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Ophiopogonis Radix (OR) is a traditional Chinese medicine. In recent years, in order to achieve the purpose of drying, bleaching, sterilizing and being antiseptic, improving appearance, and easy storage, people often use sulfur fumigation for its processing. However, changes in the chemical composition of medicinal herbs caused by sulfur fumigation can lead to the transformation and loss of potent substances. Therefore, the development of methods to rapidly reveal the chemical transformation of medicinal herbs induced by sulfur fumigation can guarantee the safe clinical use of medicines. In this study, a combined full scan-parent ions list-dynamic exclusion acquisition-diagnostic product ions analysis strategy based on UHPLC-LTQ-Orbitrap MS was proposed for the analysis of steroidal saponins and their transformed components in sulfur-fumigated Ophiopogonis Radix (SF-OR). Based on precise mass measurements, chromatographic behavior, neutral loss ions, and diagnostic product ions, 286 constituents were screened and identified from SF-OR, including 191 steroidal saponins and 95 sulfur-containing derivatives (sulfates or sulfites). The results indicated that the established strategy was a valuable and effective analytical tool for comprehensively characterizing the material basis of SF-OR, and also provided a basis for potential chemical changes in other sulfur-fumigated herbs. [ABSTRACT FROM AUTHOR]

The active peptide (APE) of Eupolyphaga sinensis Walker, which is prepared by bioenzymatic digestion, has significant antihyperlipidemic effects in vivo, but its mechanism of action on hyperlipidemia is not clear. Recent studies on amino acid metabolism suggested a possible link between it and hyperlipidemia. In this study, we first characterized the composition of APE using various methods. Then, the therapeutic effects of APE on hyperlipidemic rats were evaluated, including lipid levels, the inflammatory response, and oxidative stress. Finally, the metabolism-regulating mechanisms of APE on hyperlipidemic rats were analyzed using untargeted and targeted metabolomic approaches. The results showed that APE significantly reduced the accumulation of fat, oxidative stress levels, and serum pro-inflammatory cytokine levels. Untargeted metabolomic analysis showed that the mechanism of the hypolipidemic effect of APE was mainly related to tryptophan metabolism, phenylalanine metabolism, arginine biosynthesis, and purine metabolism. Amino-acid-targeted metabolomic analysis showed that significant differences in the levels of eight amino acids occurred after APE treatment. Among them, the expression of tryptophan, alanine, glutamate, threonine, valine, and phenylalanine was upregulated, and that of arginine and proline was downregulated in APE-treated rats. In addition, APE significantly downregulated the mRNA expression of SREBP-1, SREBP-2, and HMGCR. Taking these points together, we hypothesize that APE ameliorates hyperlipidemia by modulating amino acid metabolism in the metabolome of the serum and feces, mediating the SREBP/HMGCR signaling pathway, and reducing oxidative stress and inflammation levels. [ABSTRACT FROM AUTHOR]

Modern data center power distribution networks place greater demands on the stability and reliability of power supply. Growing network computing demands and complex network environments can cause network congestion, which in turn leads to network traffic overload and power supply equipment overload. Therefore, network congestion is one of the most important problems faced by data center power distribution networks. In this paper, we propose an approach called ACC-RL based on reinforcement learning (RL), which can effectively avoid network congestion and improve energy performance. ACC-RL models the congestion control task as a Partially Observable Markov Decision Process (POMDP). It is independent of the estimated value function and supports deterministic policies. It also sets the reward value function using real-time network information such as the transmission rate, RTT, and switch queue length, with the target transmission rate as the target equilibrium point. ACC-RL is highly general, can be trained on datasets running in different network environments, and generates a robust congestion control policy. The experimental results show that ACC-RL can solve the congestion problem without any predefined scenarios in different network environments. It can control the network traffic well, thus ensuring the stability and reliability of the power supply in the distribution network. We conduct network simulation experiments through NS-3. We set up different scenarios for experiments and data analysis in many-to-one, all-to-all, and long–short network environments. Compared with the popular rule-based congestion control algorithms such as TIMELY, DCQCN, and HPCC, ACC-RL shows different degrees of energy performance advantages in network metrics such as fairness, link utilization, and throughput. [ABSTRACT FROM AUTHOR]

Xanthohumol (XN), a natural prenylated flavonoid extracted and isolated from the hop plant (Humulus lupulus), possesses diverse pharmacological activities. Although the metabolites of XN have been investigated in the previous study, a comprehensive metabolic profile has been insufficient in vivo or in vitro until now. The current study was aimed at systematically elucidating the metabolic pathways of XN after oral administration to rats. Herein, a UHPLC-Q-Exactive Orbitrap MS was adopted for the potential metabolites detection. A stepwise targeted matching strategy for the overall identification of XN metabolites was proposed. A metabolic net (53 metabolites included) on XN in vivo and in vitro, as well as the metabolic profile investigation, were designed, preferably characterizing XN metabolites in rat plasma, urine, liver, liver microsomes, and feces. On the basis of a stepwise targeted matching strategy, the net showed that major in vivo metabolic pathways of XN in rats include glucuronidation, sulfation, methylation, demethylation, hydrogenation, dehydrogenation, hydroxylation, and so on. The proposed metabolic pathways in this research will provide essential data for further pharmaceutical studies of prenylated flavonoids and lay the foundation for further toxicity and safety studies. [ABSTRACT FROM AUTHOR]

The Internet-of-Things (IoT) based monitoring system has significantly promoted the intelligence and automation of power grids. The inspection robots and wireless sensors used in the monitoring system usually have multiple network interfaces to achieve high throughput and reliability transmission. The concurrent usage of these available interfaces with Multipath TCP (MPTCP) can enhance the quality of service of the communications. However, traditional MPTCP scheduling algorithms may bring about data disorder and even buffer blocking, which severely affects the transmission performance of MPTCP. And the common MPTCP improvement mechanisms for IoT lack sufficient attention to energy consumption, which is important for the battery-limited wireless sensors. With the aim to promote conservative energy without loss of throughput, this paper develops an integrated multipath scheduler for energy consumption optimization named energy-efficient MPTCP (EE-MPTCP). EE-MPTCP first constructs a target optimization function which considers both network throughput and energy consumption. Then, based on the proposed MPTCP transmission model and existing energy efficiency model, the network throughput and energy consumption of each path can be estimated. Finally, a heuristic scheduling algorithm is proposed to find a suitable set of paths for each application. As confirmed by experiments based on Linux testbed as well as the NS3 simulation platform, the proposed scheduler can shorten the average completion time and reduce the energy consumption by up to 79.9% and 79.2%, respectively. [ABSTRACT FROM AUTHOR]

The excessive release of phosphorus is a prime culprit for eutrophication and algal bloom in the aquatic environment, and there is always an urgent need to develop effective methods to deal with phosphorus pollution. Ce-based oxide is a type of compelling adsorbent for phosphate removal, and a self-templating strategy is used to construct high-performance Ce-based oxides for phosphate adsorption in this study. A "sea anemone"-like CeFe cyanometallate (CM) with a 3D microstructure is fabricated to provide a precursor for synthesizing CeFe-based oxides (CeFe-CM-T) by high-temperature pyrolysis. The as-prepared CeFe-CM-T maintains the "sea anemone" morphology well and has abundant micropores/mesopores, which render its superior phosphate adsorption capacity 1~2 orders of magnitude higher than that of the commercial CeO2 and Fe3O4 materials. Moreover, CeFe-CM-T shows high selectivity for phosphate removal when it co-exists with other anions and natural organic matter and exhibits excellent recycling performance. It demonstrates that both Ce3+ and Ce4+ are reserved in the oxides, where Ce3+ serves as the main active site for phosphate capture, which forms stable Ce-PO4 compounds via a ligand-exchange mechanism. Thus, the self-templating strategy using CM as a precursor is a potential method for synthesizing porous Ce-based oxides for phosphate removal. [ABSTRACT FROM AUTHOR]

Under global climate change, the frequency of typhoons and their strong wind, heavy rain, and storm surge increase, seriously threatening the life and property of human society. However, traditional tropical cyclone track prediction methods have difficulties in processing large amounts of complex data in terms of prediction efficiency and accuracy. Recently, deep learning methods have shown a potential capability to process complex data efficiently and accurately. In this paper, we propose a novel data-driven approach based on auto-encoder (AE) and gated recurrent unit (GRU) models to forecast tropical cyclone landing locations using the historical tropical cyclone tracks and various meteorological attributes. This approach fuses a data preprocessing layer, an AE layer, and a GRU layer with a customized batch process. The model is trained on a real-world tropical cyclone dataset from the years 1945–2017. Through a comparison with existing forecasting methods, the results verified that our proposed model performed around 15%, 42%, and 56% better than the Numerical Weather Prediction model (NWP) in 24, 48, and 72 h forecasts, and 27%, 13%, 17%, and 17% better than RNN, AE-RNN, GRU, and LSTM, respectively, in 24 h forecasts, using the absolute position error. In addition, a comparison of the meteorological variables indicated that the variable maximum sustained wind speed had the most significant effect on tropical cyclone track prediction. [ABSTRACT FROM AUTHOR]

Multipath TCP (MPTCP) allows the system to use available paths through multiple network interfaces. This aggregation of bandwidth increases the throughput of the network but also consumes more energy to maintain connections between multiple interfaces. Existing algorithms tend to shift traffic from the high energy path to the low energy path to save energy. However, when the performance of the less energy-intensive path deteriorates, continued use of that path will reduce throughput. To alleviate this issue and reduce energy consumption while ensuring network throughput, this paper proposes an energy-saving scheduling system named ES-MPTCP, which can be combined with the path management module to manage subflows, thereby determining the priority of subflows and which subflows to be used for data transmission. The ES-MPTCP algorithm designs a target optimization function based on path throughput and energy consumption to find different sets of suitable subflows for different applications to reduce energy consumption and guarantee throughput. The extensive experimental results based on the Linux real platform demonstrate that the ES-MPTCP algorithm has the best performance, which can improve throughput by 13.6%, reduce energy consumption by 16.2% and increase the energy efficiency up to 23%. [ABSTRACT FROM AUTHOR]

Cellular-based networks keep large buffers at base stations to smooth out the bursty data traffic, which has a negative impact on the user's Quality of Experience (QoE). With the boom of smart vehicles and phones, this has drawn growing attention. For this paper, we first conducted experiments to reveal the large delays, thus long flow completion time (FCT), caused by the large buffer in the cellular networks. Then, a receiver-side transmission control protocol (TCP) countermeasure named Delay-based Flow Control algorithm with Service Differentiation (DFCSD) was proposed to target interactive applications requiring high throughput and low delay in cellular networks by limiting the standing queue size and decreasing the amount of packets that are dropped in the eNodeB in Long Term Evolution (LTE). DFCSD stems from delay-based congestion control algorithms but works at the receiver side to avoid the performance degradation of the delay-based algorithms when competing with loss-based mechanisms. In addition, it is derived based on the TCP fluid model to maximize the network utility. Furthermore, DFCSD also takes service differentiation into consideration based on the size of competing flows to shorten their completion time, thus improving user QoE. Simulation results confirmed that DFCSD is compatible with existing TCP algorithms, significantly reduces the latency of TCP flows, and increases network throughput. [ABSTRACT FROM AUTHOR]

With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models' complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions. [ABSTRACT FROM AUTHOR]

The calcium-dependent activator protein for secretion 1 (CAPS1) regulates exocytosis of dense-core vesicles (DCVs) in neurons and neuroendocrine cells. The role of CAPS1 in cancer biology remains unknown. The purpose of this study was to investigate the role of CAPS1 in hepatocellular carcinoma (HCC). We determined the levels of CAPS1 in eight hepatoma cell lines and 141 HCC specimens. We evaluated the prognostic value of CAPS1 expression and its association with clinical parameters. We investigated the biological consequences of CAPS1 overexpression in two hepatoma cell lines in vitro and in vivo. The results showed that loss of CAPS1 expression in HCC tissues was markedly correlated with aggressive tumor phenotypes, such as high-grade tumor node metastasis (TNM) stage ( p = 0.003) and absence of tumor encapsulation ( p = 0.016), and was associated with poor overall survival ( p = 0.008) and high recurrence ( p = 0.015). CAPS1 overexpression inhibited cell proliferation and migration by changing the exocytosis-associated tumor microenvironment in hepatoma cells in vitro. The in vivo study showed that CAPS1 overexpression inhibited xenograft tumor growth. Together, these results identified a previously unrecognized tumor suppressor role for CAPS1 in HCC development., Competing Interests: The authors declare no conflict of interest.

Tumor-associated macrophages (TAMs), the most abundant infiltrating immune cells in tumor microenvironment, have distinct functions in hepatocellular carcinoma (HCC) progression. CD68⁺ TAMs represent multiple polarized immune cells mainly containing CD86⁺ antitumoral M1 macrophages and CD206⁺ protumoral M2 macrophages. TAMs expression and density were assessed by immunohistochemical staining of CD68, CD86, and CD206 in tissue microarrays from 253 HCC patients. Clinicopathologic features and prognostic value of these markers were evaluated. We found that CD68⁺ TAMs were not associated with clinicopathologic characteristics and prognosis in HCC. Low presence of CD86⁺ TAMs and high presence of CD206⁺ TAMs were markedly correlated with aggressive tumor phenotypes, such as multiple tumor number and advanced tumor-node-metastasis (TNM) stage; and were associated with poor overall survival (OS) (p = 0.027 and p = 0.024, respectively) and increased time to recurrence (TTR) (p = 0.037 and p = 0.031, respectively). In addition, combined analysis of CD86 and CD206 provided a better indicator for OS (p = 0.011) and TTR (p = 0.024) in HCC than individual analysis of CD86 and CD206. Moreover, CD86⁺/CD206⁺ TAMs predictive model also had significant prognosis value in α-fetoprotein (AFP)-negative patients (OS: p = 0.002, TTR: p = 0.005). Thus, these results suggest that combined analysis of immune biomarkers CD86 and CD206 could be a promising HCC prognostic biomarker.