Interesterified palm olein (IEPalm) and interesterified stearic acid-rich fat blend (IEStear) have no adverse effects on insulin resistance: a randomized control trial

Chemically-interesterified (CIE) fats are trans-fat free and are increasingly being used as an alternative to hydrogenated oils for food manufacturing industries to optimize their products’ characteristics and nutrient compositions. The metabolic effects of CIE fats on insulin activity, lipids, and adiposity in humans are not well established. We investigated the effects of CIE fats rich in palmitic (C16:0, IEPalm) and stearic (C18:0, IEStear) acids on insulin resistance, serum lipids, apolipoprotein concentrations, and adiposity, using C16:0-rich natural palm olein (NatPO) as the control. We designed a parallel, double-blind clinical trial. Three test fats were used to prepare daily snacks for consumption with a standard background diet over a period of 8 weeks by three groups of a total of 85 healthy, overweight adult volunteers. We measured the outcome variables at weeks 0, 6, and at the endpoint of 8. After 8 weeks, there was no significant difference in surrogate biomarkers of insulin resistance in any of the IE fat diets (IEPalm and IEStear) compared to the NatPO diet. The change in serum triacylglycerol concentrations was significantly lower with the IEStear diet, and the changes in serum leptin and body fat percentages were significantly lower in the NatPO-diet compared to the IEPalm diet. We conclude that diets containing C16:0 and C18:0-rich CIE fats do not affect markers of insulin resistance compared to a natural C16:0-rich fat (NatPO) diet. Higher amounts of saturated fatty acids (SFAs) and longer chain SFAs situated at the sn-1,3 position of the triacylglycerol (TAG) backbones resulted in less weight gain and lower changes in body fat percentage and leptin concentration to those observed in NatPO and IEStear

Synthesis of Cs-ABW nanozeolite in organotemplate-free system

Cesium-aluminosilicate zeolite nanocrystals with ABW framework structure are synthesized free of organic template using hydrothermal approach. The crystallization process of Cs-ABW zeolite nanocrystals by varying the initial gel molar composition, heating temperature and crystallization time was studied. More detailed investigations of the formation of Cs-ABW nanozeolite using a reactive clear precursor hydrogel (4SiO2:1Al2O3:16Cs2O:160H2O) were then carried out. Fully crystalline Cs-ABW nanozeolites were obtained within 120 min at 180 °C and 22 bar, which is considerably faster and safer in comparison to the currently available method involving treatment at 695 °C, 1000 bar and 46 h. The Cs-ABW nanocrystals have grain shape morphology with a mean size of 32 nm and they do not agglomerate for long durations. The nanosized Cs-ABW zeolite has high alumina content (Si/Al ratio = 1.04). These nanocrystals can be prepared in high solid yield (ca. 82%) thus offering a promising route for large-scale production of highly basic zeolite nanoparticles

Natural Organochlorines as precursors of 3-monochloropropanediol esters in vegetable oils

During high-temperature refining of vegetable oils, 3-monochloropropanediol (3-MCPD) esters, possible carcinogens, are formed from acylglycerol in the presence of a chlorine source. To investigate organochlorine compounds in vegetable oils as possible precursors for 3-MCPD esters, we tested crude palm, soybean, rapeseed, sunflower, corn, coconut, and olive oils for the presence of organochlorine compounds. Having found them in all vegetable oils tested, we focused subsequent study on oil palm products. Analysis of the chlorine isotope mass pattern exhibited in high-resolution mass spectrometry enabled organochlorine compound identification in crude palm oils as constituents of wax esters, fatty acid, diacylglycerols, and sphingolipids, which are produced endogenously in oil palm mesocarp throughout ripening. Analysis of thermal decomposition and changes during refining suggested that these naturally present organochlorine compounds in palm oils and perhaps in other vegetable oils are precursors of 3-MCPD esters. Enrichment and dose-response showed a linear relationship to 3-MCPD ester formation and indicated that the sphingolipid-based organochlorine compounds are the most active precursors of 3-MCPD esters

A novel deep learning based neural network for heartbeat detection in ballistocardiograph

Ballistocardiography (BCG) is a revamped technology for cardiac function monitoring. Detecting individual heart beats in BCG remains a challenging task due to various artifacts and low signal-to-noise ratio, which are not well addressed by conventional approaches based on intuitive observations of BCG waveforms. In this paper, we propose to employ deep learning networks to capture the characteristics of the variations of BCG waveforms within and across individual subjects. Particularly, we design a neural network that combines Convolutional-Neural-Network (CNN) and Extreme Learning Machine (ELM). We test the new learning method on a real BCG data set and show better detection result compared with a state-of-the-art method. We demonstrate how the advanced machine learning technology can learn and detect BCG waveforms robustly.Accepted versio

Evaluation of Program Evaluation and Review Technique (PERT) for project management using Monte Carlo Simulation.

This study was done to investigate the criticisms that PERT has received from researchers. The assumptions made in the procedures seem to cause the inaccurate results that users often get. A number of modifications have been made to the original procedure, resulting in methods like PNET. These circumvent the inherent deficiencies in the original method and give more accurate results. It is also possible to employ Monte Carlo simulation as an alternative to the original PERT method

Microbend fiber optic sensor for perioperative pediatric vital signs monitoring

We have demonstrated a highly sensitive microbend fiber optic sensor for perioperative pediatric vital signs monitoring that is free from direct contact with skin, cableless, electromagnetic interference free and low cost. The feasibility of our device was studied on infants undergoing surgery and 10 participants ranging from one month to 12 months were enrolled. The sensor was placed under a barrier sheet on the operating table. All patients received standard intraoperative monitoring. The results showed good agreement in heart rate and respiratory rate between our device and the standard physiological monitoring when signals are clean.Published versio

A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention

Decoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of the idle state immediately preceding the motion initiation needs to be addressed explicitly. We therefore design a hierarchical dynamic Bayesian learning network model that integrates an array of Gaussian mixture model - hidden Markov models (GMM-HMMs), where each GMM-HMM learns the multi-sEMG processes either during the idle state, or during the motion initiation phase of a particular motion task. To test the hypothesis and evaluate the new learning network, we design and build a upper-limb sEMG-joystick motion study system, and collect data from 11 healthy volunteers. The data collection protocol adapted from the psychomotor vigilance task includes repeated and randomized binary hand motion tasks (push or pull) starting from either of two designated idle states: relaxed (with minimal muscle tones), or prepared (with muscle tones). We run a series of cross-validation tests to examine the performance of the method in comparison with the conventional techniques. The results suggest that the idle state recognition favors the dynamic Bayesian model over a static classification model. The results also show a statistically significant improvement in motion prediction accuracy by the proposed method (93.83±6.41%) in comparison with the conventional GMM-HMM method (89.71±8.98%) that does not explicitly account for the idle state. Moreover, we examine the progress of prediction accuracy over the course of motion initiation and identify the important hidden states that warrant future research.Agency for Science, Technology and Research (A*STAR)Published versionThis study was supported in part by the National Robotics Programme, Singapore under Grant No. 1922500046 and Grant No. M22NBK0074, and in part by the Science and Engineering Research Council, Agency of Science, Technology and Research, Singapore, through the National Robotics Programme under Grant No. 1922500054

Scalp EEG-based pain detection using convolutional neural network

Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This paper proposes a pain detection framework based on Electroencephalogram (EEG) and deep convolutional neural networks (CNN). The feasibility of CNN is investigated for distinguishing induced pain state from resting state in the recruitment of 10 chronic back pain patients. The experimental study recorded EEG signals in two phases: 1. movement stimulation (MS), where induces back pain by executing predefined movement tasks; 2. video stimulation (VS), where induces back pain perception by watching a set of video clips. A multi-layer CNN classifies the EEG segments during the resting state and the pain state. The novel approach offers high and robust performance and hence is significant in building a powerful pain detection algorithm. The area under the receiver operating characteristic curve (AUC) of our approach is 0.83 ± 0.09 and 0.81 ± 0.15, in MS and VS, respectively, higher than the state-of-the-art approaches. The sub-brain-areas are also analyzed, to examine distinct brain topographies relevant for pain detection. The results indicate that MS-induced pain tends to evoke a generalized brain area, while the evoked area is relatively partial under VS-induced pain. This work may provide a new solution for researchers and clinical practitioners on pain detection.Published versio

Development of an electrochemical membrane-based nanobiosensor for ultrasensitive detection of dengue virus

A sensitive membrane-based electrochemical nanobiosensor is developed for the detection of dengue type 2 virus (DENV-2) using nanoporous alumina-modified platinum electrode. Its sensing mechanism relies on the monitoring of electrode's Faradaic current response toward redox probe, ferrocenemethanol, which is sensitive toward the formation of immune complexes within the alumina nanochannels. Anti-DENV-2 monoclonal antibody (clone 3H5, isotype IgG) is used as the biorecognition element in this work. The stepwise additions of antibody, bovine serum albumin (BSA) and DENV-2 are characterized by differential pulse voltammetry (DPV). A low detection limit of 1 pfu mL−1 with linear range from 1 to 103 pfu mL−1 (R2 = 0.98) can be achieved by the nanobiosensor. The nanobiosensor is selective toward DENV-2 with insignificant cross reaction with non-specific viruses, Chikungunya virus, West Nile virus and dengue type 3 virus (DENV-3). Relative standard deviation (RSD) for triplicate analysis of 5.9% indicates an acceptable level of reproducibility. The first direct quantitation of DENV-2 concentration in whole mosquito vector is demonstrated using this electrochemical nanobiosensor

Synthesis of Cs-ABW nanozeolite in organotemplate-free system

International audienceCesium-aluminosilicate zeolite nanocrystals with ABW framework structure are synthesized free of organic template using hydrothermal approach. The crystallization process of Cs-ABW zeolite nanocrystals by varying the initial gel molar composition, heating temperature and crystallization time was studied. More detailed investigations of the formation of Cs-ABW nanozeolite using a reactive clear precursor hydrogel (4SiO2:1Al2O3:16Cs2O:160H2O) were then carried out. Fully crystalline Cs-ABW nanozeolites were obtained within 120 min at 180 °C and 22 bar, which is considerably faster and safer in comparison to the currently available method involving treatment at 695 °C, 1000 bar and 46 h. The Cs-ABW nanocrystals have grain shape morphology with a mean size of 32 nm and they do not agglomerate for long durations. The nanosized Cs-ABW zeolite has high alumina content (Si/Al ratio = 1.04). These nanocrystals can be prepared in high solid yield (ca. 82%) thus offering a promising route for large-scale production of highly basic zeolite nanoparticles