Transcriptomics of Haemophilus (Glässerella) parasuis serovar 5 subjected to culture conditions partially mimetic to natural infection for the search of new vaccine antigens

11 p.Haemophilus (Glässerella) parasuis is the etiological agent of Glässer’s disease in pigs. Control of this disorder has been traditionally based on bacterins. The search for alternative vaccines has focused mainly on the study of outer membrane proteins. This study investigates the transcriptome of H. (G.) parasuis serovar 5 subjected to in vitro conditions mimicking to those existing during an infection (high temperature and iron-restriction), with the aim of detecting the overexpression of genes coding proteins exposed on bacterial surface, which could represent good targets as vaccine candidates. The transcriptomic approach identified 13 upregulated genes coding surface proteins: TbpA, TbpB, HxuA, HxuB, HxuC, FhuA, FimD, TolC, an autotransporter, a protein with immunoglobulin folding domains, another large protein with a tetratricopeptide repeat and two small proteins that did not contain any known domains. Of these, the first six genes coded proteins being related to iron extraction. Six of the proteins have already been tested as vaccine antigens in murine and/or porcine infection models and showed protection against H. (G.) parasuis. However, the remaining seven have not yet been tested and, consequently, they could become useful as putative antigens in the prevention of Glässer’s disease. Anyway, the expression of this seven novel vaccine candidates should be shown in other serovars different from serovar 5.S

Dysphagia Management in Iran: Knowledge, Attitude and Practice of Healthcare Providers

Despite the remarkable burden of dysphagia, appropriate multidisciplinary management is lacking in Iran and patients are often deprived of effective treatments. Obtaining a full understanding of knowledge, attitude and practice (KAP) of healthcare providers is necessary to determine the gaps in improvement of the quality of care for dysphagic patients. A questionnaire was designed covering demographic information and the parameters of KAP. Face and content validity were determined. Test�retest reliability confirmed that the questionnaire scores are stable over time (r = 0.77, p value < 0.01). Participants were healthcare providers employed in university-affiliated hospitals in three major cities of Iran; Tehran, Shiraz and Mashhad. In total, 312 healthcare professionals completed our survey. The majority (96.8) were familiar with the term �dysphagia or swallowing disorders�. Most of the participants believed their profession (88.5), as well as other disciplines (92.3) can play an important role in the management of dysphagia; and this problem should be recognized in a multidisciplinary manner (96.2). Also, 60.9 had encountered a patient with dysphagia. 52.2 had used at least one assessment method, while 49.9 had applied at least one treatment method. However, very few participants were familiar with a standard test for screening and assessment of dysphagia (11.9). 74.7 were willing to participate in a workshop on dysphagia. As the main pitfalls of care lie in diagnosis and treatment expertise, the policy of hospitals should prioritize educating and updating the skills of healthcare professionals, encourage multidisciplinary teamwork, establishing clear guidelines and facilitate access to advanced tools. © 2018 Springer Science+Business Media, LLC, part of Springer Natur

Context-Based Object Recognition: Indoor Versus Outdoor Environments

Object recognition is a challenging problem in high-level vision. Models that perform well for the outdoor domain, perform poorly in the indoor domain and the reverse is also true. This is due to the dramatic discrepancies of the global properties of each environment, for instance, backgrounds and lighting conditions. Here, we show that inferring the environment before or during the recognition process can dramatically enhance the recognition performance. We used a combination of deep and shallow models for object and scene recognition, respectively. Also, we used three novel topologies that can provide a trade-off between classification accuracy and decision sensitivity. We achieved a classification accuracy of 97.91%, outperforming the performance of a single GoogLeNet by 13%. In another experiment, we achieved an accuracy of 95% to categorise indoor and outdoor scenes by inference