Bacillus cereus is an important foodborne pathogen, which can cause severe food poisoning. The aim of this study was (i) to evaluate the quantitative prevalence of B. cereus in retail prepackaged infant formula and ready-to-eat rice flour in China and (ii) to gain the basic information on pheno- and genotypic characteristics of B. cereus isolates. We found that 40 out of the 587 samples were positive for B. cereus. B. cereus in 3.5% of infant formula samples and 1.0% of rice flour samples outnumbered 100 Colony-Forming Units (CFU)/g. B. cereus level even attained 10-10 CFU/g in four infant formula samples and one rice flour sample. Furthermore, we identified the distribution patterns of toxin genes in B. cereus isolates. The results showed that 97.5% of B. cereus isolates harbored at least one enterotoxin gene. Antibiotic susceptibility tests revealed that all isolated B. cereus strains were resistant to penicillin and 50% of them were multidrug resistant. Thirteen new sequence types (STs) and four new alleles were identified via multilocus sequence typing. Clonal Complex (CC) ST-205 and CC ST-142 were predominant clonal complexes. Interestingly, we revealed the special relationship between STs of B. cereus isolates and the geographical distributions of infant food manufacturers for the first time. The data implied that B. cereus of different STs might have a distinct ecological niche in China. In view of relatively high contamination level of enterotoxin- producing B. cereus in a proportion of infant foods, especially in those suitable for the ≤6-month-old infant group, appropriate safety criteria and hygienic control measures for infant foods should be drafted in China to prevent B. cereus infection.
Comparison of two methods for estimation of soil water content from measured reflectance. Can. J. Soil Sci. 92: 845Á857. Remote sensing (RS) technology has increasingly been used in soil water content estimation, but highly accurate estimates of soil water content are still difficult to obtain using this technique. This study aims to determine the wavelengths at which the reflectance is most sensitive to changes in soil water contents (u). Four types of soils were selected and light reflectance (l) was measured at different u. Results showed that parabolic functions fit u with measured l very well but only for individual wavelengths. Multivariate linear functions of u with measured l at visually selected characteristic wavelengths led to improved predictions, but the coefficients of determination between the soil water content and measured reflectance (R 2 ranged from 0.788 to 0.925 for the four soils) were still not high for the studied soils. Stepwise multiple linear regressions between u and measured l showed higher coefficients of determination (R 2 increased to 0.99 when the number of the statistically selected wavelengths increased) than the multiple regression, but had lower coefficients of determination than the stepwise multiple linear regressions between u and the normalized band depths (D n). The multi-variable linear functions fitted the measured u vs. D n best with much higher R 2 values, even when a single wavelength was used. Re-sampling wavelengths of less than 20 nm preserved the main features of the original reflectance for the studied soils. Parameters were fitted for the quadratic functions using the re-sampled reflectance data at 20-nm wavelength intervals for further estimation of soil water content, which was considered potentially applicable in RS technology for estimating soil water content provided that soils are relatively homogeneous. In conclusion, stepwise multiple linear regression functions between u vs. D n are statistically precise and are recommended for estimating soil water content from reflectance. Parameters for quadratic functions relating soil water content to the observed reflectance could be potentially used in RS technology for estimating soil water content.
The rise in efficiency of Satisfiability Modulo Theories (SMT) solvers has created numerous uses for them in software verification, program synthesis, functional programming, refinement types, etc. In all of these applications, SMT solvers are used for generating satisfying assignments (e.g., a witness for a bug) or proving unsatisfiability/validity(e.g., proving that a subtyping relation holds). We are often interested in finding not just an arbitrary satisfying assignment, but one that optimizes (minimizes/maximizes) certain criteria. For example, we might be interested in detecting program executions that maximize energy usage (performance bugs), or synthesizing short programs that do not make expensive API calls. Unfortunately, none of the available SMT solvers offer such optimization capabilities. In this paper, we present SYMBA, an efficient SMT-based optimization algorithm for objective functions in the theory of linear real arithmetic (LRA). Given a formula φ and an objective function t , SYMBA finds a satisfying assignment of φthat maximizes the value of t . SYMBA utilizes efficient SMT solvers as black boxes. As a result, it is easy to implement and it directly benefits from future advances in SMT solvers. Moreover, SYMBA can optimize a set of objective functions, reusing information between them to speed up the analysis. We have implemented SYMBA and evaluated it on a large number of optimization benchmarks drawn from program analysis tasks. Our results indicate the power and efficiency of SYMBA in comparison with competing approaches, and highlight the importance of its multi-objective-function feature.
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