Antibodies against Schmallenberg virus detected in cattle in the Otjozondjupa region, Namibia

Several ruminant species have been shown to be susceptible to Schmallenberg virus (SBV), but adult animals usually recover after showing mild or no clinical signs. However, transplacental infection can occur and lead to abortion, malformations and stillborn lambs, calves and goat kids. During November and December 2014, malformations were observed in 11 stillborn calves from two farms in the north-eastern region of Namibia. Blood samples were collected from 9 of the 11 cows that delivered stillborn and malformed calves. All these animals tested negative for Rift Valley fever, bovine viral diarrhoea and infectious bovine rhinotracheitis and were serologically positive for bluetongue virus, SBV and epizootic haemorrhagic disease virus. Clinical findings and serological results suggested that SBV may be circulating in Namibia

Multi-views Embedding for Cattle Re-identification

People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural Networks (CNN) and the availability of large datasets. However, little research has been conducted on animal identification and re-identification, even if this knowledge may be useful in a rich variety of different scenarios. Here, we tackle cattle re-identification exploiting deep CNN and show how this task is poorly related to the human one, presenting unique challenges that make it far from being solved. We present various baselines, both based on deep architectures or on standard machine learning algorithms, and compared them with our solution. Finally, a rich ablation study has been conducted to further investigate the unique peculiarities of this task

Scoring pleurisy in slaughtered pigs using convolutional neural networks

Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short lifespan of pigs, lesions can be still evident at slaughter, where they can be usefully recorded and scored. Therefore, the slaughterhouse represents a key check-point to assess the health status of pigs, providing unique and valuable feedback to the farm, as well as an important source of data for epidemiological studies. Although relevant, scoring lesions in slaughtered pigs represents a very time-consuming and costly activity, thus making difficult their systematic recording. The present study has been carried out to train a convolutional neural network-based system to automatically score pleurisy in slaughtered pigs. The automation of such a process would be extremely helpful to enable a systematic examination of all slaughtered livestock. Overall, our data indicate that the proposed system is well able to differentiate half carcasses affected with pleurisy from healthy ones, with an overall accuracy of 85.5%. The system was better able to recognize severely affected half carcasses as compared with those showing less severe lesions. The training of convolutional neural networks to identify and score pneumonia, on the one hand, and the achievement of trials in large capacity slaughterhouses, on the other, represent the natural pursuance of the present study. As a result, convolutional neural network-based technologies could provide a fast and cheap tool to systematically record lesions in slaughtered pigs, thus supplying an enormous amount of useful data to all stakeholders in the pig industry

A new member of the Pteropine Orthoreovirus species isolated from fruit bats imported to Italy

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Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs

Nowadays, Vector-Borne Diseases (VBDs) raise a severe threat for public health, accounting for a considerable amount of human illnesses. Recently, several surveillance plans have been put in place for limiting the spread of such diseases, typically involving on-field measurements. Such a systematic and effective plan still misses, due to the high costs and efforts required for implementing it. Ideally, any attempt in this field should consider the triangle vectors-host-pathogen, which is strictly linked to the environmental and climatic conditions. In this paper, we exploit satellite imagery from Sentinel-2 mission, as we believe they encode the environmental factors responsible for the vector's spread. Our analysis - conducted in a data-driver fashion - couples spectral images with ground-truth information on the abundance of Culicoides imicola. In this respect, we frame our task as a binary classification problem, underpinning Convolutional Neural Networks (CNNs) as being able to learn useful representation from multi-band images. Additionally, we provide a multi-instance variant, aimed at extracting temporal patterns from a short sequence of spectral images. Experiments show promising results, providing the foundations for novel supportive tools, which could depict where surveillance and prevention measures could be prioritized

First external quality assessment of molecular and serological detection of rift valley fever in the western Mediterranean region

Pas de clé UTRift Valley fever (RVF) is a mosquito-borne viral zoonosis which affects humans and a wide range of domestic and wild ruminants. The large spread of RVF in Africa and its potential to emerge beyond its geographic range requires the development of surveillance strategies to promptly detect the disease outbreaks in order to implement efficient control measures, which could prevent the widespread of the virus to humans. The Animal Health Mediterranean Network (REMESA) linking some Northern African countries as Algeria, Egypt, Libya,Mauritania, Morocco, Tunisia with Southern European ones as France, Italy, Portugal and Spain aims at improving the animal health in the Western Mediterranean Region since 2009. In this context, a first assessment of the diagnostic capacities of the laboratories involved in the RVF surveillance was performed. The first proficiency testing (external quality assessment— EQA) for the detection of the viral genome and antibodies of RVF virus (RVFV) was carried out from October 2013 to February 2014. Ten laboratories participated from 6 different countries (4 from North Africa and 2 from Europe). Six laboratories participated in the ring trial for both viral RNA and antibodies detection methods, while four laboratories participated exclusively in the antibodies detection ring trial. For the EQA targeting the viral RNA detection methods 5 out of 6 laboratories reported 100% of correct results. One laboratory misidentified 2 positive samples as negative and 3 positive samples as doubtful indicating a need for corrective actions. For the EQA targeting IgG and IgM antibodies methods 9 out of the 10 laboratories reported 100% of correct results, whilst one laboratory reported all correct results except one false-positive. These two ring trials provide evidence that most of the participating laboratories are capable to detect RVF antibodies and viral RNA thus recognizing RVF infection in affected ruminants with the diagnostic methods currently available

Preliminary report of transfrontier disease surveillance in free-ranging buffalo in the Caprivi Strip, Namibia

A capture operation to ascertain the health status of free-ranging buffalo (Syncerus caffer) from six areas in the Caprivi Strip in the north-east corner of Namibia is described. In-depth reports on buffalo capture operations and their cost, with detailed descriptions of diseases for research purposes, sampling techniques, field processing of samples and laboratory-related costs are still lacking in the literature. This paper describes materials, methods and the related costs of a disease surveillance operation conducted among buffalo in Namibia. The survey attempted to provide information designed to improve the control of infectious diseases in the Caprivi Strip, a key area bordering Angola, Zambia, Botswana and Zimbabwe

Rapporto preliminare dell’attività di sorveglianza delle malattie transfrontaliere nei bufali africani nella regione del Caprivi, Namibia

Questo lavoro descrive le fasi organizzative e lo svolgimento delle operazioni di cattura nel Caprivi, a nordest della Namibia, per accertare lo stato sanitario dei bufali africani. Un report completo, che descriva le operazioni di cattura e i costi sostenuti oltre alle finalità di ricerca, le tecniche di campionamento, la lavorazione dei campioni in campo e le spese di laboratorio è assente in letteratura. In questo lavoro vengono descritti materiali, metodi e relativi costi di un’operazione di sorveglianza svolta sui bufali in Namibia. L’attività di sorveglianza qui descritta vuole fornire delle indicazioni preliminari che permettano di migliorare le strategie di controllo delle malattie infettive nel Caprivi, che è una zona di grande importanza sanitaria al confine con Angola, Zambia, Botswana e Zimbabwe