Chemical Safety Board finalizes two fatal accident investigations

We formulated the principles of designing bioluminescent enzyme tests for assessing the quality of complex media which consist in providing the maximum sensitivity to potentially toxic chemicals at a minimal impact of uncontaminated complex media. The developed principles served as a basis for designing a new bioluminescent method for an integrated rapid assessment of chemical safety of fruits and vegetables which is based on using the luminescent bacterium enzymes (NAD(P)H:FMN oxidoreductase and luciferase) as a test system.

Download Full-text

Better Living Through Chemicals (Regulation)? The Chemical Safety Improvement Act of 2013 Through an Environmental Public Health Law Lens

Vermont Journal of Environmental Law ◽

10.2307/vermjenvilaw.15.3.490 ◽

2014 ◽

Vol 15 (3) ◽

pp. 490

Author(s):

Tracy Bach

Keyword(s):

Public Health ◽

Health Law ◽

Chemical Safety ◽

Public Health Law ◽

Environmental Public Health ◽

Safety Improvement ◽

Chemicals Regulation

Download Full-text

Simplifying COSHH and improving chemical safety

Process Safety and Environmental Protection ◽

10.1016/j.psep.2019.12.027 ◽

2020 ◽

Vol 137 ◽

pp. 66-72

Author(s):

Stuart Morgan ◽

Mark Stewart ◽

Tasha Bennett

Keyword(s):

Chemical Safety

Download Full-text

Beleaguered Chemical Safety Board could get more support

C&EN Global Enterprise ◽

10.1021/cen-09904-cover9 ◽

2021 ◽

Vol 99 (4) ◽

pp. 37-37

Author(s):

Jyllian Kemsley

Keyword(s):

Chemical Safety

Download Full-text

Effect of Organic Modifier Types on the Physical–Mechanical Properties and Overall Migration of Post-Consumer Polypropylene/Clay Nanocomposites for Food Packaging

Polymers ◽

10.3390/polym13091502 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1502

Author(s):

Eliezer Velásquez ◽

Sebastián Espinoza ◽

Ximena Valenzuela ◽

Luan Garrido ◽

María José Galotto ◽

...

Keyword(s):

Mechanical Properties ◽

Food Packaging ◽

Clay Nanocomposites ◽

Organic Modifier ◽

Thermal And Mechanical Properties ◽

Chemical Safety ◽

Elongation At Break ◽

Fatty Food ◽

Recycled Plastics ◽

Thermal Stability Of

The deterioration of the physical–mechanical properties and loss of the chemical safety of plastics after consumption are topics of concern for food packaging applications. Incorporating nanoclays is an alternative to improve the performance of recycled plastics. However, properties and overall migration from polymer/clay nanocomposites to food require to be evaluated case-by-case. This work aimed to investigate the effect of organic modifier types of clays on the structural, thermal and mechanical properties and the overall migration of nanocomposites based on 50/50 virgin and recycled post-consumer polypropylene blend (VPP/RPP) and organoclays for food packaging applications. The clay with the most hydrophobic organic modifier caused higher thermal stability of the nanocomposites and greater intercalation of polypropylene between clay mineral layers but increased the overall migration to a fatty food simulant. This migration value was higher from the 50/50 VPP/RPP film than from VPP. Nonetheless, clays reduced the migration and even more when the clay had greater hydrophilicity because of lower interactions between the nanocomposite and the fatty simulant. Conversely, nanocomposites and VPP/RPP control films exhibited low migration values in the acid and non-acid food simulants. Regarding tensile parameters, elongation at break values of PP film significantly increased with RPP addition, but the incorporation of organoclays reduced its ductility to values closer to the VPP.

Download Full-text

Text Mining of Hazard and Operability Analysis Reports Based on Active Learning

Processes ◽

10.3390/pr9071178 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1178

Author(s):

Zhenhua Wang ◽

Beike Zhang ◽

Dong Gao

Keyword(s):

Active Learning ◽

Named Entity Recognition ◽

Entity Recognition ◽

Chemical System ◽

Chemical Safety ◽

High Quality ◽

Data Set ◽

Final Model ◽

Named Entity ◽

Sampling Algorithms

In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize the hidden dangers in the system, and be of great significance to improve the safety of the whole chemical system. However, due to the standardization and professionalism of chemical safety analysis text, it is difficult to improve the performance of traditional models. To solve this problem, in this study, an improved method based on active learning is proposed, and three novel sampling algorithms are designed, Variation of Token Entropy (VTE), HAZOP Confusion Entropy (HCE) and Amplification of Least Confidence (ALC), which improve the ability of the model to understand HAZOP text. In this method, a part of data is used to establish the initial model. The sampling algorithm is then used to select high-quality samples from the data set. Finally, these high-quality samples are used to retrain the whole model to obtain the final model. The experimental results show that the performance of the VTE, HCE, and ALC algorithms are better than that of random sampling algorithms. In addition, compared with other methods, the performance of the traditional model is improved effectively by the method proposed in this paper, which proves that the method is reliable and advanced.

Download Full-text

Generalized Read-Across prediction using genra-py

Bioinformatics ◽

10.1093/bioinformatics/btab210 ◽

2021 ◽

Author(s):

Imran Shah ◽

Tia Tate ◽

Grace Patlewicz

Keyword(s):

Risk Assessment ◽

Human Health Risk ◽

Chemical Properties ◽

Predictive Performance ◽

Hazard Identification ◽

Data Driven ◽

Chemical Safety ◽

Physico Chemical ◽

Data Driven Approach ◽

Assessment Problems

Abstract Motivation Generalized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert’s manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties. Results We have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation. Availability and implementation The package is available from github.com/i-shah/genra-py.

Download Full-text