A Crayfish-Inspired Sensor Fusion Platform for Super Additive Integration of Visual, Chemical, and Tactile Information.

The concept of cross-sensor modulation, wherein one sensor modality can influence another's response, is often overlooked in traditional sensor fusion architectures, leading to missed opportunities for enhancing data accuracy and robustness. In contrast, biological systems, such as aquatic animals like crayfish, demonstrate superior sensor fusion through multisensory integration. These organisms adeptly integrate visual, tactile, and chemical cues to perform tasks such as evading predators and locating prey. Drawing inspiration from this, we propose a neuromorphic platform that integrates graphene-based chemitransistors, monolayer molybdenum disulfide (MoS2) based photosensitive memtransistors, and triboelectric tactile sensors to achieve "Super-Additive" responses to weak chemical, visual, and tactile cues and demonstrate contextual response modulation, also referred to as the "Inverse Effectiveness Effect." We hold the view that integrating bio-inspired sensor fusion principles across various modalities holds promise for a wide range of applications.

Ficus hirta Vahl. alleviate LPS induced apoptosis via down-regulating of miR-411 in orange-spotted grouper (Epinephelus coioides) spleen cell.

Ficus hirta Vahl. (FhV) has been shown to have antimicrobial and antiviral efficacy. To further ascertain the pharmacological properties of FhV., and to search for alternatives to antibiotics. An in vitro experiment was carried out to evaluate what influence FhV. would have on LPS-induced apoptosis. In this study, Fas, an apoptosis receptor, was cloned, which included a 5'-UTR of 39 bp, an ORF of 951 bp, a protein of 316 amino acids, and a 3'-UTR of 845 bp. EcFas was most strongly expressed in the spleen tissue of orange-spotted groupers. In addition, the apoptosis of fish spleen cells induced by LPS was concentration-dependent. Interestingly, appropriate concentrations of FhV. alleviated LPS-induced apoptosis. Inhibition of miR-411 further decreased the inhibitory effect of Fas on apoptosis, which reduced Bcl-2 expression and mitochondrial membrane potential, enhanced the protein expression of Bax and Fas. More importantly, the FhV. could activate miR-411 to improve this effect. In addition, luciferase reporter assays showed that miR-411 binds to Fas 3'-UTR to inhibit Fas expression. These findings provide evidence that FhV. alleviates LPS-induced apoptosis by activating miR-411 to inhibit Fas expression and, therefore, provided possible strategies for bacterial infections in fish.

A Butterfly-Inspired Multisensory Neuromorphic Platform for Integration of Visual and Chemical Cues.

Unisensory cues are often insufficient for animals to effectively engage in foraging, mating, and predatory activities. In contrast, integration of cues collected from multiple sensory organs enhances the overall perceptual experience and thereby facilitates better decision-making. Despite the importance of multisensory integration in animals, the field of artificial intelligence (AI) and neuromorphic computing has primarily focused on processing unisensory information. This lack of emphasis on multisensory integration can be attributed to the absence of a miniaturized hardware platform capable of co-locating multiple sensing modalities and enabling in-sensor and near-sensor processing. In this study, this limitation is addressed by utilizing the chemo-sensing properties of graphene and the photo-sensing capability of monolayer molybdenum disulfide (MoS2) to create a multisensory platform for visuochemical integration. Additionally, the in-memory-compute capability of MoS2 memtransistors is leveraged to develop neural circuits that facilitate multisensory decision-making. The visuochemical integration platform is inspired by intricate courtship of Heliconius butterflies, where female species rely on the integration of visual cues (such as wing color) and chemical cues (such as pheromones) generated by the male butterflies for mate selection. The butterfly-inspired visuochemical integration platform has significant implications in both robotics and the advancement of neuromorphic computing, going beyond unisensory intelligence and information processing.

Observation of Rich Defect Dynamics in Monolayer MoS2.

Defects play a pivotal role in limiting the performance and reliability of nanoscale devices. Field-effect transistors (FETs) based on atomically thin two-dimensional (2D) semiconductors such as monolayer MoS2 are no exception. Probing defect dynamics in 2D FETs is therefore of significant interest. Here, we present a comprehensive insight into various defect dynamics observed in monolayer MoS2 FETs at varying gate biases and temperatures. The measured source-to-drain currents exhibit random telegraph signals (RTS) owing to the transfer of charges between the semiconducting channel and individual defects. Based on the modeled temperature and gate bias dependence, oxygen vacancies or aluminum interstitials are probable defect candidates. Several types of RTSs are observed including anomalous RTS and giant RTS indicating local current crowding effects and rich defect dynamics in monolayer MoS2 FETs. This study explores defect dynamics in large area-grown monolayer MoS2 with ALD-grown Al2O3 as the gate dielectric.

A Graphene-Based Straintronic Physically Unclonable Function.

Physically unclonable functions (PUFs) are an integral part of modern-day hardware security. Various types of PUFs already exist, including optical, electronic, and magnetic PUFs. Here, we introduce a novel straintronic PUF (SPUF) by exploiting strain-induced reversible cracking in the contact microstructures of graphene field-effect transistors (GFETs). We found that strain cycling in GFETs with a piezoelectric gate stack and high-tensile-strength metal contacts can lead to an abrupt transition in some GFET transfer characteristics, whereas other GFETs remain resilient to strain cycling. Strain sensitive GFETs show colossal ON/OFF current ratios >107, whereas strain-resilient GFETs show ON/OFF current ratios <10. We fabricated a total of 25 SPUFs, each comprising 16 GFETs, and found near-ideal performance. SPUFs also demonstrated resilience to regression-based machine learning (ML) attacks in addition to supply voltage and temporal stability. Our findings highlight the opportunities for emerging straintronic devices in addressing some of the critical needs of the microelectronics industry.

Graphene Strain-Effect Transistor with Colossal ON/OFF Current Ratio Enabled by Reversible Nanocrack Formation in Metal Electrodes on Piezoelectric Substrates.

Extraordinarily high carrier mobility in graphene has led to many remarkable discoveries in physics and at the same time invoked great interest in graphene-based electronic devices and sensors. However, the poor ON/OFF current ratio observed in graphene field-effect transistors has stymied its use in many applications. Here, we introduce a graphene strain-effect transistor (GSET) with a colossal ON/OFF current ratio in excess of 107 by exploiting strain-induced reversible nanocrack formation in the source/drain metal contacts with the help of a piezoelectric gate stack. GSETs also exhibit steep switching with a subthreshold swing (SS) < 1 mV/decade averaged over ∼6 orders of magnitude change in the source-to-drain current for both electron and hole branch amidst a finite hysteresis window. We also demonstrate high device yield and strain endurance for GSETs. We believe that GSETs can significantly expand the application space for graphene-based technologies beyond what is currently envisioned.

QACL: Quartet attention aware closed-loop learning for abdominal MR-to-CT synthesis via simultaneous registration.

Synthesis of computed tomography (CT) images from magnetic resonance (MR) images is an important task to overcome the lack of electron density information in MR-only radiotherapy treatment planning (RTP). Some innovative methods have been proposed for abdominal MR-to-CT synthesis. However, it is still challenging due to the large misalignment between preprocessed abdominal MR and CT images and the insufficient feature information learned by models. Although several studies have used the MR-to-CT synthesis to alleviate the difficulty of multi-modal registration, this misalignment remains unsolved when training the MR-to-CT synthesis model. In this paper, we propose an end-to-end quartet attention aware closed-loop learning (QACL) framework for MR-to-CT synthesis via simultaneous registration. Specifically, the proposed quartet attention generator and mono-modal registration network form a closed-loop to improve the performance of MR-to-CT synthesis via simultaneous registration. In particular, a quartet-attention mechanism is developed to enlarge the receptive fields in networks to extract the long-range and cross-dimension spatial dependencies. Experimental results on two independent abdominal datasets demonstrate that our QACL achieves impressive results with MAE of 55.30±10.59 HU, PSNR of 22.85±1.43 dB, and SSIM of 0.83±0.04 for synthesis, and with Dice of 0.799±0.129 for registration. The proposed QACL outperforms the state-of-the-art MR-to-CT synthesis and multi-modal registration methods.

A Monolithic Stochastic Computing Architecture for Energy Efficient Arithmetic.

As the energy and hardware investments necessary for conventional high-precision digital computing continue to explode in the era of artificial intelligence (AI), a change in paradigm that can trade precision for energy and resource efficiency is being sought for many computing applications. Stochastic computing (SC) is an attractive alternative since, unlike digital computers, which require many logic gates and a high transistor volume to perform basic arithmetic operations such as addition, subtraction, multiplication, sorting, etc., SC can implement the same using simple logic gates. While it is possible to accelerate SC using traditional silicon complementary metal-oxide-semiconductor (CMOS) technology, the need for extensive hardware investment to generate stochastic bits (s-bits), the fundamental computing primitive for SC, makes it less attractive. Memristor and spin-based devices offer natural randomness but depend on hybrid designs involving CMOS peripherals for accelerating SC, which increases area and energy burden. Here, the limitations of existing and emerging technologies are overcome, and a standalone SC architecture embedded in memory and based on 2D memtransistors is experimentally demonstrated. The monolithic and non-von-Neumann SC architecture occupies a small hardware footprint and consumes a miniscule amount of energy (<1 nJ) for both s-bit generation and arithmetic operations, highlighting the benefits of SC.

Hardware implementation of Bayesian network based on two-dimensional memtransistors.

Bayesian networks (BNs) find widespread application in many real-world probabilistic problems including diagnostics, forecasting, computer vision, etc. The basic computing primitive for BNs is a stochastic bit (s-bit) generator that can control the probability of obtaining '1' in a binary bit-stream. While silicon-based complementary metal-oxide-semiconductor (CMOS) technology can be used for hardware implementation of BNs, the lack of inherent stochasticity makes it area and energy inefficient. On the other hand, memristors and spintronic devices offer inherent stochasticity but lack computing ability beyond simple vector matrix multiplication due to their two-terminal nature and rely on extensive CMOS peripherals for BN implementation, which limits area and energy efficiency. Here, we circumvent these challenges by introducing a hardware platform based on 2D memtransistors. First, we experimentally demonstrate a low-power and compact s-bit generator circuit that exploits cycle-to-cycle fluctuation in the post-programmed conductance state of 2D memtransistors. Next, the s-bit generators are monolithically integrated with 2D memtransistor-based logic gates to implement BNs. Our findings highlight the potential for 2D memtransistor-based integrated circuits for non-von Neumann computing applications.

Protective effects of P2X7R antagonist in sepsis-induced acute lung injury in mice via regulation of circ_0001679 and circ_0001212 and downstream Pln, Cdh2, and Nprl3 expression.

BACKGROUND: Sepsis induces pulmonary P2X7 receptor (P2X7 R) expression and P2X7 R-knockout reduced lung inflammation in mice. The present study investigated the expression of circular RNA (circRNA) and mRNA in sepsis-induced acute lung injury (ALI) treated with a P2X7 R antagonist. METHODS: Sepsis was induced by tracheal administration of lipopolysaccharide (LPS), and the mice were then divided into two groups: without [sepsis + dimethyl sulfoxide (DMSO)] or with P2X7 R antagonist treatment (sepsis + P2X7 A). Sham mice were administrated sterile normal saline. Serum levels of interleukin (IL)-1ß and tumor necrosis factor (TNF)-α, pathological changes, cell apoptosis and P2X7 R expression in lung were assessed, followed by RNA sequencing (RNA-seq) and bioinformatics analyses. A quantitative reverse transcriptase-polymerase chain reaction (RT-qPCR) was used to validate circRNAs and mRNAs. RESULTS: Compared to the sham group, LPS-induced sepsis produced obvious pathological changes in lung tissue, as well as increased apoptotic lung cells, serum TNF-α and IL-1ß levels, and P2X7 R expression; P2X7 R antagonism significantly ameliorated these changes. RNA-seq identified many dysregulated circRNAs and mRNAs during sepsis, whereas this changed with P2X7 R antagonism. RT-qPCR confirmed that Mus musculus (mmu)_circ_0001679, mmu_circ_0001212, phospholamban (Pln), cadherin-2 (Cdh2) and nitrogen permease regulator 3-like (Nprl3) expression were significantly increased in the sepsis + DMSO group compared to that in the sham group but were decreased in the sepsis + P2X7 A group compared to that in the sepsis + DMSO group. The circRNA-microRNA-mRNA coexpression network indicated that mmu_circ_0001679 may regulate Nprl3 and that mmu_circ_0001212 may similarly regulate Pln, Cdh2 and Nprl3 as a competing endogenous RNA. CONCLUSIONS: P2X7 R antagonism attenuates sepsis-induced ALI by inhibiting dysregulated expression of circRNA (circ_0001679, circ_0001212) and mRNA (Pln, Cdh2 and Nprl3).