Role of aerobic physical training on cardiac autonomic and morphophysiological dysfunction in hypertensive rats subjected to ovarian hormone deprivation

Here we investigated the effects of physical training on cardiovascular autonomic control and cardiac morphofunctional parameters in spontaneously hypertensive rats (SHRs) subjected to ovarian hormone deprivation. Forty-eight 10-week-old SHRs were divided into two groups: ovariectomized (OVX, n=24) and sham (SHAM, n=24). Half of each group (n=12) was trained by swimming for 12 weeks (OVX-T and SHAM-T). Cardiac morphology and functionality were assessed using echocardiography, and autonomic parameters were assessed using double pharmacological autonomic block, baroreflex sensitivity (BRS), and analyses of heart rate variability (HRV) and blood pressure variability (BPV). Ovariectomy did not influence the cardiac autonomic tonus balance unlike physical training, which favored greater participation of the vagal autonomic tonus. Ovariectomy and aerobic physical training did not modify HRV and BRS, unlike BPV, for which both methods reduced low-frequency oscillations, suggesting a reduction in sympathetic vascular modulation. Untrained ovariectomized animals showed a reduced relative wall thickness (RWT) and increased diastolic and systolic volumes and left ventricular diameters, resulting in increased stroke volume. Trained ovariectomized animals presented reduced posterior wall thickness and RWT as well as increased final diastolic diameter, left ventricular mass, and stroke volume. Ovarian hormone deprivation in SHRs promoted morphofunctional adaptations but did not alter the evaluation of cardiac autonomic parameters. In turn, aerobic physical training contributed to a more favorable cardiac autonomic balance to the vagal autonomic component and promoted morphological adaptations but had little effect on cardiac functionality.

Experienced and inexperienced observers achieved relatively high within-observer agreement on video mobility scoring of dairy cows.

Assessment of lameness prevalence and severity requires visual evaluation of thelocomotion of a cow. Welfare schemes including locomotion assessments are increasingly being adopted, and more farmers and their veterinarians might implement a locomotion-scoring routine together. However, high within-observer agreement is a prerequisite for obtaining valid mobility scorings, and within-observer agreement cannot be estimated in a barn, because the gait of cows is dynamic and may change between 2 occasions. The objective of this study was to estimate the within-observer agreement according to the observers' educational background and experience with cattle, based on video recordings with very diverse types of gait. Groups of farmers, bovine veterinarians, first- and fourth-year veterinary students, researchers, and cattle-inexperienced sensory assessors evaluated mobility using a 5-point mobility score system developed specifically for walking cows (n=102 observers). The evaluation sessions were similar for all groups, lasted 75 min, and were organized as follows: introduction, test A, short training session, break, and test B. In total, video recordings of 22 cows were displayed twice in a random order (11 cows in each test × 2 replicates). Data were analyzed applying kappa coefficient, logistic regression, and testing for random effects of observers. The crude estimates of 95% confidence interval for weighted kappa in test A and B ranged, respectively, from 0.76 to 0.80 and 0.70 to 0.75. When adjusting for the fixed effects of video sample and gait scoring preferences, the probability of assigning the same mobility score twice to the same cow varied from 55% (sensory assessors) to 72% (fourth-year veterinary students). The random effect of the individual observers was negligible. That is, in general observers could categorize the mobility characteristics of cows quite well. Observers who preferred to assess the attributes back arch or the overall mobility score (based on uneven gait) had the highest agreement, respectively, 69 or 68%. The training session seemed insufficient to improve agreement. Nonetheless, even novice observers were able to achieve perfect agreement up to 60% of the 22 scorings with merely the experience obtained during the study (introduction and training session). The relatively small differences between groups, together with a high agreement, demonstrate that the new system is easy to follow compared with previously described scoring systems. The mobility score achieves sufficiently high within-observer repeatability to allow between-observer agreement estimates, which are reliable compared with other more-complex scoring systems. Consequently, the new scoring scale seems feasible for on-farm applications as a tool to monitor mobility within and between cows, for communication between farmers and veterinarians with diverse educational background, and for lamenessbenchmarking of herds.

Maternal obesity and offspring dietary patterns at 9 months of age.

BACKGROUND/OBJECTIVES: Differences in the quality of complementary feeding between infants of obese and nonobese mothers have not been examined sufficiently. The aim of this paper was to compare dietary patterns, foods, nutrients and energy intakes of 9-month-old Danish infants in a cohort comprising obese mothers (SKOT II, n=184; SKOT, Danish abbreviation of small children's diet and well-being) with a cohort consisting mainly of nonobese mothers (SKOT I, n=329). SUBJECTS/METHODS: Dietary intake was assessed by 7-day records, and dietary patterns were identified by principal component analysis. RESULTS: SKOT I was characterized by a lower maternal body mass index (BMI) and a higher social class than SKOT II in relation to parental education and occupation. Infants in SKOT II had lower scores on a Health-Conscious Food pattern reflected at the food group level, for example, with lower intake of the food groups Fruit and Vegetable but higher intake of WheatBreadNoWholegrain in SKOT II compared with SKOT I. Moreover, SKOT II had shorter durations of breastfeeding, earlier introductions of complementary feeding, higher energy intake from protein but lower energy intakes from monounsaturated fatty acids and polyunsaturated fatty acids at 9 months. SKOT II had higher weight-for-age and length-for-age z-scores, but no differences in BMI z-scores, as compared with SKOT I at 9 months. CONCLUSIONS: Infants of obese mothers from a lower social class seem to have a less healthy diet and higher weight and length z-scores at 9 months. Therefore, the promotion of healthy complementary feeding might be beneficial for the prevention of health implications, such as obesity, later in life for these infants.

Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis.

Lameness causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods. Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3 or 4/4) or not lame (score 1/4). Both models achieved sensitivity and specificity values around 80%, both in calibration and cross-validation. At the optimum values in the receiver operating characteristic curve, the false-positive rate was 28% in the parity 1 model, whereas in the parity 2 model it was about half (16%), which makes it more suitable for practical application; the model error rates were, 23 and 19%, respectively. Based on data registered automatically from one AMS farm, we were able to discriminate nonlame and lame cows, where partial least squares discriminant analysis achieved similar performance to the reference method.

Image analysis for maintenance of coating quality in nickel electroplating baths--real time control.

The aim of this paper is to show how it is possible to extract analytical information from images acquired with a flatbed scanner and make use of this information for real time control of a nickel plating process. Digital images of plated steel sheets in a nickel bath are used to follow the process under degradation of specific additives. Dedicated software has been developed for making the obtained results accessible to process operators. This includes obtaining the RGB image, to select the red channel data exclusively, to calculate the histogram of the red channel data and to calculate the mean colour value (MCV) and the standard deviation of the red channel data. MCV is then used by the software to determine the concentration of the additives Supreme Plus Brightner (SPB) and SA-1 (for confidentiality reasons, the chemical contents cannot be further detailed) present in the bath (these two additives degrade and their concentration changes during the process). Finally, the software informs the operator when the bath is generating unsuitable quality plating and suggests the amount of SPB and SA-1 to be added in order to recover the original plating quality.

Olive oil quantification of edible vegetable oil blends using triacylglycerols chromatographic fingerprints and chemometric tools.

The present work studies the effectiveness of the use of triacylglycerols (TAGs) for the quantification of olive oil in blends with vegetable oils. The determinations were obtained using high-performance liquid chromatography (HPLC) coupled to a Charged Aerosol Detector (CAD), in combination with Partial Least Squares (PLS) regression and using interval PLS (iPLS) for variable selection. Results revealed that PLS models can predict olive oil concentrations with reasonable errors. Variable selection through iPLS did not improve predictions significantly, but revealed the chemical information important in the chromatogram to quantify olive oil in vegetable oil blends.

Flatbed scanners as a source of imaging. Brightness assessment and additives determination in a nickel electroplating bath.

Desktop flatbed scanners are very well-known devices that can provide digitized information of flat surfaces. They are practically present in most laboratories as a part of the computer support. Several quality levels can be found in the market, but all of them can be considered as tools with a high performance and low cost. The present paper shows how the information obtained with a scanner, from a flat surface, can be used with fine results for exploratory and quantitative purposes through image analysis. It provides cheap analytical measurements for assessment of quality parameters of coated metallic surfaces and monitoring of electrochemical coating bath lives. The samples used were steel sheets nickel-plated in an electrodeposition bath. The quality of the final deposit depends on the bath conditions and, especially, on the concentration of the additives in the bath. Some additives become degraded with the bath life and so is the quality of the plate finish. Analysis of the scanner images can be used to follow the evolution of the metal deposit and the concentration of additives in the bath. Principal component analysis (PCA) is applied to find significant differences in the coating of sheets, to find directions of maximum variability and to identify odd samples. The results found are favorably compared with those obtained by means of specular reflectance (SR), which is here used as a reference technique. Also the concentration of additives SPB and SA-1 along a nickel bath life can be followed using image data handled with algorithms such as partial least squares (PLS) regression and support vector regression (SVR). The quantitative results obtained with these and other algorithms are compared. All this opens new qualitative and quantitative possibilities to flatbed scanners.

Discriminating olive and non-olive oils using HPLC-CAD and chemometrics.

This work presents a method for an efficient differentiation of olive oil and several types of vegetable oils using chemometric tools. Triacylglycerides (TAGs) profiles of 126 samples of different categories and varieties of olive oils, and types of edible oils, including corn, sunflower, peanut, soybean, rapeseed, canola, seed, sesame, grape seed, and some mixed oils, have been analyzed. High-performance liquid chromatography coupled to a charged aerosol detector was used to characterize TAGs. The complete chromatograms were evaluated by PCA, PLS-DA, and MCR in combination with suitable preprocessing. The chromatographic data show two clusters; one for olive oil samples and another for the non-olive oils. Commercial oil blends are located between the groups, depending on the concentration of olive oil in the sample. As a result, a good classification among olive oils and non-olive oils and a chemical justification of such classification was achieved.

Cross-validation of component models: a critical look at current methods.

In regression, cross-validation is an effective and popular approach that is used to decide, for example, the number of underlying features, and to estimate the average prediction error. The basic principle of cross-validation is to leave out part of the data, build a model, and then predict the left-out samples. While such an approach can also be envisioned for component models such as principal component analysis (PCA), most current implementations do not comply with the essential requirement that the predictions should be independent of the entity being predicted. Further, these methods have not been properly reviewed in the literature. In this paper, we review the most commonly used generic PCA cross-validation schemes and assess how well they work in various scenarios.

Quantitative analysis of NMR spectra with chemometrics.

The number of applications of chemometrics to series of NMR spectra is rapidly increasing due to an emerging interest for quantitative NMR spectroscopy e.g. in the pharmaceutical and food industries. This paper gives an analysis of advantages and limitations of applying the two most common chemometric procedures, Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR), to a designed set of 231 simple alcohol mixture (propanol, butanol and pentanol) (1)H 400 MHz spectra. The study clearly demonstrates that the major advantage of chemometrics is the visualisation of larger data structures which adds a new exploratory dimension to NMR research. While robustness and powerful data visualisation and exploration are the main qualities of the PCA method, the study demonstrates that the bilinear MCR method is an even more powerful method for resolving pure component NMR spectra from mixtures when certain conditions are met.

Active photosensitizers in butter detected by fluorescence spectroscopy and multivariate curve resolution.

In this study, fluorescence excitation and emission matrices and multivariate curve resolution (PARAFAC) were used to detect and characterize active photosensitizers spectrally in butter. Butter samples were packed under high (air) and low oxygen (<0.05%) atmospheres and exposed to violet, green, or red light. Six photosensitizers were found: riboflavin, protoporphyrin, hematoporphyrin, a chlorophyll a-like molecule, and two unidentified tetrapyrrols. By estimation of relative concentrations, we could follow how each sensitizer was photodegraded as function of wavelength, oxygen level, and time. The degradation rate of protoporphyrin, hematoporphyrin, chlorophyll a, and one of the tetrapyrrols correlated well (0.83-0.91) with the formation of sensory measured oxidation. The results suggest that mainly type I photoreactions were responsible for the degradation of photosensitizers in both high and low oxygen atmosphere. Type II photoreactions (generation of singlet oxygen) were involved in the oxidation of butter stored in air. The study shows that PARAFAC modeling of fluorescence landscapes is an excellent tool for studying photooxidation in complex systems.

Attempt to separate the fluorescence spectra of adrenaline and noradrenaline using chemometrics.

An investigation was conducted on whether the fluorescence spectra of the very similar catecholamines adrenaline and noradrenaline could be separated using chemometric methods. The fluorescence landscapes (several excitation and emission spectra were measured) of two data sets with respectively 16 and 6 samples were measured, the smaller data set with higher resolution and i.e. precision. The samples were artificial urine (pH approximately equal to 3) spiked with the catecholamines in the concentration ranges 40--1200 nmol/L and 5.5--18 micromol/L, respectively. Unfold partial least squares regression (Unfold-PLSR) on the larger data set and parallel factor analysis (PARAFAC) of the six samples of the smaller set showed that there was no difference between the fluorescence landscapes of adrenaline and noradrenaline. It can be concluded that chemometric separation of adrenaline and noradrenaline is not obtainable using this type of fluorescence measurement. Raman scatter, which overlaps the catecholamine spectra, was shown not to have any influence on the models calculated.