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Mixed‐valence europium ions‐activated phosphors have distinct advantages in color modulation, dynamic anti‐counterfeiting, and optical sensors. Nevertheless, it is still a challenge to obtain mixed‐valence europium ions in single compounds by facile self‐reduction. Herein, the crystal structure of a 3D hexagonal network formed by SiO4/AlO4 tetrahedra is demonstrated to play a significant role in the spontaneous reduction of Eu3+ to Eu2+ based on SrAl2Si2O8, Sr2SiO4, SrAl2O4 hosts. The crystal field theory and Judd‐Ofelt theory provide a deeper understanding of Eu2+ and Eu3+ luminescence behavior, namely, the low energy spectra of Eu2+ are more easily observed in crystal structure with high polarizability and octahedral coordination, whereas the spectra properties of Eu3+ are affected by the symmetry of local environment and crystal rigidity. For SrAl2Si2O8: 0.02Eu2+/Eu3+, multi‐mode thermometry is explored in terms of the luminescence intensity ratio (LIR) of Eu2+/Eu3+, luminescence intensity (LI) and full‐width at half maximum (FWHM) of Eu2+ with maximal relative sensitivity reaching 3.83% K−1. This study presents the first exploration of optical manometry based on the LIR mode of Eu2+/Eu3+ with excellent sensitivity (Sr = 18.13% GPa−1). This work not only provides a novel strategy for the design of mixed‐valence ions‐activated materials but also constructs promising optical thermometry, and manometry candidates. [ABSTRACT FROM AUTHOR]

Increased acid phosphatase (APase) activity is a prominent feature of tomato (Solanum lycopersicum) responses to inorganic phosphate (Pi) restriction. SlPHL1, a phosphate starvation response (PHR) transcription factor, has been identified as a positive regulator of low Pi (LP)‐induced APase activity in tomato. However, the molecular mechanism underlying this regulation remains to be elucidated. Here, SlPHL1 was found to positively regulate the LP‐induced expression of five potential purple acid phosphatase (PAP) genes, namely SlPAP7, SlPAP10b, SlPAP12, SlPAP15, and SlPAP17b. Furthermore, we provide evidence that SlPHL1 can stimulate transcription of these five genes by binding directly to the PHR1 binding sequence (P1BS) located on their promoters. The P1BS mutation notably weakened SlPHL1 binding to the promoters of SlPAP7, SlPAP12, and SlPAP17b but almost completely abolished SlPHL1 binding to the promoters of SlPAP10b and SlPAP15. As a result, the transcriptional activation of SlPHL1 on SlPAP10b and SlPAP15 was substantially diminished. In addition, not only did transient overexpression of either SlPAP10b or SlPAP15 in tobacco leaves increase APase activity, but overexpression of SlPAP15 in Arabidopsis and tomato also increased APase activity and promoted plant growth. Subsequently, two SPX proteins, SlSPX1 and SlSPX4, were shown to physically interact with SlPHL1. Moreover, SlSPX1 inhibited the transcriptional activation of SlPHL1 on SlPAP10b and SlPAP15 and negatively regulated the activity of APase. Taken together, these results demonstrate that SlPHL1‐mediated LP signaling promotes APase activity by activating the transcription of SlPAP10b and SlPAP15, which may provide valuable insights into the mechanisms of tomato response to Pi‐limited stress. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the association between cigarette smoking and retinal capillary plexus (RCP) using optical coherence tomography angiography (OCTA) and to examine whether potential vascular risk factors could impact their association. Methods: This is a cross‐sectional, community‐based study. The Jidong Eye Cohort Study included participants aged ≥18 years in the Jidong community (Tangshan city, northern China) from August 2019 to January 2020. All participants underwent comprehensive ophthalmic examination and completed detailed smoking questionnaires. Retinal vessel density in the superficial and deep RCP was automatically measured using OCTA. Results: Of the 2598 participants included in the study, 2026 (78.0%) never smoked and 572 (22.0%) had a history of smoking (494 [19.0%] current smokers and 78 [3.0%] former smokers). The median (interquartile range) age was 41 (34–52) years for the non‐smoking group and 45 (35–54.5) years for the smoking group. Multivariable analysis showed that smoking history is associated with a low deep RCP vessel density in the parafovea (β, −0.53; 95% confidence interval [CI], −0.82 to −0.24) and four quadrants. Increased smoking pack‐years were associated with reduced deep RCP vessel density in the parafovea (p for trend <0.001) and four quadrants. The significant interaction between diabetes and smoking only was found for superficial RCP vessel density in the parafovea (p for interaction = 0.014) and four quadrants except for the temporal quadrants. Conclusions: Cigarette smoking is an independent risk factor for reduced deep RCP vessel density. Our findings imply the potential detrimental effect of smoking on the occurrence of ocular diseases. [ABSTRACT FROM AUTHOR]

Background: Regulatory T (Treg) cells are important components of the tumour microenvironment (TME) that play roles in gastric cancer (GC) metastasis. Although tumour cells that undergo epithelial‐mesenchymal transition (EMT) regulate Treg cell function, their regulatory mechanism in GC remains unclear. Methods: The miR‐192‐5p was identified by examining three Gene Expression Omnibus GC miRNA expression datasets. RNA immunoprecipitation (RIP) and dual‐luciferase reporter assays were conducted to identify interactions between miR‐192‐5p and RB1. The role of miR‐192‐5p/RB1 in GC progression was evaluated based on EdU incorporation, wound healing and Transwell assays. An in vitro co‐culture assay was performed to measure the effect of miR‐192‐5p/RB1 on Treg cell differentiation. In vivo experiments were conducted to explore the role of miR‐192‐5p in GC progression and Treg cell differentiation. Results: MiR‐192‐5p was overexpressed in tumour and was associated with poor prognosis in GC. MiR‐192‐5p bound to the RB1 3′‐untranslated region, resulting in GC EMT, proliferation, migration and invasion. MiR‐192‐5p/RB1 mediated interleukin‐10 (IL‐10) secretion by regulating nuclear factor‐kappaBp65 (NF‐κBp65), affecting Treg cell differentiation. NF‐κBp65, in turn, promoted miR‐192‐5p expression and formed a positive feedback loop. Furthermore, in vivo experiments confirmed that miR‐192‐5p/RB1 promotes GC growth and Treg cell differentiation. Conclusion: Collectively, our studies indicate that miR‐192‐5p/RB1 promotes EMT of tumour cells, and the miR‐192‐5p/RB1/NF‐κBp65 signaling axis induces Treg cell differentiation by regulating IL‐10 secretion in GC. Our results suggest that targeting miR‐192‐5p/RB1/NF‐κBp65 /IL‐10 may pave the way for the development of new immune treatments for GC. [ABSTRACT FROM AUTHOR]

Background: Many clinical biomarkers have been associated with dementia. This paper uses a novel data set from an understudied population – a national sample of older Indians ‐ to assess the relative importance of 41 biomarkers using a variety of approaches. The value of these data is the extensiveness of the biomarker collection, the validated classification of dementia, and the uniqueness of the population with low average education. Many approaches to both summarizing and reducing data complexity have been suggested. Some of these are based on theory about biological mechanisms and some are agnostic and based on machine‐learning approaches. The results are informative for others collecting and analyzing biomarker data in population samples. Method: We use 6 approaches to assessing the value of the biomarkers in explaining variance in dementia diagnosis and cognitive functioning – some traditional social science approaches and some based on machine learning. The aim of the different approaches is to determine how to best characterize the influence of biology as well as how to trim and combine the data. The five approaches to measuring biological risk: 41 individual biomarkers; identification of subsets of biomarkers with elastic net; support vector machine learning; factors developed with factor analysis; a principal components approach; and a factor classification based on a theoretical approach. Results: Results indicate if the aim is explain variance in dementia, all the biomarkers or a reduced set of biomarkers identified by elastic net do the best job at explaining variability. However, biological markers chosen as most important do not fit well our understanding of biological mechanisms. Conclusion: If the aim is understanding the relative importance of biological systems, traditional social science approaches, e.g. the factor analysis and principal components approach, allow better understanding and interpretation of the biomarkers associated with cognitive performance. [ABSTRACT FROM AUTHOR]