Light-activated ferroelectric transition in layer dependent Bi2O2Se films

Bi2O2Se has attracted intensive attention due to its potential in electronics, optoelectronics, as well as ferroelectric applications. Despite that, there have only been a handful of experimental studies based on ultrafast spectroscopy to elucidate the carrier dynamics in Bi2O2Se thin films, Different groups have reported various ultrafast timescales and associated mechanisms across films of different thicknesses. A comprehensive understanding in relation to thickness and fluence is still lacking. In this work, we have systematically explored the thickness-dependent Raman spectroscopy and ultrafast carrier dynamics in chemical vapor deposition (CVD)-grown Bi2O2Se thin films on mica substrate with thicknesses varying from 22.44 nm down to 4.62 nm at both low and high pump fluence regions. Combining the thickness dependence and fluence dependence of the slow decay time, we demonstrate a ferroelectric transition in the thinner (< 8 nm) Bi2O2Se films, influenced by substrate-induced compressive strain and non-equilibrium states. Moreover, this transition can be manifested under highly non-equilibrium states. Our results deepen the understanding of the interplay between the ferroelectric phase and semiconducting characteristics of Bi2O2Se thin films, providing a new route to manipulate the ferroelectric transition

High mobility group box 1 promotes radioresistance in esophageal squamous cell carcinoma cell lines by modulating autophagy

Resistance to radiotherapy results in relapse and treatment failure in locally advanced esophageal squamous cell carcinoma (ESCC). High mobility group box 1 (HMGB1) is reported to be associated with the radioresistance in bladder and breast cancer. However, the role of HMGB1 in the radiotherapy response in ESCC has not been fully elucidated. Here, we investigated the role of HMGB1 to radioresistance in ESCC clinical samples and cell lines. We found that HMGB1 expression was associated with tumor recurrence after postoperative radiotherapy in locally advanced ESCC patients. HMGB1 knockdown in ESCC cells resulted in increased radiosensitivity both in vitro and in vivo. Autophagy level was found depressed in HMGB1 inhibition cells and activation of autophagy brought back cell's radioresistance. Our results demonstrate that HMGB1 activate autophagy and consequently promote radioresistance. HMGB1 may be used as a predictor of poor response to radiotherapy in ESCC patients. Our finding also highlights the importance of the utility of HMGB1 in ESCC radiosensitization.Peer reviewe

Amino Acid Sensor Kinase Gcn2 Is Required for Conidiation, Secondary Metabolism, and Cell Wall Integrity in the Taxol-Producer Pestalotiopsis microspora

The canonical Gcn2/Cpc1 kinase in fungi coordinates the expression of target genes in response to amino acid starvation. To investigate its possible role in secondary metabolism, we characterized a gcn2 homolog in the taxol-producing fungus Pestalotiopsis microspora. Deletion of the gene led to severe physiological defects under amino acid starvation, suggesting a conserved function of gcn2 in amino acid sensing. The mutant strain Δgcn2 displayed retardation in vegetative growth. It generated dramatically fewer conidia, suggesting a connection between amino acid metabolism and conidiation in this fungus. Importantly, disruption of the gene altered the production of secondary metabolites by HPLC profiling. For instance, under amino acid starvation, the deletion strain Δgcn2 barely produced secondary metabolites including the known natural product pestalotiollide B. Even more, we showed that gcn2 played critical roles in the tolerance to several stress conditions. Δgcn2 exhibited a hypersensitivity to Calcofluor white and Congo red, implying a role of Gcn2 in maintaining the integrity of the cell wall. This study suggests that Gcn2 kinase is an important global regulator in the growth and development of filamentous fungi and will provide knowledge for the manipulation of secondary metabolism in P. microspora

PERK-Mediated Cholesterol Excretion from IDH Mutant Glioma Determines Anti-Tumoral Polarization of Microglia

Isocitrate dehydrogenase (IDH) mutation, a known pathologic classifier, initiates metabolic reprogramming in glioma cells and has been linked to the reaction status of glioma-associated microglia/macrophages (GAMs). However, it remains unclear how IDH genotypes contribute to GAM phenotypes. Here, it is demonstrated that gliomas expressing mutant IDH determine M1-like polarization of GAMs, while archetypal IDH induces M2-like polarization. Intriguingly, IDH-mutant gliomas secrete excess cholesterol, resulting in cholesterol-rich, pro-inflammatory GAMs without altering their cholesterol biosynthesis, and simultaneously exhibiting low levels of tumoral cholesterol due to expression remodeling of cholesterol transport molecules, particularly upregulation of ABCA1 and downregulation of LDLR. Mechanistically, a miR-19a/LDLR axis-mediated novel post-transcriptional regulation of cholesterol uptake is identified, modulated by IDH mutation, and influencing tumor cell proliferation and invasion. IDH mutation-induced PERK activation enhances cholesterol export from glioma cells via the miR-19a/LDLR axis and ABCA1/APOE upregulation. Further, a synthetic PERK activator, CCT020312 is introduced, which markedly stimulates cholesterol efflux from IDH wild-type glioma cells, induces M1-like polarization of GAMs, and consequently suppresses glioma cell invasion. The findings reveal an essential role of the PERK/miR-19a/LDLR signaling pathway in orchestrating gliomal cholesterol transport and the subsequent phenotypes of GAMs, thereby highlighting a novel potential target pathway for glioma therapy

Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%

Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%

Image Denoising of Adaptive Fractional Operator Based on Atangana–Baleanu Derivatives

A fractional integral operator can preserve an image edge and texture details as a denoising filter. Recently, a newly defined fractional-order integral, Atangana–Baleanu derivatives (ABC), has been used successfully in image denoising. However, determining the appropriate order requires numerous experiments, and different image regions using the same order may cause too much smoothing or insufficient denoising. Thus, we propose an adaptive fractional integral operator based on the Atangana–Baleanu derivatives. Edge intensity, global entropy, local entropy, and local variance weights are used to construct an adaptive order function that can adapt to changes in different regions of an image. Then, we use the adaptive order function to improve the masks based on the Grumwald–Letnikov scheme (GL_ABC) and Toufik–Atangana scheme (TA_ABC), namely, Ada_GL_ABC and Ada_TA_ABC, respectively. Finally, multiple evaluation indicators are used to assess the proposed masks. The experimental results demonstrate that the proposed adaptive operator can better preserve texture details when denoising than other similar operators. Furthermore, the image processed by the Ada_TA_ABC operator has less noise and more detail, which means the proposed adaptive function has universality