Showing papers by "Qiang He published in 2023"

Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

Abstract: Edge computing, as a new computing paradigm, brings cloud computing’s computing and storage capacities to network edge for providing low latency services for users. The networked edge servers in a specific area constitute edge storage systems (ESSs), where popular data can be stored to serve the users in the area. The novel ESSs raise many new opportunities as well as unprecedented challenges. Most existing studies of ESSs focus on the storage of data replicas in the system to ensure low data retrieval latency for users. However, replica-based edge storage strategies can easily incur high storage costs. It is not cost-effective to store massive replicas of large-size data, especially those that do not require real-time access at the edge, e.g., system upgrade files, popular app installation files, videos in online games. It may not even be possible due to the constrained storage resources on edge servers. In this article, we make the first attempt to investigate the use of erasure codes in cost-effective data storage at the edge. The focus is to find the optimal strategy for placing coded data blocks on the edge servers in an ESS, aiming to minimize the storage cost while serving all the users in the system. We first model this novel Erasure Coding based Edge Data Placement (EC-EDP) problem as an integer linear programming problem and prove its $\mathcal {NP}$NP-hardness. Then, we propose an optimal approach named EC-EDP-O based on integer programming. Another approximation algorithm named EC-EDP-V is proposed to address the high computation complexity of large-scale EC-EDP scenarios efficiently. The extensive experimental results demonstrate that EC-EDP-O and EC-EDP-V can save an average of 68.58% (and up to 81.16% in large-scale scenarios) storage cost compared with replica-based storage approaches.

Semantic Tradeoff for Heterogeneous Graph Embedding

Abstract: Recently, many data mining models based on heterogeneous graph (HG) have emerged. Among these, HG embedding is an important and indispensable process. However, the existing HG embedding models usually use graph neural network to learn embeddings separately on different meta-paths, ignoring the fact that different meta-paths contain not only their unique semantics but also related semantics. This may result in semantic overlap or irrelevance, which needs to be a feasible and effective tradeoff for high-quality HG embedding, yet studies thereof have rarely been reported. In this article, we propose semantic tradeoff HG embedding (STHGE) by first introducing the Hilbert–Schmidt independence criterion (HSIC) as restriction. The main idea of STHGE is to regard semantic tradeoff as independence tradeoff (or correlation) between different meta-path spaces. Specifically, we first transform the original features of nodes into different meta-path feature spaces with HSIC restriction between them. Then, we use graph attention network to learn the embeddings of nodes on different meta-paths with HSIC restrictions. Finally, we concatenate the embeddings on different meta-paths to perform prediction. Experimental results on three heterogeneous datasets not only demonstrate the effectiveness of STHGE but also demonstrate that STHGE can achieve a new semantic tradeoff between different meta-paths. Furthermore, we demonstrate the robustness of STHGE.

Routing Optimization With Deep Reinforcement Learning in Knowledge Defined Networking

Abstract: Traditional routing algorithms cannot dynamically change network environments due to the limited information for routing decisions. Meanwhile, they are prone to performance bottlenecks in the face of increasingly complex business requirements. Some approaches, such as deep reinforcement learning (DRL) have been proposed to address the routing problems. However, they hardly utilize the information about the network environment fully. The Knowledge Defined Networking (KDN) architecture inspires us to develop new learning mechanisms adapted to the dynamic characteristics of the network topology. In this paper, we propose an effective scheme to solve the routing optimization problem by adding a graph neural network (GNN) structure to DRL, called Message Passing Deep Reinforcement Learning (MPDRL). MPDRL uses the characteristics of GNN to interact with the network topology environment and extracts exploitable knowledge through the message passing process of information between links in the topology. The goal is to achieve the load balance of network traffic and improve network performance. We have conducted experiments on three Internet Service Provider (ISP) network topologies. The evaluation results show that MPDRL obtains better network performance than the baseline algorithms.

ST-EUA: Spatio-Temporal Edge User Allocation With Task Decomposition

Abstract: Recently, edge user allocation (EUA) problem has received much attentions. It aims to appropriately allocate edge users to their nearby edge servers. Existing EUA approaches suffer from a series of limitations. First, considering users’ service requests only as a whole, they neglect the fact that in many cases a service request may be partitioned into multiple tasks to be performed by different edge servers. Second, the impact of the spatial distance between edge users and servers on users’ quality of experience is not properly considered. Third, the temporal dynamics of users’ service requests has not been fully considered. To overcome these limitations systematically, this article focuses on the problem of spatio-temporal edge user allocation with task decomposition (ST-EUA). We first formulate the ST-EUA problem. Then, we transform ST-EUA problem as an optimization problem with multiple objectives and global constraints and prove its $\mathcal {NP}$NP-hardness. To tackle the ST-EUA problem effectively and efficiently, we propose a novel genetic algorithm-based heuristic approach called GA-ST, aiming to maximize users’ overall QoE while minimizing the cost of task migration in different time slots. Extensive experiments are conducted on two widely-used real-world datasets to evaluate the performance of our approach. The results demonstrate that GA-ST significantly outperforms state-of-the-art approaches in finding approximate solutions in terms of the trade-off among multiple metrics.

Biogeochemical behavior and pollution control of arsenic in mining areas: A review

Abstract: Arsenic (As) is one of the most toxic metalloids that possess many forms. As is constantly migrating from abandoned mining area to the surrounding environment in both oxidation and reducing conditions, threatening human health and ecological safety. The biogeochemical reaction of As included oxidation, reduction, methylation, and demethylation, which is closely associated with microbial metabolisms. The study of the geochemical behavior of arsenic in mining areas and the microbial remediation of arsenic pollution have great potential and are hot spots for the prevention and remediation of arsenic pollution. In this study, we review the distribution and migration of arsenic in the mining area, focus on the geochemical cycle of arsenic under the action of microorganisms, and summarize the factors influencing the biogeochemical cycle of arsenic, and strategies for arsenic pollution in mining areas are also discussed. Finally, the problems of the risk control strategies and the future development direction are prospected.

APIMatchmaker: Matching the Right APIs for Supporting the Development of Android Apps

Abstract: Android developers are often faced with the need to learn how to use different APIs suitable for their projects. Automated API recommendation approaches have been invented to help fill this gap, and these have been demonstrated to be useful to some extent. Unfortunately, most state-of-the-art works are not proposed for Android developers, and the ones dedicated to Android app development often suffer from high redundancy and poor run-time performance, or do not target the problem of recommending API usage patterns. To address this gap we propose to the community a new tool, namely APIMatchmaker, to recommend API usages by learning directly from similar real-world Android apps. Unlike existing recommendation approaches, which leverage a single context to find similar projects, we innovatively introduce a multi-dimensional, context-aware, collaborative filtering approach to better achieve the purpose. Specifically, in addition to code similarity, we also take app descriptions (or topics) into consideration to ensure that similar apps also provide similar functions. We evaluate APIMatchmaker on a large number of real-world Android apps and observe that APIMatchmaker yields a high success rate in recommending APIs for Android apps under development, and it is also able to outperform the state-of-the-art.

Maximizing spin Hall magnetoresistance in heavy metal/crystalline metallic ferromagnet multilayers with opposite spin Hall angles.

Abstract: Interconversion between charge and spin through spin-orbit coupling at a heavy metal (HM)/ferromagnet (FM) interface plays a key role in determining the amplitude of spin Hall magnetoresistance (SMR), which might maximally facilitate its applications in novel electronics. In this study, annealed NiFe films grown on MgO (100) substrates capped with Pt and Ta are reported to exhibit a maximum SMR. When the measuring temperature is reduced, the SMR rises and is significantly larger in crystalline NiFe than in amorphous NiFe. Another physical process for the negative SMR in Ta(dTa)/Pt(3 nm)/annealed NiFe samples is attributed to the interfacial spin-orbit coupling (ISOC) driven spin current (Js) generation and its reciprocal effects. Moreover, spin accumulation is enhanced at Pt(3 nm)/annealed NiFe interfaces after capping with a Ta layer, which functions as a spin sink in a certain thinner thickness range. With the cooperative interaction of choosing the proper Ta's thickness and annealing NiFe layers, the maximum SMR is obtained. Our results pave the way for rational interface engineering to enhance SMR for developing high-efficiency spintronic devices.

B-Detection: Runtime Reliability Anomaly Detection for MEC Services With Boosting LSTM Autoencoder

Abstract: By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic MEC systems, MEC services are challenged by the risks of runtime reliability anomalies. Detecting runtime reliability anomalies for MEC services is challenging yet critical to ensuring the stability of MEC systems. The effectiveness of existing anomaly detection methods suffers from poor performance when handling MEC services' large-volume, continuous, and volatile reliability streaming data. The key is to identify significant changes in the distribution of MEC services' current reliability streaming data compared with their historical performance. Inspired by concept drift, this paper proposes B-Detection, a boosting Long Short-Term Memory (LSTM) Autoencoder for detecting MEC services' runtime reliability anomalies based on distribution dissimilarity evaluation. B-Detection employs a deep learning method named LSTM Autoencoder to characterize the MEC services' historical reliability data distribution. To cope with the challenge of modeling complex distribution characteristics of MEC services' historical reliability streaming data and guarantee the real-time performance of B-Detection, we enhance LSTM Autoencoder with a weight-based reservoir sampling technique and an LSTM boosting algorithm. The reconstruction loss of the trained LSTM Autoencoder model is estimated for the up-to-date reliability streaming data, and the result is used to infer MEC services' runtime reliability anomalies. The performance of B-Detection is verified through a series of experiments conducted on a real-world dataset.

Task Computation Offloading for Multi-Access Edge Computing via Attention Communication Deep Reinforcement Learning

Abstract: —This paper investigates how to enhance the Multi-access Edge Computing (MEC) systems performance with the aid of device-to-device (D2D) communication computation offloading. By adequately exploiting a novel computation offloading mechanism based on D2D collaboration, users can efficiently share computational resources with each other. However, it is challenging to distinguish valuable information that truly promotes a collaborative decision, as worthless information can hinder collaboration among users. In addition, the transmission of large volumes of information requires high bandwidth and incurs significant latency and computational complexity, resulting in unacceptable costs. In this paper, we propose an efficient D2D-assisted MEC computation offloading framework based on Attention Communication Deep Reinforcement Learning (ACDRL), which simulates the interactions between related entities, including device-to-device collaboration in the horizontal and device-to-edge offloading in the vertical. Secondly, we developed a distributed cooperative reinforcement learning algorithm that includes an attention mechanism that skews computational resources towards active users to avoid unnecessary resource wastage in large-scale MEC systems. Finally, to improve the effectiveness and rationality of cooperation among users, we introduce a communication channel to integrate information from all users in a communication group, thus facilitating cooperative decision-making. The proposed framework is benchmarked, and the experimental results show that the proposed framework can effectively reduce latency and provide valuable insights for practical design compared to other baseline approaches.

CoopEdge+: Enabling Decentralized, Secure and Cooperative Multi-Access Edge Computing Based on Blockchain

Abstract: Multi-access Edge Computing (MEC) has emerged as a new distributed computing paradigm for its ability to offer low-latency services to users. Suffering from constrained computational resources because of their limited physical sizes, edge servers usually cannot handle all the incoming compute tasks on time when they operate independently. Thus, they need to cooperate by peer-offloading. Incentive and trust are the two major challenges towards to cooperative computing among edge servers operating in a distrusted environment. Another specific challenge in the MEC environment is to facilitate incentive and trust in a decentralized manner. This article proposes CoopEdge+, a novel blockchain-based decentralized platform, to drive and support cooperative multi-access edge computing to tackle these challenges in a systematic manner. On CoopEdge+, an edge server can publish a compute task for other edge servers to contend for. A winner is selected from candidate edge servers as the task executor based on their reputation to perform the compute task. After that, CoopEdge+ employs a random leader election scheme to elect a task recorder without revealing its leadership until its consensus epoch. The task recorder will coordinate a consensus among edge servers to record the task executor's performance on blockchain. We implement CoopEdge+ based on Hyperledger fabric and evaluate it experimentally against a baseline implementation and three state-of-the-art implementations in a simulated MEC environment. The results validate the usefulness of CoopEdge+ and demonstrate its performance.

FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures

Abstract: Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world’s web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed; and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×.

Vertically stratified methane, nitrogen and sulphur cycling and coupling mechanisms in mangrove sediment microbiomes

Abstract: Mangrove ecosystems are considered as hot spots of biogeochemical cycling, yet the diversity, function and coupling mechanism of microbially driven biogeochemical cycling along the sediment depth of mangrove wetlands remain elusive. Here we investigated the vertical profile of methane (CH4), nitrogen (N) and sulphur (S) cycling genes/pathways and their potential coupling mechanisms using metagenome sequencing approaches.Our results showed that the metabolic pathways involved in CH4, N and S cycling were mainly shaped by pH and acid volatile sulphide (AVS) along a sediment depth, and AVS was a critical electron donor impacting mangrove sediment S oxidation and denitrification. Gene families involved in S oxidation and denitrification significantly (P < 0.05) decreased along the sediment depth and could be coupled by S-driven denitrifiers, such as Burkholderiaceae and Sulfurifustis in the surface sediment (0-15 cm). Interestingly, all S-driven denitrifier metagenome-assembled genomes (MAGs) appeared to be incomplete denitrifiers with nitrate/nitrite/nitric oxide reductases (Nar/Nir/Nor) but without nitrous oxide reductase (Nos), suggesting such sulphide-utilizing groups might be an important contributor to N2O production in the surface mangrove sediment. Gene families involved in methanogenesis and S reduction significantly (P < 0.05) increased along the sediment depth. Based on both network and MAG analyses, sulphate-reducing bacteria (SRB) might develop syntrophic relationships with anaerobic CH4 oxidizers (ANMEs) by direct electron transfer or zero-valent sulphur, which would pull forward the co-existence of methanogens and SRB in the middle and deep layer sediments.In addition to offering a perspective on the vertical distribution of microbially driven CH4, N and S cycling genes/pathways, this study emphasizes the important role of S-driven denitrifiers on N2O emissions and various possible coupling mechanisms of ANMEs and SRB along the mangrove sediment depth. The exploration of potential coupling mechanisms provides novel insights into future synthetic microbial community construction and analysis. This study also has important implications for predicting ecosystem functions within the context of environmental and global change. Video Abstract.

Enabling Balanced Data Deduplication in Mobile Edge Computing

Abstract: In the mobile edge computing (MEC) environment, edge servers with storage and computing resources are deployed at base stations within users’ geographic proximity to extend the capabilities of cloud computing to the network edge. Edge storage system (ESS), is comprised by connected edge servers in a specific area, which ensures low-latency services for users. However, high data storage overheads incurred by edge servers’ limited storage capacities is a key challenge in ensuring the performance of applications deployed on an ESS. Data deduplication, as a classic data reduction technology, has been widely applied in cloud storage systems. It also offers a promising solution to reducing data redundancy in ESSs. However, the unique characteristics of MEC, such as edge servers’ geographic distribution and coverage, render cloud data deduplication mechanisms obsolete. In addition, data distribution must be balanced over edge storage systems to accommodate future data demands, which cannot be undermined by data deduplication. Thus, balanced edge data deduplication (BEDD) must consider deduplication ratio, data storage benefits, and resource balance systematically under the latency constraint. In this article, we model the novel BEDD problem formally and prove its $\mathcal {NP}$NP-hardness. Then, we propose an optimal approach for solving the BEDD problem exactly in small-scale scenarios and a sub-optimal approach to solve large-scale BEDD problems with a theoretical performance guarantee. Extensive and comprehensive experiments conducted on a real-world dataset demonstrate the significant performance improvements of our approaches against four representative approaches.

Reinforcement-Learning-Based Dynamic Opinion Maximization Framework in Signed Social Networks

Abstract: Dynamic opinion maximization (DOM) is a significant optimization issue, whose target is to select some nodes in the network and prorogate the opinions of network nodes, and produce the optimum node opinions. Until now, the node opinions of related researches are unchanged and seldom focus on social relationships. In the real scenario, the dynamic process of network nodes over time and user preference have existed. Therefore, this article proposes the ${Q}$ -learning-based DOM (QDOM) framework in signed social networks to solve the OM problem, which is made up of two phases: 1) the activated dynamic opinion model and 2) the ${Q}$ -learning-based seeding process. We propose the activated dynamic opinion model based on stateless ${Q}$ -learning theory to derive the opinion propagation process. Moreover, we design the ${Q}$ -learning-based seeding algorithm to obtain the seed nodes. The experimental results on the four signed social network data sets demonstrate that the proposed framework outperforms the state-of-the-art approaches on positive opinions, the ratio of positive opinions, and activated nodes.

Automatic brain structure segmentation for 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance images via deep learning

Abstract: Background Brain structure segmentation is of great value in diagnosing brain disorders, allowing radiologists to quickly acquire regions of interest and assist in subsequent analyses, diagnoses and treatment. Current brain structure segmentation methods are usually applied to magnetic resonance (MR) images, which provide higher soft tissue contrast and better spatial resolution. However, fewer segmentation methods are conducted on a positron emission tomography/magnetic resonance imaging (PET/MRI) system that combines functional and structural information to improve analysis accuracy. Methods In this paper, we explore a dual-modality image segmentation model to segment brain 18F-fluorodeoxyglucose (18F-FDG) PET/MR images based on the U-Net architecture. This model takes registered PET and MR images as parallel inputs, and four evaluation metrics (Dice score, Jaccard coefficient, precision and sensitivity) are used to evaluate segmentation performance. Moreover, we also compared the proposed approach with other single-modality segmentation strategies, including PET-only segmentation and MRI-only segmentation. Results The experiments were conducted on the clinical head data of 120 patients, and the results show that the proposed algorithm accurately delineates brain volumes of interest (VOIs), achieving superior performance with 84.24%±1.44% Dice score, 74.36%±2.40% Jaccard, 84.33%±1.56% precision and 84.73%±1.56% sensitivity. Furthermore, compared with directly using the FreeSurfer toolkit, the proposed method reduced the segmentation time, which only needs 20 seconds to segment the whole brain for each patient. Conclusions We present a deep learning-based method for the joint segmentation of anatomical and functional PET/MR images. Compared with other single-modality methods, our method greatly improved the accuracy of brain structure delineation, which shows great potential for brain analysis.

Macrophage contribution to the survival of transferred expanded skin flap through angiogenesis

Abstract: Background Despite the application of tissue expansion in the reconstruction of significant tissue defects, complications with expanded random-pattern skin flaps remain a major challenge. Insufficient angiogenesis is one of the keys factors in flap ischemia and dysfunction. Macrophages play a key role in promoting tissue angiogenesis, but their effects on expanded flap angiogenesis and the survival of the transferred skin flap are still unknown. Methods A rat scalp expansion model was established to evaluate the dynamic changes of macrophages in expanded skin. Clodronate liposomes (Clo-lipo) were injected into the expanded scalps to deplete the macrophages, and the expanded scalp flaps with macrophage depletion were orthotopically transferred. The remaining expanded rat scalp flaps were treated with either a macrophage-colony stimulating factor (M-CSF) alone or M-CSF in combination with Clo-lipo and transferred. The number of macrophages, blood perfusion, microvascular densities (MVDs), flap survival, histological changes, and gene expression related to macrophage polarization and angiogenesis were determined with immunofluorescence (IF) staining, full-field laser perfusion imager, hematoxylin and eosin (HE) staining, and quantitative real-time polymerase chain reaction. Results The number of pan-macrophages significantly increased in the expanded scalp on days 14 and 21 after expander placement. The depletion rate after treatment with Clo-lipo was 29.06%, and the number of macrophages was significantly reduced in the group that underwent Clo-lipo treatment on day 14 before flap transfer (P<0.05). Macrophage depletion resulted in decreased blood perfusion, reduced MVDs, lower expression of factors, and poor survival rate. The recruitment of macrophages with a M-CSF led to higher blood perfusion, increased MVDs, greater expression of angiogenic factors, and better flap survival after flap transfer. Conclusions Alternatively activated macrophages in the expanded flap could significantly promote angiogenesis, improve blood perfusion, and ultimately increase the flap survival rate. Modulating alternatively activated macrophages may provide a key therapeutic strategy to promote expanded skin flap survival. Our study has provided a basis for clinically improving random-pattern skin flap survival.

The modified properties of sludge-based biochar with ferric sulfate and its effectiveness in promoting carbon release from particulate organic matter in rural household wastewater.

Abstract: The scarcity of carbon sources presents a significant challenge for the bio-treatment of rural domestic wastewater (RDW). This paper presented an innovative approach to address this issue by investigating the supplementary carbon source through in-situ degradation of particulate organic matter (POM) facilitated by ferric sulfate modified sludge-based biochar (SBC). To prepare SBC, five different contents of ferric sulfate (0%, 10%, 20%, 25%, and 33.3%) were added to sewage sludge. The results revealed that the pore and surface of SBC were enhanced, providing active sites and functional groups to accelerate the biodegradation of protein and polysaccharide. During the 8-day hydrolysis period, the concentration of soluble chemical oxidation demand (SCOD) increased and peaked (1087–1156 mg L−1) on the fourth day. The C/N ratio increased from 3.50 (control) to 5.39 (25% ferric sulfate). POM was degraded the five dominant phyla, which were Actinobacteriota, Firmicutes, Synergistota, Proteobacteria, and Bacteroidetes. Although the relative abundance of dominant phyla changed, the metabolic pathway remained unchanged. The leachate of SBC (<20% ferric sulfate) was beneficial for microbes, but an excessive amount of ferric sulfate (33.3% ferric sulfate) could have inhibition effects on bacteria. In conclusion, ferric sulfate modified SBC holds the potential for the carbon degradation of POM in RDW, and further improvements should be made in future studies.

Synthetic Denitrifying Communities Reveal a Positive and Dynamic Biodiversity-Ecosystem Functioning Relationship during Experimental Evolution

Abstract: Despite the consensus that biodiversity supports ecosystem functioning, not all experimental models of macro-organisms support this notion with positive, negative, or neutral biodiversity-ecosystem functioning (BEF) relationships reported. The fast-growing, metabolically versatile, and easy manipulation nature of microbial communities allows us to explore well the BEF relationship and further interrogate if the BEF relationship remains constant during long-term community evolution. ABSTRACT Biodiversity is vital for ecosystem functions and services, and many studies have reported positive, negative, or neutral biodiversity-ecosystem functioning (BEF) relationships in plant and animal systems. However, if the BEF relationship exists and how it evolves remains elusive in microbial systems. Here, we selected 12 Shewanella denitrifiers to construct synthetic denitrifying communities (SDCs) with a richness gradient spanning 1 to 12 species, which were subjected to approximately 180 days (with 60 transfers) of experimental evolution with generational changes in community functions continuously tracked. A significant positive correlation was observed between community richness and functions, represented by productivity (biomass) and denitrification rate, however, such a positive correlation was transient, only significant in earlier days (0 to 60) during the evolution experiment (180 days). Also, we found that community functions generally increased throughout the evolution experiment. Furthermore, microbial community functions with lower richness exhibited greater increases than those with higher richness. Biodiversity effect analysis revealed positive BEF relationships largely attributable to complementary effects, which were more pronounced in communities with lower richness than those with higher richness. This study is one of the first studies that advances our understanding of BEF relationships and their evolutionary mechanisms in microbial systems, highlighting the crucial role of evolution in predicting the BEF relationship in microbial systems. IMPORTANCE Despite the consensus that biodiversity supports ecosystem functioning, not all experimental models of macro-organisms support this notion with positive, negative, or neutral biodiversity-ecosystem functioning (BEF) relationships reported. The fast-growing, metabolically versatile, and easy manipulation nature of microbial communities allows us to explore well the BEF relationship and further interrogate if the BEF relationship remains constant during long-term community evolution. Here, we constructed multiple synthetic denitrifying communities (SDCs) by randomly selecting species from a candidate pool of 12 Shewanella denitrifiers. These SDCs differ in species richness, spanning 1 to 12 species, and were monitored continuously for community functional shifts during approximately 180-day parallel cultivation. We demonstrated that the BEF relationship was dynamic with initially (day 0 to 60) greater productivity and denitrification among SDCs of higher richness. However, such pattern was reversed thereafter with greater productivity and denitrification increments in lower-richness SDCs, likely due to a greater accumulation of beneficial mutations during the experimental evolution.

PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing

Abstract: Abstract Summary De novo peptide sequencing for tandem mass spectrometry data is not only a key technology for novel peptide identification, but also a precedent task for many downstream tasks, such as vaccine and antibody studies. In recent years, neural network models for de novo peptide sequencing have manifested a remarkable ability to accommodate various data sources and outperformed conventional peptide identification tools. However, the excellent model is computationally expensive, taking up to 1 week to process about 400 000 spectrums. This article presents PGPointNovo, a novel neural network-based tool for parallel de novo peptide sequencing. PGPointNovo uses data parallelization technology to accelerate training and inference and optimizes the training obstacles caused by large batch sizes. The results of extensive experiments conducted on multiple datasets of different sizes demonstrate that compared with PointNovo the excellent neural network-based de novo peptide sequencing tool, PGPointNovo, accelerates de novo peptide sequencing by up to 7.35× without precision or recall compromises. Availability and implementation The source code and the parameter settings are available at https://github.com/shallFun4Learning/PGPointNovo. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Re-Scheduling IoT Services in Edge Networks

Abstract: With the explosive growth of the Internet of Things (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated as IoT services, and user requests can be achieved through the composition of data and/or computation-intensive IoT services. Considering the prediction-uncertainty of forthcoming requests, certain IoT services may (i) not be hosted currently by appropriate IoT devices, or (ii) such an IoT service exists, but its non-functional properties may hardly be satisfied with respect to certain constraints prescribed by requests. To address this challenge, this paper proposes an efficiency-aware service Migration Scheduling (denoted eMS) mechanism in edge networks, in order to migrate IoT services on-demand, and thus, to optimally settle non-satisfiable constraints. Specifically, IoT services are re-scheduled, such that certain IoT services are migrated from their hosting IoT devices to neighboring ones, while minimizing the energy consumption and average delay caused by this service re-scheduling operation. We formulate this service re-scheduling as a multi-objective and multi-constraint optimization problem, which is solved through integrating the greedy algorithm into the fast non-dominated sorting and crowded-comparison operators as the hybrid genetic algorithm (G-NSGA-II). Based on real-life datasets provided by an oil pipeline monitoring project, extensive experiments are conducted, and evaluation results show that our eMS is promising in reducing the energy consumption and average delay of service re-scheduling in comparison with the state-of-art’s techniques.

Perpendicular antiferromagnetic ordering and canted spins in van der Waals FePS3 probed by spin Hall magnetoresistance

Abstract: Van der Waals antiferromagnets (AFMs) provide a two-dimensional (2D) platform for spintronic devices with exceptional properties. However, the electric transport features of the magnetic order of van der Waals AFM influenced by different field directions and amplitudes has not been demonstrated systematically. In this letter, we investigate the magnetic texture of the exfoliated van der Waals FePS3, a uniaxial AFM with perpendicular anisotropy, detected by the spin Hall magnetoresistance (SMR). Magnetic field- and temperature- dependent longitudinal magnetoresistance measurements in three orthogonal directions for the exfoliated FePS3/Pt nanostructures are conducted. The modulations in the SMR signal enable the separation of two contributions to the SMR, one of which corresponds to the negative signature of AFM SMR caused by in-plane field rotations, and the other of which is caused by canted spins in perpendicular AFM order. Our findings offer great guidance for further research and investigation using SMR approach of the magnetic texture in van der Waals AFMs.

Effect of the N-hexanoyl-L-homoserine Lactone on the Carbon Fixation Capacity of the Algae–Bacteria System

Abstract: Algae–bacteria systems are used widely in wastewater treatment. N-hexanoyl-L-homoserine lactone (AHL) plays an important role in algal-bacteria communication. However, little study has been conducted on the ability of AHLs to regulate algal metabolism and the carbon fixation ability, especially in algae–bacteria system. In this study, we used the Microcystis aeruginosa + Staphylococcus ureilyticus strain as a algae–bacteria system. The results showed that 10 ng/L C6-HSL effectively increased the chlorophyll-a (Chl-a) concentration and carbon fixation enzyme activities in the algae–bacteria group and algae group, in which Chl-a, carbonic anhydrase activity, and Rubisco enzyme increased by 40% and 21%, 56.4% and 137.65%, and 66.6% and 10.2%, respectively, in the algae–bacteria group and algae group, respectively. The carbon dioxide concentration mechanism (CCM) model showed that C6-HSL increased the carbon fixation rate of the algae–bacteria group by increasing the CO2 transport rate in the water and the intracellular CO2 concentration. Furthermore, the addition of C6-HSL promoted the synthesis and secretion of the organic matter of algae, which provided biogenic substances for bacteria in the system. This influenced the metabolic pathways and products of bacteria and finally fed back to the algae. This study provided a strategy to enhance the carbon fixation rate of algae–bacteria consortium based on quorum sensing.

SPatiotemporal-ENcoded acoustic radiation force imaging of focused ultrasound

Abstract: Neuromodulation technology has provided novel therapeutic approaches for diseases caused by neural circuit dysfunction. Transcranial focused ultrasound (FU) is an emerging neuromodulation approach that combines noninvasiveness with relatively sharp focus, even in deep brain regions. It has numerous advantages such as high precision and good safety in neuromodulation, allowing for modulation of both peripheral and central nervous systems. To ensure accurate treatment targeting in FU neuromodulation, a magnetic resonance acoustic radiation force imaging (MR-ARFI) sequence is crucial for the visualization of the focal point. Currently, the commonly used 2D Spin Echo ARFI (2D SE-ARFI) sequence suffers from the long acquisition time, while the echo planar imaging ARFI (EPI-ARFI) sequence with a shorter acquisition time is vulnerable to the magnetic field inhomogeneities. To address these problems, we proposed a spatiotemporal-encoded acoustic radiation force imaging sequence (i.e., SE-SPEN-ARFI, shortened to SPEN-ARFI) in this study. The displacement at the focal spot obtained was highly consistent with that of the SE-ARFI sequence. Our research shows that SPEN-ARFI allows for rapid image acquisition and has less image distortions even under great field inhomogeneities. Therefore, a SPEN-ARFI sequence is a practical alternative for the treatment planning in ultrasound neuromodulation.

PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain

Abstract: Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads.