Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.
Thousands of plasmid sequences are now publicly available in the NCBI nucleotide database, but they are not reliably annotated to distinguish complete plasmids from plasmid fragments, such as gene or contig sequences; therefore, retrieving complete plasmids for downstream analyses is challenging. Here we present a curated dataset of complete bacterial plasmids from the clinically relevant Enterobacteriaceae family. The dataset was compiled from the NCBI nucleotide database using curation steps designed to exclude incomplete plasmid sequences, and chromosomal sequences misannotated as plasmids. Over 2000 complete plasmid sequences are included in the curated plasmid dataset. Protein sequences produced from translating each complete plasmid nucleotide sequence in all 6 frames are also provided. Further analysis and discussion of the dataset is presented in an accompanying research article: "Ordering the mob: insights into replicon and MOB typing…" (Orlek et al., 2017) [1]. The curated plasmid sequences are publicly available in the Figshare repository.
The implementation of next generation sequencing techniques, such as whole-genome sequencing (WGS), in tuberculosis (TB) research has enabled timely, cost-effective, and comprehensive insights into the genetic repertoire of the human pathogens of the Mycobacterium tuberculosis complex (MTBC). WGS data allow for detailed epidemiological analysis based on genomic distance of the MTBC strains under investigation, e.g., for tracing outbreaks; it can accelerate diagnostics by predicting drug resistance from a mutation catalogue (Fig 1). Indeed, specific mutations even permit predictions on the possible clinical treatment course and outcome [1–4].
Open in a separate window
Fig 1
Principle of pathogen-tailored individualized treatment design.
(A) Mutations are obtained from a whole-genome sequencing reference mapping approach that can be also transferred into a cgMLST for molecular outbreak surveillance. (B) Individual mutations are further interpreted towards their biological phenotype employing a validated consensus mutation catalogue. (C) When canonical and/or high-level resistance-conferring mutations are present, this drug should not be used. However, mutations associated with a moderate or intermediate resistance level may allow the use of drugs at increased doses. Moreover, mutations can be used to predict different treatment outcomes. Thus, by also considering phylogenetic benign mutations that do not confer resistance, a comprehensive molecular drug susceptibility profile could be inferred for a pathogen-tailored individualized treatment regimen in the future. cgMLST,core genome multilocus sequencing type; TB, tuberculosis.
Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages.We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space.The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php.Supplementary data are available at Bioinformatics online.
This chapter looks at the various channels available to banks and discusses the challenges and opportunities presented by each channel and the technology that supports them. In a more modern bank, with an online core banking platform, the automated teller machine host can now go directly to the core banking platform to get the latest available balance for an account and to update the available balance on the core banking platform immediately following a cash withdrawal. With the centralisation of core banking platforms, it was also possible to centralise telephone services, and large banks embraced this in the 1980s and 1990s and set up large call centres to handle telephone enquiries, to the extent banks often made it impossible for a customers to call their local branch. The chapter also looks at the technology that was used to handle telephone calls and then at modern multimedia contact handling, including telephone calls, chat and video calls.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)