This platform is readily constructed based on excellent catalytic activity of novel nanocomposite (Pd NFs/C60-NH2), and demonstrated remarkable advantages such as rapidity and universality. Highly ...degradation of 4-nitrophenol (4-NP) on remarkable UV–vis spectra technology.
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In this study, we report the synthesis of novel palladium nanoflowers (Pd NFs) on amino-functionalized fullerene (C60-NH2) by hydrothermal self-assembly growth using ethylenediamine (EA) as a functional reagent. The successful formation of Pd nanoflowers supported amino-functionalized fullerene (C60-NH2/Pd NFs) is evidenced by UV–vis and powder X-ray diffraction (XRD). The morphology of Pd NFs over the C60-NH2 surface has been investigated by high-resolution transmission electron microscopy (TEM) and Fourier-transform infrared (FT-IR) techniques. The supported Pd nanoflowers (Pd NFs/C60-NH2) exhibit remarkably superior catalytic activity toward the reduction of 4-nitrophenol (4-NP). It exhibits remarkable UV–vis spectra response from 4-nitrophenol to 4-aminophenol (4-AP) (99% in 2.0 min) with a turnover frequency of 12.35 min-1. Its excellent catalytic stability and durability offer the promising application in catalysis.
Fatigued driving is one of the major causes of traffic accidents. Frequent repetition of driving behavior for a long time may lead to driver fatigue, which is closely related to the central nervous ...system. In the present work, we designed a fatigue driving simulation experiment and collected the electroencephalogram (EEG) signals. Complex network theory was introduced to study the evolution of brain dynamics under different rhythms of EEG signals during several periods of the simulated driving. The results show that as the fatigue degree deepened, the functional connectivity and the clustering coefficients increased while the average shortest path length decreased for the delta rhythm. In addition, there was a significant increase of the degree centrality in partial channels on the right side of the brain for the delta rhythm. Therefore, it can be concluded that driving fatigue can cause brain complex network characteristics to change significantly for certain brain regions and certain rhythms. This exploration may provide a theoretical basis for further finding objective and effective indicators to evaluate the degree of driving fatigue and to help avoid fatigue driving.
Ginsenoside Rh2 (G-Rh2), a rare ginsenoside isolated from red ginseng, has considerable anti-cancer activity and induces apoptosis in a variety of cancer cells, but its activity in esophageal cancer ...cells is unclear. In this study, we examined the cytotoxic activity of (20S) G-Rh2 in highly differentiated esophageal squamous ECA109 cells and poorly differentiated esophageal squamous TE-13 cells. (20S) G-Rh2 exerted intense cytotoxicity in ECA109 and TE-13 cells with an IC50 of 2.9 and 3.7 μg/mL, respectively. After treatment with G-Rh2, Bcl-2, and Bcl-xL, the two main anti-apoptosis Bcl-2 family proteins upregulated, and Bax and Bak, the two key pro-apoptosis proteins translocated to mitochondria in both cell lines. At the same time, cytochrome c and Smac released from mitochondria, followed by caspase-9 activation, indicating that a mitochondria-mediated intrinsic apoptosis pathway was activated in both cell lines upon treatment with (20S) G-Rh2. It is noteworthy that (20S) G-Rh2 upregulated the transcription and protein expression of two death receptors, Fas and DR5, and subsequently activated Caspase-8 in the TE-13 cells but not in the ECA109 cells. Taken together, we demonstrated the potent anti-esophageal cancer cell activity of (20S) G-Rh2 and showed its working mechanism in two differentiated esophageal cancer cells, which can provide important evidence for developing an effective strategy for anti-esophageal cancer treatment.
The recognition and localization of strawberries are crucial for automated harvesting and yield prediction. This article proposes a novel RTF-YOLO (RepVgg-Triplet-FocalLoss-YOLO) network model for ...real-time strawberry detection. First, an efficient convolution module based on structural reparameterization is proposed. This module was integrated into the backbone and neck networks to improve the detection speed. Then, the triplet attention mechanism was embedded into the last two detection heads to enhance the network’s feature extraction for strawberries and improve the detection accuracy. Lastly, the focal loss function was utilized to enhance the model’s recognition capability for challenging strawberry targets, which thereby improves the model’s recall rate. The experimental results demonstrated that the RTF-YOLO model achieved a detection speed of 145 FPS (frames per second), a precision of 91.92%, a recall rate of 81.43%, and an mAP (mean average precision) of 90.24% on the test dataset. Relative to the baseline of YOLOv5s, it showed improvements of 19%, 2.3%, 4.2%, and 3.6%, respectively. The RTF-YOLO model performed better than other mainstream models and addressed the problems of false positives and false negatives in strawberry detection caused by variations in illumination and occlusion. Furthermore, it significantly enhanced the speed of detection. The proposed model can offer technical assistance for strawberry yield estimation and automated harvesting.
Increasing production and application of nanomaterials lead to their environmental release possible. The nanomaterials with different properties may transport together in porous media, and ...consequently affect their environmental fates. In this study, column experiments were conducted to investigate the co-transport of two typical nanomaterials, graphene oxide (GO) and nano-titanium dioxide (nTiO2), in saturated quartz sand in NaCl and CaCl2 electrolyte solutions under both favorable and unfavorable conditions. The breakthrough curves as well as the retained profiles of single and binary nanoparticles were examined. The results indicated that nTiO2 significantly enhanced the GO retention under all examined conditions, especially at lower pH, higher ionic strength and the presence of divalent cation Ca2+. This might be attributed to the formation of less negatively charged and larger-sized GO-nTiO2 agglomerates as well as the increased retention sites on sand surface by preferentially deposited nTiO2. However, GO merely slightly enhanced the transport of nTiO2 in NaCl solutions, whereas had negligible effect on nTiO2 transport and retention in CaCl2 solutions. The highly hydrophilic and mobile GO served as a carrier and facilitated the transport of nTiO2 in NaCl solutions. In CaCl2 solutions, the strong attachment affinity between positively charged nTiO2 and negatively charged quartz sand (at pH 4.5), and dramatical accumulation of large nTiO2 agglomerates near the column inlets (at pH 6.5) led to significant deposition of nTiO2 on quartz sand. The co-presence of GO failed to counteract the retention of nTiO2 particles on sand.
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•nTiO2 inhibited GO transport, especially at lower pH, higher ionic strength and the presence of Ca2+.•The formation of GO-nTiO2 agglomerates and increased retention sites by deposited nTiO2 decreased GO transport.•Highly hydrophilic and mobile GO slightly enhanced the transport of nTiO2 in NaCl solutions.•GO had negligible effects on the transport of nTiO2 in CaCl2 solutions.
Capsule: nTiO2 significantly inhibits the transport of GO, whereas GO has slight or negligible enhancement effects on the transport of nTiO2 in saturated quartz sand.
Background
The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as ...Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.
Objective
The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.
Methods
First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.
Results
Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.
Conclusion
These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.
Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the ...primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.
Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD ...medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.
This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.
First, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.
Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.
These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.
Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver’s attention and vigilance, manifesting a fatigue ...effect. This paper proposes a means of revealing the effects of driving fatigue on the brain’s information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain’s local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.
Severe combined immunodeficiency (SCID) is a group of fatal primary immunodeficiencies characterized by the severe impairment of T-cell differentiation. IL7R deficiency is a rare form of SCID that ...usually presents in the first months of life with severe and opportunistic infections, failure to thrive, and a high risk of mortality unless treated. Although recent improvements in early diagnosis have been achieved through newborn screening, few IL7R-related SCID patients had been reported in the Chinese population.
Here, we retrospectively analyzed a case of SCID in a 5-month-old girl with symptoms, including severe T-cell depletion, recurrent fever, oral ulcers, pneumonia, hepatosplenomegaly, bone marrow hemophagocytosis, and bacterial and viral infections. Whole-exome sequencing (WES), quantitative PCR (qPCR), and chromosome microarray analysis (CMA) were performed to identify the patient's genetic etiology. We identified a 268 kb deletion and a splicing variant, c.221 + 1G > A, in the proband. These two variants of IL7R were inherited from the father and mother.
To our knowledge, this is the first report of whole IL7R gene deletion in combination with a pathogenic splicing variant in a patient with SCID. This deletion also expands the pathogenic variation spectrum of SCID caused by IL7R. The incorporation of exome-based copy number variant analysis makes WES a powerful molecular diagnostic technique for the clinical diagnosis of pediatric patients.
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