Models with three Higgs doublets in the triplet representations of A(4) or S-4

We consider the quark sector of theories containing three scalar SU(2)(L) doublets in the triplet representation of A(4) (or S-4) and three generations of quarks in arbitrary A(4) (or S-4) representations. We show that for all possible choices of quark field representations and for all possible alignments of the Higgs vacuum expectation values that can constitute global minima of the scalar potential, it is not possible to obtain simultaneously nonvanishing quark masses and a nonvanishing CP-violating phase in the Cabibbo-Kobayashi-Maskawa quark mixing matrix. As a result, in this minimal form, models with three scalar fields in the triplet representation of A(4) or S-4 cannot be extended to the quark sector in a way consistent with experiment. DOI: 10.1103/PhysRevD.87.055010.

Biodiesel production from waste frying oils over lime catalysts

Biodiesel production from semi-refined oils (SRO) and waste frying oils (WFO) was studied using commercial CaO as heterogeneous catalyst. The methanolysis tests were carried out in mild reaction conditions (62 A degrees C, atmospheric pressure). With such conditions, SRO (soybean and rapeseed) allowed to produce a biodiesel containing 97-98 % of methyl esters (FAME), whereas WFO only provided 86-87 % of FAME. The lower FAME yield for WFO oil is ascribable to the partial neutralization of the catalyst by free fatty acids. Also, soaps formation from the WFO oil reduced the weight yield of the oil phase (containing FAME) obtained and increased the MONG content of the glycerin phase. The catalysts stability tests showed high stability even when WFO oil was processed. Catalytic tests performed with blends of WFO/semi-refined oils showed blending as a good strategy to process low value raw oils with minor decay of the catalyst performance. Both WFO and semi-refined oils showed S-shape kinetics curves thus discarding significant differences of the reaction mechanisms.

The D1822V APC polymorphism interacts with fat, calcium, and fiber intakes in modulating the risk of colorectal cancer in Portuguese persons

Background - Both genetic and environmental factors affect the risk of colorectal cancer (CRC). Objective - We aimed to examine the interaction between the D1822V polymorphism of the APC gene and dietary intake in persons with CRC. Design - Persons with CRC (n = 196) and 200 healthy volunteers, matched for age and sex in a case-control study, were evaluated with respect to nutritional status and lifestyle factors and for the D1822V polymorphism. Results - No significant differences were observed in energy and macronutrient intakes. Cases had significantly (P < 0.05) lower intakes of carotenes, vitamins C and E, folate, and calcium than did controls. Fiber intake was significantly (P = 0.004) lower in cases than in controls, whereas alcohol consumption was associated with a 2-fold risk of CRC. In addition, cases were significantly (P = 0.001) more likely than were controls to be sedentary. The homozygous variant for the APC gene (VV) was found in 4.6% of cases and in 3.5% of controls. Examination of the potential interactions between diet and genotype found that a high cholesterol intake was associated with a greater risk of colorectal cancer only in noncarriers (DD) of the D1822V APC allele (odds ratio: 1.66; 95% CI: 1.00, 2.76). In contrast, high fiber and calcium intakes were more markedly associated with a lower risk of CRC in patients carrying the polymorphic allele (DV/VV) (odds ratio: 0.50; 95% CI: 0.27, 0.94 for fiber; odds ratio: 0.51; 95% CI: 0.28, 0.93 for calcium) than in those without that allele. Conclusion - These results suggest a significant interaction between the D1822V polymorphism and the dietary intakes of cholesterol, calcium, and fiber for CRC risk.

Tunnig optical A-Sic/A-Si active filters by UV bias light in the visible and infrared spectral ranges

Visible range to telecom band spectral translation is accomplished using an amorphous SiC pi'n/pin wavelength selector under appropriate front and back optical light bias. Results show that background intensity works as selectors in the infrared region, shifting the sensor sensitivity. Low intensities select the near-infrared range while high intensities select the visible part according to its wavelength. Here, the optical gain is very high in the infrared/red range, decreases in the green range, stays close to one in the blue region and strongly decreases in the near-UV range. The transfer characteristics effects due to changes in steady state light intensity and wavelength backgrounds are presented. The relationship between the optical inputs and the output signal is established. A capacitive optoelectronic model is presented and tested using the experimental results. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

In Situ Near-Infrared (NIR) Versus High-Throughput Mid- Infrared (MIR) Spectroscopy to Monitor Biopharmaceutical Production

The development of biopharmaceutical manufacturing processes presents critical constraints, with the major constraint being that living cells synthesize these molecules, presenting inherent behavior variability due to their high sensitivity to small fluctuations in the cultivation environment. To speed up the development process and to control this critical manufacturing step, it is relevant to develop high-throughput and in situ monitoring techniques, respectively. Here, high-throughput mid-infrared (MIR) spectral analysis of dehydrated cell pellets and in situ near-infrared (NIR) spectral analysis of the whole culture broth were compared to monitor plasmid production in recombinant Escherichia coil cultures. Good partial least squares (PLS) regression models were built, either based on MIR or NIR spectral data, yielding high coefficients of determination (R-2) and low predictive errors (root mean square error, or RMSE) to estimate host cell growth, plasmid production, carbon source consumption (glucose and glycerol), and by-product acetate production and consumption. The predictive errors for biomass, plasmid, glucose, glycerol, and acetate based on MIR data were 0.7 g/L, 9 mg/L, 0.3 g/L, 0.4 g/L, and 0.4 g/L, respectively, whereas for NIR data the predictive errors obtained were 0.4 g/L, 8 mg/L, 0.3 g/L, 0.2 g/L, and 0.4 g/L, respectively. The models obtained are robust as they are valid for cultivations conducted with different media compositions and with different cultivation strategies (batch and fed-batch). Besides being conducted in situ with a sterilized fiber optic probe, NIR spectroscopy allows building PLS models for estimating plasmid, glucose, and acetate that are as accurate as those obtained from the high-throughput MIR setup, and better models for estimating biomass and glycerol, yielding a decrease in 57 and 50% of the RMSE, respectively, compared to the MIR setup. However, MIR spectroscopy could be a valid alternative in the case of optimization protocols, due to possible space constraints or high costs associated with the use of multi-fiber optic probes for multi-bioreactors. In this case, MIR could be conducted in a high-throughput manner, analyzing hundreds of culture samples in a rapid and automatic mode.

Comparative analysis of near and mid-infrared spectroscopy to monitor recombinant cyprosin production

Infrared spectroscopy, either in the near and mid (NIR/MIR) region of the spectra, has gained great acceptance in the industry for bioprocess monitoring according to Process Analytical Technology, due to its rapid, economic, high sensitivity mode of application and versatility. Due to the relevance of cyprosin (mostly for dairy industry), and as NIR and MIR spectroscopy presents specific characteristics that ultimately may complement each other, in the present work these techniques were compared to monitor and characterize by in situ and by at-line high-throughput analysis, respectively, recombinant cyprosin production by Saccharomyces cerevisiae. Partial least-square regression models, relating NIR and MIR-spectral features with biomass, cyprosin activity, specific activity, glucose, galactose, ethanol and acetate concentration were developed, all presenting, in general, high regression coefficients and low prediction errors. In the case of biomass and glucose slight better models were achieved by in situ NIR spectroscopic analysis, while for cyprosin activity and specific activity slight better models were achieved by at-line MIR spectroscopic analysis. Therefore both techniques enabled to monitor the highly dynamic cyprosin production bioprocess, promoting by this way more efficient platforms for the bioprocess optimization and control.