[Design Method Analysis and Performance Comparison of Wall Filter for Ultrasound Color Flow Imaging].

The successful suppression of clutter arising from stationary or slowly moving tissue is one of the key issues in medical ultrasound color blood imaging. Remaining clutter may cause bias in the mean blood frequency estimation and results in a potentially misleading description of blood-flow. In this paper, based on the principle of general wall-filter, the design process of three classes of filters, infinitely impulse response with projection initialization (Prj-IIR), polynomials regression (Pol-Reg), and eigen-based filters are previewed and analyzed. The performance of the filters was assessed by calculating the bias and variance of a mean blood velocity using a standard autocorrelation estimator. Simulation results show that the performance of Pol-Reg filter is similar to Prj-IIR filters. Both of them can offer accurate estimation of mean blood flow speed under steady clutter conditions, and the clutter rejection ability can be enhanced by increasing the ensemble size of Doppler vector. Eigen-based filters can effectively remove the non-stationary clutter component, and further improve the estimation accuracy for low speed blood flow signals. There is also no significant increase in computation complexity for eigen-based filters when the ensemble size is less than 10.

Effects of particle composition and environmental parameters on catalytic hydrodechlorination of trichloroethylene by nanoscale bimetallic Ni-Fe.

Catalytic nickel was successfully incorporated into nanoscale iron to enhance its dechlorination efficiency for trichloroethylene (TCE), one of the most commonly detected chlorinated organic compounds in groundwater. Ethane was the predominant product. The greatest dechlorination efficiency was achieved at 22 molar percent of nickel. This nanoscale Ni-Fe is poorly ordered and inhomogeneous; iron dissolution occurred whereas nickel was relatively stable during the 24-hr reaction. The morphological characterization provided significant new insights on the mechanism of catalytic hydrodechlorination by bimetallic nanoparticles. TCE degradation and ethane production rates were greatly affected by environmental parameters such as solution pH, temperature and common groundwater ions. Both rate constants decreased and then increased over the pH range of 6.5 to 8.0, with the minimum value occurring at pH 7.5. TCE degradation rate constant showed an increasing trend over the temperature range of 10 to 25°C. However, ethane production rate constant increased and then decreased over the range, with the maximum value occurring at 20°C. Most salts in the solution appeared to enhance the reaction in the first half hour but overall they displayed an inhibitory effect. Combined ions showed a similar effect as individual salts.

CT-Net: an interpretable CNN-Transformer fusion network for fNIRS classification.

Functional near-infrared spectroscopy (fNIRS), an optical neuroimaging technique, has been widely used in the field of brain activity recognition and brain-computer interface. Existing works have proposed deep learning-based algorithms for the fNIRS classification problem. In this paper, a novel approach based on convolutional neural network and Transformer, named CT-Net, is established to guide the deep modeling for the classification of mental arithmetic (MA) tasks. We explore the effect of data representations, and design a temporal-level combination of two raw chromophore signals to improve the data utilization and enrich the feature learning of the model. We evaluate our model on two open-access datasets and achieve the classification accuracy of 98.05% and 77.61%, respectively. Moreover, we explain our model by the gradient-weighted class activation mapping, which presents a high consistent between the contributing value of features learned by the model and the mapping of brain activity in the MA task. The results suggest the feasibility and interpretability of CT-Net for decoding MA tasks.

Bifunctional drug delivery system with carbonic anhydrase IX targeting and glutathione-responsivity driven by host-guest amphiphiles for effective tumor therapy.

It remains a critical issue to deliver anticancer drugs to tumor tissues and reducing the toxic effects on normal tissues. The drug delivery system (DDS) based on self-assembly provides a multi-functional way for drug delivery. In this work, a supramolecular host (L-CD) with targeting function based on a ß-cyclodextrin (ß-CD) backbone was synthesized with carbonic anhydrase IX (CAIX) overexpressed on tumor cells as a target, and the methotrexate prodrug (MTX-SS-Ad) modified by adamantane and disulfide bond was prepared to be used as the guest. The amphiphilic complex was prepared between L-CD and MTX-SS-Ad through host-guest interactions and could further self-assemble into supramolecular nanoparticles (SNPs) with active targeting and stimulus release functions. The interaction between host and guest was investigated by UV, NMR, IR, XRD and TGA. The characteristic of SNPs was observed by DLS and TEM. Throng the study of molecular docking, in vitro inhibition, cell uptake experiments, and western blotting, SNPs have showed CAIX inhibitory effects both inside and outside the cells. The in vitro release experiments indicated that SNPs can undergo disintegration and release drugs under acidic and GSH conditions. Moreover, SNP can effectively inhibit the proliferation of cancer cells without generating additional toxic side effects on normal cells. So, we provide a strategy of bifunctional drug delivery system with targeting and glutathione-responsivity for effective tumor therapy.

Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention.

The diagnosis and management of sleep problems depend heavily on sleep staging. For autonomous sleep staging, many data-driven deep learning models have been presented by trying to construct a large-labeled auxiliary sleep dataset and test it by electroencephalograms on different subjects. These approaches suffer a significant setback cause it assumes the training and test data come from the same or similar distribution. However, this is almost impossible in scenario cross-dataset due to inherent domain shift between domains. Unsupervised domain adaption was recently created to address the domain shift issue. However, only a few customized UDA solutions for sleep staging due to two limitations in previous UDA methods. First, the domain classifier does not consider boundaries between classes. Second, they depend on a shared model to align the domain that could miss the information of domains when extracting features. Given those restrictions, we present a novel UDA approach that combines category decision boundaries and domain discriminator to align the distributions of source and target domains. Also, to keep the domain-specific features, we create an unshared attention method. In addition, we investigated effective data augmentation in cross-dataset sleep scenarios. The experimental results on three datasets validate the efficacy of our approach and show that the proposed method is superior to state-of-the-art UDA methods on accuracy and MF1-Score.

CSF-GTNet: A novel multi-dimensional feature fusion network based on Convnext-GeLU-BiLSTM for EEG-signals-enabled fatigue driving detection.

Electroencephalography(EEG) signal has been recognized as an effective fatigue detection method, which can intuitively reflect the drivers' mental state. However, the research on multi-dimensional features in existing work could be much better. The instability and complexity of EEG signals will increase the difficulty of extracting data features. More importantly, most current work only treats deep learning models as classifiers. They ignored the features of different subjects learned by the model. Aiming at the above problems, this paper proposes a novel multi-dimensional feature fusion network, CSF-GTNet, based on time and space-frequency domains for fatigue detection. Specifically, it comprises Gaussian Time Domain Network (GTNet) and Pure Convolutional Spatial Frequency Domain Network (CSFNet). The experimental results show that the proposed method effectively distinguishes between alert and fatigue states. The accuracy rates are 85.16% and 81.48% on the self-made and SEED-VIG datasets, respectively, which are higher than the state-of-the-art methods. Moreover, we analyze the contribution of each brain region for fatigue detection through the brain topology map. In addition, we explore the changing trend of each frequency band and the significance between different subjects in the alert state and fatigue state through the heat map. Our research can provide new ideas in brain fatigue research and play a specific role in promoting the development of this field. The code is available on https://github.com/liio123/EEG_Fatigue.

A Zero-Padding Frequency Domain Convolutional Neural Network for SSVEP Classification.

The brain-computer interface (BCI) of steady-state visual evoked potential (SSVEP) is one of the fundamental ways of human-computer communication. The main challenge is that there may be a nonlinear relationship between different SSVEP in other states. For improving the performance of SSVEP BCI, a novel CNN algorithm model is proposed in this study. Based on the discrete Fourier transform to calculate the signal's power spectral density (PSD), we perform zero-padding in the signal's time domain to improve its performance on the PSD and make it more refined. In this way, the frequency point interval in the PSD of the SSVEP is consistent with the minimum gap between the stimulation frequency. Combining the nonlinear transformation capabilities of CNN in deep learning, a zero-padding frequency domain convolutional neural network (ZPFDCNN) model is proposed. Extensive experiments based on the SSVEP dataset validate the effectiveness of our method. The study verifies that the proposed ZPFDCNN method can improve the effectiveness of the SSVEP-based high-speed BCI ITR. It has massive potential in the application of BCI.

Onlays/partial crowns versus full crowns in restoring posterior teeth: a systematic review and meta-analysis.

BACKGROUND: Tooth-colored onlays and partial crowns for posterior teeth have been used increasingly in clinics. However, whether onlays/partial crowns could perform as well as full crowns in the posterior region was still not evaluated thoroughly. METHODS: A literature search was conducted without language restrictions in Pubmed, Embase, Cochrane Central Register of Controlled Trial and Web of science until September 2021. RCTs, prospective and retrospective observational studies with a mean follow-up of 1 year were selected. Cochrane Collaboration's tool was adopted for quality assessment of the RCT. The quality of observational studies was evaluated following Newcastle-Ottawa scale. The random-effects and fixed-effects model were employed for meta-analysis. RESULTS: Four thousand two hundred fifty-seven articles were initially searched. Finally, one RCT was identified for quality assessment and five observational studies for qualitative synthesis and meta-analysis. The RCT was of unclear risk of bias while five observational studies were evaluated as low risk. The meta-analysis indicated no statistically significant difference in the survival between onlays/partial crowns and full crowns after 1 year (OR = 0.55, 95% CI: 0.02-18.08; I2 = 57.0%; P = 0.127) and 3 years (OR = 0.65, 95% CI: 0.20-2.17; I2 = 0.0%; P = 0.747). For the success, onlays/partial crowns performed as well as crowns (OR = 0.58, 95% CI: 0.20-1.72; I2 = 0.0%; P = 0.881) at 3 years. No significant difference of crown fracture existed between the two methods (RD = 0.00, 95% CI: - 0.03-0.03; I2 = 0.0%; P = 0.972). CONCLUSIONS: Tooth-colored onlays/partial crowns performed as excellently as full crowns in posterior region in a short-term period. The conclusions should be further consolidated by RCTs with long-term follow-up.