Preparation and Characterization of TiO2-Coated Hollow Glass Beads for Functionalization of Deproteinized Natural Rubber Latex via UVA-Activated Photocatalytic Degradation.

The photochemical degradation of natural rubber (NR) is a prevalent method used to modify its inherent properties. Natural rubber, predominantly derived from the Hevea Brasiliensis tree, exhibits an exceptionally high molecular weight (MW), often reaching a million daltons (Da). This high MW restricts its solubility in various solvents and its reactivity with polar compounds, thereby constraining its versatile applications. In our previous work, we employed TiO2 in its powdered form as a photocatalyst for the functionalization of NR latex. However, the post-process separation and reuse of this powder present substantial challenges. In this present study, we aimed to functionalize deproteinized NR (DPNR) latex. We systematically reduced its MW via photochemical degradation under UVA irradiation facilitated by H2O2. To enhance the efficiency of the degradation process, we introduced TiO2-coated hollow glass beads (TiO2-HGBs) as photocatalysts. This approach offers the advantage of easy collection and repeated reuse. The modified DPNR showed a reduction in its number-average MW from 9.48 × 105 to 0.28 × 105 Da and incorporated functional groups, including hydroxyl, carbonyl, and epoxide. Remarkably, the TiO2-HGBs maintained their performance over seven cycles of reuse. Due to their superior efficacy, TiO2-HGBs stand out as promising photocatalysts for the advanced functionalization of NR across various practical applications.

Prediction of the histologic upgrade of ductal carcinoma in situ using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging.

Objective: To 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 methods: This 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. Results: The 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. Conclusion: Our 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.

The splicing factor 1-FLOWERING LOCUS M module spatially regulates temperature-dependent flowering by modulating FLOWERING LOCUS T and LEAFY expression.

KEY MESSAGE: The AtSF1-FLM module spatially controls temperature-dependent flowering by negatively regulating the expression of FT and LFY in the leaf and shoot apex, respectively. Alternative splicing mediated by various splicing factors is important for the regulation of plant growth and development. Our recent reports have shown that a temperature-dependent interaction between Arabidopsis thaliana splicing factor 1 (AtSF1) and FLOWERING LOCUS M (FLM) pre-mRNA introns controls the differential production of FLM-ß transcripts at different temperatures, eventually resulting in temperature-responsive flowering. However, the molecular and genetic interactions between the AtSF1-FLM module and floral activator genes remain unknown. Here, we aimed to identify the interactions among AtSF1, FLM, FLOWERING LOCUS T (FT), and LEAFY (LFY) by performing molecular and genetic analyses. FT and TWIN SISTER OF FT (TSF) expression in atsf1-2 mutants significantly increased in the morning and middle of the night at 16 and 23 °C, respectively, under long-day conditions. In addition, ft mutation suppressed the early flowering of atsf1-2 and atsf1-2 flm-3 mutants and masked the temperature response of atsf1-2 flm-3 mutants, suggesting that FT is a downstream target gene of the AtSF1-FLM module. LFY expression significantly increased in the diurnal samples of atsf1-2 mutants and in the shoot apex regions of atsf1-2 ft-10 mutants at different temperatures. The chromatin immunoprecipitation (ChIP) assay revealed that FLM directly binds to the genomic regions of LFY but not of APETALA1 (AP1). Moreover, lfy mutation suppressed the early flowering of flm-3 mutants, suggesting that LFY is another target of the AtSF1-FLM module. Our results reveal that the AtSF1-FLM module spatially modulates temperature-dependent flowering by regulating FT and LFY expressions.

Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms.

The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated the potential of using deep learning models for differentiating between upgraded versus pure DCIS in DCIS diagnosed by core-needle biopsy. Preoperative axial dynamic contrast-enhanced magnetic resonance imaging (MRI) data from 352 lesions were used to train, validate, and test three different types of deep learning models. The highest performance was achieved by Recurrent Residual Convolutional Neural Network using Regions of Interest (ROIs) with an accuracy of 75.0% and area under the receiver operating characteristic curve (AUC) of 0.796. Our results suggest that the deep learning approach may provide an assisting tool to predict the histologic upgrade of DCIS and provide personalized treatment strategies to patients with underestimated invasive disease.

Role of Arabidopsis Splicing factor SF1 in Temperature-Responsive Alternative Splicing of FLM pre-mRNA.

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-ß, 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-ß 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-ß transcripts. Moreover, alterations in other splicing factors such as ABA HYPERSENSITIVE1/CBP80 (ABH1/CBP80) and STABILIZED1 (STA1) did not impact the FLM-ß/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.

Spheroid Formation of Hepatocarcinoma Cells in Microwells: Experiments and Monte Carlo Simulations.

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.