In-Plane Mesoporous 3D Flower-Like Mo2Ti2C3Clx MXene Anodes for Li-Ion Batteries: From Structure to Performance.

MXenes are known for their exceptional electrical conductivity and surface functionality, gaining interest as promising anode materials for Li-ion batteries. However, conventional 2D multilayered MXenes often exhibit limited electrochemical applicability due to slow ion transport kinetics and low structural stability. Addressing these challenges, this study develops a 3D flower-type double transition metal MXene, Mo2Ti2C3Clx, with precisely engineered in-plane mesoporosity using HF-free Lewis acid-assisted molten salt method, coupled with intercalation and freeze-drying. The molar ratio of Lewis acid to eutectic salts is meticulously controlled to create the mesoporosity, which is preserved through freeze-drying. Molecular dynamics (MD) simulations assess the impact of in-plane pore size on the structure and transport dynamics of electrolyte components. Density functional theory (DFT) shows that chlorine surface functional groups significantly reduce Li-ion diffusion barriers, thereby enhancing ion transport and battery performance. Electrochemical evaluations reveal that small-sized (2-5 nm) mesoporous Mo2Ti2C3Clx achieves a specific capacity of 324 mAh g-1 at 0.2 A g-1 and maintains 97% capacity after 500 cycles at 0.5 A g-1, outperforming larger-pored (10 nm) and non-porous variants. This research highlights a scalable strategy for designing mesoporous materials that optimize ion transport and structural stability, essential for advancing next-generation high-performance energy storage solutions.

Molecular structure and characterization of the Thermochromatium tepidum light-harvesting 1 photocomplex produced in a foreign host.

Purple phototrophic bacteria possess light-harvesting 1 and reaction center (LH1-RC) core complexes that play a key role in converting solar energy to chemical energy. High-resolution structures of LH1-RC and RC complexes have been intensively studied and have yielded critical insight into the architecture and interactions of their proteins, pigments, and cofactors. Nevertheless, a detailed picture of the structure and assembly of LH1-only complexes is lacking due to the intimate association between LH1 and the RC. To study the intrinsic properties and structure of an LH1-only complex, a genetic system was constructed to express the Thermochromatium (Tch.) tepidum LH1 complex heterologously in a modified Rhodospirillum rubrum mutant strain. The heterologously expressed Tch. tepidum LH1 complex was isolated in a pure form free of the RC and exhibited the characteristic absorption properties of Tch. tepidum. Cryo-EM structures of the LH1-only complexes revealed a closed circular ring consisting of either 14 or 15 αß-subunits, making it the smallest completely closed LH1 complex discovered thus far. Surprisingly, the Tch. tepidum LH1-only complex displayed even higher thermostability than that of the native LH1-RC complex. These results reveal previously unsuspected plasticity of the LH1 complex, provide new insights into the structure and assembly of the LH1-RC complex, and show how molecular genetics can be exploited to study membrane proteins from phototrophic organisms whose genetic manipulation is not yet possible.

Multilevel Laser-Induced Pain Measurement with Wasserstein Generative Adversarial Network - Gradient Penalty Model.

Pain is an experience of unpleasant sensations and emotions associated with actual or potential tissue damage. In the global context, billions of people are affected by pain disorders. There are particular challenges in the measurement and assessment of pain, and the commonly used pain measuring tools include traditional subjective scoring methods and biomarker-based measures. The main tools for biomarker-based analysis are electroencephalography (EEG), electrocardiography and functional magnetic resonance. The EEG-based quantitative pain measurements are of immense value in clinical pain management and can provide objective assessments of pain intensity. The assessment of pain is now primarily limited to the identification of the presence or absence of pain, with less research on multilevel pain. High power laser stimulation pain experimental paradigm and five pain level classification methods based on EEG data augmentation are presented. First, the EEG features are extracted using modified S-transform, and the time-frequency information of the features is retained. Based on the pain recognition effect, the 20-40[Formula: see text]Hz frequency band features are optimized. Afterwards the Wasserstein generative adversarial network with gradient penalty is used for feature data augmentation. It can be inferred from the good classification performance of features in the parietal region of the brain that the sensory function of the parietal lobe region is effectively activated during the occurrence of pain. By comparing the latest data augmentation methods and classification algorithms, the proposed method has significant advantages for the five-level pain dataset. This research provides new ways of thinking and research methods related to pain recognition, which is essential for the study of neural mechanisms and regulatory mechanisms of pain.

Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients.

Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.

A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge-Cloud Computing.

With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge-LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge-LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network's total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.

Unraveling the Biosynthesis of Carvacrol in Different Tissues of Origanum vulgare.

Origanum vulgare, belonging to the Lamiaceae family, is a principal culinary herb used worldwide which possesses great antioxidant and antibacterial properties corresponding to various volatile organic components (VOCs). However, the metabolite profiles and underlying biosynthesis mechanisms of elaborate tissues (stems, leaves, bracts, sepals, petals) of Origanum vulgare have seldom been reported. Here, solid-phase microextraction-gas chromatography/mass spectrometry results showed that Origanum vulgare 'Hot and Spicy' (O. vulgare 'HS') was extremely rich in carvacrol and had the tissue dependence characteristic. Moreover, a full-length transcriptome analysis revealed carvacrol biosynthesis and its tissue-specific expression patterns of 'upstream' MVA/MEP pathway genes and 'downstream' modifier genes of TPSs, CYPs, and SDRs. Furthermore, the systems biology method of modular organization analysis was applied to cluster 16,341 differently expressed genes into nine modules and to identify significant carvacrol- and peltate glandular trichome-correlated modules. In terms of these positive and negative modules, weighted gene co-expression network analysis results showed that carvacrol biosynthetic pathway genes are highly co-expressed with TF genes, such as ZIPs and bHLHs, indicating their involvement in regulating the biosynthesis of carvacrol. Our findings shed light on the tissue specificity of VOC accumulation in O. vulgare 'HS' and identified key candidate genes for carvacrol biosynthesis, which would allow metabolic engineering and breeding of Origanum cultivars.

Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery.

The motor imagery brain-computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the MI-BCI system require lots of training samples, but the electroencephalogram (EEG) data of stroke patients is quite scarce. Therefore, the expansion of EEG data has become an important part of stroke clinical rehabilitation research. In this paper, a deep convolution generative adversarial network (DCGAN) model is proposed to generate artificial EEG data and further expand the scale of the stroke dataset. First, multichannel one-dimensional EEG data is converted into a two-dimensional EEG spectrogram using EEG2Image based on the modified S-transform. Then, DCGAN is used to artificially generate EEG data based on MI. Finally, the validity of the generated artificial EEG data is proved. This paper preliminarily indicates that generating artificial stroke data is a promising strategy, which contributes to the further development of stroke clinical rehabilitation.

Biocompatible Nanotomography of Tightly Focused Light.

Tightly focusing a spatially modulated laser beam lays the foundations for advanced optical techniques, such as a holographic optical tweezer and deterministic super-resolution imaging. Precisely mapping the subwavelength features of those highly confined fields is critical to improving the spatial resolution, especially in highly scattering biotissues. However, current techniques characterizing focal fields are mostly limited to conditions such as under a vacuum and on a glass surface. An optical probe with low cytotoxicity and resistance to autofluorescence is the key to achieving in vivo applications. Here, we use a newly emerging quantum reference beacon, the nitrogen-vacancy (NV) center in the nanodiamond, to characterize the focal field of the near-infrared (NIR) laser focus in Caenorhabditis elegans (C. elegans). This biocompatible background-free focal field mapping technique has the potential to optimize in vivo optical imaging and manipulation.