Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs

We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks. The primary goal of the package is to make community detection, node and whole graph embedding available to a wide audience of machine learning researchers and practitioners. We designed Karate Club with an emphasis on a consistent application interface, scalability, ease of use, sensible out of the box model behaviour, standardized dataset ingestion, and output generation. This paper discusses the design principles behind this framework with practical examples. We show Karate Club's efficiency with respect to learning performance on a wide range of real world clustering problems, classification tasks and support evidence with regards to its competitive speed.Comment: The frameworks is available at: https://github.com/benedekrozemberczki/karateclu

Combination of Fluoride and SO2 Induce DNA Damage and Morphological Alterations in Male Rat Kidney

Background/Aims: We investigated the combined toxic effect of sodium fluoride (NaF) and sulfur dioxide (SO2) on kidney morphological changes and DNA damage in male Wistar rats. Methods: In this study we selected totally 96 male Wistar rats (12-week-old) then randomly group-housed them into four cages, treated with deionized water, NaF, SO2 and co-treatment of NaF and SO2 respectively. Morphological changes of kidney were detected by hematoxylin and eosin (H&E) staining at 2, 4, 6 and 8 weeks. Correspondingly, tailing ratio and comet length were measured by BAB Bs Comet Assay System, including DNA damage special unit were calculated to evaluate the grades of kidney DNA damage at the same time. Results: Treated groups showed a body weight decrease when compared to control group. However, no significant difference in the relative weight of kidney was found in all four groups. It is noteworthy that at 2, 4, 6 and 8 weeks after exposure, the morphological alteration of renal tubules were observed in all treated groups, especially in group-IV. Also, at 4 and 6 weeks, notable DNA damage was found in all treated groups, as assessed by significantly increasing trend of comet length tailing ratio. Conclusion: The study manifests that presence of NaF and SO2 will not only induce renal tissue lesions but also impact DNA integrity. In addition, this combined exposure exhibits a synergistic effect, characterizing a dose-dependence and time correlation. These findings may provide novel insights regarding perturbations of DNA damage and its functions as a potential new mechanism, by which cautious interpretation of NaF and SO2 co-exposure evolved in both animals and human beings is necessary

A novel marker integrating multiple genetic alterations better predicts platinum sensitivity in ovarian cancer than HRD score

Introduction: Platinum-based chemotherapy is the first-line treatment strategy for ovarian cancer patients. The dismal prognosis of ovarian cancer was shown to be stringently associated with the heterogeneity of tumor cells in response to this therapy, therefore understanding platinum sensitivity in ovarian cancer would be helpful for improving patients’ quality of life and clinical outcomes. HRDetect, utilized to characterize patients’ homologous recombination repair deficiency, was used to predict patients’ response to platinum-based chemotherapy. However, whether each of the single features contributing to HRD score is associated with platinum sensitivity remains elusive.Methods: We analyzed the whole-exome sequencing data of 196 patients who received platinum-based chemotherapy from the TCGA database. Genetic features were determined individually to see if they could indicate patients’ response to platinum-based chemotherapy and prognosis, then integrated into a Pt-score employing LASSO regression model to assess its predictive performance.Results and discussion: Multiple genetic features, including bi-allelic inactivation of BRCA1/2 genes and genes involved in HR pathway, multiple somatic mutations in genes involved in DNA damage repair (DDR), and previously reported HRD-related features, were found to be stringently associated with platinum sensitivity and improved prognosis. Higher contributions of mutational signature SBS39 or ID6 predicted improved overall survival. Besides, arm-level loss of heterozygosity (LOH) of either chr4p or chr5q predicted significantly better disease-free survival. Notably, some of these features were found independent of HRD. And SBS3, an HRD-related feature, was found irrelevant to platinum sensitivity. Integrated all candidate markers using the LASSO model to yield a Pt-score, which showed better predictive ability compared to HRDetect in determining platinum sensitivity and predicting patients’ prognosis, and this performance was validated in an independent cohort. The outcomes of our study will be instrumental in devising effective strategies for treating ovarian cancer with platinum-based chemotherapy

Design Strategy of Information Construction Based on User Participation

In the design of the Internet products, the information construction is one of the important factors to determine that whether a product is friendly. It is a challenge for every information architect to discover new innovations on the basis of established user habits. In practice, we often found that the development of many new functions did not meet the needs of users and many functions would be improved by users themselves, the fact of which shows the strong desire of users to participate in the development of products. Therefore, this article proposes an information architecture method based on the “users’ partially involved”, that is, when the main framework of a product is determined and the detailed architecture of part of the functions is proposed by users themselves, builders will help users to dig and organize their requirements and eventually integrate them into the design of architecture. In order to verify the effectiveness of this method, the article takes the virtual products for social service based on "surrounding geographic information" as an example. A comparison experiment was conducted on the effectiveness of the design of user-participant information architecture, and the experimental results were evaluated

Generating reliable friends via adversarial training to improve social recommendation

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often untenable as the online social networks are quite sparse and a majority of users only have a small number of friends. Besides, explicit friends may not share similar interests because of the randomness in the process of building social networks. Therefore, discovering a number of reliable friends for each user plays an important role in advancing social recommendation. Unlike other studies which focus on extracting valuable explicit social links, our work pays attention to identifying reliable friends in both the observed and unobserved social networks. Concretely, in this paper, we propose an end-to-end social recommendation framework based on Generative Adversarial Nets (GAN). The framework is composed of two blocks: a generator that is used to produce friends that can possibly enhance the social recommendation model, and a discriminator that is responsible for assessing these generated friends and ranking the items according to both the current user and her friends' preferences. With the competition between the generator and the discriminator, our framework can dynamically and adaptively generate reliable friends who can perfectly predict the current user' preference at a specific time. As a result, the sparsity and unreliability problems of explicit social relations can be mitigated and the social recommendation performance is significantly improved. Experimental studies on real-world datasets demonstrate the superiority of our framework and verify the positive effects of the generated reliable friends

Damage Monitoring of Engineered Cementitious Composite Beams Reinforced with Hybrid Bars Using Piezoceramic-Based Smart Aggregates

In order to explore the crack development mechanism and damage self-repairing capacity of ECC beams reinforced with hybrid bars, the smart aggregate-based active sensing approach were herein adopted to conduct damage monitoring of ECC beams under cyclic loading. A total of six beams, including five engineered cementitious composite (ECC) beams reinforced with different bars and one reinforcement concrete counterpart, were fabricated and tested under cyclic loading. The ultimate failure modes and hysteresis curves were obtained and discussed herein, demonstrating the multiple crack behavior and excellent ductility of ECC material. The damage of the tested beams was monitored by smart aggregate-based (SA) active sensing method, in which two SAs pasted on both beam ends were used as actuator and sensor, respectively. The time domain analysis, wavelet packet-based energy analysis and wavelet packet-based damage index analysis were performed to quantitatively evaluate the crack development. To evaluate the self-repairing capacity of the beams, a self-repairing index defined by the difference of damage index at loading and unloading peak points was proposed. The results in time domain and wavelet packed analysis were in close agreement with the observed crack development, revealing the feasibility of smart aggregate-based active sensing approach in damage detection for ECC beams. Especially, the proposed damage self-repairing index can describe the same structural re-centering phenomena with the test results, showing the proposed index can be used to evaluate the damage self-repairing capacity

Adaptive implicit friends identification over heterogeneous network for social recommendation

The explicitly observed social relations from online social platforms have been widely incorporated into recommender systems to mitigate the data sparsity issue. However, the direct usage of explicit social relations may lead to an inferior performance due to the unreliability (e.g., noises) of observed links. To this end, the discovery of reliable relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to adaptively identify implicit friends toward discovering more credible user relations. Particularly, implicit friends are those who share similar tastes but could be distant from each other on the network topology of social relations. Methodologically, to find the implicit friends for each user, we first model the whole system as a heterogeneous information network, and then capture the similarity of users through the meta-path based embedding representation learning. Finally, based on the intuition that social relations have varying degrees of impact on different users, our approach adaptively incorporates different numbers of similar users as implicit friends for each user to alleviate the adverse impact of unreliable social relations for a more effective recommendation. Experimental analysis on three real-world datasets demonstrates the superiority of our method and explain why implicit friends are helpful in improving social recommendation