Effect of Seven-Valent Pneumococcal Conjugate Vaccine on Staphylococcus aureus Colonisation in a Randomised Controlled Trial

Background: Heptavalent pneumococcal conjugate vaccine (PCV7) shifts nasopharyngeal colonisation with vaccine serotype pneumococci towards nonvaccine serotypes. Because of the reported negative association of vaccine serotype pneumococci and Staphylococcus aureus in the nasopharynx, we explored the effect of PCV7 on nasopharyngeal colonisation with S. aureus in children and parents. Methodology/Principal Findings: This study was part of a randomised controlled trial on the effect of PCV7 on pneumococcal carriage, enrolling healthy newborns who were randomly assigned (1: 1: 1) to receive PCV7 (1) at 2 and 4 months of age (2) at 2, 4 and 11 months or (3) no PCV7 (controls). Nasopharyngeal colonisation of S. aureus was a planned secondary outcome. Nasopharyngeal swabs were obtained from all children over a 2-year period with 6-months interval and from one parent at the child's age of 12 and 24 months and cultured for Streptococcus pneumoniae and S. aureus. Between July 2005 and February 2006, 1005 children were enrolled and received either 2-doses of PCV7 (n = 336), 2+1-doses (336) or no dose (n = 333) before PCV7 implementation in the Dutch national immunization program. S. aureus colonisation had doubled in children in the 2+1-dose group at 12 months of age compared with unvaccinated controls (10.1% versus 5.0%; p = 0.019). A negative association for co-colonisation of S. pneumoniae and S. aureus was observed for both vaccine serotype (adjusted odds ratio (aOR) 0.53, 95% confidence interval (CI) 0.38-0.74) and nonvaccine serotype pneumococci (aOR 0.67, 95% CI 0.52-0.88). Conclusions/Significance: PCV7 induces a temporary increase in S. aureus colonisation in children around 12 months of age after a 2+1-dose PCV7 schedule. The potential clinical consequences are unknown and monitoring is warranted

A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

The strength of long short-term memory neural networks (LSTMs) that have been applied is more located in handling sequences of variable length than in handling geometric variability of the image patterns. In this paper, an end-to-end convolutional LSTM neural network is used to handle both geometric variation and sequence variability. The best results for LSTMs are often based on large-scale training of an ensemble of network instances. We show that high performances can be reached on a common benchmark set by using proper data augmentation for just five such networks using a proper coding scheme and a proper voting scheme. The networks have similar architectures (convolutional neural network (CNN): five layers, bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporal classification (CTC) processing step). The approach assumes differently scaled input images and different feature map sizes. Three datasets are used: the standard benchmark RIMES dataset (French); a historical handwritten dataset KdK (Dutch); the standard benchmark George Washington (GW) dataset (English). Final performance obtained for the word-recognition test of RIMES was 96.6%, a clear improvement over other state-of-the-art approaches which did not use a pre-trained network. On the KdK and GW datasets, our approach also shows good results. The proposed approach is deployed in the Monk search engine for historical-handwriting collections

The disruption of proteostasis in neurodegenerative diseases

Cells count on surveillance systems to monitor and protect the cellular proteome which, besides being highly heterogeneous, is constantly being challenged by intrinsic and environmental factors. In this context, the proteostasis network (PN) is essential to achieve a stable and functional proteome. Disruption of the PN is associated with aging and can lead to and/or potentiate the occurrence of many neurodegenerative diseases (ND). This not only emphasizes the importance of the PN in health span and aging but also how its modulation can be a potential target for intervention and treatment of human diseases.info:eu-repo/semantics/publishedVersio