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Polymer dielectrics possessing the superiorities of easy processing and high power density are widely used in pulsed power and power electronics. However, the low energy storage density (Ue) of polymer dielectrics limits their application in the modern electronic industries. In this work, we present the sea-island structure multilayered composites based on polymethylmethacrylate (PMMA) matrix and polyvinylidene fluoride (PVDF) nanoparticles, which is constructed by the solution blending with typical solubility differences method and the layer-by-layer solution casting method. The PVDF/PMMA composite incorporated with high PVDF content is placed in the middle of the sandwich-structured composite as a non-traditional intermediate polarization layer. The PVDF/PMMA composite with low PVDF loading is placed in the outer layer to improve the insulating property of the composite. The results show that the PVDF/PMMA composites with inner and outer PVDF volume content of 30% and 10%, respectively, obtained an excellent dielectric constant value (εr ~ 4.35), which is 45% higher than that of PMMA (εr ~ 3). Meanwhile, the maximum Ue (~ 4.95 J cm−3) of the PVDF/PMMA composites at an electric field of 422.48 MV m−1 was obtained, which is 207% higher than that of PMMA (Ue ~ 1.61 J cm−3). The improved energy storage capability could be attributed to the introduction of highly polarizing particles, the increased number of heterogeneous interfaces, and the multilayered construction of composite. This work provides a strategy for the preparation of advanced all-organic dielectrics with a higher discharge energy density. [ABSTRACT FROM AUTHOR]

Background: The clinical value and molecular characteristics of tumor differentiation in oral squamous cell carcinoma (OSCC) remain unclear. There is a lack of a related molecular classification prediction system based on pathological images for precision medicine. Methods: Integration of epidemiology, genomics, experiments, and deep learning to clarify the clinical value and molecular characteristics, and develop a novel OSCC molecular classification prediction system. Results: Large-scale epidemiology data (n = 118,817) demonstrated OSCC differentiation was a significant prognosis indicator (p < 0.001), and well-differentiated OSCC was more chemo-resistant than poorly differentiated OSCC. These results were confirmed in the TCGA database and in vitro. Furthermore, we found chemo-resistant related pathways and cell cycle-related pathways were up-regulated in well- and poorly differentiated OSCC, respectively. Based on the characteristics of OSCC differentiation, a molecular grade of OSCC was obtained and combined with pathological images to establish a novel prediction system through deep learning, named ShuffleNetV2-based Molecular Grade of OSCC (SMGO). Importantly, our independent multi-center cohort of OSCC (n = 340) confirmed the high accuracy of SMGO. Conclusions: OSCC differentiation was a significant indicator of prognosis and chemotherapy selection. Importantly, SMGO could be an indispensable reference for OSCC differentiation and assist the decision-making of chemotherapy. [ABSTRACT FROM AUTHOR]

The influence mechanism of tensile load on the conductivity of thin films is a key issue in evaluating the conductivity of engineering structural thin films under complex loading conditions. How to reveal the correlation mechanisms of different physical quantities of metal films, and to predict the variation of film resistance with tensile load is a very critical and challenging problem. In this paper, a multiscale model of film resistance under tensile neching is established to describe the mechanical behavior, resistivity, and resistance of metal films under tensile load. The results show that the tensile load has a significant effect on the resistivity and conductivity of the film. This provides a reference for the evaluation and optimization of the conductivity of thin films with complex engineering structures. [ABSTRACT FROM AUTHOR]

Objective: To compare the predictive performance of the current clinical prediction models for predicting intravesical recurrence (IVR) after radical nephroureterectomy (RNU) in patients with upper tract urothelial carcinoma (UTUC). Methods: We retrospectively analysed upper tract urothelial carcinoma patients who underwent radical nephroureterectomy in our centre from January 2009 to December 2019. We used the propensity score matching (PSM) method to adjust the confounders between the IVR and non-IVR groups. Additionally, Xylinas' reduce model and full model, Zhang's model, and Ishioka's risk stratification model were used to retrospectively calculate predictions for each patient. Receiver operating characteristic (ROC) curves were generated, and the areas under the curves (AUCs) were compared to identify the method with the highest predictive value. Results: We included 217 patients with a median follow-up of 41 months, of which 57 had IVR. After PSM analysis, 52 pairs of well-matched patients were included in the comparative study. No significant difference was found in clinical indicators besides hydronephrosis. The model comparison showed that the AUCs of the reduced Xylinas' model for 12 months, 24 months, and 36 months were 0.69, 0.73, and 0.74, respectively, and those of the full Xylinas' model were 0.72, 0.75, and 0.74, respectively. The AUC of Zhang's model for 12 months, 24 months, and 36 months was 0.63, 0.71, and 0.71, respectively, the performance of Ishioka's model is that the AUC of 12 months, 24 months and 36 months was 0.66, 0.71, and 0.74, respectively. Conclusion: The external verification results of the four models show that more comprehensive data and a larger sample size of patients are needed to strengthen the models' derivation and updating procedure, to better apply them to different populations. [ABSTRACT FROM AUTHOR]

Plants have evolved sophisticated signaling networks to adjust flowering time, ensuring successful reproduction. Two crucial flowering regulators, FLOWERING LOCUS T (FT) and CONSTANS (CO), play pivotal roles in regulating flowering across various species. Previous studies have indicated that suppressing Gossypium hirsutum CONSTANS-LIKE 2 (GhCOL2), a homolog of Arabidopsis CO, leads to delayed flowering in cultivated cotton. However, the underlying regulatory mechanisms remain unknown. In this study, a yeast one-hybrid and dual-LUC expression assays were used to elucidate the molecular mechanism through which GhCOL2 regulates the transcription of GhHD3A. RT-qPCR was used to examine the expression of GhCOL2 and GhHD3A. Our findings reveal that GhCOL2 directly binds to CCACA cis-elements and atypical CORE (TGTGTATG) cis-elements in the promoter regions of HEADING DATE 3 A (HD3A), thereby activating GhHD3A transcription. Notably, GhCOL2 and GhHD3A exhibited high expression levels in the adult stage and low levels in the juvenile stage. Interestingly, the expression of GhCOL2 and GhHD3A varied significant between the two cotton varieties (Tx2094 and Maxxa). In summary, our study enhances the understanding of the molecular mechanism by which cotton GhCOL2-GhHD3A regulates flowering at the molecular level. Furthermore, it contributes to a broader comprehension of the GhCOL2-GhHD3A model in G. hirsutum. [ABSTRACT FROM AUTHOR]