Fault localization, a core element in network fault management, is the process of inferring the exact failure in a network from the set of observed symptoms. Since faults in network systems can be unavoidable, their quick and accurate detection and diagnosis is important for the stability, consistency, and performance of a communication system. In this paper, we discuss the challenges of fault localization in complex communication systems and present an overview of recent techniques proposed in the literature along with their advantages and limitations. We start by briefly surveying passive monitoring techniques which were previously reviewed in a survey by Steinder [1]. We then describe more recent fault localization research in five categories: active monitoring techniques, techniques for overlay and virtual networks, decentralized probabilistic management techniques, temporal correlation techniques, and learning techniques.

200 words) 1 In monocots other than maize and rice, the repertoire and diversity of microRNAs (miRNAs) and 2 the populations of phased, secondary, small interfering RNAs (phasiRNAs) are poorly 3 characterized. To remedy this, we sequenced small RNAs from vegetative and dissected 4 inflorescence tissue in 28 phylogenetically diverse monocots and from several early-diverging 5 angiosperm lineages, as well as publicly available data from 10 additional monocot species. We 6 annotated miRNAs, siRNAs and phasiRNAs across the monocot phylogeny, identifying miRNAs 7 apparently lost or gained in the grasses relative to other monocot families, as well as a number 8 of tRNA fragments misannotated as miRNAs. Using our miRNA database cleaned of these 9 misannotations, we identified conservation at the 8 th , 9 th , 19 th and 3' end positions that we 10 hypothesize are signatures of selection for processing, targeting, or Argonaute sorting. We show 11 that 21-nt reproductive phasiRNAs are far more numerous in grass genomes than other 12 monocots. Based on sequenced monocot genomes and transcriptomes, DICER-LIKE5 (DCL5), 13 important to 24-nt phasiRNA biogenesis, likely originated via gene duplication before the 14 diversification of the grasses. This curated database of phylogenetically diverse monocot miRNAs, 15 siRNAs, and phasiRNAs represents a large collection of data that should facilitate continued 16 exploration of small RNA diversification in flowering plants. 17 18 19 20 55 2018). Rice, Brachypodium, and maize are the most studied of the grasses, with miRNAs 56 characterized using varying genotypes, tissue types, growth and stress conditions (Jeong et al., 57 2011; Zhang et al., 2009). With the major goal of assessing the diversity and origins of miRNAs 58 4in monocots, we analyzed sRNA data from 38 phylogenetically diverse monocots, spanning 59 orders from the Acorales to the Zingiberales. We described sRNA size classes, miRNA 60 conservation, divergence, sequence variability, 5' and 3' end nucleotide preferences, and single 61 nucleotide sequence profile characterizing positional biases and providing novel insights within 62 the miRNA sequences. We performed comparative analysis of miR2118 and miR2275 and their 63 long non-coding RNA (lncRNA) targets in monocots relative to other flowering plants, 64 demonstrating their presence and absence in these species. We found that both miR2118 and 65 miR2275 are conserved across diverse monocot species, and are present in vegetative tissues but 66 are found at high abundances predominantly in inflorescence tissues. The 21-and 24-nt PHAS 67 loci are most numerous in the genomes of grasses, relative to other monocots, and are similarly 68 most abundant in inflorescence tissues. Fewer PHAS loci were identified in non-grass monocots. 69 Overall, our study provides a deep comparative analysis of sRNAs in monocots, including a refined 70 database of monocot miRNAs. 71 72 Results 73 74Sequencing from diverse monocots demonstrates atypically abundant 22-nt siRNAs 75 We c...

The Solanaceae or “nightshade" family is an economically important group with remarkable diversity. To gain a better understanding of how the unique biology of the Solanaceae relates to the family’s small RNA genomic landscape, we downloaded over 255 publicly available small RNA datasets that comprise over 2.6 billion reads of sequence data. We applied a suite of computational tools to predict and annotate two major small RNA classes: (1) microRNAs (miRNAs), typically 20- to 22-nt RNAs generated from a hairpin precursor and functioning in gene silencing, and (2) short interfering RNAs (siRNAs), including 24-nt heterochromatic siRNAs (hc-siRNAs) typically functioning to repress repetitive regions of the genome via RNA-directed DNA methylation, as well as secondary phased siRNAs (phasiRNAs) and trans-acting siRNAs (tasiRNAs) generated via miRNA-directed cleavage of a Pol II-derived RNA precursor. Our analyses described thousands of small RNA loci, including poorly understood clusters of 22-nt siRNAs that accumulate during viral infection. The birth, death, expansion, and contraction of these small RNA loci are dynamic evolutionary processes that characterize the Solanaceae family. These analyses indicate that individuals within the same genus share similar small RNA landscapes, whereas comparisons between distinct genera within the Solanaceae reveal relatively few commonalities.

We developed public web sites and resources for data access, display, and analysis of plant small RNAs. These web sites are interconnected with related data types. The current generation of these informatics tools was developed for Illumina data, evolving over more than 15 years of improvements. Our online databases have customized web interfaces to uniquely handle and display RNA-derived data from diverse plant species, ranging from Arabidopsis (Arabidopsis thaliana) to wheat (Triticum spp.), including many crop and model species. The web interface displays the abundance and genomic context of data for small RNAs, parallel analysis of RNA ends/degradome reads, RNA sequencing, and even chromatin immunoprecipitation sequencing data; it also provides information about potentially novel transcripts (antisense transcripts, alternative splice isoforms, and regulatory intergenic transcripts). Numerous options are included for downloading data as tables or via web services. Interpretation of these data is facilitated by the inclusion of extensive repeat or transposon data in our genome viewer. We have developed graphical and analytical tools, including a new viewer and a query page for the analysis of phased small RNAs; these are particularly useful for understanding the complex small RNA pathways of plants. These public databases are accessible at https://mpss.danforthcenter.org.