Burglary in London: insights from statistical heterogeneous spatial point processes

To obtain operational insights regarding the crime of burglary in London, we consider the estimation of the effects of covariates on the intensity of spatial point patterns. Inspired by localized properties of criminal behaviour, we propose a spatial extension to mixtures of generalized linear models from the mixture modelling literature. The Bayesian model proposed is a finite mixture of Poisson generalized linear models such that each location is probabilistically assigned to one of the groups. Each group is characterized by the regression coefficients, which we subsequently use to interpret the localized effects of the covariates. By using a blocks structure of the study region, our approach enables specifying spatial dependence between nearby locations. We estimate the proposed model by using Markov chain Monte Carlo methods and we provide a Python implementation

Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach

BACKGROUND: Screening for Barrett's oesophagus relies on endoscopy, which is invasive and few who undergo the procedure are found to have the condition. We aimed to use machine learning techniques to develop and externally validate a simple risk prediction panel to screen individuals for Barrett's oesophagus. METHODS: In this prospective study, machine learning risk prediction in Barrett's oesophagus (MARK-BE), we used data from two case-control studies, BEST2 and BOOST, to compile training and validation datasets. From the BEST2 study, we analysed questionnaires from 1299 patients, of whom 880 (67·7%) had Barrett's oesophagus, including 40 with invasive oesophageal adenocarcinoma, and 419 (32·3%) were controls. We randomly split (6:4) the cohort using a computer algorithm into a training dataset of 776 patients and a testing dataset of 523 patients. We compiled an external validation cohort from the BOOST study, which included 398 patients, comprising 198 patients with Barrett's oesophagus (23 with oesophageal adenocarcinoma) and 200 controls. We identified independently important diagnostic features of Barrett's oesophagus using the machine learning techniques information gain and correlation-based feature selection. We assessed multiple classification tools to create a multivariable risk prediction model. Internal validation of the model using the BEST2 testing dataset was followed by external validation using the BOOST external validation dataset. From these data we created a prediction panel to identify at-risk individuals. FINDINGS: The BEST2 study included 40 diagnostic features. Of these, 19 added information gain but after correlation-based feature selection only eight showed independent diagnostic value including age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and taking antireflux medication, of which all were associated with increased risk of Barrett's oesophagus, except frequency of stomach pain, with was inversely associated in a case-control population. Logistic regression offered the highest prediction quality with an area under the receiver-operator curve (AUC) of 0·87 (95% CI 0·84–0·90; sensitivity set at 90%; specificity of 68%). In the testing dataset, AUC was 0·86 (0·83–0·89; sensitivity set at 90%; specificity of 65%). In the external validation dataset, the AUC was 0·81 (0·74–0·84; sensitivity set at 90%; specificity of 58%). INTERPRETATION: Our diagnostic model offers valid predictions of diagnosis of Barrett's oesophagus in patients with symptomatic gastro-oesophageal reflux disease, assisting in identifying who should go forward to invasive confirmatory testing. Our predictive panel suggests that overweight men who have been taking antireflux medication for a long time might merit particular consideration for further testing. Our risk prediction panel is quick and simple to administer but will need further calibration and validation in a prospective study in primary care. FUNDING: Charles Wolfson Charitable Trust and Guts UK

Identification and characterization of a previously undescribed family of sequence-specific DNA-binding domains

Sequence-specific DNA-binding proteins are among the most important classes of gene regulatory proteins, controlling changes in transcription that underlie many aspects of biology. In this work, we identify a transcriptional regulator from the human fungal pathogen Candida albicans that binds DNA specifically but has no detectable homology with any previously described DNA- or RNA-binding protein. This protein, named White–Opaque Regulator 3 (Wor3), regulates white–opaque switching, the ability of C. albicans to switch between two heritable cell types. We demonstrate that ectopic overexpression of WOR3 results in mass conversion of white cells to opaque cells and that deletion of WOR3 affects the stability of opaque cells at physiological temperatures. Genome-wide chromatin immunoprecipitation of Wor3 and gene expression profiling of a wor3 deletion mutant strain indicate that Wor3 is highly integrated into the previously described circuit regulating white–opaque switching and that it controls a subset of the opaque transcriptional program. We show by biochemical, genetic, and microfluidic experiments that Wor3 binds directly to DNA in a sequence-specific manner, and we identify the set of cis-regulatory sequences recognized by Wor3. Bioinformatic analyses indicate that the Wor3 family arose more recently in evolutionary time than most previously described DNA-binding domains; it is restricted to a small number of fungi that include the major fungal pathogens of humans. These observations show that new families of sequence-specific DNA-binding proteins may be restricted to small clades and suggest that current annotations—which rely on deep conservation—underestimate the fraction of genes coding for transcriptional regulators