Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance : a retrospective cohort study

BACKGROUND : Diagnosing drug-resistance remains an obstacle to the elimination of tuberculosis. Phenotypic drugsusceptibility testing is slow and expensive, and commercial genotypic assays screen only common resistancedetermining mutations. We used whole-genome sequencing to characterise common and rare mutations predicting drug resistance, or consistency with susceptibility, for all fi rst-line and second-line drugs for tuberculosis. METHODS : Between Sept 1, 2010, and Dec 1, 2013, we sequenced a training set of 2099 Mycobacterium tuberculosis genomes. For 23 candidate genes identifi ed from the drug-resistance scientifi c literature, we algorithmically characterised genetic mutations as not conferring resistance (benign), resistance determinants, or uncharacterised. We then assessed the ability of these characterisations to predict phenotypic drug-susceptibility testing for an independent validation set of 1552 genomes. We sought mutations under similar selection pressure to those characterised as resistance determinants outside candidate genes to account for residual phenotypic resistance. FINDINGS : We characterised 120 training-set mutations as resistance determining, and 772 as benign. With these mutations, we could predict 89·2% of the validation-set phenotypes with a mean 92·3% sensitivity (95% CI 90·7–93·7) and 98·4% specifi city (98·1–98·7). 10·8% of validation-set phenotypes could not be predicted because uncharacterised mutations were present. With an in-silico comparison, characterised resistance determinants had higher sensitivity than the mutations from three line-probe assays (85·1% vs 81·6%). No additional resistance determinants were identifi ed among mutations under selection pressure in non-candidate genes. INTERPRETATION : A broad catalogue of genetic mutations enable data from whole-genome sequencing to be used clinically to predict drug resistance, drug susceptibility, or to identify drug phenotypes that cannot yet be genetically predicted. This approach could be integrated into routine diagnostic workfl ows, phasing out phenotypic drugsusceptibility testing while reporting drug resistance early.Wellcome Trust, National Institute of Health Research, Medical Research Council, and the European Union.http://www.thelancet.com/infectionhb201

First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage

AbstractAfter psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peri-traumatic brain activation was able to predict later intrusive memories (i.e. before they had happened). To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma film) via a prospective event-related design was able to capture an individual's later intrusive memories. Results showed widespread increases in brain activation at encoding when viewing a scene in the scanner that would later return as an intrusive memory in the real world. These fMRI results were replicated in a second study. While traditional mass univariate regression analysis highlighted an association between brain processing and symptomatology, this is not the same as prediction. Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory. We also report here brain networks key in intrusive memory prediction. MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms