Sentiment-based topic suggestion for micro-reviews

Location-based social sites, such as Foursquare or Yelp, are gaining increasing popularity. These sites allow users to check in at venues and leave a short commentary in the form of a micro-review. Micro-reviews are rich in content as they offer a distilled and concise account of user experience. In this paper we consider the problem of predicting the topic of a micro-review by a user who visits a new venue. Such a prediction can help users make informed decisions, and also help venue owners personalize users' experiences. However, topic modeling for micro-reviews is particularly difficult, due to their short and fragmented nature. We address this issue using pooling strategies, which aggregate micro-reviews at the venue or user level, and we propose novel probabilistic models based on Latent Dirichlet Allocation (LDA) for extracting the topics related to a user-venue pair. Our best topic model integrates influences from both venue inherent properties and user preferences, considering at the same the sentiment orientation of the users. Experimental results on real datasets demonstrate the superiority of this model compared to simpler models and previous work; they also show that venue-inherent properties have higher influence on the topics of micro-reviews. © Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.postprin

A network-specific approach to percolation in networks with bidirectional links

Methods for determining the percolation threshold usually study the behavior of network ensembles and are often restricted to a particular type of probabilistic node/link removal strategy. We propose a network-specific method to determine the connectivity of nodes below the percolation threshold and offer an estimate to the percolation threshold in networks with bidirectional links. Our analysis does not require the assumption that a network belongs to a specific ensemble and can at the same time easily handle arbitrary removal strategies (previously an open problem for undirected networks). In validating our analysis, we find that it predicts the effects of many known complex structures (e.g., degree correlations) and may be used to study both probabilistic and deterministic attacks.Comment: 6 pages, 8 figure

Liver transplantation as last-resort treatment for patients with bile duct injuries following cholecystectomy: A multicenter analysis

Background Liver transplantation (LT) has been used as a last resort in patients with end-stage liver disease due to bile duct injuries (BDI) following cholecystectomy. Our study aimed to identify and evaluate factors that cause or contribute to an extended liver disease that requires LT as ultimate solution, after BDI during cholecystectomy. Methods Data from 8 high-volume LT centers relating to patients who underwent LT after suffering BDI during cholecystectomy were prospectively collected and retrospectively analyzed. Results Thirty-four patients (16 men, 18 women) with a median age of 45 (range 22-69) years were included in this study. Thirty of them (88.2%) underwent LT because of liver failure, most commonly as a result of secondary biliary cirrhosis. The median time interval between BDI and LT was 63 (range 0-336) months. There were 23 cases (67.6%) of postoperative morbidity, 6 cases (17.6%) of post-transplant 30-day mortality, and 10 deaths (29.4%) in total after LT. There was a higher probability that patients with concomitant vascular injury (hazard ratio 10.69, P=0.039) would be referred sooner for LT. Overall survival following LT at 1, 3, 5 and 10 years was 82.4%, 76.5%, 73.5% and 70.6%, respectively. Conclusion LT for selected patients with otherwise unmanageable BDI following cholecystectomy yields acceptable long-term outcomes

Degree and connectivity of the Internet's scale-free topology

In this paper we theoretically and empirically study the degree and connectivity of the Internet's scale-free topology at the autonomous system (AS) level. The basic features of the scale-free network have influence on the normalization constant of the degree distribution p(k). We develop a mathematics model of the Internet's scale-free topology. On this model we theoretically get the formulas of the average degree, the ratios of the kmin-degree (minimum degree) nodes and the kmax-degree (maximum degree) nodes, the fraction of the degrees (or links) in the hands of the richer (top best-connected) nodes. We find the average degree is larger for smaller power-law exponent {\lambda} and larger minimum or maximum degree. The ratio of the kmin-degree nodes is larger for larger {\lambda} and smaller kmin or kmax. The ratio of the kmax-degree ones is larger for smaller {\lambda} and kmax or larger kmin. The richer nodes hold most of the total degrees of the AS-level Internet topology. In addition, we reveal the ratio of the kmin-degree nodes or the rate of the increase of the average degree has power-law decay with the increase of the kmin. The ratio of the kmax-degree nodes has power-law decay with the increase of the kmax, and the fraction of the degrees in the hands of the richer 27% nodes is about 73% (the '73/27 rule'). At last, we empirically calculate, based on empirical data extracted from BGP, the average degree and the ratio and fraction using our method and other methods, and find that our method is rigorous and effective for the AS-level Internet topology.Comment: 22 pages, 8 figure

Evaluating the effect of marine diagenesis on late Miocene pre-evaporitic sedimentary successions of Eastern Mediterranean Sea

Unidad de excelencia María de Maeztu MdM-2015-0552The microstructure and geochemical composition of foraminiferal tests are valuable archives for the reconstruction of paleoclimatic and paleoecological changes. In this context, the late Miocene Globigerinoides obliquus shells from Faneromeni section (Crete Island) were investigated through Scanning Electron Microscopy (SEM) imaging, Energy Dispersive System (EDS) analysis and X-Ray Diffraction (XRD) spectroscopy in order to evaluate their potential as paleoenvironmental archives in the eastern Mediterranean. Investigation of diagenetic features, in late Miocene sediments from the Faneromeni section, shows that carbonate precipitation and cementation occur in various lithologies, particularly in carbonate-rich portions, such as bioclastic or clayey limestones. We identified 3 different diagenetic stages (early, intermediate, advanced), as a function of taphonomy in the study area. The comparison of microstructural and geochemical characteristics reveals a sequence of preservation states with "glassy" to "frosty" to "chalky" shells, indicative of the progressive diagenetic alteration of late Miocene planktic foraminiferal calcite. The early diagenetic stage occurs during the Tortonian, and consists of intermediates between "glassy" and "frosty" individuals. Around the Tortonian/Messinian (T/M) boundary at the second diagenetic stage, planktonic foraminifera have a clear "frosty" appearance, showing a gradual high-Mg calcite (to dolomite) crystal overgrowth development and dissolution of biogenic calcite. During the late Messinian and progressively through the Messinian Salinity Crisis (MSC), planktonic foraminifera present a "chalky" taphonomy. The additional precipitation of authigenic high-Mg inorganic calcite and dolomite crystals in the exterior of the tests characterizes the advanced diagenetic stage. The measured amount of diagenetic Mg-rich (10-14% molar Mg on average) calcite and/or dolomite coatings is compatible with results obtained on modern eastern Mediterranean core-top sediments. The assessment of such a diagenetic alteration contributes to a more precise reconstruction of sea surface temperatures (SSTs) during the Neogene, such that only when the changing proportions of the texture are accounted for, would geochemical measurements and subsequent paleoenvironmental interpretations be more meaningful. However, further investigations should extend this approach to test the robustness of our findings across a range of taphonomies, ages and burial settings

A general co-expression network-based approach to gene expression analysis: comparison and applications

<p>Abstract</p> <p>Background</p> <p>Co-expression network-based approaches have become popular in analyzing microarray data, such as for detecting functional gene modules. However, co-expression networks are often constructed by ad hoc methods, and network-based analyses have not been shown to outperform the conventional cluster analyses, partially due to the lack of an unbiased evaluation metric.</p> <p>Results</p> <p>Here, we develop a general co-expression network-based approach for analyzing both genes and samples in microarray data. Our approach consists of a simple but robust rank-based network construction method, a parameter-free module discovery algorithm and a novel reference network-based metric for module evaluation. We report some interesting topological properties of rank-based co-expression networks that are very different from that of value-based networks in the literature. Using a large set of synthetic and real microarray data, we demonstrate the superior performance of our approach over several popular existing algorithms. Applications of our approach to yeast, Arabidopsis and human cancer microarray data reveal many interesting modules, including a fatal subtype of lymphoma and a gene module regulating yeast telomere integrity, which were missed by the existing methods.</p> <p>Conclusions</p> <p>We demonstrated that our novel approach is very effective in discovering the modular structures in microarray data, both for genes and for samples. As the method is essentially parameter-free, it may be applied to large data sets where the number of clusters is difficult to estimate. The method is also very general and can be applied to other types of data. A MATLAB implementation of our algorithm can be downloaded from <url>http://cs.utsa.edu/~jruan/Software.html</url>.</p

Characterization and Comparison of the Tissue-Related Modules in Human and Mouse

BACKGROUND: Due to the advances of high throughput technology and data-collection approaches, we are now in an unprecedented position to understand the evolution of organisms. Great efforts have characterized many individual genes responsible for the interspecies divergence, yet little is known about the genome-wide divergence at a higher level. Modules, serving as the building blocks and operational units of biological systems, provide more information than individual genes. Hence, the comparative analysis between species at the module level would shed more light on the mechanisms underlying the evolution of organisms than the traditional comparative genomics approaches. RESULTS: We systematically identified the tissue-related modules using the iterative signature algorithm (ISA), and we detected 52 and 65 modules in the human and mouse genomes, respectively. The gene expression patterns indicate that all of these predicted modules have a high possibility of serving as real biological modules. In addition, we defined a novel quantity, "total constraint intensity," a proxy of multiple constraints (of co-regulated genes and tissues where the co-regulation occurs) on the evolution of genes in module context. We demonstrate that the evolutionary rate of a gene is negatively correlated with its total constraint intensity. Furthermore, there are modules coding the same essential biological processes, while their gene contents have diverged extensively between human and mouse. CONCLUSIONS: Our results suggest that unlike the composition of module, which exhibits a great difference between human and mouse, the functional organization of the corresponding modules may evolve in a more conservative manner. Most importantly, our findings imply that similar biological processes can be carried out by different sets of genes from human and mouse, therefore, the functional data of individual genes from mouse may not apply to human in certain occasions

Computing Immutable Regions for Subspace Top-k Queries

National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiativ

Systematic identification of functional modules and cis-regulatory elements in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Several large-scale gene co-expression networks have been constructed successfully for predicting gene functional modules and cis-regulatory elements in Arabidopsis (<it>Arabidopsis thaliana</it>)<it>.</it> However, these networks are usually constructed and analyzed in an <it>ad hoc</it> manner. In this study, we propose a completely parameter-free and systematic method for constructing gene co-expression networks and predicting functional modules as well as cis-regulatory elements.</p> <p>Results</p> <p>Our novel method consists of an automated network construction algorithm, a parameter-free procedure to predict functional modules, and a strategy for finding known cis-regulatory elements that is suitable for consensus scanning without prior knowledge of the allowed extent of degeneracy of the motif. We apply the method to study a large collection of gene expression microarray data in Arabidopsis. We estimate that our co-expression network has ~94% of accuracy, and has topological properties similar to other biological networks, such as being scale-free and having a high clustering coefficient. Remarkably, among the ~300 predicted modules whose sizes are at least 20, 88% have at least one significantly enriched functions, including a few extremely significant ones (ribosome, <it>p</it> < 1E-300, photosynthetic membrane, <it>p</it> < 1.3E-137, proteasome complex, <it>p</it> < 5.9E-126). In addition, we are able to predict cis-regulatory elements for 66.7% of the modules, and the association between the enriched cis-regulatory elements and the enriched functional terms can often be confirmed by the literature. Overall, our results are much more significant than those reported by several previous studies on similar data sets. Finally, we utilize the co-expression network to dissect the promoters of 19 Arabidopsis genes involved in the metabolism and signaling of the important plant hormone gibberellin, and achieved promising results that reveal interesting insight into the biosynthesis and signaling of gibberellin.</p> <p>Conclusions</p> <p>The results show that our method is highly effective in finding functional modules from real microarray data. Our application on Arabidopsis leads to the discovery of the largest number of annotated Arabidopsis functional modules in the literature. Given the high statistical significance of functional enrichment and the agreement between cis-regulatory and functional annotations, we believe our Arabidopsis gene modules can be used to predict the functions of unknown genes in Arabidopsis, and to understand the regulatory mechanisms of many genes.</p