A magnetic CdS quantum dot (Fe3O4/polydopamine (PDA)/CdS) was synthesized through a facile and convenient method from inexpensive starting materials. Characterization of the prepared catalyst was ...performed by means of FTIR spectroscopy, XRD, SEM, TEM, energy‐dispersive X‐ray spectroscopy, and vibrating‐sample magnetometer techniques. Fe3O4/PDA/CdS was found to be a highly active photocatalyst for the amidation of aromatic aldehydes by using air as a clean oxidant under mild conditions. The photocatalyst can be recovered by magnetic separation and successfully reused for five cycles without considerable loss of its catalytic activity.
Going greener: Magnetic CdS quantum dots are designed for application as an efficient photocatalyst for the oxidative amidation of aldehydes by using air as the oxidant under mild conditions. The photocatalyst can be recovered by magnetic separation and successfully reused in five cycles without considerable loss of catalytic activity.
Fast End-to-End Trainable Guided Filter Huikai Wu; Shuai Zheng; Junge Zhang ...
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Conference Proceeding
Odprti dostop
Image processing and pixel-wise dense prediction have been advanced by harnessing the capabilities of deep learning. One central issue of deep learning is the limited capacity to handle joint ...upsampling. We present a deep learning building block for joint upsampling, namely guided filtering layer. This layer aims at efficiently generating the high-resolution output given the corresponding low-resolution one and a high-resolution guidance map. The proposed layer is composed of a guided filter, which is reformulated as a fully differentiable block. To this end, we show that a guided filter can be expressed as a group of spatial varying linear transformation matrices. This layer could be integrated with the convolutional neural networks (CNNs) and jointly optimized through end-to-end training. To further take advantage of end-to-end training, we plug in a trainable transformation function that generates task-specific guidance maps. By integrating the CNNs and the proposed layer, we form deep guided filtering networks. The proposed networks are evaluated on five advanced image processing tasks. Experiments on MIT-Adobe FiveK Dataset demonstrate that the proposed approach runs 10-100Ã- faster and achieves the state-of-the-art performance. We also show that the proposed guided filtering layer helps to improve the performance of multiple pixel-wise dense prediction tasks. The code is available at https://github.com/wuhuikai/DeepGuidedFilter.
Detecting visually salient regions in images is one of the fundamental problems in computer vision. We propose a novel method to decompose an image into large scale perceptually homogeneous elements ...for efficient salient region detection, using a soft image abstraction representation. By considering both appearance similarity and spatial distribution of image pixels, the proposed representation abstracts out unnecessary image details, allowing the assignment of comparable saliency values across similar regions, and producing perceptually accurate salient region detection. We evaluate our salient region detection approach on the largest publicly available dataset with pixel accurate annotations. The experimental results show that the proposed method outperforms 18 alternate methods, reducing the mean absolute error by 25.2% compared to the previous best result, while being computationally more efficient.
Multiple studies have investigated the effect of perioperative blood transfusion (PBT) for patients with radical cystectomy (RC), but the results have been inconsistent. We conducted a systematic ...review and meta-analysis to investigate the relationship between PBT and the clinical outcomes of RC patients.
We searched MEDLINE, EMBASE, the Cochrane library and BIOSIS previews to identify relevant literature for studies that focused on the relationship of PBT and outcomes of patients undergoing RC. A fixed or random effects model was used in this meta-analysis to calculate the pooled hazard ratio (HR) with 95% confidence intervals (CIs).
A total of 7080 patients in 6 studies matched the selection criteria. Aggregation of the data suggested that PBT in patients who underwent RC correlated with increased all-cause mortality, cancer-specific mortality and cancer recurrence. The combined HRs were 1.19 (n = 6 studies, 95% CI: 1.11-1.27, Z = 4.71, P<0.00001), 1.17 (n = 4 studies, 95% CI: 1.06-1.30, Z = 3.06, P = 0.002), 1.14 (n = 3 studies, 95% CI: 1.03-1.27, Z = 2.50, P = 0.01), respectively. The all-cause mortality associated with PBT did not vary by the characteristics of the study, including number of study participants, follow-up period and the median blood transfusion ratio of the study.
Our data showed that PBT significantly increased the risks of all-cause mortality, cancer-specific mortality and cancer recurrence in patients undergoing RC for bladder cancer.
A layered covalent organic framework (COF) material based on an imine-linkage with high thermal and chemical stability was prepared using a deep eutectic solvent (DES) as the green medium. The ...as-synthesized COF materials were modified with catalytic copper. This crystalline and highly porous catalyst shows excellent photocatalytic performance in visible-light-driven coupling reactions of terminal alkynes with
H
-phosphonates at room temperature. In addition, the photocatalyst could be recovered and recycled eight times without significant loss of its reactivity.
A copper decorated covalent organic framework has been prepared and identified as an efficient heterogeneous photocatalyst for the phosphorylation of terminal alkynes.
Background:
The overall incidence and mortality of gastric cancer have steadily declined in the United States over the past few decades, but it is still a serious disease burden for patients. ...Therefore, it is of great significance to evaluate the latest survival rate of gastric cancer.
Methods:
Based on the Surveillance, Epidemiology, and End Results database, this study analyzed the age-standardized relative survival rates and survival trends of gastric cancer cases in 2007–2011 and 2012–2016 using period analysis, and the survival rate 2017–2021 was predicted using a generalized linear model based on the period analysis.
Results:
During 2007–2016, the 5-year relative survival rate of patients with gastric cancer continued to rise, and the same trend was observed in 2017–2021. The 5-year overall age-standardized relative survival rates in 2007–2011, 2012–2016, and 2017–2021 were 38.3%, 40.6%, and 42.9%, respectively. However, despite these favorable trends, the overall relative survival of patients with gastric cancer remains at a low level. There were significant differences in the relative survival rates of patients with gastric cancer in terms of age, sex, race, primary site, stage, and socioeconomic status. Notably, the survival rate of patients with distant-stage gastric cancer remains very low (10%).
Conclusion:
We found that the survival rate of patients with gastric cancer showed different degrees of improvement in each subgroup. However, the overall relative survival rate of patients with gastric cancer remains low. Analyzing the changes of patients with gastric cancer in the last 10 years will be helpful in predicting the changing trend of cancer in the future. It also provides a scientific basis for relevant departments to formulate effective tumor prevention and control measures.
Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a ...mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.
Malnutrition is very common in patients with chronic kidney disease, especially in those on maintenance dialysis. Malnutrition is one of the major factors affecting survival and death of dialysis ...patients, and reducing their activity tolerance and immunity. There are numerous and interacting risk factors for malnutrition, such as reduced nutritional intake, increased energy expenditure, hormonal disorders, and inflammation. Selenium, in the form of selenoproteins, is involved in many physiological processes in the body and plays an important role in maintaining redox homeostasis. Oxidative stress and infection are very common in dialysis patients, and selenium levels in dialysis patients are significantly lower than those in the healthy population. It has been shown that there is a correlation between selenium levels in hemodialysis patients and their nutrition-related indicators, and that selenium supplementation may improve malnutrition in patients. However, further studies are needed to support this conclusion and there is a lack of basic research to further characterize the potential mechanisms by which selenium may improve malnutrition in dialysis patients. The purpose of this review is to provide a comprehensive overview of factors associated with malnutrition in dialysis patients and to describe the progress of research on nutritional status and selenium levels in dialysis patients.
Malnutrition is very common and one of the main factors affecting the survival and mortality of dialysis patients
Risk factors for malnutrition in dialysis patients are numerous and interact with each other; controlling and reducing these risk factors is important to improve the nutritional status of patients.
The trace element selenium acts to improve the nutritional status of patients by reducing oxidative stress and inflammation in their bodies.
Recently, smart cities, smart homes, and smart medical systems have challenged the functionality and connectivity of the large-scale Internet of Things (IoT) devices. Thus, with the idea of ...offloading intensive computing tasks from them to edge nodes (ENs), edge computing emerged to supplement these limited devices. Benefit from this advantage, IoT devices can save more energy and still maintain the quality of the services they should provide. However, computational offload decisions involve federation and complex resource management and should be determined in the real-time face to dynamic workloads and radio environments. Therefore, in this work, we use multiple deep reinforcement learning (DRL) agents deployed on multiple edge nodes to indicate the decisions of the IoT devices. On the other hand, with the aim of making DRL-based decisions feasible and further reducing the transmission costs between the IoT devices and edge nodes, federated learning (FL) is used to train DRL agents in a distributed fashion. The experimental results demonstrate the effectiveness of the decision scheme and federated learning in the dynamic IoT system.
The rapid and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the early stage of virus infection can effectively prevent the spread of the virus and control the ...epidemic. Here, a colorimetric and fluorescent dual-functional lateral flow immunoassay (LFIA) biosensor was developed for the rapid and sensitive detection of spike 1 (S1) protein of SARS-CoV-2. A novel dual-functional immune label was fabricated by coating a single-layer shell formed by mixing 20 nm Au nanoparticles (Au NPs) and quantum dots (QDs) on SiO2 core to produce strong colorimetric and fluorescence signals and ensure good monodispersity and high stability. The colorimetric signal was used for visual detection and rapid screening of suspected SARS-CoV-2 infection on sites. The fluorescence signal was utilized for sensitive and quantitative detection of virus infection at the early stage. The detection limits of detecting S1 protein via colorimetric and fluorescence functions of the biosensor were 1 and 0.033 ng/mL, respectively. Furthermore, we evaluated the performance of the biosensor for analyzing real samples. The novel biosensor developed herein had good repeatability, specificity and accuracy, which showed great potential as a tool for rapidly detecting SARS-CoV-2.
•The composite nanospheres with colorimetric and fluorescent dual-functional and excellent stability was synthesized.•The sensitivity of the colorimetric signal and the fluorescence signal of the SiO2@Au/QDs-based LFIA strip was 10-fold and 300-fold higher than those of Au-based LFIA strip, respectively.•The novel biosensor for rapid antigen detection demonstrated excellent specificity and reproducibility, RSD< 7.16%.•The LOD of the novel LFIA biosensor for the inactivated SARS-CoV-2 virus could reach 1.02 × 104 copies/mL.•A dual-functional platform for rapid and sensitive detection of SARS-CoV-2 spike 1 protein in the field was established. The proposed LFIA system could be applied to detect other pathogens.
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