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The structure of N-glycans on specific proteins can regulate innate and adaptive immunity via sensing environmental signals. Meanwhile, the structural diversity of N-glycans poses analytical challenges that limit the exploration of specific glycosylation functions. In this work, we used THP-1-derived macrophages as examples to show the vast potential of a N-glycan structural interpretation tool StrucGP in N-glycoproteomic analysis. The intact glycopeptides of macrophages were enriched and analyzed using mass spectrometry (MS)-based glycoproteomic approaches, followed by the large-scale mapping of site-specific glycan structures via StrucGP. Results revealed that bisected GlcNAc, core fucosylated, and sialylated glycans (e.g., HexNAc

Despite the known hazards to wildlife, plastic waste to waterways and oceans continues to increase at alarming rates every year. Nanoplastics are a breakdown product of plastic debris in the environment (0.1–100 nm). Their small size renders them bioavailable, and they are likely to accumulate/adsorb persistent organic pollutants, which could increase their toxicity. In this study, we evaluated the effects of nanoplastics and phenanthrene on the early life stages of zebrafish. Zebrafish embryos were exposed to fluorescent nanoparticles and observed with fluorescence microscopy. Polystyrene nanoplastics (80 nm) attached to the embryonic chorionic membrane and were ingested when zebrafish reached 144 h after fertilization, the point at which they first open their mouth, as observed by the fluorescence in their intestines. Exposure alone or in combination with phenanthrene resulted in zebrafish pericardial edema, spinal curvature, increased embryonic heart rate, shortened body length, and increased mortality. Co-exposure to 5 mg/L nanoplastics and 0.1 mg/L phenanthrene caused more negative effects on length, teratogenicity and mortality of zebrafish embryos than the corresponding concentrations of single exposure. This study provides clear evidence that nanoplastics can be ingested by zebrafish larvae, thus future work to assess the toxicological effects is important to marine wildlife management.

Based on the synergistic effect of MoS2 and rGO, a MoS2-rGO nanocomposite film shows a wide linear range and high sensitivity towards cyproterone acetate.

Coronavirus disease-2019 (COVID-19) has become a worldwide emergency and has had a severe impact on human health. Inflammatory factors have the potential to either enhance the efficiency of host immune responses or damage the host organs with immune overreaction in COVID-19. Therefore, there is an urgent need to investigate the functions of inflammatory factors and serum markers that participate in disease progression.In total, 54 COVID-19 patients were enrolled in this study. Disease severity was evaluated by clinical evaluation, laboratory tests, and computed tomography (CT) scans. Data were collected at: admission, 3-5 days after admission, when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detection became negative, and composite endpoint.We found that the positive rate in sputum was three times higher than that in throat swabs. Higher levels of C-reactive protein (CRP), lactate dehydrogenase (LDH), D-dimer (D-D), interleukin-6 (IL-6) and neutrophil-to-lymphocyte ratio (NLR) or lower lymphocyte counts suggested more severe disease, and the levels of cytokines and serum markers were intrinsically correlated with disease progression. When SARS-CoV-2 RNA detection became negative, the receiver operating characteristic (ROC) curve demonstrated that LDH had the highest sensitivity independently, and four indicators (NLR, CRP, LDH, and D-D) when combined had the highest sensitivity in distinguishing critically ill patients from mild ones.Monitoring dynamic changes in NLR, CRP, LDH, IL-6, and D-D levels, combined with CT imaging and viral RNA detection in sputum, could aid in severity evaluation and prognosis prediction and facilitate COVID-19 treatment.

Accurate identification of core fucosylation on

Rationale: Epithelial-mesenchymal transition (EMT) has been recognized as an important step toward high invasion and metastasis of many cancers including hepatocellular carcinoma (HCC), while the mechanism for EMT promotion is still ambiguous. Methods: The dynamic alterations of site-specific glycosylation during HGF/TGF-β1-induced EMT process of three HCC cell lines were systematically investigated using precision glycoproteomic methods. The possible roles of EMT-related glycoproteins and site-specific glycans were further confirmed by various molecular biological approaches. Results: Using mass spectrometry-based glycoproteomic methods, we totally identified 2306 unique intact glycopeptides from SMMC-7721 and HepG2 cell lines, and found that core-fucosylated glycans were accounted for the largest proportion of complex N-glycans. Through quantification analysis of intact glycopeptides, we found that the majority of core-fucosylated intact glycopeptides from folate receptor α (FOLR1) were up-regulated in the three HGF-treated cell lines. Similarly, core-fucosylation of FOLR1 were up-regulated in SMMC-7721 and Hep3B cells with TGF-β1 treatment. Using molecular approaches, we further demonstrated that FUT8 was a driver for HGF/TGF-β1-induced EMT. The silencing of FUT8 reduced core-fucosylation and partially blocked the progress of HGF-induced EMT. Finally, we confirmed that the level of core-fucosylation on FOLR1 especially at the glycosite Asn-201 positively regulated the cellular uptake capacity of folates, and enhanced uptake of folates could promote the EMT of HCC cells. Conclusions: Based on the results, we proposed a potential pathway for HGF or TGF-β1-induced EMT of HCC cells: HGF or TGF-β1 treatment of HCC cells can increase the expression of glycosyltransferase FUT8 to up-regulate the core-fucosylation of N-glycans on glycoproteins including the FOLR1; core-fucosylation on FOLR1 can then enhance the folate uptake capacity to finally promote the EMT progress of HCC cells.

In the smart grid, the end-users have the opportunity to integrate renewable energy sources (RESs) and participate in two-way energy trading. At the same time, an increasing number of flexible loads (FLs) have been developed for use on the demand side. Thus, this article considers joint energy trading and load scheduling at a end-user with integrated renewable generation. With unknown statistics on renewable generation, loads and electricity prices, we aim at optimizing both energy trading and load scheduling to maximize the long-term average profit of the end-user, subject to load delay constraints. We employ the Lyapunov optimization theory to solve this stochastic problem with a series of problem corrections and transformations that enable us to design a dynamic energy trading and load scheduling algorithm. Within the performance analysis of the algorithm, we further demonstrate that the algorithm not only provides a bounded performance guarantee to the optimal solution that has complete future information, but is also asymptotically equivalent to it as the battery capacity or the delay time of FLs tend to infinity. The simulation results show that the proposed algorithm is superior to other algorithms both in terms of performance and service delay. Moreover, we can achieve a trade-off between comfort and total profit by adjusting the values of the parameters, and analyze the effect of battery capacity on algorithm performance to provide a theoretical basis for the end-user to determine battery capacity.

Primary liver cancer, mainly comprising hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), remains a major global health problem. Although ICC is clinically different from HCC, their molecular differences are still largely unclear. In this study, precision N-glycoproteomic analysis was performed on both ICC and HCC tumors as well as paracancer tissues to investigate their aberrant site-specific N-glycosylation. By using our newly developed glycoproteomic methods and novel algorithm, termed 'StrucGP', a total of 486 N-glycan structures attached on 1235 glycosites were identified from 894 glycoproteins in ICC and HCC tumors. Notably, glycans with uncommon LacdiNAc (GalNAcβ1-4GlcNAc) structures were distinguished from their isomeric glycans. In addition to several bi-antennary and/or bisecting glycans that were commonly elevated in ICC and HCC, a number of LacdiNAc-containing, tri-antennary, and core-fucosylated glycans were uniquely increased in ICC. More interestingly, almost all LacdiNAc-containing N-glycopeptides were enhanced in ICC tumor but not in HCC tumor, and this phenomenon was further confirmed by lectin histochemistry and the high expression of β1-4 GalNAc transferases in ICC at both mRNA and protein expression levels. The novel N-glycan alterations uniquely detected in ICC provide a valuable resource for future studies regarding to the discovery of ICC diagnostic biomarkers, therapeutic targets, and mechanism investigations.

Energy storage can save end user costs in local energy markets that have time-varying pricing. However, energy storage device incur fixed acquisition costs which depend on their capacity. End user is faced with sophisticated energy scheduling tradeoffs in the local energy markets to account for these costs. In this paper, we consider a typical energy usage scenario where the end user draws energy from multiple types of energy supplies: the local power provider, the external power grid, and the user’s own energy storage device. Our objective is to minimize the user’s total costs (the total of purchased energy and storage) while meeting their energy demand in each time slot. Furthermore, the end user’s energy demand, the local power supplier’s prices, and the external power grid prices all vary over time. To deal with this variability, we formulated the energy scheduling problem as a stochastic optimization. We propose a dynamic algorithm based on Lyapunov optimization, and it is theoretically proved that the proposed algorithm can make the optimization target infinitely close to optimum. Finally, the effectiveness of the proposed algorithm is verified by simulation comparison. The algorithm provides a tool for end user energy scheduling where the user is equipped with energy storage device.

Background: Coronavirus disease-2019 (COVID-19) has become a worldwide emergency and has had a severe impact on human health. Inflammatory factors have the potential to either enhance the efficiency of host immune responses or damage the host organs with immune overreaction in COVID-19. Therefore, there is an urgent need to investigate the functions of inflammatory factors and serum markers that participate in disease progression. Methods: In total, 54 COVID-19 patients were enrolled in this study. Disease severity was evaluated by clinical evaluation, laboratory tests, and computed tomography (CT) scans. Data were collected at: admission, 3–5 days after admission, when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detection became negative, and composite endpoint. Results: We found that the positive rate in sputum was three times higher than that in throat swabs. Higher levels of C-reactive protein (CRP), lactate dehydrogenase (LDH), D-dimer (D-D), interleukin-6 (IL-6) and neutrophil-to-lymphocyte (NLR) or lower lymphocyte counts suggested more severe disease, and the levels of cytokines and serum markers were intrinsically correlated with disease progression. When SARS-CoV-2 RNA detection became negative, the receiver operating characteristic (ROC) curve demonstrated that LDH had the highest sensitivity independently, and four indicators (NLR, CRP, LDH, and D-D) when combined had the highest sensitivity in distinguishing critically ill patients from mild ones. Conclusions: Monitoring dynamic changes in NLR, CRP, LDH, IL-6, and D-D levels, combined with CT imaging and viral RNA detection in sputum, could aid in severity evaluation and prognosis prediction and facilitate COVID-19 treatment.

The problems of insufficient power consumption and delay sensitivity are challenging issues of Software Defined-Wireless Body Area Networks (SD-WBANs) for smart health monitoring. The migration and offloading problems in mobile edge computing (MEC) system complicate the resource allocation on low delay and energy consumption. In this paper, we propose an Asynchronous Advantage Actor-Critic (A3C)-based joint optimization offloading and migration algorithm to address this issue. Based on Actor-Critic network, the proposed algorithm optimizes the neural network with asynchronous gradient descent to maximize the long-term benefits of SD-WBAN. Moreover, we define the total system cost as the combination of SD-WBAN migration cost, offloading cost and quality of service. Simulation results show that this algorithm can effectively get better long-term benefits of different priority tasks and highly improve the service quality of the users.

Real-time and accurate short-term traffic prediction enables efficient operation of road traffic via real-time vehicle status information. However, the traffic prediction model based on deep learning or machine learning is generally not efficient due to the long training time. Thus, we propose a Bidirectional Connection Broad Learning System (Bi-BLS) model to characterize the relationship between road flow and vehicle speed, so as to perform real-time and short-term traffic prediction. The Bi-BLS consists of two parts, namely the mapped feature nodes layer and the enhancement nodes layer. The mapped nodes layer employs cascade connection to extract hierarchical features from the data set by edge computing servers. The enhancement nodes layer uses bidirectional connection to extract hierarchical features from the mapped nodes layer. Moreover, it can efficiently add enhancement nodes or mapped feature nodes. To validate the effectiveness of our proposed model, the simulations are conducted based on the England Freeway Data set. Numerical results show that our proposed model has better prediction performance in short-term traffic prediction than other benchmark models.

Due to the excellent ion insertion and exchange capacity, the layered double hydroxide (LDH) is considered to be an ideal electrode material for pseudocapacitor. However, the fast redox reaction of LDH always results in low power density and poor cycle stability. In this work, the C/NiMn-LDH/Ni3S2 is successfully prepared on the nickel foam (NF) and exhibits excellent electrochemical performance. The specific capacitance of C/NiMn-LDH/Ni3S2/NF electrode could reach 3490 mF cm−2 at a current density of 2 mA cm−2 and retains 84.25% capacitance after 5000 continuous cycles. In addition, the two-electrode device assembled by C/NiMn-LDH/Ni3S2/NF and activated carbon (C/NiMn-LDH/Ni3S2/NF//AC) shows an excellent specific energy of 64.46 Wh kg−1 at 1279.96 W kg−1. The extraordinary electrochemical performance is owing to the synergistic effect of the components.

Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that, compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.

In this paper, we investigate the problem of dynamic power allocation for a multiuser transmitter supplied by hybrid energy sources in details. Specifically, we focus on the hybrid energy sources which include both the traditional power grid and various renewable sources whereby there are a few issues in considerations: (1) The energy harvested jointly from various renewable sources is time-varying and possibly unpredictable and is stored in a limited capacity buffer with battery leakage. (2) At the meantime, the data arrives randomly to the transmitter and queues according to the individual receivers to wait to be transmitted. (3) In addition, the wireless channels fluctuate randomly due to fading. Taking into account the time variant and dynamic features of this system, we develop a dynamic power allocation algorithm for the transmitter with the aim of minimizing the average amount of energy consumption from the power grid over an infinite horizon, subject to all data in queues cannot exceed a given deadline of receivers. The research question is formulated as a stochastic optimization problem, then we utilize Lyapunov optimization to exploit an online algorithm with low complexity, and it does not require prior statistical knowledge of the stochastic processes. Performance analysis of the proposed algorithm is carried out in theory, which shows that the proposed algorithm performs arbitrarily close to the optimal objective value; meanwhile, the algorithm ensures that the maximum delay of all data queues cannot exceed a given value. Finally, performance comparison shows that our proposed algorithm provides not only better performance but also less time delay than other two algorithms.

In this paper, we consider a wireless communication node with hybrid energy harvesting (EH) sources which results in great difficulty in obtaining the statistical knowledge of joint EH process. In addition, the wireless channel fluctuates randomly due to fading. Our goal is, under this condition, to develop a dynamic power control policy for the transmitter such that the time average throughput of the system is maximized over an infinite horizon, taking into account the circuit energy consumption and inefficiency of the rechargeable battery. Such a dynamic power control problem is formulated as a stochastic network optimization problem. The problem is solved by utilizing Lyapunov optimization and an efficient on-line algorithm with quite low complexity is obtained. Simulation results illustrate that the proposed algorithm has the same performance as the optimal one with giving statistical knowledge of the stochastic processes.

This study investigated the dynamics of CO2 flux in a rain-fed wheat field ecosystem using an eddy covariance technique during the 2011 to 2012 wheat-growing season at the Loess Plateau, China. Results showed that the daily CO2 flux was closely related to photosynthetically active radiation (PAR), growth stage, soil temperature and rainfall. The average CO2 flux at different growth stages followed the order jointing and booting > erecting > reviving > heading > wintering > seeding and tillering > grain filling > ripening. The first four stages were carbon sinks, whereas the last four stages were carbon sources. The relationship between nighttime CO2 flux and air temperature was significant and fitted the index model (y=aebt). The relationship between daytime CO2 flux and PAR was also significant and fitted the quadratic model (y=ax2+bx+c). Moreover, daytime CO2 flux was significantly correlated with air temperature and PAR at the erecting, jointing and booting, and heading stages. Nighttime CO2 flux was also significantly correlated with soil temperature at 5 cm depth at the heading as well as jointing and booting stages. The carbon budget in the rain-fed wheat ecosystem was -401 g C m-2 yr-1, which was higher than those in other wheat ecosystems. This study implies that the ability of carbon-sequestration in different wheat field ecosystems may respond differently to climate and environment change.

Drought inhibits wheat grain filling. Polyamines (PAs) are closely associated with plant resistance due to drought and grain filling of cereals. However, little is known about the effect of PAs on the grain filling of wheat under drought stress. This study investigated whether and how PAs are involved in regulating wheat grain filling under drought stress. Two wheat genotypes differing in drought resistance were used, and endogenous PA levels were measured during grain filling under different water treatments. Additionally, external PAs were used, and the variation of hormone levels in grains was measured during grain filling under drought stress. The results indicated that spermidine (Spd) and spermine (Spm) relieve the inhibition caused by drought stress, and putrescine (Put) has the opposite effect. The higher activities of S-adenosylmethionine decarboxylase and Spd synthase in grains promotes the synthetic route from Put to Spd and Spm and notably increases the free Spd and Spm concentrations in grains, which promotes grain filling and drought resistance in wheat. The effect of PA on the grain filling of wheat under drought stress was closely related to the endogenous ethylene (ETH), zeatin (Z) + zeatin riboside (ZR) and abscisic acid (ABA). Spd and Spm significantly increased the Z + ZR and ABA concentrations and decreased the ETH evolution rate in grains, which promoted wheat grain filling under drought. Put significantly increased the ETH evolution rate, which led to excessive ABA accumulation in grains, subsequently aggravating the inhibition of drought on wheat grain filling. This means that the interaction of hormones, rather than the action of a single hormone, was involved in the regulation of wheat grain filling under drought.

Plastic film mulching (PM) is widely utilized for maize production in China. However, the effect of PM on the grain yield of crops has not been established, and the biochemical mechanism underlying the increase or decrease in grain yield under PM is not yet understood. Grain filling markedly affects the grain yield. The objective of this study was to investigate the effects of PM on maize grain filling under different irrigation levels and the relationship of such effects with hormonal changes. In the present study, PM was compared with traditional nonmulching management (TN) under 220 mm, 270 mm and 320 mm irrigation amount, and the grain filling characters of the grains located in various parts of the ear and the hormonal changes in the grains were measured. The results indicated that at 220 mm irrigation, PM significantly increased the grain filling rate of the middle and basal grains and decreased the grain filling rate of the upper grains. At 270 mm irrigation, the PM significantly increased the grain filling rate of the all grains. At 320 mm irrigation, the PM only significantly increased the grain filling rate of the upper grains. The IAA, Z+ZR and ABA content in the grains was positively correlated with the grain weight and grain-filling rates; however, the ETH evolution rate of the grains was negatively correlated with the grain weight and grain-filling rates. These results show that the effect of PM on maize grain filling is related to the irrigation amount and that the grain position on the ear and the grain filling of the upper grains was more sensitive to PM and irrigation than were the other grains. In addition, the PM and irrigation regulated the balance of hormones rather than the content of individual hormones to affect the maize grain filling.

The aim of this study is to explore the mucosal and systemic antibody immune responses induced by recombinant L.casei expressing the major immunoprotective antigen VP7 of bovine rotavirus (BRV). Two inducible expression vectors containing the antigenic dominant region of VP7 gene, pPG-1-VP7 (cell-surface expression vector) and pPG-2-VP7 (secretory vector), were constructed and electrotransformed into L. casei 393 to obtain positive recombinant bacteria designated rLc::pPG-1-VP7 or rLc::pPG-2-VP7. The expressions of VP7 protein induced by 2% lactose in MRS media were confirmed by western blot and indirect immunofluorescent staining using anti-VP7 antibody. Oral immunization of mice with the recombinant strains significantly increased anti-VP7 sIgA level in mouse vaginal lavages and feces samples, and anti-VP7 IgG level in serum compared with empty plasmid transformed bacteria. Results of neutralization assay clearly indicated that serum antibodies induced by oral vaccination have neutralization activity against BRV in vitro. The neutralizing titers are 1:62.9 and 1:50.6 for rLc::pPG-1-VP7 and rLc::pPG-2-VP7, respectively. Our results demonstrated that Lactobacillus casei expressing rBRV-VP7 have application potentials in producing oral vaccine against BRV. (Abstract)

The representation form of most signals in practice is non-stationary signal, and the main analysis methods for these signals are centralized in the field of time frequency (or time scale), and a series of results have been acquired. A reversible transformation method based on time sequence bit is proposed from another angle, and this method can be used to analyze non-stationary wide-band noise signals according to the difference of some characters of noises and speech signals (such as the randomness degree). The experiment result indicates that this method can reversibly convert random sequences with unordered time sequence into approximately ordered time sequences, and the difference between the wide-band noise character and the speech character is relatively obvious. The method and the principle are simple, and the algorithm can be easily realized real time.

Data sharing in Internet of Vehicles (IoV) makes it possible to provide personalized services for users by service providers in Intelligent Transportation Systems (ITS). As IoV is a multi-user mobile scenario, the reliability and efficiency of data sharing need to be further enhanced. Federated learning allows the server to exchange parameters without obtaining private data from clients so that the privacy is protected. Broad learning system is a novel artificial intelligence technology that can improve training efficiency of data set. Thus, we propose a federated bidirectional connection broad learning scheme (FeBBLS) to solve the data sharing issues. Firstly, we adopt the bidirectional connection broad learning system (BiBLS) model to train data set in vehicular nodes. The server aggregates the collected parameters of BiBLS from vehicular nodes through the federated broad learning system (FedBLS) algorithm. Moreover, we propose a clustering FedBLS algorithm to offload the data sharing into clusters for improving the aggregation capability of the model. Some simulation results show our scheme can improve the efficiency and prediction accuracy of data sharing and protect the privacy of data sharing.