A consensus genome of sika deer (Cervus nippon) and transcriptome analysis provided novel insights on the regulation mechanism of transcript factor in antler development.

BACKGROUND: Sika deer (Cervus nippon) holds significance among cervids, with three genomes recently published. However, these genomes still contain hundreds of gaps and display significant discrepancies in continuity and accuracy. This poses challenges to functional genomics research and the selection of an appropriate reference genome. Thus, obtaining a high-quality reference genome is imperative to delve into functional genomics effectively. FINDINGS: Here we report a high-quality consensus genome of male sika deer. All 34 chromosomes are assembled into single-contig pseudomolecules without any gaps, which is the most complete assembly. The genome size is 2.7G with 23,284 protein-coding genes. Comparative genomics analysis found that the genomes of sika deer and red deer are highly conserved, an approximately 2.4G collinear regions with up to 99% sequence similarity. Meanwhile, we observed the fusion of red deer's Chr23 and Chr4 during evolution, forming sika deer's Chr1. Additionally, we identified 607 transcription factors (TFs) that are involved in the regulation of antler development, including RUNX2, SOX6, SOX8, SOX9, PAX8, SIX2, SIX4, SIX6, SPI1, NFAC1, KLHL8, ZN710, JDP2, and TWST2, based on this consensus reference genome. CONCLUSIONS: Our results indicated that we acquired a high-quality consensus reference genome. That provided valuable resources for understanding functional genomics. In addition, discovered the genetic basis of sika-red hybrid fertility and identified 607 significant TFs that impact antler development.

STIRUnet: SwinTransformer and inverted residual convolution embedding in unet for Sea-Land segmentation.

Extraction of coastline from optical remote sensing images is of paramount importance for coastal zone management, erosion monitoring, and intelligent ocean construction. However, nearshore marine environment complexity presents a challenge when capturing small-scale and detailed information regarding coastlines. Furthermore, the presence of numerous tidal flats, suspended sediments, and coastal biological communities exacerbates the reduction in segmentation accuracy, which is particularly noticeable in medium-high-resolution remote sensing image segmentation tasks. Most previous related studies, based primarily on convolutional neural networks (CNNs) or traditional feature extraction methods, faced challenges in detailed pixel-level refinement and lacked comprehensive understanding of the studied images. Therefore, we proposed a new U-shaped deep learning model (STIRUnet) that combines the excellent global modeling ability of SwinTransformer with an improved CNN using an inverted residual module. The proposed method has the capability of global supervised feature learning and layer-by-layer feature extraction, and we conducted sea-land segmentation experiments using GF-HNCD and BSD remote sensing image datasets to validate the performance of the proposed model. The results indicate the following: 1) suspended sediments and coastal biological communities are major contributors to coastline blurring, and 2) the recovery of minute features (e.g., narrow watercourses and microscale artificial structures) effectively enhances edge details and leads to more realistic segmentation outcomes. The findings of this study are highly important in relation of accurate extraction of sea-land information in complex marine environments, and they offer novel insights regarding mixed-pixel identification.

The spatio-temporal expression analysis of parathyroid hormone like hormone gene provide a new insight for bone growth of the antler tip tissue in sika deer.

Objective: Parathyroid hormone like hormone (PTHLH), as an essential factor for bone growth, is involved in a variety of physiological processes. The aim of this study was to explore the role of PTHLH gene in the growth of antlers. Methods: The coding sequence (CDS) of PTHLH gene cDNA was obtained by cloning in sika deer (Cervus nippon), and the bioinformatics was analyzed. The quantitative real-time polymerase chain reaction (qRT-PCR) was used to analyze the differences expression of PTHLH mRNA in different tissues of the antler tip at different growth periods (early period, EP; middle period, MP; late period, LP). Results: The CDS of PTHLH gene was 534bp in length and encoded 177 amino acids. Predictive analysis results revealed that the PTHLH protein was a hydrophilic protein without transmembrane structure, with its secondary structure consisting mainly of random coil. The PTHLH protein of sika deer had the identity of 98.31%, 96.82%, 96.05% and 94.92% with Cervus canadensis, Bos mutus, Oryx dammah and Budorcas taxicolor, which were highly conserved among the artiodactyls. The qRT-PCR results showed that PTHLH mRNA had a unique spatio-temporal expression pattern in antlers. In the dermis, precartilage, and cartilage tissues, the expression of PTHLH mRNA was extremely significantly higher in MP than in EP, LP (p<0.01). In the mesenchyme tissue, the expression of PTHLH mRNA in MP was significantly higher than that of EP (p<0.05), but extremely significantly lower than that of LP (p<0.01). The expression of PTHLH mRNA in antler tip tissues at all growth periods had approximately the same trend, that is, from distal to basal, it was first down-regulated from the dermis to the mesenchyme and then continuously up-regulated to the cartilage tissue. Conclusion: PTHLH gene may promote the rapid growth of antler mainly through its extensive regulatory effect on the antler tip tissue.

DNA methylation pattern and mRNA expression of OPN promoter in sika deer antler tip tissues.

In order to explore the biological role of OPN gene during the growth of sika deer antler, the dermis, mesenchyme, precartilage and cartilage tissues of sika deer antler tip at the early period of the antler with a saddle-like appearance (30 days), the rapid growth period of the antler with two branches (60 days), and the final period of the antler with three branches (90 days) were analyzed. Bisulfite sequencing PCR (BSP) and quantitative real-time PCR (qRT-PCR) were used to explore the DNA promoter methylation and mRNA expression of OPN in sika deer antler from the perspective of space and time. The test results showed that: 1) The methylation rates of OPN promoter at the early, middle and late periods of dermis tissue were (40.48 ± 0.82)%, (40.00 ± 1.43)%, and (39.05 ± 0.82)%; The methylation rates in mesenchyme tissue were (37.62 ± 0.82)%, (34.76 ± 2.18)%, and (38.57 ± 1.43)%; The methylation rates in precartilage tissue were (36.67 ± 0.28)%, (29.52 ± 1.65)%, (28.10 ± 2.18)%; The methylation rates in cartilage tissue were (31.90 ± 1.65)%, (26.67 ± 1.65)%, (24.29 ± 1.43)%. 2) There are 7 CpG sites in the OPN promoter region, and the 3 CpG sites of -367 bp, -245 bp and -31 bp are all methylated to different level. 3) The methylation level of OPN in the dermis, mesenchyme, precartilage and cartilage tissues decreased in sequence at the same growth period. At the middle and late periods, the methylation level of the promoter region of the precartilage tissue was significantly different from that of the dermis and mesenchyme tissues (P < 0.05); At different growth periods, the methylation level of the promoter region of cartilage tissue was extremely significantly different from that of dermis and mesenchyme tissues (P < 0.01); In the same tissue, the methylation level of the promoter region at the middle period was down-regulated compared with the early period, and the methylation level of the promoter region at the early period and the middle period was extremely significantly different in the precartilage and cartilage (P < 0.01). 4) OPN mRNA is highly expressed in precartilage and cartilage tissues. 5) The methylation level of OPN promoter was negatively correlated with mRNA expression level. In summary, it is speculated that the OPN gene, which may be regulated by the DNA methylation level of the promoter, promotes the growth and development of deer antler mainly by regulating the growth of precartilage and cartilage tissues.

Remote Sensing Image Fusion Based on Morphological Convolutional Neural Networks with Information Entropy for Optimal Scale.

Remote sensing image fusion is a fundamental issue in the field of remote sensing. In this paper, we propose a remote sensing image fusion method based on optimal scale morphological convolutional neural networks (CNN) using the principle of entropy from information theory. We use an attentional CNN to fuse the optimal cartoon and texture components of the original images to obtain a high-resolution multispectral image. We obtain the cartoon and texture components using sparse decomposition-morphological component analysis (MCA) with an optimal threshold value determined by calculating the information entropy of the fused image. In the sparse decomposition process, the local discrete cosine transform dictionary and the curvelet transform dictionary compose the MCA dictionary. We sparsely decompose the original remote sensing images into a texture component and a cartoon component at an optimal scale using the information entropy to control the dictionary parameter. Experimental results show that the remote sensing image fusion method proposed in this paper can effectively retain the information of the original image, improve the spatial resolution and spectral fidelity, and provide a new idea for image fusion from the perspective of multi-morphological deep learning.