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Water vapor plays a vital role in weather variations, making it essential to monitor atmospheric water vapor content for reliable weather forecasts. This study investigates the feasibility of utilizing a low-cost GNSS network to monitor water vapor transport during a heavy precipitation event. The zenith wet delay (ZWD) products are retrieved in GNSS data processing and then transformed to integrated water vapor (IWV). In addition, the impact of various factors, including near real-time products, weighted mean temperature ( ) estimation models, and the sensitivity of the conversion factor to variations are investigated in this study. Results demonstrate that: (1) Phase center variation (PCV) corrections, often unavailable for low-cost antennas, are crucial for accurate ZWD estimation, and the absence of these corrections may result in underestimations of the ZWD by several millimeters. (2) Near real-time GNSS products demonstrate comparable accuracy to final products, enabling timely IWV monitoring. (3) ZWD estimated from low-cost stations exhibit strong agreement with those from geodesic-grade stations, demonstrating their reliability. (4) GPT3, GTrop, and GGNTm models could effectively convert ZWD to IWV, with negligible differences despite slight variations in estimation accuracy. (5) The network effectively captures the spatio-temporal evolution of IWV during the precipitation event, demonstrating its potential for high-resolution water vapor monitoring. These findings highlight the effectiveness of low-cost GNSS networks in providing valuable insights into atmospheric water vapor dynamics, contributing to improved weather forecasting and hydrological modeling.

Differential Code Bias (DCB) is a crucially systematic error in satellite positioning and ionospheric modeling. This study aims to estimate the BeiDou-3 global navigation satellite system (BDS-3) satellite DCBs by using the single-frequency (SF) uncombined Precise Point Positioning (PPP) model. The experiment utilized BDS-3 B1 observations collected from 25 International GNSS Service (IGS) stations located at various latitudes during March 2023. The results reveal that the accuracy of estimating B1I-B3I DCBs derived from single receiver exhibits latitude dependence. Stations in low-latitude regions show considerable variability in the root mean square (RMS) of absolute offsets for satellite DCBs estimation, covering a wide range of values. In contrast, mid- to high-latitude stations demonstrate a more consistent pattern with relatively stable RMS values. Moreover, it has been observed that the stations situated in the Northern Hemisphere display a higher level of consistency in the RMS values when compared to those in the Southern Hemisphere. When incorporating estimates from all 25 stations, the RMS of the absolute offsets in satellite DCBs estimation consistently remained below 0.8 ns. Notably, after excluding 8 low-latitude stations and utilizing data from the remaining 17 stations, the RMS of absolute offsets in satellite DCBs estimation decreased to below 0.63 ns. These enhancements underscore the importance of incorporating a sufficient number of mid- and high-latitude stations to mitigate the effects of ionospheric variability when utilizing SF observations for satellite DCBs estimation.

Background Wheat landraces are considered a valuable source of genetic diversity for breeding programs. It is useful to evaluate the genetic diversity in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection. In addition, constructing a core germplasm set that represents the genetic diversity of the entire variety set is of great significance for the efficient conservation and utilization of wheat landrace germplasms. Results To understand the genetic diversity in wheat landrace, 2,023 accessions in the Jiangsu Provincial Crop Germplasm Resource Bank were used to explore the molecular diversity and population structure using the Illumina 15 K single nucleotide polymorphism (SNP) chip. These accessions were divided into five subpopulations based on population structure, principal coordinate and kinship analysis. A significant variation was found within and among the subpopulations based on the molecular variance analysis (AMOVA). Subpopulation 3 showed more genetic variability based on the different allelic patterns (Na, Ne and I). The M strategy as implemented in MStratv 4.1 software was used to construct the representative core collection. A core collection with a total of 311 accessions (15.37%) was selected from the entire landrace germplasm based on genotype and 12 different phenotypic traits. Compared to the initial landrace collections, the core collection displayed higher gene diversity (0.31) and polymorphism information content (PIC) (0.25), and represented almost all phenotypic variation. Conclusions A core collection comprising 311 accessions containing 100% of the genetic variation in the initial population was developed. This collection provides a germplasm base for effective management, conservation, and utilization of the variation in the original set.

Powdery mildew poses a significant threat to wheat crops worldwide, emphasizing the need for durable disease control strategies. The wheat- Dasypyrum villosum T5AL·5 V#4 S and T5DL·5 V#4 S translocation lines carrying powdery mildew resistant gene Pm55 shows developmental-stage and tissue-specific resistance, whereas T5DL·5 V#5 S line carrying Pm5V confers resistance at all stages. Here, we clone Pm55 and Pm5V , and reveal that they are allelic and renamed as Pm55a and Pm55b , respectively. The two Pm55 alleles encode coiled-coil, nucleotide-binding site-leucine-rich repeat (CNL) proteins, conferring broad-spectrum resistance to powdery mildew. However, they interact differently with a linked inhibitor gene, SuPm55 to cause different resistance to wheat powdery mildew. Notably, Pm55 and SuPm55 encode unrelated CNL proteins, and the inactivation of SuPm55 significantly reduces plant fitness. Combining SuPm55 / Pm55a and Pm55b in wheat does not result in allele suppression or yield penalty. Our results provide not only insights into the suppression of resistance in wheat, but also a strategy for breeding durable resistance. Powdery mildew threatens worldwide wheat production. Here, the authors report the cloning of two powdery mildew resistant Pm55 alleles and show that they exhibit distinct interactions with the inhibitor SuPm55 to cause different resistance.

It is of great significance to detect faults correctly in continental sandstone reservoirs in the east of China to understand the distribution of remaining structural reservoirs for more efficient development operation. However, the majority of the faults is characterized by small displacements and unclear components, which makes it hard to recognize them in seismic data via traditional methods. We consider fault detection as an end-to-end binary image-segmentation problem of labeling a 3D seismic image with ones as faults and zeros elsewhere. Thus, we developed a fully convolutional network (FCN) based method to fault segmentation and used the synthetic seismic data to generate an accurate and sufficient training data set. The architecture of FCN is a modified version of the VGGNet (A convolutional neural network was named by Visual Geometry Group). Transforming fully connected layers into convolution layers enables a classification net to create a heatmap. Adding the deconvolution layers produces an efficient network for end-to-end dense learning. Herein, we took advantage of the fact that a fault binary image is highly biased with mostly zeros but only very limited ones on the faults. A balanced crossentropy loss function was defined to adjust the imbalance for optimizing the parameters of our FCN model. Ultimately, the FCN model was applied on real field data to propose that our FCN model can outperform conventional methods in fault predictions from seismic images in a more accurate and efficient manner.

Summary STAUROSPORINE AND TEMPERATURE SENSITIVE3 (STT3) is a catalytic subunit of oligosaccharyltransferase, which is important for asparagine‐linked glycosylation. Sharp eyespot, caused by the necrotrophic fungal pathogen Rhizoctonia cerealis, is a devastating disease of bread wheat. However, the molecular mechanisms underlying wheat defense against R. cerealis are still largely unclear. In this study, we identified TaSTT3a and TaSTT3b, two STT3 subunit genes from wheat and reported their functional roles in wheat defense against R. cerealis and increasing grain weight. The transcript abundance of TaSTT3b‐2B was associated with the degree of wheat resistance to R. cerealis and induced by both R. cerealis and exogenous jasmonic acid (JA). Overexpression of TaSTT3b‐2B significantly enhanced resistance to R. cerealis, grain weight, and JA content in transgenic wheat subjected to R. cerealis stress, while silencing of TaSTT3b‐2B compromised resistance of wheat to R. cerealis. Transcriptomic analysis showed that TaSTT3b‐2B affected the expression of a series of defense‐related genes and JA biosynthesis–related genes, as well as genes coding starch synthase and sucrose synthase. Application of exogenous JA elevated expression levels of the abovementioned defense‐ and grain weight–related genes, and rescuing the resistance of TaSTT3b‐2B–silenced wheat to R. cerealis, while pretreatment with sodium diethyldithiocarbamate, an inhibitor of JA synthesis, attenuated the TaSTT3b‐2B–mediated resistance to R. cerealis, suggesting that TaSTT3b‐2B played critical roles in regulating R. cerealis resistance and grain weight via JA biosynthesis. Altogether, this study reveals new functional roles of TaSTT3b‐2B in regulating plant innate immunity and grain weight, and illustrates its potential application value for wheat molecular breeding.

Fault imaging follows the processing and migration imaging of seismic data, which is very important in oil and gas exploration and development. Conventional fault imaging methods are easily influenced by seismic data and interpreters’ experience and have limited ability to identify complex fault areas and micro-faults. Conventional convolutional neural network uniformly processes feature maps of the same layer, resulting in the same receptive field of the neural network in the same layer and relatively single local information obtained, which is not conducive to the imaging of multi-scale faults. To solve this problem, our research proposes a modified U-Net architecture. Two functional modules containing dilated convolution are added between the encoder and decoder to enhance the network’s ability to select multi-scale information, enhance the consistency between the receptive field and the target region of fault recognition, and finally improve the identification ability of micro-faults. Training on synthetic seismic data and testing on real data were carried out using the modified U-Net. The actual fault imaging shows that the proposed scheme has certain advantages.

Due to the lack of drilling data and poor quality of seismic data in deep-water offshore areas, conventional methods cannot effectively predict the total organic carbon (TOC) content. In this paper, the BP neural network method is used to predict the TOC of the strata overlying the target layer, which adds to the TOC information in the study area. Then, the highest TOC value of the strata overlying the target layer is used to select the most sensitive seismic attributes. Finally, the sensitive seismic attributes are used to evaluate the source rocks with no or few wells. A set of TOC prediction technology flows is established for TOC combined with seismic attributes under the condition of no wells and few wells in deep-water areas. The application example shows the reliability of TOC prediction by this technical process, and the study has a certain reference significance for the evaluation of hydrocarbon source rocks in offshore deep water.

Common wheat is a leading global food crop that impacts food security. Wheat powdery mildew (PM), caused by f. sp. ( ), poses a significant threat to grain yield and flour quality. The identification and utilization of broad-spectrum resistance genes against PM are essential for effective disease control. The resistance spectrum test during the seedling stage and the identification of resistance during the adult stage were conducted to evaluate the wheat landrace Honghuamai (HHM). Five segregating populations were investigated to assess the inheritance of PM resistance in HHM. To map its PM resitance gene, bulked segregant analysis, molecular mapping and comparative genomic analysis were also used in the present study. HHM shows remarkable adult resistance in the field and is nearly immune to all 25 isolates used in seedling tests, making it an excellent source of PM resistance. PM resistance in HHM was determined by a single dominant gene, temporarily named . It was then fine-mapped to an interval with a genetic distance of 0.0031 cM and a physical distance of 187.4 kb on chromosome 4AL of the Chinese Spring reference sequence v.2.1. Four genes were identified in the target region, three of which encode nucleotide-binding leucine-rich repeat (NLR) proteins. Comparative genomic analysis revealed presence/absence variations (PAVs) of the locus among common wheat varieties. These closely linked molecular markers will not only benefit the cloning of the gene underlying but also facilitate the efficient utilization of the gene in breeding programs.