DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks

Influence maximization aims to find a subset of seeds that maximize the influence spread under a given budget. In this paper, we mainly address the data-driven version of this problem, where the diffusion model is not given but needs to be inferred from the history cascades. Several previous works have addressed this topic in a statistical way and provided efficient algorithms with theoretical guarantee. However, in their settings, though the diffusion parameters are inferred, they still need users to preset the diffusion model, which can be an intractable problem in real-world practices. In this paper, we reformulate the problem on the attributed network and leverage the node attributes to estimate the closeness between the connected nodes. Specifically, we propose a machine learning-based framework, named DSCom, to address this problem in a heuristic way. Under this framework, we first infer the users' relationship from the diffusion dataset through attention mechanism and then leverage spectral clustering to overcome the influence overlap problem in the lack of exact diffusion formula. Compared to the previous theoretical works, we carefully designed empirical experiments with parameterized diffusion models based on real-world social networks, which prove the efficiency and effectiveness of our algorithm

Adeno-Associated Virus-Mediated Gene Transfer to Renal Tubule Cells via a Retrograde Ureteral Approach

www.karger.com/nne This is an Open Access article licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License (www.karger.com/OA-license), applicable to the online version of the article only. Distribution for non-commercial purposes only

Self-compression of stimulated Raman backscattering by flying focus

A novel regime of self-compression is proposed for plasma-based backward Raman amplification(BRA) upon flying focus. By using a pumping focus moving with a speed equal to the group velocity of stimulated Raman backscattering(SRBS), only a short part of SRBS which does always synchronize with the flying focus can be amplified. Due to the asymmetrical amplification, the pulse can be directly compressed in the linear stage of BRA. Therefore, instead of a short pulse, the Raman spontaneous or a long pulse can seed the BRA amplifiers. The regime is supported by the 2D particle-in-cell(PIC) simulation without a seed, presenting that the pump pulse is compressed from 26ps to 116fs, with an output amplitude comparable with the case of a well-synchronized short seed. This method provides a significant way to simplify the Raman amplifiers and overcome the issue of synchronization jitter between the pump and the seed

Using field measurements across land cover types to evaluate albedo-based wind friction velocity and estimate sediment transport

The soil surface wind friction velocity (us*) is an essential parameter for predicting sediment transport on rough surfaces. However, this parameter is difficult and time-consuming to obtain over large areas due to its spatiotemporal heterogeneity. The albedo-based approach calibrates normalized shadow retrieved from any source of albedo data with laboratory measurements of aerodynamic properties. This enables direct and cross-scale us* retrieval but has not been evaluated against field measurements for different cover types. We evaluated the approach's performance using wind friction velocity (u*) measurements from ultrasonic anemometers. We retrieved coincident field pyranometer and satellite albedo at 48 sites that were spread over approximately 1,800 km on the Inner Mongolia Plateau, including grassland, artificial shrubland, open shrubland, and gobi land. For all cover types, u* estimates from ultrasonic anemometers were close to the albedo-based results approach. Our results confirm and extend the findings that the approach works across scales from lab to field measurements and permits large-area assessments using satellite albedo. We compared the seasonal sediment transport across the region calculated from albedo-based us* with results from an exemplar traditional transport model (QT) driven by u* with aerodynamic roughness length varying with land cover type and fixed over time. The traditional model could not account for spatiotemporal variation in roughness elements and considerably over-estimated sediment transport, particularly in partially vegetated and gravel-covered central and western parts of the Inner Mongolia Plateau. The albedo-based sediment transport (QA) estimates will enable dynamic monitoring of the interaction between wind and surface roughness to support Earth System models

Tuning Oxygen Redox Reaction through the Inductive Effect with Proton Insertion in Li-Rich Oxides.

As a parent compound of Li-rich electrodes, Li2MnO3 exhibits high capacity during the initial charge; however, it suffers notoriously low Coulombic efficiency due to oxygen and surface activities. Here, we successfully optimize the oxygen activities toward reversible oxygen redox reactions by intentionally introducing protons into lithium octahedral vacancies in the Li2MnO3 system with its original structural integrity maintained. Combining structural probes, theoretical calculations, and resonant inelastic X-ray scattering results, a moderate coupling between the introduced protons and lattice oxygen at the oxidized state is revealed, which stabilizes the oxygen activities during charging. Such a coupling leads to an unprecedented initial Coulombic efficiency (99.2%) with a greatly improved discharge capacity of 302 mAh g-1 in the protonated Li2MnO3 electrodes. These findings directly demonstrate an effective concept for controlling oxygen activities in Li-rich systems, which is critical for developing high-energy cathodes in batteries

Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network

Epilepsy is one of the most common chronic neurological diseases. High-frequency oscillations (HFOs) have emerged as promising biomarkers for the epileptogenic zone. However, visual marking of HFOs is a time-consuming and laborious process. Several automated techniques have been proposed to detect HFOs, yet these are still far from being suitable for application in a clinical setting. Here, ripples and fast ripples from intracranial electroencephalograms were detected in six patients with intractable epilepsy using a convolutional neural network (CNN) method. This approach proved more accurate than using four other HFO detectors integrated in RIPPLELAB, providing a higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples and 79.36% for fast ripples) for HFO detection. Furthermore, for one patient, the Cohen's kappa coefficients comparing automated detection and visual analysis results were 0.541 for ripples and 0.777 for fast ripples. Hence, our automated detector was capable of reliable estimates of ripples and fast ripples with higher sensitivity and specificity than four other HFO detectors. Our detector may be used to assist clinicians in locating epileptogenic zone in the future

The prognostic value of high expression of FKBP1A in gastric cancer and the regulatory effect of targeted PI3K/AKT on glucose metabolism

Background and purpose: Gastric cancer is one of the common gastrointestinal malignancies. FKBP1A has been reported to be involved in the occurrence and development of various tumors, but the biological role and mechanism of it in gastric cancer remain unclear. This study aimed to investigate the expression level and prognostic value of FKBP1A in gastric cancer tissues, and to analyze the possible pathways and mechanisms of its regulation of gastric cancer progression. Methods: The expression of FKBP1A in gastric cancer was observed by immunohistochemistry, and its correlation with poor prognosis was analyzed by combining bioinformatics and clinicopathological parameters. Kaplan-Meier survival curve was used to analyze the effect of FKBP1A on the 5-year survival rate of patients with gastric cancer after surgery, and COX multivariate regression analysis was used to explore the independent prognostic factors affecting the 5-year survival rate of patients with gastric cancer after surgery. The diagnostic value of FKBP1A expression in patients with gastric cancer was analyzed using receiver operating characteristic (ROC) curve. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to enrich and analyze the biological function of FKBP1A and the possible signal pathways involved. We constructed the MGC803 cell model transfected with lentivirus in vitro, explored the influence of FKBP1A on the glucose metabolism and malignant biological behavior of MGC803 cells, and the possible molecular mechanism involved, and established a nude mouse transplantation tumor model in vitro to verify it. Results: Immunohistochemical results showed that FKBP1A was highly expressed in gastric cancer (P<0.01), and bioinformatics and clinical parameter analysis showed that FKBP1A was associated with poor prognosis. Kaplan-Meier survival analysis and COX regression analysis showed that the expression level of FKBP1A was negatively correlated with 5-year survival. And carcinoembryonic antigen (CEA)≥5 μg/L, carbohydrate antigen (CA) 19-9≥37 kU/L, T stage (T3-T4) and N stage (N2-N3) were independent prognostic factors affecting the 5-year survival rate of gastric cancer patients after surgery (P<0.05). ROC analysis showed that high expression of FKBP1A had good prognostic value (P<0.01). Enrichment of GO and KEGG suggested that FKBP1A was involved in regulating glucose metabolism in gastric cancer cells. In vitro experiments showed that overexpression of FKBP1A promoted glucose metabolism and proliferation, invasion and migration of MGC803 cells, while silencing of FKBP1A did the opposite (P<0.05). In vivo experiments showed that overexpression of FKBP1A in gastric cancer cells promoted the growth of transplanted tumor in nude mice, while silencing FKBP1A inhibited it (P<0.05). Mechanism analysis showed that overexpression of FKBP1A upregulated the expressions of phosphatidylinositol 3-kinase (PI3K) and protein kinase B (AKT) in gastric cancer cells, while silencing FKBP1A downregulated the expressions (P<0.05). Conclusion: FKBP1A is highly expressed in gastric cancer tissues, and is associated with poor prognosis, which may be due to the promotion of glucose metabolism and malignant biological behavior of gastric cancer cells by activating PI3K/AKT

Measles Resurgence Associated with Continued Circulation of Genotype H1 Viruses in China, 2005

Measles morbidity and mortality decreased significantly after measles vaccine was introduced into China in 1965. From 1995 to 2004, average annual measles incidence decreased to 5.6 cases per 100,000 population following the establishment of a national two-dose regimen. Molecular characterization of wild-type measles viruses demonstrated that genotype H1 was endemic and widely distributed throughout the country in China during 1995-2004. A total of 124,865 cases and 55 deaths were reported from the National Notifiable Diseases Reporting System (NNDRS) in 2005, which represented a 69.05% increase compared with 2004. Over 16,000 serum samples obtained from 914 measles outbreaks and the measles IgM positive rate was 81%. 213 wild-type measles viruses were isolated from 18 of 31 provinces in China during 2005, and all of the isolates belonged to genotype H1. The ranges of the nucleotide sequence and predicted amino acid sequence homologies of the 213 genotype H1 strains were 93.4%-100% and 90.0%-100%, respectively. H1-associated cases and outbreaks caused the measles resurgence in China in 2005. H1 genotype has the most inner variation within genotype, it could be divided into 2 clusters, and cluster 1 viruses were predominant in China throughout 2005

Dysbiosis of the Salivary Microbiome Is Associated With Non-smoking Female Lung Cancer and Correlated With Immunocytochemistry Markers

Background: Association between oral bacteria and increased risk of lung cancer have been reported in several previous studies, however, the potential association between salivary microbiome and lung cancer in non-smoking women have not been evaluated. There is also no report on the relationship between immunocytochemistry markers and salivary microbiota.Method: In this study, we assessed the salivary microbiome of 75 non-smoking female lung cancer patients and 172 matched healthy individuals using 16S rRNA gene amplicon sequencing. We also calculated the Spearman's rank correlation coefficient between salivary microbiota and three immunohistochemical markers (TTF-1, Napsin A and CK7).Result: We analyzed the salivary microbiota of 247 subjects and found that non-smoking female lung cancer patients exhibited oral microbial dysbiosis. There was significantly lower microbial diversity and richness in lung cancer patients when compared to the control group (Shannon index, P < 0.01; Ace index, P < 0.0001). Based on the analysis of similarities, the composition of the microbiota in lung cancer patients also differed from that of the control group (r = 0.454, P < 0.001, unweighted UniFrac; r = 0.113, P < 0.01, weighted UniFrac). The bacterial genera Sphingomonas (P < 0.05) and Blastomonas (P < 0.0001) were relatively higher in non-smoking female lung cancer patients, whereas Acinetobacter (P < 0.001) and Streptococcus (P < 0.01) were higher in controls. Based on Spearman's correlation analysis, a significantly positive correlation can be observed between CK7 and Enterobacteriaceae (r = 0.223, P < 0.05). At the same time, Napsin A was positively associated with genera Blastomonas (r = 0.251, P < 0.05). TTF-1 exhibited a significantly positive correlation with Enterobacteriaceae (r = 0.262, P < 0.05). Functional analysis from inferred metagenomes indicated that oral microbiome in non-smoking female lung cancer patients were related to cancer pathways, p53 signaling pathway, apoptosis and tuberculosis.Conclusions: The study identified distinct salivary microbiome profiles in non-smoking female lung cancer patients, revealed potential correlations between salivary microbiome and immunocytochemistry markers used in clinical diagnostics, and provided proof that salivary microbiota can be an informative source for discovering non-invasive lung cancer biomarkers