Deep features optimization based on a transfer learning, genetic algorithm, and extreme learning machine for robust content-based image retrieval.

The recent era has witnessed exponential growth in the production of multimedia data which initiates exploration and expansion of certain domains that will have an overwhelming impact on human society in near future. One of the domains explored in this article is content-based image retrieval (CBIR), in which images are mostly encoded using hand-crafted approaches that employ different descriptors and their fusions. Although utilization of these approaches has yielded outstanding results, their performance in terms of a semantic gap, computational cost, and appropriate fusion based on problem domain is still debatable. In this article, a novel CBIR method is proposed which is based on the transfer learning-based visual geometry group (VGG-19) method, genetic algorithm (GA), and extreme learning machine (ELM) classifier. In the proposed method, instead of using hand-crafted features extraction approaches, features are extracted automatically using a transfer learning-based VGG-19 model to consider both local and global information of an image for robust image retrieval. As deep features are of high dimension, the proposed method reduces the computational expense by passing the extracted features through GA which returns a reduced set of optimal features. For image classification, an extreme learning machine classifier is incorporated which is much simpler in terms of parameter tuning and learning time as compared to other traditional classifiers. The performance of the proposed method is evaluated on five datasets which highlight the better performance in terms of evaluation metrics as compared with the state-of-the-art image retrieval methods. Its statistical analysis through a nonparametric Wilcoxon matched-pairs signed-rank test also exhibits significant performance.

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage. The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people. This article presents detection of pancreatic cancer in CT scan images using machine PSO SVM and image processing. The Gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images. The K means algorithm uses a partitioning technique to separate the image into its component parts. The process of identifying objects in an image and determining the regions of interest is aided by image segmentation. The PCA method is used to extract important information from digital photographs. PSO SVM, naive Bayes, and AdaBoost are the algorithms that are used to perform the classification. Accuracy, sensitivity, and specificity of the PSO SVM algorithm are better.

Antibodies to influenza nucleoprotein cross-react with human hypocretin receptor 2.

The sleep disorder narcolepsy is linked to the HLA-DQB1*0602 haplotype and dysregulation of the hypocretin ligand-hypocretin receptor pathway. Narcolepsy was associated with Pandemrix vaccination (an adjuvanted, influenza pandemic vaccine) and also with infection by influenza virus during the 2009 A(H1N1) influenza pandemic. In contrast, very few cases were reported after Focetria vaccination (a differently manufactured adjuvanted influenza pandemic vaccine). We hypothesized that differences between these vaccines (which are derived from inactivated influenza viral proteins) explain the association of narcolepsy with Pandemrix-vaccinated subjects. A mimic peptide was identified from a surface-exposed region of influenza nucleoprotein A that shared protein residues in common with a fragment of the first extracellular domain of hypocretin receptor 2. A significant proportion of sera from HLA-DQB1*0602 haplotype-positive narcoleptic Finnish patients with a history of Pandemrix vaccination (vaccine-associated narcolepsy) contained antibodies to hypocretin receptor 2 compared to sera from nonnarcoleptic individuals with either 2009 A(H1N1) pandemic influenza infection or history of Focetria vaccination. Antibodies from vaccine-associated narcolepsy sera cross-reacted with both influenza nucleoprotein and hypocretin receptor 2, which was demonstrated by competitive binding using 21-mer peptide (containing the identified nucleoprotein mimic) and 55-mer recombinant peptide (first extracellular domain of hypocretin receptor 2) on cell lines expressing human hypocretin receptor 2. Mass spectrometry indicated that relative to Pandemrix, Focetria contained 72.7% less influenza nucleoprotein. In accord, no durable antibody responses to nucleoprotein were detected in sera from Focetria-vaccinated nonnarcoleptic subjects. Thus, differences in vaccine nucleoprotein content and respective immune response may explain the narcolepsy association with Pandemrix.

One dose of an MF59-adjuvanted pandemic A/H1N1 vaccine recruits pre-existing immune memory and induces the rapid rise of neutralizing antibodies.

Protective antibody responses to a single dose of 2009 pandemic vaccines have been observed in the majority of healthy subjects aged more than 3 years. These findings suggest that immune memory lymphocytes primed by previous exposure to seasonal influenza antigens are recruited in the response to A/H1N1 pandemic vaccines and allow rapid seroconversion. However, a clear dissection of the immune memory components favoring a fast response to pandemic vaccination is still lacking. Here we report the results from a clinical study where antibody, CD4+ T cell, plasmablast and memory B cell responses to one dose of an MF59-adjuvanted A/H1N1 pandemic vaccine were analyzed in healthy adults. While confirming the rapid appearance of antibodies neutralizing the A/H1N1 pandemic virus, we show here that the response is dominated by IgG-switched antibodies already in the first week after vaccination. In addition, we found that vaccination induces the rapid expansion of pre-existing CD4+ T cells and IgG-memory B lymphocytes cross-reactive to seasonal and pandemic A/H1N1 antigens. These data shed light on the different components of the immune response to the 2009 H1N1 pandemic influenza vaccination and may have implications in the design of vaccination strategies against future influenza pandemics.