Search

MotivationAdvances in sequencing technologies have led to a surge in genomic data, although the functions of many gene products coded by these genes remain unknown. While in-depth, targeted experiments that determine the functions of these gene products are crucial and routinely performed, they fail to keep up with the inflow of novel genomic data. In an attempt to address this gap, high-throughput experiments are being conducted in which a large number of genes are investigated in a single study. The annotations generated as a result of these experiments are generally biased towards a small subset of less informative Gene Ontology (GO) terms. Identifying and removing biases from protein function annotation databases is important since biases impact our understanding of protein function by providing a poor picture of the annotation landscape. Additionally, as machine learning methods for predicting protein function are becoming increasingly prevalent, it is essential that they are trained on unbiased datasets. Therefore, it is not only crucial to be aware of biases, but also to judiciously remove them from annotation datasets.ResultsWe introduce GOThresher, a Python tool that identifies and removes biases in function annotations from protein function annotation databases.Implementation and AvailabilityGOThresher is written in Python and released via PyPIhttps://pypi.org/project/gothresher/and on the Bioconda Anaconda channelhttps://anaconda.org/bioconda/gothresher. The source code is hosted on GitHubhttps://github.com/FriedbergLab/GOThresherand distributed under the GPL 3.0 license.Contact{idoerg@iastate.edu|parnal@iastate.edu}

Coronavirus disease caused by the virus SARS-nCOV2 rst detected in 2019 in Wuhan, China and then spread throughout the world creating a havoc and severe burden on the available health care resources worldwide. Multiple studies have been done to nd out the exact pathogenesis of this disease. However, few studies have focussed on the aspect of endothelial injury as marker of pulmonary damage in covid 19 patients .This study aims to review the already existing articles on the importance of endothelial biomarkers like VCAM, ICAM , P- selectin, Ang-2 and others as hallmark of pulmonary endothelial injury in critically ill COVID-19patients and to draw a meaningful conclusion. Further, it also aims to nd the role of Dexamethasone in reducing the inammation and endothelial injury in such patients.

Background Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative. Results We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species. Conclusions FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision—ideal for bench researchers with limited experience in handling computational tools.

This study was designed to investigate whether the glucose lowering effects of Potentilla fulgens acts by modulating GLUT4, AKT2 and AMPK expression in the skeletal muscle and liver tissues.Alloxan...

Plant disease resistance often occurs upon direct or indirect recognition of pathogen effectors by host nucleotide-binding leucine-rich-repeat (NLR) receptors. The barley powdery mildew fungus,Blumeria graminisf. sp.hordei(Bgh), secretes hundreds of candidate secreted effector proteins (CSEPs) to facilitate pathogen infection and colonization. One of these, CSEP0008, is directly recognized by the barley NLR, MLA1, and therefore designated AVRA1. Here we show that AVRA1and the sequence-unrelatedBgheffector BEC1016 (CSEP0491) suppress immunity in barley cells. We then used yeast 2-hybrid next-generation-interaction screens (Y2H-NGIS), followed by binary Y2H and bimolecular fluorescence complementation, to identify a common barley target of AVRA1and BEC1016, the endoplasmic reticulum (ER)-localized J-domain protein,HvERdj3B. This is an ER protein quality control (ERQC) protein and silencing it increased theBghpenetration rate in barley.HvERdj3B is localized to the ER lumen, and AVRA1and BEC106 translocation into the ER was confirmed using a split GFP system. Together, these results suggest that the barley innate immunity, preventingBghentry into epidermal cells, is dependent on ERQC, which in turn is regulated by J-domain proteinHvERdj3B and the two effectors. Previous work has shown that AVRA1is directly recognized in the cytosol by the immune receptor, MLA1. We speculate whether the AVRA1J-domain target being inside the ER, where it is inapproachable by NLRs, has forced the plant to evolve this challenging direct recognition.

The topological quantum field-effect transition in buckled 2D-Xenes can potentially be engineered to enable sub-thermionic transistor operation coupled with dissipationless ON-state conduction. Substantive device design strategies to harness this will necessitate delving into the physics of the quantum field effect transition between the dissipationless topological phase and the band insulator phase. Investigating workable device structures, we uncover fundamental sub-threshold limits posed by the gating mechanism that effectuates such a transition, thereby emphasizing the need for innovations on materials and device structures. Detailing the complex band translation physics related to the quantum spin Hall effect phase transition, it is shown that a gating strategy to beat the thermionic limit can be engineered at the cost of sacrificing the dissipationless ON-state conduction. It is then demonstrated that an out-of-plane antiferromagnetic exchange introduced in the material via proximity coupling can incite transitions between the quantum spin-valley Hall and the spin quantum anomalous Hall phase, which can ultimately ensure the topological robustness of the ON state while surpassing the thermionic limit. Our work thus underlines the operational criteria for building topological transistors using quantum materials that can overcome the Boltzmann's tyranny while preserving the topological robustness., 8 pages, 6 figures, supplementary material included. Comments welcome

Advancement in technology has enabled sequencing machines to produce vast amounts of genetic data, causing an increase in storage demands. Most genomic software utilizes read alignments for several purposes including transcriptome assembly and gene count estimation. Herein we present, ABRIDGE, a state-of-the-art compressor for SAM alignment files offering users both lossless and lossy compression options. This reference-based file compressor achieves the best compression ratio among all compression software ensuring lower space demand and faster file transmission. Central to the software is a novel algorithm that retains non-redundant information. This new approach has allowed ABRIDGE to achieve a compression 16% higher than the second-best compressor for RNA-Seq reads and over 35% for DNA-Seq reads. ABRIDGE also offers users the option to randomly access location without having to decompress the entire file. ABRIDGE is distributed under MIT license and can be obtained from GitHub (https://github.com/sagnikbanerjee15/Abridge) and docker hub. We anticipate that the user community will adopt ABRIDGE within their existing pipeline encouraging further research in this domain.

Metamaterial absorbers, on account of their inherent property of electromagnetic radiation absorption, have become a center of attraction for many researchers in recent times. This paper proposes a unique design of a terahertz metamaterial absorber that can be used to sense biomedical samples. The proposed design consists of two identical circular ring resonators (CRRs) made of aluminum on top of a gallium arsenide (GaAs) substrate. On account of its high field confinement in the sensing regime, a near-to-perfect absorption rate of 99.50% is achieved at a frequency of 2.64 THz, along with a large quality factor (Q-Factor) of 44. The design is highly sensitive to the refractive index changes in the encompassing medium. Hence, the proposed absorber can be used as a refractive index sensor exhibiting a reasonable sensitivity of 1500 GHz/RIU and a figure of merit (FoM) of 25. The refractive index range has been varied in the range of 1.34 to 1.39. As many biomedical samples, including cancerous cells, reside within this range, the proposed sensor can be used for biomedical sensing applications.

Background Plants encounter pathogenic and non-pathogenic microorganisms on a nearly constant basis. Small RNAs such as siRNAs and miRNAs/milRNAs influence pathogen virulence and host defense responses. We exploited the biotrophic interaction between the powdery mildew fungus, Blumeria graminis f. sp. hordei (Bgh), and its diploid host plant, barley (Hordeum vulgare) to explore fungal and plant sRNAs expressed during Bgh infection of barley leaf epidermal cells. Results RNA was isolated from four fast-neutron immune-signaling mutants and their progenitor over a time course representing key stages of Bgh infection, including appressorium formation, penetration of epidermal cells, and development of haustorial feeding structures. The Cereal Introduction (CI) 16151 progenitor carries the resistance allele Mla6, while Bgh isolate 5874 harbors the AVRa6 avirulence effector, resulting in an incompatible interaction. Parallel Analysis of RNA Ends (PARE) was used to verify sRNAs with likely transcript targets in both barley and Bgh. Bgh sRNAs are predicted to regulate effectors, metabolic genes, and translation-related genes. Barley sRNAs are predicted to influence the accumulation of transcripts that encode auxin response factors, NAC transcription factors, homeodomain transcription factors, and several splicing factors. We also identified phasing small interfering RNAs (phasiRNAs) in barley that overlap transcripts that encode receptor-like kinases (RLKs) and nucleotide-binding, leucine-rich domain proteins (NLRs). Conclusions These data suggest that Bgh sRNAs regulate gene expression in metabolism, translation-related, and pathogen effectors. PARE-validated targets of predicted Bgh milRNAs include both EKA (effectors homologous to AVRk1 and AVRa10) and CSEP (candidate secreted effector protein) families. We also identified barley phasiRNAs and miRNAs in response to Bgh infection. These include phasiRNA loci that overlap with a significant proportion of receptor-like kinases, suggesting an additional sRNA control mechanism may be active in barley leaves as opposed to predominant R-gene phasiRNA overlap in many eudicots. In addition, we identified conserved miRNAs, novel miRNA candidates, and barley genome mapped sRNAs that have PARE validated transcript targets in barley. The miRNA target transcripts are enriched in transcription factors, signaling-related proteins, and photosynthesis-related proteins. Together these results suggest both barley and Bgh control metabolism and infection-related responses via the specific accumulation and targeting of genes via sRNAs. Electronic supplementary material The online version of this article (10.1186/s12864-019-5947-z) contains supplementary material, which is available to authorized users.

Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking., Author summary Organisms respond to their environment through networks of interacting proteins and other biomolecules. In order to investigate these interacting proteins, many in vitro and in vivo techniques have been used. Among these, yeast two-hybrid (Y2H) has been integrated with next generation sequencing (NGS) to approach protein-protein interactions on a genome-wide scale. The fusion of these two methods has been termed next-generation-interaction screening, abbreviated as Y2H-NGIS. However, the diverse data sets resulting from this technology have presented unique challenges to analysis. To address these challenges, we optimized the computational and statistical evaluation of Y2H-NGIS to provide metrics to identify high-confidence interacting proteins under a variety of dataset scenarios. Our proposed framework can be extended to different yeast-based interaction settings, utilizing the general principles of enrichment, specificity, and in-frame prey selection to accurately assemble interactome networks. Lastly, we showed how the pipeline works experimentally, by identifying and validating novel interactions between the barley powdery mildew resistance protein, MLA6, and fourteen targets, including proteins involved in signaling, transcriptional regulation, and intracellular trafficking. Y2H-SCORES software is available at GitHub repository https://github.com/Wiselab2/Y2H-SCORES/tree/master/Software.

Additional file 9. Supplementary text document outlining methods and some results in more details.

Additional file 1. Supplementary figures (S1–S9).

We propose and investigate thermometry of a setup employing dual quantum dots with staircase ground state configuration. The stair-case ground state configuration actuates thermally controlled inelastic tunnelling, which translates into a temperature sensitive conductance, thereby inducing thermometry. The performance of the set-up is then analyzed employing quantum master equation (QME) for such systems in the sequential tunnelling regime. In particular, it is demonstrated that the system performance, in terms of temperature sensitivity and efficiency, is maximum in the regime of low temperature, making such system suitable for cryogenic thermometry. The proposed set-up can pave the path towards realization of high performance cryogenic nano temperature sensors., Comment: 8 pages, 6 figures

Clerodin (C24H34O7), a clerodane diterpenoid, is a bitter principle of Clerodendrum viscosum. The present study aimed to decipher colchicine-like actions of clerodin in terms of microtubule destabilization based mitotic abnormalities, G2-M arrest, and plant polyploidy. Purified clerodin showed increased metaphase frequency in Human Peripheral Blood Lymphocytes (HPBLs), Human Embryonic Kidney cells (HEK-293), and Allium cepa root apical meristem cells. Both squashed slide of the onion root tip and flow cytometric analysis of radish protoplast revealed a significantly increased frequency of polyploid cells. Flow cytometric analysis showed an increase in frequencies of G2-M in MCF-7 cells from 6.10 to 16.25% after clerodin (200μg/mL) treatment for 24 h. Confocal microscopy imaging of tubulin in clerodin-treated MCF-7 cells revealed microtubule destabilization. Molecular docking and LIGPLOT analysis indicate that clerodin interact in the colchicine binding site, including, single hydrogen bond with Asn 101 of α-tubulin. In summary, our experimental data revealed that clerodin has metaphase arresting, microtubule destabilization, and polyploidy inducing ability similar to colchicine. Molecular docking analysis revealed for the first time that clerodin and colchicine interact at the common site of tubulin residue indicating a common mechanism of action. The results also indicate similar cytotoxic potentialities of both clerodin and colchicine even though they belong to different chemical groups. Thus, clerodin may be used in place of colchicine as a plant polyploidy inducing agent in plant breeding programs in Agriculture.

Mapping protein–protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate complementary DNA (cDNA) libraries represent an alternative approach that optimizes scale, cost and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and it recognized all validated interactions. NGPINT can process interaction data from any biosystem with an available genome or transcriptome reference, thus facilitating the discovery of protein–protein interactions in model and non-model organisms.

An important yet challenging step of genome annotation is the process of finding the location of genes and transcripts within chromosomes. Complexities in eukaryotic genomes, like the presence of transposable elements, make the job of annotation difficult. To complicate matters even more, there are genes that produce numerous transcripts and also overlap with other genes. Perfect annotation of genes, originating from such complicated loci, is possible with full-length transcripts obtained from long-read sequencing platforms like PacBio or Nanopore. Unfortunately, these platforms require specialized library preps, produce high number of errors and are less cost-effective as sequencing on platforms like Illumina. Prevalence of complexities in the genome hinders accurate reconstructing of full-length transcripts from short Illumina reads, thereby impeding the progress of novel gene discovery and precise genome annotation. Reduction in sequencing costs have enabled numerous small labs to undertake whole genome expression studies to understand their respective biosystems better. These studies have been extended to non-model organisms for which a comprehensive gene annotation is often lacking. Absence of gene models and partially annotated genes lead to false negatives in differential expression study thereby preventing a holistic understanding of the biosystem. Correct identification of transcription start sites and proper construction of gene structures is pivotal to downstream applications (e.g. promoter mining, protein interaction study, etc.) making genome annotation extremely relevant. Annotation approaches undertaken by tools like BRAKER2 and MAKER2 involve predicting protein-coding genes from mapped RNA-Seq data. MAKER2 also allows users to enter EST sequences for annotation. These pipelines implement statistical gene predictors like SNAP and Augustus to annotate coding regions of genes. The approach of annotating only protein-coding regions lead to partial gene structures that interfere with epigenetic analysis which directly impacts about 13,000 transcripts in the well-annotated Arabidopsis thaliana genome since those transcripts have 5' UTR of length greater than 250 bp. Most BRAKER2 gene models have only one transcript thereby causing a plunge in transcript sensitivity. Also, BRAKER2 does not predict any non-coding genes. To overcome the shortcomings of previous genome annotators, we have designed a pipeline called FINDER which integrates predicted gene models with assembled expressed transcript models. FINDER performs optimized read mapping with STAR and uses PsiCLASS to conduct meta-assembly. The pipeline is designed to assess each gene model based on expression coverage and modify transcript boundaries to better comply with the expression pattern. Finally, FINDER incorporates predicted gene models from BRAKER2 if those do not overlap with assembled models and have some representation in the provided proteome. Arabidopsis thaliana is a model organism with one of the best gene annotations making it ideal to validate and evaluate gene annotation pipelines. We compared our assembled gene models with predicted models from BRAKER2 and in all the categories PsiCLASS gene models were superior. Most approaches use Stringtie and/or Scallop to generate assemblies and then merge those using Stringtie merge. PsiCLASS meta-assembler was able to generate much better transcript models recoding the highest F1 score. Some transcript assemblies generated by PsiCLASS represent merged genes and exhibit exon extension for overlapping genes on the opposite strand. Signals present in expression profiles are utilized through changepoint detection to split up merged gene models. This improved the transcript F1 score from 42.37 to 49.28. Finally, gene models predicted by BRAKER2 that have high similarity with proteins and do not overlap with the assembled transcripts are included in the final annotations boosting the F1 score to 50.03. FINDER executes OLEGO - an aligner optimized to detect micro-exons that further improves gene predictions. Our results demonstrate that overall FINDER generates better and more consistent gene and transcript models. BRAKER2 is designed to annotate only the protein-coding regions of genes thereby generating incomplete gene models impacting epigenetics research. Annotating only a portion of a gene and reporting very few transcripts per gene reduces the sensitivity of BRAKER2. Stringtie and Scallop are popular assemblers and are widely used to generate assemblies. Stringtie merge is used to combine individual assemblies giving rise to a meta-assembly. Stringtie merge produces poor meta assemblies, because it does not take into account the expression profile of the transcripts. Changepoint detection implemented to refine PsiCLASS assemblies improves the overall gene structures showing the importance of expression profiles in redefining transcript boundaries. We are confident that FINDER will pave the way for better gene structure annotation in the future.

Interactomes embody one of the most effective representations of cellular behavior by revealing function through protein associations. In order to build these models at the organism scale, high-throughput techniques are required to identify interacting pairs of proteins. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate protein-protein interaction networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we describe a statistical framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens that considers key aspects of experimental design, normalization, and controls. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS identified conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, accelerating the biological interpretation of experimental results. Proof-of-concept was demonstrated by discovery and validation of a novel interaction between the barley powdery mildew effector, AVRA13, with the vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1.Author SummaryOrganisms respond to their environment through networks of interacting proteins and other biomolecules. In order to investigate these interacting proteins, many in vitro and in vivo techniques have been used. Among these, yeast two-hybrid (Y2H) has been integrated with next generation sequencing (NGS) to approach protein-protein interactions on a genome-wide scale. The fusion of these two methods has been termed next-generation-interaction screening, abbreviated as Y2H-NGIS. However, the massive and diverse data sets resulting from this technology have presented unique challenges to analysis. To address these challenges, we optimized the computational and statistical evaluation of Y2H-NGIS to provide metrics to identify high-confidence interacting proteins under a variety of dataset scenarios. Our proposed framework can be extended to different yeast-based interaction settings, utilizing the general principles of enrichment, specificity, and in-frame prey selection to accurately assemble protein-protein interaction networks. Lastly, we showed how the pipeline works experimentally, by identifying and validating a novel interaction between the barley powdery mildew effector AVRA13 and the barley vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1. Y2H-SCORES software is available at GitHub repository https://github.com/Wiselab2/Y2H-SCORES.

Figure S2. PhasiRNA size distributions for genotype-specific phasing. (PDF 382 kb)

Figure S1. Median counts of Bgh genome mapped sRNAs for each barley line and time point combination. Reads were mapped to the Bgh genome with Bowtie, and median counts from all three replicates for each condition were compared via ANOVA analysis. The null hypothesis was not rejected if the median values are not statistically different with an alpha of 0.05. Standard error bars are shown for each condition. (PDF 24 kb)

We investigate a quadruple quantum dot setup that can be employed to sense the temperature of an electrically isolated remote target reservoir. Such a setup was conceived earlier by S\'anchez et. al. (New Journal of Physics, 19, 113040) as non-local thermodynamic engine and relies on the electrostatic interaction between Coulomb-coupled quantum dots. The conjugation of Coulomb-coupling and energy-filtering results in an overall change in conductance with remote reservoir temperature. The performance of the thermometer is then theoretically investigated using density matrix formulation, and it is demonstrated that the quadruple quantum dot design ensures a superior temperature sensitivity and noise robustness compared to a simple thermometer consisting of two Coulomb-coupled quantum dots. In the end, we investigate the regime of operation and comment on the ground state configuration for optimal performance of the thermometer. The setup investigated in this paper can be employed to construct highly efficient non-local cryogenic thermometers., Comment: 18 Pages, 11 Figures

Phakopsora pachyrhizi, which causes Asian soybean rust (ASR), secretes effector proteins to manipulate host immunity and promote disease. To date, only a small number of effectors have been identified from transcriptome studies. To obtain a more comprehensive understanding of P. pachyrhizi candidate secreted effector proteins (CSEPs), we sequenced the transcriptome using two next-generation sequencing technologies. Short-read Illumina RNA-Seq data was used for reducing base-calling errors for long-read PacBio Iso-Seq. After initial de novo assemblies for RNA-seq and error correction of transcripts for Iso-Seq followed by filtering, we obtained 8,528, 27,647, 26,895, and 17,141 non-plant, non-soybean transcripts at 3, 7, 10, and 14 days after inoculation, respectively. We identified a repertoire of CSEPs of which a majority was expressed during the later stages of infection, and many that could be bioinformatically associated with haustoria. This approach for identifying CSEPs improves our current understanding of the P. pachyrhizi effectorome, and these CSEPs are expected to be a valuable resource for future studies of P. pachyrhizi-soybean interactions.

We report on our experiments with N-gram and embedding based feature representations for Native Language Identification (NLI) as a part of the NLI Shared Task 2017 (team name: NLI-ISU). Our best performing system on the test set for written essays had a macro F1 of 0.8264 and was based on word uni, bi and trigram features. We explored n-grams covering word, character, POS and word-POS mixed representations for this task. For embedding based feature representations, we employed both word and document embeddings. We had a relatively poor performance with all embedding representations compared to n-grams, which could be because of the fact that embeddings capture semantic similarities whereas L1 differences are more stylistic in nature.

method of finding a sequence of characters, called the pattern, in another much longer sequence of characters, called the text, is known as pattern matching. Several pattern- matching algorithms exist, that locate all the positions where a pattern occurs in a text. In this paper we have presented an algorithm which implements a divide and conquer technique, which divides the text in smaller independent sub-texts and then looks for the existence of the pattern in each such sub- text. The said divide and conquer method thus eventually finds all the occurrences of the pattern in the given text. The points of division have been chosen using a genetic algorithm.