Docosahexaenoic Acid Levels in Blood and Metabolic Syndrome in Obese Children: Is There a Link?

Prevalence of metabolic syndrome is increasing in the pediatric population. Considering the different existing criteria to define metabolic syndrome, the use of the International Diabetes Federation (IDF) criteria has been suggested in children. Docosahexaenoic acid (DHA) has been associated with beneficial effects on health. The evidence about the relationship of DHA status in blood and components of the metabolic syndrome is unclear. This review discusses the possible association between DHA content in plasma and erythrocytes and components of the metabolic syndrome included in the IDF criteria (obesity, alteration of glucose metabolism, blood lipid profile, and blood pressure) and non-alcoholic fatty liver disease in obese children. The current evidence is inconsistent and no definitive conclusion can be drawn in the pediatric population. Well-designed longitudinal and powered trials need to clarify the possible association between blood DHA status and metabolic syndrome.     

15‐Methylene‐Eburnamonine Kills Leukemic Stem Cells and Reduces Engraftment in a Humanized Bone Marrow Xenograft Mouse Model of Leukemia

Recent studies suggest that leukemia stem cells (LSCs) play a critical role in the initiation, propagation, and relapse of leukemia. Herein we show that (?)‐15‐methylene‐eburnamonine, a derivative of the alkaloid (?)‐eburnamonine, is cytotoxic against acute and chronic lymphocytic leukemias (ALL and CLL) and acute myelogenous leukemia (AML). The agent also decreases primary LSC frequency in vitro. The cytotoxic effects appear to be mediated via the oxidative stress pathways. Furthermore, we show that the compound kills AML, ALL, and CLL stem cells. By the use of a novel humanized bone marrow murine model of leukemia (huBM/NSG), it was found to decrease progenitor cell engraftment.     

Kernel ridge regression for out-of-sample mapping in supervised manifold learning

Manifold learning methods for unsupervised nonlinear dimensionality reduction have proven effective in the visualization of high dimensional data sets. When dealing with classification tasks, supervised extensions of manifold learning techniques, in which class labels are used to improve the embedding of the training points, require an appropriate method for out-of-sample mapping.In this paper we propose multi-output kernel ridge regression (KRR) for out-of-sample mapping in supervised manifold learning, in place of general regression neural networks (GRNN) that have been adopted by previous studies on the subject. Specifically, we consider a supervised agglomerative variant of Isomap and compare the performance of classification methods when the out-of-sample embedding is based on KRR and GRNN, respectively. Extensive computational experiments, using support vector machines and k-nearest neighbors as base classifiers, provide statistical evidence that out-of-sample mapping based on KRR consistently dominates its GRNN counterpart, and that supervised agglomerative Isomap with KRR achieves a higher accuracy than direct classification methods on most data sets.     

Multiple clusterings of heterogeneous information networks

Machine Learning - Traditional clustering algorithms focus on a single clustering result; as such, they cannot explore potential diverse patterns of complex real world data. To deal with this...     

Locally adaptive metric nearest-neighbor classification

Nearest-neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest-neighbor rule. We propose a locally adaptive nearest-neighbor classification method to try to minimize bias. We use a chi-squared distance analysis to compute a flexible metric for producing neighborhoods that are highly adaptive to query locations. Neighborhoods are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities are smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using both simulated and real-world data     

Visually impaired people and grocery shopping in store: First evidence from brain oscillations electroencephalogram

Grocery shopping represents a challenging task for visually impaired (VI), but the neuroscientific literature on the consumption patterns of this group is still scarce. The aim of the study was to analyse the relationship between explicit consumer experience and neuropsychological measures. A group of VI and sighted explored and manipulated three different product categories inside the supermarket, while EEG, behavioral and self-report data were collected. Electroencephalogram (EEG) findings showed a generalized delta band activity in pasta compared to frozen food and it was interpreted as higher emotional activation probably required by selecting the correct stimuli in a multisensory environment. A delta band activation was also found in frontal area in VI compared to control and it was supposed to be an index of greater cognitive control. Finally, higher delta band activity in parieto-occipital and temporal areas were related to greater sense of disorientation. In conclusion, it was found that VI experience grocery shopping more stressfully and with greater cognitive effort (parieto-occipital area) than people without visual disability. In general, VI use the sense of touch (temporal area) more and have more difficulties in orienting themselves internally in the store. The results could encourage the use of tactile touchpoints, braille maps, or an initial guided exploration of the supermarket, to allow the VI to memorize the internal layout of the different product categories and allow them to shop independently. Another suggestion would be to make products within the same product category more distinctive, perhaps by adding additional tactile information.     

Mesoporous carbons supported non-noble metal Fe–N X electrocatalysts for PEM fuel cell oxygen reduction reaction

Three types of iron–nitrogen-containing non-noble metal catalysts, supported on an ultrasonic spray pyrolysis mesoporous carbon (USPMC), a hollow core mesoporous shell carbon (HCMSC), and a standard carbon (Ketjen Black CJ600, KB), respectively, are synthesized using a wet-impregnation method. The morphologies and structure as well as composition of the synthesized carbon supports and their corresponding supported Fe–N _X catalysts (namely Fe–N _X /USPMC, Fe–N _X /HCMSC, and Fe–N _X /KB, respectively) are physically characterized using EDX, SEM, FESEM, and BET analysis, respectively. The catalytic activities of these three electrocatalysts toward oxygen reduction reaction (ORR) are measured using rotating disk electrode technique in O₂-saturated 0.5 M H₂SO₄ solution. The catalyzed ORR exchange current densities are also obtained using the Tafel method based on the measured data. Among these three electrocatalysts, Fe–N _X /HCMSC can give the best ORR performance, which is correlated to its higher nitrogen, mesopore, and micropore contents, compared to the other electrocatalysts. It is rationalized that the performance improvement of these electrocatalysts may be achieved as long as an optimal relationship among mesopores, micropores, and even macropores for increasing both ORR kinetics and reactant gases accessibility to the active sites can be found.     

Risks and benefits related to alimentary exposure to xenoestrogens

Xenoestrogens are widely diffused in the environment and in food, thus a large portion of human population worldwide is exposed to them. Among alimentary xenoestrogens, phytoestrogens (PhyEs) are increasingly being consumed because of their potential health benefits, although there are also important risks associated to their ingestion. Furthermore, other xenoestrogens that may be present in food are represented by other chemicals possessing estrogenic activities, that are commonly defined as endocrine disrupting chemicals (EDCs). EDCs pose a serious health concern since they may cause a wide range of health problems, starting from pre-birth till adult lifelong exposure. We herein provide an overview of the main classes of xenoestrogens, which are classified on the basis of their origin, their structures and their occurrence in the food chain. Furthermore, their either beneficial or toxic effects on human health are discussed in this review.