The coordination structure determines the electrocatalytic performances of single atom catalysts (SACs), while it remains a challenge to precisely regulate their spatial location and coordination environment. Herein, ...
详细信息
The coordination structure determines the electrocatalytic performances of single atom catalysts (SACs), while it remains a challenge to precisely regulate their spatial location and coordination environment. Herein, we report a universal sub-nanoreactor strategy for synthesis of yolk-shell MoS 2 supported single atom electrocatalysts with dual-anchored microenvironment of vacancy-enriched MoS 2 and intercalation carbon toward robust hydrogen-evolution reaction. Theoretical calculations reveal that the “E-Lock” and “E-Channel” are conducive to stabilize and activate metal single atoms. A group of SACs is subsequently produced with the assistance of sulfur vacancy and intercalation carbon in the yolk-shell sub-nanoreactor. The optimized C−Co−MoS 2 yields the lowest overpotential (η 10 =17 mV) compared with previously reported MoS 2 -based electrocatalysts to date, and also affords a 5–9 fold improvement in activity even comparing with those as-prepared single-anchored analogues. Theoretical results and in situ characterizations unveil its active center and durability. This work provides a universal pathway to design efficient catalysts for electro-refinery.
It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learning-based approximation algorithms for att...
详细信息
Accurate Lung cancer (LC) identification is a big medical problem in the AI-based healthcare systems. Various deep learning-based methods have been proposed for Lung cancer diagnosis. In this study, we proposed a Deep...
Accurate Lung cancer (LC) identification is a big medical problem in the AI-based healthcare systems. Various deep learning-based methods have been proposed for Lung cancer diagnosis. In this study, we proposed a Deep learning techniques-based integrated model (CNN-GRU) for Lung cancer detection. In the proposed model development Convolutional neural networks (CNNs), and gated recurrent units (GRU) models are integrated to design an intelligent model for lung cancer detection. The CNN model extracts spatial features from lung CT images through convolutional and pooling layers. The extracted features from data are embedded in the GRUs model for the final prediction of LC. The model (CNN-GRU) was validated using LC data using the holdout validation technique. data augmentation techniques such as rotation, and brightness were used to enlarge the data set size for effective training of the model. The optimization techniques Stochastic Gradient Descent(SGD) and Adaptive Moment Estimation(ADAM) were applied during model training for model training parameters optimization. Additionally, evaluation metrics were used to test the model performance. The experimental results of the model presented that the model achieved 99.77% accuracy as compared to previous models. The (CNN-GRU) model is recommended for accurate LC detection in AI-based healthcare systems due to its improved diagnosis accuracy.
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from t...
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.
Lipid nanoparticle-based drug delivery systems have a profound clinical impact on nucleic acid-based therapy and vaccination. Recombinant human insulin, a negatively-charged biomolecule like mRNA, may also be delivere...
详细信息
Lipid nanoparticle-based drug delivery systems have a profound clinical impact on nucleic acid-based therapy and vaccination. Recombinant human insulin, a negatively-charged biomolecule like mRNA, may also be delivered by rationally-designed positively-charged lipid nanoparticles with glucose-sensing elements and be released in a glucose-responsive manner. Herein, we have designed phenylboronic acid-based quaternary amine-type cationic lipids that can self-assemble into spherical lipid nanoparticles in an aqueous solution. Upon mixing insulin and the lipid nanoparticles, a heterostructured insulin complex is formed immediately arising from the electrostatic attraction. In a hyperglycemia-relevant glucose solution, lipid nanoparticles become less positively charged over time, leading to reduced attraction and subsequent insulin release. Compared with native insulin, this lipid nanoparticle-based glucose-responsive insulin shows prolonged blood glucose regulation ability and blood glucose-triggered insulin release in a type 1 diabetic mouse model.
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. ...
详细信息
Background:Neuroblastoma(NB)is a heterogeneous disease with respect to genomic abnormalities and clinical *** recent advances in our understanding of the association between the genetic aberrations and clinical featur...
详细信息
Background:Neuroblastoma(NB)is a heterogeneous disease with respect to genomic abnormalities and clinical *** recent advances in our understanding of the association between the genetic aberrations and clinical features,it remains one of the major challenges to predict prognosis and stratify patients for determining personalized therapy in this *** aim of this study was to develop an effective prognosis prediction model for NB ***:We integrated diverse computational analyses to define gene signatures that reflect MYCN activity and chromosomal aberrations including deletion of chromosome 1p(Chr1p_del)and chromosome 11q(Chr11q_del)as well as chromosome 11q whole loss(Chr11q_wls).We evaluated the prognostic and predictive values of these signatures in seven NB gene expression datasets(the number of samples ranges from 94 to 498,with a total of 2120)generated from both RNA sequencing and microarray ***:MYCN signature was a more effective prognostic marker than MYCN amplification status and MYCN ***,the Chr1p_del score was more prognostic than Chr1p *** activity scores of MYCN,Chr1p_del and Chr11q_del were associated with poor prognosis,while the Chr11q_wls score was linked to good *** integrated the activity scores of MYCN,Chr1p_del,Chr11q_del,and Chr11q_wls and clinical variables into an integrative prognostic model,which displayed significant performance over the clinical variables or each genomic aberration ***:Our integrative gene signature model shows a significantly improved forecast performance with prognostic and predictive information,and thereby can be served as a biomarker to stratify NB patients for prognosis evaluation and surveillance programs.
Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state...
详细信息
暂无评论