An informatics approach to prioritizing risk assessment for chemicals and chemical combinations based on near-field exposure from consumer products

Over 80,000 chemicals are registered under the U.S. Toxic Substances Control Act of 1976, but only a few hundred have been screened for human toxicity. Not even those used in everyday consumer products, and known to have widespread exposure in the general population, have been screened. Toxicity screening is time-consuming, expensive, and complex because simultaneous or sequential exposure to multiple environmental stressors can affect chemical toxicity. Cumulative risk assessments consider multiple stressors but it is impractical to test every chemical combination and environmental stressor to which people are exposed. The goal of this research is to prioritize the chemical ingredients in consumer products and their most prevalent combinations for risk assessment based on likely exposure and retention. This work is motivated by two concerns. The first, as noted above, is the vast number of environmental chemicals with unknown toxicity. Our body burden (or chemical load) is much greater today than a century ago. The second motivating concern is the mounting evidence that many of these chemicals are potentially harmful. This makes us the unwitting participants in a vast, uncontrolled biochemistry experiment. An informatics approach is developed here that uses publicly available data to estimate chemical exposure from everyday consumer products, which account for a significant proportion of overall chemical load. Several barriers have to be overcome in order for this approach to be effective. First, a structured database of consumer products has to be created. Even though such data is largely public, it is not readily available or easily accessible. The requisite consumer product information is retrieved from online retailers. The resulting database contains brand, name, ingredients, and category for tens of thousands of unique products. Second, chemical nomenclature is often ambiguous. Synonymy (i.e., different names for the same chemical) and homonymy (i.e., the same name for different chemicals) are rampant. The PubChem Compound database, and to a lesser extent the Universal Medical Language System, are used to map chemicals to unique identifiers. Third, lists of toxicologically interesting chemicals have to be compiled. Fortunately, several authoritative bodies (e.g., the U.S. Environmental Protection Agency) publish lists of suspected harmful chemicals to be prioritized for risk assessment. Fourth, tabulating the mere presence of potentially harmful chemicals and their co-occurrence within consumer product formulations is not as interesting as quantifying likely exposure based on consumer usage patterns and product usage modes, so product usage patterns from actual consumers are required. A suitable dataset is obtained from the Kantar Worldpanel, a market analysis firm that tracks consumer behavior. Finally, a computationally feasible probabilistic approach has to be developed to estimate likely exposure and retention for individual chemicals and their combinations. The former is defined here as the presence of a chemical in a product used by a consumer. The latter is exposure combined with the relative likelihood that the chemical will be absorbed by the consumer based on a product’s usage mode (e.g., whether the product is rinsed off or left on after use). The results of four separate analyses are presented here to show the efficacy of the informatics approach. The first is a proof-of-concept demonstrating that the first two barriers, creating the consumer product database and dealing with chemical synonymy and homonymy, can be overcome and that the resulting system can measure the per-product prevalence of a small set of target chemicals (55 asthma-associated and endocrine disrupting compounds) and their combinations. A database of 38,975 distinct consumer products and 32,231 distinct ingredient names was created by scraping Drugstore.com, an online retailer. Nearly one-third of the products (11,688 products, 30%) contained ≥1 target chemical and 5,229 products (13%) contained >1. Of the 55 target chemicals, 31 (56%) appear in ≥1 product and 19 (35%) appear under more than one name. The most frequent 3-way chemical combination (2 phenoxyethanol, methyl paraben, and ethyl paraben) appears in 1,059 products. The second analysis demonstrates that the informatics approach can scale to several thousand target chemicals (11,964 environmental chemicals compiled from five authoritative lists). It repeats the proof-of-concept using a larger product sample (55,209 consumer products). In the third analysis, product usage patterns and usage modes are incorporated. This analysis yields unbiased, rational prioritizations of potentially hazardous chemicals and chemical combinations based on their prevalence within a subset of the product sample (29,814 personal care products), combined exposure from multiple products based on actual consumer behavior, and likely chemical retention based on product usage modes. High-ranking chemicals, and combinations thereof, include glycerol; octamethyltrisiloxane; citric acid; titanium dioxide; 1,2 propanediol; octadecan 1 ol; saccharin; hexitol; limonene; linalool; vitamin e; and 2 phenoxyethanol. The fourth analysis is the same as the third except that each authoritative list is prioritized individually for side-by-side comparison. The informatics approach is a viable and rationale way to prioritize chemicals and chemical combinations for risk assessment based on near-field exposure and retention. Compared to spectrographic approaches to chemical detection, the informatics approach has the advantage of a larger product sample, so it often detects chemicals that are missed during spectrographic analysis. However, the informatics approach is limited to the chemicals that are actually listed on product labels. Manufacturers are not required to specify the chemicals in fragrance or flavor mixtures, so the presence of some chemicals may be underestimated. Likewise, chemicals that are not part of the product formulation (e.g., chemicals leached from packaging, degradation byproducts) cannot be detected. Therefore, spectrographic and informatics approaches are complementary

A Method to Automatically Identify the Results from Journal Articles

The idea of automating systematic reviews has been motivated by both advances in technology that have increased the availability of full-text scientific articles and by sociological changes that have increased the adoption of evidence-based medicine. Although much work has focused on automating the information retrieval step of the systematic review process with a few exceptions the information extraction and analysis have been largely overlooked. In particular, there is a lack of systems that automatically identify the results of an empirical study. Our goal in this paper is to fill that gap. We frame the problem as a classification task and employ three different objective, domain-independent feature selection strategies and two different classifiers. Additionally, special attention is paid to the selection of the data set used in this experiment, the feature selection metrics as well as the classification algorithms, and parameters of the algorithms used for classification in order to show the situatedness of this experiment and its dependence on each of the three parameters.ye

Fatigue Life of Haynes 188 Superalloy in Direct Connect Combustor Durability Rig

The Direct Connect Combustor Durability Rig (DCR) will provide NASA a flexible and efficient test bed to demonstrate the durability of actively cooled scramjet engine structure, static and dynamic sealing technologies, and thermal management techniques. The DCR shall be hydrogen fueled and cooled, and test hydrogen coolded structural panels at Mach 5 and 7. Actively cooled Haynes 188 superalloy DCR structural panels exposed to the combustion environment shall have electrodischarge machined (EDM) internal cooling holes with flowing liquid hydrogen. Hydrogen combustion could therefore produce severe thermal conditions that could challenge low cycle fatigue durability of this material. The objective of this study was to assess low cycle fatigue capability of Haynes 188 for DCR application. Tests were performed at 25 and 650 C, in hydrogen and helium environments, using specimens with low stress ground (LSG) and electro-discharge machined (EDM) surface finish. Initial fatigue tests in helium and hydrogen indicate the low cycle fatigue life capability of Haynes 188 in hydrogen appears quite satisfactory for the DCR application. Fatigue capability did not decrease with increasing test temperature. Fatigue capability also did not decrease with EDM surface finish. Failure evaluations indicate retention of ductility in all conditions. Additional tests are planned to reconfirm these positive trends

Fatigue Life of Haynes 188 Superalloy in Hydrogen

No abstract availabl

The LDBC Financial Benchmark

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The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space

Graph data management is instrumental for several use cases such as recommendation, root cause analysis, financial fraud detection, and enterprise knowledge representation. Efficiently supporting these use cases yields a number of unique requirements, including the need for a concise query language and graph-aware query optimization techniques. The goal of the Linked Data Benchmark Council (LDBC) is to design a set of standard benchmarks that capture representative categories of graph data management problems, making the performance of systems comparable and facilitating competition among vendors. LDBC also conducts research on graph schemas and graph query languages. This paper introduces the LDBC organization and its work over the last decade

The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space

Graph data management is instrumental for several use cases such as recommendation, root cause analysis, financial fraud detection, and enterprise knowledge representation. Efficiently supporting these use cases yields a number of unique requirements, including the need for a concise query language and graph-aware query optimization techniques. The goal of the Linked Data Benchmark Council (LDBC) is to design a set of standard benchmarks that capture representative categories of graph data management problems, making the performance of systems comparable and facilitating competition among vendors. LDBC also conducts research on graph schemas and graph query languages. This paper introduces the LDBC organization and its work over the last decade

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