Many illnesses and infections are exacerbated and/or caused by biofilms. Neisseria gonorrhoeae, the etiologic agent of gonorrhea, is frequently asymptomatic in women, which can lead to persistent infection. Persistent infection can result in pelvic inflammatory disease, tubo-ovarian abscesses, infertility, and ectopic pregnancy. N. gonorrhoeae has been shown to form biofilms over glass, primary...

Neisseria gonorrhoeae has been shown to form biofilms during cervical infection. Thus, biofilm formation may play an important role in the infection of women. The ability of N. gonorrhoeae to form membrane blebs is crucial to biofilm formation. Blebs contain DNA and outer membrane structures, which have been shown to be major constituents of the biofilm matrix. The organism expresses a DNA ther...

Denitrification is an anaerobic process performed by several soil bacteria as an alternative to aerobic respiration. A key-step in denitrification (the N-Nbond is made) is catalyzed by nitric oxide reductase (NOR); 2NO + 2e + 2H → N2O + H2O. NOR from Paracoccus denitrificans is a member of the heme copper oxidase superfamily (HCuOs), where the mitochondrial cytochrome c oxidase is the classical...

Recent work in machine learning has been proven successful on object recognition task. For instance, best digit-classi cation accuracy on MNIST dataset rivals that of human-beings (Cire3an et al., 2012); (Coates et al., 2011) has achieved state-of-art performance on both CIFAR and NORB benchmarks; breakthrough on scalable visual recognition has been made by (Krizhevsky et al., 2012) via e cient...

We propose a method that exploits pose information in order to improve object classification. A lot of research has focused in other strategies, such as engineering feature extractors, trying different classifiers and even using transfer learning. Here, we use neural network architectures in a multi-task setup, whose outputs predict both the class and the camera azimuth. We investigate both Mul...

Recently, it was shown that deep neural networks can perform very well if the activities of hidden units are regularized during learning, e.g, by randomly dropping out 50% of their activities. We describe a method called ‘standout’ in which a binary belief network is overlaid on a neural network and is used to regularize of its hidden units by selectively setting activities to zero. This ‘adapt...

With lot of research and advancement of deep learning, complex unsupervised learning is applied for extracting deep hierarchies of features especially to images. But, off-the-shelf unsupervised learning algorithms combined with deep learning techniques would yield results similar to complext,time consuming Deep learning algorithms. In this report, I would use K-means algorithm based on [1][3] a...

Generic object recognition is the classification of an individual object to a generic category. Intra-class variabilities, such as different objects of the same category, different poses and lighting conditions, cause big troubles for this task. Traditional methods involve plenty of pre-processing steps, such as shape model construction, extraction of hand-crafted features, etc. Moreover, these...

While vector quantization (VQ) has been applied widely to generate features for visual recognition problems, much recent work has focused on more powerful methods. In particular, sparse coding has emerged as a strong alternative to traditional VQ approaches and has been shown to achieve consistently higher performance on benchmark datasets. Both approaches can be split into a training phase, wh...