Agricultural robotic agents have been becoming useful helpers in a wide range of agricultural tasks. However, they still heavily rely on manual operations or fixed railways for movement. To address this limitation, the AgriVLN method and the A2A benchmark pioneeringly extend Vision-and-Language Navigation (VLN) to the agricultural domain, enabling agents to navigate to the target positions following the natural language instructions. We observe that AgriVLN can effectively understands the simple instructions, but often misunderstands the complex ones. To bridge this gap, we propose the T-araVLN method, in which we build the instruction translator module to translate noisy and mistaken instructions into refined and precise representations. When evaluated on A2A, our T-araVLN successfully improves Success Rate (SR) from 0.47 to 0.63 and reduces Navigation Error (NE) from 2.91 m to 2.28 m, demonstrating the state-of-the-art performance in the agricultural VLN domain. Code: https://github.com/AlexTraveling/T-araVLN .

MOTIVATION: Tumor viruses account for ∼10% of cancer diagnoses. Virally induced tumorigenesis is understood as direct signaling through oncogenes such as E6 and E7 genes in the case of human papillomavirus. Furthermore, pathogen characteristics such as viral oncogene dose may impact the disease course. To our knowledge, no tool has been proposed to assess the intra-viral copy number alterations that define the gene dose of viral oncogenes and associated suppressive pathways native to the pathogen's normal life cycle. RESULTS: We propose an R package, "ELViS," that analyzes viral copy number changes from DNA sequencing of whole viral genomes. The method adjusts for viral load with 2D transformation and segmentation to offer the relative viral gene doses. AVAILABILITY AND IMPLEMENTATION: The ELViS R package is available from https://bioconductor.org/packages/ELViS. This article used controlled access data from dbGaP (phs001713.v1.p1).

MOTIVATION: Somatic DNA copy number alterations (CNAs) arise in tumor tissue because of underlying genomic instability. Recurrent CNAs that occur in the same genomic region across multiple independent samples are of interest to researchers because they may contain genes that contribute to the cancer phenotype. However, differences in copy number states between cancers are also commonly of interest, for example when comparing tumors with distinct morphologies in the same anatomic location. Current methodologies are limited by their inability to perform direct comparisons of CNAs between tumor cohorts, and thus they cannot formally assess the statistical significance of observed copy number differences or identify regions of the genome where these differences occur. RESULTS: We introduce the DiNAMIC.Duo R package that can be used to identify recurrent CNAs in a single cohort or recurrent copy number differences between two cohorts, including when neither cohort is copy neutral. The package utilizes Python scripts for computational efficiency and provides functionality for producing figures and summary output files. AVAILABILITY AND IMPLEMENTATION: The DiNAMIC.Duo R package is available from CRAN at https://cran.r-project.org/web/packages/DiNAMIC.Duo/index.html. This article uses publicly available data from the Broad Institute TCGA Genome Data Analysis Center, https://doi.org/10.7908/C11G0KM9. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: GMD (generalized minimum distance of distributions) is an R package to assess the similarity between spatial distributions of read-based sequencing data such as ChIP-seq and RNA-seq. GMD calculates the optimal distance between pairs of normalized signal distributions, optionally sliding one distribution over the other to ‘align’ the distributions. GMD also provides graphical and downstream clustering tools. Availability: The R package GMD source code is available at http://cran.r-project.org/web/packages/GMD/ under GPL license Contact: [email protected]; mailto:[email protected] Supplementary information: Supplementary data are available at Bioinformatics online.