Sentinel-1 (S1) extra-wide (EW) swath data in cross-polarization (horizontal-vertical, HV or vertical-horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this paper, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e. multiplicative noise) at the sub-swath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization SAR images in wide swaths for intensive sea ice monitoring in polar regions.

In this paper, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization (vertical-horizontal, VH or horizontal-vertical, HV) data in extra wide (EW) swath mode based on the machine learning algorithm support vector machine (SVM). The classification basis includes the S1 radar backscatter coefficients and texture features that are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e. entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparison shows good agreement between the SAR-derived sea ice cover using the proposed method and a visual inspection, of which the accuracy reaches approximately 90% - 95% based on a few cases. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of extracted sea ice cover by using S1 data is more than 80%.

The valley pseudospin properties of electrons in two–dimensional hexagonal materials result in lots of fascinating physical phenomena, which opens up the new field of valleytronics. The valley-contrasting physics aims at distinguishing the valley degree of freedom based on valley–dependent effects. Here, we theoretically demonstrate that both of the valley–selective high harmonic generation and valley–selective electronic excitation can be achieved by using an orthogonal two–color (OTC) laser field in gapped graphene. It is shown that the asymmetry degrees of harmonic yields in the plateaus, cutoff energies of generated harmonics and electron populations from two different valleys can be precisely controlled by the relative phase of the OTC laser field. Thus the selectivity of the dominant valley for the harmonic radiation and electronic polarization can be switched by adjusting the relative phase of the OTC laser field. Our work offers an all–optical route to produce the valley–resolved high harmonic emissions and manipulate the ultrafast valley polarization on a femtosecond timescale in condensed matter.

Due to the growth of tourism, mining and manufacturing, economy of the Hainan Island, southern China has swiftly expanded, but also poses a risk of soil pollution by mercury (Hg) due to the traffic and mineral processing. In order to investigate the characteristics of soil Hg pollution in the Hainan Island and assess the health risk, a total of 239 samples were gathered from five representative regions across the island. A vehicle-mounted potable mercury analyzer was used to measure the atmospheric Hg and soil Hg concentrations in the collected samples. The results indicate that the soil Hg concentration of the five sub-study areas exhibits notable fluctuations, which was found to be influenced by various factors, including wind direction, mining activities, and economic development conditions. The degree of Hg pollution was assessed using both the Single Pollution Index Method and the Geoaccumulation Index Method. The results showed that although nearly half of the sampling points were contaminated, the current soil Hg level does not pose a non-carcinogenic risk to human health.

The future of the Colorado River water supply (WS) affects millions of people and the U.S. economy. A recent study suggested a cross-basin correlation between the Colorado River and its neighboring Great Salt Lake (GSL). Following that study, the feasibility of using the previously developed multi-year prediction of the GSL water level to forecast the Colorado River WS was tested. Time-series models were developed to predict the changes in WS out to 10 years. Regressive methods and the GSL water level data were used for the depiction of decadal variability of the Colorado River WS. Various time-series models suggest a decline in the 10-year-averaged WS since 2013 before starting to increase around 2020. Comparison between this WS prediction and the WS projection published in a 2012 government report (derived from climate models) reveals a widened imbalance between supply and demand by 2020. Further research to update similar multi-year prediction of the Colorado River WS is needed. Such information could aid in management decision making in the face of future water shortages.

bZIP transcription factors, basic leucine zipper, play significant roles in plants’ growth and development processes, as well as in response to biological and abiotic stress. Hypericum perforatum is one of the world's top three best-selling herbal medicines, mainly used to treat depression. However, there is no systematic identification or functional analysis of the bZIP gene family in H. perforatum. In this study, 79 HpbZIP genes were identified. Based on phylogenetic analysis, the HpbZIP gene family was divided into ten groups, designated A-I, and S. The physicochemical properties, gene structures, protein conserved motifs, and Gene Ontology enrichments of all HpbZIPs were systematically analyzed. The expression patterns of all genes in different tissues of H. perforatum (root, stem, leaf, and flower) were analyzed by qRT-PCR, revealing the different expression patterns of HpbZIP under abiotic stresses. HpbZIP69 protein is localized in the nucleus. According to the results of the yeast one-hybrid (Y1H) assays, HpbZIP69 can bind to the HpASMT2 (N-Acetylserotonin O-methyltransferase) gene promoter ( G-box cis-element ) to activate its activity. Overexpressing HpbZIP69 transgenic Arabidopsis lines enhanced tolerance to drought. The content of MDA and H2O2 content was significantly decreased, and the activity of superoxide dismutase (SOD) was considerably increased under the drought stress. These results may aid in additional functional studies of HpbZIP transcription factors and cultivating drought-resistant medicinal plants.

Background Critical patients with novel coronavirus pneumonia ( COVID-19) have worse outcome and high mortality. However, the histopathology of critical patient with COVID-19 remains undisclosed. Methods We performed the whole lung biopsy, and described the pathological changes of critical COVID-19 patient done with transplant by HE staining, immunohistochemistry and special staining observed under the microscopy. Findings The whole lungs displayed diffuse congestive appearance and partly haemorrhagic necrosis on gross examination. The haemorrhagic necrosis was prominently present in outer edge of the right lower lung. The cut surfaces of the lung displayed severe congestive and haemorrhagic changes. The main pathological changes showed massive pulmonary interstitial fibrosis, and partly hyaline degeneration, variable degrees of hemorrhagic pulmonary infarction. Small vessels hyperplasia, vessel wall thickening, lumen stenosis, occlusion and microthrombosis formation. Focal monocytes, lymphocytes and plasma cells infiltrating into pulmonary interstitium. Bronchiolitis and alveolitis with proliferation, atrophy, desquamation and squamous metaplasia of epithelial cells. Atrophy, vacuolar degeneration, proliferation, desquamation and squamous metaplasia in alveolar epithelial cells. Alveolar cavity congestion was prominent, and contained mucus, edema fluid, desquamated epithelial cells, and inflammatory cells. We also found several multinucleate giant cells and intracytoplasmic viral inclusion bodies. Special stains including Masson stain, sirius red staining, reticular fibers staining indicated massive pulmonary interstitial fibrosis. Immunohistochemistry showed positive for immunity cells including CD3, CD4, CD8, CD20, CD79a, CD5, CD38 and CD68. Interpretation We demonstrate the pathological findings of critical patient with COVID-19, which might provide a deep insight of the pathogenesis and severity of this disease.

Alfalfa is an important forage crop around the world. LncRNAs are considered to be a class of functional biomacromolecules, while little is known about lncRNAs in alfalfa. In this study, RNAs from different tissues of alfalfa were sequenced and anlyzed with full-length transcriptome sequencing technology, yielding the full-length transcripts dataset of alfalfa. Based on sequencing and public RNA-seq data, lncRNAs of alfalfa were predicted genome-wide by CPC2 and PLEK. The results showed most lncRNAs shared low sequence conservation with those in other plant species, part of which seems originate from plastid genome. We also identified 88563 lncRNAs, approximately 99.8% of total lncRNAs, with possibility of coding small ORFs using two prediction tools. Our research generated the biggest sequence set of alfalfa lncRNAs, and revealed some plastid originated lncRNAs with high sequence conservation.

Mercury (Hg) pollution not only poses a threat to the environment but also adversely affects the growth and development of plants, with potential repercussions for animals and humans through bioaccumulation in the food chain. Maize, a crucial source of food, industrial materials, and livestock feed, requires special attention in understanding the genetic factors influencing mercury accumulation. Developing maize varieties with low mercury accumulation is vital for both maize production and human health. In this study, a comprehensive genome-wide association study (GWAS) was conducted using an enlarged SNP panel comprising 1.25 million single nucleotide polymorphisms (SNPs) in 230 maize inbred lines across three environments. The analysis identified 111 significant SNPs within 78 quantitative trait loci (QTL), involving 169 candidate genes under the Q model. Compared to the previous study, the increased marker density and optimized statistical model led to the discovery of 74 additional QTL, demonstrating improved statistical power. Gene Ontology (GO) enrichment analysis revealed that most genes participate in arsenate reduction and stress responses. Notably, GRMZM2G440968, associated with the significant SNP chr6.S_155668107 of axis tissue, encodes a cysteine proteinase inhibitor, suggesting its potential role in alleviating Hg toxicity by inhibiting cysteine. Haplotype analyses provided further insights, indicating that lines carrying hap3 exhibited the lowest mercury content compared to other haplotypes. In summary, our study significantly enhances the statistical power of GWAS, identifying additional genes related to mercury accumulation and metabolism. These findings offer valuable insights into unraveling the genetic basis of mercury content in maize and contribute to the development of maize varieties with low mercury accumulation.