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.

Workers and respirators: What factors influence medical clearance? Experience in the petrochemical industry 2004--2008

This retrospective cohort study analyzed data from more than 2200 OSHA-mandated respirator medical evaluations performed between 2004 and 2008, with information initially obtained using an online questionnaire, to determine what factors influence medical clearance and the ability to safely wear respiratory protection in a large petrochemical company.^ The employees were mostly white males with a high school education, ranging in age from 25 to 60 years of age, who had been employed with the company an average of eight years. Their work was typically performed outdoors in a rural or offshore setting. Respirators were typically required for emergency response – escape or rescue only – and/or limited to less than four hours per month.^ Approximately 90% of the population achieved medical clearance by utilizing the online questionnaire. Of the remaining 10%, 66% were cleared after additional "hands-on" medical examination exam; 28% of the individuals' jobs were modified by their supervisor in order to not use a respirator, and 6% of the individuals (n=13) were excluded from wearing a respirator on the basis of the medical examination. The primary causes for exclusion from respirator use were cardiovascular (37.5%) and respiratory (31.3%) issues, followed by psychological (18.8%) and musculoskeletal (12.5%) concerns. Ultimately, over 99% of workers evaluated under this system were found capable of using respiratory protection safely. This questionnaire has proven to be an excellent health screening tool capable of initiating early detection and further investigation of potentially serious medical conditions within a large and diverse population in multiple locations. ^

The development of poultry farms risk assessment tool for avian influenza in Imo State, Nigeria

Background: Nigeria was one of the 13 countries where avian influenza outbreak in poultry farms was reported during the 2006 avian influenza pandemic threat and was also the first country in Africa to report the presence of H5N1influenza among its poultry population. There are multiple hypotheses on how the avian influenza outbreak of 2006 was introduced to Nigeria, but the consensus is that once introduced, poultry farms and their workers were responsible for 70% of the spread of avian influenza virus to other poultry farms and the population. ^ The spread of avian influenza has been attributed to lack of compliance by poultry farms and their workers with poultry farm biosecurity measures. When poultry farms fail to adhere to biosecurity measures and there is an outbreak of infectious diseases like in 2006, epidemiological investigations usually assess poultry farm biosecurity—often with the aid of a questionnaire. Despite the importance of questionnaires in determining farm compliance with biosecurity measures, there have been few efforts to determine the validity of questionnaires designed to assess poultry farms risk factors. Hence, this study developed and validated a tool (questionnaire) that can be used for poultry farm risk stratification in Imo State, Nigeria. ^ Methods: Risk domains were generated using literature and recommendations from agricultural organizations and the Nigeria government for poultry farms. The risk domains were then used to develop a questionnaire. Both the risk domain and questionnaire were verified and modified by a group of five experts with a research interest in Nigeria's poultry industry and/or avian influenza prevention. Once a consensus was reached by the experts, the questionnaire was distributed to 30 selected poultry farms in Imo State, Nigeria that participated in this study. Survey responses were received for all the 30 poultry farms that were selected. The same poultry farms were visited one week after they completed the questionnaires for on-site observation. Agreement among survey and observation results were analyzed using a kappa test and rated as poor, fair, moderate, substantial, or nearly perfect; and internal consistency of the survey was also computed. ^ Result: Out of the 43 items on the questionnaire, 32 items were validated by this study. The agreement between the survey result and onsite observation was analyzed using kappa test and ranged from poor to nearly perfect. Most poultry farms had their best agreements in the contact section of the survey. The least agreement was noted in the farm management section of the survey. Thirty-two questions on the survey had a coefficient alpha > 0.70, which is a robust internal consistency for the survey. ^ Conclusion: This study developed 14 risk domains for poultry farms in Nigeria and validated 32 items from the original questionnaire that contained 43 items. The validated items can be used to determine the risk of introduction and spread of avian influenza virus in poultry farms in Imo State, Nigeria. After further validations in other states, regions and poultry farm sectors in Nigeria; this risk assessment tool can then be used to determine the risk profile of poultry farms across Nigeria.^

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.^