Viromes of Hungarian Peach Trees Identified by High-Throughput Sequencing of Small RNAs.

Peach trees can be infected with viruses and viroids. As we do not have efficient plant protection methods against these pathogens, the prevention of infection is crucial. Fruit trees are maintained by vegetative propagation. Planting material such as certified mother trees and rootstocks should be free from viruses and viroids, and this status has to be regularly checked to prevent infections. We surveyed certified peach trees for the presence of viruses and viroids using small RNA high-throughput sequencing (HTS), an unbiased virus diagnostic method. The results of the bioinformatic analysis of HTS were validated by other molecular methods including RT-PCR, Northern blot hybridization and loop-mediated isothermal amplification (LAMP). We found the presence of plum pox virus and peach latent mosaic viroid (PLMVd) in the vector-free isolator houses, whose presence should be regularly tested. Moreover, we detected frequent infection with recently described viruses such as nectarine stem pitting-associated virus and peach-associated luteovirus (PaLV). During the survey, PLMVd and PaLV were detected for the first time in Hungary. The analysis of the presenting virus variants and possible sources of infection suggests that the source of the viral infection could be the infected propagating material. Our study emphasizes the importance of using sensitive and trustworthy diagnostic techniques to be able to detect viral infections and successfully prevent their spread by propagation material.

Graph-Cut RANSAC: Local Optimization on Spatially Coherent Structures.

We propose Graph-Cut RANSAC, GC-RANSAC in short, a new robust geometric model estimation method where the local optimization step is formulated as energy minimization with binary labeling, applying the graph-cut algorithm to select inliers. The minimized energy reflects the assumption that geometric data often form spatially coherent structures - it includes both a unary component representing point-to-model residuals and a binary term promoting spatially coherent inlier-outlier labelling of neighboring points. The proposed local optimization step is conceptually simple, easy to implement, efficient with a globally optimal inlier selection given the model parameters. Graph-Cut RANSAC, equipped with "the bells and whistles" of USAC and MAGSAC++, was tested on a range of problems using a number of publicly available datasets for homography, 6D object pose, fundamental and essential matrix estimation. It is more geometrically accurate than state-of-the-art robust estimators, fails less often and runs faster or with speed similar to less accurate alternatives. The source code is available at https://github.com/danini/graph-cut-ransac.

Marginalizing Sample Consensus.

A new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not make inlier-outlier decisions, and a novel marginalization procedure formulated as an M-estimation with a novel class of M-estimators (a robust kernel) solved by an iteratively re-weighted least squares procedure. Instead of the inlier-outlier threshold, it requires only its loose upper bound which can be chosen from a significantly wider range. Also, we propose a new termination criterion and a technique for selecting a set of inliers in a data-driven manner as a post-processing step after the robust estimation finishes. On a number of publicly available real-world datasets for homography, fundamental matrix fitting and relative pose, MAGSAC++ produces results superior to the state-of-the-art robust methods. It is more geometrically accurate, fails fewer times, and it is often faster. It is shown that MAGSAC++ is significantly less sensitive to the setting of the threshold upper bound than the other state-of-the-art algorithms to the inlier-outlier threshold. Therefore, it is easier to be applied to unseen problems and scenes without acquiring information by hand about the setting of the inlier-outlier threshold. The source code and examples both in C++ and Python are available at https://github.com/danini/magsac.

Analysis of a novel RNA virus in a wild northern white-breasted hedgehog (Erinaceus roumanicus).

Tombusviruses are generally considered plant viruses. A novel tombus-/carmotetravirus-like RNA virus was identified in a faecal sample and blood and muscle tissues from a wild northern white-breasted hedgehog (Erinaceus roumanicus). The complete genome of the virus, called H14-hedgehog/2015/HUN (GenBank accession number MN044446), is 4,118 nucleotides in length with a readthrough stop codon of type/group 1 in ORF1 and lacks a poly(A) tract at the 3' end. The predicted ORF1-RT (RdRp) and the capsid proteins had low (31-33%) amino acid sequence identity to unclassified tombus-/noda-like viruses (Hubei tombus-like virus 12 and Beihai noda-like virus 10), respectively, discovered recently in invertebrate animals. An in vivo experimental plant inoculation study showed that an in vitro-transcribed H14-hedgehog/2015/HUN viral RNA did not replicate in Nicotiana benthamiana, Chenopodium quinoa, or Chenopodium murale, the most susceptible hosts for plant-origin tombusviruses.

Optimal Multi-View Surface Normal Estimation Using Affine Correspondences.

An optimal, in the least squares sense, method is proposed to estimate surface normals in both stereo and multi-view cases. The proposed algorithm exploits exclusively photometric information via affine correspondences and estimates the normal for each correspondence independently. The normal is obtained as a root of a quartic polynomial. Therefore, the processing time is negligible. Eliminating the outliers, we propose a robust extension of the algorithm that combines maximum likelihood estimation and iteratively re-weighted least squares. The method has been validated on both synthetic and publicly available real-world datasets. It is superior to the state of the art in terms of accuracy and processing time. Besides, we demonstrate two possible applications: 1) using our algorithm as the seed-point generation step of patch-based multi-view stereo method, the obtained reconstruction is more accurate, and the error of the 3D points is reduced by 30% on average and 2) multi-plane fitting becomes more accurate applied to the resulting oriented point cloud.

Efficient Recovery of Essential Matrix From Two Affine Correspondences.

We propose a method to estimate the essential matrix using two affine correspondences for a pair of calibrated perspective cameras. Two novel, linear constraints are derived between the essential matrix and a local affine transformation. The proposed method is also applicable to the over-determined case. We extend the normalization technique of Hartley to local affinities and show how the intrinsic camera matrices modify them. Even though perspective cameras are assumed, the constraints can straightforwardly be generalized to arbitrary camera models since they describe the relationship between local affinities and epipolar lines (or curves). Benefiting from the low number of exploited points, it can be used in robust estimators, e.g. RANSAC, as an engine, thus leading to significantly less iterations than the traditional point-based methods. The algorithm is validated both on synthetic and publicly available data sets and compared with the state-of-the-art. Its applicability is demonstrated on two-view multi-motion fitting, i.e., finding multiple fundamental matrices simultaneously, and outlier rejection.

Small RNA NGS Revealed the Presence of Cherry Virus A and Little Cherry Virus 1 on Apricots in Hungary.

Fruit trees, such as apricot trees, are constantly exposed to the attack of viruses. As they are propagated in a vegetative way, this risk is present not only in the field, where they remain for decades, but also during their propagation. Metagenomic diagnostic methods, based on next generation sequencing (NGS), offer unique possibilities to reveal all the present pathogens in the investigated sample. Using NGS of small RNAs, a special field of these techniques, we tested leaf samples of different varieties of apricot originating from an isolator house or open field stock nursery. As a result, we identified Cherry virus A (CVA) and little cherry virus 1 (LChV-1) for the first time in Hungary. The NGS results were validated by RT-PCR and also by Northern blot in the case of CVA. Cloned and Sanger sequenced viral-specific PCR products enabled us to investigate their phylogenetic relationships. However, since these pathogens have not been described in our country before, their role in symptom development and modification during co-infection with other viruses requires further investigation.

Use of siRNAs for Diagnosis of Viruses Associated to Woody Plants in Nurseries and Stock Collections.

Woody perennial plants like grapevine and fruit trees can be infected by several viruses even as multiple infections. Since they are propagated vegetatively, the phytosanitary status of the propagation material (both the rootstock and the variety) can have a profound effect on the lifetime and health of the new plantations. The fast evolution of sequencing techniques provides a new opportunity for metagenomics-based viral diagnostics. Viral derived small RNAs produced by the host immune system during viral infection can be sequenced by next-generation techniques and analyzed for the presence of viruses, revealing the presence of all known viral pathogens in the sample. This method is based on Illumina sequencing of short RNAs and bioinformatics analysis of virus-derived small RNAs in the host. Here we describe a protocol for this challenging technique step by step with notes, in order to ensure success for every user.