Early subclinical inflammation in kidney transplants is associated with later graft fibrosis and dysfunction. Regulatory T cells (Tregs) can reverse established inflammation in animal models. We ...conducted a pilot safety and feasibility trial of autologous Treg cell therapy in three kidney transplant recipients with subclinical inflammation noted on 6‐month surveillance biopsies. Tregs were purified from peripheral blood and polyclonally expanded ex vivo using medium containing deuterated glucose to label the cells. All patients received a single infusion of ~320 × 106 (319, 321, and 363.8 × 106) expanded Tregs. Persistence of the infused Tregs was tracked. Graft inflammation was monitored with follow‐up biopsies and urinary biomarkers. Nearly 1 × 109 (0.932, 0.956, 1.565 × 109) Tregs were successfully manufactured for each patient. There were no infusion reactions or serious therapy‐related adverse events. The infused cells demonstrated patterns of persistence and stability similar to those observed in non‐immunosuppressed subjects receiving the same dose of Tregs. Isolation and expansion of Tregs is feasible in kidney transplant patients on immunosuppression. Infusion of these cells was safe and well tolerated. Future trials will test the efficacy of polyclonal and donor alloantigen‐reactive Tregs for the treatment of inflammation in kidney transplants.
The first interventional trial utilizing ex vivo expanded autologous regulatory T cells to control graft inflammation in kidney transplantation demonstrates the therapy's safety and feasibility.
A novel Ag/oxide‐based threshold switching device with attractive features including ≈1010 nonlinearity is developed. High‐resolution transmission electron microscopic analysis of the nanoscale ...crosspoint device suggests that elongation of an Ag nanoparticle under voltage bias followed by spontaneous reformation of a more spherical shape after power off is responsible for the observed threshold switching.
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of ...the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
Tantalum oxide memristors can switch continuously from a low-conductance semiconducting to a high-conductance metallic state. At the boundary between these two regimes are quantized conductance ...states, which indicate the formation of a point contact within the oxide characterized by multistable conductance fluctuations and enlarged electronic noise. Here, we observe diverse conductance-dependent noise spectra, including a transition from 1/f(2) (activated transport) to 1/f (flicker noise) as a function of the frequency f, and a large peak in the noise amplitude at the conductance quantum GQ=2e(2)/h, in contrast to suppressed noise at the conductance quantum observed in other systems. We model the stochastic behaviour near the point contact regime using Molecular Dynamics-Langevin simulations and understand the observed frequency-dependent noise behaviour in terms of thermally activated atomic-scale fluctuations that make and break a quantum conductance channel. These results provide insights into switching mechanisms and guidance to device operating ranges for different applications.
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units have led to major advances in artificial intelligence. However, state-of-the-art LSTM models with ...significantly increased complexity and a large number of parameters have a bottleneck in computing power resulting from both limited memory capacity and limited data communication bandwidth. Here we demonstrate experimentally that the synaptic weights shared in different time steps in an LSTM can be implemented with a memristor crossbar array, which has a small circuit footprint, can store a large number of parameters and offers in-memory computing capability that contributes to circumventing the ‘von Neumann bottleneck’. We illustrate the capability of our crossbar system as a core component in solving real-world problems in regression and classification, which shows that memristor LSTM is a promising low-power and low-latency hardware platform for edge inference.Deep neural networks are increasingly popular in data-intensive applications, but are power-hungry. New types of computer chips that are suited to the task of deep learning, such as memristor arrays where data handling and computing take place within the same unit, are required. A well-used deep learning model called long short-term memory, which can handle temporal sequential data analysis, is now implemented in a memristor crossbar array, promising an energy-efficient and low-footprint deep learning platform.
Neuromorphic computers based on analogue neural networks aim to substantially lower computing power by reducing the need to shuttle data between memory and logic units. Artificial synapses containing ...nonvolatile analogue conductance states enable direct computation using memory elements; however, most nonvolatile analogue memories require high write voltages and large current densities and are accompanied by nonlinear and unpredictable weight updates. Here, we develop an inorganic redox transistor based on electrochemical lithium-ion insertion into LiXTiO2 that displays linear weight updates at both low current densities and low write voltages. The write voltage, as low as 200 mV at room temperature, is achieved by minimizing the open-circuit voltage and using a low-voltage diffusive memristor selector. We further show that the LiXTiO2 redox transistor can achieve an extremely sharp transistor subthreshold slope of just 40 mV/decade when operating in an electrochemically driven phase transformation regime.
Abstract
The impact of a series resistor (R
S
) on the variability and endurance
performance of memristor was studied in the TaO
x
memristive system. A
dynamic voltage divider between the R
S
and ...memristor during both the set
and the reset switching cycles can suppress the inherent irregularity of the voltage
dropped on the memristor, resulting in a greatly reduced switching variability. By
selecting the proper resistance value of R
S
for the set and reset cycles
respectively, we observed a dramatically improved endurance of the TaO
x
memristor. Such a voltage divider effect can thus be critical for the memristor
applications that require low variability, high endurance and fast speed.
A wide reservoir computing system is an advanced architecture composed of multiple reservoir layers in parallel, which enables more complex and diverse internal dynamics for multiple time-series ...information processing. However, its hardware implementation has not yet been realized due to the lack of a high-performance physical reservoir and the complexity of fabricating multiple stacks. Here, we achieve a proof-of-principle demonstration of such hardware made of a multilayered three-dimensional stacked 3 × 10 × 10 tungsten oxide memristive crossbar array, with which we further realize a wide physical reservoir computing for efficient learning and forecasting of multiple time-series data. Because a three-layer structure allows the seamless and effective extraction of intricate three-dimensional local features produced by various temporal inputs, it can readily outperform two-dimensional based approaches extensively studied previously. Our demonstration paves the way for wide physical reservoir computing systems capable of efficiently processing multiple dynamic time-series information.
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low ...communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.
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