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Abstract The study aims to evaluate whether rhythm control by catheter ablation is superior to medical therapy for the patients with atrial fibrillation (AF) and heart failure (HF). The literatures were searched by using PubMed, Cochrane Library, Embase, and Web of Science databases up to 12 October 2023. The randomized controlled trials (RCTs) comparing rhythm control using catheter ablation vs. medical therapy in AF patients with HF were pooled. The primary outcomes included all‐cause mortality, HF re‐hospitalization, and stroke, and the secondary outcomes included left ventricular ejection fraction (LVEF), atrial tachyarrythmia recurrence, quality of life (Minnesota Living with Heart Failure Questionnaire score, MLHFQ score), 6 min walking distance (6MWD), the level of N‐terminal B‐type natriuretic peptide precursor (NT‐proBNP), and adverse events. Nine RCTs involving in 2293 patients met the inclusion criteria. Compared with medical therapy, catheter ablation reduced all‐cause mortality [10.07% (121/1201) vs. 15.26% (175/1147), risk ratio (RR):0.60, 95% confidence interval (CI): 0.48–0.74, P

Abstract Although aberrant splicing events of genes are closely related to the development and progression of colorectal cancer (CRC), the mapping of abnormal splicing events, especially alternative splicing (AS) event types and the underlying effects, remain investigational. In the present study, we analyzed a public RNA-seq database (GSE138202) and identified 14,314 significant AS events in CRC patients compared to healthy individuals. Most of the key genes such as oncogenes involved in the development of CRC have different AS event types. Moreover, the results demonstrate that certain AS events may play a significant role in the functioning of key genes involved in splicing factors and microRNAs. Furthermore, we observed that the oncogene CDK4 in CRC tends to undergo exon 2 skipping AS events, resulting in a stronger tendency for protein expression to form complexes with CCND1, thereby inhibiting the cell cycle and weakening cell proliferation, while enhancing cell migration capability. These findings not only provide new insights into the mechanism of AS in regulating CRC, but also offers a theoretical basis for targeted splicing therapy in CRC.

Establishing the corresponding forecasting models based on numerical model that fails to capture gale events is helpful to improve the ability to forecast gales in coastal areas of China. First, we screened for gale events occurred in coastal areas of Qingdao from 2016 to 2019, and established the dataset of gale events that are missed by the forecast with ECMWF (European Center for Medium-range Weather Forecast) high resolution model (hereinafter referred to as EC model). Second, based on support vector machine (SVM), artificial neural network (ANN) and long short-term memory network (LSTM) algorithms, we established the forecast models for gales along the coast of Qingdao, and used these models to revise the wind speed forecasted by the EC model. Finally, after comparative analysis, a model suitable for forecasting gale along the coast of Qingdao, i.e. the forecast model SVM_2 based on SVM algorithm, was selected and its operational forecasting results were ex⁃ amined. Results show that the SVM_2 model has the smallest forecast error for wind speed compared to other models. In order to evaluate the prediction performance of SVM_2 model in terms of the gale events, two gale events occurred in the coast of Qingdao and influenced by differ⁃ ent weather systems are selected to further examine the prediction error of SVM_2 model and EC model for the gale events. The results show that the error of the maximum wind speed forecasted by the SVM_2 model against the observations is significantly smaller than that of the EC model, and the SVM_2 model also has some improvement on the weak gale along the coast of Qingdao compared to the forecast by EC model.

Deep learning, a data-driven technology, has attracted widespread attention from various disciplines due to the rapid advancements in the Internet of Things (IoT) big data, machine learning algorithms and computational hardware in recent years. It proves to achieve comparable or even more accurate results than traditional methods in a more flexible manner in existing applications in various fields. In the field of physical oceanography, an important scientific field of oceanography, the abundance of ocean surface data and high dynamic complexity pave the way for an extensive application of deep learning. Moreover, researchers have already conducted a great deal of work to innovate traditional approaches in ocean circulation, ocean dynamics, ocean climate, ocean remote sensing and ocean geophysics, leading oceanographic studies into the “AI ocean era”. In our study, we categorize numerous research topics in physical oceanography into four aspects: surface elements, subsurface elements, typical ocean phenomena, and typical weather and climate phenomena. We review the cutting-edge applications of deep learning in physical oceanography over the past three years to provide comprehensive insights into its development. From the perspective of three application scenarios, namely spatial data, temporal data and data generation, three corresponding deep learning model types are introduced, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative adversarial networks (GANs), and also their principal application tasks. Furthermore, this study discusses the current bottlenecks and future innovative prospects of deep learning in oceanography. Through summarizing and analyzing the existing research, our aim is to delve into the potential and challenges of deep learning in physical oceanography, providing reference and inspiration for researchers in future oceanographic studies.

Objectives: To evaluate the correlation of oxidative stress and vascular endothelial dysfunction with hippocampal perfusion in patients with atrial fibrillation and cognitive impairment. Methods: In total, 41 atrial fibrillation patients with cognitive impairment were compared to 45 atrial fibrillation patients without cognitive impairment. Oxidative stress, vascular endothelial dysfunction, hippocampal perfusion, and cognitive function were measured. Results: Serum level of oxidized low-density lipoprotein was significantly higher in the atrial fibrillation + cognitive impairment group than in the atrial fibrillation group. Serum levels of intercellular adhesion molecule-1 and vascular cell adhesion molecule-1 were significantly higher, and nitric oxide was lower, in the atrial fibrillation + cognitive impairment group than in the atrial fibrillation group. The regional cerebral blood volume, mean transit time, and time to peak were significantly higher in the atrial fibrillation + cognitive impairment group than in the atrial fibrillation group. Moreover, regional cerebral blood flow was significantly lower in the atrial fibrillation + cognitive impairment group than in the atrial fibrillation group. Age, left atrial diameter, and regional cerebral blood volume were negatively correlated with the cognitive function score in the atrial fibrillation + cognitive impairment group. Serum levels of oxidized low-density lipoprotein, regional cerebral blood volume, regional cerebral blood flow, mean transit time, and time to peak were significantly correlated with cognitive impairment in atrial fibrillation patients after multivariate logistic regression analysis. Conclusion: Hippocampal perfusion and oxidative stress were significantly correlated with cognitive impairment in atrial fibrillation patients.

The pathogenesis of inflammatory bowel disease (IBD) remains unclear and is associated with an increased risk of developing colitis-associated cancer (CAC). Under sustained inflammatory stimulation in the intestines, loss of early DNA damage response genes can lead to tumor formation. Many proteins are involved in the pathways of DNA damage response and play critical roles in protecting genes from various potential damages that DNA may undergo. ERCC4 is a structure-specific endonuclease that participates in the nucleotide excision repair (NER) pathway. The catalytic site of ERCC4 determines the activity of NER and is an indispensable gene in the NER pathway. ERCC4 may be involved in the imbalanced process of DNA damage and repair in IBD-related inflammation and CAC. This article primarily reviews the function of ERCC4 in the DNA repair pathway and discusses its potential role in the processes of IBD-related inflammation and carcinogenesis. Finally, we explore how this knowledge may open novel avenues for the treatment of IBD and IBD-related cancer.

Inflammatory bowel disease (IBD) is a chronic, recurrent gastrointestinal disorder with elusive etiology. Interleukin-12 (IL-12) and IL-23 have emerged as key proinflammatory mediators/cytokines in IBD pathogenesis. Ustekinumab (UST), targeting IL-12 and IL-23, has demonstrated promising efficacy and safety in the treatment of IBD. Recently, UST has become increasingly favored as a potential first-line treatment option. This review delineates UST’s mechanism of action, its clinical applications in IBD, including the response rates, strategies for dose optimization for case of partial or lost response, and potential adverse events. This review aims to offer a comprehensive understanding of UST’s role as a therapeutic option in IBD management.

Abstract Atrial fibrosis is a crucial contributor to initiation and perpetuation of atrial fibrillation (AF). This study aimed to identify a circRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) regulatory network related to atrial fibrosis in AF, especially to validate hsa_circ_0000672/hsa_miR-516a-5p/TRAF6 ceRNA axis in AF preliminarily. The circRNA-miRNA-mRNA ceRNA network associated with AF fibrosis was constructed using bioinformatic tools and literature reviews. Left atrium (LA) low voltage was used to represent LA fibrosis by using LA voltage matrix mapping. Ten controls with sinus rhythm (SR), and 20 patients with persistent AF including 12 patients with LA low voltage and 8 patients with LA normal voltage were enrolled in this study. The ceRNA regulatory network associated with atrial fibrosis was successfully constructed, which included up-regulated hsa_circ_0000672 and hsa_circ_0003916, down-regulated miR-516a-5p and five up-regulated hub genes (KRAS, SMAD2, TRAF6, MAPK11 and SMURF1). In addition, according to the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, these hub genes were clustered in TGF-beta and MAPK signaling pathway. In the patients with persistent AF, hsa_circ_0000672 expression in peripheral blood monocytes was significantly higher than those in controls with SR by quantitative real-time polymerase chain reaction (p-value

Abstract In recent years, circular RNAs (circRNAs) have been found to play an essential regulatory role in hepatocellular carcinoma (HCC) through various mechanisms, particularly the endogenous competitive RNA (ceRNA) mechanism. Therefore, it is significant to explore the circRNAs in hepatoma. In this study, we constructed the ceRNA and survival network using Cytoscape. We also used R, Perl software, and multiple online databases and platforms, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), to perform overall survival, immune cell infiltration, immune checkpoints, pathway activity, and anticancer drug sensitivity analysis of the genes. Finally, the receiver operator characteristic curve (ROC) analysis was performed to identify the diagnosis value of the genes. KEGG analysis revealed the T cell receptor signaling pathway as the main enrichment pathway. A total of 29 genes related to survival and prognosis were screened out. The findings suggest that ZNF544, WDR76, ACTG1, RASSF3, E2F3, ASRGL1, and POGK are associated with multilevel immune cell infiltration. Additionally, immune checkpoint analysis screened out the ACTG1, E2F3, RASSF3, and WDR76. It was also revealed that the WDR76, E2F3, ASRGL1, and POGK mainly activated the cell cycle and DNA damage response (DDR) pathway. The results suggest that the sensitivity toward trametinib, refametinib (RDEA119), and selumetinib correlates to the expression of WDR76. ROC analysis showed that the area under the curve (AUC) of all genes in the regulatory axis was greater than 0.7. The identified hsa_circ_0000417/hsa_circ_0002688/hsa_circ_0001387--hsa-miR-199a-5p--WDR76 regulatory axis may provide new insights into the progression, clinical diagnosis, and treatment of HCC.

BackgroupIbutilide has already been used for cardioversion of persistent atrial fibrillation (PsAF) after radiofrequency catheter ablation (RFCA). The purpose of this study was to determine the effect of Ibutilide-guided cardioversion on clinical outcomes after individualized ablation of PsAF.MethodsFrom October 2020 to September 2021, consecutive patients with PsAF accepted for RFCA were prospectively enrolled. After individualized ablation including pulmonary vein isolation plus left atrial roof line ablation and personalized linear ablation based on left atrial low-voltage zones, patients were divided into the spontaneous conversion (SCV) group, direct current synchronized cardioversion (DCC) group and Ibutilide group according to different cardioversion types during ablation. The rates of freedom from atrial tachyarrhythmia (ATT) among the three groups were evaluated after follow-up.ResultsIn this study, 110 patients were enrolled, including 12 patients with SCV, 50 patients receiving DCC and 48 patients receiving Ibutilide cardioversion after individualized ablation. Among the three groups, the SCV group had shorter AF duration {12 months [interquartile range (IQR) 12–16], P = 0.042} and smaller left atrial diameter (LAD) [35 mm (IQR: 33–42), P = 0.023]. A 12-month freedom from ATT rate was 83.3% in SCV group, 69.4% in DCC group, and 79.2% in Ibutilide group, respectively (Log-rank, P = 0.745). During the follow-up [17 months (IQR: 15–19)], the rate of freedom from ATT of SCV group (83.3%), and Ibutilide group (72.9%) were both higher than that of DCC group (53.1%, P = 0.042). Moreover, Kaplan–Meier analysis showed a significantly higher sinus rhythm (SR) maintenance in Ibutilide group than in DCC group (Log-rank, P = 0.041). After adjusting for risk factors of AF recurrence, the hazard ratio for AF recurrence of the DCC group with reference to the Ibutilide group was 4.10 [95% confidence interval (CI) (1.87–8.98), P

The taxonomic composition and abundance of phytoplankton have a direct impact on marine ecosystem dynamics and global environment change. Phytoplankton classification is crucial for phytoplankton analysis, but it is challenging due to their large quantity and small size. Machine learning is the primary method for automatically performing phytoplankton image classification. As large-scale research on marine phytoplankton generates overwhelming amounts of data, more powerful computational resources are required for the success of machine learning methods. Recently, quantum machine learning has emerged as a potential solution for large-scale data processing by harnessing the exponentially computational power of quantum computers. Here, for the first time, we demonstrate the feasibility of using quantum deep neural networks for phytoplankton classification. Hybrid quantum-classical convolutional and residual neural networks are developed based on the classical architectures. These models strike a balance between the limited function of current quantum devices and the large size of phytoplankton images, making it possible to perform phytoplankton classification on near-term quantum computers. Our quantum models demonstrate superior performance compared to their classical counterparts, exhibiting faster convergence, higher classification accuracy and lower accuracy fluctuation. The present quantum models are versatile and can be applied to various tasks of image classification in the field of marine science.

Inflammatory bowel disease (IBD), a general term encompassing Crohn’s disease (CD) and ulcerative colitis (UC), and other conditions, is a chronic and relapsing autoimmune disease that can occur in any part of the digestive tract. While the cause of IBD remains unclear, it is acknowledged that the disease has much to do with the dysregulation of intestinal immunity. In the intestinal immune regulatory system, Cholesterol-25-hydroxylase (CH25H) plays an important role in regulating the function of immune cells and lipid metabolism through catalyzing the oxidation of cholesterol into 25-hydroxycholesterol (25-HC). Specifically, CH25H focuses its mechanism of regulating the inflammatory response, signal transduction and cell migration on various types of immune cells by binding to relevant receptors, and the mechanism of regulating lipid metabolism and immune cell function via the transcription factor Sterol Regulator-Binding Protein. Based on this foundation, this article will review the function of CH25H in intestinal immunity, aiming to provide evidence for supporting the discovery of early diagnostic and treatment targets for IBD.

RNA splicing is the process of forming mature mRNA, which is an essential phase necessary for gene expression and controls many aspects of cell proliferation, survival, and differentiation. Abnormal gene-splicing events are closely related to the development of tumors, and the generation of oncogenic isoform in splicing can promote tumor progression. As a main process of tumor-specific splicing variants, alternative splicing (AS) can promote tumor progression by increasing the production of oncogenic splicing isoforms and/or reducing the production of normal splicing isoforms. This is the focus of current research on the regulation of aberrant tumor splicing. So far, AS has been found to be associated with various aspects of tumor biology, including cell proliferation and invasion, resistance to apoptosis, and sensitivity to different chemotherapeutic drugs. This article will review the abnormal splicing events in colorectal cancer (CRC), especially the tumor-associated splicing variants arising from AS, aiming to offer an insight into CRC-targeted splicing therapy.

Mesoscale eddies play a significant role in marine energy and matter transportation. Due to their huge impact on the ocean, mesoscale eddy detection has been studied for many years. However, existing methods mainly use single-modal data, such as the sea surface height (SSH), to detect mesoscale eddies, resulting in inaccurate detection results. In this paper, we propose an end-to-end mesoscale eddy detection method based upon multi-modal data fusion. Particularly, we don’t only use SSH, but also add data of other two modals, i.e., the sea surface temperature (SST) and the velocity of flow, which are closely related to mesoscale eddy detection. Moreover, we design a novel network named SymmetricNet, which is able to achieve multi-modal data fusion in mesoscale eddy detection. The proposed SymmetricNet mainly contains a downsampling pathway and an upsampling pathway, where the low-level feature maps from the downsampling pathway and the high-level feature maps from the upsampling pathway are merged through lateral connections. In addition, we apply dilated convolutions to the network structure to increase the receptive field without sacrificing resolution. Experiments on multi-modal mesoscale eddy dataset demonstrate the advantages of the proposed method over previous approaches for mesoscale eddy detection.

Underwater images are generally of low quality, limiting the performance of subsequent perceptual tasks, such as underwater object detection and recognition. However, only a few methods can improve the quality of underwater images by simultaneously restoring and super-resolving underwater images. In this paper, we propose an end-to-end trainable model based on generative adversarial networks (GANs) called Simultaneous Restoration and Super-Resolution GAN (SRSRGAN) to obtain clear super-resolution underwater images automatically. In particular, our model leverages a cascaded architecture with two stages of carefully designed generative adversarial networks to restore and super-resolve corrupted underwater images in a coarse-to-fine manner. The major advantages of SRSRGAN are twofold. First, it is a unified solution that can simultaneously restore and super-resolve images. Second, SRSRGAN is not limited by the prior experience of the types and levels of underwater degraded images but can perform the inference using only observed corrupted images. These two advantages enable SRSRGAN to enjoy better flexibility and higher practicability in realistic underwater scenarios. Extensive experimental results demonstrate the superiority of SRSRGAN in underwater image restoration, super-resolution, and simultaneous restoration and super-resolution.

Background and aimsinflammation plays an important role in atrial fibrillation (AF). In this study, we investigated the significance of immune cell infiltration in AF and identified the potential Hub genes involved in the regulation of immune cell infiltration in AF.Methodswe obtained AF datasets from the GEO database and analyzed them for obtaining differentially expressed genes (DEGs) by R software. Then, we performed GO, KEGG, and GSEA enrichment analyses of DEGs. The Hub genes of AF were determined by least absolute shrinkage selection operator (LASSO) regression analysis and weighted gene co-expression network analysis (WGCNA). Their validation was verified by using quantitative polymerase chain reaction (qPCR) in the AF rat model. Finally, we used a single sample GSEA (ssGSEA) to analyze immune cell infiltration and its relationship with hub genes.ResultsWe obtained 298 DGEs from the heatmap and found that DGEs were closely related to inflammation, immunity, and cytokine interactions by enrichment analyses. We obtained 10 co-expression modules by WGCNA. Among them, the module including CLEC4A, COTL1, EVI2B, FCER1G, GAPT, HCST, NCF2, PILRA, TLR8, and TYROBP had the highest correlation with AF. Four Hub genes (PILRA, NCF2, EVI2B, GAPT) were obtained further by LASSO analysis. The results suggested that the expression level of PILRA was significantly elevated in the rats with AF by qPCR, compared to the rats without AF. The results revealed that the infiltration of neutrophils, macrophages, monocytes, mast cells, immature B cells, myeloid-derived suppressor cell (MDSC), dendritic cell, and T cells and their partial subpopulations were closely related to AF by ssGSEA analysis, and PILRA was positively correlated with immature B cell, monocyte, macrophage, mast cell, dendritic cell, and T cells and their partial subpopulations by Spearman correlation analysis.ConclusionsPILRA was closely related to multiple types of immune cell infiltration, which may be associated with AF. PILRA may be a novel target of intervention for AF.

Since the last IAEA-FEC in 2021, significant progress on the development of long pulse steady state scenario and its related key physics and technologies have been achieved, including the reproducible 403 s long-pulse steady-state H-mode plasma with pure radio frequency (RF) power heating. A thousand-second time scale (∼1056 s) fully non-inductive plasma with high injected energy up to 1.73 GJ has also been achieved. The EAST operational regime of high β _P has been significantly extended ( H _98y2 > 1.3, β _P ∼ 4.0, β _N ∼ 2.4 and n _e / n _GW ∼ 1.0) using RF and neutral beam injection (NBI). The full edge localized mode suppression using the n = 4 resonant magnetic perturbations has been achieved in ITER-like standard type-I ELMy H-mode plasmas with q _95 ≈ 3.1 on EAST, extrapolating favorably to the ITER baseline scenario. The sustained large ELM control and stable partial detachment have been achieved with Ne seeding. The underlying physics of plasma-beta effect for error field penetration, where toroidal effect dominates, is disclosed by comparing the results in cylindrical theory and MARS-Q simulation in EAST. Breakdown and plasma initiation at low toroidal electric fields (

Ensemble learning, online learning and deep learning are very effective and versatile in a wide spectrum of problem domains, such as feature extraction, multi-class classification and retrieval. In this paper, combining the ideas of ensemble learning, online learning and deep learning, we propose a novel deep learning method called deep error-correcting output codes (DeepECOCs). DeepECOCs are composed of multiple layers of the ECOC module, which combines several incremental support vector machines (incremental SVMs) as base classifiers. In this novel deep architecture, each ECOC module can be considered as two successive layers of the network, while the incremental SVMs can be viewed as weighted links between two successive layers. In the pre-training procedure, supervisory information, i.e., class labels, can be used during the network initialization. The incremental SVMs lead this procedure to be very efficient, especially for large-scale applications. We have conducted extensive experiments to compare DeepECOCs with traditional ECOC, feature learning and deep learning algorithms. The results demonstrate that DeepECOCs perform, not only better than existing ECOC and feature learning algorithms, but also related to deep learning ones in most cases.

BackgroundGlycemic variability (GV) has been associated with vascular complications in patients with diabetes. However, the relationship between GV and risk of atrial fibrillation (AF) remains not fully determined. We therefore conducted a systematic review and meta-analysis to evaluate the above association.MethodsMedline, Embase, Web of Science, Wanfang, and China National Knowledge Infrastructure were searched for longitudinal follow-up studies comparing the incidence of AF between patients with higher versus lower GV. A random-effects model incorporating the potential heterogeneity was used to pool the results.ResultsNine cohort studies with 6,877,661 participants were included, and 36,784 (0.53%) participants developed AF during follow-up. Pooled results showed that a high GV was associated with an increased risk of AF (risk ratio [RR]: 1.20, 95% confidence interval [CI]: 1.11 to 1.30, p < 0.001, I2 = 20%). Subgroup analyses suggested consistent association between GV and AF in prospective (RR: 1.29, 95% CI: 1.05 to 1.59, p = 0.01) and retrospective studies (RR: 1.18, 95% CI: 1.08 to 1.29, p = 0.002), in diabetic (RR: 1.24, 95% CI: 1.03 to 1.50, p = 0.03) and non-diabetic subjects (RR: 1.13, 95% CI: 1.00 to 1.28, p = 0.05), in studies with short-term (RR: 1.25, 95% CI: 1.11 to 1.40, p < 0.001) and long-term GV (RR: 1.18, 95% CI: 1.05 to 1.34, p = 0.006), and in studies with different quality scores (p for subgroup difference all > 0.05).ConclusionA high GV may predict an increased risk of AF in adult population.

Ocean fronts are a response to the variabilities of marine hydrographic elements and are an important mesoscale ocean phenomenon, playing a significant role in fish farming and fishing, sea-air exchange, marine environmental protection, etc. The horizontal gradients of sea surface temperature (SST) are frequently applied to reveal ocean fronts. Up to now, existing spatiotemporal prediction approaches have suffered from low prediction precision and poor prediction quality for non-stationary data, particularly for long-term prediction. It is a challenging task for medium- and long-term fine-grained prediction for SST sequences and fronts in oceanographic research. In this study, SST sequences and fronts are predicted for future variation trends based on continuous mean daily remote sensing satellite of SST data. To enhance the precision of the predicted SST sequences and fronts, this paper proposes a novel memory-contextual spatiotemporal transfer network (MCSTNet) for SST sequence and front predictions. MCSTNet involves three components: the encoder-decoder structure, a time transfer module, and a memory-contextual module. The encoder-decoder structure is used to extract the rich contextual and semantic information in SST sequences and frontal structures from the SST data. The time transfer module is applied to transfer temporal information and fuse low-level, fine-grained temporal information with high-level semantic information to improve medium- and long-term prediction precision. And the memory-contextual module is employed to fuse low-level, spatiotemporal information with high-level semantic information to enhance short-term prediction precision. In the training process, mean squared error (MSE) loss and contextual loss are combined to jointly guide the training of MCSTNet. Extensive experiments demonstrate that MCSTNet predicts more authentic and reasonable SST sequences and fronts than the state-of-the-art (SOTA) models on the SST data.

Catalytic performance of Pt based nanocatalysts significantly depends on their morphologies, compositions and structures. In this work, the generation-4 phenylazomethine-tetraphenylmethane dendrimer (TPMG4)-encapsulated PtSn bimetallic ultrafine nanoparticles with tunable ratio of Pt and Sn (PtnSn60-n@TPMG4/GMC, n = 56, 48, 32) supported on graphitic mesoporous carbon (GMC) were fabricated through co-reduction method by using the phenylazomethine dendrimer as a template. The morphologies, compositions, and structures of resulting PtnSn60-n@TPMG4/GMC were detected by UV–vis absorption spectra (UV–vis), inductively coupled plasma-optical emission spectroscopy (ICP-OES), high angle annular dark field scanning transmission electron microscopy (HAADF-STEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) measurements. These physical characterizations show that dendrimer-encapsulated PtnSn60-n nanoparticles are anchored on the surface of graphitic mesoporous carbon with highly dispersion and uniformly ultrafine size of ∼1 nm, The atomic ratio of Pt and Sn in PtnSn60-n bimetallic nanoparticles can be accurately controlled. The electrochemical characterizations demonstrate that all PtnSn60-n@TPMG4/GMC samples exhibit more excellent electrocatalytic performance for methanol oxidation than single metal Ptm@TPMG4/GMC and commercial carbon supported platinum catalyst (Pt/C). Especially, the Pt56Sn4@TPMG4/GMC displays the highest catalytic activity with 1.6 times higher than Pt60/GMC and 1.9 times higher than Pt/C. Also, the Pt56Sn4@TPMG4/GMC exhibits the high carbon monoxide (CO) tolerance and superior catalytic stability towards methanol oxidation. Consequently, constructing Pt based bimetallic nanoparticles with controllable compositions and structures is an effective way to improve the catalytic performance and reduce the amount of Pt used in the catalysts, which will be widely concerned in the field of direct methanol fuel cells.

Background: Catheter ablation combined with left atrial appendage closure is effective in treating atrial fibrillation. However, the effectiveness of this combined treatment compared with catheter ablation alone is still controversial. Methods: We searched studies in databases, including The Cochrane Library, PubMed, EMBASE, and Web of Science, that compared catheter ablation combined with left atrial appendage closure and catheter ablation alone in the treatment of atrial fibrillation. These studies reported at least one of the following outcomes: the freedom from atrial arrhythmia rate, the procedure time, the fluoroscopy time, perioperative complications, thromboembolic events, and bleeding events during follow-up. The risk ratio and standard mean difference with 95% CI were analyzed by the random-effects model. Results: Five studies involving 699 people were included in our meta-analysis. We found no significant difference in the freedom from atrial arrhythmia rate (risk ratio=0.93, 95% CI=0.83-1.04, I 2=0%, P=.21) between the 2 groups. Catheter ablation combined with left atrial appendage closure showed significantly longer procedure and fluoroscopy times than catheter ablation alone (standard mean difference=1.26, 95% CI=0.85-1.67, P

Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been proposed, but those with discriminative relationship analysis are lacking yet, as important supervisory information is usually ignored. In this paper, we propose a novel and general framework, called relational Fisher analysis (RFA), which successfully integrates relational information into the dimensionality reduction model. For nonlinear data representation learning, we adopt the kernel trick to RFA and propose the kernelized RFA (KRFA). In addition, the convergence of the RFA optimization algorithm is proved theoretically. By leveraging suitable strategies to construct the relational matrix, we conduct extensive experiments to demonstrate the superiority of our RFA and KRFA methods over related approaches.

The metric learning method has been successfully developed in few-shot learning by learning the deeply embedded metric space. However, many methods ignore the relationship between the support set and the query set. In this paper, we propose the category relevance redirection network (CRRNet), which is a novel metric learning-based few-shot learning classification network. The learning of CRRNet includes two stages. In the category relevance pre-training stage, the convolutional block attention module (CBAM) and the classifier are jointly trained using the center loss and cross entropy loss. In the category redirection training stage, the meta fusion block and the weight generator are used to represent the weighted query set features, which are then input into the spatial attention generator to obtain the discriminative features for classification. CRRNet can not only learn the relevant category characteristics of the support set samples, but emphasizes the relevant category characteristics of the current task’s query samples. Experiments on three datasets show that CRRNet can greatly improve the embedding learning performance and classification accuracy of metric-based few-shot classification. For the 5-way 1-shot and 5-way 5-shot scenarios, CRRNet may produce 9.68 percent and 12.42 percent increases over the traditional methods on the miniImageNet dataset, respectively.

The cribriform-morular variant of papillary thyroid carcinoma (CMV-PTC) is now designated as morular cribriform thyroid carcinoma (CMTC) according to the 5th edition of the World Health Organization (WHO) Classification of Thyroid Tumors. CMTC can appear within a familial adenomatous polyposis (FAP) or be sporadic. We report the first case of a young female patient in China who was diagnosed with FAP and CMTC with a mutation in exon 16 of the APC gene underlying the disease. The main purpose of this case report is to provide a special pathological type of thyroid tumors, which is expected to be helpful for clinical work in the future.

Recently, the research on drone-view object detection (DOD) has predominantly centered on efficiently identifying objects through cropping high-resolution images. However, it has overlooked the distinctive challenges posed by scale imbalance and a higher prevalence of small objects in drone images. In this paper, to address the challenges associated with the detection of drones (DODs), we introduce a specialized detector called DroneNet. Firstly, we propose a feature information enhancement module (FIEM) that effectively preserves object information and can be seamlessly integrated as a plug-and-play module into the backbone network. Then, we propose a split-concat feature pyramid network (SCFPN) that not only fuses feature information from different scales but also enables more comprehensive exploration of feature layers with many small objects. Finally, we develop a coarse to refine label assign (CRLA) strategy for small objects, which assigns labels from coarse- to fine-grained levels and ensures adequate training of small objects during the training process. In addition, to further promote the development of DOD, we introduce a new dataset named OUC-UAV-DET. Extensive experiments on VisDrone2021, UAVDT, and OUC-UAV-DET demonstrate that our proposed detector, DroneNet, exhibits significant improvements in handling challenging targets, outperforming state-of-the-art detectors.

BackgroundSalt-sensitive hypertension (SSH) is a common type of essential hypertension in China. In recent years, although an increasing number of researches have focused on SSH, few studies have been researched on patients with SSH. The objective of this study was to explore the genes and pathways linked with SSH using RNA-sequencing (RNA-seq).Materials and methodsWe used RNA-seq to analyze the transcriptome of peripheral blood mononuclear cells (PBMCs) of five SSH patients and five SRH patients. Next, we analyzed the differentially expressed genes (DEGs) using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Set Enrichment (GSEA) enrichment analysis. Then, Cytoscape was used to construct the protein-protein interaction (PPI) network and the hub genes. Finally, CMAP analysis found that several small molecular compounds could reverse the altered DEGs.ResultsA total of 431 DEGs were found in the PBMC samples, including 294 up-regulated and 137 down-regulated genes. Functional enrichment analysis found significant enrichment in immune-related associations such as inflammation, chemokine, and cytokine-cytokine receptor interaction. The hub genes of the two modules were IL-6, IL-1A, CCL2, CCL3L3, and BUB1. In addition, we identified two small molecular compounds (iopromide and iloprost) that potentially interacted with DEGs.ConclusionThis study suggests some potential biomarkers for the diagnosis of SSH. It provides new insights into SSH diagnosis and possible future clinical treatment.

As a classic and well-performed deep convolutional neural network, DenseNet links every layer to each of its preceding layers via skip connections. However, the dense connectivity of the links leads to much redundance, consuming lots of computational resources. In this paper, to automatically prune redundant skip connections in DenseNet, we introduce a novel reinforcement learning method called automatic DenseNet sparsification (ADS). In ADS, we use adjacent matrix to represent dense connections in DenseNet, and design an agent using recurrent neural networks (RNNs) to sparsify the matrix, i. e. removing redundant skip connections in DenseNet. The validation accuracies of the sparsified DenseNets are used as rewards to update the agent, which promotes the agent to generate sparsified DenseNets with high performance. Extensive experiments demonstrate the effectiveness of ADS: The performance of the sparsified DenseNet surpasses not only the original DenseNet but related models; Moreover, the sparsified DenseNet has strong transferability when it is applied to new tasks. More importantly, ADS is very efficient. For the compression of a 40-layer DenseNet, it takes less than 1 day on a single GPU.

Background. There is still no clear understanding of the pathogenesis of atrial fibrillation (AF). For this purpose, we used integrated analysis to uncover immune infiltration characteristics and investigated their relationship with competing endogenous RNA (ceRNA) network in AF. Methods. Three AF mRNA data sets (GSE14975, GSE79768, and GSE41177) were integrated using the SVA method from Gene Expression Omnibus (GEO). Together with AF circRNA data set (GSE129409) and miRNA data set (GSE70887) from GEO database, we built a ceRNA network. Then hub genes were screened by the Cytoscape plug-in cytoHubba from a protein-protein interaction (PPI) network. As well, CIBERSORT was employed to investigate immune infiltration, followed by Pearson correlation coefficients to unravel the correlation between AF-related infiltrating immune cells and hub genes. Ulteriorly, circRNA-miRNA-mRNA regulatory axises that could be immunologically related to AF were obtained. Results. Ten hub genes were identified from the constructing PPI network. The immune infiltration analysis revealed that the number of monocytes and neutrophils was higher, as well as the number of dendritic cells activated and T cells regulatory (Tregs) was lower in AF. Seven hub genes (C5AR1, CXCR4, HCK, LAPTM5, MPEG1, TLR8, and TNFSF13B) were associated with those 4 immune cells (P

A series of surface modified-gadolinium/boron/polyethylene composites composed of surface modified fillers (M−microGd2O3 and M−nanoGd2O3) are prepared for shielding neutron and gamma radiation. Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), energy dispersive X-ray energy spectrometer (EDS) reveal that the surface modification of fillers significantly improved the interfacial compatibility and dispersion in the polyethylene matrix. Thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and mechanical tensile tests show that the modification of nanoGd2O3 and microGd2O3 improve the thermal stability and mechanical property of the composites. The T5% (initial decomposition temperature), Tp (endothermic peak temperatures) and Tensile strength of 10wt% M-nanoGd2O3/20wt% B4C/70wt% HDPE reachs 463.5℃,137.2℃, and 19.6MPa, which is greatly improved compared with unmodified materials and neat HDPE. The neutron and gamma shielding mechanism of the composites is studied both by experimental measurements and Monte Carlo simulation. The results show that the improved interfacial compatibility and dispersion of fillers in the polyethylene matrix effectively enhances the neutron and gamma shielding rate. The shielding performance of the composite blended with M−nanoGd2O3 is significantly better than that of the composite blended with M−microGd2O3 and the unmodified materials with the relatively thin thickness. Finally, a superior composite containing 10 wt% M−nanoGd2O3/20 wt% B4C/70 wt% HDPE reaches a neutron shielding rate of 90% at 9.1 cm under Cf-252 environment, and a gamma shielding rate of 70% at 13.7 cm under Cs-137 environment, which have the characteristics of lead free, low specific gravity and high shielding performance and is expected to be a promising radiation shielding materials used in neutron-gamma mixed fields.

Recently, many deep models have been proposed in different fields, such as image classification, object detection, and speech recognition. However, most of these architectures require a large amount of training data and employ random initialization. In this paper, we propose to stack feature learning modules for the design of deep architectures. Specifically, marginal Fisher analysis (MFA) is stacked layer-by-layer for the initialization and we call the constructed deep architecture marginal deep architecture (MDA). When implementing the MDA, the weight matrices of MFA are updated layer-by-layer, which is a supervised pre-training method and does not need a large scale of data. In addition, several deep learning techniques are applied to this architecture, such as backpropagation, dropout, and denoising, to fine-tune the model. We have compared MDA with some feature learning and deep learning models on several practical applications, such as handwritten digits recognition, speech recognition, historical document understanding, and action recognition. The extensive experiments show that the performance of MDA is better than not only shallow feature learning models but also related deep learning models in these tasks.

In recent years, deep neural networks (DNNs) have been widely applied in many areas, such as computer vision and pattern recognition. However, we observe that most of the DNNs include redundant layers. Hence, in this paper, we introduce a novel method named incremental layers resection (ILR) to resect the redundant layers in DNNs, while preserving their learning performances. ILR uses a multistage learning strategy to incrementally resect the inconsequential layers. In each stage, it preserves the data representations learned by the original network, while connecting the two nearby layers of each resected one. Particularly, based on a teacher-student knowledge transfer framework, we have designed the layer-level learning and overall learning procedures to enforce the resected network performing similarly with the original one. Extensive experiments demonstrate that, compared to the original networks, the compressed ones by ILR need only about half of the storage space and have higher inference speed. More importantly, they even deliver higher classification accuracy than the original networks.

Bipolar membranes, a new type of composite ion exchange membrane, contain an anion exchange layer, a cation exchange layer and an interface layer. The interface layer or junction is the connection between the anion and cation exchange layers. Water is dissociated into protons and hydroxide ions at the junction, which provides solutions to many challenges in the chemical, environmental and energy fields. By combining bipolar membranes with electrodialysis technology, acids and bases could be produced with low cost and high efficiency. The interface layer or junction of bipolar membranes (BPMs) is the connection between the anion and cation exchange layers, which the membrane and interface layer modification are vital for improving the performance of BPMs. This paper reviews the effect of modification of a bipolar membrane interface layer on water dissociation efficiency and voltage across the membrane, which divides into three aspects: organic materials, inorganic materials and newly designed materials with multiple components. The structure of the interface layer is also introduced on the performance of bipolar membranes. In addition, the remainder of this review discusses the challenges and opportunities for the development of more efficient, sustainable and practical bipolar membranes.

Geopolymer is a porous inorganic material with a three-dimensional mesh structure, good mechanical properties, a simple preparation process (no sintering) and a low economic cost, and it is environmentally friendly. Geopolymer concrete has been widely used in the construction field, and many other studies have revealed that geopolymer will become one of the most promising inorganic materials with unique structure and properties. This paper provides a review of the development and current status of geopolymers and briefly explains the effects of material proportioning, experimental factors and activators on geopolymer performance. Because of the advantages of high specific surface area and high porosity, geopolymers could be used as adsorbent materials. This paper summarizes the research progresses of the adsorption of metal cations, anions, dyes, and gases by geopolymers, which emphasizes the geopolymer membranes in adsorption, and discusses the challenges and opportunities for the development of more efficient, sustainable and practical adsorption protocols.

Abstract Background In matrix completion fields, the traditional convex regularization may fall short of delivering reliable low-rank estimators with good prediction performance. Previous works use the alternation least squares algorithm to optimize the nonconvex regularization. However, this algorithm has high time complexities and requires more iterations to reach convergence, which cannot scale to large-scale matrix completion problems. We need to develop faster algorithm to examine large amount of data to uncover the correlations between items for the big data analytics. Results In this work, we adopt the randomized SVD decomposition and Nesterov’s momentum to accelerate the optimization of nonconvex matrix completion. The experimental results on four real world recommendation system datasets show that the proposed algorithm achieves tremendous speed increases whilst maintaining comparable performance with the alternating least squares (ALS) method for nonconvex (convex) matrix completions. Conclusions Our novel algorithm can achieve comparable performance with previous algorithms but with shorter running time.

Background. High power shorter duration (HPSD) ablation may lead to safe and rapid lesion formation. However, the optimal radio frequency power to achieve the desired ablation index (AI) or lesion size index (LSI) is insubstantial. This analysis aimed to appraise the clinical safety and efficacy of HPSD guided by AI or LSI (HPSD-AI or LSI) in patients with atrial fibrillation (AF). Methods. The Medline, PubMed, Embase, Web of Science, and the Cochrane Library databases from inception to November 2020 were searched for studies comparing HPSD-AI or LSI and low power longer duration (LPLD) ablation. Results. Seven trials with 1013 patients were included in the analysis. The analyses verified that HPSD-AI or LSI revealed benefits of first-pass pulmonary vein isolation (PVI) (RR: 1.28; 95% CI: 1.05–1.56, P = 0.01) and acute pulmonary vein reconnection (PVR) (RR: 0.65; 95% CI: 0.48–0.88, P = 0.005) compared with LPLD. HPSD-AI or LSI showed higher freedom from atrial tachyarrhythmia (AT) (RR = 1.32, 95% CI: 1.14–1.53, P = 0.0002) in the subgroup analysis of studies with PVI ± (with or without additional ablation beyond PVI). HPSD-AI or LSI could short procedural time (WMD: −22.81; 95% CI, −35.03 to −10.60, P = 0.0003), ablation time (WMD: −10.80; 95% CI: −13.14 to −8.46, P

Mitophagy is an autophagic response and plays essential roles in survival, development, and homeostasis of cells. It has been reported that mitophagic dysfunction is involved in several cardiovascular diseases. However, the effect of mitophagy on atrial fibrillation (AF) is still unknown. Therefore, we investigated the exact role of mitophagy in human chronic AF. Western blot was used to detect the protein abundance. The mitochondrial morphology and structure were observed by transmission electron microscopy. Immunofluorescent stainings were performed to analyze colocalization of mitochondria with autophagosomes or lysosomes. Totally, 43 patients with valvular heart disease undergoing cardiac surgery were selected, including 21 patients with chronic AF. Comparing with the sinus rhythm (SR) group, we found the size and number of mitochondria in atrial myocytes of patients with AF increased significantly. In addition, expression of LC3B II and LC3B II/LC3B I ratio was significantly decreased in the AF group. Moreover, the expression of p62 was markedly elevated in the AF group compared with that in the SR group. The results of immunofluorescence staining and western blot showed an enhanced expression of Cox IV in the AF group. Dual immunofluorescent stainings revealed that mitophagy defect in atrial myocytes of patients with AF resulted from dysfunction in the process of delivery of mitochondria into autophagosomes. For the first time, impaired mitophagy, during the phagocytosis of mitochondria, is associated with human chronic AF. Mitophagy could be a potential therapeutic target for AF.

Several studies have indicated that long noncoding RNAs (lncRNAs)-HOX transcript antisense RNA (HOTAIR) is involved in some cardiovascular diseases by regulating gene expression as a competitive endogenous RNA (ceRNA). GJA1 encoding Cx43 is one potential target gene of microRNA-613 (miR-613). Meanwhile, there is a potential target regulatory relationship between HOTAIR and miR-613. The present study is aimed at investigating whether HOTAIR functions as a ceRNA to regulate the Cx43 expression in atrial fibrillation (AF) by sponging miR-613. The expressions of HOTAIR, miR-613, and Cx43 were detected in the right atrial appendages of 45 patients with heart valve disease, including 23 patients with chronic AF. The HOTAIR overexpressed and underexpressed HL-1 cell model were constructed to confirm the effect of HOTAIR on Cx43. Then, the Cx43 expression was detected to testify the interplay between HOTAIR and miR-613 after cotransfecting HOTAIR and miR-613. Furthermore, luciferase assays were performed to verify that HOTAIR could regulate Cx43 remolding as a ceRNA by sponging miR-613. The expression of HOTAIR and Cx43 was significantly downregulated in chronic AF group. HOTAIR regulated positively the Cx43 expression in HL-1 cells. The upregulated effect of HOTAIR on the Cx43 expression could be remarkably attenuated by miR-613. Moreover, the inhibitory effect of miR-613 on the Cx43 expression could be obviously mitigated by HOTAIR. At last, luciferase assays confirmed HOTAIR functioned as a ceRNA in the Cx43 expression by sponging miR-613. Our study suggests that HOTAIR, functioning as a ceRNA by sponging miR-613, is an important contributor to Cx43 remolding in AF.

In this article, we propose a novel model for estimating sea state bias (SSB) based on multi-layer neural network and multi-source altimeter data from the Topex/Poseidon (T/P), Jason-2, and Jason-3 altimeters. Significant wave height (SWH), wind speed (U) and backscatter coefficient (σ0) are considered as the inputs of the multi-layer neural network, while the corresponding SSB as outputs. The neural network has four layers, with structure 3-3-6-1. Data from three seasons are employed for the neural network training, and the trained model is applied for the SSB estimation on the HY-2 altimeter data. To show the effectiveness of the adopted model, the correlations between SSB and SWH, U and σ0 are analyzed. Moreover, the explained variance and residual error are compared with a conventional parametric model for SSB estimation. The results demonstrate that multi-layer neural network trained on multi-source altimeter data performs superior to the conventional SSB estimation model.

The water inrush of the Shangjiawan karst tunnel is used to study the evolution pattern of precursor water inrush information in water-filled caves and to further reveal the matching mode of the information. The three-dimensional numerical method FLAC3D was used to simulate the evolution process of water inrush after damage to a water-blocking rock mass structure in a water-filled cave and to obtain the evolution pattern of precursor water-inrush information caused by the damage. The results show that the multifield response to the characteristic precursor information of the water-inrush pattern after the fracture of the water-blocking rock mass follows the order of stress-field displacement-field seepage-field. Further, the matching pattern of the information shows that the stress field increased first and then decreased, the displacement field always increased, and the seepage field increased first and then decreased.

Random Forests are powerful classification and regression tools that are commonly applied in machine learning and image processing. In the majority of random classification forests algorithms, the Gini index and the information gain ratio are commonly used for node splitting. However, these two kinds of node-split methods may pay less attention to the intrinsic structure of the attribute variables and fail to find attributes with strong discriminate ability as a group yet weak as individuals. In this paper, we propose an innovative method for splitting the tree nodes based on the cooperative game theory, from which some attributes with good discriminate ability as a group can be learned. This new random forests algorithm is called Cooperative Profit Random Forests (CPRF). Experimental comparisons with several other existing random classification forests algorithms are carried out on several real-world data sets, including remote sensing images. The results show that CPRF outperforms other existing Random Forests algorithms in most cases. In particular, CPRF achieves promising results in ocean front recognition.

Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, either linear or nonlinear, either supervised or unsupervised, either “shallow” or “deep”. Particularly, deep architectures are widely applied for representation learning in recent years, and have delivered top results in many tasks, such as image classification, object detection and speech recognition. In this paper, we review the development of data representation learning methods. Specifically, we investigate both traditional feature learning algorithms and state-of-the-art deep learning models. The history of data representation learning is introduced, while available online resources (e.g., courses, tutorials and books) and toolboxes are provided. At the end, we give a few remarks on the development of data representation learning and suggest some interesting research directions in this area.

Two high performance single crystal diamond (SCD) radiation detectors for neutrons and gamma ray have been fabricated. One was coated with a 5 μm 6LiF (95% enriched in 6Li) layer in order to detect the neutrons and the other one is without any conversion layer for comparison. The detectors are irradiated with the 241Am-Be and 252Cf neutron sources and 152Eu gamma source to test their performance. Under neutron sources, the neutron signal peak was clearly observed in the detector with the 6LiF conversion layer. Meanwhile, both detectors are responsive to the gamma rays at the low energy. The 152Eu gamma source was specially used to verify the gamma ray detection ability of the fabricated SCD detectors. The results show that the detector’s count rate and the gamma peak center position in the PHS depend on the incident gamma spectra critically. Combined, these results demonstrate that the design and fabrication process of the detectors are feasible.

A single crystal diamond (SCD) detector and a polycrystalline diamond (PCD) detector have been designed and fabricated using electronic grade CVD diamond. The fabricated detectors were tested for their dark current and X-ray photocurrent. It was found that the SCD and PCD detectors have superb signal to noise ratios (SNR) under X-ray irradiation from an Ag target with 10kV and 40kV accelerating voltage, 2000 and 7000 respectively for the SCD detector and 550 and 2000 for the PCD detector. The performance of these detectors using an 241Am α source was tested under different bias voltages and the results were benchmarked against a commercial SCD detector. The typical rise time of an α event in both of the fabricated detectors are about 1.2ns. The fabricated SCD detector has a 3.7% net energy resolution while that of the commercial detector is about 3.9%. The pulse height spectra are integrated and fitted to obtain the charge collection efficiency. For the fabricated SCD detector, this value is above 97% at bias 200V or beyond, which is 1-2% higher than that of the commercial detector at the same voltage. Finally, the fabricated PCD detector can also detect the presence of α particle although it only has a continuous and decreasing energy spectrum under α radiation. These results fully reveal that the fabricated SCD detector has good performance as a multifunctional detector for both X-ray and α radiation, and show great potential as neutron spectrometer as well.

Neutral beam injection (NBI) system has been proved to affect sawtooth activities through both producing energetic particles and supplying torque applied to the plasma. The impact of NBI on sawtooth crashes has been studied in the EAST tokamak, which is equipped with co- and counter NBIs. Statistical analysis shows that both strong co- and counter-NBI yield stronger sawtooth activities than cases when heating power is weak. A minimum sawtooth period is observed at a counter-NBI power of 0.2 MW. This is linked to zero-plasma rotation and a non-rotating precursor mode of the sawtooth instability. This indicates that the sawtooth instability is stabilized by plasma rotation as previously suggested. The fast-ion content differs between co- and counter-NBI, so energetic particles might additionally contribute to the sawtooth activities.

Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to obtain backward trajectories and then conduct clustering analysis is a common method to analyze potential sources and transmission paths of atmospheric particulate pollutants. Taking Qingdao (N36 E120) as an example, the global data assimilation system (GDAS 1°) of days from 2015 to 2018 provided by National Centers for Environmental Prediction (NCEP) is used to process the backward 72 h trajectory data of 3 arrival heights (10 m, 100 m, 500 m) through the HYSPLIT model with a data interval of 6 h (UTC 0:00, 6:00, 12:00, and 18:00 per day). Three common clustering methods of trajectory data, i.e., K-means, Hierarchical clustering (Hier), and Self-organizing maps (SOM), are used to conduct clustering analysis of trajectory data, and the results are compared with those of the HYSPLIT model released by National Oceanic and Atmospheric Administration (NOAA). Principal Component Analysis (PCA) is used to analyze the original trajectory data. The internal evaluation indexes of Davies–Bouldin Index (DBI), Silhouette Coefficient (SC), Calinski Harabasz Index (CH), and I index are used to quantitatively evaluate the three clustering algorithms. The results show that there is little information in the height data, and thus only two-dimensional plane data are used for clustering. From the results of clustering indexes, the clustering results of SOM and K-means are better than the Hier and HYSPLIT model. In addition, it is found that DBI and I index can help to select the number of clusters, of which DBI is preferred for cluster analysis.

In recent years, deep learning models have achieved remarkable successes in various applications, such as pattern recognition, computer vision, and signal processing. However, high-performance deep architectures are often accompanied by a large storage space and long computational time, which make it difficult to fully exploit many deep neural networks (DNNs), especially in scenarios in which computing resources are limited. In this paper, to tackle this problem, we introduce a method for compressing the structure and parameters of DNNs based on neuron agglomerative clustering (NAC). Specifically, we utilize the agglomerative clustering algorithm to find similar neurons, while these similar neurons and the connections linked to them are then agglomerated together. Using NAC, the number of parameters and the storage space of DNNs are greatly reduced, without the support of an extra library or hardware. Extensive experiments demonstrate that NAC is very effective for the neuron agglomeration of both the fully connected and convolutional layers, which are common building blocks of DNNs, delivering similar or even higher network accuracy. Specifically, on the benchmark CIFAR-10 and CIFAR-100 datasets, using NAC to compress the parameters of the original VGGNet by 92.96% and 81.10%, respectively, the compact network obtained still outperforms the original networks.

By contrast with the widely reported traditional two mirror-based Herriott cell, a three mirror-based dense pattern gas cell was proposed, of which the modeling and design were proven to be effective through a comparison between the simulated spot pattern and effective path length and those of the experimental results. A mechanical structure was designed to adjust the position/angle of the three mirrors for aligning the optical path. The experimentally measured reflection number was 60, resulting in an optical path length of ~11 m, which agrees well with the theoretical value of 10.95 m. Combined with a near-infrared laser with a center wavenumber located at an acetylene (C2H2) absorption line of 6521.2 cm−1, a C2H2 sensor system was established to verify the feasibility of the three mirror-based gas cell. Assisted by a data acquisition (DAQ) card, a LabVIEW platform was developed to generate the drive signal of the laser and acquire the second harmonic (2f) signal from the output of the detector. Through Allan variance analysis, the limit of detection (LoD) of the sensor system is 4.36 ppm at an average time of 0.5 s; as the average time exceeds 10 s, the LoD is

In cardiac tissues, the expression of multiple connexins (Cx40, Cx43, Cx45, and Cx30.2) is a requirement for proper development and function. Gap junctions formed by these connexins have distinct permeability and gating mechanisms. Since a single cell can express more than one connexin isoform, the formation of hetero-multimeric gap junction channels provides a tissue with an enormous repertoire of combinations to modulate intercellular communication. To study further the perm-selectivity and gating properties of channels containing Cx43 and Cx45, we studied two monoheteromeric combinations in which a HeLa cell co-transfected with Cx43 and Cx45 was paired with a cell expressing only one of these connexins. Macroscopic measurements of total conductance between cell pairs indicated a drastic reduction in total conductance for mono-heteromeric channels. In terms of Vj dependent gating, Cx43 homomeric connexons facing heteromeric connexons only responded weakly to voltage negativity. Cx45 homomeric connexons exhibited no change in Vj gating when facing heteromeric connexons. The distributions of unitary conductances (γj) for both mono-heteromeric channels were smaller than predicted, and both showed low permeability to the fluorescent dyes Lucifer yellow and Rhodamine123. For both mono-heteromeric channels, we observed flux asymmetry regardless of dye charge: flux was higher in the direction of the heteromeric connexon for MhetCx45 and in the direction of the homomeric Cx43 connexon for MhetCx43. Thus, our data suggest that co-expression of Cx45 and Cx43 induces the formation of heteromeric connexons with greatly reduced permeability and unitary conductance. Furthermore, it increases the asymmetry for voltage gating for opposing connexons, and it favors asymmetric flux of molecules across the junction that depends primarily on the size (not the charge) of the crossing molecules.