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

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Hydrophilic polymers are the center of research emphasis in nanotechnology because of their perceived “intelligence”. They can be used as thin films, scaffolds, or nanoparticles in a wide range of biomedical and biological applications. Here we highlight recent developments in engineering uncrosslinked and crosslinked hydrophilic polymers for these applications. Natural, biohybrid, and synthetic hydrophilic polymers and hydrogels are analyzed and their thermodynamic responses are discussed. In addition, examples of the use of hydrogels for various therapeutic applications are given. We show how such systems’ intelligent behavior can be used in sensors, microarrays, and imaging. Finally, we outline challenges for the future in integrating hydrogels into biomedical applications.

/pdf/hydrogels-in-biology-and-medicine-from-molecular-principles-3lowrzjjsl.pdf

Hydrogels in Biology and Medicine: From Molecular Principles to Bionanotechnology†

Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion.

/pdf/zero-shot-text-to-image-generation-3ra9fg0mfm.pdf

Zero-Shot Text-to-Image Generation

Semiconducting nanowires have the potential to act as highly sensitive sensors for the detection of pathogenic microorganisms, without the need for a label on the pathogen. Practical miniature sensors would have applications in diagnostics, homeland security and basic research. Current technologies have not been widely adopted for various reasons, including the difficulty of integrating nanoscale devices into practical sensors. Now a team spanning five departments at Yale has developed a new approach to the problem. In a state-of-the-art (CMOS-compatible) system they create miniature, ultra-sensitive sensors that can detect specific unlabelled antibodies at concentrations below 100 femtomolar and are able to monitor the cellular immune response in 'real-time'. A new approach that uses complementary metal oxide semiconductor field effect transistor compatible technology is reported, and demonstrates the specific label-free detection of below 100 femtomolar concentrations of antibodies as well as real-time monitoring of the cellular immune response. Semiconducting nanowires have the potential to function as highly sensitive and selective sensors for the label-free detection of low concentrations of pathogenic microorganisms1,2,3,4,5,6,7,8,9,10. Successful solution-phase nanowire sensing has been demonstrated for ions3, small molecules4, proteins5,6, DNA7 and viruses8; however, ‘bottom-up’ nanowires (or similarly configured carbon nanotubes11) used for these demonstrations require hybrid fabrication schemes12,13, which result in severe integration issues that have hindered widespread application. Alternative ‘top-down’ fabrication methods of nanowire-like devices9,10,14,15,16,17 produce disappointing performance because of process-induced material and device degradation. Here we report an approach that uses complementary metal oxide semiconductor (CMOS) field effect transistor compatible technology and hence demonstrate the specific label-free detection of below 100 femtomolar concentrations of antibodies as well as real-time monitoring of the cellular immune response. This approach eliminates the need for hybrid methods and enables system-scale integration of these sensors with signal processing and information systems. Additionally, the ability to monitor antibody binding and sense the cellular immune response in real time with readily available technology should facilitate widespread diagnostic applications.

/pdf/label-free-immunodetection-with-cmos-compatible-39gztg5mmq.pdf

Label-free immunodetection with CMOS-compatible semiconducting nanowires

We report on a pH sensor with ultrahigh sensitivity based on a microcantilever structure with a lithographically-defined crosslinked copolymeric hydrogel. Silicon-on-insulator wafers were used to fabricate cantilevers on which a polymer consisting of poly(methacrylic acid) (PMAA) with poly(ethylene glycol) dimethacrylate was patterned using free-radical UV polymerization. As the pH around the cantilever was increased above the pKa of PMAA, the polymer network expanded and resulted in a reversible change in surface stress causing the microcantilever to bend. Excellent mechanical amplification of polymer swelling as a function of pH change within the dynamic range was obtained, with a maximum deflection sensitivity of 1 nm/5×10−5 ΔpH.

/pdf/micromechanical-cantilever-as-an-ultrasensitive-ph-gupw3fsfqn.pdf

Micromechanical cantilever as an ultrasensitive pH microsensor

Deep neural networks are commonly developed and trained in 32-bit floating point format. Significant gains in performance and energy efficiency could be realized by training and inference in numerical formats optimized for deep learning. Despite advances in limited precision inference in recent years, training of neural networks in low bit-width remains a challenging problem. Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications. Flexpoint tensors have a shared exponent that is dynamically adjusted to minimize overflows and maximize available dynamic range. We validate Flexpoint by training AlexNet, a deep residual network and a generative adversarial network, using a simulator implemented with the \emph{neon} deep learning framework. We demonstrate that 16-bit Flexpoint closely matches 32-bit floating point in training all three models, without any need for tuning of model hyperparameters. Our results suggest Flexpoint as a promising numerical format for future hardware for training and inference.

/pdf/flexpoint-an-adaptive-numerical-format-for-efficient-542kdhnyz2.pdf

Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks

Semiconductor device-based sensing of chemical and biological entities has been demonstrated through the use of micro- and nanoscale field-effect devices and close variants. Although carbon nanotubes and silicon nanowires have been demonstrated as single molecule biosensors, the fabrication methods that have been used for creating these devices are typically not compatible with modern semiconductor manufacturing techniques and their large scale integration is problematic. These shortcomings are addressed by recent advancements in microelectronic fabrication techniques which resulted in the realization of nanowire-like structures. Here we report a method to fabricate silicon nanowires at precise locations using such techniques. Our method allows for the realization of truly integrated sensors capable of production of dense arrays. Sensitivity of these devices to changes in the ambient gas composition is also shown.

/pdf/integrated-nanoscale-silicon-sensors-using-top-down-3j0wj1lq0v.pdf

Integrated nanoscale silicon sensors using top-down fabrication

In one aspect, described herein are field effect chemical sensor devices useful for chemical and/or biochemical sensing. Also provided herein are methods for single molecule detection. In another aspect, described herein are methods useful for amplification of target molecules by PCR.

DNA sequencing and amplification systems using nanoscale field effect sensor arrays

Over the last decade, field-effect transistors (FETs) with nanoscale dimensions have emerged as possible label-free biological and chemical sensors capable of highly sensitive detection of various entities and processes. While significant progress has been made towards improving their sensitivity, much is yet to be explored in the study of various critical parameters, such as the choice of a sensing dielectric, the choice of applied front and back gate biases, the design of the device dimensions, and many others. In this work, we present a process to fabricate nanowire and nanoplate FETs with Al2O3 gate dielectrics and we compare these devices with FETs with SiO2 gate dielectrics. The use of a high-k dielectric such as Al2O3 allows for the physical thickness of the gate dielectric to be thicker without losing sensitivity to charge, which then reduces leakage currents and results in devices that are highly robust in fluid. This optimized process results in devices stable for up to 8 h in fluidic environments. Using pH sensing as a benchmark, we show the importance of optimizing the device bias, particularly the back gate bias which modulates the effective channel thickness. We also demonstrate that devices with Al2O3 gate dielectrics exhibit superior sensitivity to pH when compared to devices with SiO2 gate dielectrics. Finally, we show that when the effective electrical silicon channel thickness is on the order of the Debye length, device response to pH is virtually independent of device width. These silicon FET sensors could become integral components of future silicon based Lab on Chip systems.

Oguz H. Elibol

Papers

Micromechanical cantilever as an ultrasensitive pH microsensor

Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks

Integrated nanoscale silicon sensors using top-down fabrication

DNA sequencing and amplification systems using nanoscale field effect sensor arrays

High-k dielectric Al2O3 nanowire and nanoplate field effect sensors for improved pH sensing