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

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able to recognize small instances (e.g., pedestrians, cyclists) very well due to the low-resolution voxelization and aggressive downsampling. To this end, we propose Sparse Point-Voxel Convolution (SPVConv), a lightweight 3D module that equips the vanilla Sparse Convolution with the high-resolution point-based branch. With negligible overhead, this point-based branch is able to preserve the fine details even from large outdoor scenes. To explore the spectrum of efficient 3D models, we first define a flexible architecture design space based on SPVConv, and we then present 3D Neural Architecture Search (3D-NAS) to search the optimal network architecture over this diverse design space efficiently and effectively. Experimental results validate that the resulting SPVNAS model is fast and accurate: it outperforms the state-of-the-art MinkowskiNet by 3.3%, ranking 1st on the competitive SemanticKITTI leaderboard. It also achieves 8x computation reduction and 3x measured speedup over MinkowskiNet with higher accuracy. Finally, we transfer our method to 3D object detection, and it achieves consistent improvements over the one-stage detection baseline on KITTI.Comment: ECCV 2020. The first two authors contributed equally to this work. Project page: http://spvnas.mit.edu

Efficient selection of globally optimal rules on large imbalanced data based on rule coverage relationship analysis

Copyright © SIAM. Rule-based anomaly and fraud detection systems often suffer from massive false alerts against a huge number of enterprise transactions. A crucial and challenging problem is to effectively select a globally optimal rule set which can capture very rare anomalies dispersed in large-scale background transactions. The existing rule selection methods which suffer significantly from complex rule interactions and overlapping in large imbalanced data, often lead to very high false positive rate. In this paper, we analyze the interactions and relationships between rules and their coverage on transactions, and propose a novel metric, Max Coverage Gain. Max Coverage Gain selects the optimal rule set by evaluating the contribution of each rule in terms of overall performance to cut out those locally significant but globally redundant rules, without any negative impact on the recall. An effective algorithm, MCGminer, is then designed with a series of built-in mechanisms and pruning strategies to handle complex rule interactions and reduce computational complexity towards identifying the globally optimal rule set. Substantial experiments on 13 UCI data sets and a real time online banking transactional database demonstrate that MCGminer achieves significant improvement on both accuracy, scalability, stability and efficiency on large imbalanced data compared to several state-of-the-art rule selection techniques

Interactions between landscape changes and host communities can regulate echinococcus multilocularis transmission

An area close to the Qinghai-Tibet plateau region and subject to intensive deforestation contains a large focus of human alveolar echinococcosis while sporadic human cases occur in the Doubs region of eastern France. The current review analyses and compares epidemiological and ecological results obtained in both regions. Analysis of rodent species assemblages within quantified rural landscapes in central China and eastern France shows a significant association between host species for the pathogenic helminth Echinococcus multilocularis, with prevalences of human alveolar echinococcosis and with land area under shrubland or grassland. This suggests that at the regional scale landscape can affect human disease distribution through interaction with small mammal communities and their population dynamics. Lidicker's ROMPA hypothesis helps to explain this association and provides a novel explanation of how landscape changes may result in increased risk of a rodent-borne zoonotic disease

The underpinning factors affecting the classroom air quality, thermal comfort and ventilation in 30 classrooms of primary schools in London

The health and academic performance of children are significantly impacted by air quality in classrooms. However, there is a lack of understanding of the relationship between classroom air pollutants and contextual factors such as physical characteristics of the classroom, ventilation and occupancy. We monitored concentrations of particulate matter (PM), CO2 and thermal comfort (relative humidity and temperature) across five schools in London. Results were compared between occupied and unoccupied hours to assess the impact of occupants and their activities, different floor coverings and the locations of the classrooms. In-classroom CO2 concentrations varied between 500 and 1500 ppm during occupancy; average CO2 (955 ± 365 ppm) during occupancy was ∼150% higher than non-occupancy. Average PM10 (23 ± 15 μgm-3), PM2.5 (10 ± 4 μgm-3) and PM1 (6 ± 3 μg m-3) during the occupancy were 230, 125 and 120% higher than non-occupancy. Average RH (29 ± 6%) was below the 40–60% comfort range in all classrooms. Average temperature (24 ± 2 °C) was >23 °C in 60% of classrooms. Reduction in PM10 concentration (50%) by dual ventilation (mechanical + natural) was higher than for PM2.5 (40%) and PM1 (33%) compared with natural ventilation (door + window). PM10 was higher in classrooms with wooden (33 ± 19 μg m-3) and vinyl (25 ± 20 μgm-3) floors compared with carpet (17 ± 12 μgm-3). Air change rate (ACH) and CO2 did not vary appreciably between the different floor levels and types. PM2.5/PM10 was influenced by different occupancy periods; highest value (∼0.87) was during non-occupancy compared with occupancy (∼0.56). Classrooms located on the ground floor had PM2.5/PM10 > 0.5, indicating an outdoor PM2.5 ingress compared with those located on the first and third floors (300 m3) classroom showed ∼33% lower ACH compared with small-volume (100–200 m3). These findings provide guidance for taking appropriate measures to improve classroom air quality

Computer-aided extraction of select MRI markers of cerebral small vessel disease: A systematic review

Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures

A novel process for preparing PZT thick films

2000-2001 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Promyelocytic leukemia nuclear bodies associate with transcriptionally active genomic regions

Cancer Research UK

Serosurvey of Coxiella burnetii (Q fever) in Dromedary Camels (Camelus dromedarius) in Laikipia County, Kenya

Dromedary camels (Camelus dromedarius) are an important protein source for people in semi-arid and arid regions of Africa. In Kenya, camel populations have grown dramatically in the past few decades resulting in the potential for increased disease transmission between humans and camels. An estimated four million Kenyans drink unpasteurized camel milk, which poses a disease risk. We evaluated the seroprevalence of a significant zoonotic pathogen, Coxiella burnetii (Q fever), among 334 camels from nine herds in Laikipia County, Kenya. Serum testing revealed 18.6% positive seroprevalence of Coxiella burnetii (n = 344). Increasing camel age was positively associated with C. burnetii seroprevalence (OR = 5.36). Our study confirmed that camels living in Laikipia County, Kenya, have been exposed to the zoonotic pathogen, C. burnetii. Further research to evaluate the role of camels in disease transmission to other livestock, wildlife and humans in Kenya should be conducted