Mapping of European transnational collaborative partnerships in higher education

This report aimed to map the existing transnational collaborative partnerships between higher education institutions in Europe. In doing so it surveyed representatives from such partnerships. Their responses provided interesting insights which are analysed in this report.JRC.B.7-Knowledge for Finance, Innovation and Growt

STUDIES OF A BIRNAVIRUS ISOLATED FROM SNAKEHEAD, OPHICEPHALUS MICROPELTES

Ph.DDOCTOR OF PHILOSOPH

A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes

The prediction of severe weather events such as hurricanes is always a challenging task in the history of climate research, and many deep learning models have been developed for predicting the severity of weather events. When a disastrous hurricane strikes a coastal region, it causes serious hazards to human life and habitats and also reflects a prodigious amount of economic losses. Therefore, it is necessary to build models to improve the prediction accuracy and to avoid such significant losses in all aspects. However, it is impractical to predict or monitor every storm formation in real time. Though various techniques exist for diagnosing the tropical cyclone intensity such as convolutional neural networks (CNN), convolutional auto-encoders, recurrent neural network (RNN), etc., there are some challenges involved in estimating the tropical cyclone intensity. This study emphasizes estimating the tropical cyclone intensity to identify the different categories of hurricanes and to perform post-disaster management. An improved deep convolutional neural network (CNN) model is used for predicting the weakest to strongest hurricanes with the intensity values using infrared satellite imagery data and wind speed data from HURDAT2 database. The model achieves a lower Root mean squared error (RMSE) value of 7.6 knots and a Mean squared error (MSE) value of 6.68 knots by adding the batch normalization and dropout layers in the CNN model. Further, it is crucial to predict and evaluate the post-disaster damage for implementing advance measures and planning for the resources. The fine-tuning of the pre-trained visual geometry group (VGG 19) model is accomplished to predict the extent of damage and to perform automatic annotation for the image using the satellite imagery data of Greater Houston. VGG 19 is also trained using video datasets for classifying various types of severe weather events and to annotate the weather event automatically. An accuracy of 98% is achieved for hurricane damage prediction and 97% accuracy for classifying severe weather events. The results proved that the proposed models for hurricane intensity estimation and its damage prediction enhances the learning ability, which can ultimately help scientists and meteorologists to comprehend the formation of storm events. Finally, the mitigation steps in reducing the hurricane risks are addressed

Phylogenetic position of Riemerella anatipestifer based on 16S rRNA gene sequences

Riemerella anatipestifer, the causative agent of septicemia anserum exsudativa (also called new duckling disease), belongs to the family Flavobacteriaceae of gram-negative bacteria. We determined the DNA sequences of the rrs genes encoding the 16S rRNAs of four R. anatipestifer strains by directly sequencing PCR-amplified rrs genes. A sequence similarity analysis confirmed the phylogenetic position of R. anatipestifer in the family Flavobacteriaceae in rRNA superfamily V and allowed fine mapping of R. anatipestifer on a separate rRNA branch comprising the most closely related species, Bergeyella zoohelcum, as well as Chryseobacterium balustinum, Chryseobacterium indologenes, and Chryseobacterium gleum. The sequences of the rrs genes of the four R. anatipestifer strains varied between 0.5 and 1.0%, but all of the strains occupied the same position on the phylogenetic tree. In general, differences in rrs genes were observed among R. anatipestifer strains, even within a given serotype, as shown by restriction fragment length polymorphism of PCR-amplified rrs genes

Identification and Characterization of CAMP Cohemolysin as a Potential Virulence Factor of Riemerella anatipestifer

Riemerella anatipestifer is responsible for exudative septicemia in ducks. The genetic determinant of the CAMP cohemolysin, cam, from a strain of R. anatipestifer was cloned and expressed in Escherichia coli. Chromosomal DNA from serotype 19 strain 30/90 was used to construct a gene library in pBluescript II SK(−) vector in E. coli XL-1-Blue strain. The clones containing recombinant plasmids were screened for the CAMP reaction with Staphylococcus aureus. Those that showed cohemolysis were chosen for further analysis by sequencing. One of these clones, JFRA8, was subcloned to identify the smallest possible DNA fragment containing the CAMP cohemolysin determinant, which was located on a 3,566-bp BamHI-BstXI fragment which specified a 1,026-bp open reading frame. Clones containing recombinant plasmids carrying cam obtained by PCR cloning into E. coli M15 strain secreted an active CAMP cohemolysin. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis and Western blot analyses confirmed that the recombinant strain expressed a protein with a molecular mass of 37 kDa and that strains from serotypes 1, 2, 3, 5, 6, and 19 expressed the cohemolysin. The deduced amino acid sequence showed high homology to those of O-sialoglycoprotein endopeptidases. Hydrolysis of radioiodinated glycophorin A confirmed that Cam is a sialoglycoprotease

The EAN Brain Health Strategy: One Brain, One Life, One Approach.

BACKGROUND Brain health is essential for health, well-being productivity and creativity across the entire life. Its definition goes beyond the absence of disease embracing all cognitive, emotional, behavioural and social functions which are necessary to cope with life situations. METHODS The EAN Brain Health Strategy responds to the high and increasing burden of neurological disorders. It aims to develop a non-disease, non-age centred holistic and positive approach ('one brain, one life, one approach') to prevent neurological disorders (e.g., Alzheimer's disease and other dementias, stroke, epilepsy, headache/migraine, Parkinson's disease, multiple sclerosis, sleep disorders, brain cancer) but also to preserve brain health and promote recovery after brain damage. RESULTS The pillars of the EAN Brain Health strategy are: 1) Contribute to a global and international Brain Health approach (together with national and subspecialty societies, other medical societies, WHO, WFN, patients' organizations, industry, and other stakeholders); 2) Supporting the 47 European national societies, healthcare and policymakers in the implementation of integrated and people-centred campaigns; 3) Fostering Research (e.g. on prevention of neurological disorders, determinants and assessments of brain health), 4) Promoting Education of students, neurologists, general practitioners, other medical specialists and health professionals, patients, caregivers, and general public; 5) Raising public awareness of neurological disorders and brain health. CONCLUSIONS By adopting this 'one brain, one life, one approach' strategy in cooperation with partner societies, international organisations, and policymakers, a significant number of neurological disorders may be prevented while enhancing the overall well-being of individuals by maintaining brain health through the life course