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Graphene-like materials are currently showing great potential in different fields, especially in energy storage. We synthesized N-GLC/MOF-74 composites by combining nitrogen-doped graphene-like carbon (N-GLC) with MOF-74. Three porous N-GLC/metal oxide complexes (A-1, A-2, and A-3) were successfully synthesized by calcining the synthesized composites at different temperatures (300°C, 400°C, and 500°C) under air atmosphere. The conversion of the MOF into metal oxides under high-temperature conditions and the oxidation of N-GLC into porous structures was revealed by characterization. The coulombic efficiency of A-1 reached 97.7% after 3500 long cycles at 5 A g−1, indicating an improvement in the stability of the material. The results of this work support the potential use of graphene-like derivatives in energy storage applications. [ABSTRACT FROM AUTHOR]

The lunar orbiter laser altimeter (LOLA) onboard the lunar reconnaissance orbiter has performed high-precision, full-coverage, and high-density laser ranging observations for the entire lunar surface since its launch. Statistics have shown that LOLA has collected 6.94 billion effective altimeter data up to June 2022. Most of the typical orbits in the LOLA dataset have a high quality and exhibit horizontal offsets of almost 7 m and radial offsets of almost 0.5 m. However, there is still a category of orbits in the dataset that will cause apparent noise in the constructed DEM, which is attributed to the orbits with large or anomalous errors. We call such orbits as flawed orbits in this paper. The flawed orbits can be identified and screened by the elevation discrepancy at the crossovers of the orbits. The results show that the flawed orbits are caused by significant along-track errors, which also result in the radial error of up to several kilometers. Moreover, most of the flawed orbits are concentrated in several consecutive time intervals. A correction method is then proposed to correct the flawed orbits in the local region. The position of the flawed orbits is reconstructed using the feature points matching of the DEMs before and after they are removed. Some experimental analyzes show that the apparent terrain artifacts have been eliminated and more identifiable terrain details are reappeared. Identifying and correcting these flawed orbits with significant along-track offsets paves the way for improving the quality of the LOLA data and reconstructing the topography of the Moon. [ABSTRACT FROM AUTHOR]

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations. [ABSTRACT FROM AUTHOR]

Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP (https://kipp.idruglab.cn) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing. [ABSTRACT FROM AUTHOR]

Quantizing is a promising approach to facilitate deploying deep neural networks on resource-limited devices. However, existing methods are challenged by obtaining computation acceleration and parameter compression while maintaining excellent performance. To achieve this goal, we propose PSE, a mixed quantization framework which combines product quantization (PQ), scalar quantization (SQ), and error correction. Specifically, we first employ PQ to obtain the floating-point codebook and index matrix of the weight matrix. Then, we use SQ to quantize the codebook into integers and reconstruct an integer weight matrix. Finally, we propose an error correction algorithm to update the quantized codebook and minimize the quantization error. We extensively evaluate our proposed method on various backbones, including VGG-16, ResNet-18/50, MobileNetV2, ShuffleNetV2, EfficientNet-B3/B7, and DenseNet-201 on CIFAR-10 and ILSVRC-2012 benchmarks. The experiments demonstrate that PSE reduces computation complexity and model size with acceptable accuracy loss. For example, ResNet-18 achieves 1.8 × acceleration ratio and 30.4 × compression ratio with less than 1.54% accuracy loss on CIFAR-10. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks and even irreversible damage to soft tissues. Here, we propose in-ear visual and auditory BCIs based on in-ear bioelectronics, named as SpiralE, which can adaptively expand and spiral along the auditory meatus under electrothermal actuation to ensure conformal contact. Participants achieve offline accuracies of 95% in 9-target steady state visual evoked potential (SSVEP) BCI classification and type target phrases successfully in a calibration-free 40-target online SSVEP speller experiment. Interestingly, in-ear SSVEPs exhibit significant 2nd harmonic tendencies, indicating that in-ear sensing may be complementary for studying harmonic spatial distributions in SSVEP studies. Moreover, natural speech auditory classification accuracy can reach 84% in cocktail party experiments. The SpiralE provides innovative concepts for designing 3D flexible bioelectronics and assists the development of biomedical engineering and neural monitoring. In-ear visual and auditory brain-computer interfaces typically have issues with poor interfacial adhesion or user irritation. Here, Wang et al. presents an in-ear hollow bioelectronic device that adaptively conforms to the ear canal, under electrothermal actuation, for electroencephalogram sensing. [ABSTRACT FROM AUTHOR]

Objectives: Preventing the expansion of perihematomal edema (PHE) represents a novel strategy for the improvement of neurological outcomes in intracerebral hemorrhage (ICH) patients. Our goal was to predict early and delayed PHE expansion using a machine learning approach. Methods: We enrolled 550 patients with spontaneous ICH to study early PHE expansion, and 389 patients to study delayed expansion. Two imaging researchers rated the shape and density of hematoma in non-contrast computed tomography (NCCT). We trained a radiological machine learning (ML) model, a radiomics ML model, and a combined ML model, using data from radiomics, traditional imaging, and clinical indicators. We then validated these models on an independent dataset by using a nested 4-fold cross-validation approach. We compared models with respect to their predictive performance, which was assessed using the receiver operating characteristic curve. Results: For both early and delayed PHE expansion, the combined ML model was most predictive (early/delayed AUC values were 0.840/0.705), followed by the radiomics ML model (0.799/0.663), the radiological ML model (0.779/0.631), and the imaging readers (reader 1: 0.668/0.565, reader 2: 0.700/0.617). Conclusion: We validated a machine learning approach with high interpretability for the prediction of early and delayed PHE expansion. This new technique may assist clinical practice for the management of neurocritical patients with ICH. Key Points: • This is the first study to use artificial intelligence technology for the prediction of perihematomal edema expansion. • A combined machine learning model, trained on data from radiomics, clinical indicators, and imaging features associated with hematoma expansion, outperformed all other methods. [ABSTRACT FROM AUTHOR]

Clearance of myelin debris caused by acute demyelination is an essential process for functional restoration following spinal cord injury (SCI). Microvascular endothelial cells, acting as "amateur" phagocytes, have been confirmed to engulf and degrade myelin debris, promoting the inflammatory response, robust angiogenesis, and persistent fibrosis. However, the effect of myelin debris engulfment on the function of endothelial tight junctions (TJs) remains unclear. Here, we demonstrate that myelin debris uptake impairs TJs and gap junctions of endothelial cells in the lesion core of the injured spinal cord and in vitro, resulting in increased permeability and leakage. We further show that myelin debris acts as an inducer to regulate the endothelial-to-mesenchymal transition in a dose-dependent manner and promotes endothelial cell migration through the PI3K/AKT and ERK signaling pathways. Together, our results indicate that myelin debris engulfment impairs TJs and promotes the migration of endothelial cells. Accelerating myelin debris clearance may help maintain blood–spinal cord barrier integrity, thus facilitating restoration of motor and sensory function following SCI. [ABSTRACT FROM AUTHOR]

Stroke has become the second leading cause of death in people aged higher than 60 years, with cancer being the first. Intracerebral hemorrhage (ICH) is the most lethal type of stroke. Using imaging techniques to evaluate the evolution of intracranial hematomas in patients with hemorrhagic stroke is worthy of ongoing research. The difficulty in obtaining ultra-early imaging data and conducting intensive dynamic radiographic imaging in actual clinical settings has led to the application of experimental animal models to assess the evolution of intracranial hematomas. Herein, we review the current knowledge on primary intracerebral hemorrhage mechanisms, focus on the progress of animal studies related to hematoma development and secondary brain injury, introduce preclinical therapies, and summarize related challenges and future directions. [ABSTRACT FROM AUTHOR]

Image captioning is a challenging task which requires not only to extract semantic information but also to generate descriptions with correct sentences. Most of the previous researches employ one-layer or two-layer Recurrent Neural Network (RNN) as the language model to predict sentence words. The language model may easily deal with the word information for a noun or an object, however, it may not be able to learn a verb or an adjective. To address this issue, a deep attention based language model is proposed to learn more abstract word information and three stacked approaches are designed to process attention. The proposed model makes full use of the Long Short Term Memory (LSTM) network and employs the transferred current attention to enhance extra spatial information. The experimental results on the benchmark MSCOCO and Flickr30K datasets have verified the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Prussian blue (PB) films have been widely used for blood glucose monitoring. Here, we present the strategy to improve the performance and sensitivity of PB film for hydrogen peroxide (H2O2) monitoring by using modified gold electrode for hydrogen peroxide monitoring. The microstructure of the studied electrodes was characterized using scanning electron microscopy and atomic force microscopy. The electrochemical properties of experimental electrodes were obtained via cyclic voltammograms and chronoamperometry methods. The results show that the thickness of deposited PB film is increased with the deposition time. The PB-modified electrode exhibits the widest linear range and best operational stability after being electrochemically deposited for 240 s. The highest sensitivity for experimental electrodes is obtained on samples deposited for 40 s (341 mA cm-2 M-1), indicating that a thinner PB film with certain critical thickness can accelerate the exchanging rate of K+ between PB lattice and the tested solution. [ABSTRACT FROM AUTHOR]

A series of novel white light-emitting LaAl2.03B4O10.54: Dy3+ phosphors for light-emitting diode applications were synthesized by high-temperature solid-state reactions. The results of X-ray diffraction measurement indicate that the crystal structure of the phosphors is LaAl2.03B4O10.54. The photoluminescence spectra of Dy3+-doped samples under the excitation at 347 nm consist of two main groups of emission lines peaking at 480 and 571 nm, which can be assigned to the optical transitions 4F9/2 → 6H15/2 and 4F9/2 → 6H13/2 of Dy3+, respectively. Based on the recorded UV-visible diffuse reflectance spectra, the values of the optical band gaps of LaAl2.03B4O10.54 and La0.96Al2.03B4O10.54: 0.04 Dy3+ were ascertained to be 3.87 and 3.62 eV, respectively. The decay lifetimes decreases with the increasing doping concentration of Dy3+. The Commission International de I’Eclairage color coordinates of Dy3+-doped samples fall in the white region. The study about the photoluminescence spectra at rising temperature shows that our phosphors own excellent thermal stability. All these investigation results indicate that LaAl2.03B4O10.54: Dy3+ as white light-emitting phosphor has potential applications in white light-emitting diodes. [ABSTRACT FROM AUTHOR]

A series of novel white light-emitting LaAl2.03B4O10.54: Dy3+ phosphors for light-emitting diode applications were synthesized by high-temperature solid-state reactions. The results of X-ray diffraction measurement indicate that the crystal structure of the phosphors is LaAl2.03B4O10.54. The photoluminescence spectra of Dy3+-doped samples under the excitation at 347 nm consist of two main groups of emission lines peaking at 480 and 571 nm, which can be assigned to the optical transitions 4F9/2 → 6H15/2 and 4F9/2 → 6H13/2 of Dy3+, respectively. Based on the recorded UV-visible diffuse reflectance spectra, the values of the optical band gaps of LaAl2.03B4O10.54 and La0.96Al2.03B4O10.54: 0.04 Dy3+ were ascertained to be 3.87 and 3.62 eV, respectively. The decay lifetimes decreases with the increasing doping concentration of Dy3+. The Commission International de I’Eclairage color coordinates of Dy3+-doped samples fall in the white region. The study about the photoluminescence spectra at rising temperature shows that our phosphors own excellent thermal stability. All these investigation results indicate that LaAl2.03B4O10.54: Dy3+ as white light-emitting phosphor has potential applications in white light-emitting diodes. [ABSTRACT FROM AUTHOR]

Flexible electronics attached to skin for healthcare, such as epidermal electronics, has to struggle with biocompatibility and adapt to specified environment of skin with respect to breath and perspiration. Here, we report a strategy for biocompatible flexible temperature sensors, inspired by skin, possessing the excellent permeability of air and high quality of water-proof by using semipermeable film with porous structures as substrate. We attach such temperature sensors to underarm and forearm to measure the axillary temperature and body surface temperature respectively. The volunteer wears such sensors for 24 hours with two times of shower and the in vitro test shows no sign of maceration or stimulation to the skin. Especially, precise temperature changes on skin surface caused by flowing air and water dropping are also measured to validate the accuracy and dynamical response. The results show that the biocompatible temperature sensor is soft and breathable on the human skin and has the excellent accuracy compared to mercury thermometer. This demonstrates the possibility and feasibility of fully using the sensors in long term body temperature sensing for medical use as well as sensing function of artificial skin for robots or prosthesis. [ABSTRACT FROM AUTHOR]