Yuanchun Zhou

Wild bird migration across the Qinghai-Tibetan Plateau: A transmission route for highly pathogenic H5N1

Background Qinghai Lake in central China has been at the center of debate on whether wild birds play a role in circulation of highly pathogenic avian influenza virus H5N1. In 2005, an unprecedented epizootic at Qinghai Lake killed more than 6000 migratory birds including over 3000 bar-headed geese (Anser indicus). H5N1 subsequently spread to Europe and Africa, and in following years has re-emerged in wild birds along the Central Asia flyway several times. Methodology/Principal Findings To better understand the potential involvement of wild birds in the spread of H5N1, we studied the movements of bar-headed geese marked with GPS satellite transmitters at Qinghai Lake in relation to virus outbreaks and disease risk factors. We discovered a previously undocumented migratory pathway between Qinghai Lake and the Lhasa Valley of Tibet where 93% of the 29 marked geese overwintered. From 2003–2009, sixteen outbreaks in poultry or wild birds were confirmed on the Qinghai-Tibet Plateau, and the majority were located within the migratory pathway of the geese. Spatial and temporal concordance between goose movements and three potential H5N1 virus sources (poultry farms, a captive bar-headed goose facility, and H5N1 outbreak locations) indicated ample opportunities existed for virus spillover and infection of migratory geese on the wintering grounds. Their potential as a vector of H5N1 was supported by rapid migration movements of some geese and genetic relatedness of H5N1 virus isolated from geese in Tibet and Qinghai Lake. Conclusions/Significance This is the first study to compare phylogenetics of the virus with spatial ecology of its host, and the combined results suggest that wild birds play a role in the spread of H5N1 in this region. However, the strength of the evidence would be improved with additional sequences from both poultry and wild birds on the Qinghai-Tibet Plateau where H5N1 has a clear stronghold.

Geographical Analysis of the Distribution and Spread of Human Rabies in China from 2005 to 2011

Background Rabies is a significant public health problem in China in that it records the second highest case incidence globally. Surveillance data on canine rabies in China is lacking and human rabies notifications can be a useful indicator of areas where animal and human rabies control could be integrated. Previous spatial epidemiological studies lacked adequate spatial resolution to inform targeted rabies control decisions. We aimed to describe the spatiotemporal distribution of human rabies and model its geographical spread to provide an evidence base to inform future integrated rabies control strategies in China. Methods We geo-referenced a total of 17,760 human rabies cases of China from 2005 to 2011. In our spatial analyses we used Gaussian kernel density analysis, average nearest neighbor distance, Spatial Temporal Density-Based Spatial Clustering of Applications with Noise and developed a model of rabies spatiotemporal spread. Findings Human rabies cases increased from 2005 to 2007 and decreased during 2008 to 2011 companying change of the spatial distribution. The ANN distance among human rabies cases increased between 2005 and 2011, and the degree of clustering of human rabies cases decreased during that period. A total 480 clusters were detected by ST-DBSCAN, 89.4% clusters initiated before 2007. Most of clusters were mainly found in South of China. The number and duration of cluster decreased significantly after 2008. Areas with the highest density of human rabies cases varied spatially each year and in some areas remained with high outbreak density for several years. Though few places have recovered from human rabies, most of affected places are still suffering from the disease. Conclusion Human rabies in mainland China is geographically clustered and its spatial extent changed during 2005 to 2011. The results provide a scientific basis for public health authorities in China to improve human rabies control and prevention program.

Exploring the wild birds’ migration data for the disease spread study of H5N1: a clustering and association approach

Knowledge about the wetland use of migratory bird species during the annual life circle is very interesting to biologists, as it is critically important in many decision-making processes such as for conservation site construction and avian influenza control. The raw data of the habitat areas and the migration routes are usually in large scale and with high complexity when they are determined by high-tech GPS satellite telemetry. In this paper, we convert these biological problems into computational studies and introduce efficient algorithms for the data analysis. Our key idea is the concept of hierarchical clustering for migration habitat localizations, and the notion of association rules for the discovery of migration routes from the scattered location points in the GIS. One of our clustering results is a tree structure, specially called spatial-tree, which is an illusive map depicting the breeding and wintering home range of bar-headed geese. A related result to this observation is an association pattern that reveals a high possibility that bar-headed geese’s potential autumn migration routes are likely between the breeding sites in the Qinghai Lake, China and the wintering sites in Tibet river valley. Given the susceptibility of geese to spread H5N1, and on the basis of the chronology and the rates of the bar-headed geese migration movements, we can conjecture that bar-headed geese play an important role in the spread of the H5N1 virus at a regional scale in Qinghai-Tibetan Plateau.

Mining quantitative association rules on overlapped intervals

Mining association rules is an important problem in data mining. Algorithms for mining boolean data have been well studied and documented, but they cannot deal with quantitative and categorical data directly. For quantitative attributes, the general idea is partitioning the domain of a quantitative attribute into intervals, and applying boolean algorithms to the intervals. But, there is a conflict between the minimum support problem and the minimum confidence problem, while existing partitioning methods cannot avoid the conflict. Moreover, we expect the intervals to be meaningful. Clustering in data mining is a discovery process which groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. The discovered clusters are used to explain the characteristics of the data distribution. The present paper will propose a novel method to find quantitative association rules by clustering the transactions of a database into clusters and projecting the clusters into the domains of the quantitative attributes to form meaningful intervals which may be overlapped. Experimental results show that our approach can efficiently find quantitative association rules, and can find important association rules which may be missed by the previous algorithms.

Discovery of Migration Habitats and Routes of Wild Bird Species by Clustering and Association Analysis

Knowledge about the wetland use of migratory bird species during the annual life circle is very interesting to biologists, as it is critically important for conservation site construction and avian influenza control. The raw data of the habitat areas and the migration routes can be determined by high-tech GPS satellite telemetry, that usually are large scale with high complexity. In this paper, we convert these biological problems into computational studies, and introduce efficient algorithms for the data analysis. Our key idea is the concept of hierarchical clustering for migration habitat localization, and the notion of association rules for the discovery of migration routes. One of our clustering results is the Spatial-Tree, an illusive map which depicts the home range of bar-headed geese. A related result to this observation is an association pattern that reveals a high possibility of bar-headed geeses potential migration routes. Both of them are of biological novelty and meaning.

A New Framework for Traffic Anomaly Detection

Trajectory data is becoming more and more popular nowadays and extensive studies have been conducted on trajectory data. One important research direction about trajectory data is the anomaly detection which is to find all anomalies based on trajectory patterns in a road network. In this paper, we introduce a road segment-based anomaly detection problem, which is to detect the abnormal road segments each of which has its “real” traffic deviating from its “expected” traffic and to infer the major causes of anomalies on the road network. First, a deviation-based method is proposed to quantify the anomaly of reach road segment. Second, based on the observation that one anomaly from a road segment can trigger other anomalies from the road segments nearby, a diffusionbased method based on a heat diffusion model is proposed to infer the major causes of anomalies on the whole road network. To validate our methods, we conduct intensive experiments on a large real-world GPS dataset of about 23,000 taxis in Shenzhen, China to demonstrate the performance of our algorithms.

Construction of the Platform for Phylogenetic Analysis

Based on discussing the history of advancement to building the tree of life using genetic and genomic information, effective strategies and methods for the construction of the tree of life, this paper carried out business process analysis and application design. It implements a phylogenetic analysis platform for the land plants based on this analysis. The platform extracts molecular data from the international public databases in batch, which is automated acquisition, cleaning function for users to understand the situation of peer data. The process of phylogenetic reconstruction includes several public modes and tools, such as batch extraction, multiple sequence alignment, cleaning & editing, tree reconstruction, phylogeny evaluation and visualization. All these procedures demand a number of interactive interfaces for phylogenetic tree automatic generation and decision-making aids experiment.

Analyze the wild birds' migration tracks by MPI-based parallel clustering algorithm

Aiming at the avian influenza outbreak in Qinghai Lake area, the satellite tracking of migratory birds in Qinghai Lake is studied to analyze the relationship between bird migration, virus spread and ecological environment. These biological problems have been converted into computational studies in previous studies in which spatial clustering is the key factor. A bird migration data analysis system based on DBSCAN algorithm was designed in previous work, by which data can be systematically analyzed, and knowledge patterns are subsequently available for deep biological studies. As the GPS (Global Positioning System) raw data grows rapidly which is large scale with high complexity, DBSCAN takes long time (several minutes) to get the result. In this paper, parallel STING (statistical information grid) algorithm is designed and implemented based on MPI (message passing interface) for spatial clustering. By using parallel STING algorithm, it only takes several seconds to get the result.

Benchmarking Replication and Consistency Strategies in Cloud Serving Databases: HBase and Cassandra

Databases serving OLTP operations generated by cloud applications have been widely researched and deployed nowadays. Such cloud serving databases like BigTable, HBase, Cassandra, Azure and many others are designed to handle a large number of concurrent requests performed on the cloud end. Such systems can elastically scale out to thousands of commodity hardware by using a shared nothing distributed architecture. This implies a strong need of data replication to guarantee service availability and data access performance. Data replication can improve system availability by redirecting operations against failed data blocks to their replicas and improve performance by rebalancing load across multiple replicas. However, according to the PACELC model, as soon as a distributed database replicates data, another tradeoff between consistency and latency arises. This tradeoff motivates us to figure out how the latency changes when we adjust the replication factor and the consistency level. The replication factor determines how many replicas a data block should maintain, and the consistency level specifies how to deal with read and write requests performed on replicas. We use YCSB to conduct several benchmarking efforts to do this job. We report benchmark results for two widely used systems: HBase and Cassandra.

Generating syntactic tree templates for feature-based opinion mining

Feature-based sentiment analysis aims to recognize appraisal expressions and identify the targets and the corresponding semantic polarity. State-of-the-art syntactic-based approaches mainly focused on designing effective features for machine learning algorithms and/or predefine some rules to extract opinion words, target words and other opinion-related information. In this paper, we present a novel approach for identifying the relation between target words and opinion words. The proposed algorithm generates tree templates by mining syntactic structures of the annotated corpus. The proposed dependency tree templates cover not only the nodes directly linked with sentiment words and target words, but also subtrees of the nodes on syntactic path, which proved to be effective features for link relation extraction between opinions and targets. Experiment results show that the proposed approach achieves the best performance on the benchmark data set and can work well when syntactic tree templates are applied to different domains.