Nanoencapsulation of Antioxidant-Rich Fraction of Roasted <i>Moringa oleifera</i>L. Leaf Extract: Physico-Chemical Properties and <i>in Vitro</i>Release Mechanisms

Nanocapsules (NC) of antioxidant rich fraction of roasted Moringa leaves were prepared using emulsion coacervation technique with alginate (ALG) and/or chitosan (CTS) as biopolymers. NC were characterized based on particle size, polydispersity index (PDI), zeta potential, encapsulation efficiency (EE) and loading capacity (LC). Substituting CTS with ALG in NC caused a reduction in particle size and PDI, and enhanced EE. Mean particle size dropped from 1209 nm in 1:3 to 413 nm in 3:1 ALG/CTS-NC;PDI decreased from 0.9% to 0.2% and zeta potential from -5.4 to -28.1 mV. The highest EE (87.6%) and LC (13%) were obtained with ALG-CTS-NC (3:1). ALG-NC were spherical while both CTS and ALG-CTS-NC were ovoid. ALG and ALG-CTS-NC were oil/water emulsions while CTS-NC formed water/oil emulsions. 60% and 70% of bioactives in ALG-CTS-NC (3:1) were released in simulated gastric and intestinal fluids respectively after 400 min. Release of antioxidants from NC is concentration-dependent (First order model) and involves simultaneously diffusion (Higuchi model), swelling (korsmeyer-Peppas model) and erosion (Hixson-Crowell model) mechanisms.

Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

This paper introduces a comparative analysis of the proficiencies of various textures and geometric features in the diagnosis of breast masses on mammograms.An improved machine learning-based framework was developed for this study.The proposed system was tested using 106 full field digital mammography images from the INbreast dataset,containing a total of 115 breast mass lesions.The proficiencies of individual and various combinations of computed textures and geometric features were investigated by evaluating their contributions towards attaining higher classification accuracies.Four state-of-the-art filter-based feature selection algorithms(Relief-F,Pearson correlation coefficient,neighborhood component analysis,and term variance)were employed to select the top 20 most discriminative features.The Relief-F algorithm outperformed other feature selection algorithms in terms of classification results by reporting 85.2%accuracy,82.0%sensitivity,and 88.0%specificity.A set of nine most discriminative features were then selected,out of the earlier mentioned 20 features obtained using Relief-F,as a result of further simulations.The classification performances of six state-of-the-art machine learning classifiers,namely k-nearest neighbor(k-NN),support vector machine,decision tree,Naive Bayes,random forest,and ensemble tree,were investigated,and the obtained results revealed that the best classification results(accuracy=90.4%,sensitivity=92.0%,specificity=88.0%)were obtained for the k-NN classifier with the number of neighbors having k=5 and squared inverse distance weight.The key findings include the identification of the nine most discriminative features,that is,FD26(Fourier Descriptor),Euler number,solidity,mean,FD14,FD13,periodicity,skewness,and contrast out of a pool of 125 texture and geometric features.The proposed results revealed that the selected nine features can be used for the classification of breast masses in mammograms.

Adaptive Cell Segmentation and Tracking for Volumetric Confocal Microscopy Images of a Developing Plant Meristem

Automated segmentation and tracking of cells in actively developing tissues can provide high-throughput and quantitative spatiotemporal measurements of a range of cell behaviors; cell expansion and cell-division kinetics leading to a better understanding of the underlying dynamics of morphogenesis. Here, we have studied the problem of constructing cell lineages in time-lapse volumetric image stacks obtained using Confocal Laser Scanning Microscopy （CLSM）. The novel contribution of the work lies in its ability to segment and track cells in densely packed tissue, the shoot apical meristem （SAM）, through the use of a close-loop, adaptive segmentation, and tracking approach. The tracking output acts as an indicator of the quality of segmentation and, in turn, the segmentation can be improved to obtain better tracking results. We construct an optimization function that minimizes the segmentation error, which is, in turn, estimated from the tracking results. This adaptive approach significantly improves both tracking and segmentation when compared to an open loop framework in which segmentation and tracking modules operate separately.

Impact of post harvest treatment on antioxidant activity and phenolic profile of Moringa oleifera lam leaves

Moringa oleifera leaves are an important source of dietary phytochemicals,such as flavonoids with high antioxidant activity(AOA).These components are however influenced by the post-harvest treatments applied as well as the processing conditions.Hence,it is crucial to determine the most appropriate post-harvest treatment that preserves or enhances AOA.To this effect the influence of steam blanching,fermentation/oxidation,oven drying and roasting of fresh Moringa leaves on their AOA was investigated.Processing conditions of time and temperature for each treatment were optimised using response surface methodology.The effect of the different treatments at optimal conditions on phenolic profile and AOA were compared.Roasting achieved the most significant(p<0.05)improvement in phenolics(43%)and AOA(22–31%),which was accompanied by the formation of 2 new compounds,quercetin-3-O-acetylglucoside and Quercetine-3-O-rhamnoside.Steam blanching had the most deleterious effect on phenolics(-31%)and AOA.Post-harvest treatments qualitatively and quantitatively affect phytochemical profile of Moringa leaves.