Abstract Neuromorphic computing systems convert multimodal signals to electrical responses for artificial intelligence recognition. Energy is consumed during both the response enhancement and depression, making the systems suffer from high energy consumption. This study presents a neuromorphic computing pathway based on supercapacitors. MXene Ti₃C₂Tx supercapacitors are fabricated and convert current stimuli to voltage responses. The response enhancement and depression are tunable through adjusting charging and discharging current stimuli, thus exhibiting synaptic plasticity. Typical synaptic behaviors are demonstrated, including short‐term memory, long‐term memory, paired‐pulse facilitation, and learning experience. Next, the voltage responses are used to recognize Braille numbers represented by 3 × 4 arrays. A charging/discharging current pulse train representing each Braille array is applied to the supercapacitor. The voltage responses are collected and converted to 12‐pixel greyscale images. Once the images representing Braille numbers 0–9 are input into artificial neural networks and deep diffraction neural networks, 100% accuracy can be achieved for recognizing the ten numbers. Because energy is stored during response enhancement in the supercapacitor and released once the response declines, this research demonstrates the potential applications of energy storage devices in neuromorphic computing, providing an innovative way to develop energy‐efficient brain‐like computing systems.
In recent years, gaze estimation has received a lot of interest in areas including human–computer interface, virtual reality, and user engagement analysis. Despite significant advances in convolutional neural network (CNN) techniques, directly and effectively predicting the point of gaze (PoG) in unconstrained situations remains a difficult task. This study proposes a gaze point estimation network (L1fcs-Net) that combines facial features with positional features derived from a two-dimensional array obtained by projecting the face relative to the screen. Our approach incorporates a Face-grid branch to enhance the network’s ability to extract features such as the relative position and distance of the face to the screen. Additionally, independent fully connected layers regress x and y coordinates separately, enabling the model to better capture gaze movement characteristics in both horizontal and vertical directions. Furthermore, we employ a multi-loss approach, balancing classification and regression losses to reduce gaze point prediction errors and improve overall gaze performance. To evaluate our model, we conducted experiments on the MPIIFaceGaz dataset, which was collected under unconstrained settings. The proposed model achieves state-of-the-art performance on this dataset with a gaze point prediction error of 2.05 cm, demonstrating its superior capability in gaze estimation.
Stroke, primarily ischemic (85%), results from inadequate blood supply and is worsened by ferroptosis, characterized by free radical generation and lipid peroxidation. Monitoring ferroptosis is essential for understanding its mechanisms and developing treatments. Glutathione (GSH) is a key ferroptosis biomarker, but current probes are limited by short excitation/emission wavelengths, small Stokes shifts, and inability to monitor dynamic GSH changes at the cellular membrane, where ferroptosis plays a crucial role. To address these issues, we developed the PM-Red-GSH, a novel near-infrared (NIR) probe based on the Excited-state intramolecular proton transfer (ESIPT) mechanism. It shows strong NIR emission (715 nm), large Stokes shift (290 nm), and enhanced membrane binding (PCC = 0.95) due to its alkyl group. PM-Red-GSH enables dynamic GSH monitoring in an MCAO mouse model. These findings offer new insights into ferroptosis and stroke treatment.
Semantic segmentation of rural roads presents unique challenges due to the unstructured nature of these environments, including irregular road boundaries, mixed surfaces, and diverse obstacles. In this study, we propose an enhanced PP-LiteSeg model specifically designed for rural road segmentation, incorporating a novel Strip Pooling Simple Pyramid Module (SP-SPPM) and a Bottleneck Unified Attention Fusion Module (B-UAFM). These modules improve the model’s ability to capture both global and local features, addressing the complexity of rural roads. To validate the effectiveness of our model, we constructed the Rural Roads Dataset (RRD), which includes a diverse set of rural scenes from different regions and environmental conditions. Experimental results demonstrate that our model significantly outperforms baseline models such as UNet, BiSeNetv1, and BiSeNetv2, achieving higher accuracy in terms of mean intersection over union (MIoU), Kappa coefficient, and Dice coefficient. Our approach enhances segmentation performance in complex rural road environments, providing practical applications for autonomous navigation, infrastructure maintenance, and smart agriculture.
Sung Jin Kim, Seung-Hoon Baek, Ki Sung Kang
et al.
Abstract The worldwide persistence of infectious diseases is a significant public health issue. Consequently, studying immunomodulatory ingredients present in natural products, such as ginseng, is important for developing new treatment options. Here, we extracted three different types of polysaccharides from white (P-WG), red (P-RG), and heat-processed (P-HPG) ginseng and analyzed their chemical properties and immunostimulatory activity against RAW 264.7 murine macrophages. Carbohydrates were the main components of all three polysaccharide types, while uronic acid and protein levels were relatively low. Chemical analysis indicated that the content of carbohydrates (total sugar) increased with processing temperature, while that of uronic acid decreased. Treatment with P-WG, P-RG or P-HPG stimulated nitric oxide (NO) production and increased tumor necrosis factor alpha (TNF-α) and interleukin (IL)-6 levels in RAW 264.7 macrophages, with P-WG showing the highest activity among the three polysaccharides. The expression of inducible NO synthase, which affects NO secretion, was highest in the macrophages treated with P-WG. Analysis of intracellular signaling pathways showed that mitogen-activated protein kinases (ERK, JNK, and p38) and NF-kB p65 were strongly phosphorylated by P-WG in macrophages but were only moderately phosphorylated by P-RG and P-HPG. Collectively, these results suggest that the polysaccharides isolated from ginseng undergo different changes in response to heat processing and display different chemical compositions and immune-enhancing activities.
Supeshala Nawarathnage, Sara Soleimani, Moriah H. Mathis
et al.
While conducting pilot studies into the usefulness of fusion to TELSAM polymers as a potential protein crystallization strategy, we observed novel properties in crystals of two TELSAM–target protein fusions, as follows. (i) A TELSAM–target protein fusion can crystallize more rapidly and with greater propensity than the same target protein alone. (ii) TELSAM–target protein fusions can be crystallized at low protein concentrations. This unprecedented observation suggests a route to crystallize proteins that can only be produced in microgram amounts. (iii) The TELSAM polymers themselves need not directly contact one another in the crystal lattice in order to form well-diffracting crystals. This novel observation is important because it suggests that TELSAM may be able to crystallize target proteins too large to allow direct inter-polymer contacts. (iv) Flexible TELSAM–target protein linkers can allow target proteins to find productive binding modes against the TELSAM polymer. (v) TELSAM polymers can adjust their helical rise to allow fused target proteins to make productive crystal contacts. (vi). Fusion to TELSAM polymers can stabilize weak inter-target protein crystal contacts. We report features of these TELSAM–target protein crystal structures and outline future work needed to validate TELSAM as a crystallization chaperone and determine best practices for its use.