Usability and Effects of a Combined Physical and Cognitive Intervention Based on Active Video Games for Preschool Children.

Executive functions (EFs) are essential for early childhood development, and effective programs to improve EFs in preschool education are becoming increasingly crucial. There is rising evidence that combined physical-cognitive intervention training utilizing active video games (exergames) could be a viable strategy to improve EFs. However, there is a shortage of empirical evidence on the application of this approach in preschool education. The effectiveness of exergame intervention training in preschools must be evaluated. This study conducted a randomized controlled trial to assess the effects of exergames intervention training on preschool children's EFs. A total of 48 participants aged 4-5 years were enrolled; 24 were randomly allocated to receive exergames physical activity training, and the remaining 24 received conventional physical activity training. After a four-week intervention, the children who received the exergames intervention training exhibited considerably greater gains in all three EFs tasks than children who received the conventional physical activity program. Follow-up interviews revealed that the children accepted the exergames well. The results demonstrate the viability of incorporating exergames into preschool education to improve children's EFs, supporting prior findings and offering more empirical evidence from early childhood research.

Minor compounds and potential interferents in gas chromatographic analyses of human serum fatty acids.

Fatty acids from 100 randomly selected human serum samples were esterified to fatty acid methyl esters and analyzed by gas chromatography with flame ionization detector. A subset of the 20 samples that spans the variation in the original set of 100 samples were thereafter analyzed by gas chromatography-mass spectrometry (GC-MS). The GC-MS data were acquired using capillary columns with two different stationary phases, BP20 (polyethylene glycol) and BPX70 (cyanopropyl polysilphenylene-siloxane). Equivalent chain lengths on the two columns are reported for 69 compounds that constituted more than 0.1% of the chromatographic area in at least one sample. Of these, 39 compounds were identified as regular fatty acid methyl esters. The remaining 30 compounds were decomposition products from cholesterol, dimethylacetals, three compounds that have been linked to poor kidney function, and 13 compounds that are currently unidentified. The retention index patterns showed that on both columns there were 16 compounds that were separated by less than 0.05 equivalent chain length units from the nearest neighbor, meaning that they were overlapping or poorly resolved. The relationship between the peak threshold level and the number of peaks found above the level predicts a dramatic increase in the number of peaks that have to be resolved if the threshold is lowered below 0.1%.

A new method for wavelength interval selection that intelligently optimizes the locations, widths and combinations of the intervals.

In this study, a new algorithm for wavelength interval selection, known as interval variable iterative space shrinkage approach (iVISSA), is proposed based on the VISSA algorithm. It combines global and local searches to iteratively and intelligently optimize the locations, widths and combinations of the spectral intervals. In the global search procedure, it inherits the merit of soft shrinkage from VISSA to search the locations and combinations of informative wavelengths, whereas in the local search procedure, it utilizes the information of continuity in spectroscopic data to determine the widths of wavelength intervals. The global and local search procedures are carried out alternatively to realize wavelength interval selection. This method was tested using three near infrared (NIR) datasets. Some high-performing wavelength selection methods, such as synergy interval partial least squares (siPLS), moving window partial least squares (MW-PLS), competitive adaptive reweighted sampling (CARS), genetic algorithm PLS (GA-PLS) and interval random frog (iRF), were used for comparison. The results show that the proposed method is very promising with good results both on prediction capability and stability. The MATLAB codes for implementing iVISSA are freely available on the website: .

Evaluation of the retention pattern on ionic liquid columns for gas chromatographic analyses of fatty acid methyl esters.

Fatty acid methyl esters from marine sources were analyzed by gas chromatography-mass spectrometry on three ionic liquid columns, SLB-IL61, SLB-IL82 and SLB-IL100 (Supelco). Retention indices (equivalent chain lengths) are reported for more than 100 compounds and the overlap patterns are evaluated from these data. The influence of chromatographic conditions on the retention indices of unsaturated fatty acid methyl esters is also evaluated. Compared to typical alternative phases the retention patterns on all three columns are highly dependent on the conditions. The SLB-IL61 phase had overlaps between nutritionally important fatty acids that could not be resolved by changing the chromatographic conditions. This column is therefore regarded as unsuitable for clinical and nutritional studies of the fatty acid composition, but similar overlaps may be avoided on IL82 and IL100. On all three columns double bonds close to the carboxyl group in the analytes contribute with limited retention, which makes it challenging to predict the retention of polyunsaturated fatty acid methyl esters.

Least squares spectral resolution of liquid chromatography-mass spectrometry data of glycerophospholipids.

Liquid chromatography-mass spectrometry represents a powerful tool for the analysis of intact glycerophospholipids (GPLs), but manual data interpretation may be a bottleneck in these analyses. The present paper proposes a least square regression approach for the automated characterization and deconvolution of the main GPLs species, i.e., phosphatidylcholine and phosphatidylethanolamine analyzed by class-specific scanning methods such as precursor ion scanning and neutral loss scanning, respectively. The algorithm is based on least squares resolution of spectra and chromatograms from theoretically calculated mass spectra, and eliminates the need for isotope correction. Results from the application of the methodology on reference compounds and extracts of cod brain and mouse brain are presented.

EMCD, a hypoglycemic triterpene isolated from Momordica charantia wild variant, attenuates TNF-α-induced inflammation in FL83B cells in an AMP-activated protein kinase-independent manner.

Insulin resistance is a causative factor for type 2 diabetes, whereas the development of insulin resistance is closely related to chronic inflammation induced by factors such as tumor necrosis factor-α (TNF-α). Momordica charantia, also known as bitter melon, has been used as an herbal medicine and reported to ameliorate inflammation and hyperglycemia. Previously, a triterpene 5ß,19-epoxy-25-methoxy-cucurbita-6,23-diene-3ß,19-diol (EMCD), purified from M. charantia L. wild variant WB24, was found to activate AMP-activated protein kinase (AMPK) and have a hypoglycaemic effect in TNF-α-treated FL83B cells. AMPK has been a target for developing anti-diabetic medicine and suggested to play a role in anti-inflammation. The current study aims to investigate if EMCD might repress TNF-α-induced inflammation via AMPK. TNF-α-induced inflammation in FL83B cells was characterized using Western blotting and reverse transcriptase-polymerase chain reaction. Consequently, the expression of inflammatory markers including inducible nitric oxide synthase (iNOS), the p65 subunit of nuclear factor-κB (NF-κB), protein-tyrosine phosphatase-1B, TNF-α and interleukin-1ß were significantly elevated by TNF-α in the cell, and EMCD obviously suppressed the TNF-α-induced expression of these markers. When the effect of EMCD was tested simultaneously with epigallocatechin-3-gallate (EGCG), a catechin from green tea reported to be anti-inflammatory, EMCD showed a more obvious anti-inflammatory activity than EGCG did. Investigation of the underlying mechanism suggested that EMCD inhibited the activation of the IκB kinase (IKK) complex and the NF-κB pathway, and the effect was likely independent of AMPK. Collectively, the multiple functions of EMCD suggest it to be a potential agent in treating diabetic complications and other inflammation-related disorders.

Recipe for uncovering predictive genes using support vector machines based on model population analysis.

Selecting a small number of informative genes for microarray-based tumor classification is central to cancer prediction and treatment. Based on model population analysis, here we present a new approach, called Margin Influence Analysis (MIA), designed to work with support vector machines (SVM) for selecting informative genes. The rationale for performing margin influence analysis lies in the fact that the margin of support vector machines is an important factor which underlies the generalization performance of SVM models. Briefly, MIA could reveal genes which have statistically significant influence on the margin by using Mann-Whitney U test. The reason for using the Mann-Whitney U test rather than two-sample t test is that Mann-Whitney U test is a nonparametric test method without any distribution-related assumptions and is also a robust method. Using two publicly available cancerous microarray data sets, it is demonstrated that MIA could typically select a small number of margin-influencing genes and further achieves comparable classification accuracy compared to those reported in the literature. The distinguished features and outstanding performance may make MIA a good alternative for gene selection of high dimensional microarray data. (The source code in MATLAB with GNU General Public License Version 2.0 is freely available at http://code.google.com/p/mia2009/).

Microfluidic cell counter/sorter utilizing multiple particle tracing technique and optically switching approach.

This paper proposes a novel microfluidic system based on a computer controlled digital image processing (DIP) technique and optical tweezers for automatic cell/microparticle recognition, counting and sorting in a continuous flow environment. In the proposed system, the cells/microparticles are focused electrokinetically into a narrow sample stream and are then driven through the region of interest (ROI), where they are recognized and traced in real time using a proprietary DIP system. Synchronized control signals generated by the DIP system are then used to actuate a focused IR laser beam to displace the target cells from the main sample stream into a neighboring sheath flow, which carries them to a downstream collection channel where they are automatically counted. Experimental trials show that the microchip is capable of continuously sorting and counting microparticles with diameters of 5 and 10 mum. In addition, a sample composed of yeast cells and polystyrene (PS) beads is successfully sorted and collected with a 100% of yield ratio and 91.9% of recovery ratio. The proposed system provides a simple, low-cost, high-performance solution for cell manipulation in microfluidic devices.