Spatio-Temporal Low Count Processes with Application to Violent Crime Events

There is significant interest in being able to predict where crimes will happen, for example to aid in the efficient tasking of police and other protective measures. We aim to model both the temporal and spatial dependencies often exhibited by violent crimes in order to make such predictions. The temporal variation of crimes typically follows patterns familiar in time series analysis, but the spatial patterns are irregular and do not vary smoothly across the area. Instead we find that spatially disjoint regions exhibit correlated crime patterns. It is this indeterminate inter-region correlation structure along with the low-count, discrete nature of counts of serious crimes that motivates our proposed forecasting tool. In particular, we propose to model the crime counts in each region using an integer-valued first order autoregressive process. We take a Bayesian nonparametric approach to flexibly discover a clustering of these region-specific time series. We then describe how to account for covariates within this framework. Both approaches adjust for seasonality. We demonstrate our approach through an analysis of weekly reported violent crimes in Washington, D.C. between 2001-2008. Our forecasts outperform standard methods while additionally providing useful tools such as prediction intervals

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

Identification of human nephron progenitors capable of generation of kidney structures and functional repair of chronic renal disease

Identification of tissue-specific renal stem/progenitor cells with nephrogenic potential is a critical step in developing cell-based therapies for renal disease. In the human kidney, stem/progenitor cells are induced into the nephrogenic pathway to form nephrons until the 34 week of gestation, and no equivalent cell types can be traced in the adult kidney. Human nephron progenitor cells (hNPCs) have yet to be isolated. Here we show that growth of human foetal kidneys in serum-free defined conditions and prospective isolation of NCAM1(+) cells selects for nephron lineage that includes the SIX2-positive cap mesenchyme cells identifying a mitotically active population with in vitro clonogenic and stem/progenitor properties. After transplantation in the chick embryo, these cells—but not differentiated counterparts—efficiently formed various nephron tubule types. hNPCs engrafted and integrated in diseased murine kidneys and treatment of renal failure in the 5/6 nephrectomy kidney injury model had beneficial effects on renal function halting disease progression. These findings constitute the first definition of an intrinsic nephron precursor population, with major potential for cell-based therapeutic strategies and modelling of kidney disease

Ssn6 Defines a New Level of Regulation of White-Opaque Switching in Candida albicans and Is Required For the Stochasticity of the Switch

The human commensal and opportunistic pathogen Candida albicans can switch between two distinct, heritable cell types, named “white” and “opaque,” which differ in morphology, mating abilities, and metabolic preferences and in their interactions with the host immune system. Previous studies revealed a highly interconnected group of transcriptional regulators that control switching between the two cell types. Here, we identify Ssn6, the C. albicans functional homolog of the Saccharomyces cerevisiae transcriptional corepressor Cyc8, as a new regulator of white-opaque switching. In a or α mating type strains, deletion of SSN6 results in mass switching from the white to the opaque cell type. Transcriptional profiling of ssn6 deletion mutant strains reveals that Ssn6 represses part of the opaque cell transcriptional program in white cells and the majority of the white cell transcriptional program in opaque cells. Genome-wide chromatin immunoprecipitation experiments demonstrate that Ssn6 is tightly integrated into the opaque cell regulatory circuit and that the positions to which it is bound across the genome strongly overlap those bound by Wor1 and Wor2, previously identified regulators of white-opaque switching. This work reveals the next layer in the white-opaque transcriptional circuitry by integrating a transcriptional regulator that does not bind DNA directly but instead associates with specific combinations of DNA-bound transcriptional regulators

Ssn6 Defines a New Level of Regulation of White-Opaque Switching in Candida albicans and Is Required For the Stochasticity of the Switch.

UnlabelledThe human commensal and opportunistic pathogen Candida albicans can switch between two distinct, heritable cell types, named "white" and "opaque," which differ in morphology, mating abilities, and metabolic preferences and in their interactions with the host immune system. Previous studies revealed a highly interconnected group of transcriptional regulators that control switching between the two cell types. Here, we identify Ssn6, the C. albicans functional homolog of the Saccharomyces cerevisiae transcriptional corepressor Cyc8, as a new regulator of white-opaque switching. In A: or α mating type strains, deletion of SSN6 results in mass switching from the white to the opaque cell type. Transcriptional profiling of ssn6 deletion mutant strains reveals that Ssn6 represses part of the opaque cell transcriptional program in white cells and the majority of the white cell transcriptional program in opaque cells. Genome-wide chromatin immunoprecipitation experiments demonstrate that Ssn6 is tightly integrated into the opaque cell regulatory circuit and that the positions to which it is bound across the genome strongly overlap those bound by Wor1 and Wor2, previously identified regulators of white-opaque switching. This work reveals the next layer in the white-opaque transcriptional circuitry by integrating a transcriptional regulator that does not bind DNA directly but instead associates with specific combinations of DNA-bound transcriptional regulators.ImportanceThe most common fungal pathogen of humans, C. albicans, undergoes several distinct morphological transitions during interactions with its host. One such transition, between cell types named "white" and "opaque," is regulated in an epigenetic manner, in the sense that changes in gene expression are heritably maintained without any modification of the primary genomic DNA sequence. Prior studies revealed a highly interconnected network of sequence-specific DNA-binding proteins that control this switch. We report the identification of Ssn6, which defines an additional layer of transcriptional regulation that is critical for this heritable switch. Ssn6 is necessary to maintain the white cell type and to properly express the opaque cell transcriptional program. Ssn6 does not bind DNA directly but rather associates with specific combinations of DNA-bound transcriptional regulators to control the switch. This work is significant because it reveals a new level of regulation of an important epigenetic switch in the predominant fungal pathogen of humans

A rapid in vivo assay system for analyzing the organogenetic capacity of human kidney cells

Transplantation of human kidney-derived cells is a potential therapeutic modality for promoting regeneration of diseased renal tissue. However, assays that determine the ability of candidate populations for renal cell therapy to undergo appropriate differentiation and morphogenesis are limited. We report here a rapid and humane assay for characterizing tubulogenic potency utilizing the well-established chorioallantoic membrane (CAM) of the chick embryo

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

G protein-coupled receptors: In silico drug discovery in 3D

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 “virtual hit” compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12–21% (full dose–response curves, K(i) < 5 μM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery