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Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.

Recently a positive treatment effect on disorders of consciousness (DOCs) with high-definition transcranial direct-current stimulation (HD-tDCS) has been reported; however, the neural modulation mechanisms of this treatment efficacy remain need further investigation. In order to better understand the processing of HD-tDCS interventions, a long-lasting HD-tDCS protocol was applied to 15 unresponsive wakefulness syndrome (UWS) patients and 20 minimally conscious state (MCS) patients in this study. Ten minutes of resting-state electroencephalograms (EEGs) were recorded from the patients, and the coma recovery scale-revised scores (CRS-Rs) were assessed for each patient from four time points (T0, T1, T2 and T3). Brain networks were constructed by calculating the EEG spectral connectivity using the debiased weighted phase lag index (dwPLI), and then quantified the network information transmission efficiency by graph theory. We found that there was an increasing trend in local and global information processing of beta and gamma bands in resting-state functional brain networks during the 14 days of HD-tDCS modulation for MCS patients. Furthermore, the increased functional connectivity not only occurred in the local brain area surrounding the stimulation position but was also present across more global brain areas. Our results suggest that long-lasting HD-tDCS on the precuneus may facilitate the information processing among neural populations in MCS patients.

Pendrin (SLC26A4) is an anion exchanger expressed in the apical membranes of selected epithelia. Pendrin ablation causes Pendred syndrome, a genetic disorder associated with sensorineural hearing loss, hypothyroid goiter, and reduced blood pressure. However its molecular structure has remained unknown, limiting our understanding of the structural basis of transport. Here, we determine the cryo-electron microscopy structures of mouse pendrin with symmetric and asymmetric homodimer conformations. The asymmetric homodimer consists of one inward-facing protomer and the other outward-facing protomer, representing coincident uptake and secretion- a unique state of pendrin as an electroneutral exchanger. The multiple conformations presented here provide an inverted alternate-access mechanism for anion exchange. The structural and functional data presented here disclose the properties of an anion exchange cleft and help understand the importance of disease-associated variants, which will shed light on the pendrin exchange mechanism. Pendrin SLC26A4 plays an important role for anion balance. Here, authors resolve cryo-EM structures of pendrin performing co-incident uptake and secretion of anions, providing a structural basis of this anion exchange mechanism.

Prevailing crowd counting approaches primarily rely on density map regression methods. Despite wonderful progress, significant scale variations and complex background interference within the same image remain challenges. To address these issues, in this paper we propose a novel DETR-based crowd counting framework called Crowd Counting DETR (CC-DETR), which aims to extend the state-of-the-art DETR object detection framework to the crowd counting task. In CC-DETR, a DETR-like encoder–decoder structure (Hybrid Context DETR, i.e., HCDETR) is proposed to tackle complex visual information by fusing features from hybrid semantic levels through a transformer. In addition, we design a Coordinate Dilated Convolution Module (CDCM) to effectively employ position-sensitive context information in different scales. Extensive experiments on three challenging crowd counting datasets (ShanghaiTech, UCF-QNRF, and NWPU) demonstrate that our model is effective and competitive when compared against SOTA crowd counting models.

Background and Aim: QingXiaoWuWei Decoction (QXWWD) is a traditional Chinese medicine that is commonly used in clinical settings to treat inflammatory and bacterial diseases. However, there is still a lot to learn about its molecular mechanism. A network pharmacology approach was applied to investigate the pharmacological mechanisms of QXWWD in inflammation treatment. Methods: The basic mechanisms involved in the anti-inflammatory and antibacterial potentials of QXWWD were identified using network pharmacology and molecular docking. The principal components of QXWWD were identified by the HPLC-Q-Exactive-MS method. The antibacterial bioactivity of QXWWD was further investigated using the Kirby-Bauer disc diffusion method and the determination of the minimum inhibitory concentration. The anti-inflammatory activity of QXWWD was evaluated using mice ear swelling test, RAW264.7 cell culture, and pro-inflammatory cytokines measurement. Skin irritation and HE staining were employed to evaluate the safety of QXWWD topical use and to depict the drug’s potential therapeutic function. The hub genes and signaling pathways associated with inflammatory and bacterial diseases were validated by western blot in addition to biochemical and pathological markers. Results: Our findings revealed that the ethanolic extract of QXWWD had a strong inhibitory effect against Staphylococcus aureus , Enterococcus faecalis , and Streptococcus pneumoniae. Meanwhile, QXWWD was potentially effective in suppressing ear swelling, elevated white blood cell counts, and the TNF-α, IL-1, and IL-6 levels. According to skin irritation, QXWWD was found to be safe when tested for topical application. The results of HE staining showed that the possible therapeutic role of QXWWD was related to the change in skin microstructure. Also, the network pharmacology, molecular docking as well as Q-Exactive-MS and HPLC analysis suggested that the synergistic effect of quercetin, luteolin and other ingredients could serve as main contributor of QXWWD for its anti-inflammatory and antibacterial activities. Moreover, the JUN, MAPK1, RELA, NFKBIA, MYC, and AKT1 were the potential identified key targets, and MAPK/PI3K/Akt was among the possibly involved signaling pathways in the anti-inflammatory and antibacterial activities of QXWWD. Conclusions: From a therapeutic standpoint, QXWWD may be a promising antibacterial and anti-inflammatory agent for the treatment of bacterial, acute, and chronic dermatitis.

Electroencephalography (EEG) can provide objective neural marker for assessing the level of consciousness of patients with disorders of consciousness (DoC), but current research mainly focuses on the EEG features of a single modality, such as the resting-state or the evoked state, which results in less than ideal assessment accuracy. To accurately assess the level of consciousness of DoC patients, we proposed a new method by combine with resting-state and auditory-evoked EEG. The EEG data of resting-state and auditory-evoked potential were collected from 157 DoC patients. Then, nonlinear dynamics feature (NDF) include spatiotemporal correlation entropy and neuromodulation intensity of multimodal EEG were extracted. Next, the multi-form feature selection algorithm (MFFS) was adopted to optimize the extracted EEG features. Finally, a diagnosis model was constructed using support vector machine (SVM). Among them, SC-Theta, SC-Alpha, NI-Alpha and ERP features were significantly ( < 0.05) correlated with the patient's level of consciousness, resulting in an average grouping accuracy of 92.4%. The proposed diagnostic model has demonstrated its distinctive advantages, showcasing remarkable effectiveness and reliability in accurately assessing consciousness states. This method holds promise for improving the reliability of clinical awareness assessments.

Bacterial concentration is an important indicator to measure the degree of water pollution. Realizing rapid and accurate quantification of bacterial concentration in water is of great significance for ensuring water safety and maintaining human health. This paper proposes a method for rapid determination of bacterial concentration by multiwavelength transmission spectroscopy combined with partial least squares regression. Escherichia coli (E. coli) is selected because it is a common indicator microorganism for assessing water pollution status, and it is easy to handle. First, we measure the transmission spectra for E. coli suspensions in the region from 200 to 900 nm and analyze the differences in the spectral characteristics at different concentrations; subsequently, considering that the concentration is affected by the instrument linearity and other factors, the sensitivity, correlation, and detection ability of the spectra at different wavelengths with the change of concentration are analyzed, and the optimal characteristic band is selected according to its wavelength variation characteristics; finally, the determination of E. coli concentrations are completed by using the optimal characteristic band spectra combined with partial least squares regression. We calculate the bacterial concentration, compared with the plate counting, the maximum relative error is 4.500%, the average relative error is 0.677%, respectively, which is less than 5%, and their accuracy and stability are all better than those calculated by the single-wavelength method. This study provides a reference for the rapid and accurate detection of bacterial concentration in water.

Although scalp EEG functional networks have been applied to the study of motor tasks using electroencephalography (EEG), the selection of a suitable reference electrode has not been sufficiently researched. To investigate the effects of the original reference (REF-CZ), the common average reference (CAR), and the reference electrode standardization technique (REST) on scalp EEG functional network analysis during hand movement tasks, EEGs of 17 right-handed subjects performing self-paced hand movements were collected, and scalp functional networks [coherence (COH), phase-locking value (PLV), phase lag index (PLI)] with different references were constructed. Compared with the REF-CZ reference, the networks with CAR and REST references exhibited more significant increases in connectivity during the left-/right-hand movement preparation (MP) and movement execution (ME) stages. The node degree of the channel near the reference electrode was significantly reduced by the REF-CZ reference. CAR and REST both decreased this reference effect, REST more so than CAR. We confirmed that the choice of reference would affect the analysis of the functional network during hand movement tasks, and the REST reference can greatly reduce the effects of the online recording reference on the analysis of EEG connectivity.Although scalp EEG functional networks have been applied to the study of motor tasks using electroencephalography (EEG), the selection of a suitable reference electrode has not been sufficiently researched. To investigate the effects of the original reference (REF-CZ), the common average reference (CAR), and the reference electrode standardization technique (REST) on scalp EEG functional network analysis during hand movement tasks, EEGs of 17 right-handed subjects performing self-paced hand movements were collected, and scalp functional networks [coherence (COH), phase-locking value (PLV), phase lag index (PLI)] with different references were constructed. Compared with the REF-CZ reference, the networks with CAR and REST references exhibited more significant increases in connectivity during the left-/right-hand movement preparation (MP) and movement execution (ME) stages. The node degree of the channel near the reference electrode was significantly reduced by the REF-CZ reference. CAR and REST both decreased this reference effect, REST more so than CAR. We confirmed that the choice of reference would affect the analysis of the functional network during hand movement tasks, and the REST reference can greatly reduce the effects of the online recording reference on the analysis of EEG connectivity.

Background The main causes of lung cancer are smoking, environmental pollution and genetic susceptibility. It is an indisputable fact that PAHs are related to lung cancer, and benzo(a) pyrene is a representative of PAHs. The purpose of the current investigation was to investigate the interaction between AhR and HIF-1 signaling pathways in A549 cells, which provide some experimental basis for scientists to find drugs that block AhR and HIF-1 signaling pathway to prevent and treat cancer. Methods This project adopts the CYP1A1 signaling pathways and the expression of CYP1B1 is expressed as a measure of AhR strength index. The expression of VEGF and CAIX volume as a measure of the strength of the signal path HIF-1 indicators. Through the construction of plasmid vector, fluorescence resonance energy transfer, real-time quantitative PCR, western blotting and immunoprecipitation, the interaction between AhR signaling pathway and HIF-1 signaling pathway was observed. Results BaP can enhance the binding ability of HIF-1α protein to HIF-1β/ARNT in a dose-dependent manner without CoCl 2 . However, the binding ability of AhR protein to HIF-1β/ARNT is inhibited by HIF-1α signaling pathway in a dose-dependent manner with CoCl 2 . Conclusion It is shown that activation of the AhR signaling pathway does not inhibit the HIF-1α signaling pathway, but activation of the HIF-1α signaling pathway inhibits the AhR signaling pathway.

This paper strives to improve the performance of video–text retrieval. To date, many algorithms have been proposed to facilitate the similarity measure of video–text retrieval from the single global semantic to multi-level semantics. However, these methods may suffer from the following limitations: (1) largely ignore the relationship semantic which results in semantic levels are insufficient; (2) it is incomplete to constrain the real-valued features of different modalities to be in the same space only through the feature distance measurement; (3) fail to handle the problem that the distributions of attribute labels in different semantic levels are heavily imbalanced. To overcome the above limitations, this paper proposes a novel multi-level cross-modal semantic alignment network (MCSAN) for video–text retrieval by jointly modeling video–text similarity on global, entity, action and relationship semantic levels in a unified deep model. Specifically, both video and text are first decomposed into global, entity, action and relationship semantic levels by carefully designing spatial–temporal semantic learning structures. Then, we utilize KLDivLoss and a cross-modal parameter-share attribute projection layer as statistical constraints to ensure that representations from different modalities in different semantic levels are projected into a common semantic space. In addition, a novel focal binary cross-entropy (FBCE) loss function is presented, which is the first effort to model the unbalanced attribute distribution problem for video–text retrieval. MCSAN is practically effective to take the advantage of the complementary information among four semantic levels. Extensive experiments on two challenging video–text retrieval datasets, namely, MSR-VTT and VATEX, show the viability of our method.