The effects of dog management on Echinococcus spp. prevalence in villages on the eastern Tibetan Plateau, China

Background The pastoral area of the eastern Tibetan plateau is a very important human echinococcosis endemic region. Domestic dogs are the main definitive host for the transmission of Echinococcus granulosus sensu lato (s.1.) and E. multilocularis to humans. To control the infection risks, a national-level canine echinococcosis prevention and control program has been implemented since 2015 in Shiqu County, Sichuan, China, The objective of this investigation was to evaluate its effect on Echinococcus spp. prevalence in dogs. Methods We surveyed 69 households with 84 owned dogs, for dog keeping information in the villages of Rizha and Eduoma. A total of 105 dog fecal samples, consisting of 75 from owned dogs and 30 unknown dog fecal samples were collected between 2015 and 2017 to determine Echinococcus spp. prevalence using copro-PCR. Eight variables based on household surveys were included into a logistic regression model for significantly relevant factors to canine echinococcosis prevalence in dogs. Results The overall Echinococcus spp. copro-DNA prevalence decreased significantly in dogs from 51.2% (2015) to 20.0% (2017) in Rizha, and insignificantly from 11.5% (2016) to 4.3% (2017) in Eduoma. Echinococcus multilocularis was the most prevalent species continually detected during the entire research period, while E. granulosus was rare and not detected in 2017. Echinococcus shiquicus prevalence was as high as E. multilocularis , although only detected in 2015 in Rizha. Unleashed dog feces were mainly collected in Rizha Village in 2015. Although 93.2% of owned dogs were leashed, and the monthly praziquantel dosing rate reached 97%, E. multilocularis infection could still be detected in 11.1% of owned dogs in 2017. Monthly deworming, leashing dogs 24h per day, and the avoidance of dogs feeding on livestock viscera are significant measures to prevent canine echinococcosis infection in owned dogs. Conclusion Carrying out a canine echinococcosis prevention and control program can significantly decrease the Echinococcus prevalence. The potential contact between leashed dogs and wild small mammals is still a risk to re-infect owned dogs. This study shows that the long term application of regular dog dosing in the vast remote echinococcosis endemic areas of west China is still challenging

The Maximum-Weight Stable Matching Problem: Duality and Efficiency

Given a preference system (G,≺) and an integral weight function defined on the edge set of G (not necessarily bipartite), the maximum-weight stable matching problem is to find a stable matching of (G,≺) with maximum total weight. In this paper we study this NP-hard problem using linear programming and polyhedral approaches. We show that the Rothblum system for defining the fractional stable matching polytope of (G,≺) is totally dual integral if and only if this polytope is integral if and only if (G,≺) has a bipartite representation. We also present a combinatorial polynomial-time algorithm for the maximum-weight stable matching problem and its dual on any preference system with a bipartite representation. Our results generalize Király and Pap's theorem on the maximum-weight stable-marriage problem and rely heavily on their work. © 2012 Society for Industrial and Applied Mathematics.published_or_final_versio

Compressive Sensing Reconstruction for Video: An Adaptive Approach Based on Motion Estimation

This paper focuses on the problem of causally reconstructing Compressive Sensing (CS) captured video. The state-of-art causal approaches usually assume the signal support is static or changing sufficiently slowly over time, where Magnetic Resonance Imaging (MRI) is widely used as a motivating example. However, such an assumption is too restrictive for many other video applications, where the signal support changes rapidly. In this paper, we propose a framework that combines Motion Estimation (ME), the Kalman Filter (KF) and CS to adapt the reconstruction process to motions in the video so that the slowly-changing assumption on the signal support is relaxed and consequently is more suitable for video reconstruction. Explicit and implicit ME are designed to provide motion aware predictions, upon which a modified KF procedure is applied. Furthermore, three CS algorithms with embedded ME and KF are developed, and theoretical analyses are conducted via reconstruction error upper bounds, to characterize the various factors that affect reconstruction accuracy. Extensive simulations utilizing actual videos are carried out and the superiority of our methods is demonstrated.This work is supported by EPSRC Research Grant EP/K033700/1; the Natural Science Foundation of China (61401018, U1334202).This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TCSVT.2016.254007

Block-based feature adaptive compressive sensing for video

This paper focuses on the problem of feature adaptive reconstruction of Compressive Sensing (CS) captured video. In CS, sparse signals can be recovered with high probability of success from very few random samples. Utilizing the temporal correlations between video frames, it is possible to exploit improved CS reconstruction algorithms. Features that relate to the changes between frames are one of the options to benefit reconstruction. However, to choose the optimal feature for every particular region in each frame is difficult, as the true images are unknown in a CS framework. In this paper, we propose two systems for block-based feature adaptive CS video reconstruction, i.e., a Cross Validation (CV) based system and a classification based system. The CV based system achieves the selection of the optimal feature by applying the techniques of CV to the results of extra reconstructions and the classification based system reduces complexity by classifying the CS samples directly, where the optimal feature for the particular class is employed for the reconstruction. Simulations demonstrate that both of our systems work appropriately and their performance is better than uniformly using any single feature for the whole video reconstruction.This work is supported by EPSRC Research Grant (EP/K033700/1); the Natural Science Foundation of China (61401018); Beijing Jiaotong University; the Fundamental Research Funds for the Central Universities (2014JBM149).This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.25

Sparsity-fused Kalman filtering for reconstruction of dynamic sparse signals

This article focuses on the problem of reconstructing dynamic sparse signals from a series of noisy compressive sensing measurements using a Kalman Filter (KF). This problem arises in many applications, e.g., Magnetic Resonance Imaging (MRI), Wireless Sensor Networks (WSN) and video reconstruction. The conventional KF does not consider the sparsity structure presented in most practical signals and it is therefore inaccurate when being applied to sparse signal recovery. To deal with this issue, we derive a novel KF procedure which takes the sparsity model into consideration. Furthermore, an algorithm, namely Sparsity-fused KF, is proposed based upon it. The method of iterative soft thresholding is utilized to refine our sparsity model. The superiority of our method is demonstrated by synthetic data and the practical data gathered by a WSN.This work is supported by EPSRC Research Grant (EP/K033700/1); the Natural Science Foundation of China (61401018, U1334202); the State Key Laboratory of Rail Traffic Control and Safety (RCS2014ZT08), Beijing Jiaotong University; the Fundamental Research Funds for the Central Universities (2014JBM149); the Key Grant Project of Chinese Ministry of Education (313006); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICC.2015.724938