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The paper aims to study a kind of revenue allocation system with a guarantee of basic interests, which integrates the egalitarianism with the marginalism. This means that a player’s payoff consists of two parts, the basic interests and the performance-based payoff. In cooperative games with coalition structure setting, the Owen value puts emphasis on the individuals’ marginal contribution, in contrast, the equal coalitional division value (ECD-value) gives priority to the egalitarianism. Through introducing the guarantee coefficient $$\alpha$$ , we propose the compromised solution which is established as the corresponding convex combination of the Owen value and the ECD-value. We call it $$\alpha$$ -egalitarian Owen value, as the solution degenerates to the corresponding $$\alpha$$ -egalitarian Shapley value when the coalition structure is trivial. Furthermore, we provide three approaches to characterize an $$\alpha$$ -egalitarian Owen value, including axiomatization, potential function and implementation.

In mobile social networks, next point-of-interest (POI) recommendation is a very important function that can provide personalized location-based services for mobile users. In this paper, we propose a recurrent neural network (RNN)-based next POI recommendation approach that considers both the location interests of similar users and contextual information (such as time, current location, and friends’ preferences). We develop a spatial-temporal topic model to describe users’ location interest, based on which we form comprehensive feature representations of user interests and contextual information. We propose a supervised RNN learning prediction model for next POI recommendation. Experiments based on real-world dataset verify the accuracy and efficiency of the proposed approach, and achieve best F1-score of 0.196 754 on the Gowalla dataset and 0.354 592 on the Brightkite dataset.

The Information-Centric Networking (ICN) paradigm has emerged to shift the current host-based network model to a content-oriented one in order to cope with the dominant content-based services in the Internet. Congestion control is a fundamental design concern to support massive content delivery in ICN. While the existing flow-based and hop-by-hop congestion control mechanisms suffer from complexity and compatibility issues, we propose RevMax , a gateway-aware congestion control mechanism based on the architecture of NDN, a well-known ICN platform, to overcome the drawbacks. In the proposed mechanism, the gateway offers a price to each end-user, which urges the user to adjust the request rate according to the price. The optimal pricing policy for the gateway is shown to be formulated as a revenue maximization problem, and an efficient algorithm is proposed to derive the optimal solution. The proposed RevMax mechanism is implemented in NDN and compared to PCON , a state-of-the-art congestion control mechanism for ICNs. Extensive experiments show that RevMax achieves higher throughput, lower network latency, and better fairness in a variety of network scenarios.

Rapid water absorption rate has become a bottleneck that limits ultrarapid hemostatic performance of hemostatic microspheres. Herein, we reported a "lotus seedpod surface-like" polysaccharide hemostatic microsphere (PHM) with "macropits on surface" morphology and "micropores in macropits" structure. Unique macropits on surface can promote the water absorption rate because they are advantageous to quickly guide blood into the micropores. Special micropores are internally connected with each other, which endows PHM4 with high water absorption ratio. During the process of blood entering the micropores from micropits, the pore size decreases gradually. In this way, blood clotting factors could be rapidly concentrated. PHM4 showed the highest water absorption rate (40.7 mL/s/cm2) and rapid hemostatic property in vivo (hemostatic time shortened from 210 to 45 s). Lotus seedpod surface-like PHMs are believed to have further clinical application as an effective hemostasis.

The Multipath TCP (MPTCP) protocol has been standardized by the IETF as an extension of conventional TCP, which enables multi-homed devices to establish multiple paths for simultaneous data transmission. Congestion control is a fundamental mechanism for the design and implementation of MPTCP. Due to the diverse QoS characteristics of heterogeneous links, existing multipath congestion control mechanisms suffer from a number of performance problems such as bufferbloat, suboptimal bandwidth usage, etc. In this paper, we propose a learning-based multipath congestion control approach called SmartCC to deal with the diversities of multiple communication path in heterogeneous networks. SmartCC adopts an asynchronous reinforcement learning framework to learn a set of congestion rules, which allows the sender to observe the environment and take actions to adjust the subflows’ congestion windows adaptively to fit different network situations. To deal with the problem of infinite states in high-dimensional space, we propose a hierarchical tile coding algorithm for state aggregation and a function estimation approach for $Q$ -learning, which can derive the optimal policy efficiently. Due to the asynchronous design of SmartCC, the processes of model training and execution are decoupled, and the learning process will not introduce extra delay and overhead on the decision making process in MPTCP congestion control. We conduct extensive experiments for performance evaluation, which show that SmartCC improves the aggregate throughput significantly and outperforms the state-of-the-art mechanisms on a variety of performance metrics.

Based on the provincial panel data from 2008 to 2019, this paper constructs a mediating effect model to analyze the impact of China’s agricultural insurance development on the nutritional status of rural residents theoretically and empirically. This study found that agricultural insurance significantly affects the nutritional status of rural residents through two mediators: increased income level and the regional crop diversity. Meanwhile, regional heterogeneity exists in the impact of agricultural insurance on the nutritional status of rural residents. Overall, this paper argues that agricultural insurance should be one of the tools to improve the nutritional status of rural residents in China. Specifically, the design and implementation process of policy-based agricultural insurance should put the nutritional health of rural residents and their complex impact into consideration. Therefore, this paper suggests that even though agricultural insurance can facilitate rural residents’ income stability and generation, relevant stakeholders also need to draw awareness, popularize the importance of nutrition and health, and optimize the supply structure of agricultural products.

An exquisite metal-free cascade cyclization reaction of 2-acylbenzoic acids with amines was developed, which provided a powerful method for the one-pot synthesis of diverse isoindoloisoquinoline and benzoindolizinoindole derivatives. This protocol avoided the use of metal catalysts, proceeded with high efficiency and had broad substrate scope. These resulting products could be transformed into tertiary amines under the reduction of LiAlH

The existence of cancer stem cells (CSCs) poses a major obstacle for the success of current cancer therapies, especially the fact that non-CSCs can spontaneously turn into CSCs, which lead to the failure of the treatment and tumor relapse. Therefore, it is very important to develop effective strategies for the eradication of the CSCs. In this work, we have developed a CSCs-specific targeted, retinoic acid (RA)-loaded gold nanostars-dendritic polyglycerol (GNSs-dPG) nanoplatform for the efficient eradication of CSCs. The nanocomposites possess good biocompatibility and exhibit effective CSCs-specific multivalent targeted capability due to hyaluronic acid (HA) decorated on the multiple attachment sites of the bioinert dendritic polyglycerol (dPG). With the help of CSCs differentiation induced by RA, the self-renewal of breast CSCs and tumor growth were suppressed by the high therapeutic efficacy of photothermal therapy (PTT) in a synergistic inhibitory manner. Moreover, the stemness gene expression and CSC-driven tumorsphere formation were significantly diminished. In addition, the in vivo tumor growth and CSCs were also effectively eliminated, which indicated superior anticancer activity, effective CSCs suppression, and prevention of relapse. Taken together, we developed a CSCs-specific targeted, RA-loaded GNSs-dPG nanoplatform for the targeted eradication of CSCs and for preventing the relapse.

Over the past decade, repeated studies demonstrated that the vertebrate brain had a specialized lymphatic transport pathway, which overturned the traditional concept of central nervous system (CNS) immune privilege. Despite the lack of lymphatic vessels, the glymphatic system and the meningeal lymphatic vessels provide a unique pathway for solutes transport and metabolites clearance in the brain. Sleep, circadian rhythm, arterial pulsation, and other physiological factors modulate this specialized lymphatic drainage pathway. It has also changed significantly under pathological conditions. These modulatory mechanisms may arise critical targets for the therapeutic of CNS disorders. This review highlights the latest research progress on the modulation of lymphatic transport in the CNS under physiological and pathological conditions. Furthermore, we examined the possible upstream and downstream relation networks between these regulatory mechanisms.

Elevated levels of low-density lipoproteins (LDL) are recognized as a crucial indicator of hyperlipidemia (HLP) and lowering of LDL levels represents an effective clinical treatment strategy. Inspired by the conjugation of phospholipid monolayers and the lipid content of the LDL particle, the current study describes the preparation of an innovative hemoperfusion adsorbent. The adsorbent was prepared by attachment of phosphatidyl ethanolamine to poly(acrylic acid) modified poly(vinyl alcohol-co-triallyl isocyanurate) beads (PVA@PAA-PE). The interaction between LDL and adsorbent mimics the lipoprotein microemulsion present in the blood and thus promotes efficient binding with high affinity. In vitro adsorption using serum from patients with HLP revealed that the LDL adsorption of PVA@PAA-PE was 4.44 times higher than that of controls and the removal rate of LDL using PVA@PAA-PE was about twice as high as that of the anti-atherogenic high-density lipoprotein (HDL). In vivo whole blood perfusion demonstrated the superior affinity of PVA@PAA-PE for LDL since LDL concentration was significantly reduced from 10.71 ± 2.36 mmol L-1 to 6.21 ± 1.45 mmol L-1, while the HDL level was not severely reduced (from 0.98 ± 0.12 mmol L-1 to 0.56 ± 0.15 mmol L-1). Additionally, PVA@PAA-PE exhibited excellent hemocompatibility and low cytotoxicity. Therefore, PVA@PAA-PE is a potential adsorbent for whole blood perfusion to treat hyperlipidemia.

Consider a group of agents located along a polluted river where every agent must pay a certain cost for cleaning up the polluted river. Following the model of Ni and Wang (2007), we propose the class of alpha-Local Responsibility Sharing methods, which generalizes the Local Responsibility Sharing (LRS) method and the Upstream Equal Sharing (UES) method. We fi rst show that the UES method is characterized by relaxing independence of upstream costs appearing in Ni and Wang (2007). Then we provide two axiomatizations with endogenous responsibility of the alpha-Local Responsibility Sharing method, one using this weak independence axiom (taken from the UES method) and one using a weak version of the no blind cost axiom (taken from the LRS method). Moreover, we also provide an axiomatization with exogenous responsibility by introducing alpha-responsibility balance. Finally, we defi ne a pollution cost-sharing game, and show that, interestingly, the Half Local Responsibility Sharing (HLRS) method coincides with the Shapley value, the nucleolus and the tau-value of the corresponding pollution cost-sharing game. This HLRS method can be seen as some kind of middle compromise of the LRS and UES methods.

Our society is witnessing a rapid vehicle electrification process. Even though being environmental-friendly, electric vehicles have not reached their full potentials due to prolonged charging time. Moreover, unbalanced spatiotemporal charging demand/supply along with the uneven number of charging stations between heterogeneous fleets make electric vehicle management more challenging, e.g., surplus charging stations across a city for electric buses but limited charging stations in some regions for electric taxis, which severely limit the charging performance of the whole electric vehicle network in a city. In this paper, we first analyze a large-scale real-world dataset from two heterogeneous electric vehicle fleets in the Chinese city Shenzhen. We investigate their mobility and charging patterns and then verify the practicability and necessity of shared charging. Based on the insights we found, we design a generic real-time shared charging scheduling system called sharedCharging to improve overall charging efficiency for heterogeneous electric vehicle fleets. Our sharedCharging also considers sophisticated real-world constraints, e.g., station spaces, availability of charging points, real-time timetable guarantee, etc. More importantly, we take the electric bus and electric taxi fleets as a concrete example of heterogeneous electric vehicle fleets given their different operating patterns. We implement and evaluate sharedCharging with streaming data from over 13,000 electric taxis and 16,000 electric buses, coupled with the charging station data in the Chinese city Shenzhen, which is the largest public electric vehicle network in the world. The evaluation results demonstrate that the proposed sharedCharging reduces the waiting time by 63.5% and reduces the total charging time by 15% on average for e-taxis.

The combination of physical and chemical cues from nanostructured biomaterials is quite promising but has rarely been reported in regulating the fate of stem cells. In this study, we propose to synthesize multivalent polyanion-dispersed carbon nanotubes (CNTs) for fabrication of nanostructured fibrous scaffolds with capabilities on controlling the fate of induced pluripotent stem cells (IPS). First, the CNTs are noncovalently functionalized by hyperbranched polyglycerol sulfate (hPGS) via sonication, and the stability and biocompatibility of hPGS-dispersed CNTs are compared with commercial dispersants. Then, the nanostructured fibrous scaffolds are prepared by coating hPGS-dispersed CNTs onto the electrospun polycaprolactone (PCL) fiber substrates to achieve PCL-CNT-hPGS. Our results show that these fibrous scaffolds can serve as a biocompatible platform for promoting the adhesion and proliferation of IPS cells. Meanwhile, the immunostaining shows that the nanostructured fibrous scaffolds induce higher neural differentiation efficiency compared to the control substrates. Moreover, we also validate that the aligned fibrous scaffolds can guide the orientation of generated neurites. Overall, a new multivalent polyanion-dispersed CNT has been utilized to construct highly bioactive nanostructured fibrous scaffolds, which not only combine physical and chemical cues to offer beneficial microenvironments for controlling the fate of IPS cells, but also present a new avenue for constructing carbon nanomaterials functionalized composites in tissue regeneration, bionic, biomedical, and bioelectronics.

Screen-camera communication using dynamic barcode streaming has emerged as a convenient and secure method for short-range, impromptu device-to-device communication. Conventional dynamic barcode systems adopt a rule-based approach to recognize the color barcode stream in the receiver, which is empirical, inflexible, and lacks self-adaptiveness. In this paper, we propose a novel solution framework for color barcode stream recognition basing on machine learning techniques. By including a number of training frames into the barcode stream to build a classification model, the proposed framework can achieve high accuracy in color barcode recognition and is adaptive to different ambient lighting conditions (without sudden changes during transmission). A semi-supervised learning approach basing on the Mixture of Experts (MoE) model is further proposed to reduce the start-up time. We implement MegaLight on both black-white and color barcode systems. Extensive experiments demonstrate that MegaLight can significantly reduce the frame demodulation error and reach up to 3x improvement in system goodput comparing to conventional barcode stream recognition approaches.

Despite significant developments in medical and surgical strategies, cardiac diseases remain the leading causes of morbidity and mortality worldwide. Numerous studies involving preclinical and clinical trials have confirmed that stem cell transplantation can help improve cardiac function and regenerate damaged cardiac tissue, and stem cells isolated from bone marrow, heart tissue, adipose tissue and umbilical cord are the primary candidates for transplantation. During the past decade, menstrual blood-derived endometrial stem cells (MenSCs) have gradually become a promising alternative for stem cell-based therapy due to their comprehensive advantages, which include their ability to be periodically and non-invasively collected, their abundant source material, their ability to be regularly donated, their superior proliferative capacity and their ability to be used for autologous transplantation. MenSCs have shown positive therapeutic potential for the treatment of various diseases. Therefore, aside from a brief introduction of the biological characteristics of MenSCs, this review focuses on the progress being made in evaluating the functional improvement of damaged cardiac tissue after MenSC transplantation through preclinical and clinical studies. Based on published reports, we conclude that the paracrine effect, transdifferentiation and immunomodulation by MenSC promote both regeneration of damaged myocardium and improvement of cardiac function.

While modern wireless devices are capable of using multiple WiFi interfaces, the Multipath TCP (MPTCP) protocol has been employed to make full use of the capacity of many radios by enabling multiple path communication simultaneously. To provide exceptional mobility support in wireless networks, a key question is to determine the best handoff strategy to switch between access points or among WiFi/3G interfaces during roaming. In this paper, we propose SERO, a novel model-driven SEamless ROaming framework to optimize layer-2 handoff and vertical handoff for multihomed devices using MPTCP. The proposed framework adopts a measurement-based method to derive the TCP throughput model for wireless communication during handoff. Based on the throughput model, we propose a hybrid handoff strategy that uses multiple WiFi interfaces for data transmission and employs 3G augmentation to bridge the network interruption caused by handoff and to guarantee the total throughput above a predefined threshold for roaming devices. We implement the SERO framework in a real-deployed wireless mesh network testbed, and evaluate its performance by extensive experiments, which shows that SERO achieves performance gain of 26%–180% compared with several existing handoff strategies.

A novel nano-CaCO3 (nCaCO3) particle composite-derived polystyrene (PS) resin was successfully synthesized by a suspension polymerization method. The nCaCO3 reinforced PS material (PS/nCaCO3) possessed a structure with abundant mesopores of high porosity, high specific surface area (828.3 m2 g-1) and large pore volume (1.83 cm3 g-1). It was revealed that the incorporation of nCaCO3 into the PS matrix enhanced both the mechanical strength which can prevent the fragmentation and its adsorption capacity for interleukin-6 (IL-6, MW = 24.0 kDa) from human plasma. The adsorption isotherm could be described by the Langmuir model and classified as S-3 type, showing an IL-6 uptake of up to 25.6 ng g-1 at an equilibrium concentration of about 500 ng L-1. The adsorption capacity for IL-6 of PS/nCaCO3 is not only significantly higher than that of PS (without nCaCO3), but also superior to those of currently available adsorbents that are under clinical studies (e.g., CytoSorb™ towards cytokines). In addition, the PS/nCaCO3 adsorbent also had good hemocompatibility and showed no leakage of nCaCO3 in the plasma in a flowing model system. Therefore, the synthesized PS/nCaCO3 nano-composite has a great potential to be used as an efficient adsorbent for the removal of interleukin-6 (IL-6) from blood of inflammatory and auto-immune disease patients through hemoperfusion.

With the increasing adoption of location-based social network applications, a large number of location traces of human mobility have been collected and published for the purpose of assisting mobile system design and scientific research. Most mobility traces are processed to achieve anonymity before publishing by the way of replacing the true IDs and introducing noise interference. In this paper, we show that such anonymous mobility traces are vulnerable to asynchronous side information attack from the edge: if partial movement information is exposed to some compromised edge nodes even after the data collection period, the adversary is able to identify the participant from the anonymous mobility traces with high probability. Our method to identify participants is based on exploring the accumulative temporal and spatial characteristics of individual movement. We introduce $$\delta$$ -partition to divide user locations into sub-areas, and $$\epsilon$$ -partition to group user activities into time intervals. We illustrate that a mobility trace can be uniquely represented by a set of frequent locations together with their active time intervals. We further derive a similarity measurement to be used by the adversary for asynchronous side information attack. We develop theoretical analysis to prove that an anonymous participant can be correctly identified with high probability under certain condition. Extensive experiments are conducted on three typical mobility datasets corresponding to the movement of bus, taxi and human, which show that the identification success ratio achieves 99%, 45% and 72%, respectively.

Tumor necrosis factor (TNF)-α has an important role in the pathogenesis of autoimmune and inflammatory diseases such as rheumatoid and septic arthritis. Removal of excess tumor necrosis factor-α (TNF-α) is a promising treatment. In this study, a series of functionalized carbon nanotube-embedded poly(vinyl alcohol) (PVA) nanocomposite adsorbents were prepared for TNF-α removal for the first time. The resulting nanocomposites were characterized by scanning electron microscopy and Raman spectroscopy, which demonstrated that carbon nanotubes were well-dispersed on the surface of PVA macroporous microspheres. Adsorption tests showed that the carboxylated carbon nanotube-embedded composite microspheres (PVA/MWCNTs-COOH) possessed much better adsorption capacity for TNF-α in both simulated serum solution and rat plasma compared to the aminated (PVA/MWCNTs-NH2) and raw carbon nanotube-embedded microspheres (PVA/MWCNTs-raw). In addition, the effects on hemolytic activity, the anticoagulant property, and the components of blood were negligible, indicating the excellent blood compatibility of composite beads. Our findings suggest that the carboxylated carbon nanotube-embedded composite microspheres may be potentially useful for the treatment of autoimmune and inflammatory diseases by removing TNF-α from the blood.

Today bicycle sharing system has been widely deployed around the world to provide a convenient transit connectivity for short trips. However, due to the careless usage or incident, it is inevitable to have some shared bicycles in the bad working condition. Rather than the fail-stop failure, e.g., a broken tire, which can be easily detected by users, the gray failure, i.e., subtle faults, can only be discovered when riding the bike. Such gray failure causes bad user experience and affects the reputation of bicycle sharing system provider. Unfortunately, it is not easy to detect these gray failures. Current solution to this problem mainly relies on users’ self-reporting. However, due to the lack of incentive mechanism and the knowledge on bicycle, cyclists may submit inaccurate report or ignore the subtle faults. To provide users the working condition of bicycles in advance and reduce the maintaining cost of system providers, this paper introduces an approach to automatically detect the gray failures of the shared bicycles. To this end, we leverage the smartphone to collect and analyze the data collected from the sensor-equipped bicycle. We extract patterns from sensing signals of broken bikes, and identify the problematic components of shared bicycles. Compared with the conventional user-reporting, the proposed method is cost-effective, automatic, and easy-to-deploy. Extensive experiments based on real bicycle sharing systems show that the proposed method achieves high accuracy in determining gray failures for a variety of scenarios.

System logs produced by modern computer systems are valuable resources for detecting anomalies, debugging performance issues, and recovering application failures. With the increasing scale and complexity of the log data, manual log inspection is infeasible and man-power expensive. In this paper, we proposed LogAttn, an autoencoder model that combines an encoder-decoder structure with an attention mechanism for unsupervised log anomaly detection. The unstructured normal log data is proceeded by a log parser that uses a semantic analyse and clustering algorithm to parse log data into a sequence of event count vectors and semantic vectors. The encoder combines deep neural networks with an attention mechanism that learns the weights of different features to form a latent feature representation, which is further used by a decoder to reconstruct the log event sequence. If the reconstruction error is above a predefined threshold, it detects an anomaly in the log sequence and reports the result to the administrator. We conduct extensive experiments based on three real-world log datasets, which show that LogAttn achieves the best comprehensive performance compared to the state-of-the-art methods.

The polymer cutoff wall offers the benefits of causing little disturbance to the dam body, being convenient and economical to construct and practical to use, and possessing good resistance to seismic shocks and cracks. It has been widely used to prevent seepage and to reinforce dikes and dams. However, the polymer cutoff wall is a concealed underground structure, and nondestructive testing methods to ensure its integrity are not yet mature. This paper describes a modal analysis of polymer cutoff wall models with different damage scenarios to investigate the feasibility of nondestructive testing of the polymer cutoff wall based on vibration theory. The dynamic characteristics of the first three natural frequencies of the wall and their mode shapes show that horizontal direction damage and centrally located partial damage have a noticeable impact on the dynamic characteristics of the wall, indicating that nondestructive testing based on vibration theory is useful to test for horizontal damage and moderate damage located centrally in the wall.

In the federated learning paradigm, multiple mobile clients train their local models independently based on the datasets generated by edge devices, and the server aggregates the model parameters received from multiple clients to form a global model. Conventional methods aggregate gradient parameters and statistical parameters without distinction, which leads to large aggregation bias due to cross-model distribution covariate shift (CDCS), and results in severe performance drop for federated learning under non-IID data. In this paper, we propose a novel decoupled parameter aggregation method called FedDNA to deal with the performance issues caused by CDCS. With the proposed method, the gradient parameters are aggregated using the conventional federated averaging method, and the statistical parameters are aggregated with an importance weighting method to reduce the divergence between the local models and the central model to optimize collaboratively by an adversarial learning algorithm based on variational autoencoder (VAE). Extensive experiments based on various federated learning scenarios with four open datasets show that FedDNA achieves significant performance improvement compared to the state-of-the-art methods.

Consider a group of agents located along a polluted river where every agent must pay a certain cost for cleaning up the polluted river. Following the model of Ni and Wang (2007), we propose the class of alpha-Local Responsibility Sharing methods, which generalizes the Local Responsibility Sharing (LRS) method and the Upstream Equal Sharing (UES) method. We fi rst show that the UES method is characterized by relaxing independence of upstream costs appearing in Ni and Wang (2007). Then we provide two axiomatizations with endogenous responsibility of the alpha-Local Responsibility Sharing method, one using this weak independence axiom (taken from the UES method) and one using a weak version of the no blind cost axiom (taken from the LRS method). Moreover, we also provide an axiomatization with exogenous responsibility by introducing alpha-responsibility balance. Finally, we defi ne a pollution cost-sharing game, and show that, interestingly, the Half Local Responsibility Sharing (HLRS) method coincides with the Shapley value, the nucleolus and the tau-value of the corresponding pollution cost-sharing game. This HLRS method can be seen as some kind of middle compromise of the LRS and UES methods.

A graph stream is a kind of dynamic graph representation that consists of a consecutive sequence of edges where each edge is represented by two endpoints and a weight. Graph stream is widely applied in many application scenarios to describe the relationships in social networks, communication networks, academic collaboration networks, etc. Graph sketch mechanisms were proposed to summarize large-scale graphs by compact data structures with hash functions to support fast queries in a graph stream. However, the existing graph sketches use fixed-size memory and inevitably suffer from dramatic performance drops after a massive number of edge updates. In this paper, we propose a novel Dynamic Graph Sketch (DGS) mechanism, which is able to adaptively extend graph sketch size to mitigate the performance degradation caused by memory overload. The proposed DGS mechanism incorporates deep neural network structures with graph sketch to actively detect the query errors, and dynamically expand the memory size and hash space of a graph sketch to keep the error below a pre-defined threshold. We conducted extensive experiments on three real-world graph stream datasets, which show that DGS outperforms the state-of-the-arts with regard to the accuracy of different kinds of graph queries.

The recently emerging stem-cell artificial niche engineering in induced pluripotent stem cell (iPSCs) 3D cultures has provided enormous opportunities to fully utilize the potential of these cells in biomedical applications. Although a fully chemically defined niche environment can supply cells with desirable safety for clinical use, establishing an artificial degradable niche environment for the controlled release of proliferated cells under mild conditions is still a big challenge. Here, an advanced controlled releasable iPSC 3D artificial niche is reported based on dendritic polyglycerol and poly(N-isopropylacrylamide)-co-polyethylene glycol polymers via a physical–chemical cogelation strategy. Benefiting from the chemically defined synthetic materials and their precise cooperation by covalent cross-linking and physical phase transition, the cogelation-based artificial niche system can be adjusted with optimal parameters and owns high cell biocompatibility to support the robust production of high quality iPSCs with an excellent expansion efficiency. Moreover, the expanded cells can be released out of their niche environment controllably only by adjusting the temperature. Overall, this controlled release hydrogel scaffold shows great promise in iPSC 3D culture for downstream applications.

An exquisite protocol to the synthesis of erythrina-related structural derivatives was developed, which is composed of a nucleophilic addition of tertiary enamides to ketonic carbonyls and the trapping of acyliminium by an aromatic unit. This protocol afforded a powerful method for the construction of diverse fused

Nanomaterials play an important role in mimicking the biochemical and biophysical cues of the extracellular matrix in human mesenchymal stem cells (MSCs). Increasing studies have demonstrated the crucial impact of functional groups on MSCs, while limited research is available on how the functional group's density on nanoparticles regulates MSC behavior. Herein, the effects of dendritic polyglycerol (dPG)-conjugated gold nanostars (GNSs) with different densities of functional groups on the osteogenesis of MSCs are systematically investigated. dPG@GNS nanocomposites have good biocompatibility and the uptake by MSCs is in a functional group density-dependent manner. The osteogenic differentiation of MSCs is promoted by all dPG@GNS nanocomposites, in terms of alkaline phosphatase activity, calcium deposition, and expression of osteogenic protein and genes. Interestingly, the dPGOH@GNSs exhibit a slight upregulation in the expression of osteogenic markers, while the different charged densities of sulfate and amino groups show more efficacy in the promotion of osteogenesis. Meanwhile, the sulfated nanostars dPGS20@GNSs show the highest enhancement. Furthermore, various dPG@GNS nanocomposites exerted their effects by regulating the activation of Yes-associated protein (YAP) to affect osteogenic differentiation. These results indicate that dPG@GNS nanocomposites have functional group density-dependent influence on the osteogenesis of MSCs, which may provide a new insight into regulating stem cell fate.

Recommender systems had been proposed to help people to find the interested items, such as recommending products to a buyer; identifying movies or music that a user will find interest, etc. However, the existing recommendation approaches mainly focus on capturing user-item interaction patterns for prediction, and ignore the user’s side information such as visit frequency and duration. In this paper, we study the side information aided website recommendation problem that using the browsing history of a set of users and their side information to predict the websites that will be of interest to a certain user. We propose a novel recommendation approach called SI-VAE that incorporates side information with the variational autoencoders (VAEs) model for top-k recommendation. The proposed method takes both user-website interaction information and side information as input, and adopts an encoder/decoder model to generate user’s interested websites from partial observations. The model of SI-VAE is implemented as a neural network, and trained with a multinomial likelihood objective function to form the ranking of user-website interaction probabilities. We conduct extensive experiments on two real-world datasets, which show that the proposed model outperforms the baselines in a number of performance metrics in website recommendation.

A practical and efficient catalyst-free cascade cyclization reaction of 2-acylbenzoic acids with various amino alcohols or diamines was developed. This protocol provides a powerful and straightforward method for the one-pot synthesis of diverse isoindolobenzoxazinones, isoindoloquinazolinones and their derivatives. The synthetic strategy evades the use of catalyst, shows a broad substrate scope and could be performed in gram-scale.

A class of solutions are introduced by lexicographically minimizing the complaint of coalitions for cooperative games with transferable utility. Among them, the nucleolus is an important representative. From the perspective of measuring the satisfaction of coalitions with respect to a payoff vector, we define a family of optimal satisfaction values in this paper. The proportional division value and the proportional allocation of non-separable contribution value are then obtained by lexicographically maximizing two types of satisfaction criteria, respectively, which are defined by the lower bound and the upper bound of the core from the viewpoint of optimism and pessimism respectively. Correspondingly, we characterize these two proportional values by introducing the equal minimal satisfaction property and the associated consistency property. Furthermore, we analyze the duality of these axioms and propose more approaches to characterize these two values on basis of the dual axioms.

Multidrug resistance (MDR) of cancer stem cells (CSCs) is a major hurdle to chemotherapy, and it is very important to develop CSCs-specific targeted nanocarriers for the treatment of drug resistant CSCs. In this work, we developed CSCs-specific targeted mSiO2-dPG nanocarriers simultaneous delivery chemotherapy drug DOX along with the P-glycoprotein (P-gp) inhibitor tariquidar (Tar) for enhanced chemotherapy to overcome MDR in breast CSCs. The mSiO2-dPG nanocarriers possess a high loading capability, excellent pH stimuli-responsive performance, and good biocompatibility. With the help of CSCs-specific targeting and P-gp inhibitor Tar, the accumulation of DOX delivered by the mSiO2-dPG nanocarriers could be greatly increased in drug resistant three-dimensional mammosphere of breast CSCs, and the chemotherapeutic efficacy against breast CSCs was enhanced. Moreover, the expression of stemness-associated gene and tumorspheres' formation ability was also significantly suppressed, which indicates the excellent capability for overcoming MDR of breast CSCs. Taken together, we developed a CSCs-specific targeted mSiO2-dPG nanocarriers for co-delivery DOX and Tar, which provide a promising approach to effectively eliminate the CSCs and overcome the MDR of breast CSCs.

Tumor necrosis factor alpha (TNF-α) is a pro-inflammatory cytokine active in the bodily immune response and serious inflammatory diseases. Traditional ligands targeting TNF-α focus on antibodies and receptors, which always associate with low efficacy and specificity. In the present study, two peptide ligands (T1: Ac-RKEM-NH2 and T2: Ac-RHCLS-NH2) were designed by computer simulation technology considering the weak interactions between TNF-α and its receptor TNFR1. Calculations of binding free energy (BFE) were made by the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method between T1 or T2 and TNF-α (-22.68 and -14.23 kcal mol-1, respectively). To assess the affinity levels, short peptide ligands were fixed on polyvinyl alcohol (PVA) microspheres; adsorption tests showed a stronger affinity of both PVA-T1 and PVA-T2 to TNF-α in PBS buffer than PVA microspheres (79.20 ± 1.32 and 74.27 ± 1.10 vs. 39.03 ± 1.25 pg mg-1, respectively). Moreover, PVA-T1 (74.8%, 17.60 ± 2.98 pg mg-1) and PVA-T2 (63.2%, 15.30 ± 4.81 pg mg-1) exhibit significantly enhanced TNF-α adsorption from the plasma of rats with sepsis to blank PVA and commercial XAD-7 resin. In conclusion, our results show that T1 designed by computer-aided molecular design (CAMD) exhibits a stronger affinity to TNF-α and it can significantly enhance PVA microsphere adsorption efficiency of TNF-α in plasma.

A novel nano-CaCO

Influence maximization in social networks is the problem of finding a set of seed nodes in the network that maximizes the spread of influence under certain information prorogation model, which has become an important topic in social network analysis. In this paper, we show that conventional influence maximization algorithms cause uneven spread of influence among different attribute groups in social networks, which could lead to severer bias in public opinion dissemination and viral marketing. We formulate the balanced influence maximization problem to address the trade-off between influence maximization and attribute balance, and propose a sampling based solution to solve the problem efficiently. To avoid full network exploration, we first propose an attribute-based (AB) sampling method to sample attributed social networks with respect to preserving network structural properties and attribute proportion among user groups. Then we propose an attributed-based reverse influence sampling (AB-RIS) algorithm to select seed nodes from the sampled graph. The proposed AB-RIS algorithm runs efficiently with guaranteed accuracy, and achieves the trade-off between influence maximization and attribute balance. Extensive experiments based on four real-world social network datasets show that AB-RIS significantly outperforms the state-of-the-art approaches in balanced influence maximization.

Katz centrality is a fundamental concept to measure the influence of a vertex in a social network. However, existing approaches to calculating Katz centrality in a large-scale network is unpractical and computationally expensive. In this paper, we propose a novel method to estimate Katz centrality based on graph sampling techniques. Specifically, we develop an unbiased estimator for Katz centrality using a multi-round sampling approach. We further propose SAKE, a Sampling based Algorithm for fast Katz centrality Estimation. We prove that the estimator calculated by SAKE is probabilistically guaranteed to be within an additive error from the exact value. The computational complexity of SAKE is much lower than the state-of-the-arts. Extensive evaluation experiments based on four real world networks show that the proposed algorithm achieves low mean relative error with low sampling rate, and it works well in identifying high influence vertices in social networks.

Nowadays smartphone users have installed dozens or even hundreds of APPs on their phones. Predicting APP usage not only helps the mobile phone system to speed up APP launching but also reduces the time for users to search them. In this paper, we focus on a novel session-based APP usage prediction problem that tends to predict a sequence of APPs to be used in a period. We propose a session-based embedding framework called SEM to solve the problem. To deal with the heterogeneity of APP sessions, we present a session embedding algorithm to form uniform feature representation, which alleviates the problem of user sparsity and obtains the vector representation of sessions. Based on session embedding, we train a two-layer GRU-based recursive neural network model for APP usage session prediction. Extensive experiments based on real datasets show that the proposed framework outperforms conventional APP recommendation approaches.

A high correlation between mood instability (MI), the rapid and constant fluctuation in mood, and mental health has been demonstrated. However, conventional approaches to measure MI are limited owing to the high manpower and time cost required. In this paper, we propose a smartphone-based MI detection that can automatically and passively detect MI with minimal human involvement. The proposed method trains a multi-view learning classification model using features extracted from the smartphone sensing data of volunteers and their self-reported moods. The trained classifier is then used to detect the MI of unseen users efficiently, thereby reducing the human involvement and time cost significantly. Based on extensive experiments conducted with the dataset collected from 68 volunteers, we demonstrate that the proposed multi-view learning model outperforms the baseline classifiers.

Endothelialization of the polymer substrate is limited by unspecific cell adhesion. Herein, a biodegradable microfibrous scaffold with a reversibly thermoswitchable property was developed to dynamically regulate endothelial progenitor cell adhesion by exposing or concealing cRGD motif to the surface with a thermosensitive moiety (cRGD-PNIPAM) and antifouling moiety linear polyglycerol (LPG). Owing to a reversible αvβ3 integrin–cRGD interaction and ligand presentation, the accelerated endothelial cell adhesion and spreading were achieved. Under the static and dynamic conditions, prestained endothelial cells were quickly attached to the surface at 25 °C via the integrin–cRGD interaction, and the cRGD was the headgroup of the stretched PNIPAM below the LCST of PNIPAM. With the increase of the temperature to 37 °C, a quick detachment of cells from the surface was observed due to the cRGD moiety being shielded by the antifouling LPG layer. As compared to current strategies for endothelialization, for example, ...

Social psychology and neuroscience had confirmed that emotion state exerts a significant effect on human communication, perception, social behavior and decision making. With the wide availability of smartphones equipped with microphone, accelerometer, GPS, and other source of sensors, it is worthwhile to explore the possibility of automatic emotion detection via smartphone sensing. Particularly, we focus on a novel research problem that tries to detect the compound emotion (a set of multiple dimensional basic emotions) of smartphone users. We observe that users' self-reported emotional states have high correlation with their smartphone usage patterns and sensing data. Based on the observations, we exploit a feature extraction and selection algorithm to find the most significant features. We further adopt a factor graph model to tackle the correlations between features and emotion labels, and propose a machine learning algorithm for compound emotion detection based on the smartphone sensing data. The proposed mechanism is implemented as an APP called MoodExplorer in Android platform. Extensive experiments conducted on the smartphone data collected from 30 university students show that MoodExplorer can recognize users' compound emotions with 76.0% exact match on average.