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Memory devices with high speed and high density are highly desired to address the ‘memory wall’ issue. Here we demonstrated a highly scalable, three-dimensional stackable ferroelectric diode, with its rectifying polarity modulated by the polarization reversal of Hf 0.5 Zr 0.5 O 2 films. By visualizing the hafnium/zirconium lattice order and oxygen lattice order with atomic-resolution spherical aberration-corrected STEM, we revealed the correlation between the spontaneous polarization of Hf 0.5 Zr 0.5 O 2 film and the displacement of oxygen atom, thus unambiguously identified the non-centrosymmetric Pca2 1 orthorhombic phase in Hf 0.5 Zr 0.5 O 2 film. We further implemented this ferroelectric diode in an 8 layers 3D array. Operation speed as high as 20 ns and robust endurance of more than 10 9 were demonstrated. The built-in nonlinearity of more than 100 guarantees its self-selective property that eliminates the need for external selectors to suppress the leakage current in large array. This work opens up new opportunities for future memory hierarchy evolution. Designing reliable, scalable and high speed computing systems remains a challenge. Here, the authors identify noncentrosymmetric orthorhombic phase in HZO film and demonstrate a CMOS compatible 3D Vertical HZO-based ferroelectric diode array with self-selective property and 20 ns of speed operation.

Observational studies have established a connection between body mass index (BMI) and an increased risk of cognitive decline. However, a comprehensive investigation into the causal relationships between BMI and cognitive function across diverse age groups, as well as the genetic underpinnings of this relationship, has been notably lacking. This study aims to investigate causality and the shared genetic underpinnings of between BMI and cognitive function by conducting a thorough genome-wide analysis, thereby provide valuable insights for developing personalized intervention strategies to promote cognitive health. Genetic associations between BMI and cognitive function were thoroughly investigated through covariate genetic analysis and chained imbalance score regression, utilizing data from genome-wide association studies (GWAS). Bi-directional Mendelian Randomization (MR) was employed to uncover associations and potential functional genes were further scrutinized through Cross-trait meta-analysis and Summary-data-based MR (SMR). Subsequently, a detailed examination of the expression profiles of the identified risk SNPs in tissues and cells was conducted. The study found a significant negative correlation between BMI and cognitive function (β = -0.16, = 1.76E-05), suggesting a causal linkage where higher BMI values were predictive of cognitive impairment. We identified 5 genetic loci (rs6809216, rs7187776, rs11713193, rs13096480, and rs13107325) between BMI and cognitive function by cross-trait meta-analysis and 5 gene-tissue pairs were identified by SMR analysis. Moreover, two novel risk genes and were shared by both cross-trait analysis and SMR analysis, which had not been observed in previous studies. Furthermore, significant enrichment of single nucleotide polymorphisms (SNPs) at tissue- and cell-specific levels was identified for both BMI and cognitive function, predominantly within the brain. This study uncovers a causal relationship between BMI and cognitive function, with the discovery of and as shared genetic factors associated with both conditions. This novel finding offers new insights into the development of preventative strategies for cognitive decline in obese individuals, and further enhances our understanding of the underlying pathophysiology of these conditions. Furthermore, these findings could serve as a guide for the development of innovative therapeutic approaches to address cognitive decline in obese individuals.

Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain. Dendritic computing is a promising approach to enhance the processing capability of artificial neural networks. Here, the authors report the development of a neurotransistor based on a vertical dual-gate electrolyte-gated transistor with short-term memory characteristics, a 30 nm channel length, a low read power of ~3.16 fW and read energy of ~30 fJ for dendritic computing.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low-Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during the reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5 dB and 0.2, respectively.

This brief presents an analog front-end (AFE) for the detection of electroencephalogram (EEG) signals. The AFE is composed of four sections, chopper-stabilized amplifiers, ripple suppression circuit, RRAM-based lowpass FIR filter, and 8-bit SAR ADC. This is the first time that an RRAM-based lowpass FIR filter has been introduced in an EEG AFE, where the bio-plausible characteristics of RRAM are utilized to analyze signals in the analog domain with high efficiency. The preamp uses the symmetrical OTA structure, reducing power consumption while meeting gain requirements. The ripple suppression circuit greatly improves noise characteristics and offset voltage. The RRAM-based low-pass filter achieves a 40 Hz cutoff frequency, which is suitable for the analysis of EEG signals. The SAR ADC adopts a segmented capacitor structure, effectively reducing the capacitor switching power consumption. The chip prototype is designed in 40 nm CMOS technology. The overall power consumption is approximately 13 µW, achieving ultra-low-power operation.

Huntington disease (HD) is a neurodegenerative disease caused by the abnormal expansion of a polyglutamine tract resulting from a mutation in the HTT gene. Oxidative stress has been identified as a significant contributing factor to the development of HD and other neurodegenerative diseases, and targeting anti-oxidative stress has emerged as a potential therapeutic approach. CHCHD2 is a mitochondria-related protein involved in regulating cell migration, anti-oxidative stress, and anti-apoptosis. Although CHCHD2 is highly expressed in HD cells, its specific role in the pathogenesis of HD remains uncertain. We postulate that the up-regulation of CHCHD2 in HD models represents a compensatory protective response against mitochondrial dysfunction and oxidative stress associated with HD. To investigate this hypothesis, we employed HD mouse striatal cells and human induced pluripotent stem cells (hiPSCs) as models to examine the effects of CHCHD2 overexpression (CHCHD2-OE) or knockdown (CHCHD2-KD) on the HD phenotype. Our findings demonstrate that CHCHD2 is crucial for maintaining cell survival in both HD mouse striatal cells and hiPSCs-derived neurons. Our study demonstrates that CHCHD2 up-regulation in HD serves as a compensatory protective response against oxidative stress, suggesting a potential anti-oxidative strategy for the treatment of HD.

Little is known about the perception and use of e-cigarettes by the Chinese, particularly the young people. This study reveals the awareness, attitudes, and use of e-cigarettes among young adults in China, examines the relationship between smoking behavior and e-cigarette perception and use, and demonstrates the phenomenon of e-cigarette gifting.INTRODUCTIONLittle is known about the perception and use of e-cigarettes by the Chinese, particularly the young people. This study reveals the awareness, attitudes, and use of e-cigarettes among young adults in China, examines the relationship between smoking behavior and e-cigarette perception and use, and demonstrates the phenomenon of e-cigarette gifting.We used results from a mobile app-based survey conducted in November 2015 that included 10477 young Chinese adults aged between 19 and 29 years. Bivariate tests were conducted to analyze perception and use of e-cigarettes by respondents of different smoking status. Multivariate logistic regressions were applied to examine the correlates of e-cigarette use and perception and e-cigarette gifting behavior, particularly the factors of tobacco smoking status and quitting behavior.METHODSWe used results from a mobile app-based survey conducted in November 2015 that included 10477 young Chinese adults aged between 19 and 29 years. Bivariate tests were conducted to analyze perception and use of e-cigarettes by respondents of different smoking status. Multivariate logistic regressions were applied to examine the correlates of e-cigarette use and perception and e-cigarette gifting behavior, particularly the factors of tobacco smoking status and quitting behavior.Among the surveyed young adults, 88.40% were aware of e-cigarettes, and nearly a quarter of all respondents had used e-cigarettes by the time of our survey. Multivariate regression results demonstrated that current smokers with quitting experience were more likely to be aware of and to use e-cigarettes than current smokers with no quitting experience. Smokers with quitting experience also were more inclined to promote e-cigarettes to others by either recommending them or giving them as gifts.RESULTSAmong the surveyed young adults, 88.40% were aware of e-cigarettes, and nearly a quarter of all respondents had used e-cigarettes by the time of our survey. Multivariate regression results demonstrated that current smokers with quitting experience were more likely to be aware of and to use e-cigarettes than current smokers with no quitting experience. Smokers with quitting experience also were more inclined to promote e-cigarettes to others by either recommending them or giving them as gifts.E-cigarettes have gained popularity among young adults in China and smokers, especially those who had tried quitting, were more likely to have known and used e-cigarettes. More empirical research on the relationship between e-cigarette use and smoking cessation is warranted to better inform a potential regulatory framework in China.CONCLUSIONSE-cigarettes have gained popularity among young adults in China and smokers, especially those who had tried quitting, were more likely to have known and used e-cigarettes. More empirical research on the relationship between e-cigarette use and smoking cessation is warranted to better inform a potential regulatory framework in China.

In this paper, a novel Resistive Random‐Access Memory (RRAM) read circuit has been designed and verified by simulation based on the RRAM model and parasitic capacitance of the circuit. Simulation results demonstrate the feasibility and effectiveness of the proposed circuit, with accurate reading of RRAM states and fast reading speed in the nanosecond range. The sense margin of the proposed circuit has improved as the array size increases, enhancing its application for advanced node RRAM array manufacture. Compared with conventional circuits, the proposed circuit achieved power consumption reduction of 6% and area reduction of 46.9 um 2 , resulting in a 97.5% reduction in area, providing an effective solution to address the cost and chip size challenges associated with RRAM industrialization.

Thyroid cancer (TC) is the most predominant malignancy of the endocrine system, with steadily growing occurrence and morbidity worldwide. Although diagnostic and therapeutic methods have been rapidly developed in recent years, the underlying molecular mechanisms in the pathogenesis of TC remain enigmatic. The N6-methyladenosine(m6A) RNA modification is designed to impact RNA metabolism and further gene regulation. This process is intricately regulated by a variety of regulators, such as methylases and demethylases. Aberrant m6A regulators expression is related to the occurrence and development of TC and play an important role in drug resistance. This review comprehensively analyzes the effect of m6A methylation on TC progression and the potential clinical value of m6A regulators as prognostic markers and therapeutic targets in this disease.

Visual sensors, including 3D light detection and ranging, neuromorphic dynamic vision sensor, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. However, their data are heterogeneous, causing complexity in system development. Moreover, conventional digital hardware is constrained by von Neumann bottleneck and the physical limit of transistor scaling. The computational demands of training ever-growing models further exacerbate these challenges. We propose a hardware-software co-designed random resistive memory-based deep extreme point learning machine. Data-wise, the multi-sensory data are unified as point set and processed universally. Software-wise, most weights are exempted from training. Hardware-wise, nanoscale resistive memory enables collocation of memory and processing, and leverages the inherent programming stochasticity for generating random weights. The co-design system is validated on 3D segmentation (ShapeNet), event recognition (DVS128 Gesture), and image classification (Fashion-MNIST) tasks, achieving accuracy comparable to conventional systems while delivering 6.78 × /21.04 × /15.79 × energy efficiency improvements and 70.12%/89.46%/85.61% training cost reductions. Processing heterogeneous visual data in edge-side intelligent machines is complex and inefficient. Here, the authors propose a hardware-software co-designed system using random resistive memory, achieving significant energy efficiency and training cost reductions.