Oxygen-deficient AgIO3 for efficiently photodegrading organic contaminants under natural sunlight.

Defect engineering is regarded as an effective strategy to boost the photo-activity of photocatalysts for organic contaminants removal. In this work, abundant surface oxygen vacancies (Ov) are created on AgIO3 microsheets (AgIO3-OV) by a facile and controllable hydrogen chemical reduction approach. The introduction of surface Ov on AgIO3 broadens the photo-absorption region from ultraviolet to visible light, accelerates the photoinduced charges separation and migration, and also activates the formation of superoxide radicals (•O2-). The AgIO3-OV possesses an outstanding degradation rate constant of 0.035 min-1, for photocatalytic degrading methyl orange (MO) under illumination of natural sunlight with a light intensity is 50 mW/cm2, which is 7 and 3.5 times that of the pristine AgIO3 and C-AgIO3 (AgIO3 is calcined in air without generating Ov). In addition, the AgIO3-OV also exhibit considerable photoactivity for degrading other diverse organic contaminants, including azo dye (rhodamine B (RhB)), antibiotics (sulflsoxazole (SOX), norfloxacin (NOR), chlortetracycline hydrochloride (CTC), tetracycline hydrochloride (TC) and ofloxacin (OFX)), and even the mixture of organic contaminants (MO-RhB and CTC-OFX). After natural sunlight illumination for 50 min, 41.4% of total organic carbon (TOC) for MO-RhB mixed solution can be decreased over AgIO3-OV. In a broad range of solution pH from 3 to 11 or diverse water bodies of MO solution, AgIO3-OV exhibits attractive activity for decomposing MO. The MO photo-degradation process and mechanism over AgIO3-OV under natural sunlight irradiation has been systemically investigated and proposed. The toxicities of MO and its degradation intermediates over AgIO3-OV are compared using Toxicity Estimation Software (T.E.S.T.). Moreover, the non-toxicity of both AgIO3-OV catalyst and treated antibiotic solution (CTC-OFX mixture) are confirmed by E. coli DH5a cultivation test, supporting the feasibility of AgIO3-OV catalyst to treat organic contaminants in real water under natural sunlight illumination.

Therapeutic potential of flavopiridol in diabetic retinopathy: Targeting DDX58.

Diabetic retinopathy (DR), a common complication of diabetes, is characterized by inflammation and neovascularization, and is intricately regulated by the ubiquitin-proteasome system (UPS). Despite advancements, identifying ubiquitin-related genes and drugs specifically targeting DR remains a significant challenge. In this study, bioinformatics analyses and the Connectivity Map (CMAP) database were utilized to explore the therapeutic potential of genes and drugs for DR. Through these methodologies, flavopiridol was identified as a promising therapeutic candidate. To evaluate flavopiridol's therapeutic potential in DR, an in vitro model using Human Umbilical Vein Endothelial Cells (HUVECs) induced by high glucose (HG) conditions was established. Additionally, in vivo models using mice with streptozotocin (STZ)-induced DR and oxygen-induced retinopathy (OIR) were employed. The current study reveals that flavopiridol possesses robust anti-inflammatory and anti-neovascularization properties. To further elucidate the molecular mechanisms of flavopiridol, experimental validation and molecular docking techniques were employed. These efforts identified DDX58 as a predictive target for flavopiridol. Notably, our research demonstrated that flavopiridol modulates the DDX58/NLRP3 signaling pathway, thereby exerting its therapeutic effects in suppressing inflammation and neovascularization in DR. This study unveils groundbreaking therapeutic agents and innovative targets for DR, and establishes a progressive theoretical framework for the application of ubiquitin-related therapies in DR.

Temporally Local Weighting-Based Phase-Locked Time-Shift Data Augmentation Method for Fast-Calibration SSVEP-BCI.

Various training-based spatial filtering methods have been proposed to decode steady-state visual evoked potentials (SSVEPs) efficiently. However, these methods require extensive calibration data to obtain valid spatial filters and temporal templates. The time-consuming data collection and calibration process would reduce the practicality of SSVEP-based brain-computer interfaces (BCIs). Therefore, we propose a temporally local weighting-based phase-locked time-shift (TLW-PLTS) data augmentation method to augment training data for calculating valid spatial filters and temporal templates. In this method, the sliding window strategy using the SSVEP response period as a time-shift step is to generate the augmented data, and the time filter which maximises the temporally local covariance between the original template signal and the sine-cosine reference signal is used to suppress the temporal noise in the augmented data. For the performance evaluation, the TLW-PLTS method was incorporated with state-of-the-art training-based spatial filtering methods to calculate classification accuracies and information transfer rates (ITRs) using three SSVEP datasets. Compared with state-of-the-art training-based spatial filtering methods and other data augmentation methods, the proposed TLW-PLTS method demonstrates superior decoding performance with fewer calibration data, which is promising for the development of fast-calibration BCIs.

Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition.

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) Multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) An improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) Invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.

Improved Charge Separation and CO2 Affinity of In2O3 by K Doping with Accompanying Oxygen Vacancies for Boosted CO2 Photoreduction.

The CO2 photocatalytic conversion efficiency of the semiconductor photocatalyst is always inhibited by the sluggish charge transfer and undesirable CO2 affinity. In this work, we prepare a series of K-doped In2O3 catalysts with concomitant oxygen vacancies (OV) via a hydrothermal method, followed by a low-temperature sintering treatment. Owing to the synergistic effect of K doping and OV, the charge separation and CO2 affinity of In2O3 are synchronously promoted. Particularly, when P/P0 = 0.010, at room temperature, the CO2 adsorption capacity of the optimal K-doped In2O3 (KIO-3) is 2336 cm3·g-1, reaching about 6000 times higher than that of In2O3 (0.39 cm3·g-1). As a result, in the absence of a cocatalyst or sacrificial agent, KIO-3 exhibits a CO evolution rate of 3.97 µmol·g-1·h-1 in a gas-solid reaction system, which is 7.6 times that of pristine In2O3 (0.52 µmol·g-1·h-1). This study provides a novel approach to the design and development of efficient photocatalysts for CO2 conversion by element doping.

Alterations in hippocampus-centered morphological features and function of the progression from normal cognition to mild cognitive impairment.

Mild cognitive impairment (MCI) is a significant precursor to dementia, highlighting the critical need for early identification of individuals at high risk of MCI to prevent cognitive decline. The study aimed to investigate the changes in brain structure and function before the onset of MCI. This study enrolled 19 older adults with progressive normal cognition (pNC) to MCI and 19 older adults with stable normal cognition (sNC). The gray matter (GM) volume and functional connectivity (FC) were estimated via magnetic resonance imaging during their normal cognition state 3 years prior. Additionally, spatial associations between FC maps and neurochemical profiles were examined using JuSpace. Compared to the sNC group, the pNC group showed decreased volume in the left hippocampus and left amygdala. The significantly positive correlation was observed between the GM volume of the left hippocampus and the MMSE scores after 3 years in pNC group. Besides, it showed that the pNC group had increased FC between the left hippocampus and the anterior-posterior cingulate gyrus, which was significantly correlated with the spatial distribution of dopamine D2 and noradrenaline transporter. Taken together, the study identified the abnormal brain characteristics before the onset of MCI, which might provide insight into clinical research.

Atypical characteristic changes of surface morphology and structural covariance network in developmental dyslexia.

BACKGROUND: Developmental dyslexia (DD) is a neurodevelopmental disorder that is characterized by difficulties with all aspects of information acquisition in the written word, including slow and inaccurate word recognition. The neural basis behind DD has not been fully elucidated. METHOD: The study included 22 typically developing (TD) children, 16 children with isolated spelling disorder (SpD), and 20 children with DD. The cortical thickness, folding index, and mean curvature of Broca's area, including the triangular part of the left inferior frontal gyrus (IFGtriang) and the opercular part of the left inferior frontal gyrus, were assessed to explore the differences of surface morphology among the TD, SpD, and DD groups. Furthermore, the structural covariance network (SCN) of the triangular part of the left inferior frontal gyrus was analyzed to explore the changes of structural connectivity in the SpD and DD groups. RESULTS: The DD group showed higher curvature and cortical folding of the left IFGtriang than the TD group and SpD group. In addition, compared with the TD group and the SpD group, the structural connectivity between the left IFGtriang and the left middle-frontal gyrus and the right mid-orbital frontal gyrus was increased in the DD group, and the structural connectivity between the left IFGtriang and the right precuneus and anterior cingulate was decreased in the DD group. CONCLUSION: DD had atypical structural connectivity in brain regions related to visual attention, memory and which might impact the information input and integration needed for reading and spelling.

Automatic brain extraction for rat magnetic resonance imaging data using U2-Net.

Objective.Skull stripping is a key step in the pre-processing of rodent brain magnetic resonance images (MRI). This study aimed to develop a new skull stripping method via U2-Net, a neural network model based on deep learning method, for rat brain MRI.Approach.In this study, 599 rats were enrolled and U2-Net was applied to segment MRI images of rat brain. The intercranial tissue of each rat was manually labeled. 476 rats (approximate 80%) were used for training set while 123 rats (approximate 20%) were used to test the performance of the trained U2-Net model. For evaluation, the segmentation result by the U2-Net model is compared with the manual label, and traditional segment methods. Quantitative evaluation, including Dice coefficient, Jaccard coefficient, Sensitivity, Specificity, Pixel accuracy, Hausdorff coefficient, True positive rate, False positive rate and the volumes of whole brain, were calculated to compare the segmentation results among different models.Main results.The U2-Net model was performed better than the software of RATS and BrainSuite, in which the quantitative values of training U2-Net model were 0.9907 ± 0.0016 (Dice coefficient), 0.9816 ± 0.0032 (Jaccard coefficient), 0.9912 ± 0.0020 (Sensitivity), 0.9989 ± 0.0002 (Specificity), 0.9982 ± 0.0003 (Pixel accuracy), 5.2390 ± 2.5334 (Hausdorff coefficient), 0.9902 ± 0.0025 (True positive rate), 0.0009 ± 0.0002(False positive rate) respectively.Significance.This study provides a new method that achieves reliable performance in rat brain skull stripping of MRI images, which could contribute to the processing of rat brain MRI.

Cross-Subject Transfer Method Based on Domain Generalization for Facilitating Calibration of SSVEP-Based BCIs.

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the time-consuming calibration session would increase the visual fatigue of subjects and reduce the usability of the BCI system. The key idea of this study is to propose a cross-subject transfer method based on domain generalization, which transfers the domain-invariant spatial filters and templates learned from source subjects to the target subject with no access to the EEG data from the target subject. The transferred spatial filters and templates are obtained by maximizing the intra- and inter-subject correlations using the SSVEP data corresponding to the target and its neighboring stimuli. For SSVEP detection of the target subject, four types of correlation coefficients are calculated to construct the feature vector. Experimental results estimated with three SSVEP datasets show that the proposed cross-subject transfer method improves the SSVEP detection performance compared to state-of-art methods. The satisfactory results demonstrate that the proposed method provides an effective transfer learning strategy requiring no tedious data collection process for new users, holding the potential of promoting practical applications of SSVEP-based BCI.

The current status and trend of the functional magnetic resonance combined with stimulation in animals.

As a non-radiative, non-invasive imaging technique, functional magnetic resonance imaging (fMRI) has excellent effects on studying the activation of blood oxygen levels and functional connectivity of the brain in human and animal models. Compared with resting-state fMRI, fMRI combined with stimulation could be used to assess the activation of specific brain regions and the connectivity of specific pathways and achieve better signal capture with a clear purpose and more significant results. Various fMRI methods and specific stimulation paradigms have been proposed to investigate brain activation in a specific state, such as electrical, mechanical, visual, olfactory, and direct brain stimulation. In this review, the studies on animal brain activation using fMRI combined with different stimulation methods were retrieved. The instruments, experimental parameters, anesthesia, and animal models in different stimulation conditions were summarized. The findings would provide a reference for studies on estimating specific brain activation using fMRI combined with stimulation.

Latency Aligning Task-Related Component Analysis Using Wave Propagation for Enhancing SSVEP-Based BCIs.

Due to the high robustness to artifacts, steady-state visual evoked potential (SSVEP) has been widely applied to construct high-speed brain-computer interfaces (BCIs). Thus far, many spatial filtering methods have been proposed to enhance the target identification performance for SSVEP-based BCIs, and task-related component analysis (TRCA) is among the most effective ones. In this paper, we further extend TRCA and propose a new method called Latency Aligning TRCA (LA-TRCA), which aligns visual latencies on channels to obtain accurate phase information from task-related signals. Based on the SSVEP wave propagation theory, SSVEP spreads from posterior occipital areas over the cortex with a fixed phase velocity. Via estimation of the phase velocity using phase shifts of channels, the visual latencies on different channels can be determined for inter-channel alignment. TRCA is then applied to aligned data epochs for target recognition. For the validation purpose, the classification performance comparison between the proposed LA-TRCA and TRCA-based expansions were performed on two different SSVEP datasets. The experimental results illustrated that the proposed LA-TRCA method outperformed the other TRCA-based expansions, which thus demonstrated the effectiveness of the proposed approach for enhancing the SSVEP detection performance.

Auricular acupuncture for shoulder pain: A protocol for systematic review and meta-analysis.

BACKGROUND: Shoulder pain is a common problem in outpatient medical practice. Recent studies show that acupuncture has therapeutic effect on releasing symptoms of shoulder pain. The aim of this systematic review and meta-analysis is to access the efficacy and safety of auricular acupuncture for shoulder pain. METHODS: Eight databases will be searched for randomized controlled trials of auricular acupuncture in the treatment of shoulder pain with retrieval time up to September 2020, including PubMed, Embase, The Cochrane Library, Web of science, CNKI, VIP, CBM, and Wangfang Data databases. We will evaluate the methodological quality of the included studies by using Cochrane Risk of Bias tool and conduct data analysis with Review Manager Software. RESULTS: The results of this study will be disseminated through a peer-reviewed journal publication. CONCLUSION: The systematic review will provide up-to-date evidence for the efficacy and safety of auricular acupuncture in treating shoulder pain. PROSPERO REGISTRATION NUMBER: CRD 42021238797.

Effects of the antibiotic component on in-vitro bacterial killing, physico-chemical properties, aerosolization and dissolution of a ternary-combinational inhalation powder formulation of antibiotics for pan-drug resistant Gram-negative lung infections.

Combinational antibiotic formulations have emerged as an important strategy to combat antibiotic resistance. The main objective of this study was to examine effects of individual components on the antimicrobial activity, physico-chemical properties, aerosolization and dissolution of powder aerosol formulations when three synergistic drugs were co-spray dried. A ternary dry powder formulation consisting of meropenem (75.5 %w/w), colistin (15.1 %w/w) and rifampicin (9.4 %w/w) at the selected ratio was produced by spray drying. The ternary formulation was characterized for in-vitro antibacterial activity, physico-chemical properties, surface composition, aerosol performance and dissolution. All of the formulations demonstrated excellent aerosolization behavior achieving a fine particle fraction of >70%, which was substantially higher than those for the Meropenem-SD and Colistin-Meropenem formulations. The results indicated that rifampicin controlled the surface morphology of the ternary and binary combination formulations resulting in the formation of highly corrugated particles. Advanced characterization of surface composition by XPS supported the hypothesis that rifampicin was enriched on the surface of the combination powder formulations. All spray-dried formulations were amorphous and absorbed substantial amount of water at the elevated humidity. Storage at the elevated humidity caused a substantial decline in aerosolization performance for the Meropenem-SD and Colistin-Meropenem, which was attributed to increased inter-particulate capillary forces or particle fusion. In contrast, the ternary combination and binary Meropenem-Rifampicin formulations showed no change in aerosol performance at the elevated storage humidity conditions; attributable to the enriched hydrophobicity of rifampicin on the particle surface that acted as a barrier against moisture condensation and particle fusion. Interestingly, in the ternary formulation rifampicin enrichment on the surface did not interfere with the dissolution of other two components (i.e. meropenem and colistin). Our study provides an insight on the impact of each component on the performance of co-spray dried combinational formulations.