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Polybrominated biphenyls (PBBs) and polybrominated biphenyl ethers (PBDEs) are substances used in electrical and electronic products. They are restricted substances due to their potential risk to human health and the environment. In this research work, a test method was evaluated for the quantitative analysis of polybrominated diphenyl ethers (PBDEs) and polybrominated biphenyls (PBBs) in acrylonitrile-butadiene–styrene (ABS) plastic and polyethylene materials, based on solvent extraction of a plastic sample with soxhlet extraction techniques and detection by gas chromatography/mass spectrometry. We have also developed a computer program for checking the suitability of the analytical method by evaluating validation characteristics, including accuracy, precision, specificity/selectivity, limit of detection (LOD), limit of quantification (LOQ), linearity, range and sensitivity. The computer program was successfully applied for evaluation of the analytical method for prohibited PBBs and PBDEs in plastics. Graphical charts for the evaluation of the analytical method are presented along with their values, and researchers can get insight into evaluating several parameters together. It was found that the analytical method was able to quantify monobrominated - decabrominated biphenyl and monobrominated diphenyl ether–nonabrominated diphenyl ether between 100-1750 mg/kg and decabrominated diphenyl ether between 100–8,000 mg/kg. In addition, ERM-EC590 Polyethylene certified reference material from the Institute for Reference Materials and Measurements (IRMM) was used to validate the analytical method for PBBs and PBDEs in plastics. The obtained accuracy and precision indicate that the method successfully meets the required accuracy expressed as % recovery (70-130%) and the required precision expressed as percentage relative standard deviation (less than 20% RSD) of the IEC 62321:2008 international standard method.

This paper proposed data mining techniques to improve efficiency and reliability in diabetes classification. The real data set collected from Sawanpracharak Regional Hospital, Thailand, was fist analyzed by using gain-ratio feature selection techniques. Three well known algorithms; naïve bayes, k-nearest neighbors and decision tree, were used to construct classification models on the selected features. Then, the popular ensemble learning; bagging and boosting were applied using the three base classifiers. The results revealed that the best model with the highest accuracy was bagging with base classifier decision tree algorithm (95.312%). The experiments also showed that ensemble classifier models performed better than the base classifiers alone.

An international intercomparison involving eight national metrology institutes (NMIs) was conducted to establish their current measurement capabilities for determining five selected congeners from the brominated flame retardant classes polybrominated diphenyl ethers and polybrominated biphenyls. A candidate reference material consisting of polypropylene fortified with technical mixtures of penta-, octa- and decabromo diphenyl ether and decabromo biphenyl, which was thoroughly assessed for material homogeneity and stability, was used as study material. The analytical procedures applied by the participants differed with regard to sample pre-treatment, extraction, clean-up, employed calibrants and type of calibration procedure as well as regarding analytical methods used for separation, identification and quantification of the flame retardant congeners (gas chromatography coupled to an electron capture detector (GC-ECD), gas chromatography-mass spectrometry in the electron ionisation mode (GC-EI-MS), gas chromatography-mass spectrometry in the electron capture negative ionisation mode (GC-ECNI-MS), and liquid chromatography-inductive coupled plasma-mass spectrometry (LC-ICP-MS)). The laboratory means agreed well with relative standard deviations of the mean of means of 1.9%, 4.8%, 5.5% and 5.4% for brominated diphenyl ether (BDE) 47, 183 and 209 and for the brominated biphenyl (BB) congener 209, respectively. For BDE 206, a relative standard deviation of 28.5% was obtained. For all five congeners, within-laboratory relative standard deviations of six measurements obtained under intermediate precision conditions were between 1% and 10%, and reported expanded measurements uncertainties typically ranged from 4% to 10% (8% to 14% for BDE 206). Furthermore, the results are in good agreement with those obtained in the characterization exercise for determining certified values for the flame retardant congeners in the same material. The results demonstrate the state-of-the-art measurement capabilities of NMIs for quantifying representative BDE congeners and BB 209 in a polymer. The outcome of this intercomparison (pilot study) in conjunction with possible improvements for employing exclusively calibrants with thoroughly assessed purity suggests that a key comparison aiming at underpinning calibration and measurement capability (CMC) claims of NMIs can be conducted.

Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.

This paper discusses how Random Forests, ensembles of weak decision trees, can be improved by excluding less important features from the model. Gain Ratio Feature Selection was used as the basis for tuning the algorithm parameters. Backwards elimination of the features to obtain the minimum subset with the highest accuracy was the key methodology of this experiment. The results of the proposed model were better in terms of accuracy and number of features used. The objective of this paper was to create a base-line, which will be useful for the classification on diabetes complications data. We recommend using the Random Forest with Feature Selection technique for other type of classification problems. Future work also includes an extension study of the different types of learning settings to improve the feature construction process.

BACKGROUND: Fears of emerging artemisinin resistance in western Cambodia have prompted a series of clinical trials investigating whether slow responses to antimalarial treatment can be overcome by increasing doses of drug. METHODS: Patients with uncomplicated malaria were allocated 1 of 3 oral artesunate monotherapy regimens (2, 4, or 6 mg/kg/day for 7 days) and were observed for 42 days. A series of safety measures, including complete blood count on days 0, 3, 6, and 14, was implemented because of a lack of safety data for these experimental doses. RESULTS: After 3 doses, geometric mean absolute neutrophil counts were reduced in all groups, and 2 patients required artesunate to be discontinued because of neutropenia (absolute neutrophil count

Background Increasing rates of failure of artemisinin-based combination therapy have highlighted the possibility of emerging artemisinin resistance along the Thai-Cambodian border. We used an integrated in vivo-in vitro approach to assess the presence of artemisinin resistance in western Cambodia. This article provides additional data from a clinical trial that has been published in The New England Journal of Medicine. Methods Ninety-four adult patients from Battambang Province, western Cambodia, who presented with uncomplicated falciparum malaria were randomized to receive high-dose artesunate therapy (4 mg/kg/day orally for 7 days) or quinine-tetracycline. Plasma concentrations of dihydroartemisinin, in vitro drug susceptibility, and molecular markers were analyzed. Cases meeting all the following criteria were classified as artemisinin resistant: failure to clear parasites within 7 days of treatment or reemergence of parasites within 28 days of follow-up; adequate plasma concentrations of dihydroartemisinin; prolonged parasite clearance; and increased in vitro drug susceptibility levels for dihydroartemisinin. Results Two (3.3%) of 60 artesunate-treated patients were classified as artemisinin resistant. Their parasite clearance times were prolonged (133 and 95 h, compared with a median of 52.2 h in patients who were cured). These patients had 50% inhibitory concentrations of dihydroartemisinin that were almost 10 times higher than the reference clone W2. Resistance did not appear to be mediated by the pfmdr1 copy number or selected PfATPase6 polymorphisms previously proposed to confer artemisinin resistance. Conclusion Artemisinin resistance has emerged along the Thai-Cambodian border. The potentially devastating implications of spreading resistance to a drug that currently has no successor call for further studies of this emerging problem. Clinical trial registration ClinicalTrials.gov identifier NCT00479206.

Background Despite widespread coverage of the emergence of artemisinin resistance, relatively little is known about the parasite populations responsible. The use of PCR genotyping around the highly polymorphic Plasmodium falciparum msp1, msp2 and glurp genes has become well established both to describe variability in alleles within a population of parasites, as well as classify treatment outcome in cases of recurrent disease. The primary objective was to assess the emergence of minority parasite clones during seven days of artesunate (AS) treatment in a location with established artemisinin resistance. An additional objective was to investigate whether the classification of clinical outcomes remained valid when additional genotyping was performed. Methods Blood for parasite genotyping was collected from 143 adult patients presenting with uncomplicated falciparum malaria during a clinical trial of AS monotherapy in Western Cambodia. Nested allelic type-specific amplification of the genes encoding the merozoite surface proteins 1 and 2 (msp1 and msp2) and the glutamate-rich protein (glurp) was performed at baseline, daily during seven days of treatment, and again at failure. Allelic variants were analysed with respect to the size of polymorphisms using Quantity One software to enable identification of polyclonal infections. Results Considerable variation of msp2 alleles but well-conserved msp1 and glurp were identified. At baseline, 31% of infections were polyclonal for one or more genes. Patients with recurrent malaria were significantly more likely to have polyclonal infections than patients without recurrence (seven of nine versus 36 of 127, p = 0.004). Emergence of minority alleles during treatment was detected in only one of twenty-three cases defined as being artemisinin resistant. Moreover, daily genotyping did not alter the final outcome classification in any recurrent cases. Conclusions The parasites responsible for artemisinin-resistant malaria in a clinical trial in Western Cambodia comprise the dominant clones of acute malaria infections rather than minority clones emerging during treatment. Additional genotyping during therapy was not beneficial. Disproportionately high rates of polyclonal infections in cases of recurrence suggest complex infections lead to poor treatment outcomes. Current research objectives should be broadened to include identification and follow-up of recurrent polyclonal infections so as to define their role as potential agents of emerging resistance.

Guided, open-ended investigations (practicals) using environmentally important unknowns allow undergraduate students in an instrumental methods course to explore more than one way to solve an analytical problem. Here we show that analysis of acrolein and acrylonitrile using SPME–GC/MS is much less problematic than the EPA method using HPLC. The availability of both methods significantly enhances the learning experience, particularly with the SPME sampling technique, which is not expensive or difficult to implement and improves both sampling selectivity and sensitivity of detection (reducing interference problems). Even where the goal is not to teach environmental chemistry, the realistic problems such analyses provide are useful in learning instrumental methods of analysis.