Hardware implementation of memristor-based artificial neural networks.

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.

Serpin peptidase inhibitor, clade E, member 2 in physiology and pathology: recent advancements.

Serine protease inhibitors (serpins) are the most numerous and widespread multifunctional protease inhibitor superfamily and are expressed by all eukaryotes. Serpin E2 (serpin peptidase inhibitor, clade E, member 2), a member of the serine protease inhibitor superfamily is a potent endogenous thrombin inhibitor, mainly found in the extracellular matrix and platelets, and expressed in numerous organs and secreted by many cell types. The multiple functions of serpin E2 are mainly mediated through regulating urokinase-type plasminogen activator (uPA, also known as PLAU), tissue-type plasminogen activator (tPA, also known as PLAT), and matrix metalloproteinase activity, and include hemostasis, cell adhesion, and promotion of tumor metastasis. The importance serpin E2 is clear from its involvement in numerous physiological and pathological processes. In this review, we summarize the structural characteristics of the Serpin E2 gene and protein, as well as its roles physiology and disease.

VO2 memristor-based frequency converter with in-situ synthesize and mix for wireless internet-of-things.

Wireless internet-of-things (WIoT) with data acquisition sensors are evolving rapidly and the demand for transmission efficiency is growing rapidly. Frequency converter that synthesizes signals at different frequencies and mixes them with sensor datastreams is a key component for efficient wireless transmission. However, existing frequency converters employ separate synthesize and mix circuits with complex digital and analog circuits using complementary metal-oxide semiconductor (CMOS) devices, naturally incurring excessive latency and energy consumption. Here we report a highly uniform and calibratable VO2 memristor oscillator, based on which we build memristor-based frequency converter using 8[Formula: see text]8 VO2 array that can realize in-situ frequency synthesize and mix with help of compact periphery circuits. We investigate the self-oscillation based on negative differential resistance of VO2 memristors and the programmability with different driving currents and calibration resistances, demonstrating capabilities of such frequency converter for in-situ frequency synthesize and mix for 2 ~ 8 channels with frequencies up to 48 kHz for low frequency transmission link. When transmitting classical sensor data (acoustic, vision and spatial) in an end-to-end WIoT experimental setup, our VO2-based memristive frequency converter presents up to 1.45× ~ 1.94× power enhancement with only 0.02 ~ 0.21 dB performance degradations compared with conventional CMOS-based frequency converter. This work highlights the potential in solving frequency converter's speed and energy efficiency problems in WIoT using high crystalline quality epitaxially grown VO2 and calibratable VO2-based oscillator array, revealing a promising direction for next-generation WIoT system design.

High-order sensory processing nanocircuit based on coupled VO2 oscillators.

Conventional circuit elements are constrained by limitations in area and power efficiency at processing physical signals. Recently, researchers have delved into high-order dynamics and coupled oscillation dynamics utilizing Mott devices, revealing potent nonlinear computing capabilities. However, the intricate yet manageable population dynamics of multiple artificial sensory neurons with spatiotemporal coupling remain unexplored. Here, we present an experimental hardware demonstration featuring a capacitance-coupled VO2 phase-change oscillatory network. This network serves as a continuous-time dynamic system for sensory pre-processing and encodes information in phase differences. Besides, a decision-making module for special post-processing through software simulation is designed to complete a bio-inspired dynamic sensory system. Our experiments provide compelling evidence that this transistor-free coupling network excels in sensory processing tasks such as touch recognition and gesture recognition, achieving significant advantages of fewer devices and lower energy-delay-product compared to conventional methods. This work paves the way towards an efficient and compact neuromorphic sensory system based on nano-scale nonlinear dynamics.

Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning.

In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.

Regulation of the integrin αVß3- actin filaments axis in early osteogenic differentiation of human mesenchymal stem cells under cyclic tensile stress.

BACKGROUND: Integrins are closely related to mechanical conduction and play a crucial role in the osteogenesis of human mesenchymal stem cells. Here we wondered whether tensile stress could influence cell differentiation through integrin αVß3. METHODS: We inhibited the function of integrin αVß3 of human mesenchymal stem cells by treating with c(RGDyk). Using cytochalasin D and verteporfin to inhibit polymerization of microfilament and function of nuclear Yes-associated protein (YAP), respectively. For each application, mesenchymal stem cells were loaded by cyclic tensile stress of 10% at 0.5 Hz for 2 h daily. Mesenchymal stem cells were harvested on day 7 post-treatment. Western blotting and quantitative RT-PCR were used to detect the expression of alkaline phosphatase (ALP), RUNX2, ß-actin, integrin αVß3, talin-1, vinculin, FAK, and nuclear YAP. Immunofluorescence staining detected vinculin, actin filaments, and YAP nuclear localization. RESULTS: Cyclic tensile stress could increase the expression of ALP and RUNX2. Inhibition of integrin αVß3 activation led to rearrangement of actin filaments and downregulated the expression of ALP, RUNX2 and promoted YAP nuclear localization. When microfilament polymerization was inhibited, ALP, RUNX2, and nuclear YAP nuclear localization decreased. Inhibition of YAP nuclear localization could reduce the expression of ALP and RUNX2. CONCLUSIONS: Cyclic tensile stress promotes early osteogenesis of human mesenchymal stem cells via the integrin αVß3-actin filaments axis. YAP nuclear localization participates in this process of human mesenchymal stem cells. Video Abstract.

Polyetheretherketone (PEEK) as a Potential Material for the Repair of Maxillofacial Defect Compared with E-poly(tetrafluoroethylene) (e-PTFE) and Silicone.

Silicone and e-poly(tetrafluoroethylene) (e-PTFE) are the most commonly used artificial materials for repairing maxillofacial bone defects caused by facial trauma and tumors. However, their use is limited by poor histocompatibility, unsatisfactory support, and high infection rates. Polyetheretherketone (PEEK) has excellent mechanical strength and biocompatibility, but its application to the repair of maxillofacial bone defects lacks a theoretical basis. The microstructure and mechanical properties of e-PTFE, silicone, and PEEK were evaluated by electron microscopy, BOSE machine, and Fourier transformed infrared spectroscopy. Mouse fibroblast L929 cells were incubated on the surface of the three materials to assess cytotoxicity and adhesion. The three materials were implanted onto the left femoral surface of 90 male mice, and samples of the implants and surrounding soft tissues were evaluated histologically at 1, 2, 4, 8, and 12 weeks post-surgery. PEEK had a much higher Young's modulus than either e-PTFE or silicone (p < 0.05 each), and maintained high stiffness without degradation long after implantation. Both PEEK and e-PTFE facilitated L929 cell adhesion, with PEEK having lower cytotoxicity than e-PTFE and silicone (p < 0.05 each). All three materials similarly hindered the motor function of mice 12 weeks after implantation (p > 0.05 each). Connective tissue ingrowth was observed in PEEK and e-PTFE, whereas a fibrotic peri-prosthetic capsule was observed on the surface of silicone. The postoperative infection rate was significantly lower for both PEEK and silicone than for e-PTFE (p < 0.05 each). PEEK shows excellent biocompatibility and mechanical stability, suggesting that it can be effective as a novel implant to repair maxillofacial bone defects.

A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface.

Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for traditional systems. Here, we propose a highly efficient neuromorphic physiological signal processing system based on VO2 memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO2 memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO2 memristors is utilized in compact Leaky Integrate and Fire (LIF) and Adaptive-LIF (ALIF) neurons, which are incorporated into a decision-making Long short-term memory Spiking Neural Network. The system demonstrates superior computing capabilities, needing only small-sized LSNNs to attain high accuracies of 95.83% and 99.79% in arrhythmia classification and epileptic seizure detection, respectively. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces.

Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges.

Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.

Discovery of N,4-Di(1H-pyrazol-4-yl)pyrimidin-2-amine-Derived CDK2 Inhibitors as Potential Anticancer Agents: Design, Synthesis, and Evaluation.

Cyclin-dependent kinase 2 (CDK2) has been garnering considerable interest as a target to develop new cancer treatments and to ameliorate resistance to CDK4/6 inhibitors. However, a selective CDK2 inhibitor has yet to be clinically approved. With the desire to discover novel, potent, and selective CDK2 inhibitors, the phenylsulfonamide moiety of our previous lead compound 1 was bioisosterically replaced with pyrazole derivatives, affording a novel series of N,4-di(1H-pyrazol-4-yl)pyrimidin-2-amines that exhibited potent CDK2 inhibitory activity. Among them, 15 was the most potent CDK2 inhibitor (Ki = 0.005 µM) with a degree of selectivity over other CDKs tested. Meanwhile, this compound displayed sub-micromolar antiproliferative activity against a panel of 13 cancer cell lines (GI50 = 0.127-0.560 µM). Mechanistic studies in ovarian cancer cells revealed that 15 reduced the phosphorylation of retinoblastoma at Thr821, arrested cells at the S and G2/M phases, and induced apoptosis. These results accentuate the potential of the N,4-di(1H-pyrazol-4-yl)pyrimidin-2-amine scaffold to be developed into potent and selective CDK2 inhibitors for the treatment of cancer.

Discovery of Potent Inhibitors of Cyclin-Dependent Kinases 7 and 9: Design, Synthesis, Structure-Activity Relationship Analysis and Biological Evaluation.

Cyclin-dependent kinases (CDKs) 7 and 9 are deregulated in various types of human cancer and are thus viewed as therapeutic targets. Accordingly, small-molecule inhibitors of both CDKs are highly sought-after. Capitalising on our previous discovery of CDKI-73, a potent CDK9 inhibitor, medicinal chemistry optimisation was pursued. A number of N-pyridinylpyrimidin-2-amines were rationally designed, chemically synthesised and biologically assessed. Among them, N-(6-(4-cyclopentylpiperazin-1-yl)pyridin-3-yl)-4-(imidazo[1,2-a]pyrimidin-3-yl)pyrimidin-2-amine was found to be one of the most potent inhibitors of CDKs 7 and 9 as well as the most effective anti-proliferative agent towards multiple human cancer cell lines. The cellular mode of action of this compound was investigated in MV4-11 acute myeloid leukaemia cells, revealing that the compound dampened the kinase activity of cellular CDKs 7 and 9, arrested the cell cycle at sub-G1 phase and induced apoptosis.

One-Phototransistor-One-Memristor Array with High-Linearity Light-Tunable Weight for Optic Neuromorphic Computing.

The recent advances in optic neuromorphic devices have led to a subsequent rise in use for construction of energy-efficient artificial-vision systems. The widespread use can be attributed to their ability to capture, store, and process visual information from the environment. The primary limitations of existing optic neuromorphic devices include nonlinear weight updates, cross-talk issues, and silicon process incompatibility. In this study, a highly linear, light-tunable, cross-talk-free, and silicon-compatible one-phototransistor-one-memristor (1PT1R) optic memristor is experimentally demonstrated for the implementation of an optic artificial neural network (OANN). For optic image recognition in the experiment, an OANN is constructed using a 16 × 3 1PT1R memristor array, and it is trained on an online platform. The model yields an accuracy of 99.3% after only ten training epochs. The 1PT1R memristor, which shows good performance, demonstrates its ability as an excellent hardware solution for highly efficient optic neuromorphic and edge computing.

Circulating tumor DNA mutation profile is associated with the prognosis and treatment response of Chinese patients with newly diagnosed diffuse large B-cell lymphoma.

Background: Characterization of gene mutation profiles can provide new treatment options for patients with diffuse large B-cell lymphoma (DLBCL). However, this method is challenged by the limited source of tissue specimens, especially those of DLBCL patients at advanced stages. Therefore, in the current study, we aimed to describe the gene mutation landscape of DLBCL using circulating tumor DNA (ctDNA) samples obtained from patients' blood samples, as well as to explore the relationship between ctDNA mutations and the prognosis and treatment response of patients with newly diagnosed DLBCL. Methods: A total of 169 newly diagnosed Chinese DLBCL patients were included in this study, among which 85 patients were divided into a training set and 84 were assigned into a validation set. The mutation profile of a 59-gene panel was analyzed by targeted next generation sequencing (NGS) of the patients' ctDNA samples. Differences in clinical factors between patients with and without ctDNA mutations were analyzed. In addition, we also explored gene mutation frequencies between GCB and non-GCB subtypes, and the relationship between gene mutation status, clinical factors, mean VAF (variant allele frequencies) and the patients' overall survival (OS) and progression-free survival (PFS). Results: ctDNA mutations were detected in 64 (75.3%) patients of the training set and 67 (79.8%) patients of the validation set. The most commonly mutated genes in both sets were PCLO, PIM1, MYD88, TP53, KMT2D, CD79B, HIST1H1E and LRP1B, with mutation frequencies of >10%. Patients with detectable ctDNA mutations trended to present advanced Ann Arbor stages (III-IV), elevated LDH (lactate dehydrogenase) levels, shorter OS and PFS, and a lower complete response (CR) rate to the R-CHOP regimen compared with DLBCL patients without ctDNA mutations. In addition, mean VAF (≥4.94%) and PCLO mutations were associated with poor OS and PFS. Conclusion: We investigated the ctDNA mutation landscape in Chinese patients with newly diagnosed DLBCL and found that ctDNA could reflect tumor burden and patients with detectable ctDNA mutations trended to have shorter OS and PFS and a lower CR rate.

Ring finger protein 126 promotes breast cancer metastasis and serves as a potential target to improve the therapeutic sensitivity of ATR inhibitors.

BACKGROUND/AIMS: This study explores the relationship between the E3 ubiquitin ligase Ring finger protein 126 (RNF126) and early breast cancer metastasis and tests the hypothesis that RNF126 determines the efficacy of inhibitors targeting Ataxia telangiectasia mutated and Rad3-related kinase (ATR). METHODS: Various metastasis-related genes were identified by univariable Cox proportional hazards regression analysis based on the GSE11121 dataset. The RNF126-related network modules were identified by WGCNA, whereas cell viability, invasion, and migration assays were performed to evaluate the biological characteristics of breast cancer cells with or without RNF126 knockdown. MTT, immunoblotting, immunofluorescence, and DNA fiber assays were conducted to determine the efficiency of ATR inhibitor in cells with or without RNF126 knockdown. RESULTS: RNF126 was associated with early breast cancer metastasis. RNF126 promoted breast cancer cell proliferation, growth, migration, and invasion. ATR inhibitors were more effective at killing breast cancer cells with intact RNF126 due to replication stress compared with the corresponding cells with RNF126 knockdown. Cyclin-dependent kinase 2 (CDK2) was involved in regulating replication stress in breast cancer cells with intact RNF126. CONCLUSION: A high level of expression of RNF126 in early breast cancer patients without lymph node metastases may indicate a high-risk type of metastatic disease, possibly due to RNF126, which may increase breast cancer cell proliferation and invasion. RNF126-expressing breast cancer cells exhibit CDK2-mediated replication stress that makes them potential targets for ATR inhibitors.

Pan-cancer analysis of forkhead box Q1 as a potential prognostic and immunological biomarker.

Forkhead box Q1 (FOXQ1) is a member of the forkhead transcription factor family involved in the occurrence and development of different tumors. However, the specific expression patterns and functions of FOXQ1 in pan-cancer remain unclear. Therefore, we collected the expression, mutation, and clinical information data of 33 tumors from The Cancer Genome Atlas database. Via public pan-cancer transcriptome data analysis, we found that FOXQ1 is differentially expressed in various tumors at tissue and cell levels, such as liver hepatocellular carcinoma, colon adenocarcinoma, lung adenocarcinoma, lung squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Kaplan-Meier and Cox analyses suggested that FOXQ1 expression was associated with poor overall survival of cutaneous melanoma and thymoma. Its expression was also associated with good disease-specific survival (DSS) in prostate adenocarcinoma but poor DSS in liver hepatocellular carcinoma. In addition, FOXQ1 expression was associated with poor disease-free survival of pancreatic adenocarcinoma. Moreover, FOXQ1 expression was closely related to the tumor mutational burden in 14 tumor types and microsatellite instability (MSI) in 8 tumor types. With an increase in stromal and immune cells, FOXQ1 expression was increased in breast invasive carcinoma, pancreatic adenocarcinoma, thyroid carcinoma, lung adenocarcinoma, and ovarian serous cystadenocarcinoma, while its expression was decreased in pancreatic adenocarcinoma, bladder urothelial carcinoma, and stomach adenocarcinoma. We also found that FOXQ1 expression was related to the infiltration of 22 immune cell types in different tumors (p < 0.05), such as resting mast cells and resting memory CD4 T cells. Last, FOXQ1 was coexpressed with 47 immune-related genes in pan-cancer (p < 0.05). In conclusion, FOXQ1 expression is closely related to prognosis, clinicopathological parameters, cancer-related pathway activity, the tumor mutational burden, MSI, the tumor microenvironment, immune cell infiltration, and immune-related genes and has the potential to be a diagnostic and prognostic biomarker as well as an immunotherapy target for tumors. Our findings provide important clues for further mechanistic research into FOXQ1.

A distributed nanocluster based multi-agent evolutionary network.

As an important approach of distributed artificial intelligence, multi-agent system provides an efficient way to solve large-scale computational problems through high-parallelism processing with nonlinear interactions between the agents. However, the huge capacity and complex distribution of the individual agents make it difficult for efficient hardware construction. Here, we propose and demonstrate a multi-agent hardware system that deploys distributed Ag nanoclusters as physical agents and their electrochemical dissolution, growth and evolution dynamics under electric field for high-parallelism exploration of the solution space. The collaboration and competition between the Ag nanoclusters allow information to be effectively expressed and processed, which therefore replaces cumbrous exhaustive operations with self-organization of Ag physical network based on the positive feedback of information interaction, leading to significantly reduced computational complexity. The proposed multi-agent network can be scaled up with parallel and serial integration structures, and demonstrates efficient solution of graph and optimization problems. An artificial potential field with superimposed attractive/repulsive components and varied ion velocity is realized, showing gradient descent route planning with self-adaptive obstacle avoidance. This multi-agent network is expected to serve as a physics-empowered parallel computing hardware.

A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system.

Neuromorphic perception systems inspired by biology have tremendous potential in efficiently processing multi-sensory signals from the physical world, but a highly efficient hardware element capable of sensing and encoding multiple physical signals is still lacking. Here, we report a spike-based neuromorphic perception system consisting of calibratable artificial sensory neurons based on epitaxial VO2, where the high crystalline quality of VO2 leads to significantly improved cycle-to-cycle uniformity. A calibration resistor is introduced to optimize device-to-device consistency, and to adapt the VO2 neuron to different sensors with varied resistance level, a scaling resistor is further incorporated, demonstrating cross-sensory neuromorphic perception component that can encode illuminance, temperature, pressure and curvature signals into spikes. These components are utilized to monitor the curvatures of fingers, thereby achieving hand gesture classification. This study addresses the fundamental cycle-to-cycle and device-to-device variation issues of sensory neurons, therefore promoting the construction of neuromorphic perception systems for e-skin and neurorobotics.

EWI2 prevents EGFR from clustering and endocytosis to reduce tumor cell movement and proliferation.

EWI2 is a transmembrane immunoglobulin superfamily (IgSF) protein that physically associates with tetraspanins and integrins. It inhibits cancer cells by influencing the interactions among membrane molecules including the tetraspanins and integrins. The present study revealed that, upon EWI2 silencing or ablation, the elevated movement and proliferation of cancer cells in vitro and increased cancer metastatic potential and malignancy in vivo are associated with (i) increases in clustering, endocytosis, and then activation of EGFR and (ii) enhancement of Erk MAP kinase signaling. These changes in signaling make cancer cells (i) undergo partial epithelial-to-mesenchymal (EMT) for more tumor progression and (ii) proliferate faster for better tumor formation. Inhibition of EGFR or Erk kinase can abrogate the cancer cell phenotypes resulting from EWI2 removal. Thus, to inhibit cancer cells, EWI2 prevents EGFR from clustering and endocytosis to restrain its activation and signaling.