Inhibitory pheromonal activity promoted by sulfur analogs of the sex pheromone of the female processionary mothThaumetopoea pityocampa (Denis and schiff)

New sulfur analogs of the sex pheromone of the female processionary mothThaumetopoea pityocampa have been found to be effective inhibitors of the natural pheromone activity both in EAG bioassays and field tests. The structures of these analogs have been derived from replacement of the oxygen atom(s) of the acetate group by sulfur (compounds 3-5) and the olefinic moiety of the enyne function by the isosteric SCH₂ group (compounds 6 and 7). The synthesis and biological activity of 3-[(Z)-12-pentadecen-10-ynylthio]-1,1,1-trifluoropropan-2-one (8), a closely related structure to the pheromone is also described.     

Compressive Creep of SiC-Whisker-Reinforced Al2O3

Compressive creep of SiC-whisker-reinforced Al₂O₃ composites (0, 5, 15, and 25 wt% SiC) was measured in the temperature range of 1300° to 1500°C in air and argon. The creep resistance increased with increasing whisker concentration. The results indicated that the whiskers degraded in air, increasing strain rates compared to those in argon. Stress exponents between 1.0 and 2.0 and an activation energy of 620 ± 100 kJ/mol were measured. Transmission electron microscopy observations indicated that cavitation was minimal and that the deformed composites had the same dislocation structure as did the as-received samples.     

Simple and stereoselective synthesis of sex pheromone of processionary mothThaumetopoea pityocampa (Denis and Schiff.)

A simple and stereoselective synthesis of (Z)-13-hexadecen-11-yn-1-yl acetate, the major component of the sex pheromone ofThaumetopoea pityocampa (Denis and Schiff.) is described. The procedure essentially involves formylation of a terminal acetylene to the corresponding aldehyde followed by a stereochemically controlled Wittig reaction, which has been studied under a variety of conditions.     

Reduction of Escherichia coli O157:H7 viability on leafy green vegetables by treatment with a bacteriophage mixture and trans-cinnamaldehyde

Enterohemorrhagic Escherichia coli (EHEC) O157:H7 has been recognized as a major foodborne pathogen responsible for frequent gastroenteritis outbreaks. Phages and essential oils can be used as a natural antimicrobial method to reduce bacterial pathogens from the food supply. The objective of this study was to determine the effect of a bacteriophage cocktail, BEC8, alone and in combination with the essential oil trans-cinnameldehyde (TC) on the viability of a mixture of EHEC O157:H7 strains applied on whole baby romaine lettuce and baby spinach leaves. The EHEC O157:H7 strains used were Nal^R mutants of EK27, ATCC 43895, and 472. Exponentially growing cells from tryptic soy (TS) broth cultures were spot inoculated on leaves and dried. EHEC cells were placed at low, medium, and high inoculum levels (10⁴, 10⁵, and 10⁶ CFU/mL, respectively). Appropriate controls, BEC8 (approx. 10⁶ PFU/leaf), and TC (0.5% v/v) were applied on treated leaves. The leaves were incubated at 4, 8, 23, and 37 °C in Petri dishes with moistened filter papers. EHEC survival was determined using standard plate count on nalidixic acid (50 μg/mL) Sorbitol MacConkey agar. No survivors were detected when both leaves were treated with BEC8 or TC individually at low inoculum levels after 24 h at 23 and 37 °C. When the EHEC inoculum size increased and/or incubation temperature decreased, the efficacy of BEC8 and TC decreased. However, when the two treatments were combined, no survivors were detected after 10 min at all temperatures and inoculum levels on both leafy greens. These results indicated that the BEC8/TC combination was highly effective against EHEC on both leafy greens. This combination could potentially be used as an antimicrobial to inactivate EHEC O157:H7 and reduce their incidence in the food chain.     

Bioprospecting for brewers: Exploiting natural diversity for naturally diverse beers

The burgeoning interest in archaic, traditional, and novel beer styles has coincided with a growing appreciation of the role of yeasts in determining beer character as well as a better understanding of the ecology and biogeography of yeasts. Multiple studies in recent years have highlighted the potential of wild Saccharomyces and non-Saccharomyces yeasts for production of beers with novel flavour profiles and other desirable properties. Yeasts isolated from spontaneously fermented beers as well as from other food systems (wine, bread, and kombucha) have shown promise for brewing application, and there is evidence that such cross-system transfers have occurred naturally in the past. We review here the available literature pertaining to the use of nonconventional yeasts in brewing, with a focus on the origins of these yeasts, including methods of isolation. Practical aspects of utilizing nondomesticated yeasts are discussed, and modern methods to facilitate discovery of yeasts with brewing potential are highlighted.     

High-Performance Liquid Chromatography Method for the Monitoring of the <Emphasis Type="Italic">Allium</Emphasis> Derivative Propyl Propane Thiosulfonate Used as Natural Additive in Animal Feed

A new simple analytical method for monitoring propyl propane thiosulfonate (PTSO) in animal feed is presented. PTSO is an active ingredient from Allium spp. (like onion and shallot), proposed as a natural additive for feed being an efficient alternative to antibiotics used as growth promoter due to its efficiency improving animal health. Reversed-phase liquid chromatography with UV detection has been used and a previous sample treatment based on solid-liquid extraction has been developed and optimized in order to extract PTSO from a feed for laying hens using acetone as extraction solvent. The method has been characterized obtaining limits of detection and quantification of 11.2 and 37.3 mg kg^?1, respectively, which are lower than the concentrations expected in samples containing this additive. The intra- and interday relative standard deviations (RSDs) were lower than 8.3 % in all the cases, and recoveries varied from 90.2 to 94.6 %. Finally, in order to check the unequivocal identification of PTSO, mass spectrometry detection was applied. The proposed method is a simple procedure for monitoring PTSO in commercial feed, being possible to implement it in routine laboratories for quantification purposes and stability studies of the distributed products.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.