Optimal operation of cold–heat–electricity multi-energy collaborative system based on price demand response

In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improve their individual operating efficiency and overall economic benefits. Demand response, as a multi-energy supply and demand balance method, can further improve system flexibility and economy. Therefore, a multi-energy cooperative system optimization model has been proposed, which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system. The main components of the multi-energy collaborative system have been analyzed. The multi-energy coupling characteristics have been identified based on the energy hub model. Using market elasticity as a basis, a price-based demand response model has been built. The model has been optimized to minimize daily operating cost of the multi-energy collaborative system. Using data from an actual situation, the model has been verified, and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.

MEX3A在非小细胞肺癌中的表达与功能研究

目的:探究MEX3A基因在非小细胞肺癌(non-small cell lung cancer,NSCLC)中的表达水平及沉默MEX3A基因后对NSCLC增殖、侵袭、迁移和凋亡及周期的影响.方法:通过mRNA转录组分析及癌症基因图谱(The Cancer Genome Atlas,TCGA)数据库信息分析,筛选出在NSCLC中显著高表达的MEX3A基因.通过qRT-PCR检测MEX3A在4种常见NSCLC细胞系中的mRNA表达水平,发现MEX3A在A549和NCI-H292中显著高表达.利用RNA干扰技术(RNA interference,RNAi)沉默A549和NCI-H292中MEX3A的表达,CCK8及Transwell试验检测沉默MEX3A表达后对NSCLC增殖、侵袭及迁移的影响,流式细胞术分析沉默MEX3A表达后对A549和NCI-H292细胞周期及凋亡的影响.结果:MEX3A在A549和NCI-H292中高表达.沉默MEX3A后NSCLC增殖、侵袭及迁移被显著抑制,细胞周期阻滞于G2/M期,促进细胞凋亡.结论:MEX3A作为NSCLC的促癌基因,参与了NSCLC的生长及增殖过程.

Preparation and photoelectric properties of cadmium sulfide quantum dots

Cadmium sulfide quantum dots(CdS QDs) are widely used in solar cells, light emitting diodes, photocatalysis, and biological imaging because of their unique optical and electrical properties. However, there are some drawbacks in existing preparation techniques for CdS QDs, such as protection of inert gas, lengthy reaction time, high reaction temperature, poor crystallinity, and non-uniform particle size distribution. In this study, we prepared CdS QDs by liquid phase synthesis under ambient room temperature and atmospheric pressure using sodium alkyl sulfonate, CdCl_2, and Na_2S as capping agent, cadmium, and sulfur sources respectively. This technique offers facile preparation, efficient reaction, low-cost, and controllable particle size. The as-prepared CdS QDs exhibited good crystallinity, excellent monodispersity, and uniform particle size. The responsivity of CdS QDs-based photodetector is greater than 0.3 μA/W, which makes them suitable for use as ultra-violet(UV) detectors.

Automatic Segmentation for Intracoronary OCT Image Based on Convolutional Neural Network and Support Vector Machine Methods

Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(IVUS)were unable to identify vulnerable plaques due to their limited resolution.Fortunately,optical coherence tomography(OCT)is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque.In particular,it has the ability to identify plaques with fibrous cap thickness<65μm,an accepted threshold value for vulnerable plaques.However,segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective.To overcome time consumption,several methodologies have been proposed for automatic segmentation of OCT images but most of these methods were still limited by intricate image preprocessing and expensive computation.In this research,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque components.Methods In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained.OCT were obtained from ILUMIEN OPTIS System(St.Jude,Minnesota,MN).The OCT catheter was traversed to the segment of interest and the catheter pullback was limited at a rate of 20 mm/sec.Following the OCT image acquisition,the IVUS catheter was traversed distally though the artery to the same coronary segment(Volcano Therapeutics,Rancho Cordova)and the catheter pullback speed was at a standard rate of 0.5 mm/sec.Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for our segmentation study.Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations.VH-IVUS was used as references and guide by the experts in the manual segmentation process.Three plaque component tissue classes were identified from OCT images in this work:lipid tissue(LT),fibrous tissue(FT)and background(BG).Procedures using two machine learning methods(CNN and SVM)were developed to segment OCT images,respectively.For CNN method,the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images.For SVM method,local binary patterns(LBPs),gray level co-occurrence matrices(GLCMs)which contains contrast,correlation,energy and homogeneity,entropy and mean value were calculated as features and assembled to feed SVM classifier.The accuracies of two segmentation methods were evaluated and compared using the OCT dataset.Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels.Results The overall classification accuracy based CNN method reached 95.8%,and the accuracies for LT,FT and BG were 86.8%,83.4%,and 98.2%,respectively.The overall classification accuracy based SVM was 71.9%,and per-class accuracy for LT,FT and BG was 75.4%,78.3%,and67.0%,respectively.Conclusions The two methods proposed can automatically identify plaque region and characterize plaque compositions for OCT images and potentially reduce the time spent by doctors in segmenting and evaluating coronary plaque OCT images.CNN provided better segmentation accuracies compared to those achieved by SVM.

Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification

Optical coherence tomography(OCT)is a new intravascular imaging technique with high resolution and could provide accurate morphological information for plaques in coronary arteries.However,its segmentation is still commonly performed manually by experts which is time-consuming.The aim of this study was to develop automatic techniques to characterize plaque components and quantify plaque cap thickness using 3 machine learning methods including convolutional neural network(CNN)with U-Net architecture,CNN with Fully convolutional DenseNet(FC-DenseNet)architecture and support vector machine(SVM).In vivo OCT and intravascular ultrasound(IVUS)images were acquired from two patients at Emory University with informed consent obtained.Eighteen OCT image slices which included lipid core and with acceptable image quality were selected for our study.Manual segmentation from imaging experts was used as the gold standard for model training and validation.Since OCT has limited penetration,virtual histology IVUS was combined with OCT data to improve reliability.A 3-fold cross-validation method was used for model training and validation.The overall tissue classification accuracy for the 18 slices studied(total classification database sample size was 8580096 pixels)was 96.36%and 92.72%for U-Net and FC-DenseNet,respectively.The best average prediction accuracy for lipid was 91.29%based on SVM,compared to 82.84%and 78.91%from U-Net and FC-DenseNet,respectively.The overall average accuracy(Acc)differentiating lipid and fibrous tissue were 95.58%,92.33%and 81.84%for U-Net,FC-DenseNet and SVM,respectively.The average errors of U-Net,FC-DenseNet and SVM from the 18 slices for cap thickness quantification were 8.83%,10.71%and 15.85%.The average relative errors of minimum cap thickness from 18 slices of U-Net,FC-DenseNet and SVM were 17.46%,13.06%and 22.20%,respectively.To conclude,CNN-based segmentation methods can better characterize plaque compositions and quantify plaque cap thickness on OCT images and are more likely to be used in the clinical arena.Large-scale studies are needed to further develop the methods and validate our findings.

A highly efficient in vivo plasmid editing tool based on CRISPR-Cas12a and phage λ Red recombineering

Plasmids are useful tools for studying genetic information in living cells,as well as heterologous expression of genes and pathways in cells(Lauritsen et al.,2018).Various methods have been developed for plasmid manipulation both in vivo and in vitro(Aslanidis and de Jong,1990;Li and Elledge,2007;Xia et al.,2018).However,large plasmids,such as P1-based artificial chromosomes(PACs),bacterial artificial chromosomes(BACs),and fosmids,are difficult to manipulate.