UV-Vis and CIELAB based chemometric characterization of manihot esculenta carotenoid contents

Vitamin A deficiency is a prevalent health problem in many areas of the world, where cassava genotypes with high pro-vitamin A content have been identified as a strategy to address this issue. In this study, we found a positive correlation between the color of the root pulp and the total carotenoid contents and, importantly, showed how CIELAB color measurements can be used as a non-destructive and fast technique to quantify the amount of carotenoids in cassava root samples, as opposed to traditional methods. We trained several machine learning models using UV-visible spectrophotometry data, CIELAB data and a low-level data fusion of the two. Best performance models were obtained for the total carotenoids contents calculated using the UV-visible dataset as input, with R2 values above 90 %. Using CIELAB and fusion data, values around 60 % and above 90 % were found. Importantly, these results demonstrated how data fusion can lead to a better model performance for prediction when comparing to the use of a single data source. Considering all these findings, the use of colorimetric data associated with UV-visible and HPLC data through statistical and machine learning methods is a reliable way of predicting the content of total carotenoids in cassava root samples.To CNPq (National Counsel of Technological and Scientific Development) for financial support (Process n 407323/2013-9), to CAPES (Coordination for the Improvement of Higher Education Personnel (CAPES), and EPAGRI(AgriculturalResearchandRuralExtensionCompanyofSantaCatarina).Theresearchfellowshipfrom CNPqonbehalfofM.Maraschinisacknowledged.TheworkispartiallyfundedbyProjectPropMine,funded bytheagreementbetweenPortugueseFCT(FoundationforScienceandTechnology)andBrazilianCNPq.info:eu-repo/semantics/publishedVersio

Development of an automatic identification algorithm for antibiogram analysis

Routinely, diagnostic and microbiology laboratories perform antibiogram analysis which can present some difficulties leading to misreadings and intra and inter-reader deviations. An Automatic Identification Algorithm (AIA) has been proposed as a solution to overcome some issues associated with the disc diffusion method, which is the main goal of this work. AIA allows automatic scanning of inhibition zones obtained by antibiograms. More than 60 environmental isolates were tested using susceptibility tests which were performed for 12 different antibiotics for a total of 756 readings. Plate images were acquired and classified as standard or oddity. The inhibition zones were measured using the AIA and results were compared with reference method (human reading), using weighted kappa index and statistical analysis to evaluate, respectively, inter-reader agreement and correlation between AIA-based and human-based reading. Agreements were observed in 88% cases and 89% of the tests showed no difference or a o4 mm difference between AIA and human analysis, exhibiting a correlation index of 0.85 for all images, 0.90 for standards and 0.80 for oddities with no significant difference between automatic and manual method. AIA resolved some reading problems such as overlapping inhibition zones, imperfect microorganism seeding, non-homogeneity of the circumference, partial action of the antimicrobial, and formation of a second halo of inhibition. Furthermore, AIA proved to overcome some of the limitations observed in other automatic methods. Therefore, AIA may be a practical tool for automated reading of antibiograms in diagnostic and microbiology laboratoriesinfo:eu-repo/semantics/acceptedVersio

Excess body fat negatively affects bone mass in adolescents

AbstractObjectiveThe aim of this study was to investigate the effects of excess body fat on bone mass in overweight, obese, and extremely obese adolescents.MethodsThis study included 377 adolescents of both sexes, ages 10 to 19 y. Weight, height, body mass index (BMI), bone age, bone mineral content (BMC), and bone mineral density (BMD) were obtained by dual-energy x-ray absorptiometry. The results were adjusted for chronological age and bone age. Comparisons according to nutritional classification were performed by analysis of variance, followed by Tukey test. Linear regression models were used to explain the variation in BMD and BMC in the L1–L4 lumbar spinal region, proximal femur, and whole body in relation to BMI, lean mass, fat mass (FM), and body fat percentage (BF%), considering P < 0.05.ResultsFor all nutritional groups, average bone age was higher than chronological age. In both sexes, weight and BMI values increased from eutrophic to extremely obese groups, except for BMD and BMC, which did not differ among male adolescents, and were smaller in extremely obese than in obese female adolescents (P < 0.01). Significant differences were observed for FM and BF% values among all nutritional groups (P < 0.01). Positive, moderate to strong correlations were detected between BMD and BMC for BMI, lean mass, and FM. A negative and moderate correlation was found between BMC and BF%, and between BMD and BF% at all bone sites analyzed in males and between BF% and spine and femur BMD, in females.ConclusionThe results reveal a negative effect of BF% on bone mass in males and indicate that the higher the BF% among overweight adolescents, the lower the BMD and BMC values

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz)

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in -carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis--carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (redfleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.FAPESC (Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina) and CNPq (Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico) for financial support. The research fellowship from CNPq on behalf of the last author is acknowledged. The work is partially funded by Project PropMine, funded by the agreement between Portuguese FCT and Brazilian CNPq. The authors also thank the FCT Strategic Project of UID/BIO/04469/2013 uni

The influence of Fe2O3 doping on the pore structure and mechanical strength of TiO2-containing alumina obtained by freeze-casting

This work investigated TiO/FeO doped alumina prepared by the freeze-casting technique and using camphene as the solvent. Dendritic pores were formed in the TiO doped alumina, a structure conferred by the frozen camphene. Contrary to this trend, further FeO doping of TiO-containing alumina resulted in the formation of non-dendritic structures. This behavior was attributed to the higher density of α-FeO (5.24 g cm) when compared to α-AlO (3.95 g cm) and anatase TiO (3.89 g cm), which reduced critical solidification front velocity, thus forming material with different pore shape. FeO doping also improved the densification of TiO-alumina and inhibited the formation of cracks, reflected by superior mechanical strength with best results ∼150% higher for 10% FeO loaded samples as compared to TiO-alumina samples

Effect of titania addition on the properties of freeze-cast alumina samples

This work investigated the behavior of TiO2-containing α-Al2O3 samples prepared by the freeze-casting technique. Camphene and liquid nitrogen were used as the solvent and cooling fluid, respectively. Camphene resulted in the formation of dendritic pores, in the direction of the freeze-casting cold front during sample preparation. The formation of β-Al2TiO5 phase occurred at 1300°C, and became more evident as the sintering temperatures reached 1500°C. The TiO2 loading did not affect the sample porosity at a given temperature, but it was detrimental in the case of mechanical properties under certain conditions. For instance, the flexural strength slightly improved with increasing the TiO2 loading and sintering temperature from 1100 to 1300°C. This effect was attributed to the occurrence of a more effective sintering of alumina. However, as the heat treatment temperature was raised from 1300 to 1500°C, the flexural strength did not increase as a function of the TiO2 loading, even though the densification occurred with loss of porosity. The loss of mechanical strength was found to be associated with the formation of microcracks which stemmed from the formation of β-Al2TiO5 phase for TiO2 loadings in excess of 4wt% at these high sintering temperatures

Robust automated cardiac arrhythmia detection in ECG beat signals

Nowadays, millions of people are affected by heart diseases worldwide, whereas a considerable amount of them could be aided through an electrocardiogram (ECG) trace analysis, which involves the study of arrhythmia impacts on electrocardiogram patterns. In this work, we carried out the task of automatic arrhythmia detection in ECG patterns by means of supervised machine learning techniques, being the main contribution of this paper to introduce the optimum-path forest (OPF) classifier to this context. We compared six distance metrics, six feature extraction algorithms and three classifiers in two variations of the same dataset, being the performance of the techniques compared in terms of effectiveness and efficiency. Although OPF revealed a higher skill on generalizing data, the support vector machines (SVM)-based classifier presented the highest accuracy. However, OPF shown to be more efficient than SVM in terms of the computational time for both training and test phases

Whole-genome sequencing of 1,171 elderly admixed individuals from Brazil

As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~2 million are absent from large public databases. WGS enables identification of ~2,000 previously undescribed mobile element insertions without previous description, nearly 5 Mb of genomic segments absent from the human genome reference, and over 140 alleles from HLA genes absent from public resources. We reclassify and curate pathogenicity assertions for nearly four hundred variants in genes associated with dominantly-inherited Mendelian disorders and calculate the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observe that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS