Microbial natural products represent a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class that include antibiotics, immunosuppressants, anticancer agents, toxins, siderophores, pigments, and cytostatics. The discovery of novel NRPs remains a laborious process because many NRPs consist of nonstandard amino acids that are assembled by nonribosomal peptide synthetases (NRPSs). Adenylation domains (A-domains) in NRPSs are responsible for selection and activation of monomers appearing in NRPs. During the past decade, several support vector machine-based algorithms have been developed for predicting the specificity of the monomers present in NRPs. These algorithms utilize physiochemical features of the amino acids present in the A-domains of NRPSs. In this article, we benchmarked the performance of various machine learning algorithms and features for predicting specificities of NRPSs and we showed that the extra trees model paired with one-hot encoding features outperforms the existing approaches. Moreover, we show that unsupervised clustering of 453 560 A-domains reveals many clusters that correspond to potentially novel amino acids. While it is challenging to predict the chemical structure of these amino acids, we developed novel techniques to predict their various properties, including polarity, hydrophobicity, charge, and presence of aromatic rings, carboxyl, and hydroxyl groups.

We investigate the use of fractal analysis (FA) as the basis of a system for multiclass prediction of the progression of glaucoma. FA is applied to pseudo two-dimensional images converted from one-dimensional retinal nerve fiber layer (RNFL) data obtained from the eyes of normal subjects, and from subjects with progressive and non-progressive glaucoma. FA features are obtained using a box-counting method and a multi-fractional Brownian motion method that incorporates texture and multiresolution analyses. Both features are used for Gaussian kernel-based multiclass classification. Sensitivity, specificity, and area under receiver operating characteristic curve (AUROC) are computed for the FA features and for metrics obtained using wavelet-Fourier analysis (WFA) and fast-Fourier analysis (FFA). The AUROCs that predict progressors from non-progressors based on classifiers trained using a dataset comprised of non-progressors and ocular normal subjects are 0.70, 0.71 and 0.82 for WFA, FFA, and FA, respectively. The correct multiclass classification rates among progressors, non-progressors, and ocular normal subjects are 0.82, 0.86 and 0.88 for WFA, FFA, and FA, respectively. Simultaneous multiclass classification among progressors, non-progressors, and ocular normal subjects has not been previously described. The novel FA-based features achieve better performance with fewer features and less computational complexity than WFA and FFA.

A smart-phone generates lots of contextual information related with user environment in Social Network Service (SNS). Especially, communication data is an important context to recognize a close-relationship among mobile users. The related works using the communication data in this research area have actively studied. However, the most of the studies were to analyze social network studying on a personal distance or to measure centrality of mobile communication network. Therefore, we propose a model of close-relationship among mobile users on mobile social network. This proposed model can measure closeness between a user and the user's acquaintances. And the model is designed with users' communication log generated among mobile devices. To realize this, we collect and analyze context data of mobile users using context-aware platform and context server.

Cellular simultaneous recurrent networks (CSRN)s have been successfully exploited to solve the conventional maze traversing problem. In this work, for the first time, we investigate the use of CSRNs for image registration under affine transformations. In our simulations, we consider binary images with in-plane rotations between plusmn20deg. First, we experiment with a readily available CSRN with generalized multilayer perceptrons (GMLP)s as the basic core. We identify performance criteria for such CSRNs in affine correction. We then propose a modified MLP architecture with multi-layered feedback as the core for a CSRN to improve binary image registration performance. Simulation results show that while both the GMLP network and our modified network are able to achieve localized image registration, our modified architecture is more effective in moving pixels for registration. Finally, we use sub-image processing with our modified MLP architecture, to reduce training time and increase global registration accuracy. Overall, both CSRN architectures show promise for correctly registering a binary image.

In this paper, we investigate two novel techniques based on Cellular Simultaneous Recurrent Network (CSRN) that can address the problem of small in-plane image rotation and large out-of-plane pose invariant face recognition in image sequences. In our first technique, for the first time in literature, we investigated the CSRNs for static image registration under affine transformations. Both the readily available CSRN with generalized multilayer perceptrons (GMLPs) and a modified MLP architecture with multi-layered feedback are implemented. Simulation results show that while both the GMLP network and our modified network are able to achieve localized image registration, our modified architecture is more effective in registering image pixels. In our second technique, we investigate the recognition problem for face image sequences with large pose variation as an implicit temporal prediction task for CSRN. CSRN is trained by image sequences to capture the temporal information. The Euclidian distances between successive frames of test and output image sequences indicate either a match or mismatch between the two corresponding face classes. We extensively evaluate our CSRN-based face recognition technique with 5 persons using publicly available VidTIMIT Audio-Video face dataset. In order to verify the performance of CSRN, we also implement Elman neural network for comparison.

Integrating notification with shared memory applications is an interesting problem. This paper looks at implementing notification in a shared memory, object-oriented, distributed transaction environment.