Sirindhorn International Institute of Technology

About: Sirindhorn International Institute of Technology is a based out in . It is known for research contribution in the topics: Supply chain & Combustion. The organization has 1048 authors who have published 1678 publications receiving 30067 citations.

Chemical synthesis of all phosphatidylinositol mannoside (PIM) glycans from Mycobacterium tuberculosis.

Abstract: The emergence of multidrug-resistant tuberculosis (TB) and problems with the BCG tuberculosis vaccine to protect humans against TB have prompted investigations into alternative approaches to combat this disease by exploring novel bacterial drug targets and vaccines. Phosphatidylinositol mannosides (PIMs) are biologically important glycoconjugates and represent common essential precursors of more complex mycobacterial cell wall glycolipids including lipomannan (LM), lipoarabinomannan (LAM), and mannan capped lipoarabinomannan (ManLAM). Synthetic PIMs constitute important biochemical tools to elucidate the biosynthesis of this class of molecules, to reveal PIM interactions with host cells, and to investigate the function of PIMs as potential antigens and/or adjuvants for vaccine development. Here, we report the efficient synthesis of all PIMs including phosphatidylinositol (PI) and phosphatidylinositol mono- to hexa-mannoside (PIM1 to PIM6). Robust synthetic protocols were developed for utilizing bicyclic a...

A framework for automatic heart sound analysis without segmentation

Abstract: A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs. Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors. The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration. The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.