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Large-scale genotyping plays an important role in genetic association studies. It has provided new opportunities for gene discovery, especially when combined with high-throughput sequencing technologies. Here, we report an efficient solution for large-scale genotyping. We call it specific-locus amplified fragment sequencing (SLAF-seq). SLAF-seq technology has several distinguishing characteristics: i) deep sequencing to ensure genotyping accuracy; ii) reduced representation strategy to reduce sequencing costs; iii) pre-designed reduced representation scheme to optimize marker efficiency; and iv) double barcode system for large populations. In this study, we tested the efficiency of SLAF-seq on rice and soybean data. Both sets of results showed strong consistency between predicted and practical SLAFs and considerable genotyping accuracy. We also report the highest density genetic map yet created for any organism without a reference genome sequence, common carp in this case, using SLAF-seq data. We detected 50,530 high-quality SLAFs with 13,291 SNPs genotyped in 211 individual carp. The genetic map contained 5,885 markers with 0.68 cM intervals on average. A comparative genomics study between common carp genetic map and zebrafish genome sequence map showed high-quality SLAF-seq genotyping results. SLAF-seq provides a high-resolution strategy for large-scale genotyping and can be generally applicable to various species and populations.

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We predict enormous piezoelectric effects in intrinsic monolayer group IV monochalcogenides (MX, M=Sn or Ge, X=Se or S), including SnSe, SnS, GeSe and GeS.Using first-principle simulations based on the modern theory of polarization, we find that their piezoelectric coefficients are about one to two orders of magnitude larger than those of other 2D materials, such as MoS2 and GaSe, and bulk quartz and AlN which are widely used in industry. This enhancement is a result of the unique "puckered" C2v symmetry and weaker chemical bonds of monolayer group IV monochalcogenides. Given the achieved experimental advances in fabrication of monolayers, their flexible character and ability to withstand enormous strain, these 2D structures with giant piezoelectric effects may be promising for a broad range of applications, such as nano-sized sensors, piezotronics, and energy harvesting in portable electronic devices.

Background: The process of malignant transformation, progression and metastasis of melanoma is poorly understood. Gene expression profiling of human cancer has allowed for a unique insight into the genes that are involved in these processes. Thus, we have attempted to utilize this approach through the analysis of a series of primary, non-metastatic cutaneous tumors and metastatic melanoma samples.

Few-shot learning in image classification aims to learn a classifier to classify images when only few training examples are available for each class. Recent work has achieved promising classification performance, where an image-level feature based measure is usually used. In this paper, we argue that a measure at such a level may not be effective enough in light of the scarcity of examples in few-shot learning. Instead, we think a local descriptor based image-to-class measure should be taken, inspired by its surprising success in the heydays of local invariant features. Specifically, building upon the recent episodic training mechanism, we propose a Deep Nearest Neighbor Neural Network (DN4 in short) and train it in an end-to-end manner. Its key difference from the literature is the replacement of the image-level feature based measure in the final layer by a local descriptor based image-to-class measure. This measure is conducted online via a k-nearest neighbor search over the deep local descriptors of convolutional feature maps. The proposed DN4 not only learns the optimal deep local descriptors for the image-to-class measure, but also utilizes the higher efficiency of such a measure in the case of example scarcity, thanks to the exchangeability of visual patterns across the images in the same class. Our work leads to a simple, effective, and computationally efficient framework for few-shot learning. Experimental study on benchmark datasets consistently shows its superiority over the related stateof-the-art, with the largest absolute improvement of 17% over the next best. The source code can be available from https://github.com/WenbinLee/DN4.git.

The two-dimensionally connected metal-organic frameworks (MOFs) Ni(HIB) and Cu(HIB) (HIB = hexaiminobenzene) are bulk electrical conductors and exhibit ultraviolet-photoelectron spectroscopy (UPS) signatures expected of metallic solids. Electronic band structure calculations confirm that in both materials the Fermi energy lies in a partially filled delocalized band. Together with additional structural characterization and microscopy data, these results represent the first report of metallic behavior and permanent porosity coexisting within a metal-organic framework.

Progress in nanocrystal synthesis and self-assembly enables the formation of highly ordered superlattices. Recent studies focused on spherical particles with tunable attraction and polyhedral particles with anisotropic shape, and excluded volume repulsion, but the effects of shape on particle interaction are only starting to be exploited. Here we present a joint experimental-computational multiscale investigation of a class of highly faceted planar lanthanide fluoride nanocrystals (nanoplates, nanoplatelets). The nanoplates self-assemble into long-range ordered tilings at the liquid-air interface formed by a hexane wetting layer. Using Monte Carlo simulation, we demonstrate that their assembly can be understood from maximization of packing density only in a first approximation. Explaining the full phase behaviour requires an understanding of nanoplate-edge interactions, which originate from the atomic structure, as confirmed by density functional theory calculations. Despite the apparent simplicity in particle geometry, the combination of shape-induced entropic and edge-specific energetic effects directs the formation and stabilization of unconventional long-range ordered assemblies not attainable otherwise.