Study on heat integration of supercritical coal-fired power plant with post-combustion CO₂ capture process through process simulation

Coal-fired power plant (CFPP) is one of the main sources of anthropogenic CO₂ emissions. Capturing CO₂ from CFPP by post-combustion process plays an important role to mitigate CO₂ emissions. However, a significant thermal efficiency drop was observed when integrating CFPP with post-combustion carbon capture (PCC) process due to the steam extraction for capture solvent regeneration. Thus research efforts are required to decrease this energy penalty. In this study, a steady state model for 600 MWe supercritical CFPP was developed as a reference case with a low heating value (LHV) based efficiency of 41.6%. A steady state model for MEA-based PCC process was also developed and scaled up to match the capacity of the CFPP. CO₂ compression process was simulated to give an accurate prediction of its electricity consumption and cooling requirement. Different integration cases were set up according to different positions of steam extraction from the CFPP. The results show that the efficiency penalty is 12.29% and 14.9% when steam was extracted at 3.64 bar and at 9.1 bar respectively. Obvious improvements were achieved by utilizing waste heat from CO₂ capture and compression process, taking part of low pressure cylinders out of service, and adding an auxiliary turbine to decompress the extracted steam. The efficiency penalty of the best case decreases to 9.75%. This study indicates that comprehensive heat integrations can significantly improve the overall energy efficiency when the CFPP is integrated with PCC and compression process

Context based mixture model for cell phase identification in automated fluorescence microscopy

BACKGROUND: Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task. RESULTS: The data is generated from Hela H2B GFP cells imaged during a 2-day period with images acquired 15 minutes apart using an automated time-lapse fluorescence microscopy. The patterns are described with four kinds of features, including twelve general features, Haralick texture features, Zernike moment features, and wavelet features. To generate a new set of features with more discriminate power, the commonly used feature reduction techniques are used, which include Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Maximum Margin Criterion (MMC), Stepwise Discriminate Analysis based Feature Selection (SDAFS), and Genetic Algorithm based Feature Selection (GAFS). Then, we propose a Context Based Mixture Model (CBMM) for dealing with the time-series cell sequence information and compare it to other traditional classifiers: Support Vector Machine (SVM), Neural Network (NN), and K-Nearest Neighbor (KNN). Being a standard practice in machine learning, we systematically compare the performance of a number of common feature reduction techniques and classifiers to select an optimal combination of a feature reduction technique and a classifier. A cellular database containing 100 manually labelled subsequence is built for evaluating the performance of the classifiers. The generalization error is estimated using the cross validation technique. The experimental results show that CBMM outperforms all other classifies in identifying prophase and has the best overall performance. CONCLUSION: The application of feature reduction techniques can improve the prediction accuracy significantly. CBMM can effectively utilize the contextual information and has the best overall performance when combined with any of the previously mentioned feature reduction techniques

Salvianolic Acid B Prevents Arsenic Trioxide-Induced Cardiotoxicity In Vivo and Enhances Its Anticancer Activity In Vitro

Clinical attempts to reduce the cardiotoxicity of arsenic trioxide (ATO) without compromising its anticancer activities remain to be an unresolved issue. In this study, we determined whether Sal B can protect against ATO-induced cardiac toxicity in vivo and increase the toxicity of ATO toward cancer cells. Combination treatment of Sal B and ATO was investigated using BALB/c mice and human hepatoma (HepG2) cells and human cervical cancer (HeLa) cells. The results showed that the combination treatment significantly improved the ATO-induced loss of cardiac function, attenuated damage of cardiomyocytic structure, and suppressed the ATO-induced release of cardiac enzymes into serum in BALB/c mouse models. The expression levels of Bcl-2 and p-Akt in the mice treated with ATO alone were reduced, whereas those in the mice given the combination treatment were similar to those in the control mice. Moreover, the combination treatment significantly enhanced the ATO-induced cytotoxicity and apoptosis of HepG2 cells and HeLa cells. Increases in apoptotic marker cleaved poly (ADP-ribose) polymerase and decreases in procaspase-3 expressions were observed through western blot. Taken together, these observations indicate that the combination treatment of Sal B and ATO is potentially applicable for treating cancer with reduced cardiotoxic side effects

Universal conductance fluctuations and phase-coherent transport in a semiconductor Bi2_2O2_2Se nanoplate with strong spin-orbit interaction

We report on phase-coherent transport studies of a Bi$_2$O$_2$Se nanoplate and on observation of universal conductance fluctuations and spin-orbit interaction induced reduction in fluctuation amplitude in the nanoplate. Thin-layered Bi$_2$O$_2$Se nanoplates are grown by chemical vapor deposition (CVD) and transport measurements are made on a Hall-bar device fabricated from a CVD-grown nanoplate. The measurements show weak antilocalization at low magnetic fields at low temperatures, as a result of spin-orbit interaction, and a crossover toward weak localization with increasing temperature. Temperature dependences of characteristic transport lengths, such as spin relaxation length, phase coherence length, and mean free path, are extracted from the low-field measurement data. Universal conductance fluctuations are visible in the low-temperature magnetoconductance over a large range of magnetic fields and the phase coherence length extracted from the autocorrelation function is in consistence with the result obtained from the weak localization analysis. More importantly, we find a strong reduction in amplitude of the universal conductance fluctuations and show that the results agree with the analysis assuming strong spin-orbit interaction in the Bi$_2$O$_2$Se nanoplate.Comment: 11 pages, 4 figures, supplementary material

A MIC-LSTM based parameter extraction method for single-diode PV model

In recent years, the installed capacity of renewable energy systems has seen rapid growth, particularly in photovoltaic (PV) power. Photovoltaic modules, being the fundamental elements of the PV system, play a crucial role in determining system performance. However, the challenge arises from the inconsistent decay rates of PV modules, which significantly impact the accuracy of PV system modeling. To address this issue, this paper introduces a novel MIC-LSTM based parameter extraction method for the single-diode PV model. This method focuses on accurately deriving PV module model parameters under various decay rates. By establishing a mapping relationship between the current-voltage (I-V) curve characteristics and the five unknown parameters in the photovoltaic module model, the proposed method demonstrates high precision in parameter extraction. Simulation and experimental verifications are carried out to validate the proposed method, where the extraction accuracy is 99.3%, 98.39%, 98.85%, 97.91%, and 98.36% for the five unknown model parameters

Epstein-barr virus-encoded microRNA-BART18-3p promotes colorectal cancer progression by targeting de novo lipogenesis

The Epstein-Barr virus (EBV) genome encodes a cluster of 22 viral microRNAs, called miR-BamHI-A rightward transcripts (miR-BARTs), which are shown to promote the development of cancer. Here, this study reports that EBV-miR-BART18-3p is highly expressed in colorectal cancer (CRC) and is closely associated with the pathological and advanced clinical stages of CRC. Ectopic expression of EBV-miR-BART18-3p leads to increased migration and invasion capacities of CRC cells in vitro and causes tumor metastasis in vivo. Mechanistically, EBV-miR-BART18-3p activates the hypoxia inducible factor 1 subunit alpha/lactate dehydrogenase A axis by targeting Sirtuin, which promotes lactate accumulation and acetyl-CoA production in CRC cells under hypoxic condition. Increased acetyl-CoA utilization subsequently leads to histone acetylation of fatty acid synthase and fatty acid synthase-dependent fat synthesis, which in turn drives de novo lipogenesis. The oncogenic role of EBV-miR-BART18-3p is confirmed in the patient-derived tumor xenograft mouse model. Altogether, the findings define a novel mechanism of EBV-miR-BART18-3p in CRC development through the lipogenesis pathway and provide a potential clinical intervention target for CRC