MEMS Oscillators-Network-Based Ising Machine with Grouping Method.

Combinatorial optimization (CO) has a broad range of applications in various fields, including operations research, computer science, and artificial intelligence. However, many of these problems are classified as nondeterministic polynomial-time (NP)-complete or NP-hard problems, which are known for their computational complexity and cannot be solved in polynomial time on traditional digital computers. To address this challenge, continuous-time Ising machine solvers have been developed, utilizing different physical principles to map CO problems to ground state finding. However, most Ising machine prototypes operate at speeds comparable to digital hardware and rely on binarizing node states, resulting in increased system complexity and further limiting operating speed. To tackle these issues, a novel device-algorithm co-design method is proposed for fast sub-optimal solution finding with low hardware complexity. On the device side, a piezoelectric lithium niobate (LiNbO3) microelectromechanical system (MEMS) oscillator network-based Ising machine without second-harmonic injection locking (SHIL) is devised to solve Max-cut and graph coloring problems. The LiNbO3 oscillator operates at speeds greater than 9 GHz, making it one of the fastest oscillatory Ising machines. System-wise, an innovative grouping method is used that achieves a performance guarantee of 0.878 for Max-cut and 0.658 for graph coloring problems, which is comparable to Ising machines that utilize binarization.

Gate-Tunable Positive and Negative Photoconductance in Near-Infrared Organic Heterostructures for In-Sensor Computing.

The rapid growth of sensor data in the artificial intelligence often causes significant reductions in processing speed and power efficiency. Addressing this challenge, in-sensor computing is introduced as an advanced sensor architecture that simultaneously senses, memorizes, and processes images at the sensor level. However, this is rarely reported for organic semiconductors that possess inherent flexibility and tunable bandgap. Herein, an organic heterostructure that exhibits a robust photoresponse to near-infrared (NIR) light is introduced, making it ideal for in-sensor computing applications. This heterostructure, consisting of partially overlapping p-type and n-type organic thin films, is compatible with conventional photolithography techniques, allowing for high integration density of up to 520 devices cm-2 with a 5 µm channel length. Importantly, by modulating gate voltage, both positive and negative photoresponses to NIR light (1050 nm) are attained, which establishes a linear correlation between responsivity and gate voltage and consequently enables real-time matrix multiplication within the sensor. As a result, this organic heterostructure facilitates efficient and precise NIR in-sensor computing, including image processing and nondestructive reading and classification, achieving a recognition accuracy of 97.06%. This work serves as a foundation for the development of reconfigurable and multifunctional NIR neuromorphic vision systems.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance.

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Ultralow-power optoelectronic synaptic transistors based on polyzwitterion dielectrics for in-sensor reservoir computing.

Bio-inspired transistor synapses use solid electrolytes to achieve low-power operation and rich synaptic behaviors via ion diffusion and trapping. While these neuromorphic devices hold great promise, they still suffer from challenges such as high leakage currents and power consumption, electrolysis risk, and irreversible conductance changes due to long-range ion migrations and permanent ion trapping. In addition, their response to light is generally limited because of "exciton-polaron quenching", which restricts their potential in in-sensor neuromorphic visions. To address these issues, we propose replacing solid electrolytes with polyzwitterions, where the cation and anion are covalently concatenated via a flexible alkyl chain, thus preventing long-range ion migrations while inducing good photoresponses to the transistors via interfacial charge trapping. Our detailed studies reveal that polyzwitterion-based transistors exhibit optoelectronic synaptic behavior with ultralow-power consumption (~250 aJ per spike) and enable high-performance in-sensor reservoir computing, achieving 95.56% accuracy in perceiving the trajectory of moving basketballs.

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors.

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.

miR-29a-KLF4 signaling inhibits breast tumor initiation by regulating cancer stem cells.

Cancer stem cells (CSCs) are known for their potent ability to drive tumor initiation and recurrence, yet the molecular mechanisms regulating CSCs are still unclear. Our study found a positive correlation between increased levels of miR-29a and better survival rates in early-stage breast cancer patients, but a negative correlation in late-stage patients, suggesting a dual function of miR-29a in regulating breast cancer. Furthermore, miR-29a showed significant downregulation in the ALDH+ breast cancer stem cell population compared to non-stem cancer cells. Overexpression of miR-29a in human breast cancer cells reduced the proportion of CSCs, suppressed their ability to form mammospheres, and inhibited the expression of stemness genes SOX2, KLF4, and hTERT in vitro. Conversely, knockdown of miR-29a in breast cancer cells showed opposite effects. Tumor xenograft experiments revealed that miR-29a overexpression significantly inhibited tumorigenesis initiated by MDA-MB-231 cell transplantation in nude mice. We further demonstrated that Krüppel-like factor 4 (KLF4), a key gene that regulates cell stemness, was a direct target of miR-29a in breast cancer cells. miR-29a suppressed the expression of KLF4 at both mRNA and protein levels. Reintroduction of KLF4 into breast cancer cells rescued the miR-29a-induced CSC suppression phenotype. In summary, our study is the first to demonstrate that miR-29a-KLF4 signaling inhibits breast tumor initiation by regulating CSCs, which provides novel therapeutic targets for preventing breast tumor initiation.

Optically Tunable Electrical Oscillations in Oxide-Based Memristors for Neuromorphic Computing.

The application of hardware-based neural networks can be enhanced by integrating sensory neurons and synapses that enable direct input from external stimuli. This work reports direct optical control of an oscillatory neuron based on volatile threshold switching in V3O5. The devices exhibit electroforming-free operation with switching parameters that can be tuned by optical illumination. Using temperature-dependent electrical measurements, conductive atomic force microscopy (C-AFM), in situ thermal imaging, and lumped element modelling, it is shown that the changes in switching parameters, including threshold and hold voltages, arise from overall conductivity increase of the oxide film due to the contribution of both photoconductive and bolometric characteristics of V3O5, which eventually affects the oscillation dynamics. Furthermore, V3O5 is identified as a new bolometric material with a temperature coefficient of resistance (TCR) as high as -4.6% K-1 at 423 K. The utility of these devices is illustrated by demonstrating in-sensor reservoir computing with reduced computational effort and an optical encoding layer for spiking neural network (SNN), respectively, using a simulated array of devices.

Risk and protective factors of suicidal tendencies among freshmen in China revealed by a hierarchical regression model.

This study aimed to identify risk and protective factors for suicidal tendencies among college students by exploring current mental health, personal experiences, family environment, and school adaptation. A total of 11,504 freshmen in China were recruited. Suicidal tendencies were assessed using the Adolescents Suicidal Tendencies Scale (ASTS), while explored risk and protective factors included mental health assessed by the Symptom Checklist-90 (SCL-90), campus adaptation using the College Student School Adaptation Scale, and Personal Situation Survey. Single-factor Logistic regression analysis, correlation analysis, and hierarchical regression analysis were used to analyze the risk and protective factors affecting suicidal tendencies. The results showed that in terms of personal experience, self-injury behavior (OR = 3.522, 95% CI [3.256, 3.811]), sexual assault experience (OR = 2.603, 95% CI [2.374, 2.855]) and lack of friendship relationship (OR = 2.249, 95% CI [2.076, 2.436]) were the most significant risk factors. Regarding family environment, parenting style (OR = 2.455, 95% CI [2.272, 2.652]), parent-child relationship (OR = 2.255, 95% CI [2.092, 2.429]) and violent conflict (OR = 2.164, 95% CI [2.015, 2.324]) were the most prominent risk factors. For protective factors, life satisfaction (OR = 0.330, 95% CI [0.304, 0.359]) and rest quality (OR = 0.415, 95% CI [0.386, 0.447]) were the most significant protective factors. In addition, Symptom Checklist-90 was positively correlated with suicidal tendencies (r = 0.541, 95% CI [0.522, 0.560], p < 0.001), while school adaptation was negatively correlated with suicidal tendencies (r = - 0.590, 95% CI [- 0.579, - 0.601], p < 0.001). After considering demographic variables, psychological symptoms, school adaptation and other risk and protective factors, the hierarchical regression model could explain 48.9% of the variance of suicidal tendencies. The study emphasizes a range of multidimensional risk and protective factors for suicidal tendencies. This enhanced understanding is crucial in aiding the design of future intervention studies targeted at improving the mental health of college students.

Lithium Niobate MEMS Antisymmetric Lamb Wave Resonators with Support Structures.

The piezoelectric thin film composed of single-crystal lithium niobate (LiNbO3) exhibits a remarkably high electromechanical coupling coefficient and minimal intrinsic losses, making it an optimal material for fabricating bulk acoustic wave resonators. However, contemporary first-order antisymmetric (A1) Lamb mode resonators based on LiNbO3 thin films face specific challenges, such as inadequate mechanical stability, limited power capacity, and the presence of multiple spurious modes, which restrict their applicability in a broader context. In this paper, we present an innovative design for A1 Lamb mode resonators that incorporates a support-pillar structure. Integration of support pillars enables the dissipation of spurious wave energy to the substrate, effectively mitigating unwanted spurious modes. Additionally, this novel approach involves anchoring the piezoelectric thin film to a supportive framework, consequently enhancing mechanical stability while simultaneously improving the heat dissipation capabilities of the core.

A 2D Heterostructure-Based Multifunctional Floating Gate Memory Device for Multimodal Reservoir Computing.

The demand for economical and efficient data processing has led to a surge of interest in neuromorphic computing based on emerging two-dimensional (2D) materials in recent years. As a rising van der Waals (vdW) p-type Weyl semiconductor with many intriguing properties, tellurium (Te) has been widely used in advanced electronics/optoelectronics. However, its application in floating gate (FG) memory devices for information processing has never been explored. Herein, an electronic/optoelectronic FG memory device enabled by Te-based 2D vdW heterostructure for multimodal reservoir computing (RC) is reported. When subjected to intense electrical/optical stimuli, the device exhibits impressive nonvolatile electronic memory behaviors including ≈108 extinction ratio, ≈100 ns switching speed, >4000 cycles, >4000-s retention stability, and nonvolatile multibit optoelectronic programmable characteristics. When the input stimuli weaken, the nonvolatile memory degrades into volatile memory. Leveraging these rich nonlinear dynamics, a multimodal RC system with high recognition accuracy of 90.77% for event-type multimodal handwritten digit-recognition is demonstrated.

miR-186-ANXA9 signaling inhibits tumorigenesis in breast cancer.

Breast cancer (BC) ranks as the highest incidence among cancer types in women all over the world. MicroRNAs (miRNAs) are a class of short endogenous non-coding RNA in cells mostly functioning to silence the target mRNAs. In the current study, a miRNA screening analysis identified miR-186-5p to be downregulated in human breast cancer tumors. Functional studies in vitro demonstrated that overexpression of miR-186-5p inhibited cellular proliferation and induced cell apoptosis in multiple breast cancer cell lines including MDA-MB-231, MCF-7, and BT549 cells. Transplantation of the miR-186-5p-overexpressing MDA-MB-231 cells into nude mice significantly inhibited mammary tumor growth in vivo. Sequence blast analysis predicted annexin A9 (ANXA9) as a target gene of miR-186-5p, which was validated by luciferase reporter assay, QRT-PCR analysis, and western blot. Additional gene expression analysis of clinical tumor samples indicated a negative correlation between miR-186-5p and ANXA9 in human breast cancer. Knockdown of ANXA9 mimicked the phenotype of miR-186-5p overexpression. Reintroduction of ANXA9 back rescued the miR-186-5p-induced cell apoptosis. In addition, miR-186-5p decreased the expression of Bcl-2 and increased the expression of p53, suggesting a mechanism regulating miR-186-5p-induced cellular apoptosis. In summary, our study is the first to demonstrate miR-186-5p-ANXA9 signaling in suppressing human breast cancer. It provided a potential therapeutic target in breast cancer.

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing.

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.

CMOS backend-of-line compatible memory array and logic circuitries enabled by high performance atomic layer deposited ZnO thin-film transistor.

The development of high-performance oxide-based transistors is critical to enable very large-scale integration (VLSI) of monolithic 3-D integrated circuit (IC) in complementary metal oxide semiconductor (CMOS) backend-of-line (BEOL). Atomic layer deposition (ALD) deposited ZnO is an attractive candidate due to its excellent electrical properties, low processing temperature below copper interconnect thermal budget, and conformal sidewall deposition for novel 3D architecture. An optimized ALD deposited ZnO thin-film transistor achieving a record field-effect and intrinsic mobility (µFE /µo) of 85/140 cm2/V·s is presented here. The ZnO TFT was integrated with HfO2 RRAM in a 1 kbit (32 × 32) 1T1R array, demonstrating functionalities in RRAM switching. In order to co-design for future technology requiring high performance BEOL circuitries implementation, a spice-compatible model of the ZnO TFTs was developed. We then present designs of various ZnO TFT-based inverters, and 5-stage ring oscillators through simulations and experiments with working frequency exceeding 10's of MHz.

Breaking the Trade-Off Between Polymer Dielectric Constant and Loss via Aluminum Oxo Macrocycle Dopants for High-Performance Neuromorphic Electronics.

The dielectric layer is crucial in regulating the overall performance of field-effect transistors (FETs), the key component in central processing units, sensors, and displays. Despite considerable efforts being devoted to developing high-permittivity (k) dielectrics, limited progress is made due to the inherent trade-off between dielectric constant and loss. Here, a solution is presented by designing a monodispersed disk-shaped Ce-Al-O-macrocycle as a dopant in polymer dielectrics. The molecule features a central Ce(III) core connected with eight Al atoms through sixteen bridging hydroxyls and eight 3-aminophenyl peripheries. The incorporation of this macrocycle in polymer dielectrics results in an up to sevenfold increase in dielectric constants and up to 89% reduction in dielectric loss at low frequencies. Moreover, the leakage-current densities decrease, and the breakdown strengths are improved by 63%. Relying on the above merits, FETs bearing cluster-doped polymer dielectrics give near three-orders source-drain current increments while maintaining low-level leakage/off currents, resulting in much higher charge-carrier mobilities (up to 2.45 cm2  V-1  s-1 ) and on/off ratios. This cluster-doping strategy is generalizable and shows great promise for ultralow-power photoelectric synapses and neuromorphic retinas. This work successfully breaks the trade-off between dielectric constant and loss and offers a unique design for polymer composite dielectrics.

Exploring the impact of childhood maltreatment and BPD on impulsivity in crimes of passion.

Background: Crimes of passion, characterized as unpremeditated impulsive aggression, have garnered increasing attention in recent years. Impulsivity, a major factor in crimes of passion, is also a common feature of various health conditions, including Borderline Personality Disorder (BPD). Childhood maltreatment is considered a significant precursor to BPD and is closely related to impulsivity. Although prior research has affirmed the relationship between impulsivity, childhood maltreatment, BPD, and criminal behavior, few studies have examined these variables' interconnections within the context of crimes of passion. This study seeks to explore the relationship between childhood maltreatment, BPD, and impulsivity in crimes of passion, assessing the former's impact on the latter. Method: Our research examined 133 crimes of passion offenders and 149 other offenders from the Shenzhen male prison in China. All 282 participants completed The Abbreviated Version of The Barratt Impulsiveness Scale (ABIS), The UPPS Impulsivity Scale (Urgency, Premeditation, Perseverance, Sensation Seeking), The McLean Screening Instrument for Borderline Personality Disorder (MSI-BPD), and The Childhood Trauma Questionnaire (CTQ). Results: Our findings indicated that (1) crimes of passion offenders scored significantly higher in emotional neglect, physical neglect, and overall childhood maltreatment than did other offenders, and childhood maltreatment scores were notably higher in the high BPD trait group. (2) Crimes of passion offenders demonstrated significantly elevated impulsivity in attention and nonplanning scales compared to other offenders. Impulsivity scores across all scales were also significantly higher in the high BPD trait group. (3) Emotional neglect was found to significantly influence the urgency scale in crimes of passion offenders. An interaction effect was noted between physical abuse and high BPD traits, leading to increased impulsivity in crimes of passion offenders. Conclusion: This study underscores the predictive roles of childhood maltreatment and BPD in determining impulsivity within the context of crimes of passion.

Dual-energy Spectral CT Imaging of Primary Anorectal Malignant Melanoma: A Case Report.

BACKGROUND: Primary anorectal malignant melanoma (ARMM) is a rare tumor. It is often misdiagnosed as hemorrhoids, polyps or colorectal cancer due to the lack of specificity of their clinical symptoms and imaging manifestations. CASE PRESENTATION: In this study, we reported an 83-year-old female patient with ARMM. Computed tomography (CT) and Magnetic Resonance Imaging (MRI) showed uneven thickening of the intestinal wall about 7.0 cm from the anal margin, and no typical T1 high signal was seen on MRI. Dual-energy spectral CT showed that the effective atomic number (Zeff) of the tumor and the iodine concentration in the arterial phase (AP) and venous phase (VP) were different from other rectal malignancies reported in the previous literature. Sigmoidoscopy showed a large polypoid mass approximately 7.0 cm from the anal verge. Immunohistochemical staining showed that about 60% of Melan A and HMB-45 were positive, S-100 protein and Ki-67 were positive, and the pathological diagnosis was ARMM. CONCLUSION: This was the first dual-energy spectral CT imaging report of ARMM. The Zeff and iodine concentration in the arterial phase and venous phase could help distinguish between ARMM and other rectal malignancies.

Cyclic Macroscopic Assembly and Disassembly Driven by Ionic Strength Fuel: A Waste-Free Approach.

Nonequilibrium assembling systems developed so far have relied on chemical fuels to drive the programmable pH cycles, redox reactions, and metastable bond formations. However, these methods often result in the unwanted accumulation of chemical waste. Herein, we present a novel strategy for achieving cyclic and waste-free nonequilibrium assembly and disassembly of macroscopic hydrogels, utilizing an ionic strength-mediated approach. Our strategy involves using ammonium carbonate as a chemical fuel to temporally regulate the attractions between oppositely charged hydrogels via ionic strength-controlled charge screening and hydrogel elasticity changes. This chemical fuel effectively mediates the assembly/disassembly processes and prevents waste accumulation, as ammonium carbonate can completely decompose into volatile chemical waste. The cyclic and reversible assembly process can be achieved without significant damping due to the self-clearance mechanism, as long as the chemical fuel is repeatedly supplied. This concept holds promise for creating macroscopic and microscopic nonequilibrium systems and self-adaptive materials.

Transient regulation of gel properties by chemical reaction networks.

Transient regulation of gel properties by chemical reaction networks (CRNs) represents an emerging and effective strategy to program or temporally control the structures, properties, and functions of gel materials in a self-regulated manner. CRNs provide significant opportunities to construct complex or sustainable gels with excellent dynamic features, thus expanding the application scope of these materials. CRN-based methods for transiently regulating the gel properties are receiving increasing attention, and the related fields are worth further studying. This feature article focuses on the CRN-mediated transient regulation of six properties of gels, which are transient gelation, transient liquefaction of gels, transient assembly of macroscopic gels, temporary actuation of gels, transient healing ability of kinetically inert gels, and cascade reaction-based self-reporting of external stimuli. Recent advances that showcase the six properties of gels controlled by CRNs are featured, the characterization and structural elucidation of gels are detailed, and the significance, achievements, and expectations of this field are discussed. The strategy of transient regulation of gel properties via CRNs is potentially useful for building the next generation of adaptive functional materials.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems.

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

Spontaneous Threshold Lowering Neuron using Second-Order Diffusive Memristor for Self-Adaptive Spatial Attention.

Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In-memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first-order dynamics. Here, a second-order memristor is experimentally demonstrated using yttria-stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second-order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL-based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second-order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high-efficiency, compact footprint, and hardware-encoded plasticity.