piRNAdb: A piwi-interacting RNA database

Motivation: Found in several metazoan species, piwi-interacting RNAs (piRNAs) regulate the expression of a wide variety of transposable elements and genes associated with cellular development, differentiation, and growth. Despite their importance, piRNAs are not well known and are still underexplored. To facilitate piRNA research, a comprehensive and easy-to-use piRNA database is still needed. Results: Here, we present piRNAdb, an integrative and user-friendly database designed to encompass several aspects of piRNAs. We selected piRNAs from four reliable human RNA-sequencing datasets to start our database. After data processing, we displayed these sequences, their genomic location, clustering information, putative targets on known genes and transposable elements, as well as direct links to other databases. In this first release, piRNAdb catalogues 27,329 piRNAs, as well as 23,380 genes that are putative targets and 47,060 associated gene ontology terms, both of which are organized and linked to each respective piRNA. Finally, to improve information exchange and increase the confidence of sequences, a feedback system is provided to users of piRNAdb. Conclusion: The inclusion of new features to facilitate piRNA analyses, data visualization, and integration is the major pillar of piRNAdb. Our main goal was to make this database an easy interface between the data and the user. We believe that this web tool achieves this objective by providing a streamlined and well-organized data repository for piRNAs and that it will be extremely useful to those already studying piRNAs and to the broader community. Availability: piRNAdb is available freely and is compatible with smartphones and tablets: https://www.pirnadb.org/ .

Collaborative Filtering Method of Web Service Recommendation Based on Location Aware and Personalized

With the rapid growth of the number of Web services, it is necessary to build an efficient web service recommendation system in the face of massive web services. In order to recommend high-quality services to users, the key problem is how to obtain the s value of Web services. This paper proposes a collaborative web service recommendation method based on location clustering. Firstly, users are clustered according to the autonomous system by using the correlation between QoS and user location. According to the clustering results, the system fills in the vacancy Qos value; Then, the vacancy Qos value is filled in in advance and the similarity between active users and each user is calculated. Based on this, to P-K algorithm is used to obtain the most similar Qos value to predict the unknown service for active users to complete the recommendation. The method proposed in this paper can effectively solve the problem of data sparsity and cold start of Web services. At the same time, a better balance between accuracy and coverage is obtained.

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation. In the bicycle drop-off location clustering module, candidate bicycle stations are clustered from each spatio-temporal subset of the large-scale cycling trajectory records. In the bicycle-station graph modeling module, a weighted digraph model is built based on the clustering results and inferior stations with low station revenue and utility are filtered. Then, graph models across time periods are combined to create a graph sequence model. In the bicycle-station location prediction module, the GGNN model is used to train the graph sequence data and dynamically predict bicycle stations in the next period. In the bicycle-station layout recommendation module, the predicted bicycle stations are fine-tuned according to the government urban management plan, which ensures that the recommended station layout is conducive to city management, vendor revenue, and user convenience. Experiments on actual DL-PBS networks verify the effectiveness, accuracy, and feasibility of the proposed BSDP system.

Analysis of microseismicity in the Hengill Geothermal Area, SW Iceland

<p>Geothermal systems in the vicinity of the Hengill volcano, SW Iceland, started to be exploited for electrical power and heat production since the late 1960s, and today the two largest operating geothermal power plants are located at the Nesjavellir and the Hellisheidi. This area is a complex tectonic and geothermal site, being located at the triple junction between the Reykjanes Peninsula (RP), the Western Volcanic Zone (WVZ), and the South Iceland Seismic Zone (SISZ). The region is seismically highly active with several thousand earthquakes located yearly. The origin of such earthquakes may be either natural or anthropogenic. The analysis of microseismicity can provide useful information on natural active processes in tectonic, geothermal and volcanic environments as well as on physical mechanisms governing induced events. Here, we investigate the microseismicity occurring in Hengill area to understand physical source mechanisms and the origin of these microseismic events. We use a very dense broadband monitoring network deployed since November 2018 with support of the GEOTHERMICA project COSEISMIQ and apply robust and full-waveform based methods for earthquake location, clustering analysis and source mechanism determination. Our dataset consists of about 637 events with M<sub>L</sub> ranging between 0.8 and 4.7 from December 2018 to January 2019. We use this rich and large dataset for testing a workflow for automated processing. Earthquake location and clustering analysis show that seismicity is spatially clustered, with shallower events at the center of geothermal site in proximity to geothermal plants, and deeper earthquakes in the southern part of the study area. Most of our moment tensors can suggest the influence of geothermal activity and geothermal energy exploitation operations on the subsurface. This work is supported by the COSEISMIQ project of the EU GEOTHERMICA program .</p>

Discriminative Bayesian Nonparametric Clustering

We propose a general framework for discriminative Bayesian nonparametric clustering to promote the inter-discrimination among the learned clusters in a fully Bayesian nonparametric (BNP) manner. Our method combines existing BNP clustering and discriminative models by enforcing latent cluster indices to be consistent with the predicted labels resulted from probabilistic discriminative model. This formulation results in a well-defined generative process wherein we can use either logistic regression or SVM for discrimination. Using the proposed framework, we develop two novel discriminative BNP variants: the discriminative Dirichlet process mixtures, and the discriminative-state infinite HMMs for sequential data. We develop efficient data-augmentation Gibbs samplers for posterior inference. Extensive experiments in image clustering and dynamic location clustering demonstrate that by encouraging discrimination between induced clusters, our model enhances the quality of clustering in comparison with the traditional generative BNP models.