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White spot syndrome virus (WSSV) is a large, enveloped, double-stranded DNA virus that threatens shrimp aquaculture worldwide. So far, the mechanisms of WSSV-host interactions are ill-defined. Recent studies have revealed that IE1, an immediate-early protein of WSSV, is a multifunctional modulator implicated in virus–host interactions. In this study, the functions of IE1 were further explored by identifying its interacting proteins using GST-pull down and mass spectrometry analysis. A total of 361 host proteins that potentially bind to IE1 were identified. Bioinformatics analysis revealed that the identified IE1-interactors wereinvolved in various signaling pathways such as prophenoloxidase (proPO) system, PI3K-AKT, and MAPK. Among these, the regulatory role of IE1 in shrimp proPO system was further studied. The Co-immunoprecipitation results confirmed that IE1 interacted with the Ig-like domain of Penaeus vannamei proPO or proPO-like protein (hemocyanin). Additionally, we found that knockdown of IE1 reduced viral genes expression and viral loads and increased the hemocytes’ PO activity, whereas recombinant IE1 protein inhibited the PO activity in a dose-dependent manner. Finally, we demonstrated that WSSV could suppress the hemocytes’ PO activity at the early infection stage. Collectively, our current data indicate that IE1 is a novel viral regulator that negatively modulates the shrimp proPO system.

Abstract Friend and point-of-interest (POI) recommendation are two primary individual services in location-based social networks (LBSNs). Major social platforms such as Foursquare and Instagram are all capable of recommending friends or POIs to individuals. However, most of these social websites make recommendations only based on similarity, popularity, or geographical influence; social trust among individuals has not been considered in those recommendation system. Recently, trust relationship has been proved to be helpful in collaborative recommendation. In this paper, we first propose algorithm to identify trust clusters and then give a trust prediction method based on these trust clusters. Then we combine the trust value and similarity among individuals to recommend friends to the target user. As for the POI recommendation, we devise a hybrid framework that integrates user preference, geographical influence, and trust relationship to improve the recommendation quality. In order to validate the effectiveness and efficiency of our methods, a series of experiments on two real social networks Foursquare and Instagram are conducted. The experiment results show that the trust cluster-based recommendation approach outperforms the baseline recommendation approaches in precision and recall.

With the rapid development of location-based social networks (LBSNs), point of interest (POI) recommendation has become more and more popular personalized service. Cold start problem and poor interpretability are two main challenges of the traditional recommendation system. In this paper, we propose a novel social and sequence-aware next POI recommendation model. Our model utilizes an improved DeepWalk on heterogeneous network to learn better POI representation which contains social relationship and geographical influence. In this way, the cold start problem and interpretability problem can be solved to some extend. As for the user preference learning, we use LSTM (Long Short-Term Memory) mechanism to infer user interest, taking both long term and short term intentions into consideration. The model is trained in a metric learning framework. The experimental results on two real LBSN datasets show our model has outstanding performance in terms of AUC, Recall, and ACC. Furthermore, we can obtain 20% performance improvement on spare dataset which indicates that our model can well solve the cold start problem.

POI (point of interest) recommendation is an important personalized service in the LBSN (location-based social network) which has wide applications such as popular sights recommendation and travel routes planning.Most existing collaborative filter algorithms make recommendation according to user similarity and location similarity,they don’t consider the trust relationship between users.And trust relationship is helpful to improve recommendation accuracy,robustness and user satisfaction.Firstly,the propagation property of trust and distrust relationship was analyzed.Then,the measurement and computation method of trust were given.Finally,a hybrid recommendation system which combined user similarity,geographical location similarity and trust relationship was proposed.The experiments results show that the hybrid recommendation is obviously superior to the traditional collaborative filtering in terms of results accuracy and user satisfaction.

The problem of influence maximization (IM) in a social network is to determine a set of nodes that could maximize the spread of influence. The IM problem has been vitally applied to marketing, advertising, and public opinion monitoring. Although recent studies have studied the IM problem, they are generally greedy or heuristic-based algorithms, which are time consuming for practical use in large-scale social networks. Based on the observation that structural hole nodes usually are much more influential than other nodes, in this paper, we develop a structure-hole-based influence maximization algorithm (SHIM) with an emphasis on time efficiency. The SHIM algorithm utilizes structure hole information to significantly decrease the number of candidates of seed nodes. To measure the structure importance of nodes, we propose an structure hole value calculate algorithm to calculate the structural hole value of nodes. We prove the SHIM is NP-hard and propose a structure-based greedy algorithm to select seeds with wide influence spread and high structural hole value. We conduct experiments on real data sets to verify our algorithm's time efficiency and accuracy, and the experimental results show that comparing with the existing algorithms, our algorithms are much more efficient and scalable.

Honeydew is a watery fluid excreted by plant sap-feeding insects. It is a waste product for the insect hosts. However, it plays important roles for other organisms, such as serving as a nutritional source for beneficial insects and bacteria, as well as elicitors and effectors modulating plant responses. In this study, shotgun LC–MS/MS analyses were used to identify the proteins in the honeydew from two important rice hemipteran pests, the brown planthopper (Nilaparvata lugens, BPH) and green rice leafhopper (Nephotettix cincticeps, GRH). A total of 277 and 210 proteins annotated to insect proteins were identified in the BPH and GRH honeydews, respectively. These included saliva proteins that may have similar functions as the saliva proteins, such as calcium-binding proteins and apolipophorin, involved in rice plant defenses. Additionally, a total of 52 and 32 Oryza proteins were identified in the BPH and GRH honeydews, respectively, some of which are involved in the plant immune system, such as Pathogen-Related Protein 10, ascorbate peroxidase, thioredoxin and glutaredoxin. Coincidently, 570 and 494 bacteria proteins were identified from the BPH and GRH honeydews, respectively, which included several well-known proteins involved in the plant immune system: elongation factor Tu, flagellin, GroEL and cold-shock proteins. The results of our study indicate that the insect honeydew is a complex fluid cocktail that contains abundant proteins from insects, plants and microbes, which may be involved in the multitrophic interactions of plants–insects–microbes.

In the routing protocol for wireless sensor network, the cluster size is generally fixed in clustering routing algorithm for wireless sensor network, which can easily lead to the “hot spot” problem. Furthermore, the majority of routing algorithms barely consider the problem of long distance communication between adjacent cluster heads that brings high energy consumption. Therefore, this paper proposes a new cross unequal clustering routing algorithm based on the EEUC algorithm. In order to solve the defects of EEUC algorithm, this algorithm calculating of competition radius takes the node’s position and node’s remaining energy into account to make the load of cluster heads more balanced. At the same time, cluster adjacent node is applied to transport data and reduce the energy-loss of cluster heads. Simulation experiments show that, compared with LEACH and EEUC, the proposed algorithm can effectively reduce the energy-loss of cluster heads and balance the energy consumption among all nodes in the network and improve the network lifetime

In recent years, the big data emerged as a hot topic because of the rapid growth of the information and wireless communication technology. One of the significant sources of the big data is wireless sensor networks. Due to the power limitation of sensor nodes, energy-efficient big data collecting is a challenging task in wireless sensor networks. Being considered as an effective solution to address this challenge is to utilize sink’s mobility to assist data collecting. Although this method can reduce the volume of data transfer between sensor nodes and thus save energy consumption of nodes, the low speed of mobile sink hinders its use in data-intensive sensing applications with time constraint. In this article, we propose a four-phase mobility-assisted data collecting protocol consisting of network clustering, routes planning, routes combination, and data collecting. Two heuristic routes planning algorithms are presented to build a set of trajectories which satisfy the deadline constraint and have the minimum overall movement cost. Numeric results show that our approaches have better performance in terms of energy saving, latency, and movement cost.