In this article, the terrain classifications of polarimetric synthetic aperture radar (PolSAR) images are studied. A novel semi-supervised method based on improved Tri-training combined with a neighborhood minimum spanning tree (NMST) is proposed. Several strategies are included in the method: 1) a high-dimensional vector of polarimetric features that are obtained from the coherency matrix and diverse target decompositions is constructed; 2) this vector is divided into three subvectors and each subvector consists of one-third of the polarimetric features, randomly selected. The three subvectors are used to separately train the three different base classifiers in the Tri-training algorithm to increase the diversity of classification; and 3) a help-training sample selection with the improved NMST that uses both the coherency matrix and the spatial information is adopted to select highly reliable unlabeled samples to increase the training sets. Thus, the proposed method can effectively take advantage of unlabeled samples to improve the classification. Experimental results show that with a small number of labeled samples, the proposed method achieves a much better performance than existing classification methods.

Motivation: Metagenomic read classification is a critical step in the identification and quantification of microbial species sampled by high-throughput sequencing. Although many algorithms have been developed to date, they suffer significant memory and/or computational costs. Due to the growing popularity of metagenomic data in both basic science and clinical applications, as well as the increasing volume of data being generated, efficient and accurate algorithms are in high demand. Results: We introduce MetaOthello, a probabilistic hashing classifier for metagenomic sequencing reads. The algorithm employs a novel data structure, called l-Othello, to support efficient querying of a taxon using its k-mer signatures. MetaOthello is an order-of-magnitude faster than the current state-of-the-art algorithms Kraken and Clark, and requires only one-third of the RAM. In comparison to Kaiju, a metagenomic classification tool using protein sequences instead of genomic sequences, MetaOthello is three times faster and exhibits 20-30% higher classification sensitivity. We report comparative analyses of both scalability and accuracy using a number of simulated and empirical datasets. Availability and implementation: MetaOthello is a stand-alone program implemented in C ++. The current version (1.0) is accessible via https://doi.org/10.5281/zenodo.808941. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

In this paper, a model of radar backscattering from rain-generated stalks on the ocean surface in a rain field is proposed. In the model, stalks in the rain field form an array and are considered as finite water cylinders standing out of an infinite water surface. The radar backscattering coefficient from these stalks is derived. Both incoherent and coherent backscattering mechanisms from the stalks are considered. The model shows that the radar backscattering intensity is a function of the average distance between stalks on the water surface, the radar wave frequency, and the incident angle of radar waves. For light/moderate rain (at low rain rates), the radar backscattering intensity increases with increasing rain rate. For heavy rain (at high rain rates), the radar backscattering intensity decreases with increasing rain rate. The maximum radar backscattering intensity occurs at a rain rate that depends on the radar wave frequency and the incident angle of radar waves. The present model is used to explain the radar signatures of a rainfall event simultaneously observed by C-band ENVISAT (European satellite) Advanced Synthetic Aperture Radar (ASAR) and ground-based weather radar in the Northwest Pacific. The relationship between the radar return intensity extracted from the C-band ASAR image and the rain rate obtained from ground-based weather radar is in agreement with the model's calculation. Also, the air-sea interface in rain fields and its effects on the attenuation of radar backscattering are experimentally studied in the laboratory.

Alternative splicing (AS) is a regulated process that enables the production of multiple mRNA transcripts from a single multi-exon gene. The availability of large-scale RNA-seq datasets has made it possible to predict splice junctions, as well as splice sites through spliced alignment to the reference genome. This greatly enhances the capability to decipher gene structures and explore the diversity of splicing variants. However, existing ab initio aligners are vulnerable to false positive spliced alignments as a result of sequence errors and random sequence matches. These spurious alignments can lead to a significant set of false positive splice junction predictions, confusing downstream analyses of splice variant detection and abundance estimation. In this work, we illustrate that splice junction sequence characteristics can be ascertained from experimental data with deep learning techniques. We employ deep convolutional neural networks for a novel splice junction classification tool named DeepSplice that (i) outperforms state-of-the-art methods for predicting splice sites, (ii) shows high computational efficiency and (iii) can be applied to self-defined training data by users.

Interim results of a laboratory investigation of the low-grazing angle radar backscatter generated by plunging breakers are presented. A primary objective of this investigation is to experimentally verify a scattering mechanism through which the horizontally-polarized backscatter generated by the breaker can exceed that at vertical polarization, without involving multipath propagation between the front face of the wave and its crest. The experiments were conducted at the University of Maryland-College Park wave tank, and involved the deployment of a dual-polarized, ultrawideband radar, multiple high-speed visible cameras, and an ultra-sensitive infrared camera. All instruments were deployed simultaneously on a moving carriage, located above the wave tank channel, that followed the breakers as they evolved in space and time. Representative ultrawideband radar data and optical images are presented in this paper

Radar scattering experiments were carried out at the wavetank facility at the University of Maryland-College Park. Spilling and plunging breakers were generated by means of a chirped wave packet, and were then imaged with a high-speed camera. Simultaneously, the radar backscatter generated by the breakers at a nominal grazing angle of 12 degrees was measured by an ultrawideband, dual-polarized, X-band radar with a range resolution of approximately 4 cm. This experimental setup also included a moving instrument carriage that allowed the sensors to follow the breakers throughout their entire evolution. In conjunction with numerical simulations, these experiments can guide the development of realistic scattering models by identifying the important wave features and the corresponding scattering mechanisms.