Structural and Optical Characterization of ZnO/Mg_<i>x</i>Zn_1–<i>x</i>O Multiple Quantum Wells Based Random Laser Diodes

Two kinds of laser diodes (LDs) comprised of ZnO/Mg_<i>x</i>Zn_1–<i>x</i>O (ZnO/MZO) multiple quantum wells (MQWs) grown on GaN (MQWs/GaN) and Si (MQWs/Si) substrates, respectively, have been constructed. The LD with MQWs/GaN exhibits ultraviolet random lasing under electrical excitation, while that with MQWs/Si does not. In the MQWs/Si, ZnO/MZO MQWs consist of nanoscaled crystallites, and the MZO layers undergo a phase separation of cubic MgO and hexagonal ZnO. Moreover, the Mg atom predominantly locates in the MZO layers along with a significant aggregation at the ZnO/MZO interfaces; in sharp contrast, the ZnO/MZO MQWs in the MQWs/GaN show a well-crystallized structure with epitaxial relationships among GaN, MZO, and ZnO. Notably, Mg is observed to diffuse into the ZnO well layers. The structure–optical property relationship of these two LDs is further discussed

Autophagy-mediated desiccation tolerance in <i>Tripogon loliiformis</i>.

<p><i>Tripogon loliiformis</i> uses a multi-pronged strategy to survive desiccation stress. I) rapid shutdown of photosynthesis triggers caloric deficiency and the onset of autophagy pathways during the early stages of dehydration. ii) autophagic removal of damaged, unfolded and redundant organelles and proteins as well as ROS mitigates stress to delay the onset of apoptosis. The efficacy of autophagy pathways is bolstered by the accumulation of trehalose throughout dehydration. iii) Nutrient recycling as a product of autophagy mediated breakdown of proteins and organelles suspends “aging” senescence pathways.</p

<i>T</i>. <i>loliiformis</i> suppresses PCD pathway during dehydration and desiccation.

<p>Hydrated, dehydrating, desiccated and rehydrated <i>T</i>. <i>loliiformis</i> leaf tissues were harvested and subjected to TUNEL assay and propidium iodide counter staining for the detection of DNA fragmentation and Apoptotic-like cell death. Hydrated leaves treated with DNase were used as the positive control.</p

Data_Sheet_1_Bioinformatic and functional characterization of cyclic-di-GMP metabolic proteins in Vibrio alginolyticus unveils key diguanylate cyclases controlling multiple biofilm-associated phenotypes.pdf

The biofilm lifestyle is critical for bacterial survival and proliferation in the fluctuating marine environment. Cyclic diguanylate (c-di-GMP) is a key second messenger during bacterial adaptation to various environmental signals, which has been identified as a master regulator of biofilm formation. However, little is known about whether and how c-di-GMP signaling regulates biofilm formation in Vibrio alginolyticus, a globally dominant marine pathogen. Here, a large set of 63 proteins were predicted to participate in c-di-GMP metabolism (biosynthesis or degradation) in a pathogenic V. alginolyticus strain HN08155. Guided by protein homology, conserved domains and gene context information, a representative subset of 22 c-di-GMP metabolic proteins were selected to determine which ones affect biofilm-associated phenotypes. By comparing phenotypic differences between the wild-type and mutants or overexpression strains, we found that 22 c-di-GMP metabolic proteins can separately regulate different phenotypic outputs in V. alginolyticus. The results indicated that overexpression of four c-di-GMP metabolic proteins, including VA0356, VA1591 (CdgM), VA4033 (DgcB) and VA0088, strongly enhanced rugose colony morphotypes and strengthened Congo Red (CR) binding capacity, both of which are indicators of biofilm matrix overproduction. Furthermore, rugose enhanced colonies were accompanied by increased transcript levels of extracellular polysaccharide (EPS) biosynthesis genes and decreased expression of flagellar synthesis genes compared to smooth colonies (WTpBAD control), as demonstrated by overexpression strains WTp4033 and ∆VA4033p4033. Overall, the high abundance of c-di-GMP metabolic proteins in V. alginolyticus suggests that c-di-GMP signaling and regulatory system could play a key role in its response and adaptation to the ever-changing marine environment. This work provides a robust foundation for the study of the molecular mechanisms of c-di-GMP in the biofilm formation of V. alginolyticus.</p

Exogenous application of trehalose triggers autophagy in <i>T</i>.<i>loliiformis</i>.

<p>Hydrated leaves from three month old glasshouse grown <i>T</i>. <i>loliiformis</i> plants were harvested, divided into 1 cm segments, vacuum infiltrated with 1 and 5 mM trehalose solution containing 1 μM concamycin A prepared in ½ MS basal salts and incubated for 24 hrs. Autophagosomes were visualised by Transmission Electron Microscopy. Top Panel–(A) Concanamycin control, (B) 1 mM trehalose, (C) 1 mM trehalose, higher magnification, (D) 5 mM trehalose, (E) 5 mM trehalose, higher magnification, (F) Tunicamycin control, Bottom panel—The sucrose and trehalose contents of hydrated, dehydrating and desiccated leaves were assessed by GCMS analysis.</p

<i>Tripogon loliiformis</i> leaf tissues are alive at desiccation.

<p><i>T</i>. <i>loliiformis</i> plants were grown from seed harvested from a single source and grown in the glasshouse for two months, dehydrated to desiccation and rehydrated with the addition of water. Left Panels—fully hydrated (Top), dehydrating and desiccated (Middle) and 48h after rehydration (Bottom). Right Panels—Leaf tissue was harvested from hydrated and dehydrating plants at 60, 40 and <10% RWC and stained with Evans Blue. As a control, a hydrated leaf was stained after immersion in boiling water for 5 minutes (samples as labelled).</p

Image3_A novel diagnostic model for predicting immune microenvironment subclass based on costimulatory molecules in lung squamous carcinoma.TIF

There is still no ideal predictive biomarker for immunotherapy response among patients with non-small cell lung cancer. Costimulatory molecules play a role in anti-tumor immune response. Hence, they can be a potential biomarker for immunotherapy response. The current study comprehensively investigated the expression of costimulatory molecules in lung squamous carcinoma (LUSC) and identified diagnostic biomarkers for immunotherapy response. The costimulatory molecule gene expression profiles of 627 patients were obtained from the The Cancer Genome Atlas, GSE73403, and GSE37745 datasets. Patients were divided into different clusters using the k-means clustering method and were further classified into two discrepant tumor microenvironment (TIME) subclasses (hot and cold tumors) according to the immune score of the ESTIMATE algorithm. A high proportion of activated immune cells, including activated memory CD4 T cells, CD8 T cells, and M1 macrophages. Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms. Based on the five CMGs, a diagnostic nomogram for predicting individual tumor immune microenvironment subclasses in the TCGA dataset was developed, and its predictive performance was validated using GSE73403 and GSE37745 datasets. The predictive accuracy of the diagnostic nomogram was satisfactory in all three datasets. Therefore, it can be used to identify patients who may benefit more from immunotherapy.</p

Table2_A novel diagnostic model for predicting immune microenvironment subclass based on costimulatory molecules in lung squamous carcinoma.XLSX

There is still no ideal predictive biomarker for immunotherapy response among patients with non-small cell lung cancer. Costimulatory molecules play a role in anti-tumor immune response. Hence, they can be a potential biomarker for immunotherapy response. The current study comprehensively investigated the expression of costimulatory molecules in lung squamous carcinoma (LUSC) and identified diagnostic biomarkers for immunotherapy response. The costimulatory molecule gene expression profiles of 627 patients were obtained from the The Cancer Genome Atlas, GSE73403, and GSE37745 datasets. Patients were divided into different clusters using the k-means clustering method and were further classified into two discrepant tumor microenvironment (TIME) subclasses (hot and cold tumors) according to the immune score of the ESTIMATE algorithm. A high proportion of activated immune cells, including activated memory CD4 T cells, CD8 T cells, and M1 macrophages. Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms. Based on the five CMGs, a diagnostic nomogram for predicting individual tumor immune microenvironment subclasses in the TCGA dataset was developed, and its predictive performance was validated using GSE73403 and GSE37745 datasets. The predictive accuracy of the diagnostic nomogram was satisfactory in all three datasets. Therefore, it can be used to identify patients who may benefit more from immunotherapy.</p

Table1_A novel diagnostic model for predicting immune microenvironment subclass based on costimulatory molecules in lung squamous carcinoma.DOCX

There is still no ideal predictive biomarker for immunotherapy response among patients with non-small cell lung cancer. Costimulatory molecules play a role in anti-tumor immune response. Hence, they can be a potential biomarker for immunotherapy response. The current study comprehensively investigated the expression of costimulatory molecules in lung squamous carcinoma (LUSC) and identified diagnostic biomarkers for immunotherapy response. The costimulatory molecule gene expression profiles of 627 patients were obtained from the The Cancer Genome Atlas, GSE73403, and GSE37745 datasets. Patients were divided into different clusters using the k-means clustering method and were further classified into two discrepant tumor microenvironment (TIME) subclasses (hot and cold tumors) according to the immune score of the ESTIMATE algorithm. A high proportion of activated immune cells, including activated memory CD4 T cells, CD8 T cells, and M1 macrophages. Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms. Based on the five CMGs, a diagnostic nomogram for predicting individual tumor immune microenvironment subclasses in the TCGA dataset was developed, and its predictive performance was validated using GSE73403 and GSE37745 datasets. The predictive accuracy of the diagnostic nomogram was satisfactory in all three datasets. Therefore, it can be used to identify patients who may benefit more from immunotherapy.</p

Image5_A novel diagnostic model for predicting immune microenvironment subclass based on costimulatory molecules in lung squamous carcinoma.TIF

There is still no ideal predictive biomarker for immunotherapy response among patients with non-small cell lung cancer. Costimulatory molecules play a role in anti-tumor immune response. Hence, they can be a potential biomarker for immunotherapy response. The current study comprehensively investigated the expression of costimulatory molecules in lung squamous carcinoma (LUSC) and identified diagnostic biomarkers for immunotherapy response. The costimulatory molecule gene expression profiles of 627 patients were obtained from the The Cancer Genome Atlas, GSE73403, and GSE37745 datasets. Patients were divided into different clusters using the k-means clustering method and were further classified into two discrepant tumor microenvironment (TIME) subclasses (hot and cold tumors) according to the immune score of the ESTIMATE algorithm. A high proportion of activated immune cells, including activated memory CD4 T cells, CD8 T cells, and M1 macrophages. Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms. Based on the five CMGs, a diagnostic nomogram for predicting individual tumor immune microenvironment subclasses in the TCGA dataset was developed, and its predictive performance was validated using GSE73403 and GSE37745 datasets. The predictive accuracy of the diagnostic nomogram was satisfactory in all three datasets. Therefore, it can be used to identify patients who may benefit more from immunotherapy.</p