Divergent and Stereoselective Synthesis of β-Silyl-α-Amino Acids through Palladium-Catalyzed Intermolecular Silylation of Unactivated Primary and Secondary C−H Bonds

A general and practical Pd^(II)-catalyzed intermolecular silylation of primary and secondary C−H bonds of α-amino acids and simple aliphatic acids is reported. This method provides divergent and stereoselective access to a variety of optical pure β-silyl-α-amino acids, which are useful for genetic technologies and proteomics. It can also be readily performed on a gram scale and the auxiliary can be easily removed with retention of configuration. The synthetic importance of this method is further demonstrated by the late-stage functionalization of biological small molecules, such as (−)-santonin and β-cholic acid. Moreover, several key palladacycles were successfully isolated and characterized to elucidate the mechanism of this β−C(sp^3)-H silylation process

A Quantization-Friendly Separable Convolution for MobileNets

As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc. Quantization, as one of the key approaches, can effectively offload GPU, and make it possible to deploy DL on fixed-point pipeline. Unfortunately, not all existing networks design are friendly to quantization. For example, the popular lightweight MobileNetV1, while it successfully reduces parameter size and computation latency with separable convolution, our experiment shows its quantized models have large accuracy gap against its float point models. To resolve this, we analyzed the root cause of quantization loss and proposed a quantization-friendly separable convolution architecture. By evaluating the image classification task on ImageNet2012 dataset, our modified MobileNetV1 model can archive 8-bit inference top-1 accuracy in 68.03%, almost closed the gap to the float pipeline.Comment: Accepted At THE 1ST WORKSHOP ON ENERGY EFFICIENT MACHINE LEARNING AND COGNITIVE COMPUTING FOR EMBEDDED APPLICATIONS (EMC^2 2018

Polymorphisms of the _ENPP1_ gene are not associated with type 2 diabetes or obesity in the Chinese Han population

*Objective:* Type 2 Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia and with a major feature of insulin resistance. Genetic association studies have suggested that _ENPP1_ might play a potential role in susceptibility to type 2 diabetes and obesity. Our study aimed to examine the association between _ENPP1_ and type 2 diabetes and obesity.

*Design:* Association study between two SNPs, rs1044498 (K121Q) and rs7754561 of ENPP1 and diabetes and obesity in the Chinese Han population.

*Subjects:* 1912 unrelated patients (785 male and 1127 female with a mean age 63.8 ± 9 years), 236 IFG/IGT subjects (83 male and 153 female with a mean age 64 ± 9 years) and 2041 controls (635 male and 1406 female with a mean age 58 ± 9 years).
 
*Measurements:* Subjects were genotyped for two SNPs using TaqMan technology on an ABI7900 system and tested by regression analysis.

*Results:* By logistic regression analysis, rs1044498 (K121Q) and rs7754561 showed no statistical association with type 2 diabetes, obesity under additive, dominant and recessive models either before or after adjusting for sex and age. Haplotype analysis found a marginal association of haplotype C-G (p=0.05) which was reported in the previous study.

*Conclusion:* Our investigation did not replicated the positive association found previously and suggested that the polymorphisms of _ENPP1_ might not play a major role in the susceptibility to type 2 diabetes or obesity in the Chinese Han population

Field Emission Properties of the Graphene Double-Walled Carbon Nanotube Hybrid Films Prepared by Vacuum Filtration and Screen Printing

The graphene double-walled carbon nanotube (DWCNT) hybrid films were prepared by vacuum filtration and screen printing. Their electron field emission properties have been studied systematically. The electron emission properties of the hybrid films are much better than those of pure DWCNT films and pure graphene films. Comparing with the screen printed films, the vacuum filtered films have many advantages, such as lower turn-on field, higher emission current density, better uniformity, better long-term stability, and stronger adhesive strength with conductive substrates. The optimized hybrid films with 20% weight ratio of graphene, which were fabricated by vacuum filtration, show the best electron emission performances with a low turn-on field of 0.50 Vμm−1 (at 1 μAcm−2) and a high field enhancement factor β of 27000