RESUMEN
In recent years, bioorthogonal uncaging reactions have been developed to proceed efficiently under physiological conditions. However, limited progress has been made in the development of protecting groups combining stability under physiological settings with the ability to be quickly removed via bioorthogonal catalysis. Herein, we present a new water-soluble coumarin-derived probe bearing an internal nucleophilic group capable of promoting Tsuji-Trost deallylation under palladium catalysis. This probe can be cleaved by a bioorthogonal palladium complex at a faster rate than the traditional probe, namely N-Alloc-7-amino-4-methylcoumarin. As the deallylation process proved to be efficient in mammalian cells, we envision that this probe may find applications in chemical biology, bioengineering, and medicine.
RESUMEN
To make ionic liquids (ILs) accessible and economical, ethylene glycol was mixed in 1-ethyl-3-methylimidazolium-dicyanamide ([EMIm]DCA) to obtain droplets that could experimentally collide white fuming nitric acid. To investigate the ignition delay (ID) time theoretically in terms of hydrodynamics, alcohol fuels and kerosene were used as combustibles, while the intermiscibility between them and nitric acid (HNO3) was calculated using the ternary phase-field method alongside finite element analysis. The specific impulses of blend fuels were calculated by a thermodynamic method and compared to ILs. When the droplet was ethylene glycol/[EMIm]DCA with a 2.1 mm diameter and a 1.69 m/s colliding velocity, the ID time was the shortest. Kerosene was not an applicable additive for [EMIm]DCA owing to its lower intermiscibility with ILs and HNO3 than alcohol fuels; alcohol fuels, however, were appropriate. The concentration of ethylene glycol in the oxidizer pool increased faster than the concentration of propylene glycol, triggering more rapid hypergolic ignition in the first 50 ms. The protocols regarding the hypergolic ignition conditions were verified, i.e., the size of the droplet had to be minute when the colliding velocity was as fast as possible; this was carefully calculated using ethylene glycol. According to thermodynamic calculations, the addition of alcohol fuels can improve the specific impulse of fuels, with ethylene glycol performing the best. The feasibility of adding alcohol fuels to ILs was confirmed via experiments and thermodynamic computations, with the simulation results providing some guidance on selecting the experimental or engineering conditions or both.
RESUMEN
In this paper, the shear modulus based equivalent homogenization methods of multi-layer BCC (body-centered cubic) lattice sandwich structures have been studied using analytical, experimental, and finite element methods. In the analytical approach, the multiple strut-deformation patterns were introduced in the derivations of the shear modulus based on Euler-Bernoulli beam theory and Timoshenko beam theory according to different boundary conditions. The analytical shear modulus of three types of rectangle shaped sandwich BCC lattice structures was derived. Finite element models of the BCC lattice structures by ANSYS were conducted to estimate the analytical solutions. Butterfly style sandwich BCC lattice structures were printed by SLM technology using 304 stainless steel (06Cr19Ni10), and corresponding shear experiments using modified Arcan Rig experimental devices were conducted to validate the analytical and numerical calculations. Good agreements were observed among the analytical, numerical, and experimental results.
RESUMEN
Genome Wide Association (GWA) studies are powerful tools to identify genes involved in common human diseases, and are becoming increasingly important in genetic epidemiology research. However, the statistical approaches behind GWA studies lack capability in taking into account the possible interactions among genetic markers; and true disease variants may be lost in statistical noise due to high threshold. A typical GWA study reports a few highly suspected signals, e.g. Single-nucleotide polymorphisms (SNPs), which usually account for a tiny portion of overall genetic risks for the disease of interest. This study proposes a computational learning approach in addition to parametric statistical methods along with a filtering mechanism, to build glaucoma genetic risk assessment model. Our data set was obtained from Singapore Malay Eye Study (SiMES), genotyped on Illumina 610 quad arrays. We constructed case-control data set with 233 glaucoma and 458 healthy samples. A standard case-control association test was conducted on post-QC dataset with more than 500k SNPs. Genetic profile is constructed using genotype information from a list of 412 SNPs filtered by a relaxed pvalue threshold of 1 × 10(-3), and forms the feature space for learning. Among the five learning algorithms we performed, Support Vector Machines with radial kernel (SVM-radial) achieved the best result, with area under curve (ROC) of 99.4% and accuracy of 95.9%. The result illustrates that, learning approach in post GWAS data analysis is able to accurately assess genetic risk for glaucoma. The approach is more robust and comprehensive than individual SNPs matching method. We will further validate our results in several other data sets obtained in consequential population studies conducted in Singapore.
