias issue: both candidate endings can obtain supporting evidence from the story context, which misleads the classifier to choose an incorrect ending. To address this issue, we present a novel idea to improve story comprehension by utilizing the hints that are obtained through comparing two candidate endings. The proposed model firstly anticipates a feature vector for a possible ending solely based on the context, and then refines the feature prediction using the hints which encode the difference between two candidates. The candidate ending whose feature vector is more similar to the predicted ending vector is regarded as correct. Experimental results demonstrate that our approach can alleviate the evidence bias issue and improve story comprehension.

As an essential process in the life of a plant, natural leaf senescence is necessary for adaptation to environmental diversity, whereas early leaf senescence can reduce per‐unit yield and cause inferior harvest quality during crop production. To explore the molecular mechanism of leaf senescence in rice (Oryza sativaL.), we studied an early senescence leaf (esl11) mutant obtained by ethyl methane sulfonate (EMS) mutagenesis. The leaves of the esl11mutant showed normal growth and leaf coloration prior to the three‐leaf stage, but after this stage, all fully expanded leaves underwent yellowing and senescence from the edges of the leaf blade and tips, apart from the emerging leaves, which showed gradual growth. The net photosynthetic rate and photosynthetic pigment content of the tip and leaf base of the mutant esl11were significantly lower than those of the wild type. The role of active oxygen species O2−, H2O2, and ⋅OH in the mutant increased, whereas the activities of MDA, peroxidase, superoxide dismutase, and catalase decreased compared with the wild type. The results of trypan blue staining on the senescent part of the leaf tip of the mutant esl11indicated procedural death in part of the cell, whereas the wild type showed no change. The early leaf senescence trait in the mutant was controlled by of recessive nuclear gene, which was mapped to chromosome 7, with a physical range of 143.0 kb. The results of this study provide the foundation for important progress in gene cloning and functional analysis of the esl11gene.

A novel starch accumulation and early senescent leaf mutant esl10was identified among progenies of the rice (Oryza sativaL.) indicamaintainer line Xinong 1B raised from seeds treated with ethyl methane sulfonate (EMS). Under paddy field conditions, leaf starch accumulation commenced at the three‐leaf stage in the mutant esl10and the leaves began to yellow at the four‐leaf stage, gradually extending from the tip to the middle of the leaf. The yellowing phenotype was most obvious at the tillering stage and persisted until maturity. Compared with the wild type, photosynthetic pigment concentrations and net photosynthetic rate of esl10were significantly lower, whereas the concentrations of reactive oxygen species and malondialdehyde (MDA) were higher. Moreover, catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) activities decreased in leaves at the tillering stage. Quantitative real‐time polymerase chain reaction showed that the expression of genes associated with starch metabolism pathway changed. Excessive accumulation of leaf starch may have caused the leaf premature senescence, finally influencing grain yield and quality of esl10. Genetic analysis showed that esl10was controlled by a dominant nuclear gene (tentatively called ESL10). ESL10was mapped between the single‐nucleotide polymorphism markers SNP585 and SNP498 on chromosome 7 with a physical distance of 416.29 kb including 56 annotated genes. The results provide a foundation for gene cloning and functional analysis of ESL10.

Glioblastoma is one of the most challenging and intractable tumors with the difficult treatment and poor prognosis. Unsatisfactory traditional systemic chemotherapies for glioblastoma are mainly attributed to the insufficient and nonspecific drug delivery into the brain tumors as well as the incomplete drug release at the tumor sites. Inspired by the facts that angiopep-2 peptide is an acknowledged dual-targeting moiety for brain tumor-targeting delivery and high-intensity focused ultrasound (HIFU) is an ideal trigger for drug release with an ultrahigh energy and millimeter-sized focus ability, in the present study, a novel HIFU-responsive angiopep-2-modified small poly(lactic-co-glycolic acid) (PLGA) hybrid nanoparticle (NP) drug delivery system holding doxorubicin/perfluorooctyl bromide (ANP-D/P) was designed to increase the intratumoral drug accumulation, further trigger on-demand drug release at the glioblastoma sites, and enhance glioblastoma therapy. It was shown that the ANP-D/P was stable and had a small size of 41 nm. The angiopep-2 modification endowed the ANP-D/P with improved blood–brain barrier transportation and specific accumulation in glioblastoma tissues by 17 folds and 13.4 folds compared with unmodified NPs, respectively. Under HIFU irradiation, the ANP-D/P could release 47% of the drug within 2 min and induce the apoptosis of most tumor cells. HIFU-triggered instantaneous drug release at the glioblastoma sites eventually enabled the ANP-D/P to achieve the strongest antiglioblastoma efficacy with the longest median survival time (56 days) of glioblastoma-bearing mice and the minimum vestiges of tumor cells in the pathological slices among all groups. In conclusion, the HIFU-responsive ANP-D/P in this study provided a new way for glioblastoma therapy with a great potential for clinical applications.

This study was to evaluate the resistance of antimicrobial agents against pathogens from inpatients with nosocomial infection collected in Beijing, China, during 2011–2014. Measurement of minimum inhibitory concentrations (MICs) was carried out using the broth microdilution method with the Clinical and Laboratory Standards Institute (CLSI) guidelines as the indicator. A total of 5442 Gram-negative and 806 Gram-positive isolates were collected in this study in 2011–2014. Two carbapenem-resistant strains appeared among Escherichia coli (E. coli), while imipenem-resistant isolates increased in proportion from 0% to 8.2% among Klebsiella pneumonia (K. pneumonia)during 4 year. Acinetobacter baumannii (A. baumannii)revealed severe antibacterial resistance to most antimicrobial agents. In contrast, a decreasing trend on resistance had been observed among Pseudomonas aeruginosa (P. aeruginosa)especially after 2012, range from 1.8% for co-trimoxazole to 13.5% for piperacillin. The resistance of Staphylococcus aureus (S. aureus)also had the lowest resistant to linezolid and vancomycin (0.1%). In summary, antimicrobial-resistant nosocomial pathogens have gradually increased from 2011 to 2014, so improved surveillance of hospital-acquired infections and effective infection-control measures may be the best way to solve the present problem.

NDK5 promotes assembly of motile cilia and flagella with its structure and protein phosphorylation–related reactions instead of the canonical NDK activity. The novel mechanisms and dominant-negative effect of mutated functional NDK5 reveal the remarkable versatility of a molecular platform that is used in diverse cellular processes.

In this study, we report a novel two-photon fluorescent probe for monitoring hydrogen peroxide. Probe 1consists of a naphthalene backbone and a boric acid ester which was used as a H2O2reporter. The reaction of probe 1with H2O2triggers the cleavage of a boronate-based protecting group, and as a result, restores the fluorescence of compound 2. The probe can be applied to the quantification of hydrogen peroxide with a linear range from 1.0 × 10−6to 2.5 × 10−4mol L−1. The detection limit of probe 1toward H2O2was estimated to be 0.7 μM. Furthermore, probe 1was found to have a much higher selectivity for H2O2than other reactive oxygen species and successfully applied to cell imaging of hydrogen peroxide using two-photon microscopy in living cells. The superior properties of the probe made it highly promising for use in chemical and biological applications.

Our aim in this study was to identify the associations between growth differentiation factor 15 (GDF15) and type 2 diabetes mellitus (T2DM) complications in a community-based population in China.

Chronic circadian rhythm disruption (CRD) increases the risk for, and severity of, cancer. It also increases the risk and severity of comorbidities that interfere with cancer treatment.The circadian system regulates multiple cancer hallmarks that are often targets of cancer drugs. Consequently, pharmacological modulation of clock components is emerging as a new treatment to simultaneously affect multiple aspects of cancer.Timing of food intake affects circadian rhythm. Accordingly, a consistent daily feeding–fasting cycle, such as in time-restricted eating (TRE), sustains robust circadian rhythms.Lifestyles incorporating principles of circadian rhythms include sufficient sleep, TRE, daylight exposure, and optimum timing of exercise. Such a lifestyle can effectively reduce the risk of cancer and comorbidities in everyone and can improve health of millions of cancer survivors.