Transcriptome analysis reveals the molecular basis of the response to acute hypoxic stress in blood clam Scapharca broughtonii

Hypoxia tolerance and adaptive regulation are important for aquatic animals, especially for species with poor mobility, such as most bivalves. Previous studies have confirmed that the blood clam Scapharca broughtonii has strong hypoxia resistance. However, the molecular mechanism supporting its hypoxic tolerance is still largely limited. To further screen the genes and their potential regulation of hypoxia tolerance, the transcriptome changes of S. broughtonii after acute hypoxic stress were explored by RNA sequencing. In this study, the average value of Q30 is 92.89%, indicating that the quality of sequencing is relatively high. The Unigenes obtained were annotated using four databases, namely Interpo, KEGG, Swisspro and TrEMBL. The annotation rates in these four databases were 71.82%, 75.95%, 92.98%, and 79.26%, respectively. And also, there were 649 DEGs in group B (dissolved oxygen (DO) of 2.5 mg/L) compared with group D (DO of 7.5 mg/L), among which 252 were up-regulated, and 397 were down-regulated. There were 965 DEGs in group A (DO of 0.5 mg/L), 2.5 mg/L, and 7.5 mg/L, compared with group B, among which 530 were up-regulated, and 435 were down-regulated. Meanwhile, there were 2,040 DEGs in group A compared with group D, among which 901 were up-regulated, and 1,139 were down-regulated. The main metabolic-related pathways of KEGG enriched in this study included Insulin secretion, Insulin signaling pathway, MAPK signal transduction pathway, and PPAR signaling pathway. These pathways may be critical metabolic pathways to solve energy demand and rebuild energy balance in S. broughtonii under hypoxic conditions. This study preliminarily clarified the response of S. broughtonii to hypoxia stress on the molecular levels, providing a reference for the following study on the response laws of related genes and pathways under environmental stress of S. broughtonii

An egg holders-inspired structure design for large-volume-change anodes with long cycle life

Abstract(#br)Silicon has been considered as a potential alternative of anodes for advanced lithium ion battery as it possesses high capacity and abundance. However, it encounters excessive volume expansion and inferior electoral conductivity, which imposes restrictions on its further development. In order to address these two problems, yolk-shell structure is employed, in which there is a suitable void for the expansion with a shell to protect the core and promote the conductivity. Here, by the inspiration from the egg holders and inverse-opal structure, an egg-stacking-like Si/C composite (ES) anode with spherical air holes was fabricated to gather the yolk-shell particles in a 3D carbon network with abundant channels allowing electrolyte to enter the material, which can facilitate the cycling performance. The half-cell battery assembled with these anodes presents high capacity and good rate performance, with a capacity reduction of only 2–7% per current density. And the cycling performance of ES anode is also praiseworthy that it delivers a high reversible discharge capacity of 2175 mAh g −1 after 300 cycles at 0.5 A g −1 . This kind of structure design is expected to be applicative for most of large-volume-change anodes

A Recombinant Vaccine of H5N1 HA1 Fused with Foldon and Human IgG Fc Induced Complete Cross-Clade Protection against Divergent H5N1 Viruses

Development of effective vaccines to prevent influenza, particularly highly pathogenic avian influenza (HPAI) caused by influenza A virus (IAV) subtype H5N1, is a challenging goal. In this study, we designed and constructed two recombinant influenza vaccine candidates by fusing hemagglutinin 1 (HA1) fragment of A/Anhui/1/2005(H5N1) to either Fc of human IgG (HA1-Fc) or foldon plus Fc (HA1-Fdc), and evaluated their immune responses and cross-protection against divergent strains of H5N1 virus. Results showed that these two recombinant vaccines induced strong immune responses in the vaccinated mice, which specifically reacted with HA1 proteins and an inactivated heterologous H5N1 virus. Both proteins were able to cross-neutralize infections by one homologous strain (clade 2.3) and four heterologous strains belonging to clades 0, 1, and 2.2 of H5N1 pseudoviruses as well as three heterologous strains (clades 0, 1, and 2.3.4) of H5N1 live virus. Importantly, immunization with these two vaccine candidates, especially HA1-Fdc, provided complete cross-clade protection against high-dose lethal challenge of different strains of H5N1 virus covering clade 0, 1, and 2.3.4 in the tested mouse model. This study suggests that the recombinant fusion proteins, particularly HA1-Fdc, could be developed into an efficacious universal H5N1 influenza vaccine, providing cross-protection against infections by divergent strains of highly pathogenic H5N1 virus

Global transpiration data from sap flow measurements : the SAPFLUXNET database

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The "sapfluxnetr" R package - designed to access, visualize, and process SAPFLUXNET data - is available from CRAN.Peer reviewe

Statistical Downscaling of Temperature Distributions in Southwest China by Using Terrain-Guided Attention Network

Deep learning techniques, especially convolutional neural networks (CNNs), have dramatically boosted the performance of statistical downscaling. In this study, we propose a CNN-based 2 m air temperature downscaling model named Terrain-Guided Attention Network (TGAN), which aims at rebuilding 2 m air temperature distribution from 0.0625$^{\circ }$ to 0.01$^{\circ }$ over Southwest China. More concretely, TGAN utilizes two upsampling modules to progressively reconstruct the high-resolution temperature data from the low-resolution one. Then, to better recover the spatial detail of the low-resolution temperature data, an attentive-terrain block is proposed to introduce digital terrain model (DEM) information. It aggregates the temperature data and the corresponding-scale DEM information via the attention mechanism in a multiscale manner. Ultimately, the reconstruction module is employed to obtain the high-resolution temperature data. We use the 2019 data for training, and utilize the 2018 data to verify the effectiveness of the proposed TGAN. The experimental results showed that TGAN achieved the lowest root-mean-square error (1.12\,^\circC) when incorporating DEM data by attentive-terrain blocks in a multiscale manner, followed by incorporating DEM data in a multiscale manner (TGAN-land, 1.31\,^\circC) and only incorporating DEM data (SRCNN-land, 1.36\,^\circC). Meanwhile, TGAN showed a competitive performance when compared with several advanced deep-learning-based super-resolution algorithms and reconstructed the texture details of 2 m air temperature fields more clearly. In general, among various deep learning approaches, TGAN achieves better downscaling results for 2 m air temperature reconstruction and provides a practical method and guidance for the back-calculation of high-resolution historical meteorological grid data

The Carbon Emissions Effect of China’s OFDI on Countries along the “Belt and Road”

With the continuous practice of the “Belt and Road” initiative, the countries along the “Belt and Road” have achieved rapid social and economic development. However, environmental problems have become increasingly prominent. Around the world, there are comments that China’s “Belt and Road” initiative is a result of resource plundering, transfer of backward production capacity, and environmental degradation of countries along the line. This study quantitatively evaluated the static, dynamic, linear, and non-linear effects of China’s foreign direct investment on the carbon emissions of countries along the line. The results showed that: (1) The direct effect of China’s foreign direct investment on the carbon emissions of countries along the route was significantly negative. (2) The economic scale and industrial structure effects of China’s foreign direct investment increased the carbon emissions of countries along the route. The production technology effect suppressed the carbon emissions of countries along the route and played a leading role. (3) The estimation results of the system generalized method of moments showed that the carbon emissions of countries along the route were significantly affected by the lag period, but the impact was small. (4) The results of the threshold regressive model showed that the GDP and proportion of industrial added value had significant threshold effects on the carbon emissions effect of China’s outward foreign direct investment. When the GDP of countries along the route exceeded 7.2696, China’s outward foreign direct investment carbon emissions reduction effect could not be realized; when the proportion of the industrial added value of countries along the route was lower than 4.0106, China’s outward foreign direct investment carbon emission reduction effect could not be realized. Based on the research conclusion, we concluded that China and countries along the “Belt and Road” should strengthen cooperation on carbon emissions reduction, jointly promote low-carbon construction of industrial parks, accelerate cooperation on green energy projects, and establish a green development fund to achieve sustainable development of the countries along the “Belt and Road”

Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size

The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studies of IAs, fast and accurate patient-specific IA reconstruction becomes a research Frontier. In this study, a deep-learning-based framework for IA identification and segmentation was developed, and the impacts of image pre-processing and convolutional neural network (CNN) architectures on the framework's performance were investigated. Three-dimensional (3D) segmentation-dedicated architectures, including 3D UNet, VNet, and 3D Res-UNet were evaluated. The dataset used in this study included 101 sets of anonymized cranial computed tomography angiography (CTA) images with 140 IA cases. After the labeling and image pre-processing, a training set and test set containing 112 and 28 IA lesions were used to train and evaluate the convolutional neural network mentioned above. The performances of three convolutional neural networks were compared in terms of training performance, segmentation performance, and segmentation efficiency using multiple quantitative metrics. All the convolutional neural networks showed a non-zero voxel-wise recall (V-Recall) at the case level. Among them, 3D UNet exhibited a better overall segmentation performance under the relatively small sample size. The automatic segmentation results based on 3D UNet reached an average V-Recall of 0.797 ± 0.140 (3.5% and 17.3% higher than that of VNet and 3D Res-UNet), as well as an average dice similarity coefficient (DSC) of 0.818 ± 0.100, which was 4.1%, and 11.7% higher than VNet and 3D Res-UNet. Moreover, the average Hausdorff distance (HD) of the 3D UNet was 3.323 ± 3.212 voxels, which was 8.3% and 17.3% lower than that of VNet and 3D Res-UNet. The three-dimensional deviation analysis results also showed that the segmentations of 3D UNet had the smallest deviation with a max distance of +1.4760/-2.3854 mm, an average distance of 0.3480 mm, a standard deviation (STD) of 0.5978 mm, a root mean square (RMS) of 0.7269 mm. In addition, the average segmentation time (AST) of the 3D UNet was 0.053s, equal to that of 3D Res-UNet and 8.62% shorter than VNet. The results from this study suggested that the proposed deep learning framework integrated with 3D UNet can provide fast and accurate IA identification and segmentation.Published versionThis research was funded by the National Natural Science Foundation of China (NSFC) (12272289, 12271440)

Intestinal Microbiota of <i>Anser fabalis</i> Wintering in Two Lakes in the Middle and Lower Yangtze River Floodplain

The intestinal microbiota of migratory birds participate in the life activities of the host and are affected by external environmental factors. The difference in habitat environment provides diversity in external environmental selection pressure for the same overwintering waterfowl, which may be reflected in their intestinal microbiota. Caizi lake and Shengjin Lake in the Middle and Lower Yangtze River Floodplain are the main habitats for migratory waterfowl in winter, especially the Anser fabalis (A. fabalis). It is important to explore the changes in intestinal microbiota composition and function of A. fabalis in the early overwintering period to clarify the effect of habitat size and protection status on intestinal microbiota. In this study, the composition and structural characteristics of the intestinal microbiota of A. fabalis in Shengjin Lake (SL) and Caizi Lake (CL) were preliminarily explored in order to obtain data for the migratory birds. In both SL and CL groups, 16S rRNA amplicon sequencing analysis showed that Firmicutes was the dominant bacterial phylum, but the relative abundance showed significant differences. Lactobacillus was the most abundant genus in both SL and CL groups. At the species level, the abundance of L. aviaries was the highest, with a relative abundance in both SL and CL groups of more than 34%. When comparing the average relative abundance of the 15 most abundant genera, it was found that Subdoligranulum, Exiguobacterium, and Terrisporobacter had higher abundances in the intestinal microbiota of CL A. fabalis, while Streptococcus and Rothia had higher abundances in the intestinal microbiota of SL A. fabalis. There was only a positive correlation between Bacteroidota and Proteobacteria in the intestinal microbiota flora of SL A. fabalis, and the species were closely related. At the same time, there were positive and negative correlations between Firmicutes and Actinomycetes. However, CL is mainly associated with a positive correlation between Firmicutes and Actinomycetes, and there are also a small number of connections between Firmicutes. PICRUSt1 prediction analysis revealed that the Clusters of Orthologous Groups (COG) functions of SL and CL involve energy production and transformation, amino acid transport and metabolism, carbohydrate transport and metabolism, and transcription. Understanding the changes in intestinal microbiota in Aves during the overwintering period is of great importance to explore the adaptation mechanism of migratory Aves to the overwintering environment. This work provides basic data for an A. fabalis intestinal microbiota study