Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation

The heterogeneous information network (HIN), which contains rich semantics depicted by meta-paths, has emerged as a potent tool for mitigating data sparsity in recommender systems. Existing HIN-based recommender systems operate under the assumption of centralized storage and model training. However, real-world data is often distributed due to privacy concerns, leading to the semantic broken issue within HINs and consequent failures in centralized HIN-based recommendations. In this paper, we suggest the HIN is partitioned into private HINs stored on the client side and shared HINs on the server. Following this setting, we propose a federated heterogeneous graph neural network (FedHGNN) based framework, which facilitates collaborative training of a recommendation model using distributed HINs while protecting user privacy. Specifically, we first formalize the privacy definition for HIN-based federated recommendation (FedRec) in the light of differential privacy, with the goal of protecting user-item interactions within private HIN as well as users' high-order patterns from shared HINs. To recover the broken meta-path based semantics and ensure proposed privacy measures, we elaborately design a semantic-preserving user interactions publishing method, which locally perturbs user's high-order patterns and related user-item interactions for publishing. Subsequently, we introduce an HGNN model for recommendation, which conducts node- and semantic-level aggregations to capture recovered semantics. Extensive experiments on four datasets demonstrate that our model outperforms existing methods by a substantial margin (up to 34% in HR@10 and 42% in NDCG@10) under a reasonable privacy budget.Comment: Accepted by WWW 202

Graph Mining for Cybersecurity: A Survey

The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance. It is imperative to summarize existing graph-based cybersecurity solutions to provide a guide for future studies. Therefore, as a key contribution of this paper, we provide a comprehensive review of graph mining for cybersecurity, including an overview of cybersecurity tasks, the typical graph mining techniques, and the general process of applying them to cybersecurity, as well as various solutions for different cybersecurity tasks. For each task, we probe into relevant methods and highlight the graph types, graph approaches, and task levels in their modeling. Furthermore, we collect open datasets and toolkits for graph-based cybersecurity. Finally, we outlook the potential directions of this field for future research

An experimental observation of geometric phases for mixed states using NMR interferometry

Examples of geometric phases abound in many areas of physics. They offer both fundamental insights into many physical phenomena and lead to interesting practical implementations. One of them, as indicated recently, might be an inherently fault-tolerant quantum computation. This, however, requires to deal with geometric phases in the presence of noise and interactions between different physical subsystems. Despite the wealth of literature on the subject of geometric phases very little is known about this very important case. Here we report the first experimental study of geometric phases for mixed quantum states. We show how different they are from the well understood, noiseless, pure-state case.Comment: 4 pages, 3 figure

Inverse design of artificial skins

Mimicking the perceptual functions of human cutaneous mechanoreceptors, artificial skins or flexible pressure sensors can transduce tactile stimuli to quantitative electrical signals. Conventional methods to design such devices follow a forward structure-to-property routine based on trial-and-error experiments/simulations, which take months or longer to determine one solution valid for one specific material. Target-oriented inverse design that shows far higher output efficiency has proven effective in other fields, but is still absent for artificial skins because of the difficulties in acquiring big data. Here, we report a property-to-structure inverse design of artificial skins based on small dataset machine learning, exhibiting a comprehensive efficiency at least four orders of magnitude higher than the conventional routine. The inverse routine can predict hundreds of solutions that overcome the intrinsic signal saturation problem for linear response in hours, and the solutions are valid to a variety of materials. Our results demonstrate that the inverse design allowed by small dataset is an efficient and powerful tool to target multifarious applications of artificial skins, which can potentially advance the fields of intelligent robots, advanced healthcare, and human-machine interfaces

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn

Impact of Clinical Characteristics of Individual Metabolic Syndrome on the Severity of Insulin Resistance in Chinese Adults

The impact the metabolic syndrome (MetS) components on the severity of insulin resistance (IR) has not been reported. We enrolled 564 subjects with MetS and they were divided into quartiles according to the level of each component; and an insulin suppression test was performed to measure IR. In males, steady state plasma glucose (SSPG) levels in the highest quartiles, corresponding to body mass index (BMI) and fasting plasma glucose (FPG), were higher than the other three quartiles and the highest quartiles, corresponding to the diastolic blood pressure and triglycerides, were higher than in the lowest two quartiles. In females, SSPG levels in the highest quartiles, corresponding to the BMI and triglycerides, were higher than in all other quartiles. No significant differences existed between genders, other than the mean SSPG levels in males were greater in the highest quartile corresponding to BMI than that in the highest quartile corresponding to HDL-cholesterol levels. The factor analysis identified two underlying factors (IR and blood pressure factors) among the MetS variables. The clustering of the SSPG, BMI, triglyceride and HDL-cholesterol was noted. Our data suggest that adiposity, higher FPG and triglyceride levels have stronger correlation with IR and subjects with the highest BMI have the highest IR

Molecular Dynamics Simulations Suggest Ligand’s Binding to Nicotinamidase/Pyrazinamidase

The research on the binding process of ligand to pyrazinamidase (PncA) is crucial for elucidating the inherent relationship between resistance of Mycobacterium tuberculosis and PncA’s activity. In the present study, molecular dynamics (MD) simulation methods were performed to investigate the unbinding process of nicotinamide (NAM) from two PncA enzymes, which is the reverse of the corresponding binding process. The calculated potential of mean force (PMF) based on the steered molecular dynamics (SMD) simulations sheds light on an optimal binding/unbinding pathway of the ligand. The comparative analyses between two PncAs clearly exhibit the consistency of the binding/unbinding pathway in the two enzymes, implying the universality of the pathway in all kinds of PncAs. Several important residues dominating the pathway were also determined by the calculation of interaction energies. The structural change of the proteins induced by NAM’s unbinding or binding shows the great extent interior motion in some homologous region adjacent to the active sites of the two PncAs. The structure comparison substantiates that this region should be very important for the ligand’s binding in all PncAs. Additionally, MD simulations also show that the coordination position of the ligand is displaced by one water molecule in the unliganded enzymes. These results could provide the more penetrating understanding of drug resistance of M. tuberculosis and be helpful for the development of new antituberculosis drugs

The fruits of Xanthium sibiricum Patr: A review on phytochemistry, pharmacological activities, and toxicity

In recent years, drug development and research have gradually shifted from chemical synthesis to biopharmaceutical and natural drugs. Natural medicines, such as traditional Chinese medicine, have been among the first studied because of their long medicinal history, simplicity, and the relatively low cost of research. Among them, Xanthii Fructus (XF) is famous for the treatment of sinusitis. In this article, the achievements of research on XF from 1953 to 2020 are systematically reviewed, focusing on the aspects of chemical constituents, pharmacological effects, clinical applications, toxicity and side effects, and processing methods. To date, there have been significant advances in both the phytochemistry and pharmacology of XF. Some traditional uses have been validated and clarified in modern pharmacological studies. However, its mechanism of action in the treatment of allergic diseases has not been satisfactorily explained. Further in vitro and in vivo studies are required to rationally develop new drugs and to elucidate the therapeutic potential of XF. A comprehensive evaluation of XF and an understanding of network pharmacology are also needed. © 2020 World Journal of Traditional Chinese Medicine | Published by Wolters Kluwer ‑ Medknow