Divergent synthesis of pyrrolizine derivatives through C-H bond functionalization of pyrroles.

Presented herein is the synthesis of diversely functionalized pyrrolizines from the reaction of N-alkoxycarbamoyl pyrroles with CF3-ynones. The formation of the product is based on a C-H bond activation-initiated cascade process including N-alkoxycarbamoyl group-directed alkenylation of the pyrrole scaffold followed by simultaneous intramolecular nucleophilic addition along with cleavage and transfer of the directing group. By taking advantage of the rich chemistry of the transferred alkoxycarbamoyl moiety, the products could be transformed into a series of structurally and biologically interesting pyrrolizine derivatives. To our knowledge, this is the first example in which the N-alkoxycarbamoyl unit acted as a transferable and transformable directing group for the divergent synthesis of pyrrolizines.

Synthesis of CF3-Substituted N-Heterocyclic Compounds Based on C-H Activation-Initiated Formal [2 + 3] Annulation Featuring with a Latent Nucleophilic Site.

Presented herein is a novel synthesis of CF3-substituted pyrrolo[1,2-a]indole derivatives based on the cascade reactions of N-alkoxycarbamoyl indoles with CF3-ynones. Mechanistically, the formation of a product involves a tandem process initiated by Rh(III)-catalyzed and N-alkoxycarbamoyl group-directed regioselective C2-H alkenylation of the indole scaffold followed by in situ removal of the directing group and intramolecular N-nucleophilic addition/annulation under one set of reaction conditions. To our knowledge, this is the first example in which a N-alkoxycarbamoyl unit initially acts as a directing group for C2-H functionalization of the indole scaffold and is then removed to provide the required reactive NH-moiety for subsequent intramolecular condensation. Moreover, the products thus obtained could be conveniently transformed into structurally and biologically attractive cycloheptenone fused indole derivatives through an acid-promoted cascade transformation. In addition, studies on the activity of selected products against human cancer cell lines demonstrated their potential as lead compounds for the development of novel anticancer drugs.

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.

The effect of histo-blood group ABO system transferase (BGAT) on pregnancy related outcomes：A Mendelian randomization study.

Protein level of Histo-Blood Group ABO System Transferase (BGAT) has been reported to be associated with cardiometabolic diseases. But its effect on pregnancy related outcomes still remains unclear. Here we conducted a two-sample Mendelian randomization (MR) study to ascertain the putative causal roles of protein levels of BGAT in pregnancy related outcomes. Cis-acting protein quantitative trait loci (pQTLs) robustly associated with protein level of BGAT (P < 5 ×10-8) were used as instruments to proxy the BGAT protein level (N = 35,559, data from deCODE), with two additional pQTL datasets from Fenland (N = 10,708) and INTERVAL (N = 3301) used as validation exposures. Ten pregnancy related diseases and complications were selected as outcomes. We observed that a higher protein level of BGAT showed a putative causal effect on venous complications and haemorrhoids in pregnancy (VH) (odds ratio [OR]=1.19, 95% confidence interval [95% CI]=1.12-1.27, colocalization probability=91%), which was validated by using pQTLs from Fenland and INTERVAL. The Mendelian randomization results further showed effects of the BGAT protein on gestational hypertension (GH) (OR=0.97, 95% CI=0.96-0.99), despite little colocalization evidence to support it. Sensitivity analyses, including proteome-wide Mendelian randomization of the cis-acting BGAT pQTLs, showed little evidence of horizontal pleiotropy. Correctively, our study prioritised BGAT as a putative causal protein for venous complications and haemorrhoids in pregnancy. Future epidemiology and clinical studies are needed to investigate whether BGAT can be considered as a drug target to prevent adverse pregnancy outcomes.

SFT-Net: A Network for Detecting Fatigue From EEG Signals by Combining 4D Feature Flow and Attention Mechanism.

Fatigued driving is a leading cause of traffic accidents, and accurately predicting driver fatigue can significantly reduce their occurrence. However, modern fatigue detection models based on neural networks often face challenges such as poor interpretability and insufficient input feature dimensions. This paper proposes a novel Spatial-Frequency-Temporal Network (SFT-Net) method for detecting driver fatigue using electroencephalogram (EEG) data. Our approach integrates EEG signals' spatial, frequency, and temporal information to improve recognition performance. We transform the differential entropy of five frequency bands of EEG signals into a 4D feature tensor to preserve these three types of information. An attention module is then used to recalibrate the spatial and frequency information of each input 4D feature tensor time slice. The output of this module is fed into a depthwise separable convolution (DSC) module, which extracts spatial and frequency features after attention fusion. Finally, long short-term memory (LSTM) is used to extract the temporal dependence of the sequence, and the final features are output through a linear layer. We validate the effectiveness of our model on the SEED-VIG dataset, and experimental results demonstrate that SFT-Net outperforms other popular models for EEG fatigue detection. Interpretability analysis supports the claim that our model has a certain level of interpretability. Our work addresses the challenge of detecting driver fatigue from EEG data and highlights the importance of integrating spatial, frequency, and temporal information. Codes are available at https://github.com/wangkejie97/SFT-Net.

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 deep learning network based on CNN and sliding window LSTM for spike sorting.

Spike sorting plays an essential role to obtain electrophysiological activity of single neuron in the fields of neural signal decoding. With the development of electrode array, large numbers of spikes are recorded simultaneously, which rises the need for accurate automatic and generalization algorithms. Hence, this paper proposes a spike sorting model with convolutional neural network (CNN) and a spike classification model with combination of CNN and Long-Short Term Memory (LSTM). The recall rate of our detector could reach 94.40% in low noise level dataset. Although the recall declined with the increasing noise level, our model still presented higher feasibility and better robustness than other models. In addition, the results of our classification model presented an accuracy of greater than 99% in simulated data and an average accuracy of about 95% in experimental data, suggesting our classifier outperforms the current "WMsorting" and other deep learning models. Moreover, the performance of our whole algorithm was evaluated through simulated data and the results shows that the accuracy of spike sorting reached about 97%. It is noteworthy to say that, this proposed algorithm could be used to achieve accurate and robust automated spike detection and spike classification.

T-A-MFFNet: Multi-feature fusion network for EEG analysis and driving fatigue detection based on time domain network and attention network.

Driving fatigue detection based on EEG signals is a research hotspot in applying brain-computer interfaces. EEG signal is complex, unstable, and nonlinear. Most existing methods rarely analyze the data characteristics from multiple dimensions, so it takes work to analyze the data comprehensively. To analyze EEG signals more comprehensively, this paper evaluates a feature extraction strategy of EEG data based on differential entropy (DE). This method combines the characteristics of different frequency bands, extracts the frequency domain characteristics of EEG, and retains the spatial information between channels. This paper proposes a multi-feature fusion network (T-A-MFFNet) based on the time domain and attention network. The model is composed of a time domain network (TNet), channel attention network (CANet), spatial attention network (SANet), and multi-feature fusion network(MFFNet) based on a squeeze network. T-A-MFFNet aims to learn more valuable features from the input data to achieve good classification results. Specifically, the TNet network extracts high-level time series information from EEG data. CANet and SANet are used to fuse channel and spatial features. They use MFFNet to merge multi-dimensional features and realize classification. The validity of the model is verified on the SEED-VIG dataset. The experimental results show that the accuracy of the proposed method reaches 85.65 %, which is superior to the current popular model. The proposed method can learn more valuable information from EEG signals to improve the ability to identify fatigue status and promote the development of the research field of driving fatigue detection based on EEG signals.

Motor imagery classification method based on relative wavelet packet entropy brain network and improved lasso.

Motor imagery (MI) electroencephalogram (EEG) signals have a low signal-to-noise ratio, which brings challenges in feature extraction and feature selection with high classification accuracy. In this study, we proposed an approach that combined an improved lasso with relief-f to extract the wavelet packet entropy features and the topological features of the brain function network. For signal denoising and channel filtering, raw MI EEG was filtered based on an R2 map, and then the wavelet soft threshold and one-to-one multi-class score common spatial pattern algorithms were used. Subsequently, the relative wavelet packet entropy and corresponding topological features of the brain network were extracted. After feature fusion, mutcorLasso and the relief-f method were applied for feature selection, followed by three classifiers and an ensemble classifier, respectively. The experiments were conducted on two public EEG datasets (BCI Competition III dataset IIIa and BCI Competition IV dataset IIa) to verify this proposed method. The results showed that the brain network topology features and feature selection methods can retain the information of EEG more effectively and reduce the computational complexity, and the average classification accuracy for both public datasets was above 90%; hence, this algorithms is suitable in MI-BCI and has potential applications in rehabilitation and other fields.

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.

Automatic Sleep Staging Algorithm Based on Time Attention Mechanism.

The most important part of sleep quality assessment is the automatic classification of sleep stages. Sleep staging is helpful in the diagnosis of sleep-related diseases. This study proposes an automatic sleep staging algorithm based on the time attention mechanism. Time-frequency and non-linear features are extracted from the physiological signals of six channels and then normalized. The time attention mechanism combined with the two-way bi-directional gated recurrent unit (GRU) was used to reduce computing resources and time costs, and the conditional random field (CRF) was used to obtain information between tags. After five-fold cross-validation on the Sleep-EDF dataset, the values of accuracy, WF1, and Kappa were 0.9218, 0.9177, and 0.8751, respectively. After five-fold cross-validation on the our own dataset, the values of accuracy, WF1, and Kappa were 0.9006, 0.8991, and 0.8664, respectively, which is better than the result of the latest algorithm. In the study of sleep staging, the recognition rate of the N1 stage was low, and the imbalance has always been a problem. Therefore, this study introduces a type of balancing strategy. By adopting the proposed strategy, SEN-N1 and ACC of 0.7 and 0.86, respectively, can be achieved. The experimental results show that compared to the latest method, the proposed model can achieve significantly better performance and significantly improve the recognition rate of the N1 period. The performance comparison of different channels shows that even when the EEG channel was not used, considerable accuracy can be obtained.

Enhancement of Event-Related Desynchronization in Motor Imagery Based on Transcranial Electrical Stimulation.

Due to the individual differences controlling brain-computer interfaces (BCIs), the applicability and accuracy of BCIs based on motor imagery (MI-BCIs) are limited. To improve the performance of BCIs, this article examined the effect of transcranial electrical stimulation (tES) on brain activity during MI. This article designed an experimental paradigm that combines tES and MI and examined the effects of tES based on the measurements of electroencephalogram (EEG) features in MI processing, including the power spectral density (PSD) and dynamic event-related desynchronization (ERD). Finally, we investigated the effect of tES on the accuracy of MI classification using linear discriminant analysis (LDA). The results showed that the ERD of the µ and ß rhythms in the left-hand MI task was enhanced after electrical stimulation with a significant effect in the tDCS group. The average classification accuracy of the transcranial alternating current stimulation (tACS) group and transcranial direct current stimulation (tDCS) group (88.19% and 89.93% respectively) were improved significantly compared to the pre-and pseudo stimulation groups. These findings indicated that tES can improve the performance and applicability of BCI and that tDCS was a potential approach in regulating brain activity and enhancing valid features during noninvasive MI-BCI processing.

Multi-modal nonlinear optical and thermal imaging platform for label-free characterization of biological tissue.

The ability to characterize the combined structural, functional, and thermal properties of biophysically dynamic samples is needed to address critical questions related to tissue structure, physiological dynamics, and disease progression. Towards this, we have developed an imaging platform that enables multiple nonlinear imaging modalities to be combined with thermal imaging on a common sample. Here we demonstrate label-free multimodal imaging of live cells, excised tissues, and live rodent brain models. While potential applications of this technology are wide-ranging, we expect it to be especially useful in addressing biomedical research questions aimed at the biomolecular and biophysical properties of tissue and their physiology.

DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks.

Chromosome enumeration is an essential but tedious procedure in karyotyping analysis. To automate the enumeration process, we develop a chromosome enumeration framework, DeepACEv2, based on the region based object detection scheme. The framework is developed following three steps. Firstly, we take the classical ResNet-101 as the backbone and attach the Feature Pyramid Network (FPN) to the backbone. The FPN takes full advantage of the multiple level features, and we only output the level of feature map that most of the chromosomes are assigned to. Secondly, we enhance the region proposal network's ability by adding a newly proposed Hard Negative Anchors Sampling to extract unapparent but essential information about highly confusing partial chromosomes. Next, to alleviate serious occlusion problems, besides the traditional detection branch, we novelly introduce an isolated Template Module branch to extract unique embeddings of each proposal by utilizing the chromosome's geometric information. The embeddings are further incorporated into the No Maximum Suppression (NMS) procedure to improve the detection of overlapping chromosomes. Finally, we design a Truncated Normalized Repulsion Loss and add it to the loss function to avoid inaccurate localization caused by occlusion. In the newly collected 1375 metaphase images that came from a clinical laboratory, a series of ablation studies validate the effectiveness of each proposed module. Combining them, the proposed DeepACEv2 outperforms all the previous methods, yielding the Whole Correct Ratio(WCR)(%) with respect to images as 71.39, and the Average Error Ratio(AER)(%) with respect to chromosomes as about 1.17.

Stimulation of water and calcium dynamics in astrocytes with pulsed infrared light.

Astrocytes are non-neuronal cells that govern the homeostatic regulation of the brain through ions and water transport, and Ca2+ -mediated signaling. As they are tightly integrated into neural networks, label-free tools that can modulate cell function are needed to evaluate the role of astrocytes in brain physiology and dysfunction. Using live-cell fluorescence imaging, pharmacology, electrophysiology, and genetic manipulation, we show that pulsed infrared light can modulate astrocyte function through changes in intracellular Ca2+ and water dynamics, providing unique mechanistic insight into the effect of pulsed infrared laser light on astroglial cells. Water transport is activated and, IP3 R, TRPA1, TRPV4, and Aquaporin-4 are all involved in shaping the dynamics of infrared pulse-evoked intracellular calcium signal. These results demonstrate that astrocyte function can be modulated with infrared light. We expect that targeted control over calcium dynamics and water transport will help to study the crucial role of astrocytes in edema, ischemia, glioma progression, stroke, and epilepsy.

Prolonged post-stimulation response induced by 980-nm infrared neural stimulation in the rat primary motor cortex.

The post-stimulation response of neural activities plays an important role to evaluate the effectiveness and safety of neural modulation techniques. Previous studies have established the capability of infrared neural modulation (INM) on neural firing regulation in the central nervous system (CNS); however, the dynamic neural activity after the laser offset has not been well characterized yet. We applied 980-nm infrared diode laser light to irradiate the primary motor cortex of rats, and tungsten electrode was inserted to record the single-unit activity of neurons at the depth of 800-1000 µm (layer V of primary motor cortex). The neural activities were assessed through the change of neural firing rate and firing pattern pre- and post-stimulation with various radiant exposures. The results showed that the 980-nm laser could modulate the firing properties of neurons in the deep layer of the cortex. More neurons with post-stimulation response (78% vs. 83%) were observed at higher stimulation intensity (0.803 J/cm2 vs. 1.071 J/cm2, respectively). The change of firing rate also increased with radiant exposures increasing, and the response lasted up to 4.5 s at 1.071 J/cm2, which was significantly longer than the theoretical thermal relaxation time. Moreover, the increasing Fano factors indicated the irregularity firing pattern of post-stimulation response. Our results confirmed that neural activity maintained a prolonged post-stimulation response after INM, which may provide necessary measurable data for optimization of INM applications in CNS.

HTUPA as a new thrombolytic agent for acute myocardial infarction: a multicenter, randomized study.

BACKGROUND: It is necessary to develop a new thrombolytic agent which can be used by a single bolus at first aid sites to decrease the time to reperfusion in clinical practice. HTUPA, a genetically engineered new thrombolytic with a longer half-life, is well qualified. We aim to compare the thrombolytic efficacy and safety of human tissue urokinase type plasminogen activator (HTUPA) to recombinant tissue plasminogen activator (rt-PA) in Chinese patients with acute myocardial infarction (AMI). METHODS: AMI patients (n=221) were randomized to rt-PA (a standard protocol) or HTUPA (25 mg bolus) treatment groups. All patients also received oral aspirin and intravenous heparin. Coronary angiography was performed 90 min after therapy initiation to determine infarct-related coronary artery (IRA) patency. Clinical outcomes and changes of clotting variables, heart rate, blood pressure, left ventricular ejection fraction (LVEF), and electrocardiogram were evaluated. RESULTS: Patent IRA [thrombolysis in myocardial infarction (TIMI) grade 2 or 3] was observed in 77% of HTUPA-treated patients, compared to 76% of rt-PA-treated patients (P=0.76). TIMI grade 3 patency rates were 52% and 44% in the HTUPA and rt-PA groups, respectively (P=0.37). The total patency rate was 77% (86/111 patients) in the HTUPA group and 73% (80/110 patients) in the rt-PA group (P=0.41). Adverse events were infrequent in both groups, and no significant differences were detected in mortality, re-occlusion rate, revascularization rate, adverse effects, clotting index, LVEF, or electrocardiogram between the two groups. CONCLUSIONS: Intravenous HTUPA had a safe and efficacious profile as good as rt-PA in patients with AMI.

[Feasibility and outcome of rotational atherectomy for treating resistant chronic total occlusions].

OBJECTIVE: To assess the application of rotational atherectomy to improving the success rate and outcome of percutaneous recanalization of resistant chronic total occlusion (CTO), i.e. the guidewire could cross the lesion but it is impossible to advance any device over the wire through the occluded segment. METHODS: From August 2008 to December 2012, 26 consecutive patients with 27 resistant CTO lesions were additionally treated by high-speed rotational atherectomy (rotational atherectomy group). The control group included 751 non-resistant CTO lesions. Drug-eluting stents were implanted in two groups after the balloon catheter crossed the CTO lesions. The successful rate of rotational atherectomy and in hospital major adverse cardiovascular events (including cardiac death, interventional treatment related myocardial infarction and target vessel revascularization) were observed. RESULTS: The rate of heavily calcified coronary lesions was significantly higher in rotational atherectomy group than in the control group[63.0% (17/27) vs. 21.2% (159/751), P < 0.05] according to pre-procedural coronary angiography. Rotational atherectomy was successful in 25 out of 27 resistant CTO lesions (92.6 %). The rate of cardiac death [0 vs. 0.5% (4/751), P > 0.05], interventional treatment related myocardial infarction [38.5% (10/26) vs. 22.2% (167/751), P > 0.05] and target vessel revascularization [0 vs. 1.2% (9/751), P > 0.05] were similar between the rotational atherectomy group and the control group. CONCLUSION: Rotational atherectomy is a safe and helpful technique to overcome the inability of balloon catheter to cross a resistant CTO.