MRPL35 Induces Proliferation, Invasion, and Glutamine Metabolism in NSCLC Cells by Upregulating SLC7A5 Expression.

BACKGROUND: Mitochondrial ribosomal protein L35 (MRPL35) has been reported to contribute to the growth of non-small cell lung cancer (NSCLC) cells. However, the functions and mechanisms of MRPL35 on glutamine metabolism in NSCLC remain unclear. METHODS: The detection of mRNA and protein of MRPL35, ubiquitin-specific protease 39 (USP39), and solute carrier family 7 member 5 (SLC7A5) was conducted using qRT-PCR and western blotting. Cell proliferation, apoptosis, and invasion were evaluated using the MTT assay, EdU assay, flow cytometry, and transwell assay, respectively. Glutamine metabolism was analyzed by detecting glutamine consumption, α-ketoglutarate level, and glutamate production. Cellular ubiquitination analyzed the deubiquitination effect of USP39 on MRPL35. An animal experiment was conducted for in vivo analysis. RESULTS: MRPL35 was highly expressed in NSCLC tissues and cell lines, and high MRPL35 expression predicted poor outcome in NSCLC patients. In vitro analyses suggested that MRPL35 knockdown suppressed NSCLC cell proliferation, invasion, and glutamine metabolism. Moreover, MRPL35 silencing hindered tumor growth in vivo. Mechanistically, USP39 stabilized MRPL35 expression by deubiquitination and then promoted NSCLC cell proliferation, invasion, and glutamine metabolism. In addition, MRPL35 positively affected SLC7A5 expression in NSCLC cells in vitro and in vivo. Moreover, the anticancer effects of MRPL35 silencing could be rescued by SLC7A5 overexpression in NSCLC cells. CONCLUSION: MRPL35 expression was stabilized by USP39-induced deubiquitination in NSCLC cells, and knockdown of MRPL35 suppressed NSCLC cell proliferation, invasion, and glutamine metabolism in vitro and impeded tumor growth in vivo by upregulating SLC7A5, providing a promising therapeutic target for NSCLC.

Event-based Semantic Segmentation with Posterior Attention.

In the past years, attention-based Transformers have swept across the field of computer vision, starting a new stage of backbones in semantic segmentation. Nevertheless, semantic segmentation under poor light conditions remains an open problem. Moreover, most papers about semantic segmentation work on images produced by commodity frame-based cameras with a limited framerate, hindering their deployment to auto-driving systems that require instant perception and response at milliseconds. An event camera is a new sensor that generates event data at microseconds and can work in poor light conditions with a high dynamic range. It looks promising to leverage event cameras to enable perception where commodity cameras are incompetent, but algorithms for event data are far from mature. Pioneering researchers stack event data as frames so that event-based segmentation is converted to framebased segmentation, but characteristics of event data are not explored. Noticing that event data naturally highlight moving objects, we propose a posterior attention module that adjusts the standard attention by the prior knowledge provided by event data. The posterior attention module can be readily plugged into many segmentation backbones. Plugging the posterior attention module into a recently proposed SegFormer network, we get EvSegFormer (the event-based version of SegFormer) with state-of-the-art performance in two datasets (MVSEC and DDD-17) collected for event-based segmentation. Code is available at https://github.com/zexiJia/EvSegFormer to facilitate research on event-based vision.

Exploring user experience and performance of a tedious task through human-agent relationship.

Positive human-agent relationships can effectively improve human experience and performance in human-machine systems or environments. The characteristics of agents that enhance this relationship have garnered attention in human-agent or human-robot interactions. In this study, based on the rule of the persona effect, we study the effect of an agent's social cues on human-agent relationships and human performance. We constructed a tedious task in an immersive virtual environment, designing virtual partners with varying levels of human likeness and responsiveness. Human likeness encompassed appearance, sound, and behavior, while responsiveness referred to the way agents responded to humans. Based on the constructed environment, we present two studies to explore the effects of an agent's human likeness and responsiveness to agents on participants' performance and perception of human-agent relationships during the task. The results indicate that when participants work with an agent, its responsiveness attracts attention and induces positive feelings. Agents with responsiveness and appropriate social response strategies have a significant positive effect on human-agent relationships. These results shed some light on how to design virtual agents to improve user experience and performance in human-agent interactions.

In-Memory Mathematical Operations with Spin-Orbit Torque Devices.

Analog arithmetic operations are the most fundamental mathematical operations used in image and signal processing as well as artificial intelligence (AI).  In-memory computing (IMC) offers a high performance and energy-efficient computing paradigm. To date, in-memory analog arithmetic operations with emerging nonvolatile devices are usually implemented using discrete components, which limits the scalability and blocks large scale integration. Here, a prototypical implementation of in-memory analog arithmetic operations (summation, subtraction and multiplication) is experimentally demonstrated, based on in-memory electrical current sensing units using spin-orbit torque (SOT) devices. The proposed structures for analog arithmetic operations are smaller than the state-of-the-art complementary metal oxide semiconductor (CMOS) counterparts by several orders of magnitude. Moreover, data to be processed and computing results can be locally stored, or the analog computing can be done in the nonvolatile SOT devices, which are exploited to experimentally implement the image edge detection and signal amplitude modulation with a simple structure. Furthermore, an artificial neural network (ANN) with SOT devices based synapses is constructed to realize pattern recognition with high accuracy of ≈95%.

ROA: A Rapid Learning Scheme for In-Situ Memristor Networks.

Memristors show great promise in neuromorphic computing owing to their high-density integration, fast computing and low-energy consumption. However, the non-ideal update of synaptic weight in memristor devices, including nonlinearity, asymmetry and device variation, still poses challenges to the in-situ learning of memristors, thereby limiting their broad applications. Although the existing offline learning schemes can avoid this problem by transferring the weight optimization process into cloud, it is difficult to adapt to unseen tasks and uncertain environments. Here, we propose a bi-level meta-learning scheme that can alleviate the non-ideal update problem, and achieve fast adaptation and high accuracy, named Rapid One-step Adaption (ROA). By introducing a special regularization constraint and a dynamic learning rate strategy for in-situ learning, the ROA method effectively combines offline pre-training and online rapid one-step adaption. Furthermore, we implemented it on memristor-based neural networks to solve few-shot learning tasks, proving its superiority over the pure offline and online schemes under noisy conditions. This method can solve in-situ learning in non-ideal memristor networks, providing potential applications of on-chip neuromorphic learning and edge computing.

Self-Doping Memristors with Equivalently Synaptic Ion Dynamics for Neuromorphic Computing.

The accumulation and extrusion of Ca2+ ions in the pre- and post-synaptic terminals play crucial roles in initiating short- and long-term plasticity (STP and LTP) in biological synapses, respectively. Mimicking these synaptic behaviors by electronic devices represents a vital step toward realization of neuromorphic computing. However, the majority of reported synaptic devices usually focus on the emulation of qualitatively synaptic behaviors; devices that can truly emulate the physical behavior of the synaptic Ca2+ ion dynamics in STP and LTP are rarely reported. In this work, Ag/Ag:Ta2O5/Pt self-doping memristors were developed to equivalently emulate the Ca2+ ion dynamics of biological synapses. With conductive filaments from double sources, these memristors produced unique double-switching behavior under voltage sweeps and demonstrated several essential synaptic behaviors under pulse stimuli, including STP, LTP, STP to LTP transition, and spike-rate-dependent plasticity. Experimental results and nanoparticle dynamic simulations both showed that Ag atoms from double sources could mimic Ca2+ dynamics in the pre- and post-synaptic terminals under stimuli. A perceptron network with an STP to LTP transition layer based on the self-doping memristors was also introduced and evaluated; simulations showed that this network could solve noisy figure recognition tasks efficiently. All of these results indicate that the self-doping memristors are promising components for future hardware creation of neuromorphic systems and emulate the characteristics of the brain.

Highly Compact Artificial Memristive Neuron with Low Energy Consumption.

Neuromorphic systems aim to implement large-scale artificial neural network on hardware to ultimately realize human-level intelligence. The recent development of nonsilicon nanodevices has opened the huge potential of full memristive neural networks (FMNN), consisting of memristive neurons and synapses, for neuromorphic applications. Unlike the widely reported memristive synapses, the development of artificial neurons on memristive devices has less progress. Sophisticated neural dynamics is the major obstacle behind the lagging. Here a rich dynamics-driven artificial neuron is demonstrated, which successfully emulates partial essential neural features of neural processing, including leaky integration, automatic threshold-driven fire, and self-recovery, in a unified manner. The realization of bioplausible artificial neurons on a single device with ultralow power consumption paves the way for constructing energy-efficient large-scale FMNN and may boost the development of neuromorphic systems with high density, low power, and fast speed.

Electrochemical metallization cell with solid phase tunable Ge2Sb2Te5 electrolyte.

Electrochemical metallization (ECM) cell kinetics are strongly determined by the electrolyte and can hardly be altered after the cell has been fabricated. Solid-state property tunable electrolytes in response to external stimuli are therefore desirable to introduce additional operational degree of freedom to the ECM cells, enabling novel applications such as multistate memory and reconfigurable computation. In this work, we use Ge2Sb2Te5(GST) as the electrolyte material whose solid state is switched from the amorphous(a) to the crystalline(c) phase thermally. Electrical heating too is readily achievable. The resistive switching characteristics of the cells with different GST phases are examined. The magnitude of the high resistance, the SET voltage and the on/off ratio are found to be considerably affected by the solid phase of GST, whereas the magnitude of the low resistance is least affected. Moreover, a transition from volatile to nonvolatile SET switching is only observed for c-GST based cell under prolonged voltage sweep, but not for a-GST based cell. This work provides a springboard for more studies on the manipulation of the ECM cell kinetics by tunable electrolyte and the resulting unprecedented device functionalities.

Electrochemical metallization cell with anion supplying active electrode.

Electrochemical metallization (ECM) memories are among the various emerging non-volatile memory technologies, contending to replace DRAM and Flash and enabling novel neuromorphic computing applications. Typically, the operation of ECM cell is based on the electrochemical redox reactions of the cation supplying active electrode (e.g., Ag, Cu). Although extensively investigated, the possibility of utilizing new materials for the active electrode remains largely undiscussed. In this paper, an ECM cell with a Te active electrode is fabricated. It is found that the SET operation of the device occurs under negative voltage on the active electrode, which is opposite to that of the device with Ag electrode, indicating that the Te electrode supplies Te2- anions by electrochemical reduction. The influence of the electrolyte material on the switching properties is also found to be more significant for devices with Te electrodes. For Pt/GeS/Te and Pt/Ge2Sb2Te5/Te cells, repeatable unipolar and bipolar resistive switching are observed, respectively, which can be attributed to the rupture of the filament by Joule heating for the former and by ECM for the latter in the RESET process. The semiconducting properties of Te, the reversed operating polarity and the electrolyte dependent switching characteristics open up unprecedented prospects for ECM cells.