Fourier transform Raman and Fourier transform infrared spectra of 2-ethylpyridine-4-carbothioamide were recorded in the regions 3600-100cm(-1) and 4000-450cm(-1), respectively in the solid phase. 2-Ethylpyridine-4-carbothioamide is used as anti-tubercular agent that inhibits mycolic acid synthesis. The equilibrium geometry harmonic vibrational frequencies, infrared intensities and Raman scattering activities were calculated by Hartee Fock and density functional B3LYP methods with 6-31G (d,p) basis set, using Gaussian 03W program package on a Pentium IV/1.6GHz personal computer(.) A detailed interpretation of the vibrational spectra of this compound has been made on the basis of the calculated potential energy distribution (PED). The thermodynamic functions of the title compound were also performed at the above methods and basis set. A detailed interpretation of the infrared and Raman spectra of 2-ethylpyridine-4-carbothioamide is reported. The (1)H and (13)C nuclear magnetic resonance (NMR) chemical shifts of the molecules were calculated using the GIAO method confirms with the experimental values. Stability of the molecule arising from hyper conjugative interactions, charge delocalization has been analyzed using natural bond orbital (NBO) analysis. The linear polarizability (α) and the first order hyperpolarizability (β) values of the investigated molecule have been computed using DFT quantum mechanical calculations. The HOMO and LUMO energy gap reveals that the energy gap reflects the chemical activity of the molecule. The observed and calculated wave numbers are found to be in good agreement. The experimental spectra also coincide satisfactorily with those of theoretically constructed spectra.

Results are presented from a study that investigated the effect of data interpretation training on exposure judgment accuracy of industrial hygienists across several companies in different industry sectors. Participating companies provided monitoring information on specific exposure tasks. Forty-nine hygienists from six companies participated in the study, and 22 industrial tasks were evaluated. The number of monitoring data points for individual tasks varied between 5 and 24. After reviewing all available basic characterization information for the job, task, and chemical, hygienists were asked to provide their judgment on the probability of the 95th percentile of the underlying exposure distribution being located in one of four exposure categories relative to the occupational exposure limit as outlined in the AIHA® exposure assessment strategy. Ninety-three qualitative judgments (i.e., without reviewing monitoring data) and 2142 quantitative judgments (i.e., those made after reviewing monitoring data) were obtained. Data interpretation training, with simple rules of thumb for estimating 95th percentiles, was provided to all hygienists. A data interpretation test was administered before and after training. All exposure task judgments were collected before and after training. Data interpretation test accuracy for the hygienists increased from 48% to 67% after training (p < 0.001) and a significant underestimation bias was removed. Hygienist quantitative task judgment accuracy improved from 46% to 69%(p < 0.001) post-training. Accuracy results showed good improvement in industrial hygienists' quantitative judgments as a result of training. Hence, the use of statistical tools is promoted to improve judgments based on monitoring data and provide feedback and calibration to improve qualitative judgments. It may be worthwhile to develop standard training programs to improve exposure judgments.

The title compound, C(21)H(20)O(2)S(2), forms a propeller-shaped structure with the tetra-hedral C atom as the central hub and meth-oxy-benzene and phenyl residues as radiating blades. Short C-H⋯π contacts are observed.

In the title compound, C(15)H(17)BrN(2)O(2), both pyrrolidinone rings adopt envelope conformations. The crystal packing is characterized by short C-Br⋯O=C inter-actions [Br⋯O = 3.1730 (13) Å], leading to supra-molecular dimers. Inter-molecular C-H⋯O and C-H⋯π inter-actions are also observed.

The mechanism of amyloid fibril formation by proteins has been classically described by the nucleation-dependent polymerization (NDP) model, which makes certain predictions regarding the kinetics of fibrillation. All proteins whose aggregation conforms to the NDP model display a t(2) time dependence for their initial reaction profile. However, there are proteins whose aggregation reactions have kinetic signatures of a flat lag phase followed by an exponential rise in fibril mass, which does not conform to the NDP model. Amyloid fibril formation by tau, a microtubule-associated protein whose aggregation to form neurofibrillary tangles is implicated in Alzheimer's disease and other tauopathies, in the presence of inducers such as heparin and fatty acid micelles, has always been traditionally described by a ligand-induced NDP model. In this study, the existence of a secondary pathway for fibril growth during the aggregation of the functional, repeat domain of tau in the presence of heparin has been established. Both kinetic and accessory evidence are provided for the existence of this pathway, which is shown to augment the primary homogeneous nucleation pathway. From the kinetic data, the main secondary pathway that is operative appears to be fibril fragmentation but other pathways such as branching or secondary nucleation may also be operative.

This paper proposes a novel approach for segmenting fetal ultrasound images. This problem presents a variety of challenges including high noise, low contrast, and other US imaging properties such as similarity between texture and gray levels of two organs/ tissues. In this paper, we have proposed a Conditional Random Field (CRF) based framework to handle challenges in segmenting fetal ultrasound images. Clinically, it is known that fetus is surrounded by specific maternal tissues, amniotic fluid and placenta. We exploit this context information using CRFs for segmenting the fetal images accurately. The proposed CRF framework uses wavelet based texture features for representing the ultrasound image and Support Vector Machines (SVM) for initial label prediction. Initial results on a limited dataset of real world ultrasound images of fetus are promising. Results show that proposed method could handle the noise and similarity between fetus and its surroundings in ultrasound images.

The objective of this exposure assessment was to reconstruct cumulative historical exposures for workers who have been exposed to multiple chemicals and chemical groups to better understand a cluster of brain cancers within a research and development lab. Chemicals of interest, including acrylates, bis-chloromethyl ether (BCME), chloromethyl methyl ether (CMME), isothiazolones and nitrosoamines, were selected on the basis of the plausibility of penetrating the blood-brain barrier and the uniqueness of the chemical's biological activity. In a complicated exposure setting such as a chemical R&D facility, multiple exposure estimation methods were needed. First, similarly exposure groups (SEGs) were created for these materials based on department group, time period of the department's existence and function associated with job titles. A probabilistic framework for assessing exposures was developed using Bayesian analysis of historical monitoring data, mathematical exposure modeling and professional judgments of current and former industrial hygienists at the facility were used to reconstruct the exposure history for acrylates, BCME and CMME for each SEG over the time period of interest. Since sufficient measurement data for isothiazolones and nitrosoamines were not available, the exposure histories for each SEG for these chemicals were estimated. This was done using objective formaldehyde levels and subjective employee interviews. The interviews assessed workplace determinants of exposure as distinct surrogates for estimating inhalation and dermal exposures. The exposure assessments by these methods were compared against each other to estimate the potential for exposure misclassification. A job exposure matrix (JEM) was constructed that contained the exposures obtained from above multiple approaches for each of these chemical groups for each SEG for each year of interest. The combination of methods used in this work is a unique and potentially helpful framework that can be used in analogous workplace settings involving multiple exposures with incomplete objective measurement information.

Results are presented from a study that investigated the effect of characteristics of occupational hygienists relating to educational and professional experience and task-specific experience on the accuracy of occupational exposure judgments. A total of 49 occupational hygienists from six companies participated in the study and 22 tasks were evaluated. Participating companies provided monitoring data on specific tasks. Information on nine educational and professional experience determinants (e.g. educational background, years of occupational hygiene and exposure assessment experience, professional certifications, statistical training and experience, and the 'need for cognition (NFC)', which is a measure of an individual's motivation for thinking) and four task-specific determinants was also collected from each occupational hygienist. Hygienists had a wide range of educational and professional backgrounds for tasks across a range of industries with different workplace and task characteristics. The American Industrial Hygiene Association exposure assessment strategy was used to make exposure judgments on the probability of the 95th percentile of the underlying exposure distribution being located in one of four exposure categories relative to the occupational exposure limit. After reviewing all available job/task/chemical information, hygienists were asked to provide their judgment in probabilistic terms. Both qualitative (judgments without monitoring data) and quantitative judgments (judgments with monitoring data) were recorded. Ninety-three qualitative judgments and 2142 quantitative judgments were obtained. Data interpretation training, with simple rules of thumb for estimating the 95th percentiles of lognormal distributions, was provided to all hygienists. A data interpretation test (DIT) was also administered and judgments were elicited before and after training. General linear models and cumulative logit models were used to analyze the relationship between accuracy of judgments and the various characteristics describing the participants. Data interpretation training (P < 0.0001), the company that the hygienist worked for (P < 0.0001), the total number of years hygienists had experience doing exposure assessments (P < 0.0001), and professional certifications (P < 0.0001) held by hygienists were found to be significant determinants of accurately predicting the correct exposure category for DITs as well as for task-specific judgment accuracy. Years of experience with a particular task (P < 0.0001), task evaluated, and the number of datapoints used for making judgments were found to be significant predictors of task-specific judgment accuracy. The NFC score was a predictor of the improvement in task judgment accuracy after training. The NFC score was itself predicted by determinants, such as company, years on current job, years of exposure assessment experience, and professional certifications. The results of this study are relevant not only for the case of industrial hygienists making exposure judgments prospectively but also possibly for those hygienists engaged in retrospective exposure assessments for epidemiological studies.

This study examines the impact of several experience and education determinants on exposure judgment accuracy. The study used desktop assessments performed on several different tasks with different exposure profiles to identify correlations between determinants and judgment accuracy using logistic regression models. The exposure judgments were elicited from industrial hygienists with varying levels of experience, education, and training. Videos and written and oral information about the exposure tasks were presented to all participants as they documented a series of qualitative and quantitative exposure judgment probabilities in four exposure categories. Participants (n = 77) first documented their qualitative and then their quantitative exposure assessments after receiving the series of sampling data points. Data interpretation tests and training in simple rules-of-thumb for data interpretation were also given to each participant to investigate the impact of data interpretation skills on exposure judgment accuracy. Logistic regression analysis indicated "years of exposure assessment experience"(p < 0.05),"highest EHS degree"(p < 0.05), and a participant's "data interpretation test score"(p < 0.05) directly impacted qualitative exposure judgment accuracy. Logistic regression models of quantitative judgment accuracy showed positive correlation with "greater than 10 years of exposure assessment experience"(p < 0.05),"highest EHS degree"(p < 0.05), a participant's "data interpretation test score"(p < 0.001), rules-of-thumb data interpretation training (p < 0.001), and the number of sample data points available for a judgment (p < 0.005). Analyzing judgments in subsets for participants with less or more than 10 years' experience indicated additional correlations with Certified Industrial Hygienist and Certified Safety Professional certifications, total number of task exposure assessments, and career number of air surveys. The correlation of qualitative and quantitative exposure judgment accuracy with "greater than 10 years experience" supports similar research findings from other fields. The results of this study indicate that several determinants of experience, education, and training, in addition to the availability of sampling data, significantly impact the accuracy of exposure assessments. The findings also suggest methods for enhancing exposure judgment accuracy through statistical tools, mathematical exposure modeling, and specific training.

A high performance liquid chromatographic method for determination of moxifloxacin in human saliva was developed. The method involved deproteinisation of the sample with perchloric acid and analysis of the supernatant using a reversed-phase C18 column (150 mm) and fluorescence detection at an excitation wavelength of 290 nm and an emission wavelength of 460 nm. The assay was specific for moxifloxacin and linear from 0.25 to 10.0 μg/ml. The relative standard deviation of intra- and inter-day assays was lower than 10%. The average recovery of moxifloxacin from saliva was 101%. Due to its simplicity, the assay can be used for pharmacokinetic studies of moxifloxacin.