Contamination is a critical issue in high-throughput metagenomic studies, yet progress towards a comprehensive solution has been limited. We present SourceTracker, a Bayesian approach to estimating the proportion of a novel community that comes from a set of source environments. We apply SourceTracker to new microbial surveys from neonatal intensive care units (NICUs), offices, and molecular biology laboratories, and provide a database of known contaminants for future testing.

SignificanceHistorically, computer-assisted detection (CAD) in radiology has failed to achieve improvements in diagnostic accuracy, decreasing clinician sensitivity and leading to unnecessary further diagnostic tests. With the advent of deep learning approaches to CAD, there is great excitement about its application to medicine, yet there is little evidence demonstrating improved diagnostic accuracy in clinically-relevant applications. We trained a deep learning model to detect fractures on radiographs with a diagnostic accuracy similar to that of senior subspecialized orthopedic surgeons. We demonstrate that when emergency medicine clinicians are provided with the assistance of the trained model, their ability to accurately detect fractures significantly improves.

One way of perceptually organizing a complex visual scene is to attend selectively to information in a particular physical location. Another way of reducing the complexity in the input is to attend selectively to an individual object in the scene and to process its elements preferentially. This latter, object-based attention process was examined, and the predicted superiority for reporting features from 1 relative to 2 objects was replicated in a series of experiments. This object-based process was robust even under conditions of occlusion, although there were some boundary conditions on its operation. Finally, an account of the data is provided via simulations of the findings in a computational model. The claim is that object-based attention arises from a mechanism that groups together those features based on internal representations developed over perceptual experience and then preferentially gates these features for later, selective processing. Humans are exceptionally good at recognizing objects in natural visual scenes despite the fact that such scenes usually contain multiple, overlapping objects. One way in which individuals organize this complex input to minimize the processing load is to divide the field on the basis of spatial location and then to attend selectively to particular physical regions. This selective attentional spotlight "illuminates" areas of interest and facilitates preferential processing of information from those chosen areas (e.g., Broadbent, 1982 ;B. A. Eriksen & Eriksen, 1974 ;C. W. Eriksen & Yeh, 1985 ;Posner, 1980 ). There is now much evidence supporting this location-based selection, all of which shows that information from selected regions is processed faster and more accurately than equivalent information from unattended regions ( Posner, 1980 ;Posner, Snyder, & Davidson, 1980 ). The idea that location-based selection plays an exclusive role in organizing visual information, however, has been increasingly challenged in recent years. Studies have shown, for example, that humans can select one of two superimposed figures even when there is no spatial basis for selection ( Rock & Gutman, 1981 ) and can allocate attention to perceptual groups independent of the spatial proximity and contiguity of the component elements (e.g., Behrmann, Vecera, & McGoldrick, 1998 ;Driver & Baylis, 1989 ;Duncan, 1984 ;Kramer & Jacobson, 1991 ;Kramer & Watson, 1995 ;Lavie & Driver, 1996 ;Prinzmetal, 1981 ;Vecera & Farah, 1994 ). To account for these findings, an alternative selection process, in which attention is directed to objects, rather than to locations or unsegmented regions of space, has been proposed. This object-based mechanism, in which complex visual input is parsed into discrete units for further processing, has received considerable empirical, neuropsychological, and computational support in recent years. Object-Based Visual AttentionAn early but compelling empirical illustration of the view that attention can be directed to objects, rather than to spatial locations per se, comes...

Competition in the wireless telecommunications industry is fierce. To maintain profitability, wireless carriers must control churn, which is the loss of subscribers who switch from one carrier to another.We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include logit regression, decision trees, neural networks, and boosting. Our experiments are based on a database of nearly 47,000 U.S. domestic subscribers and includes information about their usage, billing, credit, application, and complaint history. Our experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offering incentives can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Finally, we report on a real-world test of the techniques that validate our simulation experiments.

In reaction time research, there has been an increasing appreciation that response-initiation processes are sensitive to recent experience and, in particular, the difficulty of previous trials. From this perspective, the authors propose an explanation for a perplexing property of masked priming: Although primes are not consciously identified, facilitation of target processing by a related prime is magnified in a block containing a high proportion of related primes and a low proportion of unrelated primes relative to a block containing the opposite mix (Bodner & Masson, 2001). In the present study, this phenomenon is explored with a parity (even/odd) decision task in which a prime (e.g., 2) precedes a target that can be either congruent (e.g., 4) or incongruent (e.g., 3). It is shown that the effect of congruence proportion with masked primes cannot be explained in terms of the blockwise prime-target contingency. Specifically, with masked primes, there is no congruency disadvantage in a block containing a high proportion of incongruent primes, but there is a congruency advantage when the block contains an equal proportion of congruent and incongruent primes. In qualitative contrast, visible primes are sensitive to the blockwise prime-target contingency. The authors explain the relatedness proportion effect found with masked primes in terms of a model according to which response-initiation processes adapt to the statistical structure of the environment, specifically the difficulty of recent trials. This account is supported with an analysis at the level of individual trials using the linear mixed effects model.