Advantages of GaN Based Light-Emitting Diodes with a P-InGaN Hole Reservoir Layer

A p-type InGaN hole reservoir layer (HRL) was designed and incorporated in GaN based light-emitting diodes (LEDs) to enhance hole injection efficiency and alleviate efficiency droop. The fabricated LEDs with p-type HRL exhibited higher light output power, smaller emission energy shift and broadening as compared to its counterpart. Based on electrical and optical characteristics analysis and numerical simulation, these improvements are mainly attributed to the alleviated band bending in the last couple of quantum well and electron blocking layer, and thus better hole injection efficiency. Meanwhile, the efficiency droop can be effectively mitigated when the p-InGaN HRL was used

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke. Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health

The Relative Roles of Climate Variation and Human Activities in Vegetation Dynamics in Coastal China from 2000 to 2019

Vegetation in the terrestrial ecosystem, sensitive to climate change and human activities, exerts a crucial influence on the carbon cycles in land, ocean, and atmosphere. Discrimination between climate and human-induced vegetation dynamics is advocated but still limited, especially in coastal China, which is characterized by a developed economy, a large population, and high food production, but also by unprecedented climate change and warming. Taking coastal China as the research area, our study used the normalized difference vegetation index (NDVI) in growing seasons, as well as precipitation, temperature, and sunlight hours datasets, adopted residual trend analysis at pixel and regional scales in coastal China from 2000–2019 and aims to (1) delineate the patterns and processes of vegetation changes, and (2) separate the relative contributions of climate and human activities by adopting residual trend analysis. The results indicated that (1) coastal China experienced the most vegetation greening (83.04% of the whole region) and partial degradation (16.86% of the whole region) with significant spatial heterogeneity; (2) compared with climate change, human activities have a greater positive impact on NDVI, and the regions were mainly located in the north of the North China Plain and the south of southern China; (3) the relative contribution rates of climate change and human activities were detected to be 0–60% and 60–100%, respectively; (4) in the northern coastal areas, the improvement of cultivated land management greatly promoted the greening of vegetation and thus the increase of grain yield, while in southern coastal areas, afforestation and the restoration of degraded forest were responsible for vegetation restoration; and (5) similar results obtained by partial correlation between nighttime lights and NDVI indicated the reliability of the residual trend analysis. The linear relationships of precipitation, temperature, and radiation on NDVI may limit the accurate estimation of climate drivers on vegetation, and further ecosystem process-modeling approaches can be used to estimate the relative contribution of climate change and human activities. The findings in our research emphasized that the attribution for vegetation dynamics with heterogeneity can provide evidence for the designation of rational ecological conservation policies

Genome-Wide Characterization and Expression Profiling of Squamosa Promoter Binding Protein-Like (SBP) Transcription Factors in Wheat (Triticum aestivum L.)

Transcription factors (TFs) play fundamental roles in the developmental processes of all living organisms. Squamosa Promoter Binding Protein-like (SBP/SBP-Box) is a major family of plant-specific TFs, which plays important roles in multiple processes involving plant growth and development. While some work has been done, there is a lot more that is yet to be discovered in the hexaploid wheat SBP (TaSBP) family. With the completion of whole genome sequencing, genome-wide analysis of SBPs in common hexaploid wheat is now possible. In this study, we used protein–protein Basic Local Alignment Search Tool (BLASTp) to hunt the newly released reference genome sequence of hexaploid wheat (Chinese spring). Seventy-four TaSBP proteins (belonging to 56 genes) were identified and clustered into five groups. Gene structure and motif analysis indicated that most TaSBPs have relatively conserved exon–intron arrangements and motif composition. Analysis of transcriptional data showed that many TaSBP genes responded to some biological and abiotic stresses with different expression patterns. Moreover, three TaSBP genes were generally expressed in the majority of tissues throughout the wheat growth and also responded to many environmental biotic and abiotic stresses. Collectively, the detailed analyses presented here will help in understanding the roles of the TaSBP and also provide a reference for the further study of its biological function in wheat

Two Green Protocols for Halogenative Semipinacol Rearrangement

Semipinacol rearrangement is a special type of Wagner–Meerwein rearrangement that involves carbocation 1,2-rearrangement to provide carbonyl compounds with an α-quaternary carbon center. It has been strategically used for natural product synthesis and construction of highly congested quaternary carbons. Herein, we report a safe and green protocol that uses oxone/halide and Fenton bromide to achieve halogenative semipinacol rearrangement. The key feature of this method is the green in situ generation of reactive halogenating species from oxidation of halide with oxone or H2O2, which produces a nontoxic byproduct (potassium sulfate or water). Easy operation (insensitive to air and moisture) at room temperature without using special equipment adds additional advantage over previous methods

Sequencing of methylase-accessible regions in integral circular extrachromosomal DNA reveals differences in chromatin structure

Abstract Background Although extrachromosomal DNA (ecDNA) has been intensively studied for several decades, the mechanisms underlying its tumorigenic effects have been revealed only recently. In most conventional sequencing studies, the high-throughput short-read sequencing largely ignores the epigenetic status of most ecDNA regions except for the junctional areas. Methods Here, we developed a method of sequencing enzyme-accessible chromatin in circular DNA (CCDA-seq) based on the use of methylase to label open chromatin without fragmentation and exonuclease to enrich ecDNA sequencing depth, followed by long-read nanopore sequencing. Results Using CCDA-seq, we observed significantly different patterns in nucleosome/regulator binding to ecDNA at a single-molecule resolution. Conclusions These results deepen the understanding of ecDNA regulatory mechanisms

BIND&MODIFY: a long-range method for single-molecule mapping of chromatin modifications in eukaryotes

Abstract Epigenetic modifications of histones are associated with development and pathogenesis of disease. Existing approaches cannot provide insights into long-range interactions and represent the average chromatin state. Here we describe BIND&MODIFY, a method using long-read sequencing for profiling histone modifications and transcription factors on individual DNA fibers. We use recombinant fused protein A-M.EcoGII to tether methyltransferase M.EcoGII to protein binding sites to label neighboring regions by methylation. Aggregated BIND&MODIFY signal matches bulk ChIP-seq and CUT&TAG. BIND&MODIFY can simultaneously measure histone modification status, transcription factor binding, and CpG 5mC methylation at single-molecule resolution and also quantifies correlation between local and distal elements

Graphene–bimetal plasmonic platform for ultra-sensitive biosensing

A graphene–bimetal plasmonic platform for surface plasmon resonance biosensing with ultra-high sensitivity was proposed and optimized. In this hybrid configuration, graphene nanosheets was employed to effectively absorb the excitation light and serve as biomolecular recognition elements for increased adsorption of analytes. Coating of an additional Au film prevents oxidation of the Ag substrate during manufacturing process and enhances the sensitivity at the same time. Thus, a bimetal Au–Ag substrate enables improved sensing performance and promotes stability of this plasmonic sensor. In this work we optimized the number of graphene layers as well as the thickness of the Au film and the Ag substrate based on the phase-interrogation sensitivity. We found an optimized configuration consisting of 6 layers of graphene coated on a bimetal surface consisting of a 5 nm Au film and a 30 nm Ag film. The calculation results showed the configuration could achieve a phase sensitivity as high as 1.71×106 deg/RIU, which was more than 2 orders of magnitude higher than that of bimetal structure and graphene–silver structure. Due to this enhanced sensing performance, the graphene–bimetal plasmonic platform proposed in this paper is potential for ultra-sensitive plasmonic sensing