CoronaVac (Sinovac) COVID-19 vaccine-induced molecular changes in healthy human serum by infrared spectroscopy coupled with chemometrics

From the beginning of the COVID-19 coronavirus pandemic in December of 2019, the disease has infected millions of people worldwide and caused hundreds of thousands of deaths. Since then, several vaccines have been developed. One of those vaccines is inactivated CoronaVac-Sinovac COVID-19 vaccine. In this proof of concept study, we first aimed to determine CoronaVac-induced biomolecular changes in healthy human serum using infrared spectroscopy. Our second aim was to see whether the vaccinated group can be separated or not from the non-vaccinated group by applying chemometric techniques to spectral data. The results revealed that the vaccine administration induced significant changes in some functional groups belonging to lipids, proteins and nucleic acids. In addition, the non-vaccinated and vaccinated groups were successfully separated from each other by principal component analysis (PCA) and linear discriminant analysis (LDA). This proof-of-concept study will encourage future studies on CoronaVac as well as other vaccines and will lead to make a comparison between different vaccines to establish a better understanding of the vaccination outcomes on serum biomolecules.     

EVALUATION OF WINE COMPETITION JUDGE PERFORMANCE USING PRINCIPAL COMPONENT SIMILARITY ANALYSIS

Principal component similarity (PCS) analysis was used to evaluate judge performance from a wine competition. Data were analyzed for five international judges and seven wine makers, for 42 white, 30 red and 25 specialty wines, using a 20-point quality scoring system. Principal similarity plots were used to group judges according to judging 'style' and to identify outliers, for each wine category. Judge groupings were consistent when three different references were used; however, the most interpretable PCS plot was obtained when the overall mean-judge-score was used as the reference. Results from PCS were compared to principal component analysis (PCA). PCS analysis allowed the information from all significant principal components to be graphically represented in two dimensions and was more successful in classifying judges than plots based on the first three principal components. The technique of PCS is an important complement to existing methodologies, and can provide wine competition coordinators with an objective technique for judge evaluation and selection.     

Hydrolytic Enzymes Producing Bacterial Endophytes of Some Poaceae Plants

Endophytic bacteria represent microorganisms that live during the whole life cycle within the tissues of healthy plants without causing any obvious signs of disease. In this study, the ability of 128 endophyte bacterial isolates from some cultivated and wild grain plants (Poaceae family) in Van, Turkey, were investigated in terms of producing several extracellular hydrolytic enzymes. It was demonstrated that lipases, proteases, amylases, cellulases, pectinases, and xylanases were produced by the bacteria with relative frequencies of 74.2%, 65.6%, 55.4%, 32%, 21.8%, and 7.8%, respectively. In addition, molecular identification of a certain number of isolates selected according to their enzyme-producing capabilities was performed by 16S rRNA gene sequencing using a next-generation sequencing platform. As a result of the analysis, the isolates yielded certain strains belonging to Pseudomonas, Micrococcus, Paenibacillus, Streptococcus, Curtobacterium, Chryseobacterium, and Bacillus genera. Also, the strain G117Y1T was evaluated as a member of potential novel species based on 16S rRNA sequencing results.     

MODELING DIFFERENCES BETWEEN PANELISTS IN USE OF MEASUREMENT SCALES

In sensory data sets, an important source of differences between panelists is in their use of the measurement scale. These differences can be summarized in differences in location , the overall level, and differences in dispersion , the range of the scale used. This paper discusses a method of correcting for these differences by jointly modeling location and dispersion using a see-saw algorithm. This approach is also applicable when scores are not normally distributed and when there is a (nonlinear) relationship between the dispersion and the location. The approach is illustrated with an example for flavor data of freeze-dried and hot-air dried peppers.