Small changes in temperature affect plant ecological and physiological factors that impact agricultural production. Hence, understanding how temperature affects flowering is crucial for decreasing the effects of climate change on crop yields. Recent reports have shown that FLM-β, the major spliced isoform of FLOWERING LOCUS M (FLM)—a flowering time gene, contributes to temperature-responsive flowering in Arabidopsis thaliana. However, the molecular mechanism linking pre-mRNA processing and temperature-responsive flowering is not well understood. Genetic and molecular analyses identified the role of an Arabidopsis splicing factor SF1 homolog, AtSF1, in regulating temperature-responsive flowering. The loss-of-function AtSF1 mutant shows temperature insensitivity at different temperatures and very low levels of FLM-β transcript, but a significantly increased transcript level of the alternative splicing (AS) isoform, FLM-δ. An RNA immunoprecipitation (RIP) assay revealed that AtSF1 is responsible for ambient temperature-dependent AS of FLM pre-mRNA, resulting in the temperature-dependent production of functional FLM-β transcripts. Moreover, alterations in other splicing factors such as ABA HYPERSENSITIVE1/CBP80 (ABH1/CBP80) and STABILIZED1 (STA1) did not impact the FLM-β/FLM-δ ratio at different temperatures. Taken together, our data suggest that a temperature-dependent interaction between AtSF1 and FLM pre-mRNA controls flowering time in response to temperature fluctuations.

The formation of spherical aggregates during the growth of cell population has long been observed under various conditions. We observed the formation of such aggregates during proliferation of Huh-7.5 cells, a human hepatocarcinoma cell line, in a microfabricated low-adhesion microwell system (SpheroFilm; formed of mass-producible silicone elastomer) on the length scales up to 500 μm. The cell proliferation was also tracked with immunofluorescence staining of F-actin and cell proliferation marker Ki-67. Meanwhile, our complementary 3D Monte Carlo simulations, taking cell diffusion and division, cell-cell and cell-scaffold adhesion, and gravity into account, illustrate the role of these factors in the formation of spheroids. Taken together, our experimental and simulation results provide an integrative view of the process of spheroid formation for Huh-7.5 cells.

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ObjectiveTo investigate whether support vector machine (SVM) trained with radiomics features based on breast magnetic resonance imaging (MRI) could predict the upgrade of ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) after surgical excision.Materials and methodsThis retrospective study included a total of 349 lesions from 346 female patients (mean age, 54 years) diagnosed with DCIS by CNB between January 2011 and December 2017. Based on histological confirmation after surgery, the patients were divided into pure (n = 198, 56.7%) and upgraded DCIS (n = 151, 43.3%). The entire dataset was randomly split to training (80%) and test sets (20%). Radiomics features were extracted from the intratumor region-of-interest, which was semi-automatically drawn by two radiologists, based on the first subtraction images from dynamic contrast-enhanced T1-weighted MRI. A least absolute shrinkage and selection operator (LASSO) was used for feature selection. A 4-fold cross validation was applied to the training set to determine the combination of features used to train SVM for classification between pure and upgraded DCIS. Sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC) were calculated to evaluate the model performance using the hold-out test set.ResultsThe model trained with 9 features (Energy, Skewness, Surface Area to Volume ratio, Gray Level Non Uniformity, Kurtosis, Dependence Variance, Maximum 2D diameter Column, Sphericity, and Large Area Emphasis) demonstrated the highest 4-fold mean validation accuracy and AUC of 0.724 (95% CI, 0.619–0.829) and 0.742 (0.623–0.860), respectively. Sensitivity, specificity, accuracy, and AUC using the test set were 0.733 (0.575–0.892) and 0.7 (0.558–0.842), 0.714 (0.608–0.820) and 0.767 (0.651–0.882), respectively.ConclusionOur study suggested that the combined radiomics and machine learning approach based on preoperative breast MRI may provide an assisting tool to predict the histologic upgrade of DCIS.

A number of countries are mixing wood biomass with coal in existing coal-fired power plants according to the implementation of renewable portfolio standard (RPS) and cap-and-trade systems; problems arise due to mixing of the two fuels which have different combustion reactivities. In the previous work, research on glycerol impregnated hybrid fuel (Hybrid Coal by Korea Institute of Energy Research; HCK) was conducted for the diversification of bioliquid and issues to be resolved through further study in the application of glycerol to Hybrid Coal were noted. As a solution, a hydrogel having a small quantity of poly vinyl alcohol (PVA) added to glycerol was applied in the present work. PVA allowed the solidification of glycerol and contributed to the binding energy being stronger among glycerol derivatives and the surface of coal pore; thus, the fuel loss by readsorption of water can be inhibited by setting hydrogel into coal pores.