Abstract-In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.

RNA silencing functions as an important antiviral defense mechanism in a broad range of eukaryotes. In plants, biogenesis of several classes of endogenous small interfering RNAs (siRNAs) requires RNA-dependent RNA Polymerase (RDR) activities. Members of the RDR family proteins, including RDR1and RDR6, have also been implicated in antiviral defense, although a direct role for RDRs in viral siRNA biogenesis has yet to be demonstrated. Using a crucifer-infecting strain of Tobacco Mosaic Virus (TMV-Cg) and Arabidopsis thaliana as a model system, we analyzed the viral small RNA profile in wild-type plants as well as rdr mutants by applying small RNA deep sequencing technology. Over 100,000 TMV-Cg-specific small RNA reads, mostly of 21- (78.4%) and 22-nucleotide (12.9%) in size and originating predominately (79.9%) from the genomic sense RNA strand, were captured at an early infection stage, yielding the first high-resolution small RNA map for a plant virus. The TMV-Cg genome harbored multiple, highly reproducible small RNA-generating hot spots that corresponded to regions with no apparent local hairpin-forming capacity. Significantly, both the rdr1 and rdr6 mutants exhibited globally reduced levels of viral small RNA production as well as reduced strand bias in viral small RNA population, revealing an important role for these host RDRs in viral siRNA biogenesis. In addition, an informatics analysis showed that a large set of host genes could be potentially targeted by TMV-Cg-derived siRNAs for posttranscriptional silencing. Two of such predicted host targets, which encode a cleavage and polyadenylation specificity factor (CPSF30) and an unknown protein similar to translocon-associated protein alpha (TRAP α), respectively, yielded a positive result in cleavage validation by 5′RACE assays. Our data raised the interesting possibility for viral siRNA-mediated virus-host interactions that may contribute to viral pathogenicity and host specificity.

Abstract

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets.