Identification of the contaminating psychrotrophic bacteria in refrigerated bulked raw milk and the assessment of their deteriorating potential

This work aimed to characterize, identify, and determine the deteriorating potential of the contaminating psychrotrophic bacteria in refrigerated raw milk. Samples were submitted to serial dilutions and plated in specific culture media to form a bacterial culture collection. The isolates were characterized for their morphology and biochemical characteristics. The deteriorating potential of the isolates was determined according to the proteolytic, lipolytic and lecithinase activities at 4.0 ºC, 6.5 ºC, and 25.0 ºC. The results obtained for deterioration potential were assessed by the multivariate statistical method and by the principal components analysis (PCA). A total of 159 isolates were characterized, and of these, 46 strongly proteolytic Gram-negative isolates were selected for identification using the API 20 NE kit. The predominant bacteria were Gram-negative and oxidase and catalase positive, with a predominance of bacteria of the genus Pseudomonas. Using PCA, it was shown that the bacteria with the greatest deterioration potential were lecithinase producers, and that, in the autumn, proteolytic bacteria predominated at 4.0 ºC. Of the 46 isolates identified, more than 80% belonged to the species Pseudomonas fluorescens. Thus, attention should be given to the importance of implementing microbial contamination prevention measures in the bulking process, since, even under refrigeration, psychrotrophic bacteria multiply and produce enzymes that deteriorate lipids and proteins, with consequent quality losses of the milk and its derivatives, yield losses in the production of dairy products, and economic losses.

An Exploration of Pathologies of Multilevel Principal Components Analysis in Statistical Models of Shape

3D facial surface imaging is a useful tool in dentistry and in terms of diagnostics and treatment planning. Between-groups PCA (bgPCA) is a method that has been used to analyse shapes in biological morphometrics, although various “pathologies” of bgPCA have recently been proposed. Monte Carlo (MC) simulated datasets were created here in order to explore “pathologies” of multilevel PCA (mPCA), where mPCA with two levels is equivalent to bgPCA. The first set of MC experiments involved 300 uncorrelated normally distributed variables, whereas the second set of MC experiments used correlated multivariate MC data describing 3D facial shape. We confirmed previous results of other researchers that indicated that bgPCA (and so also mPCA) can give a false impression of strong differences in component scores between groups when there is none in reality. These spurious differences in component scores via mPCA reduced strongly as the sample sizes per group were increased. Eigenvalues via mPCA were also found to be strongly effected by imbalances in sample sizes per group, although this problem was removed by using weighted forms of covariance matrices suggested by the maximum likelihood solution of the two-level model. However, this did not solve problems of spurious differences between groups in these simulations, which was driven by very small sample sizes in one group here. As a “rule of thumb” only, all of our experiments indicate that reasonable results are obtained when sample sizes per group in all groups are at least equal to the number of variables. Interestingly, the sum of all eigenvalues over both levels via mPCA scaled approximately linearly with the inverse of the sample size per group in all experiments. Finally, between-group variation was added explicitly to the MC data generation model in two experiments considered here. Results for the sum of all eigenvalues via mPCA predicted the asymptotic amount for the total amount of variance correctly in this case, whereas standard “single-level” PCA underestimated this quantity.

An Exploratory Analysis of Housing and the Distribution of COVID-19 in Sweden

The impact of COVID-19 on various aspects of our life is evident. Proximity and close contact with individuals infected with the virus, and the extent of such contact, contribute to the intensity of the spread of the virus. Healthy and infected household members who both require sanctuary and quarantine space come into close and extended contact in housing. In other words, housing and living conditions can impact the health of occupants and the spread of COVID-19. This study investigates the relationship between housing characteristics and variations in the spread of COVID-19 per capita across Sweden’s 290 municipalities. For this purpose, we have used the number of infected COVID-19 cases per capita during the pandemic period—February 2020 through April 2021—per municipality. The focus is on variables that measure housing and housing conditions in the municipalities. We use exploratory analysis and Principal Components Analysis to reduce highly correlated variables into a set of linearly uncorrelated variables. We then use the generated variables to estimate direct and indirect effects in a spatial regression analysis. The results indicate that housing and housing availability are important explanatory factors for the geographical spread of COVID-19. Overcrowding, availability, and quality are all critical explanatory factors.

Dimensions of Self-Reported Driving Difficulty in Autistic and Non-Autistic Adults and their Relationship with Autistic Traits

A survey asked autistic and non-autistic people about the driving difficulties they experience and their autistic traits. Principle components analysis was used to identify how reported difficulties clustered together in each group, and regression was used to determine which subscales of the Autism Spectrum Quotient predict these factors. For autistic drivers three factors of driving difficulty emerged: a Driving Executive factor, predicted by Attention Switching; a Driving Understanding factor, predicted by Communication; and a Driving Social Interaction factor, predicted by Attention Switching. For non-autistic drivers only one Driving General factor emerged, predicted by Communication. This suggests autistic people may experience at least three distinct domains of difficulty when driving which may relate to their particular profile of autistic features.

Anti-Phototoxicity Effect of Phenolic Compounds from Acetone Extract of Entada phaseoloides Leaves via Activation of COX-2 and iNOS in Human Epidermal Keratinocytes

The extract from Entada phaseoloides was employed as active ingredients of natural origin into cosmetic products, while the components analysis was barely reported. Using LC-DAD-MS/qTOF analysis, eleven compounds (1–11) were proposed or identified from acetone extract of E. phaseoloides leaves (AE). Among them, six phenolic compounds, protocatechuic acid (2), 4-hydroxybenzoic acid (3), luteolin-7-O-β-d-glucoside (5), cirsimaritin (6), dihydrokaempferol (9), and apigenin (10), were isolated by various chromatographic techniques. Protocatechuic acid (2), epicatechin (4), and kaempferol (11) at a concentration 100 μM increased the HaCaT cells viability of the UVB-irradiated cell without any cytotoxicity effect and reduced the expression of COX-2 and iNOS inflammation gene. Moreover, compounds 2 and 4 could have potent effects on cell migration during wound closure. These results suggest that compounds 2, 4, and 11 from AE have anti-photoaging properties and could be employed in pharmaceutical and cosmeceutical products.

Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

Morphological and microsatellite DNA diversity of Djallonké sheep in Guinea-Bissau

Background The present study aimed at characterizing the Djallonké Sheep (DS), the only local sheep breed raised in Guinea-Bissau. A total of 200 animals were sampled from four regions (Bafatá, Gabú, Oio and Cacheu) and described using 7 visual criteria and 8 measurements. These parameters have been studied by principal components analysis. The genetic diversity and population structure of 92 unrelated animals were studied using 12 microsatellite markers. Results The values of quantitative characters in the Bafatá region were significantly higher than those obtained in the other three regions. A phenotypic diversity of the DS population was observed and three genetic types distinguished: animals with “large traits” in the region of Bafatá, animals with “intermediate traits” in the regions of Gabú and Oio and animals with “small traits” in the Cacheu region. The hair coat colors are dominated by the white color, the shape of the facial head profile is mainly convex and the ears “erected horizontally”. Most of the morphobiometric characteristics were significantly influenced by the “region” and “sex of animals”. The average Polymorphism Information Content (PIC) of 0.65 ± 0.11 supports the use of markers in genetic characterization. Gabú subpopulation had the highest genetic diversity measures (He = 0.716 ± 0.089) while Cacheu DS subpopulation presented the smallest (He = 0.651 ± 0.157). Only Gabú and Bafatá subpopulations presented significant heterozygote deficiency across all loci indicating possible significant inbreeding. Mean values for FIT,FST, FIS and GST statistics across all loci were 0.09, 0.029, 0.063 and 0.043 respectively. The overall genetic differentiation observed between the four DS subpopulations studied was low. Bafatá and Gabú are the most closely related subpopulations (DS = 0.04, genetic identity = 0.96) while Bafatá and Cacheu were the most genetically distant subpopulations (DS = 0.14, genetic identity = 0.87). Using Bayesian approach, the number of K groups that best fit the data is detected between 2 and 3, which is consistent with the morphological analysis and the factorial analysis of correspondence. Conclusions The molecular results on DS population of Guinea-Bissau confirmed the ones obtained with morphological analysis. The three genetic types observed phenotypically might be due to a combination of the agro-ecological differences and the management of breeding rather than genetic factors.

Chromatographic fingerprint application possibilities in food authentication

The aim of the study was to compare the effectiveness of the use of low-peak chromatographic fingerprints for the differentiation of various food products. Three groups of unprocessed products (mushrooms, hazelnuts and tomatoes), food preparations (bread, dried herbs and tomato juice) and alcoholic beverages (vodka and two types of blended whiskey) were examined. A commercial electronic nose based on ultrafast gas chromatography (acquisition time 90 s) with a flame ionization detector was used for the research. Static headspace was used as a green procedure to extract volatile compounds without modifying the food matrix. Individual extraction conditions were used for each product group. Similarities and differences between profiles were analyzed by simple Principal Components Analysis. The similarity rating was determined using the Euclidean distances. Global model was built for recognition chromatographic fingerprints of food samples. The best recognition results were 100% and 89% for tomato juices, spices, separate champignon elements and hazelnuts. On the other hand, the worst recognition results were 56% and 77% for breads and strong alcoholic beverages.