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Ionic liquids have attracted the attention of researchers as possible electrolytes for electrochemical energy storage devices. However, their properties, such as the electrochemical stability window (ESW), ionic conductivity, and diffusivity, are influenced both by the chemical structures of cations and anions and by their combinations. Most studies in the literature focus on the understanding of common ionic liquids, and little effort has been made to find ways to improve our atomistic understanding of those systems. The goal of this paper is to explore the structural characteristics of cations and anions that form ionic liquids that can expand the HOMO/LUMO gap, a property directly linked to the ESW of the electrolyte. For that, we design a framework for randomly generating new ions by combining their fragments. Within this framework, we generate about 10 4 cations and 10 4 anions and fully optimize their structures using density functional theory. Our calculations show that aromatic cations are less stable ionic liquids than aliphatic ones, an expected result if chemical rationale is used. More importantly, we can improve the gap by adding electron-donating and electron-withdrawing functional groups to the cations and anions, respectively. The increase can be about 2 V, depending on the case. This improvement is reflected in a wider ESW.

Machine learning as a tool for chemical space exploration broadens horizons to work with known and unknown molecules. At its core lies molecular representation, an essential key to improve learning about structure-property relationships. Recently, contrastive frameworks have been showing impressive results for representation learning in diverse domains. Therefore, this paper proposes a contrastive framework that embraces multimodal molecular data. Specifically, our approach jointly trains a graph encoder and an encoder for the simplified molecular-input line-entry system (SMILES) string to perform the contrastive learning objective. Since SMILES is the basis of our method, i.e., we built the molecular graph from the SMILES, we call our framework as SMILES Contrastive Learning (SMICLR). When stacking a nonlinear regressor on the SMICLR's pretrained encoder and fine-tuning the entire model, we reduced the prediction error by, on average, 44% and 25% for the energetic and electronic properties of the QM9 data set, respectively, over the supervised baseline. We further improved our framework's performance when applying data augmentations in each molecular-input representation. Moreover, SMICLR demonstrated competitive representation learning results in an unsupervised setting.

Most machine learning applications in quantum-chemistry (QC) data sets rely on a single statistical error parameter such as the mean square error (MSE) to evaluate their performance. However, this approach has limitations or can even yield incorrect interpretations. Here, we report a systematic investigation of the two components of the MSE, i.e., the bias and variance, using the QM9 data set. To this end, we experiment with three descriptors, namely (i) symmetry functions (SF, with two-body and three-body functions), (ii) many-body tensor representation (MBTR, with two- and three-body terms), and (iii) smooth overlap of atomic positions (SOAP), to evaluate the prediction process's performance using different numbers of molecules in training samples and the effect of bias and variance on the final MSE. Overall, low sample sizes are related to higher MSE. Moreover, the bias component strongly influences the larger MSEs. Furthermore, there is little agreement among molecules with higher errors (outliers) across different descriptors. However, there is a high prevalence among the outliers intersection set and the convex hull volume of geometric coordinates (VCH). According to the obtained results with the distribution of MSE (and its components bias and variance) and the appearance of outliers, it is suggested to use ensembles of models with a low bias to minimize the MSE, more specifically when using a small number of molecules in the training set.

Machine learning (ML) models can potentially accelerate the discovery of tailored materials by learning a function that maps chemical compounds into their respective target properties. In this realm, a crucial step is encoding the molecular systems into the ML model, in which the molecular representation plays a crucial role. Most of the representations are based on the use of atomic coordinates (structure); however, it can increase ML training and predictions' computational cost. Herein, we investigate the impact of choosing free-coordinate descriptors based on the Simplified Molecular Input Line Entry System (SMILES) representation, which can substantially reduce the ML predictions' computational cost. Therefore, we evaluate a feed-forward neural network (FNN) model's prediction performance over five feature selection methods and nine ground-state properties (including energetic, electronic, and thermodynamic properties) from a public data set composed of ∼130k organic molecules. Our best results reached a mean absolute error, close to chemical accuracy, of ∼0.05 eV for the atomization energies (internal energy at 0 K, internal energy at 298.15 K, enthalpy at 298.15 K, and free energy at 298.15 K). Moreover, for the atomization energies, the results obtained an out-of-sample error nine times less than the same FNN model trained with the Coulomb matrix, a traditional coordinate-based descriptor. Furthermore, our results showed how limited the model's accuracy is by employing such low computational cost representation that carries less information about the molecular structure than the most state-of-the-art methods.

The burden of chronic diseases, including occupational respiratory diseases (ORDs), is increasing worldwide. Nevertheless, epidemiological data on these conditions are scarce in most countries. Therefore, it is important to conduct surveillance to monitor ORDs, particularly in developing countries, where the working population is especially vulnerable and the health system infrastructure is usually weak. This article provides a general framework for the implementation of ORD surveillance in developing countries. The main objectives of surveillance are to describe incidence and prevalence of ORDs, as well as to identify sentinel events and new associations between occupational exposures and health outcomes. Diseases with high morbidity and mortality and those in which early diagnosis with standardized tests are available are especially suitable for surveillance activities. Simple strategies, preferably using existing resources and technology, are the best option for surveillance in developing countries. This article offers examples of specific surveillance systems that are in place in Brazil, China, Cuba, India, and South Africa., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)

Background: The decline in asbestos use in the United States may impact mesothelioma incidence., Objective: This report provides national and state-specific estimates of mesothelioma incidence in the United States using cancer surveillance data for the entire US population., Methods: Data from the National Program for Cancer Registries and the Surveillance, Epidemiology, and End Results program were used to calculate incidence rates and annual percent change., Results: During 2003-2008, an average of 1.05 mesothelioma cases per 100 000 persons were diagnosed annually in the United States; the number of cases diagnosed each year remained level, whereas rates decreased among men and were stable among women., Conclusion: US population-based cancer registry data can be used to determine the burden of mesothelioma and track its decline. Even 30 years after peak asbestos use in the United States, 3200 mesothelioma cases are diagnosed annually, showing that the US population is still at risk.

Background: Mortality trends in the USA show that deaths from asbestosis are increasing, while deaths related to other pneumoconiosis are declining., Objectives: To analyse the association between asbestos consumption and asbestosis mortality trends., Methods: In an epidemiological time series study, we used a modern computer-intensive local regression method to evaluate the relationship between asbestos consumption per capita (1900-2006) as the predictor variable and number of deaths from asbestosis (1968-2004). The predictor variable was progressively lagged by annual increments from 30 to 60 years and the goodness of fit assessed for each lag period. The model having the smallest Akaike's Information Criteria was used to derive extrapolated estimates of future mortality based on more recent asbestos consumption data., Results: Asbestos consumption per capita reached a peak in 1951 and gradually declined until 1973, when it started to drop rapidly. In 2006, it was 0.0075 kg/person/year. There were 25 564 deaths from asbestosis over the period 1968-2004. The best-fitting model (adjusted coefficient of determination (R(2)) = 99.7%) for 1968-2004 deaths from asbestosis used asbestos consumption per capita 48 years prior (1920-1956) and the log value of asbestos consumption per capita 43 years prior (1925-1961). This model predicts a total of 29 667 deaths (95% CI 19 629 to 39 705) to occur during 2005-2027 (an average of 1290 deaths per year)., Conclusions: This study demonstrates a clear association between asbestos consumption and deaths from asbestosis and indicates that asbestosis deaths are not expected to decrease sharply in the next 10-15 years.

Metal and wood dust exposures have been identified as possible occupational risk factors for idiopathic pulmonary fibrosis (IPF). We analyzed mortality data using ICD-10 code J84.1--"Other interstitial pulmonary diseases with fibrosis," derived age-adjusted mortality rates for 1999-2003, and assessed occupational risks for 1999, by calculating proportionate mortality ratios (PMRs) and mortality odds ratios (MORs) using a matched case-control approach. We identified 84,010 IPF deaths, with an age-adjusted mortality rate of 75.7 deaths/million. Mortality rates were highest among males, whites, and those aged 85 and older. Three industry categories with potential occupational exposures recognized as risk factors for IPF were identified: "Wood buildings and mobile homes" (PMR = 4.5, 95% confidence interval (CI) 1.2-11.6 and MOR = 5.3, 95% CI 1.2-23.8), "Metal mining" (PMR = 2.4, 95% CI 1.3-4.0 and MOR = 2.2, 95% CI 1.1-4.4), and "Fabricated structural metal products" (PMR = 1.9, 95% CI 1.1-3.1 and MOR = 1.7, 95% CI 1.0-3.1). Workers in these industry categories may benefit from toxicological studies and improved surveillance for this disease.

Background: Following employee respiratory concerns, we investigated the health effects of rayon flock exposure at a card manufacturing plant., Methods: We conducted a cross-sectional survey including environmental evaluation, standardized questionnaires, spirometry, carbon monoxide diffusing capacity testing, and methacholine challenge testing., Results: From a total of 239 participants, 146 (61%) reported working at least 1 hr per week in areas where flock-coated cards are processed (flock workers) and 47 (20%) reported cleaning equipment with compressed air. These workers had generally higher prevalences of respiratory symptoms. Flock workers and employees with longer tenure at areas where flock-coated cards are processed were more likely to have restrictive impairment of lung function. Although dust and fiber samples were largely below the detection limits, peak exposures to airborne particulate occurred during cleaning with compressed air., Conclusions: Working with rayon flock and cleaning with compressed air were associated with health effects in workers at this plant.

Background: There are few population-based studies addressing hypersensitivity pneumonitis (HP) in the United States. The National Institute for Occupational Safety and Health (NIOSH) has nationally comprehensive longitudinal mortality data that can contribute to a better understanding of the epidemiology of HP., Methods: The National Center for Health Statistics multiple cause-of-death data were analyzed for the period 1980-2002. Annual death rate was age-adjusted to the 2000 U.S. standard population. Death rate time-trends were calculated using a linear regression model and geographic distribution of death rates were mapped by state and county. Proportionate mortality ratios (PMRs) by usual industry and occupation adjusted for age, sex, and race, were based on data from 26 states reporting industry and occupation during 1985-1999., Results: Overall age-adjusted death rates increased significantly (P < 0.0001) between 1980 and 2002, from 0.09 to 0.29 per million. Wisconsin had the highest rate at 1.04 per million. Among industries, PMR for HP was significantly high for agricultural production, livestock (PMR, 19.3; 95% CI, 14.0-25.9) and agricultural production, crops (PMR, 4.3; 95% CI, 3.0-6.0). Among occupations, PMR for HP was significantly elevated for farmers, except horticulture (PMR, 8.1; 95% CI, 6.4-10.2)., Conclusions: These findings indicate that agricultural industries are closely associated with HP mortality and preventive strategies are needed to protect workers in these industries.

Malignant mesothelioma is strongly associated with asbestos exposure. This paper describes demographic, geographic, and occupational distributions of mesothelioma mortality in the United States, 1999-2001. The data (n = 7,524) were obtained from the National Center for Health Statistics multiple-cause-of-death records. Mortality rates (per million per year) were age-adjusted to the 2000 U.S. standard population, and proportionate mortality ratios (PMRs) were calculated by occupation and industry, and adjusted for age, sex, and race. The overall age-adjusted mortality rate was 11.52, with males (22.34) showing a sixfold higher rate than females (3.94). Geographic distribution of mesothelioma mortality is predominantly coastal. Occupations with significantly elevated PMRs included plumbers/pipefitters and mechanical engineers. Industries with significantly elevated PMRs included ship and boat building and repairing, and industrial and miscellaneous chemicals. These surveillance findings can be useful in generating hypotheses and developing strategies to prevent mesothelioma.

For every communicable disease, the construction of a database is fundamental to prioritizing related measures (educational, prevention and control), as well as to the development of public health policies. Although various countries possess reliable systems of epidemiological surveillance, Brazil still lacks an adequate structure for the collection, processing and dissemination of data. We consider it imperative that the health professionals working in this area make a unified effort, as soon as possible, to compile a database that suitably addresses the epidemiology of occupational respiratory diseases.

Background: Despite significant progress made in reducing dust exposures in underground coal miners in the United States, severe cases of coal workers' pneumoconiosis (CWP), including progressive massive fibrosis (PMF), continue to occur among coal miners., Aims: To identify US miners with rapidly progressive CWP and to describe their geographic distribution and associated risk factors., Methods: Radiographic evidence of disease progression was evaluated for underground coal miners examined through US federal chest radiograph surveillance programmes from 1996 to 2002. A case of rapidly progressive CWP was defined as the development of PMF and/or an increase in small opacity profusion greater than one subcategory over five years. County based prevalences were derived for both CWP and rapidly progressive cases., Results: A total of 886 cases of CWP were identified among 29 521 miners examined from 1996 to 2002. Among the subset of 783 miners with CWP for whom progression could be evaluated, 277 (35.4%) were cases of rapidly progressive CWP, including 41 with PMF. Miners with rapidly progressive CWP were younger than miners without rapid progression, were more likely to have worked in smaller mines (<50 employees), and also reported longer mean tenure in jobs involving work at the face of the mine (in contrast to other underground mining jobs), but did not differ with respect to mean underground tenure. There was a clear tendency for the proportion of cases of rapidly progressive CWP to be higher in eastern Kentucky, and western Virginia., Conclusions: Cases of rapidly progressive CWP can be regarded as sentinel health events, indicating inadequate prevention measures in specific regions. Such events should prompt investigations to identify causal factors and initiate appropriate additional measures to prevent further disease.

Objective: To assess high-resolution computed tomography (HRCT) findings in silicosis and to better define the role of HRCT in early detection of parenchymal abnormalities in silica-exposed workers., Methods: Forty-one stone carvers were evaluated with chest radiographs (CR), HRCT, and pulmonary function tests (PFT). Inter-reader agreement was calculated using the kappa statistic (k). Correlation between radiographic and HRCT profusion scores and PFT was assessed using the Spearman correlation coefficient., Results: The most common HRCT findings were branching centrilobular structures, seen in 28/41 workers (68.3%). Nodules consistent with silicosis were detected in 53.7% workers on CR and in 56.1% workers on HRCT. Inter-reader agreement for diagnosis of silicosis was better on HRCT (k = 0.84) than on CR (k = 0.54). Small opacity profusion on HRCT correlated inversely with total lung capacity and FVC%., Conclusion: Profusion of opacities on HRCT correlates with functional impairment. The presence of branching centrilobular structures may be helpful in early recognition of silicosis.

A case of tracheobronchopathia osteochondroplastica in a patient with silicosis is reported, showing a rare association of disease. Etiological hypotheses and clinical aspects are discussed. Radiologic, bronchoscopic, and pathologic findings are demonstrated with emphasis on the role of computed tomography (CT) in the diagnosis of this disease.

With the implementation in 1999 of ICD-10 death certificate coding in the United States, mortality data specific to malignant mesothelioma became readily available on a national basis. To evaluate the accuracy and completeness of diagnosis and coding for mesothelioma on the death certificate, mortality information was compared with incidence data. A mortality/incidence ratio was calculated for each of the nine areas covered by the SEER Program, using National Vital Statistics mortality data from 1999 and 2000, and the SEER incidence data for 1998 and 1999. The mortality/incidence ratio for the two years combined for all areas was 0.82. Only two areas (Connecticut and Atlanta) had ratios <80%. The overall correlation coefficient between mortality and incidence rates was 0.96. Thus, mortality data coded using ICD-10 can be a valid source for mesothelioma surveillance and can be instituted without major cost if a national mortality statistics program based on ICD-10 is in place, making it feasible even for developing countries.

Background: In the city of Petrópolis, Brazil, artisans carve souvenirs from a variety of silica-containing minerals. The finding of pulmonary massive fibrosis in one of the workers motivated an investigation of the prevalence of silicosis in this group., Methods: Between January 2000 and June 2002, a cross-sectional study was performed. We obtained clinical and occupational histories, spirometry, lung volumes, and carbon monoxide diffusion capacity measurements. Chest radiographs and high-resolution computed tomographies (HRCT) were evaluated. Personal air samples were analyzed., Results: Forty-two stone carvers were examined. The prevalence of silicosis was 53.7%. HRCT better characterized silicotic lesions compared to chest radiographs. Early coalescence of small opacities was associated with lung function impairment. The concentration of dust exceeded permissible limits in 91% of the workplaces., Conclusions: Exposure to high levels of silica dust was associated with an increased prevalence of silicosis among stone carvers. Am. J. Ind. Med. 45:194-201, 2004., (Copyright 2004 Wiley-Liss, Inc.)

To obtain information about the occurrence of pleural mesothelioma on a population basis in Brazil, mortality related to pleural tumors in the State of Rio de Janeiro during 1979-2000 was examined. Death certificates with pleural tumors as the main cause of death and hospital records were analyzed, together with histopathologic material, which was reevaluated. Of 217 death certificates coded as pleural tumors, 34.1% were considered wrongly coded. Results after reclassification were: definite mesothelioma = 45 cases; probable = 7; possible = 31; inconclusive = 65; other tumors = 11. Thus, the number of mesotheliomas in Rio de Janeiro in 1979-2000 is estimated to have been 83. The analysis also suggests a problem with mortality codification in the State.

A case of broncholithiasis associated with massive silicosis is reported, showing a rare aspect of parenchymal lesions generating broncholiths as well as the presence of recurrent lithoptysis, with subsequent regression of radiological lesions. Aetiological, clinical, physiopathological, and radiological aspects of the disease are discussed, demonstrating the importance of the use of computed tomography in diagnosis. The mineralogical analysis of expectorated fragments is also shown.