Search

Inhibition of glutaminolysis was implicated in the potential hepatoprotective effects of hemp seed oil against liver fibrosis in mice with MCD diet-induced NASH.

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

Mobile social applications have been widely used by Internet users. Users can efficiently acquire many kinds of information and share their statuses by various social platforms. However, when a user intends to share information through the user's social applications, the user can set the access permission only before the information is posted. Once the information is posted, it is completely beyond the user's control. Specifically, during the recommendation process (for friend or information) in social applications, a user cannot control who can get the recommendation and access his/her information. If one user is accessed by another malicious user, his/her privacy information can be disclosed, and even be further misused for malicious attacks. In this article, we propose Trust2Privacy, a trust-based access control mechanism to protect the personalized privacy of users after posting their information, which can effectively realize the transformation from trust to privacy. First, to represent the relations accurately among users, we define the direction of trust among users according to the user-follow status. Then we combine the similarity, correlation, and interaction among users to calculate the trust values. Considering the fuzzy relationship between the multi-dimensional features and trust levels, we propose a fuzzy comprehensive evaluation algorithm to compute the fuzzy trust. Moreover, for the sake of the high mobility of mobile social networks, we exploit so-called online-to-offline trust evidence to derive the trust value, including taking the location information (e.g., distance, semantics) into consideration. To meet the personalized privacy requirement, we design a filtering algorithm based on the trust relationships and the privacy policy of the target users or posted information, according to which the access requesters can get the list of accessible users or information. This enables achieving the goal of personalized privacy protection. The theoretical analysis and simulation experiment demonstrate that Trust2Privacy is able to achieve personalized privacy protection without bringing negative impact on the availability and usability of mobile social applications.

The on-target off-tumor toxicity of chimeric antigen receptor-engineered T cells (CAR-T) might lead to fatal side effects in cancer patients, which remains as a major obstacle to the clinical application of CAR-T immunotherapy. The off-tumor on-target normal tissue toxicity of CAR-T cells needs to be evaluated in preclinical studies using rational animal models.We aim to develop a rational animal model for assessing the off-tumor on-target normal tissue toxicity of various CAR-T cell designs quickly.We used a recombinant adenovirus type 5 carrying human HER2/ERBB2 (Ad5-HER2) or CD47 gene (Ad5-CD47) to rapidly generate a mouse model with tunable human antigen expression on normal liver tissue to determine immunotoxicity of traditional CAR-T and hypoxia-response CAR-T cells in vivo.The obvious liver damage and lymphocyte infiltration were not observed in mice with human antigen-high livers 8 days post-infection. Interestingly, the lethal liver damage, systemic cytokine release and CAR-T cells infiltration in liver were only observed in mice that received traditional CAR-T cells, but not in hypoxia-response CAR-T cells.Adenovirus-based expression of target antigen in normal mouse tissue may be a useful method for assessing on-target CAR-T cell toxicity in normal tissues, especially various CAR-T cell designs that have the potency of conditional regulation in tumor microenvironment (TME).

The convergence of AI and IoT enables data to be quickly explored and turned into vital decisions, and however, there are still some challenging issues to be further addressed. For example, lacking of enough data in AI-based decision making (so called Sparse Decision Making, SDM) will decrease the efficiency\ud dramatically, or even disable the intelligent IoT networks. Taking the intelligent IoT networks as the network infrastructure, the recommendation systems have been facing such SDM problems. A naive solution is to introduce so-called trust information. However, trust information also maybe face the difficulty of sparse trust evidence (a.k.a sparse trust problem). In our work, an accurate sparse decision making model with two-way trust recommendation in the AI enabled IoT systems is proposed by us, named TT-SVD. Our model incorporates both trust information and rating information more completely, which can efficiently alleviate the above mentioned sparse trust problem and therefore be able to solve the cold start and data sparsity problems. Specifically, we first consider the two-fold trust influences from both trustees and trustors, which can be represented by a factor called trust propensity. To this end, we propose a dual model, including the trustor model (TrustorSVD) and a trustee model (TrusteeSVD) based on an existing rating-only recommendation model called SVD++, which are integrated by the weighted average and yield the final model, TT-SVD. The experimental results show that our model outperforms the state of the art including SVD and TrustSVD in both the ”all users” and ”cold start users” cases, and the accuracy improvement can reach a maximum of 29%. Complexity analysis shows that our model is equally suitable for the case of large sparse datasets. In a word, our model can effectively solve the sparse decision problem by introducing the two-way trust recommendation, and hence improve the efficiency of the intelligent recommendation systems.

Distributed multi-controller deployment is a key issue in the innovative Software Defined Network (SDN) to scale network while improving performance and reliability. It is interesting to know how many controllers should be deployed and where to locate under a wide range of performance sensitive and completive constraints, including latency, fair load distribution as well as cost. We solve this problem by minimizing propagation latency and controller cost. The required number of controllers is determined based on requests and controller capacity. Due to the uneven distribution of network load, it is more likely to deploy controllers on nodes with high request density. A clustering algorithm NWDP (Node Weight Deployment Policy) is thus proposed based on node weight to choose location of multi-controller. To achieve effectively, autonomous and dynamic deployment in large-scale networks, we further propose a supervised graph convolution network model with fusion features(FF-GCN). The open network database Internet Topology Zoo is adopted to evaluate the effectiveness of our algorithms. Simulation results show that NWDP efficiently outperforms traditional algorithms in medium-sized topology, and the trained FF-GCN can figure out the deployment in a 702 nodes large-scale topology with an average prediction accuracy of 90%.

We study server consolidation problem in clouds under simultaneous failures of multiple servers, where consolidation means that cloud providers put tenants on shared servers to improve resource utilization and thus reduce operation and maintenance costs. With replicas of each tenant put on multiple servers, our objective is to minimize the total number of opened servers and ensure that a particular failure will not result in overload on any remaining server. In this paper, we propose Rotation algorithm. It packs comparable sizes replicas into the same type of servers and adopts a cyclic shift method to quickly reuse those already-opened servers without the need of new ones for new tenants. Through experimental evaluations, we show that the proposed algorithms can achieve a better performance than existing works and produce near-optimal replications allocation.

Background: According to the WMO's (World Meteorological Organization) Greenhouse Gas Bulletin (2019), the fastest increase atmospheric concentrations of the three main greenhouse gases has been detected in the past 20 years. Researchers have paid more and more attention to the problem of greenhouse gas (GHG) emission. Among the studies, people focus much more on grassland ecosystem than that of forest ecosystem，which has a more important effect on the emission and absorption of greenhouse gases. Estimating the impact of forest ecosystem on greenhouse effect in China is of great significance. Methods: 6 deciduous broad-leaved forest communities with similar elevation, slope orientations and slope gradient in Wuling Mountain National Natural Reserve are employed to conduct the GHG flux study. Five 50cm×50cm square stainless-steel bases are set in the study site by mechanical spot arrangement method to sample the GHG. The environmental temperature and soil moisture content is measured every sampling time. Static chamber-gas chromatograph method is adopted to examine the greenhouse gas fluxes of CO 2 , CH 4 and N 2 O. Results and conclusions: The GHG flux of CO 2 , N 2 O and CH 4 in the Warm Temperate Deciduous Broad-Leaved Forest Ecosystem has great relationship with environmental temperature and soil water content. GHG flux varies with seasons. Among them, the CH 4 flux shows the character of source or sink in different seasons. The other 2 are always the source of GHG flux during the study period. The peak value of CO 2 is in summer and that of N 2 O in March. The flux of CH 4 shows the feature of source in April and sink in other seasons. The peak value of sink comes in late July. The flux of CO 2 has the greatest relationship with the soil temperature 5cm below the ground surface and the flux of N 2 O has the greatest relationship with the soil water content. The relationship between CH 4 flux and the environmental temperature or the soil water content is complex.

With the growing popularity of Android devices, Android malware is seriously threatening the safety of users. Although such threats can be detected by deep learning as a service (DLaaS), deep neural networks as the weakest part of DLaaS are often deceived by the adversarial samples elaborated by attackers. In this paper, we propose a new semi-black-box attack framework called one-feature-each-iteration (OFEI) to craft Android adversarial samples. This framework modifies as few features as possible and requires less classifier information to fool the classifier. We conduct a controlled experiment to evaluate our OFEI framework by comparing it with the benchmark methods JSMF, GenAttack and pointwise attack. The experimental results show that our OFEI has a higher misclassification rate of 98.25%. Furthermore, OFEI can extend the traditional white-box attack methods in the image field, such as fast gradient sign method (FGSM) and DeepFool, to craft adversarial samples for Android. Finally, to enhance the security of DLaaS, we use two uncertainties of the Bayesian neural network to construct the combined uncertainty, which is used to detect adversarial samples and achieves a high detection rate of 99.28%.

With the extensive application of artificial intelligence technology in 5G and Beyond Fifth Generation (B5G) networks, it has become a common trend for artificial intelligence to integrate into modern communication networks. Deep learning is a subset of machine learning and has recently led to significant improvements in many fields. In particular, many 5G-based services use deep learning technology to provide better services. Although deep learning is powerful, it is still vulnerable when faced with 5G-based deep learning services. Because of the nonlinearity of deep learning algorithms, slight perturbation input by the attacker will result in big changes in the output. Although many researchers have proposed methods against adversarial attacks, these methods are not always effective against powerful attacks such as CW. In this paper, we propose a new two-stream network which includes RGB stream and spatial rich model (SRM) noise stream to discover the difference between adversarial examples and clean examples. The RGB stream uses raw data to capture subtle differences in adversarial samples. The SRM noise stream uses the SRM filters to get noise features. We regard the noise features as additional evidence for adversarial detection. Then, we adopt bilinear pooling to fuse the RGB features and the SRM features. Finally, the final features are input into the decision network to decide whether the image is adversarial or not. Experimental results show that our proposed method can accurately detect adversarial examples. Even with powerful attacks, we can still achieve a detection rate of 91.3%. Moreover, our method has good transferability to generalize to other adversaries.

A fiber-type terahertz time-domain spectrometer was developed by combining fiber femtosecond laser with fiber-coupled terahertz photoconductive antenna. And variable angle terahertz reflectance of high-resistance silicon wafer and composite absorbent material was measured using this spectrometer. The measurement results are in good agreement with the theoretical calculation results.

BackgroundHypoxia is a striking feature of most solid tumors and could be used to discriminate tumors from normoxic tissues. Therefore, the design of hypoxia-conditioned Chimeric Antigen Receptor (CAR) T cells is a promising strategy to reduce on-target off-tumor toxicity in adoptive cell therapy. However, existing hypoxia-conditioned CAR-T designs have been only partially successful in enhancing safety profile but accompanied with reduced cytotoxic efficacy. Our goal is to further improve safety profile with retained excellent antitumor efficacy.MethodsIn this study, we designed and constructed a hypoxia-inducible transcription amplification system (HiTA-system) to control the expression of CAR in T (HiTA-CAR-T) cells. CAR expression was determined by Flow cytometry, and the activation and cytotoxicity of HiTA-CAR-T cells in vitro were evaluated in response to antigenic stimulations under hypoxic or normoxic conditions. The safety of HiTA-CAR-T cells was profiled in a mouse model for its on-target toxicity to normal liver and other tissues, and antitumor efficacy in vivo was monitored in murine xenograft models.ResultsOur results showed that HiTA-CAR-T cells are highly restricted to hypoxia for their CAR expression, activation and cytotoxicity to tumor cells in vitro. In a mouse model in vivo, HiTA-CAR-T cells targeting Her2 antigen showed undetectable CAR expression in all different normoxic tissues including human Her2-expresing liver, accordingly, no liver and systemic toxicity were observed; In contrast, regular CAR-T cells targeting Her2 displayed significant toxicity on human Her2-expression liver. Importantly, HiTA-CAR-T cells were able to achieve significant tumor suppression in murine xenograft models.ConclusionOur HiTA system showed a remarkable improvement in hypoxia-restricted transgene expression in comparison with currently available systems. HiTA-CAR-T cells presented significant antitumor activities in absence of any significant liver or systemic toxicity in vivo. This approach could be also applied to design CAR-T cell targeting other tumor antigens.

In this work, a series of shaped CoB/Ni-foam catalysts were directly synthesized by using a convenient and simple electroless plating method. Despite the low loading amount of CoB, the catalysts showed high catalytic performance in the hydrolysis of NaBH4 solution, and the maximum hydrogen generation rate reached 1930 mL min−1 (g CoB)−1 in 1 wt % NaBH4 + 5 wt % NaOH solution at 293 K. The catalysts demonstrated distinct stability, and the hydrogen generation rate was almost unchanged after 6 cycles. Furthermore, the catalysts could be easily recovered from the reaction system by a magnet. These characteristics make CoB/Ni-foam a high performance and cost effective catalyst for practical applications of hydrogen generation.

‘Hanhong’ (Pyrus ussuriensis Maxim × P. bretschneideri Rehd.) is a new high-quality Asian pear with excellent firmness and crispness and a long shelf life. Fruit of ‘Hanhong’ are very attractive and the tree is very winter-hardy. The fruit and leaves are resistant to pear scab (Venturia pirina Aderh) and black spot (Alternaria kikuchiana Tannka) disease.