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Correction for 'Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ activation' by Bingjie Guo et al., Mater. Horiz., 2024, https://doi.org/10.1039/D3MH01950K.

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Affective computing, representing the forefront of human-machine interaction, is confronted with the pressing challenges of the execution speed and power consumption brought by the transmission of massive data. Herein, we introduce a bionic organic memristor inspired by the ligand-gated ion channels (LGICs) to facilitate near-sensor affective computing based on electroencephalography (EEG). It is constructed from a coordination polymer comprising Co ions and benzothiadiazole (Co-BTA), featuring multiple switching sites for redox reactions. Through advanced characterizations and theoretical calculations, we demonstrate that when subjected to a bias voltage, only the site where Co ions bind with N atoms from four BTA molecules becomes activated, while others remain inert. This remarkable phenomenon resembles the selective in situ activation of LGICs on the postsynaptic membrane for neural signal regulation. Consequently, the bionic organic memristor network exhibits outstanding reliability (200 000 cycles), exceptional integration level (210 pixels), ultra-low energy consumption (4.05 pJ), and fast switching speed (94 ns). Moreover, the built near-sensor system based on it achieves emotion recognition with an accuracy exceeding 95%. This research substantively adds to the ambition of realizing empathetic interaction and presents an appealing bionic approach for the development of novel electronic devices.

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Whiskers are nanoscale, high-strength fibrous crystals with a wide range of potential applications in dentistry owing to their unique mechanical, thermal, electrical, and biological properties. They possess high strength, a high modulus of elasticity and good biocompatibility. Hence, adding these crystals to dental composites as reinforcement can considerably improve the mechanical properties and durability of restorations. Additionally, whiskers are involved in inducing the value-added differentiation of osteoblasts, odontogenic osteocytes, and pulp stem cells, and promoting the regeneration of alveolar bone, periodontal tissue, and pulp tissue. They can also enhance the mucosal barrier function, inhibit the proliferation of tumor cells, control inflammation, and aid in cancer prevention. This review comprehensively summarizes the classification, properties, growth mechanisms and preparation methods of whiskers and focuses on their application in dentistry. Due to their unique physicochemical properties, excellent biological properties, and nanoscale characteristics, whiskers show great potential for application in bone, periodontal, and pulp tissue regeneration. Additionally, they can be used to prevent and treat oral cancer and improve medical devices, thus making them a promising new material in dentistry.

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In response to the increasing number of agents and changing task scenarios in multi-agent collaborative systems, existing collaborative strategies struggle to effectively adapt to new task scenarios. To address this challenge, this paper proposes a knowledge distillation method combined with a domain separation network (DSN-KD). This method leverages the well-performing policy network from a source task as the teacher model, utilizes a domain-separated neural network structure to correct the teacher model's outputs as supervision, and guides the learning of agents in new tasks. The proposed method does not require the pre-design or training of complex state-action mappings, thereby reducing the cost of transfer. Experimental results in scenarios such as UAV surveillance and UAV cooperative target occupation, robot cooperative box pushing, UAV cooperative target strike, and multi-agent cooperative resource recovery in a particle simulation environment demonstrate that the DSN-KD transfer method effectively enhances the learning speed of new task policies and improves the proximity of the policy model to the theoretically optimal policy in practical tasks.

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Genome mining of Emericella sp. XL-029 achieved a new type E sesterterpene synthase, EmES, which affored a novel bipolyhydroindenol sesterterpene, emerindanol A. Heterologous coexpression with the upstream P450 oxidase revealed C-4 hydroxylated product, emerindanol B. Notably, emerindanols A and B represented the first sesterterpenes featuring a unique 5/6-6/5 coupled ring system. EmES was postulated to initiate through C1-IV-V pathway and convert the fused ring intermediate into the final coupled ring product through a spiro skeleton.

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Industrialization has caused a significant global issue with cadmium (Cd) pollution. In this study, Biochar (Bc), generated through initial pyrolysis of rice straw, underwent thorough mixing with magnetized bentonite clay, followed by activation with KOH and subsequent pyrolysis. Consequently, a magnetized bentonite modified rice straw biochar (Fe3O4@B-Bc) was successfully synthesized for effective treatment and remediation of this problem. Fe3O4@B-Bc not only overcomes the challenges associated with the difficult separation of individual bentonite or biochar from water, but also exhibited a maximum adsorption capacity of Cd(II) up to 241.52 mg g-1. The characterization of Fe3O4@B-Bc revealed that its surface was rich in C, O and Fe functional groups, which enable efficient adsorption. The quantitative calculation of the contribution to the adsorption mechanism indicates that cation exchange and physical adsorption accounted for 65.87% of the total adsorption capacity. In conclusion, Fe3O4@B-Bc can be considered a low-cost and recyclable green adsorbent, with broad potential for treating cadmium-polluted water.

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OBJECTIVES: Three-dimensional (3D) facial symmetry analysis is based on the 3D symmetry reference plane (SRP). Artificial intelligence (AI) is widely used in the dental and oral sciences. This study developed a novel deep learning model called the facial planar reflective symmetry net (FPRS-Net) to automatically construct an SRP and established a method for defining a 3D point-cloud region of interest (ROI) and high-dimensional feature computations suitable for this network model. METHODS: Overall, 240 patients were enroled. The deep learning model was trained and predicted using 200 samples, and its clinical suitability was evaluated with 40 samples. Four FPRS-Net models were prepared, each using supervised and unsupervised learning approaches based on full facial and ROI data (FPRS-NetS, FPRS-NetSR, FPRS-NetU, and FPRS-NetUR). These models were trained on 160 3D facial datasets, validated on 20 cases, and tested on another 20 cases. The model predictions were evaluated using an additional 40 clinical 3D facial datasets by comparing the mean square error of the SRP between the parameters predicted by the four FPRS-Net models and the truth plane. The clinical suitability of FPRS-Net models was evaluated by measuring the angle error between the predicted and ground-truth planes; experts evaluated the predicted SRP of the four FPRS-Net models using the visual analogue scales (VAS) method. RESULTS: The FPRS-NetSR and FPRS-NetU models achieved an average angle error of 0.84° and 0.99° in predicting 3D facial SRP, respectively, with a VAS value of >8. Using the four FPRS-Net models to create an SRP in 40 cases of 3D facial data required <4 s. CONCLUSIONS: Our study demonstrated a new solution for automatically constructing oral clinical 3D facial SRPs. CLINICAL SIGNIFICANCE: This study proposes a novel deep learning algorithm (FPRS-Net) to construct a symmetry reference plane that can reduce workload, shorten the time required for digital design, reduce dependence on expert experience, and improve therapeutic efficiency and effectiveness in dental clinics.

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The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.

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Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately addresses fault diagnosis issues across devices. To better balance the relationship between computational resources and diagnostic accuracy, a knowledge distillation-based lightweight transfer learning framework for rolling bearing diagnosis is proposed in this study. Specifically, a deep teacher-student model based on variable-scale residual networks is constructed to learn domain-invariant features relevant to fault classification within both the source and target domain data. Subsequently, a knowledge distillation framework incorporating a temperature factor is established to transfer fault features learned by the large teacher model in the source domain to the smaller student model, thereby reducing computational and parameter overhead. Finally, a multi-kernel domain adaptation method is employed to capture the feature probability distribution distance of fault characteristics between the source and target domains in Reproducing Kernel Hilbert Space (RKHS), and domain-invariant features are learned by minimizing the distribution distance between them. The effectiveness and applicability of the proposed method in situations of incomplete data across device types were validated through two engineering cases, spanning device models and transitioning from laboratory equipment to real-world operational devices.

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Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in-memory two-factor authentication (IM-2FA). The unipolar self-oscillated synaptic behavior is adopted to function as in-memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM-2FA strategy.

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High-performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain-inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high-density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as-fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle-to-cycle uniformity of 98.89% and device-to-device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking-plasticity-related algorithm for decision-making tasks.

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High-performance artificial synapse with nonvolatile memory and low power consumption is a perfect candidate for brainoid intelligence. Unfortunately, due to the energy barrier paradox between ultra-low power and nonvolatile modulation of device conductances, it is still a challenge at the moment to construct such ideal synapses. Herein, a proton-reservoir type 4,4',4″,4'''-(Porphine-5,10,15,20-tetrayl) tetrakis (benzenesulfonic acid) (TPPS) molecule and fabricated organic protonic memristors with device width of 10 µm to 100 nm is synthesized. The occurrence of sequential proton migration and interfacial self-coordinated doping will introduce new energy levels into the molecular bandgap, resulting in effective and nonvolatile modulation of device conductance over 64 continuous states with retention exceeding 30 min. The power consumptions of modulating and reading the device conductance approach the zero-power operating limits, which range from 16.25 pW to 2.06 nW and 6.5 fW to 0.83 pW, respectively. Finally, a robust artificial synapse is successfully demonstrated, showing spiking-rate-dependent plasticity (SRDP) and spiking-timing-dependent plasticity (STDP) characteristics with ultra-low power of 0.66 to 0.82 pW, as well as 100 long-term depression (LTD)/potentiation (LTP) cycles with 0.14%/0.30% weight variations.

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Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO4)]2/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device's conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device's synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor.

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Reflectional symmetry is a ubiquitous pattern in nature. Previous works usually solve this problem by voting or sampling, suffering from high computational cost and randomness. In this article, we propose a learning-based approach to intrinsic reflectional symmetry detection. Instead of directly finding symmetric point pairs, we parametrize this self-isometry using a functional map matrix, which can be easily computed given the signs of Laplacian eigenfunctions under the symmetric mapping. Therefore, we manually label the eigenfunction signs for a variety of shapes and train a novel neural network to predict the sign of each eigenfunction under symmetry. Our network aims at learning the global property of functions and consequently converts the problem defined on the manifold to the functional domain. By disentangling the prediction of the matrix into separated bases, our method generalizes well to new shapes and is invariant under perturbation of eigenfunctions. Through extensive experiments, we demonstrate the robustness of our method in challenging cases, including different topology and incomplete shapes with holes. By avoiding random sampling, our learning-based algorithm is over 20 times faster than state-of-the-art methods, and meanwhile, is more robust, achieving higher correspondence accuracy in commonly used metrics.

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Breast cancer is the leading cause of cancer-related deaths in women and remains a formidable therapeutic challenge. Mitochondria participate in a myriad of essential cellular processes, such as metabolism, and are becoming an ideal target for cancer therapy. Artemisinin and its derivatives have demonstrated multiple activities in the context of various cancers. Mitochondrial autophagy(mitophagy) is one of the important anti-tumor mechanisms of artemisinin drugs. However, the lack of specific tumor targeting ability limits the anti-tumor efficacy of artemisinin drugs. In this study, a GSH-sensitive artesunate smart conjugate (TPP-SS-ATS) was synthesized and liposomes (TPP-SS-ATS-LS) that target tumor cells and mitochondria were further prepared. The advantages of TPP-SS-ATS-LS targeting to the breast tumor were verified by in vivo and in vitro evaluations. In our study, the cytotoxicity was obviously enhanced in vitro and tumor growth inhibition rate was increased from 37.7% to 56.4% at equivalent artesunate dosage in breast cancer orthotopic implanted mice. Meanwhile, mitochondrial dysfunction, suppression of ATP production and respiratory capacity were detected in breast cancer cells. We further discovered that TPP-SS-ATS-LS inhibited tumor cells proliferation through mitophagy by regulating PHB2 and PINK1 expression. These results provide new research strategies for the development of new artemisinin-based anti-tumor drugs.

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Cerebral malaria (CM) is a life-threatening neurological complication caused by Plasmodium falciparum. About 627,000 patients died of malaria in 2020. Currently, artemisinin and its derivatives are the front-line drugs used for the treatment of cerebral malaria. However, they cannot target the brain, which decreases their effectiveness. Therefore, increasing their ability to target the brain by the nano-delivery system with brain-targeted materials is of great significance for enhancing the effects of antimalarials and reducing CM mortality. This study used glucose transporter 1 (GLUT1) on the blood-brain barrier as a target for a synthesized cholesterol-undecanoic acid-glucose conjugate. The molecular dynamics simulation found that the structural fragment of glucose in the conjugate faced the outside the phospholipid bilayers, which was conducive to the recognition of brain-targeted liposomes by GLUT1. The fluorescence intensity of the brain-targeted liposomes (na-ATS/TMP@lipoBX) in the mouse brain was significantly higher than that of the non-targeted liposomes (na-ATS/TMP@lipo) in vivo (P < 0.001) after intranasal administration. The infection and recurrence rate of the mice receiving na-ATS/TMP@lipoBX treatment were significantly decreased, which had more advantages than those of other administration groups. The analysis of pharmacokinetic data showed that na-ATS/TMP@lipoBX could enter the brain in both systemic circulation and nasal-brain pathway to treat malaria. Taken together, these results in this study provide a new approach to the treatment of cerebral malaria.

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The fungus Emericella sp. XL029 isolated from leaves of Panax notoginseng was investigated for agents with potential antibacterial and antifungal activities using a one strain-many compounds (OSMAC) strategy. Fifteen compounds, including seven undescribed structures, were obtained from this species. Their structures were confirmed by extensive spectroscopic data, single-crystal X-ray crystallography and quantum chemistry calculations. Emerlactam A exhibited better antibacterial activity against multidrug-resistant Enterococcus faecium and antifungal activity against Helminthosporium maydis, with an MIC value of 12.5 µg/mL. Quiannulatic acid displayed significant antibacterial activity against multidrug-resistant Enterococcus faecium and multidrug-resistant Enterococcus faecalis with MIC values of 1.56 µg/mL and 3.13 µg/mL, respectively. 5-alkenylresorcinol exhibited significant antifungal activity against all tested phytopathogenic fungi with MIC values ranging from 6.25 to 12.5 µg/mL.

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The lipopolysaccharide(LPS)-indused RAW264.7 cells inflammation model was used as a carrier to investigated the effects of the preparation quality markers of Yulian Tang with anti-inflammatory activity in vitro. RAW264.7 cells were treated with LPS(50 ng·mL~(-1)) or/and different concentrations(low dose 0.1 µmol·L~(-1); medium dose 1 µmol·L~(-1); high dose 10 µmol·L~(-1)) of 18 chemical components in Yulian Tang for 24 h. Then the activity of RAW264.7 cell was detected using Cell Counting Kit-8(CCK-8) and the concentrations of inflammatory factors TNF-α and IL-6 in the supernatant of RAW264.7 cell were detected by ELISA assay. As the concentrations of chemical components in Yulian Tang increased, berberine, coptisine, magnoflorine, epiberberine, columbamine and costunolide had stronger inhibitory effects on TNF-α, whereas limonin, dehydroevodiamine, chlorogenic acid, neochlorogenic acid, groenlandicine, evodiamine, rutaecarpine and phellodendrine showed weakened inhibitory effects on TNF-α. The concentrations of palmatine, jatrorrhizine, dehydrocostus lactone and cryptochlorogenic acid had no significant effect on their inhibitory effect on TNF-α. Furthermore, dehydrorutaecarpine, chlorogenic acid, neochlorogenic acid, evodiamine, rutaecarpine, costunolide, phellodendrine and cryptochlorogenic acid showed stronger inhibitory effect on IL-6 as their concentrations increased; berberine, coptisine, magnoflorine, epiberberine, limonin, columbamine, groenlandicine and dehydrocostus lactone had no changes in their inhibitory effects on IL-6 as the concentrations increased. Palmatine and jatrorrhizine had the best inhibitory effect on IL-6. Combining the previous analysis of qualitative and quantitative preparation quality markers of Yulian Tang with the above result of dose-response relationship, we finally identified 15 preparation quality markers of Yulian Tang with anti-inflammatory activity, namely berberine, coptisine, palmatine, magnoflorine, epiberberine, limonin, columbamine, jatrorrhizine, neochlorogenic acid, chlorogenic acid, groenlandicine, evodiamine, rutaecarpine, dehydrocostus lactone and costunolide. In conclusion, our study provides a quick strategy for screening the qualitative preparation quality markers of Yulian Tang with anti-inflammatory activity. Moreover, it also provides an explicit route for the determination of preparation quality markers of Yulian Tang with other activities.

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With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to build a prediction model of behavior recognition. Firstly, genetic algorithm is used to cluster the redundant data, so that the data are in fragment order, and then it is used to reduce the data redundancy of different behaviors and weaken the influence of dimension on behavior recognition. Then, the genetic algorithm clusters the data to form subgenetic particles with different dimensions and carries out coevolution and optimal location sharing for subgenetic particles with different dimensions. Through simulation test, the algorithm constructed in this paper is better than genetic algorithm and neural network algorithm in terms of calculation accuracy and convergence speed. Finally, the prediction model is constructed by setting the initial value and threshold to predict the behavior recognition, and the results show that the accuracy of the model constructed in this paper is improved in the analysis of behavior recognition.

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The rapid development of social economy not only increases people's living pressure but also reduces people's health. Looking for a healthy development prediction model has become a domestic concern. Based on the analysis of the influencing factors of health development, this paper looks for a model to predict the development of public health, so as to improve the accuracy of health development prediction. In this paper, the linear sequential extreme learning machine algorithm can be used to evaluate the health status of a large number of data, analyze the differences of each evaluation index, and construct the analysis model of health status. Therefore, this paper introduces rough set theory into linear sequential extreme learning machine algorithm. Rough set can analyze the double analysis of evaluation scheme, predict the health development of different individuals, and improve the evaluation accuracy of mass health evaluation. The simulation results show that the improved line sequential extreme learning machine algorithm can accurately analyze the mass health and meet the needs of different individuals' health evaluation.

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