抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

Memristive devices for computing

Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 10(14) synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current-voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.

Training and operation of an integrated neuromorphic network based on metal-oxide memristors

The isolation of graphene in 2004 from graphite was a defining moment for the “birth” of a field: two-dimensional (2D) materials In recent years, there has been a rapidly increasing number of papers focusing on non-graphene layered materials, including transition-metal dichalcogenides (TMDs), because of the new properties and applications that emerge upon 2D confinement Here, we review significant recent advances and important new developments in 2D materials “beyond graphene” We provide insight into the theoretical modeling and understanding of the van der Waals (vdW) forces that hold together the 2D layers in bulk solids, as well as their excitonic properties and growth morphologies Additionally, we highlight recent breakthroughs in TMD synthesis and characterization and discuss the newest families of 2D materials, including monoelement 2D materials (ie, silicene, phosphorene, etc) and transition metal carbide- and carbon nitride-based MXenes We then discuss the doping and functionalization of 2

Recent Advances in Two-Dimensional Materials beyond Graphene

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New approaches to nanofabrication: molding, printing, and other techniques.

Effect of cooling rate on the grain refinement of TiAl-based alloys by rapid heat treatment

Control of the homogeneity of the lamellar structure of a TiAl alloy refined by heat treatment

SNNs using the conversion-based approach could benefit the energy efficiency of inference and retain high accuracy of DLNs. However, transistor-based spiking neurons and synapses are not scalable and inefficient. In this work, a Mott neuron with 1T1R structure is designed to meet the requirement of the conversion-based approach, whose spiking rates dependence on voltage naturally implements the rectified linear unit (ReLU). Based on the 1T1R Mott neuron, we experimentally demonstrated a one-layer SNN (320 ×10), which consists of RRAM synaptic weight elements and Mott-type output neurons, for the first time. Attributes to the rectified linear voltage-rates relationship of the 1T1R neuron and its inherent stochasticity, 95.7% converting accuracy of the neurons and 85.7% recognition accuracy in MNIST datasets are obtained. At last, a neuron X-bar architecture is proposed for parallel multi-tasking and better system integration.

Experimental Demonstration of Conversion-Based SNNs with 1T1R Mott Neurons for Neuromorphic Inference

Embodiments of the present invention are directed to systems for performing surface-enhanced Raman spectroscopy. In one embodiment, a system for performing Raman spectroscopy includes a waveguide layer (102,402,702,902) configured with at least one array of features, and a material (110,410,710,910) disposed on at least a portion of the features. Each array of features and the waveguide layer are configured to provide guided-mode resonance for at least one wavelength of electromagnetic radiation. The electromagnetic radiation produces enhanced Raman scattered light from analyte molecules located on or in proximity to the material.

Waveguides configured with arrays of features for performing raman spectroscopy

Memristive devices have attracted considerable attention as a promising candidate for the next generation nonvolatile random access memory and unconventional computing. One obstacle for wide applications of these devices is their performance variation from device to device and from cycle to cycle. Here, the authors report that by introducing a thin HfO2 layer into the Pt/TiO2/Pt device geometry, the cycle-to-cycle uniformity of the programing voltages and the resistance states are greatly improved. The programing voltages are distributed in much narrower ranges. The uniformities of low-resistance state resistance and high-resistance state resistance were improved by 25 and 20 times, respectively. It is believed that the extra layer of HfO2 has led to fewer potential conducting filaments in the bilayer devices, contributing to the significant improvement in switching uniformity.

Qiangfei Xia

Papers

Effect of cooling rate on the grain refinement of TiAl-based alloys by rapid heat treatment

Control of the homogeneity of the lamellar structure of a TiAl alloy refined by heat treatment

Experimental Demonstration of Conversion-Based SNNs with 1T1R Mott Neurons for Neuromorphic Inference

Waveguides configured with arrays of features for performing raman spectroscopy

Improvement of resistive switching uniformity for TiO2-based memristive devices by introducing a thin HfO2 layer