scholarly journals Computer Vision Applied to Detect Lethargy through Animal Motion Monitoring: A Trial on African Swine Fever in Wild Boar

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2241 ◽  
Author(s):  
Eduardo Fernández-Carrión ◽  
Jose Ángel Barasona ◽  
Ángel Sánchez ◽  
Cristina Jurado ◽  
Estefanía Cadenas-Fernández ◽  
...  
Keyword(s):  
Computer Vision ◽  
Infectious Diseases ◽  
Wild Boar ◽  
Real Time ◽  
Disease Surveillance ◽  
Animal Health ◽  
Clinical Signs ◽  
African Swine Fever ◽  
Cost Effective ◽  
Experimental Trials

Early detection of infectious diseases is the most cost-effective strategy in disease surveillance for reducing the risk of outbreaks. Latest deep learning and computer vision improvements are powerful tools that potentially open up a new field of research in epidemiology and disease control. These techniques were used here to develop an algorithm aimed to track and compute animal motion in real time. This algorithm was used in experimental trials in order to assess African swine fever (ASF) infection course in Eurasian wild boar. Overall, the outcomes showed negative correlation between motion reduction and fever caused by ASF infection. In addition, infected animals computed significant lower movements compared to uninfected animals. The obtained results suggest that a motion monitoring system based on artificial vision may be used in indoors to trigger suspicions of fever. It would help farmers and animal health services to detect early clinical signs compatible with infectious diseases. This technology shows a promising non-intrusive, economic and real time solution in the livestock industry with especial interest in ASF, considering the current concern in the world pig industry.

scholarly journals Development of a pandemic of African Swine Fever

2019 ◽  
Vol 75 (02) ◽  
pp. 6186-2019
Author(s):  
ZYGMUNT PEJSAK ◽  
MARIAN TRUSZCZYŃSKI ◽  
KAZIMIERZ TARASIUKL
Keyword(s):  
Infectious Diseases ◽  
Wild Boar ◽  
Large Scale ◽  
Animal Health ◽  
Swine Influenza ◽  
African Swine Fever ◽  
World Health ◽  
The European Union ◽  
Leading Position ◽  
Veterinary Epidemiology

In the introduction of this paper the increasingly accepted nomenclature of basic expressions used in veterinary epidemiology is presented. This is in accordance with the 2018 Edition of Wiley-Blackwell Veterinary Epidemiology, by Michael Thrusfield. Pandemia and not Panzootia is used for large scale outbreaks of infectious diseases also in relation to animals characterizing significantly increased morbidity and mortatlity over a wide geographic area, including countries, continents or even the whole globe and causing significant economic, social and even political disruption. It is underlined that the pandemics of infectious diseases of animals will continue to increase because of the growing transboundary trade and transportation of animals and animal products and increasing international contacts of humans. In the control of pandemics of humans, particularly, for example of influenza of the years 1918-1919, and also in 1968, but also pandemics of other ethiology occurring in humans the World Health Organisation (WHO) took the leading position. In case of avian and swine influenza as well as in pandemics occurring in animals, the leading position in prevention, control and eradication belongs to World Organization for Animal Health (OIE) supported by the Food and Agriculture Organization (FAO) of the United Nations. The main part of this paper is devoted to the characterization of the pandemic of African Swine Fever (ASF) which started in 2007 in Georgia, transmitted from Africa. Countries, where ASF virus (ASFV) was confirmed by laboratory tests are mentioned in the text of this paper. They are located in Eurasia. Among them are countries, being members of the European Union (EU). The routs of spreading of the ASFV in wild boar and swine are given, the methods of ASF control in EU were presented. The main vector of the ASF spread in EU, is the wild boar and the humans. Future distribution of the pandemia spread of ASFV is difficult to predict. Veterinary services of many countries, practitioners and scientists are working intensively to understand epidemiology of ASF and to stop the spread of ASFV.

scholarly journals SARS-CoV-2 genome datasets analytics for informed infectious disease surveillance

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 919
Author(s):  
Moses Effiong Ekpenyong ◽  
Ifiok James Udo ◽  
Mercy Ernest Edoho ◽  
EnoAbasi Deborah Anwana ◽  
Francis Bukie Osang ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Real Time ◽  
Disease Surveillance ◽  
Emerging Infectious Diseases ◽  
Cost Effective ◽  
Contact Tracing ◽  
Public Healthcare ◽  
Case Definitions ◽  
Smart Healthcare ◽  
Disease Case

Background: The COVID-19 pandemic has ravaged economies, health systems, and lives globally. Concerns surrounding near total economic collapse, loss of livelihood and emotional complications ensuing from lockdowns and commercial inactivity, resulted in governments loosening economic restrictions. These concerns were further exacerbated by the absence of vaccines and drugs to combat the disease, with the fear that the next wave of the pandemic would be more fatal. Consequently, integrating disease surveillance mechanism into public healthcare systems is gaining traction, to reduce the spread of community and cross-border infections and offer informed medical decisions. Methods: Publicly available datasets of coronavirus cases around the globe deposited between December, 2019 and March 15, 2021 were retrieved from GISAID EpiFluTM and processed. Also retrieved from GISAID were data on the different SARS-CoV-2 variant types since inception of the pandemic. Results: Epidemiological analysis offered interesting statistics for understanding the demography of SARS-CoV-2 and helped the elucidation of local and foreign transmission through a history of contact travels. Results of genome pattern visualization and cognitive knowledge mining revealed the emergence of high intra-country viral sub-strains with localized transmission routes traceable to immediate countries, for enhanced contact tracing protocol. Variant surveillance analysis indicates increased need for continuous monitoring of SARS-CoV-2 variants.  A collaborative Internet of Health Things (IoHT) framework was finally proposed to impact the public health system, for robust and intelligent support for modelling, characterizing, diagnosing and real-time contact tracing of infectious diseases. Conclusions: Localizing healthcare disease surveillance is crucial in emerging disease situations and will support real-time/updated disease case definitions for suspected and probable cases. The IoHT framework proposed in this paper will assist early syndromic assessments of emerging infectious diseases and support healthcare/medical countermeasures as well as useful strategies for making informed policy decisions to drive a cost effective, smart healthcare system.

122 Bioactive Peptides for Enhancing Intestine Health

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 65-65
Author(s):  
Julang Li ◽  
Nadeem Akhtar ◽  
Celina Osakowicz ◽  
Lauren Fletcher ◽  
Karmin O ◽  
...  
Keyword(s):  
Animal Health ◽  
Clinical Signs ◽  
Body Weight Loss ◽  
Cost Effective ◽  
Significant Rise ◽  
Feed Ratio ◽  
Resistant Bacteria ◽  
Intestinal Development ◽  
Antibiotic Resistant ◽  
Animal Trials

Abstract Intestinal disorders and colitis affect both animals and humans. The pathogenesis behind the inflammation is complex and not entirely understood. Furthermore, the significant rise in antibiotic-resistant bacteria has emphasized an urgent need for alternative anti-infective therapies. Antimicrobial peptides (AMPs) is one of the appealing alternative to antibiotics due to their antimicrobial activity, mode of actions, and potential role in tissue repair. Epidermal growth factor (EGF) plays an important role in intestinal proliferation and differentiation and thus promotes intestinal development. Using food grade microorganisms such as Lactococcus lactis and yeast as hosts, our laboratory has produced recombinant porcine protegrin-1 (PG-1), a pig originated antimicrobial peptide and EGF via fermentation. Oral administration of PG-1 reduced Citrobacter rodentium induced intestinal infection in mice. This was evidenced by reduced histopathological changes in the colon, prevention of body weight loss, milder clinical signs of disease, and ultimately more effective clearance of bacterial infection. On the other hand, animal trials using the recombinant EGF demonstrated that it enhances intestinal development and growth of early weaned pig fed with antibiotic-free diet. Moreover, piglets challenged with enterotoxigenic Escherchia coli (E. coli) K88 showed similar beneficial responses to EGF as those fed diets with antibiotic in terms of improving gain to feed ratio and lowering oxidative stress. Taken together, our findings suggest the potential for cost-effective production and application of recombinant bioactive proteins as alternatives to antibiotics in animal health and production.

scholarly journals Suitability of Different Deterrents for Short-Term Reduction of Wild Boar Contacts With Carcasses of African Swine Fever-Infected Animals

2020 ◽  
Author(s):  
Nicolai Denzin ◽  
Frithjof Helmstaedt ◽  
Carolina Probst ◽  
Franz J. Conraths
Keyword(s):  
Viral Infection ◽  
Disease Control ◽  
Wild Boar ◽  
Animal Health ◽  
African Swine Fever ◽  
Short Term ◽  
Major Threat ◽  
Physical And Chemical ◽  
Important Disease

African swine fever (ASF) is a viral infection of pigs and represents a major threat to animal health and trade. Due to the high tenacity of the causative virus also in carcasses of wild boar, contacts of wild boar with infectious carcasses are regarded an important driver of the so-called habitat cycle. The latter is believed to play a major role in maintaining the present ASF situation in wild boar in Europe. Therefore, search campaigns and timely removal and disposal of carcasses are considered important disease control approaches. If timely disposal is not feasible due to logistic reasons, deterrence of wild boar could be a provisionary option. The performance of seven deterrents (physical and chemical) was tested in a forest near Greifswald, Germany. Carcasses as entities of attraction for wild boar were substituted by luring sites. It could be demonstrated that certain physical (LED-Blinkers, aluminum stripes) and chemical (Wildschwein-Stopp™, Hukinol™) deterrents are capable of reducing significantly the odds of wild boar contacts to one third. It is recommended to carry a choice of the aforementioned, reasonable and easy to apply deterrents, when carcass search campaigns are launched in case of an outbreak of ASF in wild boar.

scholarly journals Evaluation of the Efficiency of Active and Passive Surveillance in the Detection of African Swine Fever in Wild Boar

2019 ◽  
Vol 7 (1) ◽  
pp. 5 ◽  
Author(s):  
Vincenzo Gervasi ◽  
Andrea Marcon ◽  
Silvia Bellini ◽  
Vittorio Guberti
Keyword(s):  
Wild Boar ◽  
Active Surveillance ◽  
Disease Surveillance ◽  
Virus Detection ◽  
African Swine Fever ◽  
First Year ◽  
Passive Surveillance ◽  
European Wild Boar ◽  
Absolute Probability ◽  
Host Parasite

African swine fever (ASF) is one of the most severe diseases of pigs and has a drastic impact on pig industry. Wild boar populations play the role of ASF genotype II virus epidemiological reservoir. Disease surveillance in wild boar is carried out either by testing all the wild boar found sick or dead for virus detection (passive surveillance) or by testing for virus (and antibodies) all hunted wild boar (active surveillance). When virus prevalence and wild boar density are low as it happens close to eradication, the question on which kind of surveillance is more efficient in detecting the virus is still open. We built a simulation model to mimic the evolution of the host-parasite interaction in the European wild boar and to assess the efficiency of different surveillance strategies. We constructed a deterministic SIR model, which estimated the probability to detect the virus during the 8 years following its introduction, using both passive and active surveillance. Overall, passive surveillance provided a much larger number of ASF detections than active surveillance during the first year. During subsequent years, both active and passive surveillance exhibited a decrease in their probability to detect ASF. Such decrease, though, was more pronounced for passive surveillance. Under the assumption of 50% of carcasses detection, active surveillance became the best detection method when the endemic disease prevalence was lower than 1.5%, when hunting rate was >60% and when population density was lower than 0.1 individuals/km2. In such a situation, though, the absolute probability to detect the disease was very low with both methods, and finding almost every carcass is the only way to ensure virus detection. The sensitivity analysis shows that carcass search effort is the sole parameter that increases proportionally the chance of ASF virus detection. Therefore, an effort should be made to promote active search of dead wild boar also in endemic areas, since reporting wild boar carcasses is crucial to understand the epidemiological situation in any of the different phases of ASF infection at any wild boar density.

scholarly journals Cost-Effective CNNs for Real-Time Micro-Expression Recognition

2020 ◽  
Vol 10 (14) ◽  
pp. 4959
Author(s):  
Reda Belaiche ◽  
Yu Liu ◽  
Cyrille Migniot ◽  
Dominique Ginhac ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Real Time ◽  
Video Clip ◽  
Cost Effective ◽  
Compact Representation ◽  
Expression Recognition ◽  
Memory Space ◽  
Computational Resources ◽  
Micro Expression

Micro-Expression (ME) recognition is a hot topic in computer vision as it presents a gateway to capture and understand daily human emotions. It is nonetheless a challenging problem due to ME typically being transient (lasting less than 200 ms) and subtle. Recent advances in machine learning enable new and effective methods to be adopted for solving diverse computer vision tasks. In particular, the use of deep learning techniques on large datasets outperforms classical approaches based on classical machine learning which rely on hand-crafted features. Even though available datasets for spontaneous ME are scarce and much smaller, using off-the-shelf Convolutional Neural Networks (CNNs) still demonstrates satisfactory classification results. However, these networks are intense in terms of memory consumption and computational resources. This poses great challenges when deploying CNN-based solutions in many applications, such as driver monitoring and comprehension recognition in virtual classrooms, which demand fast and accurate recognition. As these networks were initially designed for tasks of different domains, they are over-parameterized and need to be optimized for ME recognition. In this paper, we propose a new network based on the well-known ResNet18 which we optimized for ME classification in two ways. Firstly, we reduced the depth of the network by removing residual layers. Secondly, we introduced a more compact representation of optical flow used as input to the network. We present extensive experiments and demonstrate that the proposed network obtains accuracies comparable to the state-of-the-art methods while significantly reducing the necessary memory space. Our best classification accuracy was 60.17% on the challenging composite dataset containing five objectives classes. Our method takes only 24.6 ms for classifying a ME video clip (less than the occurrence time of the shortest ME which lasts 40 ms). Our CNN design is suitable for real-time embedded applications with limited memory and computing resources.

scholarly journals Real-Time Evaluation of Breast Self-Examination Using Computer Vision

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Eman Mohammadi ◽  
Elmer P. Dadios ◽  
Laurence A. Gan Lim ◽  
Melvin K. Cabatuan ◽  
Raouf N. G. Naguib ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Vision ◽  
Real Time ◽  
Cost Effective ◽  
Breast Self Examination ◽  
Cost Effective Approach ◽  
Confirmatory Tests ◽  
Evaluation Algorithm ◽  
Vision Algorithm ◽  
Self Examination

Breast cancer is the most common cancer among women worldwide and breast self-examination (BSE) is considered as the most cost-effective approach for early breast cancer detection. The general objective of this paper is to design and develop a computer vision algorithm to evaluate the BSE performance in real-time. The first stage of the algorithm presents a method for detecting and tracking the nipples in frames while a woman performs BSE; the second stage presents a method for localizing the breast region and blocks of pixels related to palpation of the breast, and the third stage focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. In a correct BSE performance, all blocks must be palpated, checked, and highlighted, respectively. If any abnormality, such as masses, is detected, then this must be reported to a doctor to confirm the presence of this abnormality and proceed to perform other confirmatory tests. The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance.

scholarly journals Development and Application of a Prioritization Tool for Animal Health Surveillance Activities in Ireland

2020 ◽  
Vol 7 ◽  
Author(s):  
AnneMarie Clarke ◽  
Simon J. More ◽  
James W. Maher ◽  
Andrew W. Byrne ◽  
Michael Horan ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Health Management ◽  
Animal Health ◽  
Foot And Mouth Disease ◽  
African Swine Fever ◽  
Resource Limitation ◽  
Health Surveillance ◽  
Second Phase ◽  
Endemic Diseases ◽  
Prioritization Process

Decisions around animal health management by stakeholders are often subject to resource limitation, therefore prioritization processes are required to evaluate whether effort is attributed appropriately. The objectives of this study were to develop and apply a surveillance prioritization process for animal health surveillance activities in Ireland. An exploratory sequential mixed research methods design was utilized. A prioritization tool was developed for surveillance activities and implemented over two phases. During the first phase, a survey was conducted which asked stakeholders to prioritize diseases/conditions by importance for Irish agriculture. In the second phase, experts identified the most important surveillance objectives, and allocated resources to the activities that they considered would best meet the surveillance objectives, for each disease/condition. This study developed a process and an accompanying user-friendly practical tool for animal disease surveillance prioritization which could be utilized by other competent authorities/governments. Antimicrobial resistance and bovine tuberculosis were ranked top of the endemic diseases/conditions in the Irish context, while African swine fever and foot and mouth disease were ranked top of the exotic diseases/conditions by the stakeholders. The study showed that for most of the diseases/conditions examined in the prioritization exercise, the respondents indicated a preference for a combination of active and passive surveillance activities. Future extensions of the tool could include prioritization on a per species basis.

scholarly journals Towards risk-based surveillance of African Swine Fever in Switzerland

2021 ◽  
Author(s):  
Maria Elena Vargas-Amado ◽  
Luís Pedro Carmo ◽  
John Berezowski ◽  
Claude Fischer ◽  
Maria João Santos ◽  
...  
Keyword(s):  
High Risk ◽  
Wild Boar ◽  
Disease Transmission ◽  
Forest Cover ◽  
Disease Risk ◽  
Animal Health ◽  
African Swine Fever ◽  
Early Disease ◽  
Domestic Pig ◽  
Domestic Pigs

African Swine Fever (ASF) has emerged as a disease of great concern to swine producers and government disease control agencies because of its severe consequences to animal health and the pig industry. Early detection of an ASF introduction is considered essential for reducing the harm caused by the disease. Risk-based surveillance approaches have been used as enhancements to early disease epidemic detection systems in livestock populations. Such approaches may consider the role wildlife plays in hosting and transmitting a disease. In this study, a novel method is presented to estimate and map the risk of introducing ASF into the domestic pig population through wild boar intermediate hosts. It makes use of data about hunted wild boar, rest areas along motorways connecting ASF affected countries to Switzerland, outdoor piggeries, and forest cover. These data were used to compute relative wild boar abundance as well as to estimate the risk of both disease introduction into the wild boar population and disease transmission to domestic pigs. The way relative wild boar abundance was calculated adds to the current state of the art by considering the effect of beech mast on hunting success and the probability of wild boar occurrence when distributing relative abundance values among individual grid cells. The risk of ASF introduction into the domestic pig population by wild boar was highest near the borders of France, Germany, and Italy. On the north side of the Alps, areas of high risk were located on the unshielded side of the main motorway crossing the Central Plateau, which acts as a barrier for wild boar. The results of this study can be used to focus surveillance efforts for early disease detection on high risk areas. The developed method may also inform policies to control other diseases that are transmitted by direct contact from wild boar to domestic pigs.

Sign in / Sign up

Export Citation Format

Share Document