Commentary: Dedication of Additional Resources to Evaluation and Low-Income Schools is Critical to Increasing Levels of Safe Walking and Bicycling to School

A commentary on Mendoza et al.'s article entitled, "Ethnic Minority Children’s Active Commuting to School and Association with Physical Activity and Pedestrian Safety Behaviors."

Structure of the Hsp110:Hsc70 nucleotide exchange machine.

Hsp70s mediate protein folding, translocation, and macromolecular complex remodeling reactions. Their activities are regulated by proteins that exchange ADP for ATP from the nucleotide-binding domain (NBD) of the Hsp70. These nucleotide exchange factors (NEFs) include the Hsp110s, which are themselves members of the Hsp70 family. We report the structure of an Hsp110:Hsc70 nucleotide exchange complex. The complex is characterized by extensive protein:protein interactions and symmetric bridging interactions between the nucleotides bound in each partner protein's NBD. An electropositive pore allows nucleotides to enter and exit the complex. The role of nucleotides in complex formation and dissociation, and the effects of the protein:protein interactions on nucleotide exchange, can be understood in terms of the coupled effects of the nucleotides and protein:protein interactions on the open-closed isomerization of the NBDs. The symmetrical interactions in the complex may model other Hsp70 family heterodimers in which two Hsp70s reciprocally act as NEFs.

Effects of a dog walking program with older adults in long-term care

The purpose of this study was to determine if walking a dog would increase motivation to adhere to a walking program and result in an increase in walking endurance and mobility among institutionalized elderly. An experimental pre and post test two group randomly assigned study design was utilized. Thirty subjects, 20 females and 10 males with an average age of 72, were enrolled from three long-term care facilities. The walking program was 3 times a week for 6 weeks. The experimental group walked with a certified therapy dog and the handler. The control group walked with only the handler. The Outcome Expectations for Exercise Scale (OEES) was used to measure the perceived benefits of exercise. The 2-minute walk test and the 30 second chair stand test were administered before and after the walking program. The OEES scores did not significantly predict adherence to the program. The pre- and post-chair stand test and the 2-minute walk test did not show statistical significant differences between groups. All of the participants did show an increase (7 minutes) in walking time during the 6 week period (p=0.048). The mean pre and post walk test scores for participants with stroke/arthritis were significantly less than those without stroke/arthritis (p=0.013). The experimental group had 12 subjects with stroke/arthritis compared with 6 in the control group. The walk test means in feet walked were 362.44 ± 130.36 (control) vs. 201.27 ± 106.25 (experimental), p=0.001. The results indicate walking practice has the potential to increase walking time and endurance. Because residents of long-term care facilities were not allowed outside the facilities without accompaniment, the presence of the dog handler was key to their walking. Analysis of conversations during the walks indicated that for participants who walked with dogs, the dogs did serve as motivation for continuing in the program. ^

Resource Review of Walking the Equity Talk: A Guide for Culturally Courageous Leadership in School Communities

Resource Review of: Walking the Equity Talk: A Guide for Culturally Courageous Leadership in School Communities by Robert Brownell II.

Walking Our Talk in the Neighborhoods: Building Professional/Natural Helper Partnerships

This article describes our efforts in Tacoma, Washington, to establish professional and natural helper partnerships to work with families involved in the child protective service system. It uses our experiences to describe the ways that natural helpers and professionals can help one another in getting better results for families.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^