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Amyloid-like fibrils have a relatively high specific surface area, which makes them suitable as absorbents. In this study, we prepared absorbent composite aerogels from whey protein isolate fibril (WPIF) and calcium alginate (CA). The impact of protein fibrillation time (4–24 h) on the adsorption properties of these aerogels was assessed, as this influences the nature of the WPIFs formed. Fourier transform infrared spectroscopy analysis indicated that hydrogen bonding, hydrophobic interactions, and electrostatic interactions were the main molecular interactions in the composite aerogels. Then, the adsorption properties of the aerogels were investigated using crystal violet as a model compound. The adsorption properties of all composite aerogels were significantly improved compared to CA aerogels, which was mainly attributed to the numerous functional groups of the surfaces of the WPIFs, as well as the rougher surface topology of the composite aerogels. The adsorption capacity of the composite aerogels first increased and then decreased with increasing fibrillation time, with 8 h fibrillation leading to the largest adsorption capacity (1886.11 mg/g). Thioflavin T fluorescence, atomic force microscopy, light extinction, average particle size, and zeta potential analysis indicated that the high fibril content, mature fibril morphology, large number of available functional groups on the surface, and relatively low zeta potential of WPIF8 might be the main reasons. Adsorption kinetics and isothermal adsorption profile analysis suggested that monolayer adsorption occurred through chemical adsorption processes. The edible aerogel with high adsorption properties developed in this work has potential application in food and biomedical fields.

Deep neural networks (DNNs) have demonstrated remarkable performance in many fields, and deploying them on resource-limited devices has drawn more and more attention in industry and academia. Typically, there are great challenges for intelligent networked vehicles and drones to deploy object detection tasks due to the limited memory and computing power of embedded devices. To meet these challenges, hardware-friendly model compression approaches are required to reduce model parameters and computation. Three-stage global channel pruning, which involves sparsity training, channel pruning, and fine-tuning, is very popular in the field of model compression for its hardware-friendly structural pruning and ease of implementation. However, existing methods suffer from problems such as uneven sparsity, damage to the network structure, and reduced pruning ratio due to channel protection. To solve these issues, the present article makes the following significant contributions. First, we present an element-level heatmap-guided sparsity training method to achieve even sparsity, resulting in higher pruning ratio and improved performance. Second, we propose a global channel pruning method that fuses both global and local channel importance metrics to identify unimportant channels for pruning. Third, we present a channel replacement policy (CRP) to protect layers, ensuring that the pruning ratio can be guaranteed even under high pruning rate conditions. Evaluations show that our proposed method significantly outperforms the state-of-the-art (SOTA) methods in terms of pruning efficiency, making it more suitable for deployment on resource-limited devices.

Graph Neural Networks (GNNs) have achieved impressive performance in many tasks on graph data. Recent studies show that they are vulnerable to adversarial attacks. Deliberate and unnoticeable perturbations on topology structure could render them near-useless in applications. How to design effective methods to improve the robustness of GNNs is a crucial problem. To solve this problem, some works attempt to design more robust GNN models, while others attempt to remove perturbations from the poisoned graph. Different from the previous works, this paper proposes a general framework termed as GraphReshape to enhance the robustness of GNNs via directly correcting the shifted classification boundary of GNN models in the presence of adversarial attacks. GraphReshape consists of two modules: locating tractive nodes that could correct GNNs and reshaping local structure to improve their representations in the latent space. Extensive experiments on four real-world datasets show that GraphReshape achieves significant performance gain compared with state-of-the-art baselines against different adversarial attacks.

Inland water bodies, being the most active biogeochemical cycle reactors, play a pivotal role in the global carbon cycle and CO2budget. This study integrates existing observation dataset of CO2flux (fCO2) in rivers, lakes and reservoirs in China, to elucidate their spatial-temporal patterns and key influencing factors and to reappraise the significance for regional carbon balance. It showed that, the fCO2in rivers, lakes and reservoirs in China presented significant variability with large range of −379.3–4947.6, −160.1–785.0 and −74.0–1603.1 mg CO2m−2h−1, respectively. The median of the fCO2in rivers was of 228.5 mg CO2m−2h−1, observably higher than these in lakes and reservoirs (26.0 and 28.3 mg CO2m−2h−1, respectively). The fCO2in rivers and reservoirs exhibited similar decreasing trend from south to north as a result of universal climate restraint, the averaged fCO2in Pearl River and Yangtze River basins showed much higher than that in Northeastern rivers. While, the averaged fCO2in the Mongolia-Xinjiang lake district and the Northeast lake district were higher than other lake districts, followed by the Qinghai-Xizang lake district, and the Eastern and Yungui lake district were generally low, contradicting the climatic restriction. The water primary production enhancement resulting from human activities was the main driver of spatial variation in the fCO2in lakes. Meanwhile, the fCO2in rivers presented seasonal pattern with higher wet season than dry season, while opposite patterns were found in lakes and reservoirs. Seasonal temperature, precipitation and water primary production were main factors. Furthermore, it showed pH was a key factor indicating the variability of the fCO2levels either in rivers, lakes or in reservoirs. In lakes, the fCO2is closely linked to chlorophyll a (Chl-a)and dissolved oxygen (DO), whereas, the fCO2in rivers is primarily associated with organic carbon (OC) and total nitrogen (TN), highlighting the diverse controlling mechanisms of fCO2in various inland water types. In addition, we found that water body sizes play an important role in regulating the fCO2levels, and small waters act as hotspots of CO2flux. Additionally, widespread urbanization and agricultural activities may enhance CO2emissions from rivers but potentially mitigating that from lakes. Nevertheless, the comprehensive impact of these factors on CO2emissions in inland water requires further evaluation. Based on the extrapolation method, we re-estimated that the total CO2emission of inland waters in China is approximately 117.3 Tg yr−1, which could offset 4.6%–12.8% of the total land carbon sink in China.

High-precision photoacoustic spectroscopy (PAS) with a three-step cylindrical photoacoustic cell (TSC-PAC) was developed for acetylene (C2H2) detection. Finite element analysis was used to investigate the acoustic properties of the proposed TSC-PAC and conventional H-type cylindrical photoacoustic cell (HTC-PAC). Simulation results showed that acoustic pressure signal of the TSC-PAC was higher than that of HTC-PAC with the same dimension. Resonance frequency of the TSC-PAC was identified at 984 Hz. In experimental studies, a plane mirror was used to reflect the incident light and increase the optical length in the PAC. A silicon-cantilever-based fiber-optic sensor was positioned at the terminal point of the TSC-PAC, where the maximum photoacoustic (PA) signal was located. With C2H2 concentration of 100 ppm, the $2{f}$ signal of TSC-PAC was 1.43 times that of HTC-PAC. When the integration time was 100 s, the linear fitting ( ${R}^{{2}}\text {)}$ and minimum detection limit (MDL) of C2H2 measured by TSC-PAC at the atmospheric pressure were 0.996 and 13.74 ppb, respectively. The normalized noise equivalent absorption (NNEA) coefficient was $8.18\times 10^{-{8}}$ cm $^{-{1}}\cdot \text { W}\cdot \text { Hz}^{{-{1}/ {2}}}$ . Both simulation and experimental results proved that the developed TSC-PAC enhanced the performance of the PAS and it had certain application potential in the PAS.

Point clouds offer a novel 3D data representation that has proven pivotal in immersive visual media applications involving human perception. Developing objective point cloud quality assessment (PCQA) methods is imperative, as they can substantially reduce human evaluation costs and drive advancements for visual perceptual experiences in point cloud related applications. Point cloud quality assessment without reference remains challenging. Previous PCQA methods predominantly employ a fixed perceptual distance and often overlook the variability in quality perceived from different viewpoints, which impedes their effectiveness in multiscale or multi-granularity feature extraction and learning, particularly for deep neural networks. The single fixed observation distance fails to capture the multi-resolution characteristics intrinsic to human perception. Addressing this gap, in this paper, we introduce a novel no-reference PCQA method (MOD-PCQA) that integrates multiscale features to enhance point cloud quality perception across diverse scales and granularities. MOD-PCQA pioneers a viewpoint-aware feature learning framework, capable of capturing visual features across various granularity levels, from fine to coarse. Specifically, we process and project point clouds into images from different viewpoints. Then, we extract multi-scale features under corresponding perspectives through three branch networks. Finally, we design an alternate learning strategy to optimize the feature extraction network to continuously refine the learned feature information from both inter-scale and intra-scale perspectives. Comprehensive experiments conducted on the SJTU-PCQA and WPC databases validate the superiority of our proposed model over state-of-the-art PCQA methods. Our method achieves optimal performance on both benchmarks by a significant margin, which comprehensively validates its effectiveness for the challenging PCQA task. The source code will be available at https://openi.pcl.ac.cn/OpenPointCloud/MOD-PCQA Zhang et al.

In order to schedule resources efficiently or maintain applications’ continuity for mobile customers, edge platforms often need to adaptively migrate the applications on them. However, our measurement shows that existing migration solutions cannot solve the issue of migrating video analytics applications in edge computing because the memory states of video analytics applications have different characteristics from other applications. We conduct a breakdown analysis of the memory states of video analytics applications, and propose to treat three types of states separately with three different techniques, i.e., warm-up, sync, and replay, to minimize the negative influence of migrations on application performance. Based on this idea, we implement a prototype system in which two new components, i.e., state store and sidecar, are designed to achieve near-transparent live migration with minimal application code modifications. Evaluation experiments demonstrate that the time of application interruption caused by migrating a video analytics application with our solution is less than 405 ms, and our solution does not consume much resources.

Gait depicts individuals’ unique and distinguishing walking patterns and has become one of the most promising biometric features for human identification. As a fine-grained recognition task, gait recognition is easily affected by many factors and usually requires a large amount of completely annotated data that is costly and insatiable. This paper proposes a large-scale self-supervised benchmark for gait recognition with contrastive learning, aiming to learn the general gait representation from massive unlabelled walking videos for practical applications via offering informative walking priors and diverse real-world variations. Specifically, we collect a large-scale unlabelled gait dataset GaitLU-1M consisting of 1.02M walking sequences and propose a conceptually simple yet empirically powerful baseline model GaitSSB. Experimentally, we evaluate the pre-trained model on four widely-used gait benchmarks, CASIA-B, OU-MVLP, GREW and Gait3D with or without transfer learning. The unsupervised results are comparable to or even better than the early model-based and GEI-based methods. After transfer learning, GaitSSB outperforms existing methods by a large margin in most cases, and also showcases the superior generalization capacity. Further experiments indicate that the pre-training can save about 50% and 80% annotation costs of GREW and Gait3D. Theoretically, we discuss the critical issues for gait-specific contrastive framework and present some insights for further study. As far as we know, GaitLU-1M is the first large-scale unlabelled gait dataset, and GaitSSB is the first method that achieves remarkable unsupervised results on the aforementioned benchmarks.

Inter-DataCenter Wide Area Network (Inter-DC WAN) that connects geographically distributed data centers is becoming one of the most critical network infrastructures. Due to limited bandwidth and inevitable link failures, it is highly challenging to guarantee network availability for services, especially those with stringent bandwidth demands, over inter-DC WAN. We present $\mathsf {TEDAT}$ , a novel Traffic Engineering (TE) framework for Diverse Availability Targets (DAT), where a Service Level Agreement (SLA) is defined to ensure that each bandwidth demand must be satisfied with a stipulated probability, when subjected to the network capacity and possible failures of the inter-DC WAN. $\mathsf {TEDAT}$ has two core components, i.e., traffic scheduling and failure recovery, which are crystalized through different mathematical models and theoretically analyzed. They are also extensively compared against state-of-the-art TE schemes, using a testbed as well as real trace driven simulations across different topologies, traffic matrices and failure scenarios. Our evaluations show that, compared with the optimal admission strategy, $\mathsf {TEDAT}$ can speed up the online admission control by $30\times $ at the expense of less than 4% false rejections. On the other hand, compared with the latest TE schemes like FFC and TEAVAR, $\mathsf {TEDAT}$ can meet the bandwidth availability SLAs for 23%~60% more demands under normal loads, and when network failure causes SLA violations, it can retain 10%~20% more profit under a pricing and refunding model.

Proteins commonly perform biological functions through protein-protein interactions (PPIs). The knowledge of PPI sites is imperative for the understanding of protein functions, disease mechanisms, and drug design. Traditional biological experimental methods for studying PPI sites still incur considerable drawbacks, including long experimental time and high labor costs. Therefore, many computational methods have been proposed for predicting PPI sites. However, achieving high prediction performance and overcoming severe data imbalance remain challenging issues. In this paper, we propose a new sequence-based deep learning model called CLPPIS (standing for CNN-LSTM ensemble based PPI Sites prediction). CLPPIS consists of CNN and LSTM components, which can capture spatial features and sequential features simultaneously. Further, it utilizes a novel feature group as input, which has 7 physicochemical, biophysical, and statistical properties. Besides, it adopts a batch-weighted loss function to reduce the interference of imbalance data. Our work suggests that the integration of protein spatial features and sequential features provides important information for PPI sites prediction. Evaluation on three public benchmark datasets shows that our CLPPIS model significantly outperforms existing state-of-the-art methods.

More and more enterprises deploy applications on Function-as-a-Service (FaaS) platforms to improve resource efficiency and save monetary costs. Network Functions (NFs) suffer from staggered peaks of traffic patterns and could benefit from fine-grained resource multiplexing in FaaS platform. However, naively exploring existing FaaS platforms to support NFs can introduce significant performance overheads in three aspects, including slow instance startup, remote state access for NFs, and costly packet delivery between NFs. To address these problems, we propose ${\sf Serpens}$Serpens, a high performance FaaS platform for NFs. First, ${\sf Serpens}$Serpens proposes a reusable NF runtime design to slash instance startup overhead. Second, ${\sf Serpens}$Serpens designs a novel state management mechanism to support local state access. Third, ${\sf Serpens}$Serpens introduces an advanced service chaining approach to avoid extra packet delivery. Besides, ${\sf Serpens}$Serpens designs an NF scaling mechanism to minimize performance fluctuation. We have implemented a prototype of ${\sf Serpens}$Serpens and conducted comprehensive experiments. Compared with the NFs and Service Function Chains (SFCs) that run on existing FaaS platforms, ${\sf Serpens}$Serpens can improve the throughput by more than 10× and reduce the latency by more than 90%.

Due to the high cost of cellular networks, vehicle users would like to offload elastic traffic through vehicular networks as much as possible. This demand prompts researchers to consider how to make the vehicular network system achieve better performance for requests coming online, such as maximizing throughput. The traffic in vehicular networks is transferred through opportunistic contacts between vehicles and infrastructures. When making scheduling decisions, the scheduler must be aware of vehicles’ future trajectories. Vehicles’ future trajectories are usually predicted by trajectory prediction algorithms when users are unwilling to report their future trips. Unfortunately, no trajectory prediction algorithm can be completely accurate, and these inaccurate prediction results will degrade the throughput achieved by scheduling algorithms. In this paper, we focus on reducing the negative impact of inaccurate predictions. Specifically, we measure two data-driven trajectory prediction algorithms that have been widely used for trajectory predictions and understand the characteristics of the accuracy of predicted contacts. Based on the enlightenment from the measurement, we design a system, i.e., i-Offload, to offload elastic traffic under imperfect trajectory predictions. The experimental results show that our system has good throughput and high scheduling efficiency even under imperfect trajectory predictions. Compared with existing scheduling algorithms, our method improves the throughput by about one time.

As an emerging 3-D presentation format, point cloud plays a vital role in the field of immersive visual media, especially for applications related to human vision. The measurement of point cloud quality is of great significance for facilitating point cloud quality enhancement, optimizing the equipment chain of the point cloud, and improving human visual perception quality. In this article, we propose MMF-PCQM, a blind point cloud quality measurement network that leverages multimodal and multiscale features, i.e., multiscale texture features in 2-D images and geometric information in points, to assess the point cloud quality. MMF-PCQM consists of a preprocessing module, dual-branch fusion network module, and feature learning module. Specifically, the preprocessing module transfers a point cloud into 2-D images and 3-D point clusters. The dual-branch fusion network module aims to capture and integrate the multiscale complementary features from the multimodal data to attain the final quality measurement. The feature learning module bridges two modalities to enhance the fused feature through a collaborative adversarial learning strategy. We evaluate MMF-PCQM on two large publicly available datasets, including the Waterloo Point Cloud (WPC) dataset and the SIAT Point Cloud Quality Database (SIAT-PCQD). Extensive experimental results and ablation studies demonstrate that our MMF-PCQM achieves better results compared with the state-of-the-art methods.

At present, the flow table of the SDN switch is stored in the costly Ternary Content Addressable Memory (TCAM) cache. Due to the cost problem, the number of flow tables that the SDN switch can store is extremely limited, which is far less than the number of traffic, so it is prone to overflow problem, and leads to network paralysis. That has become a bottleneck in restricting the processing capacity of the data center, and will become a weak point focused by attackers. In this paper, we propose an algorithm for the Alarm Switch Remove (ASR) that fully loads the flow table space in SDN, and further put forward an integrated load balancing scheme in SDN. Finally, we use Mininet to verify that the scheme can ease the SDN switch flow table overflow problem and increase network throughput.

WiFi has become one of the most popular ways to access the Internet. However, in large-scale campus wireless networks, it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance. In this paper, we implement PerfMon, a network performance measurement and diagnosis system, which integrates collected multi-source datasets and analysis methods. Based on PerfMon, we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university (TWLAN), which is one of the largest campus wireless networks. Then we systematically study the application-level network performance. We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types. The performance is far from satisfactory in some cases. To diagnose these problems, we distinguish locations and device types, and further locate the most crucial factors that affect the performance. The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.

The vigorous development of wind energy will help to promoting the adjustment of energy structure, the control of atmospheric smog, and the transformation of economic development mode. However, the randomness and volatility of wind speed make it difficult to predict with high accuracy. High-precision short-term wind speed prediction is inseparable from effective variable selection. In order to select highly relevant variables for short-term wind speed prediction, Multi-Task Lasso is applied to extract features, so the change trend of wind speed can be expressed exactly, and the Multilayer Perceptron (MLP) is applied to predict based on the extracted features. Firstly, Multi-Task Lasso is used to establish a linear regression model, the leading periods and the original input variables are determined from the perspective of prediction accuracy; Secondly, according to the optimization results of Multi-Task Lasso, the optimal features that can best express the wind speed fluctuation is extracted from different influencing factors and different leading periods based on the weight; Finally, the extracted optimal feature set is used as the input variable of MLP to establish prediction model. The results show that the effect of feature selection is obvious, the prediction accuracy is improved by more than 17%, and the short-term wind speed prediction model has good prediction accuracy and good generalization ability.

Tumor recurrence and metastasis have become thorny problems in clinical tumor therapy. Vaccine-mediated antitumor immune response has emerged as a significant postoperative inhibition for tumor recurrence and metastasis. However, limited tumor antigens are not conducive to trigger complete antigen-specific T cell-mediated immune responses. Herein, the design of a hydrogel vaccine system containing a granulocyte-macrophage colony stimulating factor (GM-CSF), based on surgically removed tumor cell lysates, was reported. The hydrogel was formed by crosslinking tumor cell lysates and alginate at low temperatures. The GM-CSF was released from the hydrogel to recruit dendritic cells (DCs), which provided a completely personalized tumor antigen pool. They were combined to foster the production of powerful antigen-specific T cells. The personalized hydrogel was implanted at the surgical site and it stimulated the antitumor immune response for the inhibition of residual tumor cells. Delightfully, the personalized hydrogel inhibited the tumor recurrence and metastasis well in a post-surgical mice tumor model, in combination with a programmed death-ligand 1 antibody (αPD-L1). The results demonstrated that the development of a personalized hydrogel and a combination of αPD-L1 provided a new strategy to prevent tumor recurrence and metastasis.

Nonasil (NON-type framework) zeolite, having inner large cages but only 6-ring (R) apertures, is recognized as a nonporous material without practical application values as catalysts or adsorbents. A novel bottom-up structural construction strategy assisted with well-designed Gemini-type surfactant is proposed to “open” the noncages, constructing two novel NON-related structures with accessible and stable acid sites. The obtained derivant (named as ECNU-27) possessed hierarchical porosity with short-range ordered 8-R micropores and abundant intercrystal mesopores, serving as a promising catalyst for the 1-butene cracking to lower alkenes.

Pancreatic ductal adenocarcinoma (PDAC) has a dense extracellular matrix (ECM) surrounding tumor cells to sequester CD8+T cell infiltration and prevent drug penetration. Concomitant inhibition of both the TGF-β pathway and the PD-1/PD-L1 checkpoint is a viable strategy to increase T cell infiltration and cytotoxicity. Here, we used an acidic tumor extracellular pH (pHe) responsive clustered nanoparticle (LYiClustersiPD-L1) to deliver TGF-β receptor inhibitors (LY2157299) and siRNA targeting PD-L1 (siPD-L1) for PDAC stroma microenvironment regulation and antitumor immunotherapy. LY2157299 encapsulated in the hydrophobic core of the nanoparticle can effectively inhibit the activation of pancreatic stellate cells (PSCs) and result in a reduction in type I collagen. siPD-L1adsorbed on the surface of the nanoparticle was released with small size poly(amidoamine) (PAMAM) at the surface of LYiClustersiPD-L1under pHeand penetrated into the tumors to silence PD-L1 gene expression in tumor cells. Compared to monotherapy, LYiClustersiPD-L1significantly increased tumor infiltrating CD8+T cells and provoked antitumor immunity to synergistically suppress tumor growth in both a subcutaneous Panc02 xenograft model and an orthotopic tumor model.

The rise of the network has injected new impetus into the development of traditional networks. Due to the complexity of the network itself and its network programmability, there is a risk of routing loops occurring in the SDN network. This paper proposes a loop detection mechanism. According to the Time To Live (TTL) value of the loop packet, there is approximately periodicity in the same loop. We use sFlow to count the number of packets corresponding to each TTL value of a switch in the loop over a period of time, and perform discrete Fourier transform on the obtained finite-length sequence to observe its frequency domain performance and determine whether there are periodic features. By doing so, it is determined whether there is a routing loop, and the purpose of passively detecting the routing loop is achieved. Compared to existing algorithms, it has advantages in real-time, scalability and false positive rate. The experimental results show that the routing loop detection algorithm based on TTL statistics in this paper still maintains high judgment accuracy under the scenarios of lower stream sampling rate and smaller detection period.

It is an arduous issue to significantly improve the charge separation efficiency of polymer photocatalysts due to their inherently high exciton binding energy. Herein, based on an interfacial coupling and atom diffusion strategy, a metal-free 3D/2D van der Waals (VdW) heterojunction is fabricated through the modification of rich-vacancy wrinkle-like S8(Vs-S8) microspheres on the surface of S-doped polymeric carbon nitride (S-PCN) nanosheets. The insight into the mechanism reveals that the interfacial coupling effect induces a strong built-in electric field from S-PCN to Vs-S8, and the carrier transfer behavior abides by the type-II charge transfer pathway, thereby dramatically improving the separation efficiency and transport kinetics of photogenerated carriers. As a result, the as-prepared metal-free 3D/2D Vs-S8/S-PCN VdW heterojunction is endowed with more superior photocatalytic hydrogen evolution (PHE) performance than PCN. The highest average PHE rate is about 11.3 times higher than that of PCN, and the apparent quantum efficiency reaches 30.1% at 400 nm on the optimal 3%-Vs-S8/S-PCN sample. This work contributes a new design strategy for a metal-free VdW heterojunction and provides a feasible avenue for improving the carrier separation efficiency of polymer photocatalysts.

Zeolite nanosheets with an extremely thin thickness featuring both unique pore systems and low diffusion resistance have the potential to achieve enhanced catalytic performance in the conversion of bulky molecular biomass. The preparation of unit-cell level nanosheets generally requires complex and costly multifunctional surfactants or an organic structure-directing agent (OSDA). Commercially available and environmentally friendly ionic liquids can also direct the structure of zeolite nanosheets by π–π stacking when these kinds of OSDA are used in large amount. Herein, we first report unit-cell-sized silicogermanate nanosheets of NS-IM-20 (UWYtopology), 5 nm in thickness, which were synthesized at a relatively low ionic liquid concentration with the assistance of halide ion (Cl–). The Pd-loaded NS-IM-20 nanosheets with a hierarchical porosity and moderate acidity act as promising bifunctional catalysts for selective biomass conversion.

The aim of this study was to investigate the effects of ensiled agricultural byproducts from Qinghai-Tibet plateau on growth performance, rumen microbiota, ruminal epithelium morphology, and nutrient transport-related gene expression in Tibetan sheep. Fourteen male Tibetan sheep were randomly assigned to one of two diets: an untreated diet (without silage inoculum, CON, n= 7) or an ensiled diet (with silage inoculum, ESD, n= 7). The total experimental period lasted for 84 d, including early 14 d as adaption period and remaining 70 d for data collection. The ESD increased average daily gain (P= 0.046), dry matter intake (P< 0.001), ammonia nitrogen (P= 0.045), microbial crude protein (P= 0.034), and total volatile fatty acids concentration (P< 0.001), and decreased ruminal pH value (P= 0.014). The proportion of propionate (P= 0.006) and the copy numbers of bacteria (P= 0.01) and protozoa (P= 0.002) were higher, while the proportion of acetate (P= 0.028) was lower in the sheep fed ESD compared to CON. Pyrosequencing of the 16S ribosomal RNA gene revealed that ESD increased the relative abundance of Firmicutes, Ruminococcus, Lachnospiraceae_AC2044_group, Lachnospiraceae_XPB1014_group, and Christensenellaceae_R-7_groupin the rumen (P< 0.05), while decreased the relative abundance of Bacteroidota, Prevotellaceae_UCG-003, and Veillonellaceae_UCG-001(P< 0.05). Analyses with PICRUSt2 and STAMP indicated that the propionate metabolism pathway was enriched in the sheep fed ESD (P= 0.026). The ESD increased the rumen papillae height (P= 0.012), density (P= 0.036), and surface area (P= 0.001), and improved the thickness of the total epithelia (P= 0.018), stratum corneum (P= 0.040), stratum granulosum (P= 0.042), and stratum spinosum and basale (P= 0.004). The relative mRNA expression of cyclin-dependent Kinase 2, CyclinA2, CyclinD2, zonula occludens-1, Occludin, monocarboxylate transporter isoform 1 (MCT1), MCT4, sodium/potassium pump, and sodium/hydrogen antiporter 3 were higher in the rumen epithelial of sheep fed ESD than CON (P< 0.05). Conversely, the relative mRNA expressions of Caspase 3and B-cell lymphoma-2 were lower in the sheep fed ESD than CON (P< 0.05). In conclusion, compared with an untreated diet, feeding an ensiled diet altered the rumen microbial community, enhanced nutrient transport through rumen epithelium, and improved the growth performance of Tibetan sheep.This research demonstrated that ensiled diet improved the growth performance of Tibetan sheep by changing the rumen microbial structure and by promoting nutrient transport through rumen epithelium.Tibetan sheep on the Qinghai-Tibet Plateau experience significant nutrient stress while a substantial amount of agricultural byproducts in the region go discarded and wasted. In this study, agricultural byproducts were ensiled and fed to the Tibetan sheep to investigate their effects on growth performance, rumen microorganisms, and nutrient transport through rumen epithelial tissues. Fourteen male Tibetan sheep were randomly assigned to one of two diets: untreated diet (without silage inoculum, CON, n= 7) or ensiled diet (with silage inoculum, ESD, n= 7). After 70 d of feeding, the ESD-fed sheep had a higher body weight than CON. The ensiled diet changed the rumen microbial community and increased the relative abundance of cellulolytic bacteria in the rumen. In addition, the ensiled diet also promoted the development of rumen epithelia and improved the relative expression of gene related to nutrient transport. Overall, the ensiled diet optimized the use of agricultural byproducts and significantly contributed to the production of Tibetan sheep.

Faujasite (FAU) zeolites with low Si/Al ratios (e.g., X-type zeolites) have much more exchangeable cation sites than those with high Si/Al ratios (Y-type zeolites) for Cu(I), which could improve the adsorption performance via π-complexation. However, the structure of Cu-exchanged X-type zeolites was unstable during autoreduction at high temperatures as reported [Takahashi; et. al., Ind. Eng. Chem. Res., 2002, 41, 2487−2496]. Here, we proposed a scheme of Cu-exchanged X-type zeolites in copper–ammonia solution to improve the stability of the zeolite structure during autoreduction. Therefore, it makes us be able to study the adsorption properties of thiophenic compounds in Cu(I)-exchanged FAU zeolites from low to high Si/Al ratios. The modified zeolites were characterized by X-ray diffraction, high-resolution transmission electron microscopy, inductively coupled plasma spectroscopy, X-ray fluorescence, and Brunauer–Emmett–Teller analysis and tested by adsorption desulfurization experiments in model fuels. The results indicate that the modified zeolites with various Si/Al ratios show different adsorption properties for thiophene and benzothiophene. The adsorption capacity for thiophene decreased in the order Cu(I)Y-5.30 > Cu(I)X-1.23 > Cu(I)Y-2.30. For benzothiophene, the adsorption capacity decreased in the order Cu(I)Y-2.30 > Cu(I)Y-5.30 > Cu(I)X-1.23.

Abstract

Chemically converted graphene fiber-shaped supercapacitors (FSSCs) are highly promising flexible energy storage devices for wearable electronics. However, the ultralow specific capacitance and poor rate performance severely hamper their practical applications. They are caused by severe stacking of graphene nanosheets and tortuous ion diffusion path in graphene-based electrodes; thus, the ultralow utilization of graphene has been rarely carefully considered to date. Here, we address these issues by developing three-dimensional hierarchically porous graphene fiber with the incorporation of holey graphene for efficient utilization of graphene to achieve fast charge diffusion and good charge storage capability. Without deterioration in electrical but robust mechanical properties, the optimal graphene fiber shows ultrahigh specific capacitance of 220.1 F cm–3at current density of 0.1 A cm–3and boosted specific capacitance of 254.3 F cm–3at 0.1 A cm–3after nitrogen doping. Moreover, the nitrogen-doped 40% holey graphene hybrid fiber-assembled FSSC exhibits ultrahigh rate capability (96, 91, and 87% at current density of 0.5, 1.0, and 2.0 A cm–3, respectively, and 67% even at ultrahigh current density of 10.0 A cm–3) and excellent cycle stability (95.65% capacitance retention after 10 000 cycles). The contribution of three-dimensional interconnected hierarchically porous network to the enhanced electrochemical (EC) performance is semiquantitatively elucidated by Brunauer–Emmett–Teller and energy dispersive spectroscopy mapping. Our work gives insights into the importance of fully utilizing graphene and provides an efficient strategy for high EC performance in chemically converted graphene-based FSSCs.

Routing loops can cause packet loss and long delay problems in the traditional network. Even in future networks - Software-Defined Networks, with loop-free implementation mechanism, it still suffers loop problems. In this paper, we propose an architecture to solve the loop problem in SDN. Unlike the existing passive routing loop detection algorithm, this algorithm processes based on the SDN loop-free characteristic, by auditing third-party network forwarding rules' modification requests to avoids loop generation, thus avoid the network loop problems. Testbed is built to simulate our proposed algorithm. The evaluation shows that the loop audit algorithm proposed in this paper has better performance in extra spatial utilization and smaller number of extra interactions between SDN controller and switches.

Urban rivers are significant hotspots of CO2emissions into the atmosphere, playing important roles in global carbon emission inventories. However, little is known about the effect of ecological restoration on CO2dynamics in severely polluted urban rivers, and this strongly hindering our understanding of the positive effects of human activities on CO2emissions in urbanized aquatic ecosystems. We measured CO2partial pressure (pCO2) and fluxes in nine severely polluted urban rivers with varying degrees of ecological restoration, and assessed the relationships with water physicochemical parameters, pollution levels and basin environmental investment. Our results indicate that urban rivers with high pollution loadings were indeed hotspots of CO2evasion. However, fully restored rivers had generally lower pCO2and CO2emissions than partially restored rivers and were observably lower than unrestored rivers, suggesting that watershed eco‐restoration could effectively reduce CO2evasion. In particular, environmental investment per unit basin area had a significant negative correlation with CO2evasion and could explain 51%–85% and 51% of total variability in pCO2and CO2fluxes among nine urban rivers respectively, emphasizing the potential benefits of carbon emission regulation resulting from positive environmental management. Nutrient removal and sewage interception during watershed eco‐restoration were key processes reducing pCO2and CO2fluxes in polluted urban rivers. pH alteration kept close correlations with pCO2and acted as a sensitive regulator of CO2evasion in the nine rivers. We highlight the importance of considering the coupling effects of pollution and restoration on the spatiotemporal variability of CO2evasion and the uncertainty of related monitoring methods in urban watersheds. Urban rivers are significant hotspots of CO2emissions to the atmosphere. Positive environment policy in urban basins may significantly affect the carbon dynamics and CO2evasion in those severely polluted urban rivers. We conducted seasonal investigations of pCO2and CO2fluxes in nine small severely polluted urban rivers, which had been exerted various degrees of ecological restoration in last decade. Consistent with most previous reports, urban rivers with high pollution loadings were indeed hotspots of CO2evasion. However, not all urban rivers presented high pCO2and CO2fluxes. We found significantly lower pCO2and CO2fluxes in those urban rivers with comprehensive watershed restoration compared with these in unrestored rivers. Particularly, ecological investment per unit basin area had showed significant negative correlation with CO2evasion. Based on the comprehensive comparison, we highlighted potential regulation benefits of riverine carbon emission resulted by positive environmental management. Therefore, our study highlights the importance of considering the coupling effects of pollution and restoration on CO2evasion in urban watersheds and the need to better constrain its spatiotemporal variability and monitoring methods' uncertainty. Urban rivers with high pollution loadings were hotspots of CO2evasions, but eco‐restoration could effectively reduce their intensityEnvironmental investment per unit basin area could explain 85% of the total variability in pCO2among different urban riversNutrient removal and sewage interception were key processes reducing pCO2variations under the coupling effects of pollution and restoration Urban rivers with high pollution loadings were hotspots of CO2evasions, but eco‐restoration could effectively reduce their intensity Environmental investment per unit basin area could explain 85% of the total variability in pCO2among different urban rivers Nutrient removal and sewage interception were key processes reducing pCO2variations under the coupling effects of pollution and restoration

Currently, tumor immunotherapy is becoming a new revolution in tumor treatment. Generated from tumor cell lysate (TCL) or irradiated tumor cells, the whole tumor antigen‐based vaccines have the possibility to enhance antitumor immune response without survival from immune surveillance in personalized immunotherapy. Here, polydopamine nanoparticles (PDA NPs) after self‐polymerization are covalently coated with TCL to form PDA@CL. Engineered Salmonella(EnS) wrapped with PDA@CL (EnS@PDA@CL) is targeted the localization of the tumor site by intravenous administration. EnS@PDA@CL delivered autologous antigen‐containing nanoparticles to tumor hypoxia regions through blood circulation. The PDA@CL particles promote the maturation of dendritic cells (DCs), thus eliciting the infiltration of whole tumor antigens specific cytotoxic T lymphocytes, significantly triggering antitumor immunity. The tumor regression in the Panc02 mice confirms the therapeutic potential of EnS@PDA@CL in clinical personalized immunotherapy. Personalized bacteria loaded with autoantigens significantly inhibited the tumor cell growth by promoting the maturation of dendritic cells (DCs), eliciting the infiltration of cytotoxic T lymphocytes, and leading a long‐term immune memory effect.

Brain cancer is a fatal disease that is difficult to treat because of poor targeting and low permeability of chemotherapeutic drugs through the blood brain barrier. In a comparison to current treatments, such as surgery followed by chemotherapy and/or radiotherapy, photothermal therapy is a remarkable noninvasive therapy developed in recent years. In this work, porphyrin immobilized nanographene oxide (PNG) was synthesized and bioconjugated with a peptide to achieve enhanced and targeted photothermal therapy for brain cancer. PNG was dispersed into the agar based artificial tissue model and demonstrated a photo-to-thermal conversion efficiency of 19.93% at a PNG concentration of only 0.5 wt %, with a heating rate of 0.6 °C/s at the beginning of irradiation. In comparison, 0.5 wt % graphene oxide (GO) indicated a photo-to-thermal conversion efficiency of 12.20% and a heating rate of 0.3 °C/s. To actively target brain tumor cells without harming healthy cells and tissues surrounding the laser path, a tripeptide l-arginyl-glycyl-l-aspartic (RGD) was further grafted to PNG. The photothermal therapy effects of PNG-RGD completely eliminated the tumor in vivo, indicating its excellent therapeutic effect for the treatment of brain cancer.

Conversion of lignocellulosic biomass into value-added products provides important environmental and economic benefits. Here we report the engineering of an unconventional metabolism for the production of tricarboxylic acid (TCA)-cycle derivatives from D-xylose, L-arabinose and D-galacturonate. We designed a growth-based selection platform to identify several gene clusters functional in Escherichia coli that can perform this nonphosphorylative assimilation of sugars into the TCA cycle in less than six steps. To demonstrate the application of this new metabolic platform, we built artificial biosynthetic pathways to 1,4-butanediol (BDO) with a theoretical molar yield of 100%. By screening and engineering downstream pathway enzymes, 2-ketoacid decarboxylases and alcohol dehydrogenases, we constructed E. coli strains capable of producing BDO from D-xylose, L-arabinose and D-galacturonate. The titers, rates and yields were higher than those previously reported using conventional pathways. This work demonstrates the potential of nonphosphorylative metabolism for biomanufacturing with improved biosynthetic efficiencies.

After cartilage damage, a large number of monocytes/macrophages infiltrate into adjacent synovium and the resident macrophages in synovial tissue transform to activated macrophages (M1), which secrete pro‐inflammatory cytokines to induce sustained inflammation and chondrocyte apoptotic. However, current clinical therapies for cartilage repair can rarely achieve long‐term anti‐inflammatory regulation and satisfactory outcomes. Herein, a platelet lysate‐rich plasma macroporous hydrogel (PLPMH) scaffold with around 100 µm pore size and 1.25 MPa Young's modulus is developed to sustainedly recruit and polarize endogenous anti‐inflammatory macrophages (M2) for improving cartilage defect repair. PLPMH scaffold can steadily release sphingosine1‐phosphate and proteins via gradual degradation, thus inducing M2 macrophages migration or resting (M0) macrophages migration and then polarization to M2 phenotype, and improving the secretion of anti‐inflammatory cytokines. Furthermore, PLPMH scaffold exhibits negligible inflammatory responses in vivo and promotes endogenous M2 macrophage infiltration in large numbers and long‐time duration to provide a local anti‐inflammatory microenvironment, which even lasts for 42 d. In a rabbit model of cartilage defect, PLPMH scaffold increases the ratio of M2 macrophages and improves cartilage tissue regeneration. These studies support that PLPMH scaffold may have a great potential in articular cartilage tissue engineering by providing an anti‐inflammatory and pro‐regenerative microenvironment. A platelet lysate‐rich plasma macroporous hydrogel scaffold is prepared, which sustainedly recruits and polarizes endogenous anti‐inflammatory M2 macrophages via releasing of sphingosine1‐phosphate and proteins, thus improving the secretion of anti‐inflammatory cytokines and providing a long‐time local anti‐inflammatory microenvironment for cartilage defect repair.

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ABSTRACTA fundamental question in microbial physiology concerns why organisms prefer certain nutrients to others. For example, among different nitrogen sources, ammonium is the preferred nitrogen source, supporting fast growth, whereas alternative nitrogen sources, such as certain amino acids, are considered to be poor nitrogen sources, supporting much slower exponential growth. However, the physiological/regulatory logic behind such nitrogen dietary choices remains elusive. In this study, by engineering Escherichia coli, we switched the dietary preferences toward amino acids, with growth rates equivalent to that of the wild-type strain grown on ammonia. However, when the engineered strain was cultured together with wild-type E. coli, this growth advantage was diminished as a consequence of ammonium leakage from the transport-and-catabolism (TC)-enhanced (TCE) cells, which are preferentially utilized by wild-type bacteria. Our results reveal that the nitrogen regulatory (Ntr) system fine tunes the expression of amino acid transport and catabolism components to match the flux through the ammonia assimilation pathway such that essential nutrients are retained, but, as a consequence, the fast growth rate on amino acids is sacrificed.IMPORTANCEBacteria exhibit different growth rates under various nutrient conditions. These environmentally related behaviors reflect the coordination between metabolism and the underlying regulatory networks. In the present study, we investigated the intertwined nitrogen metabolic and nitrogen regulatory systems to understand the growth differences between rich and poor nitrogen sources. Although maximal growth rate is considered to be evolutionarily advantageous for bacteria (as remarked by François Jacob, who said that the “dream” of every cell is to become two cells), we showed that negative-feedback loops in the regulatory system inhibit growth rates on amino acids. We demonstrated that in the absence of regulatory feedback, amino acids are capable of supporting fast growth rates, but this results in ammonia leaking out from cells as “waste,” benefiting the growth of competitors. These findings provide important insights into the regulatory logic that controls metabolic flux and ensures nutrient containment but consequently sacrifices growth rate.