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The carbon market relies on market-oriented financial means to solve the problem of carbon emissions. An effective carbon pricing mechanism can improve market efficiency and better serve the implementation of carbon emission reduction. The limited attention of investors increases the uncertainty of carbon market volatility and is an important exogenous factor affecting the price of carbon assets. This study innovatively mines keywords of investor attention on the carbon market through online news texts and eliminates those that have no causal link to carbon price forecasting in order to reduce noise. The results show that the keyword extraction method based on news text mining is better than that of nontext mining. Meanwhile, a carbon price forecasting model based on a particle-swarm-optimization LSTM model structure is constructed, and the forecasting accuracy is improved. The results show that carbon market investors pay more attention to carbon quota supply and demand, carbon prices, environmental change, and the energy market. The results have important implications for the development of effective carbon market policies and risk management.

Intermediate roll shifting (IRS) is widely used for improving the strip shape in the six-high tandem cold mill, but most related studies are limited to a single stand. To fill the knowledge gap, a three-dimensional (3D) multi-stand elastic–plastic finite element (FE) model was developed for a continuously variable crown (CVC)-6 tandem cold mill using data transfer, which was then validated by industrial experimental results. Based on this FE model, the effects of the IRS on the strip crown, strip flatness, loaded roll gap profile and contact normal stress between rolls at each stand were quantitatively analysed. The results show that from Stand 1 (S1) to Stand 5 (S5), the regulation ability of the IRS on the strip crown shows a decreasing trend, which depends on the strip plastic rigidity; in contrast, the regulation ability on the quadratic flatness experiences an obvious increase from S1 to Stand 4 (S4), then a drop at S5, while the IRS exerts little effect on the quartic flatness and quartic crown of the loaded roll gap. Moreover, the most uniform distribution of contact normal stress emerges at different IRSs from S1 to S5. These findings can contribute to a better understanding of the role of the IRS in controlling the strip shape during tandem cold rolling (TCR).

Induced pluripotent stem cells (iPSCs) have become an essential research platform to study different human diseases once being discovered by Dr. Shinya Yamanaka in 2006. Another breakthrough in biomedical research is the application of CRISPR/Cas9 system for genome editing in mammalian cells. Although numerous studies have been done to develop methods for gene editing in iPSCs, the current approaches suffer from several limitations, including time and labor consuming, low editing efficiency, and potential off-target effects. In the current study, we report an electroporation-mediated plasmid CRISPR/Cas9 delivery approach for genome editing in iPSCs. With this approach, an edited iPSC cell line could be obtained within 2 weeks. In addition, the transit introducing of CRISPR/Cas9 machinery could minimize genomic integration of Cas9 gene, which avoided potential long-term side effects of Cas9 enzyme. We showed that CRISPR/Cas9-mediated genomic editing did not affect pluripotency and differentiation ability of iPSCs. With the quickly evolving of both iPSC and CRISPR/Cas9-mediated genome editing research fields, we believe that our method can significantly facilitate the application of genome editing in iPSCs research.

Ara h 1 is recognized as a major peanut allergen. The effects of high hydrostatic pressure (HHP) on the immunoreactivity and structure of Ara h 1 were investigated in this study. The immunoreactivity of Ara h 1 was considerably reduced (P

Metal oxide semiconductor sensors based on nanocrystalline In2O3 and its composites are found to be very sensitive in detecting low-concentration (~ 5 ppm) gases such as ozone, nitrogen dioxide, formaldehyde and butane. Here, we successfully obtained fiber-shaped In2O3 crystalline nanofibers via electrospun and calcination routes. The gas sensing properties of the In2O3 nanofibers were studied by exposing them to the acetic acid vapor with different concentrations from 500 ppb to 2000 ppm at the optimum operating temperature (250 °C). The device possesses ultra-high response of 66.7 toward 2000 ppm acetic acid vapor, low response and recovery times of 25 s and 37 s (100 ppm), respectively, and significant selectivity to acetic acid at 100 ppm. In particular, the sensor based on In2O3 nanofibers has very low detection limit and can reach 500 ppb. Therefore, the presented In2O3 nanofiber sensor can be used in practice in acetic acid detection area in the future.

The wear behavior of Ti–45Al–7Nb–0.3W (at%) alloy prepared by powder metallurgy (P/M) has been investigated using pin-on-disk wear tests at the room temperature. The dry sliding wear tests were performed against on a zirconia disk in different environments, viz, argon, hydrogen in nitrogen mixture, air and oxygen. The worn surfaces of Ti–45Al–7Nb–0.3W pins and wear debris were examined by scanning electron microscope, X-ray diffractometer and X-ray photoelectron spectroscopy. It was found that the high Nb containing Ti–45Al–7Nb–0.3W alloy didn’t exhibit better wear resistance in the presence of oxygen than commercial γ-TiAl (Ti–47Al–2Nb–2Cr–0.2W) alloy. The oxidation on the worn surface and spalling of oxides caused by the mechanical force during sliding were the main reason for the high wear losses in the air and oxygen. The zirconia disk underwent a phase transformation caused by the high contact temperature and stress during the sliding tests. The abrasive particles produced in the wear tests largely consisted of the zirconia particles in different environments, except in the pure oxygen. The hard zirconia debris was embedded into the friction layer on the worn surfaces of TiAl pins, which provided protection against wear to some extent. The main wear mechanisms of Ti–45Al–7Nb–0.3W alloy were abrasive wear of two-body and three-body, some delamination and plastic deformation.

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Cancer cells alter regular metabolic pathways in order to sustain rapid proliferation. One example of metabolic remodeling in cancerous tissue is the upregulation of pyruvate kinase isoenzyme M2 (PKM2), which is involved in aerobic glycolysis. Indeed, PKM2 has previously been identified as a tumor biomarker and as a potential target for cancer therapy. Here, the role of PKM2 in the anticancer efficacy of 5-fluorouracil (5-FU) was evaluated in colorectal cancer (CRC). HCT116, SW480 and HT-29 cells were used by transfection with lentiviral vectors expressing short hairpin RNA (shRNA) against PKM2. In response to 5-FU treatment, cellular proliferation was examined, the levels of ATP/ADP ratio were monitored, the intracellular accumulation of 5-FU was measured, and intracellular levels of phosphoenolpyruvate (PEP), pyruvate and lactate were evaluated by using liquid chromatography–mass spectrometry (LC–MS). A CRC subcutaneous tumor model was performed to investigate the effect of PKM2 inhibition on 5-FU efficacy in vivo. Suppression of PKM2 resulted in changes in glucose metabolism, leading to decreased synthesis of adenosine triphosphate (ATP). Reduced levels of ATP/ADP ratio resulted in the intracellular accumulation of 5-FU, consequently enhancing the therapeutic efficacy of this drug in several CRC cell lines. Furthermore, the enhanced efficacy of 5-FU by simultaneous inhibition of PKM2 was demonstrated in an in vivo HCT116 CRC model. We show that the combination treatment showed superior anticancer efficacy as compared to 5-FU alone. These findings suggest that targeting PKM2 can increase the efficacy of chemotherapy, potentially providing a new approach for improving the outcome of chemotherapy in patients with CRC.

This study aimed to evaluate the antifibrotic effects of NF-E2-Related Factor 2 (Nrf2) on intestinal fibrosis. Intestinal fibrosis is a common complication of Crohn’s disease; however, its mechanism of intestinal fibrosis is largely unclear. BALB/c mice received 2,4,6-trinitrobenzene sulfonic acid weekly via intrarectal injections to induce chronic fibrotic colitis. They also diet containing received 1% (w/w) tert-butylhydroquinone (tBHQ), which is an agonist of Nrf2. Human intestinal fibroblasts (CCD-18Co cells) were pretreated with tBHQ or si-Nrf2 followed by stimulation with transforming growth factor-β1 (TGF-β1), which transformed the cells into myofibroblasts. The main fibrosis markers such as α-smooth muscle actin, collagen I, tissue inhibitor of metalloproteinase-1, and TGF-β1/SMADs signaling pathway were detected by quantitative real-time RT-PCR, immunohistochemical analysis, and Western blot analysis. Levels of cellular reactive oxygen species (ROS) were detected by dichlorodihydrofluorescein diacetate. tBHQ suppressed the intestinal fibrosis through the TGF-β1/SMADs signaling pathway in TNBS-induced colitis and CCD-18Co cells. Moreover, Nrf2 knockdown enhanced the TGF-β1-induced differentiation of CCD-18Co cells. ROS significantly increased in TGF-β1-stimulated CCD-18Co cells. Pretreatment with H2O2, the primary component of ROS, was demonstrated to block the effect of tBHQ on reducing the expression of TGF-β1. Moreover, scavenging ROS by N-acetyl cysteine could inhibit the increasing expression of TGF-β1 promoted by Nrf2 knockdown. The results suggested that Nrf2 suppressed intestinal fibrosis by inhibiting ROS/TGF-β1/SMADs pathway in vivo and in vitro.

Traditional anaesthesia training is considered as a time-consuming task since trainees are required to go through an extended period of knowledge learning and practice their skill in the supervision of experienced anaesthetists. In this paper, a Computational Virtual Reality Environment for Anesthesia (CVREA) is proposed, which can significantly improve the training and learning performance of trainee anaesthetists in an efficient way. Virtual reality, big data, data mining and machine learning techniques will be explored and applied in this system. CVREA consists of two main parts: (1) an immersive and interactive VR-based training platform for anaesthetists. It allows trainees to hone their clinical skills in a virtual environment without placing risk to patients. (2) a knowledge learning system which records and collects clinical data with greater richness. Knowledge learning algorithms will be developed to explore these data in order to help data processing and facilitates knowledge discovery in anaesthesiology.

Bismuth ferrite (BiFeO3) acting as a significant multiferroic material exhibits unique magnetic and ferroelectric properties. Here we report the size-controlled synthesis of BiFeO3 nanoparticles via a facile and stable Pechini sol–gel method, in which uses tartaric acid as a complexing agent, ethylene glycol as a polymerizing agent. It is found that the nanoparticle size is sensitive to the gel-forming temperature. The nanoparticles with size as small as 30 nm have been obtained. A sequential reaction process and the mechanism of size controllable growth of BiFeO3 has been presented. The effects of size on optical and magnetic properties have been also investigated. The decreasing particles lead to blue shift in the band gap. BiFeO3 nanoparticles show weak ferromagnetic character due to a Fe3+ oxidation state, and the magnetization increases with decrease in the particle size.

A 3D model-based food traceability system is proposed in this paper. It implements an information extraction method for processing video surveillance data. The first step of the proposed method is to build a 3D model of the target area. Based on the 3D models, cameras deployment in the surveillance system could be optimized with view coverage analysis. Then, we map the 2D views in video cameras into the coordinate system under the generated 3D models. Next, the deep learning based object identification method is selected to locate the interesting targets and their 3D coordinates are calculated based on the 3D model. Finally, multiple trajectories from different cameras are merged to create a complete traceability event for the target object. According to the experiment, the 3D models is useful to generate the unified traceability trajectories and the deep learning based method is efficient in extract the interesting objects from video surveillance system.