Single-cell Transcriptome Study as Big Data

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies

A Four-Hierarchy method for the design of organic Rankine cycle (ORC) power plants

In this paper, a practical and general-adapted optimization and decision-making method is proposed for thermal systems designed for a wide range of energy field regarding organic Rankine cycle and thermodynamic cycles. This method is composed of four progressive hierarchies including modelling, optimization, scheme comparison and decision-making. To demonstrate the Four-Hierarchy method, performance of a basic trans-critical ORC and a recuperative trans-critical ORC are analyzed and compared. The NSGA-II algorithm is adopted to obtain the Pareto optimal frontier. Four decision-making methods which are Shannon Entropy, modified LINMAP, TOPSIS and TLFDM are applied for evaluating the Pareto set points. Furthermore, the final Pareto-optimal solution is determined by the root-mean-square difference, correlation coefficient and standard deviation in the Taylor diagram. The optimal results indicate that the final Pareto-optimal solution often appears at LINMAP and TLFDM points. In contrast with basic trans-critical ORC, the recuperative trans-critical ORC can always improve the system’s thermodynamic performance. But the techno-economics is only enhanced when the energy grade of heat source is sufficient. The most beneficial improvement is the average reduction of heat transfer area per net output power by more than 27.0% and 30.0% in the medium temperature and high temperature geothermal reservoirs, respectively. Based on the case study, the presented method has proved its application value, and has shown its promising applicability in a wide range of energy field regarding organic Rankine cycle and thermodynamic cycles for energy conversion

Numerical Study on Transient Heat Transfer of a Quartz Lamp Heating System

Thermal ground testing is an accepted and frequently used method for simulating the aerodynamic heating of high speed flight vehicles. A numerical method based on a finite volume method for a quartz lamp heating system, used in thermal testing, is proposed. In this study, the unstructured finite-volume method (UFVM) for radiation has been formulated and implemented in a fluid flow solver GTEA on unstructured grids. For comparison and validation of the proposed method, a 2D furnace with cooling pipes was chosen. The results obtained from the proposed FVM agreed well with the exact solutions. Numerical results show that the quartz lamp can be simplified as a slat with the same temperature radiation source, and a simplified 2D thermal testing case was then simulated with the coupling effects of radiation, convection, and conduction heat transfer. Different temperature loading curves and ratios of intervals between the lamps and lamp length (l/s) were studied using the proposed method. The radiation heat flux on the metal surface was a wave-shaped curve. Comparing the different interval ratios, we found that the smaller the interval ratio, the larger the maximum value and the smaller the difference between the maximum and minimum heat flux

A RNA-Sequencing approach for the identification of novel long non-coding RNA biomarkers in colorectal cancer

Abstract Long non-coding RNAs (lncRNAs) have been implicated in human pathology, however, their role in colorectal carcinogenesis have not been fully elucidated. In the current study, whole-transcriptome analysis was performed in 3 pairs of colorectal cancer (CRC) and matched normal mucosa (NM) by RNA sequencing (RNA-seq). Followed by confirmation using the Cancer Genome Atlas (TCGA) dataset, we identified 27 up-regulated and 22 down-regulated lncRNAs in CRC. Up-regulation of four lncRNAs, hereby named colorectal cancer associated lncRNA (CRCAL)-1 [AC021218.2], CRCAL-2 [LINC00858], CRCAL-3 [RP11-138J23.1] and CRCAL-4 [RP11-435O5.2], was further validated by real-time RT-PCR in 139 colorectal neoplasms and matched NM tissues. Knockdown of CRCAL-3 and CRCAL-4 in colon cancer cells reduced cell viability and colony formation ability, and induced cell cycle arrest. TCGA dataset supported the associations of CRCAL-3 and CRCAL-4 with cell cycle and revealed a co-expression network comprising dysregulated lncRNAs associated with protein-coding genes. In conclusion, RNA-seq identified numbers of novel lncRNAs dysregulated in CRC. In vitro experiments and GO term enrichment analysis indicated the functional relevance of CRCAL-3 and CRCAL-4 in association with cell cycle. Our data highlight the capability of RNA-seq to discover novel lncRNAs involved in human carcinogenesis, which may serve as alternative biomarkers and/or molecular treatment targets

A systematic study involving patent analysis and theoretical modeling of eco-friendly technologies for electric vehicles and power batteries to ease carbon emission from the transportation industry

Using natural and recycled materials in the manufacturing of electric vehicles (EVs) and power batteries (PBs) offers several environmental, economic, and technical compelling advantages. Recently, extensive study has been dedicated on the manufacturing of EVs and their power batteries to comprehensively address these advantages. This research analyzes 12,202 scientific patents from 1970 to 2021, evaluating eco-friendly materials for EVs and power batteries. The study identifies current status and gaps in research, mapping collaborations and networks, assessing core technologies, classification of innovative materials, future research directions; hence environmental and economic implications. To assess current development and forecast future technologies, hybrid autoregressive integrated moving average (ARIMA) time series model combined with an advanced machine learning logistic regression algorithm were employed. Patents analyses results unveil noteworthy insights: Dynamic analysis demonstrates a growing interest in eco-friendly EVs and PBs manufacturing from countries such as China (PF2/PF1 = 0.406), U.S.A (PF2/PF1 = 0.468), Germany (PF2/PF1 = 0.465), and Japan (PF2/PF1 = 0.427). Key companies including TOSHIBA, BYD, TOYOTA, and TESLA are leading the way in technology transfer related to the manufacturing of eco-friendly EVs and PBs. Latest technology update reveals that, synthetic “SofTex” leather has 85 % less CO2 emissions than genuine leather during the processing, recycled aluminum production emits 97 % less CO2 than new production. Ford’s aim is to reduce its carbon footprint by utilizing 1.2 billion plastic bottles from landfills waste per year for making vehicle parts, resulting in a noteworthy 50–60 % weight reduction and a 37 % decrease in CO2 emissions in new vehicles compared to traditional counterparts. S-curve analysis further highlights a remarkable surge in patent filings for EVs and PBs since 2011. Notably, the patent CN-101013763-A holds significant influence in driving innovation route and utilization of eco-friendly materials such as bio-paint, bamboo, recycled plastic, and advanced steel in the manufacturing of EVs and PBs. In the predictable future, ongoing research will pinpoint opportunities for advancing technology to lead carbon reduction and sustainability in the EV industry