DeepInf: Social Influence Prediction with Deep Learning

Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding

Content of Trace Metals in Medicinal Plants and their Extracts

The heavy metals (Fe, Cu, Zn and Mn) contents of selected plant species, grown in Southeast region of Serbia, that are traditionally used in alternative medicine were determined. Among the considered metals, iron content was the highest one and varied from 137.53 up to 423.32 mg/kg, while the contents of Cu, Zn and Mn were remarkably lower, and ranged from 8.91 to 62.20 mg/kg. In addition, an analysis of plants extracts showed a significant transfer of heavy metals during extraction procedure; therefore, the corresponding extraction coefficients reached values up to 88.8%. Those were especially high in the ethanol based extracts. Moreover, it is was established that such coefficients mostly depend on the solvent nature and also on the treated plant species. The obtained results impose that medicinal plants from Southeast region of Serbia due to rather low content of heavy metals are appropriate for preparation of teas and medicinal extracts

Antioksidativna aktivnost etanolnih ekstrakata Solanum Retroflexum

In this paper the antioxidant activity of ethanolic extracts from Solanum retroflexum Dun fruits were investigated. The extracts were obtained by classical technique and by Soxhlet extraction. In order to investigate the possibilities of future utilization of Solanum retroflexum fruits, the antioxidant activity, by FRAP and DPPH method was determined. Results showed the extract obtained by 75% ethanol and classic extraction during 45 minutes at 60°C, had the highest antioxidant activity, both for DPPH and FRAP method (EC50 was 60,67 μg/mL and 1,55 μmol Fe2+/mg, respectively). There was good correlation of results for antioxidant activity obtained by both methods and for all analyzed extracts.Rad se bavi ispitivanjem antioksidativne aktivnosti etanolnih ekstrakata ploda biljke Solanum retroflexum Dun. Ekstrakti su dobijeni tehnikama klasične i Soxhlet extrakcije. Potencijalna antioksidativna aktivnost je ispitana FRAP i DPPH metodama. Rezultati su pokazali da ekstrakt dobijen klasičnom ekstrakcijom sa 75% rastvorom etanola na 60°C i za vreme 45 minuta, ima najveću antioksidativnu aktivnost (EC50 = 60,67 μg/ml, 1,55 μmol Fe2+/mg), kao i da postoji dobra korelacija rezultata dobijenih obema metodama u slučaju svih analiziranih ekstrakata

SCE: Scalable Network Embedding from Sparsest Cut

Large-scale network embedding is to learn a latent representation for each node in an unsupervised manner, which captures inherent properties and structural information of the underlying graph. In this field, many popular approaches are influenced by the skip-gram model from natural language processing. Most of them use a contrastive objective to train an encoder which forces the embeddings of similar pairs to be close and embeddings of negative samples to be far. A key of success to such contrastive learning methods is how to draw positive and negative samples. While negative samples that are generated by straightforward random sampling are often satisfying, methods for drawing positive examples remains a hot topic. In this paper, we propose SCE for unsupervised network embedding only using negative samples for training. Our method is based on a new contrastive objective inspired by the well-known sparsest cut problem. To solve the underlying optimization problem, we introduce a Laplacian smoothing trick, which uses graph convolutional operators as low-pass filters for smoothing node representations. The resulting model consists of a GCN-type structure as the encoder and a simple loss function. Notably, our model does not use positive samples but only negative samples for training, which not only makes the implementation and tuning much easier, but also reduces the training time significantly. Finally, extensive experimental studies on real world data sets are conducted. The results clearly demonstrate the advantages of our new model in both accuracy and scalability compared to strong baselines such as GraphSAGE, G2G and DGI.Comment: KDD 202

Multivariate Relations Aggregation Learning in Social Networks

Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, and academic networks. Due to the principle of ternary closures and the trend of group formation, the multivariate relationships in social networks are complex and rich. Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important. Existing graph learning methods are based on the neighborhood information diffusion mechanism, which often leads to partial omission or even lack of multivariate relationship information, and ultimately affects the accuracy and execution efficiency of the task. To address these challenges, this paper proposes the multivariate relationship aggregation learning (MORE) method, which can effectively capture the multivariate relationship information in the network environment. By aggregating node attribute features and structural features, MORE achieves higher accuracy and faster convergence speed. We conducted experiments on one citation network and five social networks. The experimental results show that the MORE model has higher accuracy than the GCN (Graph Convolutional Network) model in node classification tasks, and can significantly reduce time cost.Comment: 11 pages, 6 figure

Collecting duct carcinoma of the kidney: an immunohistochemical study of 11 cases

BACKGROUND: Collecting duct carcinoma (CDC) is a rare but very aggressive variant of kidney carcinoma that arises from the epithelium of Bellini's ducts, in the distal portion of the nephron. In order to gain an insight into the biology of this tumor we evaluated the expression of five genes involved in the development of renal cancer (FEZ1/LZTS1, FHIT, TP53, P27(kip1), and BCL2). METHODS: We studied eleven patients who underwent radical nephrectomy for primary CDC. All patients had an adequate clinical follow-up and none of them received any systemic therapy before surgery. The expression of the five markers for tumor initiation and/or progression were assessed by immunohistochemistry and correlated to the clinicopathological parameters, and survival by univariate analysis. RESULTS: Results showed that Fez1 protein expression was undetectable or substantially reduced in 7 of the 11 (64%) cases. Fhit protein was absent in three cases (27%). The overexpression of p53 protein was predominantly nuclear and detected in 4 of 11 cases (36%). Immunostaining for p27 was absent in 5 of 11 cases (45.5%). Five of the six remaining cases (90%) showed exclusively cytoplasmic protein expression, where, in the last case, p27 protein was detected in both nucleus and cytoplasm. Bcl2 expression with 100% of the tumor cells positive was observed in 4 of 11 (36%) cases. Statistical analysis showed a statistical trend (P = 0.06) between loss and reduction of Fez1 and presence of lymph node metastases. CONCLUSIONS: These findings suggest that Fez1 may represent not only a molecular diagnostic marker but also a prognostic marker in CDC

Monitoring of species' genetic diversity in Europe varies greatly and overlooks potential climate change impacts.

Genetic monitoring of populations currently attracts interest in the context of the Convention on Biological Diversity but needs long-term planning and investments. However, genetic diversity has been largely neglected in biodiversity monitoring, and when addressed, it is treated separately, detached from other conservation issues, such as habitat alteration due to climate change. We report an accounting of efforts to monitor population genetic diversity in Europe (genetic monitoring effort, GME), the evaluation of which can help guide future capacity building and collaboration towards areas most in need of expanded monitoring. Overlaying GME with areas where the ranges of selected species of conservation interest approach current and future climate niche limits helps identify whether GME coincides with anticipated climate change effects on biodiversity. Our analysis suggests that country area, financial resources and conservation policy influence GME, high values of which only partially match species' joint patterns of limits to suitable climatic conditions. Populations at trailing climatic niche margins probably hold genetic diversity that is important for adaptation to changing climate. Our results illuminate the need in Europe for expanded investment in genetic monitoring across climate gradients occupied by focal species, a need arguably greatest in southeastern European countries. This need could be met in part by expanding the European Union's Birds and Habitats Directives to fully address the conservation and monitoring of genetic diversity

Interlaboratory Comparison Reveals State of the Art in Microplastic Detection and Quantification Methods

\ua9 2025 The Authors. Published by American Chemical Society. In this study, we investigate the current accuracy of widely used microplastic (MP) detection methods through an interlaboratory comparison (ILC) involving ISO-approved techniques. The ILC was organized under the prestandardization platform of VAMAS (Versailles Project on Advanced Materials and Standards) and gathered a large number (84) of analytical laboratories across the globe. The aim of this ILC was (i) to test and to compare two thermo-analytical and three spectroscopical methods with respect to their suitability to identify and quantify microplastics in a water-soluble matrix and (ii) to test the suitability of the microplastic test materials to be used in ILCs. Two reference materials (RMs), polyethylene terephthalate (PET) and polyethylene (PE) as powders with rough size ranges between 10 and 200 μm, were used to press tablets for the ILC. The following parameters had to be assessed: polymer identity, mass fraction, particle number concentration, and particle size distribution. The reproducibility, SR, in thermo-analytical experiments ranged from 62%-117% (for PE) and 45.9%-62% (for PET). In spectroscopical experiments, the SR varied between 121% and 129% (for PE) and 64% and 70% (for PET). Tablet dissolution turned out to be a very challenging step and should be optimized. Based on the knowledge gained, development of guidance for improved tablet filtration is in progress. Further, in this study, we discuss the main sources of uncertainties that need to be considered and minimized for preparation of standardized protocols for future measurements with higher accuracy

Modulation of hepatic inflammation and energy-sensing pathways in the rat liver by high-fructose diet and chronic stress

Purpose High-fructose consumption and chronic stress are both associated with metabolic inflammation and insulin resistance. Recently, disturbed activity of energy sensor AMP-activated protein kinase (AMPK) was recognized as mediator between nutrient-induced stress and inflammation. Thus, we analyzed the effects of high-fructose diet, alone or in combination with chronic stress, on glucose homeostasis, inflammation and expression of energy sensing proteins in the rat liver. Methods In male Wistar rats exposed to 9-week 20% fructose diet and/or 4-week chronic unpredictable stress we measured plasma and hepatic corticosterone level, indicators of glucose homeostasis and lipid metabolism, hepatic inflammation (pro- and anti-inflammatory cytokine levels, Toll-like receptor 4, NLRP3, activation of NF kappa B, JNK and ERK pathways) and levels of energy-sensing proteins AMPK, SIRT1 and peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1 alpha). Results High-fructose diet led to glucose intolerance, activation of NF kappa B and JNK pathways and increased intrahepatic IL-1 beta, TNF alpha and inhibitory phosphorylation of insulin receptor substrate 1 on Ser(307). It also decreased phospho-AMPK/AMPK ratio and increased SIRT1 expression. Stress alone increased plasma and hepatic corticosterone but did not influence glucose tolerance, nor hepatic inflammatory or energy-sensing proteins. After the combined treatment, hepatic corticosterone was increased, glucose tolerance remained preserved, while hepatic inflammation was partially prevented despite decreased AMPK activity. Conclusion High-fructose diet resulted in glucose intolerance, hepatic inflammation, decreased AMPK activity and reduced insulin sensitivity. Chronic stress alone did not exert such effects, but when applied together with high-fructose diet it could partially prevent fructose-induced inflammation, presumably due to increased hepatic glucocorticoids

Epidemiology of intra-abdominal infection and sepsis in critically ill patients: “AbSeS”, a multinational observational cohort study and ESICM Trials Group Project

Purpose: To describe the epidemiology of intra-abdominal infection in an international cohort of ICU patients according to a new system that classifies cases according to setting of infection acquisition (community-acquired, early onset hospital-acquired, and late-onset hospital-acquired), anatomical disruption (absent or present with localized or diffuse peritonitis), and severity of disease expression (infection, sepsis, and septic shock). Methods: We performed a multicenter (n = 309), observational, epidemiological study including adult ICU patients diagnosed with intra-abdominal infection. Risk factors for mortality were assessed by logistic regression analysis. Results: The cohort included 2621 patients. Setting of infection acquisition was community-acquired in 31.6%, early onset hospital-acquired in 25%, and late-onset hospital-acquired in 43.4% of patients. Overall prevalence of antimicrobial resistance was 26.3% and difficult-to-treat resistant Gram-negative bacteria 4.3%, with great variation according to geographic region. No difference in prevalence of antimicrobial resistance was observed according to setting of infection acquisition. Overall mortality was 29.1%. Independent risk factors for mortality included late-onset hospital-acquired infection, diffuse peritonitis, sepsis, septic shock, older age, malnutrition, liver failure, congestive heart failure, antimicrobial resistance (either methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, extended-spectrum beta-lactamase-producing Gram-negative bacteria, or carbapenem-resistant Gram-negative bacteria) and source control failure evidenced by either the need for surgical revision or persistent inflammation. Conclusion: This multinational, heterogeneous cohort of ICU patients with intra-abdominal infection revealed that setting of infection acquisition, anatomical disruption, and severity of disease expression are disease-specific phenotypic characteristics associated with outcome, irrespective of the type of infection. Antimicrobial resistance is equally common in community-acquired as in hospital-acquired infection