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Predicting protein structure from sequence remains a major open problem in protein biochemistry. One component of predicting complete structures is the prediction of inter-residue contact patterns (contact maps). Here, we discuss protein contact map prediction by machine learning. We describe a novel method for contact map prediction that uses the evolution of logic circuits. These logic circuits operate on feature data and output whether or not two amino acids in a protein are in contact or not. We show that such a method is feasible, and in addition that evolution allows the logic circuits to be trained on the dataset in an unbiased manner so that it can be used in both contact map prediction and the selection of relevant features in a dataset., Competing Interests: Claus O. Wilke is an Academic Editor for PeerJ.

Nationally, there has been a steady increase in the number of infants born with neonatal abstinence syndrome or drug withdrawal. Many of these infants remain in the hospital because their signs of withdrawal require pharmacologic management. The length of hospital stay can range from 1 week to several months depending on the severity of withdrawal. One way to decrease the length of stay and reduce the cost of care is to discharge the infant from the hospital sooner. To discharge infants sooner, their neonatal abstinence syndrome must be accurately assessed so that appropriate pharmacologic management can be administered. One way to increase the accuracy of assessing withdrawal signs is to implement an interobserver reliability protocol. This approach will allow healthcare professionals to gain consistency and accuracy in assessing infants for signs of withdrawal that may facilitate earlier discharge from the hospital.

Background: Exposure to traffic-related particulate matter (PM) has been associated with adverse respiratory health outcomes in children. Diesel exhaust particles (DEPs) are a local driver of urban fine PM [aerodynamic diameter < or = 2.5 microm (PM(2.5))]; however, evidence linking ambient DEP exposure to acute respiratory symptoms is relatively sparse, and susceptibilities of urban and asthmatic children are inadequately characterized., Objectives: We examined associations of daily ambient black carbon (BC) concentrations, a DEP indicator, with daily respiratory symptoms among asthmatic and nonasthmatic adolescents in New York City (NYC) and a nearby suburban community., Methods: BC and PM(2.5) were monitored continuously outside three NYC high schools and one suburban high school for 4-6 weeks, and daily symptom data were obtained from 249 subjects (57 asthmatics, 192 nonasthmatics) using diaries. Associations between pollutants and symptoms were characterized using multilevel generalized linear mixed models, and modification by urban residence and asthma status were examined., Results: Increases in BC were associated with increased wheeze, shortness of breath, and chest tightness. Multiple lags of nitrogen dioxide (NO(2)) exposure were associated with symptoms. For several symptoms, associations with BC and NO(2) were significantly larger in magnitude among urban subjects and asthmatics compared with suburban subjects and nonasthmatics, respectively. PM(2.5) was not consistently associated with increases in symptoms., Conclusions: Acute exposures to traffic-related pollutants such as DEPs and/or NO(2) may contribute to increased respiratory morbidity among adolescents, and urban residents and asthmatics may be at increased risk. The findings provide support for developing additional strategies to reduce diesel emissions further, especially in populations susceptible because of environment or underlying respiratory disease.

Relatively little is known about exposures to traffic-related particulate matter at schools located in dense urban areas. The purpose of this study was to examine the influences of diesel traffic proximity and intensity on ambient concentrations of fine particulate matter (PM(2.5)) and black carbon (BC), an indicator of diesel exhaust particles, at New York City (NYC) high schools. Outdoor PM(2.5) and BC were monitored continuously for 4-6 weeks at each of 3 NYC schools and 1 suburban school located 20 kilometers upwind of the city. Traffic count data were obtained using an automated traffic counter or video camera. BC concentrations were 2-3 fold higher at urban schools compared with the suburban school, and among the 3 urban schools, BC concentrations were higher at schools located adjacent to highways. PM(2.5) concentrations were significantly higher at urban schools than at the suburban school, but concentrations did not vary significantly among urban schools. Both hourly average counts of trucks and buses and meteorological factors such as wind direction, wind speed, and humidity were significantly associated with hourly average ambient BC and PM(2.5) concentrations in multivariate regression models. An increase of 443 trucks/buses per hour was associated with a 0.62 mug/m(3) increase in hourly average BC at a NYC school located adjacent to a major interstate highway. Car traffic counts were not associated with BC. The results suggest that local diesel vehicle traffic may be important sources of airborne fine particles in dense urban areas and consequently may contribute to local variations in PM(2.5) concentrations. In urban areas with higher levels of diesel traffic, local, neighborhood-scale monitoring of pollutants such as BC, which compared to PM(2.5), is a more specific indicator of diesel exhaust particles, may more accurately represent population exposures.