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Abstract Obesity represents a low‐grade chronic inflammation status, which is associated with compromised adaptive thermogenesis. However, the mechanisms underlying the defective activation of thermogenesis in chronic inflammation remain unclear. Here, a chronic inflammatory model is first estabolished by injecting mice with low‐dose lipopolysaccharide (LPS) before cold exposure, and then it is verified that LPS treatment can decrease the core body temperature of mice and alter the microbial distribution in epididymal white adipose tissue (eWAT). An adipose tissue‐resident bacterium Sphingomonas paucimobilis is identified as a potential inhibitor on the activation of brown fat and browning of inguinal WAT, resulting in defective adaptive thermogenesis. Mechanically, LPS and S. paucimobilis inhibit the production and release of 15‐HETE by suppressing its main metabolic enzyme 12 lipoxygenase (12‐LOX) and 15‐ Hydroxyeicosatetraenoic acid (15‐HETE) rescues the impaired thermogenesis. Interestingly, 15‐HETE directly binds to AMP‐activated protein kinase α (AMPKα) and elevates the phosphorylation of AMPK, leading to the activation of uncoupling protein 1 (UCP1) and mitochondrial oxidative phosphorylation (OXPHOS) complexes. Further analysis with human obesity subjects reveals that individuals with high body mass index displayed lower 15‐HETE levels. Taken together, this work improves the understanding of how chronic inflammation impairs adaptive thermogenesis and provides novel targets for alleviating obesity.

Abstract High‐frequency oscillatory activity in cognition‐related neural circuits during wakefulness consistently induces the growth of dendritic spines and axonal terminals. Although these structural changes are essential for cognitive functions, it is hypothesized that if these newly expanded structures fail to establish functional connections, they may become superfluous. Sleep is believed to facilitate the reduction of such redundant structures to maintain neural homeostasis. However, the mechanisms underlying this pruning process during sleep remain poorly understood. In this study, that melatonin type 3 receptors (MT3Rs) are selectively expressed in the stellate neurons of the medial entorhinal cortex (MEC) is demonstrated, an area where high melatonin levels are detected during sleep. Activation of MT3Rs during sleep initiates the shrinkage of dendritic spines in stellate neurons by downregulating neural network activity and dephosphorylating synaptic proteins in the MEC. This process is disrupted when MT3R expression is knocked down or when MT3Rs are blocked during sleep. Notably, interference with MT3Rs in the MEC during sleep impairs the acquisition of spatial memory but does not affect object memory acquisition following sleep. These findings reveal novel molecular mechanisms involving melatonin and MT3Rs in the regulation of dendritic spine shrinkage during sleep, which is crucial for the acquisition and consolidation of spatial memory.

BackgroundPulsed field ablation, as a non-thermal ablation modality, has received increasing attention. The aim of this study is to explore whether a reversible pulsed electric field (RPEF) can temporarily inhibit electrical conduction and provide a novel method for precise ablation of arrhythmia.MethodsRPEF energy was delivered from an ablation catheter to the atrium of six dogs, followed by a series of electrogram and histology assessments.ResultsRPEF ablation of ordinary myocardium resulted in an average reduction of 68.3% (range, 53.7%–83.8%) in electrogram amplitude, while 5 min later, the amplitude in eight electrograms returned to 77.9% (range, 72.4%–87.3%) of baseline. Similarly, the amplitude of the sinoatrial node electrograms reduced by an average of 73.0% (range, 60.2%–84.4%) after RPEF ablation, but recovered to 84.9% (range, 80.3%–88.5%) of baseline by 5 min. No necrotic change was detected in histopathology. Transient third-degree atrioventricular block occurred following the ablation of the maximum His potential sites with RPEF, the duration of which was voltage dependent. The histopathological results showed necrosis of the myocardium at the ablation sites but no injury to His bundle cells.ConclusionsRPEF can be applied to transiently block electrical conduction in myocardial tissues contributing to precise ablation.

Abstract Activities of daily living (ADLs) relate to people’s daily self-care activities, which reflect their living habits and lifestyle. A prior study presented a neural network model called STADLART for ADL routine learning. In this paper, we propose a cognitive model named Spatial-Temporal Episodic Memory for ADL (STEM-ADL), which extends STADLART to encode event sequences in the form of distributed episodic memory patterns. Specifically, STEM-ADL encodes each ADL and its associated contextual information as an event pattern and encodes all events in a day as an episode pattern. By explicitly encoding the temporal characteristics of events as activity gradient patterns, STEM-ADL can be suitably employed for activity prediction tasks. In addition, STEM-ADL can predict both the ADL type and starting time of the subsequent event in one shot. A series of experiments are carried out on two real-world ADL data sets: Orange4Home and OrdonezB, to estimate the efficacy of STEM-ADL. The experimental results indicate that STEM-ADL is remarkably robust in event retrieval using incomplete or noisy retrieval cues. Moreover, STEM-ADL outperforms STADLART and other state-of-the-art models in ADL retrieval and subsequent event prediction tasks. STEM-ADL thus offers a vast potential to be deployed in real-life healthcare applications for ADL monitoring and lifestyle recommendation.

Background: Recent advancements in artificial intelligence (AI) have significantly improved atrial fibrillation (AF) detection using electrocardiography (ECG) data obtained during sinus rhythm (SR). However, the utility of printed ECG (pECG) records for AF detection, particularly in developing countries, remains unexplored. This study aims to assess the efficacy of an AI-based screening tool for paroxysmal AF (PAF) using pECGs during SR. Methods: We analyzed 5688 printed 12-lead SR-ECG records from 2192 patients admitted to Beijing Chaoyang Hospital between May 2011 to August 2022. All patients underwent catheter ablation for PAF (AF group) or other electrophysiological procedures (non-AF group). We developed a deep learning model to detect PAF from these printed SR-ECGs. The 2192 patients were randomly assigned to training (1972, 57.3% with PAF), validation (108, 57.4% with PAF), and test datasets (112, 57.1% with PAF). We developed an applet to digitize the printed ECG data and display the results within a few seconds. Our evaluation focused on sensitivity, specificity, accuracy, F1 score, the area under the receiver-operating characteristic curve (AUROC), and precision-recall curves (PRAUC). Results: The PAF detection algorithm demonstrated strong performance: sensitivity 87.5%, specificity 66.7%, accuracy 78.6%, F1 score 0.824, AUROC 0.871 and PRAUC 0.914. A gradient-weighted class activation map (Grad-CAM) revealed the model’s tailored focus on different ECG areas for personalized PAF detection. Conclusions: The deep-learning analysis of printed SR-ECG records shows high accuracy in PAF detection, suggesting its potential as a reliable screening tool in real-world clinical practice.

Oxygen metabolism abnormality plays a crucial role in the pathogenesis of Alzheimer's disease (AD) via several mechanisms, including hypoxia, oxidative stress, and mitochondrial dysfunction. Hypoxia condition usually results from living in a high-altitude habitat, cardiovascular and cerebrovascular diseases, and chronic obstructive sleep apnea. Chronic hypoxia has been identified as a significant risk factor for AD, showing an aggravation of various pathological components of AD, such as amyloid β-protein (Aβ) metabolism, tau phosphorylation, mitochondrial dysfunction, and neuroinflammation. It is known that hypoxia and excessive hyperoxia can both result in oxidative stress and mitochondrial dysfunction. Oxidative stress and mitochondrial dysfunction can increase Aβ and tau phosphorylation, and Aβ and tau proteins can lead to redox imbalance, thus forming a vicious cycle and exacerbating AD pathology. Hyperbaric oxygen therapy (HBOT) is a non-invasive intervention known for its capacity to significantly enhance cerebral oxygenation levels, which can significantly attenuate Aβ aggregation, tau phosphorylation, and neuroinflammation. However, further investigation is imperative to determine the optimal oxygen pressure, duration of exposure, and frequency of HBOT sessions. In this review, we explore the prospects of oxygen metabolism in AD, with the aim of enhancing our understanding of the underlying molecular mechanisms in AD. Current research aimed at attenuating abnormalities in oxygen metabolism holds promise for providing novel therapeutic approaches for AD.

Abstract The key to skeleton-based action recognition is how to extract discriminative features from skeleton data. Recently, graph convolutional networks (GCNs) are proven to be highly successful for skeleton-based action recognition. However, existing GCN-based methods focus on extracting robust features while neglecting the information of feature distributions. In this work, we aim to introduce Fisher vector (FV) encoding into GCN to effectively utilize the information of feature distributions. However, since the Gaussian Mixture Model (GMM) is employed to fit the global distribution of features, Fisher vector encoding inevitably leads to losing temporal information of actions, which is demonstrated by our analysis. To tackle this problem, we propose a temporal enhanced Fisher vector encoding algorithm (TEFV) to provide more discriminative visual representation. Compared with FV, our TEFV model can not only preserve the temporal information of the entire action but also capture fine-grained spatial configurations and temporal dynamics. Moreover, we propose a two-stream framework (2sTEFV-GCN) by combining the TEFV model with the GCN model to further improve the performance. On two large-scale datasets for skeleton-based action recognition, NTU-RGB+D 60 and NTU-RGB+D 120, our model achieves state-of-the-art performance.

Paroxysmal atrial fibrillation originates most commonly in the pulmonary veins. However, the superior vena cava has proved to be arrhythmogenic in some cases. Pulsed field ablation, an emerging ablation technology, selectively affects myocardial tissue. Herein, we present a case of paroxysmal atrial fibrillation in a 64-year-old man who was admitted to our hospital for pulsed field ablation. The tachycardia was recurrent despite four successful pulmonary vein isolations. The superior vena cava was determined to be involved in arrhythmogenesis. The atrial fibrillation terminated immediately after the pulsed field ablation discharge at the superior vena cava.

Abstract Skeleton‐based action recognition is an inspired yet challenging task in computer vision. Recently, the latest graph convolutional network (GCN), which generalises well‐established convolutional neural networks to non‐Euclidean structures, is proven to be highly successful for action recognition from body skeleton data. However, the GCN architecture has not been fully studied. In this work, a Fisher vector (FV) encoding based GCN architecture (FV‐GCN) is proposed, which exceeds the limitations of existing GCN‐based methods by combining the GCN model with FV encoding. A channel‐wise spatial–temporal aggregation function to preserve spatial–temporal information in the whole action clip and integrate it into the FV‐GCN architecture is also presented. Since FV is different from the GCN structure, this hybrid architecture that incorporates the advantages of both algorithms can discover complementary information of feature representation effectively. On two challenging human action datasets, kinetics, and NTU‐RGBD, improved performance is demonstrated over the baseline method, and the FV‐GCN is better or comparable to some state‐of‐the‐art methods.

Abstract. Background:. Insufficient cerebral perfusion is suggested to play a role in the development of Alzheimer disease (AD). However, there is a lack of direct evidence indicating whether hypoperfusion causes or aggravates AD pathology. We investigated the effect of chronic cerebral hypoperfusion on AD-related pathology in humans. Methods:. We enrolled a group of cognitively normal patients (median age: 64 years) with unilateral chronic cerebral hypoperfusion. Regions of interest with the most pronounced hypoperfusion changes were chosen in the hypoperfused region and were then mirrored in the contralateral hemisphere to create a control region with normal perfusion. 11C-Pittsburgh compound-positron emission tomography standard uptake ratios and brain atrophy indices were calculated from the computed tomography images of each patient. Results:. The median age of the 10 participants, consisting of 4 males and 6 females, was 64 years (47–76 years). We found that there were no differences in standard uptake ratios of the cortex (volume of interest [VOI]: P = 0.721, region of interest [ROI]: P = 0.241) and grey/white ratio (VOI: P = 0.333, ROI: P = 0.445) and brain atrophy indices (Bicaudate, Bifrontal, Evans, Cella, Cella media, and Ventricular index, P > 0.05) between the hypoperfused regions and contralateral normally perfused regions in patients with unilateral chronic cerebral hypoperfusion. Conclusion:. Our findings suggest that chronic hypoperfusion due to large vessel stenosis may not directly induce cerebral β-amyloid deposition and neurodegeneration in humans.

Abstract Sevoflurane has been the most widely used inhaled anesthetics with a favorable recovery profile; however, the precise mechanisms underlying its anesthetic action are still not completely understood. Here the authors show that sevoflurane activates a cluster of urocortin 1 (UCN1+)/cocaine‐ and amphetamine‐regulated transcript (CART+) neurons in the midbrain involved in its anesthesia. Furthermore, growth hormone secretagogue receptor (GHSR) is highly enriched in sevoflurane‐activated UCN1+/CART+ cells and is necessary for sleep induction. Blockade of GHSR abolishes the excitatory effect of sevoflurane on UCN1+/CART+ neurons and attenuates its anesthetic effect. Collectively, their data suggest that anesthetic action of sevoflurane necessitates the GHSR activation in midbrain UCN1+/CART+ neurons, which provides a novel target including the nucleus and receptor in the field of anesthesia.

Objectives The aims of the study were to assess the management of low-density lipoprotein cholesterol (LDL-C) and the goal achievement, as well as to investigate the association between baseline LDL-C level, lipid-lowering treatment (LLT), and stroke recurrence in patients with ischaemic stroke or transient ischaemic attack (TIA).Design Our study was a post hoc analysis of the Third China National Stroke Registry (CNSR-III).Setting We derived data from the CNSR-III - a nationwide clinical registry of ischaemic stroke and TIA based on 201 participating hospitals in mainland China.Participants 15,166 patients were included in this study with demographic characteristics, etiology, imaging, and biological markers from August 2015 to March 2018.Primary and secondary outcome measures The primary outcome was a new stroke, LDL-C goal (LDL-C

Abstract The application of graph convolutional networks (GCN) in hyperspectral image (HSI) classification has become a promising method, thanks to its flexible convolution operation in any irregular image region. For the classification of HSI, GCN can extract more superpixel‐level features with a topological structure, in comparison to the traditional convolutional neural networks (CNNs) using fixed square kernels distilling pixel‐level features. To fully leverage the different levels of features, this study proposes a novel deep network referred to as a CNN‐combined graph residual network (C2GRN), which integrates the multilevel graph residual module and spectral‐spatial features continuous learning module. During the extraction of topology information using the former module, HSI pixels are divided into superpixels and served as input nodes of the module to reduce the computational complexity and obtain the multilevel spatial relevance between adjacent superpixels. Besides, for the latter module, the spectral‐spatial features are learnt continuously, which could obtain the finer pixel‐level features. Finally, the captured spectral‐spatial features of different levels are concatenated. This strategy could not only adequately utilize the correlation and difference of adjacent spatial but also obtain the finer and more valuable spectral‐spatial information, which makes a significant boost in the HSI classification. Additionally, the experiment results demonstrate the superiority and availability of the C2GRN on three benchmark datasets of HSI, compared with the state‐of‐the‐art methods for the classification of HSI.

Chen at al used fMRI to study the brain connectivity in two ageing cohorts and a mixed cohort of patients with neurodegenerative diseases. They demonstrated that with ageing the brain moves towards a more segregated modular structure, however this is disrupted by neurodegenerative diseases.

Left atrial appendage occlusion (LAAO) is an alternative to oral anticoagulation therapy in patients with atrial fibrillation who are at high risk for bleeding and thromboembolic events. Transesophageal echocardiography (TEE) is the standard modality for intraprocedural imaging during LAAO. We report a rare case of extensive submucosal esophageal hematoma that developed after a TEE-guided LAAO procedure. The cause, management, and prevention of this complication are explored in depth in this report.

Abstract Objective Cerebral microbleeds (CMBs) is a subtype of cerebral small vessel disease. Their underlying pathogenesis remains unclear. The aim of this study was to investigate the association between infectious burden (IB) and CMBs. Methods Seven hundred and seventy‐three consecutive patients who were hospitalized in the Department of Neurology in General Hospital of Western Theater Command without severe neurological symptoms were recruited and selected in this pilot cross‐sectional study. CMBs were assessed using the susceptibility‐weighted imaging sequence of magnetic resonance imaging. Immunoglobulin G antibodies against common pathogens, including herpes simplex virus (HSV)‐1, HSV‐2, cytomegalovirus (CMV), Chlamydia pneumoniae (C. pneumoniae), Mycoplasma pneumoniae (M. pneumoniae), Epstein‐Barr virus (EBV), Helicobacter pylori (HP), and Borrelia burgdorferi (B. burgdorferi), were measured by commercial ELISA assays. IB was defined as a composite serologic measure of exposure to these common pathogens. Results Patients with and without CMBs were defined as the CMBs group (n = 76) and the non‐CMBs group (n = 81), respectively. IB was significantly different between the CMBs and non‐CMBs groups. After adjusted for other risk factors, the increased IB was independently associated with the presence of CMBs (P = 0.031, OR = 3.00, 95% CI [1.11–8.15]). IB was significantly positively associated with the number of CMBs (Spearman ρ = 0.653, P

Abstract As the most common form of dementia, Alzheimer’s disease (AD) is characterized by progressive cognitive impairments and constitutes a major social burden. Currently, the invasiveness and high costs of tests have limited the early detection and intervention of the disease. As a unique window of the brain, retinal changes can reflect the pathology of the brain. In this review, we summarize current understanding of retinal structures in AD, mild cognitive impairment (MCI) and preclinical AD, focusing on neurodegeneration and microvascular changes measured using optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) technologies. The literature suggests that the impairment of retinal microvascular network and neural microstructure exists in AD, MCI and even preclinical AD. These findings provide valuable insights into a better understanding of disease pathogenesis and demonstrate that retinal changes are potential biomarkers for early diagnosis of AD and monitoring of disease progression.

Objective: Stereoelectroencephalogram (SEEG) has been widely adapted to detect the electrical activity of patients with epilepsy. Due to the low-quality, large-amount, high-dimensionality characteristics of SEEG data, it is still challenging to comprehensively employ the SEEG signals to automatically and precisely determine the seizure onset zone (SOZ). This is because there is lack of an effective criterion for clinicians to select the target electrodes, which is of great importance for SOZ localization. Methods: We propose a SOZ localization method via analyzing the long-term SEEG monitoring for preoperative planning of epilepsy surgery. Considering that high frequency oscillations can reflect physiological brain activity of epileptic patients, we first extract the high-frequency features of the SEEG signals and utilize the convolutional neural network (CNN) to detect the interictal and seizure segments. Then we propose a novel criterion, namely adaptive high frequency epileptogenicity index (AHFEI), to determine the target electrodes. Results: We compare our SEEG-determined target electrodes with three preoperative planning of successful focal epilepsy resective surgery cases, finding that most localization results of our method are in consistent with clinical successful decision making, while the performance of our method outperforms than the state-of-the-art method for SOZ localization. Conclusion: Our SEEG-determined SOZ localization method can assist clinicians in preliminarily selecting the potential target electrodes according to long-term SEEG data automatically and effectively. Clinical Impact: The proposed automatic SOZ localization method has achieved satisfactory performance in the preliminary study, which has the great potential to be integrated into function-structure fused clinical decision-making system.

Background Small single subcortical infarction (SSSI) may be classified as parent artery disease-related or only branch involved according to the stenosis of parent artery. The study aimed to evaluate short-term and long-term prognoses and the effectiveness of antiplatelet therapy in SSSI.Methods We prospectively enrolled 2890 patients with SSSI from the Third China National Stroke Registry (CNSR-III) database from August 2015 to March 2018. We assessed clinical outcomes and antiplatelet treatment effects in patients with SSSI with and without parent artery stenosis (PAS) identified by magnetic resonance angiography.Results Among 2890 patients with SSSI in the perforator territory of the middle cerebral artery and the basilar artery, there were 680 (23.53%) patients with PAS and 2210 (76.47%) patients without PAS, respectively. After adjusting for potential confounders, the PAS group had a greater initial stroke severity (OR 1.262, 95% CI 1.058 to 1.505; p=0.0097) and a higher risk of ischaemic stroke recurrence at 3 months (OR 2.266, 95% CI 1.631 to 3.149; p

Background: Peripheral biomarkers may be affected by various factors, their reliability in reflecting local cardiac inflammatory status in patients with atrial fibrillation (AF) needs further exploration. This prospective study was aimed to investigate the relationship between circulating biomarkers and local cardiac inflammation measured by epicardial adipose tissue (EAT) activity via18F-fluorodeoxyglucose (FDG) imaging in AF patients.Methods: From 2017 to 2018, 83 AF patients [43 persistent AF (PsAF) and 40 paroxysmal AF (PAF)] referred for radiofrequency catheter ablation (RFCA) were recruited. Pre- and post-RFCA blood samples were collected to measure IL-6, IL-8, IL-10, IL-18, TNF-α, Hsp27, Hsp60, Hsp70, PDGF-BB, MMP-2, MMP-9, MPO, TGF-β1, Gal-3, and sST2. Pre-RFCA FDG images were obtained to assess EAT activity. Sixty-seven patients (35 PAF and 32 PsAF) received RFCA were regularly followed for 27 (24, 29) months.Results: Higher hsCRP and IL-6 and lower TGF-β1 were demonstrated in PsAF patients compared with PAF patients. Multivariate logistic regression analysis demonstrated that Gal-3 (OR: 1.221, 95% CI: 1.024–1.456, P = 0.026) and MPO (OR: 1.002, 95% CI: 1.001–1.003, P = 0.027) were independently correlated with EAT activity. The percentage decrease of Hsp60 linearly correlated with that of EAT activity post-RFCA (Spearman rs = 0.455, P = 0.019). Seventeen patients (10 PsAF and 7 PAF) had AF recurrence, but none of the selected biomarkers were predictive of post-RFCA recurrence.Conclusion: Our findings demonstrated that in patients with AF, Gal-3 correlated with local cardiac inflammation, and Hsp60 was associated with the alleviation of cardiac inflammation after RFCA.

In order to mine the local behavior and dynamic characteristic of batch process data for effective process monitoring, a two-dimensional localized dynamic support vector data description (TLDSVDD) method is proposed in this article. The main contributions of the proposed method include three aspects. Firstly, considering that batch process variables may behave differently at each operation stage, a two-dimensional localization strategy is designed to mine the local behaviors of process data from the perspective of the variable dimension and the sample dimension. Secondly, for each local data segment, the slow feature analysis is applied to build the local dynamic sub-models, which can monitor the static and dynamic process changes simultaneously. Lastly, the model ensemble strategy based on Bayesian inference is employed and two holistic monitoring statistics are developed to indicate the process running status. The proposed method not only extracts the local process behaviors, but also determines whether the process fault belongs to the dynamic or static change. Finally, one case study on the simulated industrial batch process is carried out to exhibit the method performance.

Over the past decade, a growing number of studies have investigated the relationship between the structure and function of human brain by predicting the resting-state functional connectivity (rsFC) from structural connectivity (SC). Yet how the whole-brain patterns of FC emerge from SC still remains incompletely understood. Unlike previous studies, here we propose an alternative approach for addressing this issue by predicting SC from rsFC. We first hypothesize that the functional couplings among brain areas at rest are shaped at least in three phases temporally: the initial direct interplay between brain areas, the communications within and between network modules, and followed by the indirect interactions ascribed to indirect structural pathways. We then introduce a network deconvolution (ND) algorithm inspired from the mechanism of cell differentiation, named CDA, to distinguish the direct dependencies from the functional network followed by a weight trimming algorithm based on Euclidean distance kernel function for shrinking the modular effects. Finally, we keep those region pairs with shorter shortest path length (SPL) together with shorter Euclidean distance as the structural connections. We apply the model and the algorithms to three intensively studied group averaged empirical connectome datasets with different parcellation resolutions and the results demonstrate that the predicted intrahemispheric structural connections and the weights distribution are highly consistent with the empirical SC derived from diffusion magnetic resonance imaging (dMRI) and probabilistic tractography, thus strongly supporting the model and algorithms proposed.

Slow feature analysis (SFA) has been adopted for control performance monitoring (CPM) recently. However, due to the selection criterion of the dominant slow features (SFs) and the performance monitoring statistics, the traditional SFA-based CPM method has certain limitations in monitoring model predictive control (MPC) performance and fails to distinguish the direction of performance change, i.e., whether the performance becomes better or worse. In order to solve the above problems, an MPC performance monitoring and grading strategy based on improved SFA is proposed in this paper. First, a new criterion for selecting dominant SFs is proposed. On this basis, two combined monitoring indices are built to monitor steady-state and dynamic characteristics of MPC systems, respectively. Besides, an SFA-based predictable performance assessment index is proposed to indicate the direction of performance change. Finally, a performance grading strategy based on improved SFA is established to classify current MPC performance to four levels. Two simulation examples demonstrate the effectiveness and superiority of the proposed method.

In order to more accurately recognize and understand the working condition of sucker-rod pumping wells so as to maximally reduce the cost and increase the profit, a large amount of data has been collected during oil production with sucker-rod pumping wells. In view of the sucker-rod pumping production system in big data and IOT (Internet of things) of oil-gas production, to solve the limitations in the existing working condition recognition research and further improve the recognition accuracy and practicality with fewer labelled working condition samples by utilizing the measured parameters from multiple information sources effectively, in this paper, a novel working condition recognition method based on Hessian-regularized weighted multi-view canonical correlation analysis is proposed. Firstly, the features of the measured ground dynamometer cards, electrical power, wellhead temperature and wellhead pressure data are extracted as four different feature views based on the prior information, empirical knowledge and mechanism analysis. Then a model based on Hessian-regularized weighted multi-view canonical correlation analysis and cosine nearest neighbour multi-classification algorithm is established. The proposed method is applied to the recognition of eleven kinds of working conditions from sixty sucker-rod pumping wells in a certain block in Shengli Oilfield, China. In the case where there are small number of labelled training samples, based on cosine nearest neighbour classification method, the recognition rates are increased by 3.44% and 1.5% compared with traditional recognition methods based on measured ground dynamometer cards and electrical power data, respectively. In contrast to methods based on traditional multi-sources of feature connection, multi-view canonical correlation analysis as well as the unweighted Hessian-regularized multi-view canonical correlation analysis, the recognition rates are increased by 4.46%, 2.21% and 1.62%, respectively.

Abstract Background The underlying mechanism of brain glucose hypometabolism, an invariant neurodegenerative feature that tightly correlates with cognitive impairment and disease progression of Alzheimer’s disease (AD), remains elusive. Methods Positron emission tomography with 2-[18F]fluoro-2-deoxy-d-glucose (FDG-PET) was used to evaluate brain glucose metabolism, presented as the rate of 2-[18F]fluoro-2-deoxy-d-glucose standardized uptake value ratio (FDG SUVR) in patients with AD or control subjects and in mice with or without thiamine deficiency induced by a thiamine-deprived diet. Brain amyloid-β (Aβ) deposition in patients with clinically diagnosed AD was quantified by performing assays using 11C-Pittsburgh compound B PET. The levels of thiamine metabolites in blood samples of patients with AD and control subjects, as well as in blood and brain samples of mice, were detected by high-performance liquid chromatography with fluorescence detection. Results FDG SUVRs in frontal, temporal, and parietal cortices of patients with AD were closely correlated with the levels of blood thiamine diphosphate (TDP) and cognitive abilities, but not with brain Aβ deposition. Mice on a thiamine-deprived diet manifested a significant decline of FDG SUVRs in multiple brain regions as compared with those in control mice, with magnitudes highly correlating with both brain and blood TDP levels. There were no significant differences in the changes of FDG SUVRs in observed brain regions between amyloid precursor protein/presenilin-1 and wild-type mice following thiamine deficiency. Conclusions We demonstrate, for the first time to our knowledge, in vivo that TDP reduction strongly correlates with brain glucose hypometabolism, whereas amyloid deposition does not. Our study provides new insight into the pathogenesis and therapeutic strategy for AD.

Deep learning has recently attracted extensive attention and developed significantly in remote sensing image super-resolution. Although remote sensing images are composed of various scenes, most existing methods consider each part equally. These methods ignore the salient objects (e.g., buildings, airplanes, and vehicles) that have more complex structures and require more attention in recovery processing. This paper proposes a saliency-guided remote sensing image super-resolution (SG-GAN) method to alleviate the above issue while maintaining the merits of GAN-based methods for the generation of perceptual-pleasant details. More specifically, we exploit the salient maps of images to guide the recovery in two aspects: On the one hand, the saliency detection network in SG-GAN learns more high-resolution saliency maps to provide additional structure priors. On the other hand, the well-designed saliency loss imposes a second-order restriction on the super-resolution process, which helps SG-GAN concentrate more on the salient objects of remote sensing images. Experimental results show that SG-GAN achieves competitive PSNR and SSIM compared with the advanced super-resolution methods. Visual results demonstrate our superiority in restoring structures while generating remote sensing super-resolution images.

Unravelling how the human brain structure gives rise to function is a central question in neuroscience and remains partially answered. Recent studies show that the graph Laplacian of the human brain’s structural connectivity (SC) plays a dominant role in shaping the pattern of resting-state functional connectivity (FC). The modeling of FC using the graph Laplacian of the brain’s SC is limited, owing to the sparseness of the Laplacian matrix. It is unable to model the negative functional correlations. We extended the graph Laplacian to the hypergraph p-Laplacian in order to describe better the nonlinear and high-order relations between SC and FC. First we estimated those possible links showing negative correlations between the brain areas shared across subjects by statistical analysis. Then we presented a hypergraph p-Laplacian model by embedding the two matrices referring to the sign of the correlations between the brain areas relying on the brain structural connectome. We tested the model on two experimental connectome datasets and evaluated the predicted FC by estimating its Pearson correlation with the empirical FC matrices. The results showed that the proposed diffusion model based on hypergraph p-Laplacian can predict functional correlations more accurately than the models using graph Laplacian as well as hypergraph Laplacian.

In recent years, the application of deep learning has achieved a huge leap in the performance of remote sensing image super-resolution (SR). However, most of the existing SR methods employ bicubic downsampling of high-resolution (HR) images to obtain low-resolution (LR) images and use the obtained LR and HR images as training pairs. This supervised method that uses ideal kernel (bicubic) downsampled images to train the network will significantly degrade performance when used in realistic LR remote sensing images, usually resulting in blurry images. The main reason is that the degradation process of real remote sensing images is more complicated. The training data cannot reflect the SR problem of real remote sensing images. Inspired by the self-supervised methods, this paper proposes a cross-dimension attention guided self-supervised remote sensing single-image super-resolution method (CASSISR). It does not require pre-training on a dataset, only utilizes the internal information reproducibility of a single image, and uses the lower-resolution image downsampled from the input image to train the cross-dimension attention network (CDAN). The cross-dimension attention module (CDAM) selectively captures more useful internal duplicate information by modeling the interdependence of channel and spatial features and jointly learning their weights. The proposed CASSISR adapts well to real remote sensing image SR tasks. A large number of experiments show that CASSISR has achieved superior performance to current state-of-the-art methods.

Friction during the hot extrusion of Inconel 625 superalloy tubes causes severe wear of the mold and plays a decisive role in the quality of the workpieces. In this paper, a ball-to-disk method was utilized to investigate the tribological behavior of Inconel 625 using two different tribological pairs, i.e., GCr15 and Si3N4, at room and elevated temperatures. Friction coefficient, specific wear rate, and morphology of worn surfaces were systematically characterized. It was found that the friction coefficients for both tribological pairs generally increased as the testing temperature increased, while the specific wear rate increased firstly and then decreased with the rise of temperature. Along with the increasing sliding speed, the friction coefficient between Inconel 625 and Si3N4 decreased monotonically, while the specific wear rate increased firstly and then decreased. Under any given testing condition, the friction coefficient and specific wear rate of the Inconel 625 for Inconel/Si3N4 pair are less than those of the Inconel/GCr15 pair. The main wear mechanisms between GCr15 and Inconel 625 are adhesive and fatigue wear at all testing temperatures. The wear mechanisms between Si3N4 and Inconel 625 are adhesive and abrasive wear at room temperature but fatigue wear at 500°C. Our findings indicate that the use of ceramic molds in the hot extrusion of Inconel 625 may significantly improve the surface qualities of the product and reduce the wear of the mold.

Support vector data description (SVDD) has been widely applied to batch process fault detection. However, it often performs poorly, especially when incipient faults occur, because it only considers the shallow data feature and omits the probabilistic information of features. In order to provide better monitoring performance on incipient faults in batch processes, an improved SVDD method, called deep probabilistic SVDD (DPSVDD), is proposed in this work by integrating the convolutional autoencoder and the probability-related monitoring indices. For mining the hidden data features effectively, a deep convolutional features extraction network is designed by a convolutional autoencoder, where the encoder outputs and the reconstruction errors are used as the monitor features. Furthermore, the probability distribution changes of these features are evaluated by the Kullback-Leibler (KL) divergence so that the probability-related monitoring indices are developed for indicating the process status. The applications to the benchmark penicillin fermentation process demonstrate that the proposed method has a better monitoring performance on the incipient faults in comparison to the traditional SVDD methods.

Due to the wide application of model predictive control (MPC) in industrial processes, the assessment of MPC performance is essential to ensure product quality and improve energy efficiency. Recently, the slow feature analysis (SFA) algorithm has been successfully applied to assess the performance of MPC. However, the disadvantage of the traditional SFA-based predictable index is that it can only extract one-step predictable information in the monitored variables. In order to better mine the predictable information contained in the monitored variables with large lag, an enhanced method to assess MPC performance based on multi-step SFA (MSSFA) is proposed. Based on the relationship between the slowness of slow features (SFs) and data predictability, an MSSFA model SFA(τ) is built through extending the temporal derivatives of the SFs from one step to multiple steps to extract multi-step predictable information in the monitored variables, which is used to construct a multi-step predictable index. Then, the predictable information in the SFs is further extracted for enhancing the multi-step predictable index to improve its sensitivity to performance changes. The effectiveness of the proposed method has been verified through two process simulation examples.

At present, nonparametric subspace classifiers, such as collaborative representation-based classification (CRC) and sparse representation-based classification (SRC), are widely used in many pattern-classification and -recognition tasks. Meanwhile, the spatial pyramid matching (SPM) scheme, which considers spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper, we first introduce the spatial pyramid matching scheme to remote-sensing (RS)-image scene-classification tasks to improve performance. Then, we propose a weighted spatial pyramid matching collaborative-representation-based classification method, combining the CRC method with the weighted spatial pyramid matching scheme. The proposed method is capable of learning the weights of different subregions in representing an image. Finally, extensive experiments on several benchmark remote-sensing-image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm when compared with state-of-the-art approaches.

In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.

Co-training is a major multi-view learning paradigm that alternately trains two classifiers on two distinct views and maximizes the mutual agreement on the two-view unlabeled data. Traditional co-training algorithms usually train a learner on each view separately and then force the learners to be consistent across views. Although many co-trainings have been developed, it is quite possible that a learner will receive erroneous labels for unlabeled data when the other learner has only mediocre accuracy. This usually happens in the first rounds of co-training, when there are only a few labeled examples. As a result, co-training algorithms often have unstable performance. In this paper, Hessian-regularized co-training is proposed to overcome these limitations. Specifically, each Hessian is obtained from a particular view of examples; Hessian regularization is then integrated into the learner training process of each view by penalizing the regression function along the potential manifold. Hessian can properly exploit the local structure of the underlying data manifold. Hessian regularization significantly boosts the generalizability of a classifier, especially when there are a small number of labeled examples and a large number of unlabeled examples. To evaluate the proposed method, extensive experiments were conducted on the unstructured social activity attribute (USAA) dataset for social activity recognition. Our results demonstrate that the proposed method outperforms baseline methods, including the traditional co-training and LapCo algorithms.

The three-stage human brain memory model is incorporated into a multiagent coevolutionary process for finding the best match of the appearance of an object, and a memory-based multiagent coevolution algorithm for robust tracking the moving objects is presented in this paper. Each agent can remember, retrieve, or forget the appearance of the object through its own memory system by its own experience. A number of such memory-based agents are randomly distributed nearby the located object region and then mapped onto a 2D lattice-like environment for predicting the new location of the object by their coevolutionary behaviors, such as competition, recombination, and migration. Experimental results show that the proposed method can deal with large appearance changes and heavy occlusions when tracking a moving object. It can locate the correct object after the appearance changed or the occlusion recovered and outperforms the traditional particle filter-based tracking methods.