Effects of the comprehensive elimination of Spartina alterniflora along China's coast on blue carbon and scenario prediction after ecological restoration.

Salt marshes cover the largest area among the three types of traditional blue carbon ecosystems in China's coastal zone, with the introduced smooth cordgrass (Spartina alterniflora Loisel.) being dominant in these marshes. The effects of eradicating S. alterniflora nationwide and the subsequent ecological restoration on blue carbon are unclear. This paper evaluates the variation in blue carbon during the national S. alterniflora eradication campaign, which involves mechanical tillage from 2022 to 2025, and proposes three scenarios for blue carbon changes after native vegetation is reestablished by 2050. The results show that, in 2025, plant carbon stock and soil carbon stock will decrease by 1.38 Tg C and 1.21 Tg C, respectively, in the areas where S. alterniflora has been removed and managed. Although blue carbon is reduced in coastal wetlands in 2025, carbon stock is expected to increase in restored native vegetated wetlands by 2050. S. alterniflora is resilient and competitive, posing a high risk in secondary invasion. Scenario Ⅰ suggests that S. alterniflora marshes could almost recover to their original state from 2022, with 7.70 Tg C stored in plant and soil carbon stocks. Scenario Ⅱ involves native vegetated wetlands coexisting with invasive S. alterniflora marshlands, with a total carbon stock estimated at 7.15 Tg C, reflecting a decrease of 0.39 Tg C in soil carbon stock and by 0.16 Tg C in plant carbon stock. In Scenario Ⅲ, mudflats dominant and native vegetated habitats are reestablished only in suitable sites, with the total carbon stock estimated at 5.63 Tg C, a 26.9% decrease compared to 2022 levels. While eradicating invasive S. alterniflora and restoring native vegetation in China's coast enhance the ecosystem services, it reduces blue carbon stocks. Therefore, developing additional strategies to increase carbon storage in coastal wetlands is necessary.

The effects of the renin inhibitor aliskiren on neuronal toxic damage.

The neurotoxicity of amyloid-ß (Aß) and its deposition in neurons plays a critical role in the occurrence and development of Alzheimer's disease (AD). Several preclinical experiments have found that the renin inhibitor aliskiren has a wide range of physiological effects, including hindering the progression of atherosclerosis and anti-inflammatory. This study is aimed to explore the effect of aliskiren on neuronal toxic damage and the underlying mechanism. This study established an in vitro nerve injury model through Aß 1-42 induction; the effects of aliskiren on the viability, inflammatory damage and apoptosis of SH-SY5Y cells were examined. For the sake of explore the underlying mechanism; SwissTargetPrediction website and molecular docking were utilized to predict the target of aliskiren. Then the impacts of the target protein overexpression were determined to verify its mediation. The results of the current study demonstrate that aliskiren has no effect on the viability of SH-SY5Y cells while Aß1-42accumulation could significantly downregulate cell viability. In addition, aliskiren could alleviate neuronal inflammatory damage and apoptosis arise from Aß 1-42accumulation. Following confirming the high expression level of the predicted target PDE4B in damaged cells, it was found that PDE4B overexpression can reverse the influence of aliskiren on cell viability, inflammatory damage and apoptosis. In conclusion, aliskiren upregulates cell viability, reduces inflammatory damage and apoptosis induced by Aß accumulation in AD via inhibiting PDE4B. These findings have expanded the scope of future application of aliskiren and provided a theoretical basis.

Spatial and seasonal variability of CO2 flux at the air-water interface of the Three Gorges Reservoir.

Diffusive carbon dioxide (CO2) emissions from the water surface of the Three Gorges Reservoir, currently the largest hydroelectric reservoir in the world, were measured using floating static chambers over the course of a yearlong survey. The results showed that the average annual CO2 flux was (163.3 +/- 117.4) mg CO2/(m2.hr) at the reservoir surface, which was larger than the CO2 flux in most boreal and temperate reservoirs but lower than that in tropical reservoirs. Significant spatial variations in CO2 flux were observed at four measured sites, with the largest flux measured at Wushan (221.9 mg CO2/(m2.hr)) and the smallest flux measured at Zigui (88.6 mg CO2/(m(2).hr)); these differences were probably related to the average water velocities at different sites. Seasonal variations in CO2 flux were also observed at four sites, starting to increase in January, continuously rising until peaking in the summer (June-August) and gradually decreasing thereafter. Seasonal variations in CO2 flux could reflect seasonal dynamics in pH, water velocity, and temperature. Since the spatial and temporal variations in CO2 flux were significant and dependent on multiple physical, chemical, and hydrological factors, it is suggested that long-term measurements should be made on a large spatial scale to assess the climatic influence of hydropower in China, as well as the rest of the world.

Multiagent Trust Region Policy Optimization.

We extend trust region policy optimization (TRPO) to cooperative multiagent reinforcement learning (MARL) for partially observable Markov games (POMGs). We show that the policy update rule in TRPO can be equivalently transformed into a distributed consensus optimization for networked agents when the agents' observation is sufficient. By using a local convexification and trust-region method, we propose a fully decentralized MARL algorithm based on a distributed alternating direction method of multipliers (ADMM). During training, agents only share local policy ratios with neighbors via a peer-to-peer communication network. Compared with traditional centralized training methods in MARL, the proposed algorithm does not need a control center to collect global information, such as global state, collective reward, or shared policy and value network parameters. Experiments on two cooperative environments demonstrate the effectiveness of the proposed method.

Comprehensive Analysis of the Complete Chloroplast Genome of Cinnamomum daphnoides (Lauraceae), An Endangered Island Endemic Plant.

Cinnamomum daphnoides (Siebold & Zucc 1846) is a rare and endangered island species with a unique Sino-Japanese distribution pattern. However, inormation regarding the species' chloroplast (cp) genome, structural features, and the phylogenetic relationship is still lacking. We utilized high-throughput sequencing technology to assemble and annotate the first cp genome of C. daphnoides (GenBank OR654104), followed by genomic characterization and phylogenetic analysis to fill the gaps in this species' cp genome. Our analysis showed that the cp genome has a quadripartite structure spanning 152,765 bp with a GC content of 39.15%. The genome encodes 126 genes, which include 36 tRNA genes, 8 rRNA genes, and 82 mRNA genes. Specifically, 44 genes are related to photosynthesis, 59 are associated with self-replication, six are other genes, and four have unknown functionality. The Codon usage bias in the genome exhibits a preference for A/U bases. We identified 29 interspaced repeat sequences that belonging to three types of repeat sequences. A total of 217 cpSSR loci were detected with single nucleotide repeats (59.91%) being the most frequent loci, mainly composed of A/T repeats. Our selection pressure analysis revealed that the ycf2 gene experienced strong positive selection (Ka/Ks = 1.81, P > 0.844). Further, we identified three highly variable fragments (psbM, psbT, and ycf1) that can be utilized as specific DNA barcoding markers for species definition and population genetic studies. We conducted boundary analysis, which showed that the structure and gene sequence of the two species were highly conserved. Finally, our phylogenetic analysis supports that C. daphnoides is close to C. cassia in the Cinnamomum genes, indicating that the two species share a common ancestry. Overall, providing genomic information on C. daphnoides will be beneficial for the conservation and utilization of endangered plant genetic resources. It will also serve as a reference for the identification of species and the phylogenetic analysis of Cinnamomum. This information will be useful in future research.

Population genetic assessment of Viburnum japonicum in China using ddRAD-seq.

Viburnum japonicum is a rare plant species and endemic to the coastal region of Eastern Asia with extremely small populations. Within mainland China, this species can be only found in narrow habitats of the northeast coastal islands of Zhejiang Province. However, there are scarce conservation genetic studies on V. japonicum, which has limited the effective conservation and management of this rare species. Here, 51 individuals in four natural populations covering the Chinese geographic range of the species were sampled to assess the genetic diversity and population structure. A total of 445,060 high-quality single nucleotide polymorphisms (SNPs) were identified using double digest restriction-site associated sequencing (ddRAD-seq). The overall average values of observed heterozygosity (Ho), expected heterozygosity (He), and average nucleotide diversity (π), were 0.2207, 0.2595, and 0.2741, respectively. The DFS-2 population exhibited the highest level of genetic diversity among all the populations. Genetic differentiation between populations was moderate (F ST = 0.1425), and there was selfing between populations (F IS = 0.1390, S = 24.52%). Of the total genetic variation, 52.9% was found among populations through AMOVA analysis. The Mantel test (r = 0.982, p = 0.030) combined with analyses of the Maximum Likelihood (ML) phylogenetic tree, ADMIXTURE, and principal component analysis (PCA), revealed that populations of V. japonicum were genetically segregated and significantly correlated with their geographical distribution. Our study demonstrated that V. japonicum maintained a medium level of genetic diversity and differentiation with a strong population structure, and the results were mainly affected by its island distribution pattern and self-crossing characteristics. These results provide insights into the genetic diversity and population history of V. japonicum, critical information for conserving and sustainably developing its genetic resources.

Comparative transcriptome findings reveal the neuroinflammatory network and potential biomarkers to early detection of ischemic stroke.

INTRODUCTION: Ischemic stroke accounts for 70-80% of all stroke cases, leading to over two million people dying every year. Poor diagnosis and late detection are the major causes of the high death and disability rate. METHODS: In the present study, we used the middle cerebral artery occlusion (MCAO) rat model and applied comparative transcriptomic analysis, followed by a systematic advanced bioinformatic analysis, including gene ontology enrichment analysis and Ingenuity Pathway Analysis (IPA). We aimed to identify novel biomarkers for the early detection of ischemic stroke. In addition, we aimed to delineate the molecular mechanisms underlying the development of ischemic stroke, in which we hoped to identify novel therapeutic targets for treating ischemic stroke. RESULTS: In the comparative transcriptomic analysis, we identified 2657 differentially expressed genes (DEGs) in the brain tissue of the MCAO model. The gene enrichment analysis highlighted the importance of these DEGs in oxygen regulation, neural functions, and inflammatory and immune responses. We identified the elevation of angiopoietin-2 and leptin receptor as potential novel biomarkers for early detection of ischemic stroke. Furthermore, the result of IPA suggested targeting the inflammasome pathway, integrin-linked kinase signaling pathway, and Th1 signaling pathway for treating ischemic stroke. CONCLUSION: The results of the present study provide novel insight into the biomarkers and therapeutic targets as potential treatments of ischemic stroke.

Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models.

Background: Despite tremendous progress in diagnosis and prediction of Alzheimer's disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment. Methods: By differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients. Results: Screened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients. Conclusion: The LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

[Effects of aridity on carbon, nitrogen, and phosphorus contents in soils and plants of mountain swamps, Zhejiang, China]. / 旱化对浙江山地沼泽湿地土壤与植物碳氮磷含量的影响.

Mountain swamps in Zhejiang Province have been suffered from severe drouhgt threats because of climate change and artificial interruption. Sphagnum bogs and swamps were gradually degraded into arid swamps. However, the effects of arid processes on the C, N, P contents of soils and their stoichiometry in mountain swamps are still unclear. We measured C, N and P concentrations, pH values, and bulk density in the upper 0-60 cm soil layers in the stands of five mountain swamps with the different arid levels. Moreover, the aboveground biomass and the C, N, P concentrations in the crushed plant mixture were also measured. The results showed that the soils of mountain swamps in Zhejiang Province were rich in soil organic carbon (SOC), total nitrogen (TN), but infertile in phosphorus (TP). Aboveground biomass, soil pH, bulk density increased, while SOC, TN, TP, C:N, C:P, N:P decreased with aridity. Soil pH and bulk density had significant negative correlations with SOC, TN, and TP in soils, respectively. The differences in the C, N, P accumulation in the soils were probably associated with litterfall production, the oxygen condition of wetlands, and the degree of plant decomposition at the different types of mountain swamps. In all, arid trends were obvious at the mountain swamps in Zhejiang Province. Soil nutrients, such as C, N, P, deceased, while plant community succeeded from the wet swamp to the mesophyte vegetation with the arid processes. The contents of C, N and P in the plants varied across species, and were not coupled with those in the soils.

Diagnosis of Middle Cerebral Artery Stenosis Using Transcranial Doppler Images Based on Convolutional Neural Network.

BACKGROUND: The purpose of this study was to explore the diagnostic value of convolutional neural networks (CNNs) in middle cerebral artery (MCA) stenosis by analyzing transcranial Doppler (TCD) images. METHODS: Overall, 278 patients who underwent cerebral vascular TCD and cerebral angiography were enrolled and classified into stenosis and non-stenosis groups based on cerebral angiography findings. Manual measurements were performed on TCD images. The patients were divided into a training set and a test set, and the CNN architecture was used to classify TCD images. The diagnostic accuracies of manual measurements, CNNs, and TCD parameters for MCA stenosis were calculated and compared. RESULTS: Overall, 203 patients without stenosis and 75 patients with stenosis were evaluated. The sensitivity, specificity, and area under the curve (AUC) for manual measurements of MCA stenosis were 0.80, 0.83, and 0.81, respectively. After 24 iterations of the running model in the training set, the sensitivity, specificity, and AUC of the CNNs in the test set were 0.84, 0.86, and 0.80, respectively. The diagnostic value of CNNs differed minimally from that of manual measurements. Two parameters of TCD, peak systolic velocity and mean flow velocity, were higher in patients with stenosis than in those without stenosis; however, their diagnostic values were significantly lower than those of CNNs (P < 0.05). CONCLUSIONS: The diagnostic value of CNNs for MCA stenosis based on TCD images paralleled that of manual measurements. CNNs could be used as an auxiliary diagnostic tool to improve the diagnosis of MCA stenosis.

Preparative separation and purification of Cyclosporin D from fungus Hypoxylon Spp. by improved closed-loop recycling counter-current chromatography.

Improved closed-loop recycling counter-current chromatography (CLR CCC) with a two-phase solvent system composed of n-hexane-acetonitrile (1:1, v/v) was developed for separation, purification and preparation of cyclosporin D from the crude extract of fungus Hypoxylon Spp. (sj18). 28 mg cyclosporin D was successfully purified from 300 mg crude extract sample. The purity was 95.2% after five cycles, determined by HPLC. The structure of cyclosporin D was identified and assigned by 1H NMR, 13C NMR and mass spectrometric analyses. In addition, in the study, we show an interesting phenomenon that cyclosporin D can be prepared by the conventional CCC in n-hexane-ethyl acetate-methanol-water solvent system (2.5:1:2.5:1, v/v/v/v), and can also be prepared by the improved closed-loop recycling CCC in n-hexane-acetonitrile solvent system (1:1, v/v), but the efficiency of preparation varies greatly.

[Correlation Between Water Purification Capacity and Bacterial Community Composition of Different Submerged Macrophytes].

Eight submerged macrophytes are commonly found in subtropical areas, including Vallisneria natans, Vallisneria denseserrulata, Hydrilla verticillata, Elodea canadensis, Ceratophyllum demersum, Potamogeton malaianus, Potamogeton pectinatus, and Potamogeton maackianus, and these eight macrophytes were selected as research objects. The absorption capacity of nitrogen and phosphorus and water purification ability of submerged macrophytes were compared under indoor static water conditions. Furthermore, combining the bacterial community composition of submerged macrophytes, which was determined by 16S rRNA gene sequencing, the correlation between the water purification ability and the bacterial community of submerged macrophytes was determined. The results showed that all of the submerged macrophytes had obvious purification effects on nitrogen and phosphorus in water. The removal of nitrogen and phosphorus by submerged macrophytes was mainly through plant synergism, and the removal rate of plant absorption and enrichment was low. Among them, the removal rate of nitrogen and phosphorus was the highest in Vallisneria denseserrulata, reaching 91.58% and 96.81%. The self-absorption ability of nitrogen and phosphorus from water of Elodea canadensis and Ceratophyllum demersum was higher than other groups. The plant synergistic purification ability of Vallisneria denseserrulata and Vallisneria natans was the highest. The absolute dominant phyla of eight submerged macrophyte-associated bacteria were Proteobacteria (abundance values were more than 40%). At the genus level, Cupriavidus, Rhodobacter, and Gemmatimonas were the dominant genera for different submerged macrophytes. Most of these bacterial groups were degradable, which may be the main reason for the strong ability of eight submerged macrophytes to purify nitrogen and phosphorus in the water. The LEfSe analysis showed that Vallisneria denseserrulata and Vallisneria natans had the highest number of bacteria with significant differences. Among them, Rhizobiales, Burkholderiales, Flavobacteriales, Alcaligenaceae, Cupriavidus, and Bacillales may be the dominant bacteria to enhance the efficiency of plant purification of the water by Vallisneria denseserrulata. The bacteria of Deinococci, Comamonadaceae, Saprospiraceae, and Hyphomicrobium may be the dominant bacteria to enhance the efficiency of plant purification of the water by Vallisneria natans.

[Spatial and Temporal Variability of CO2 Emissions from the Xin'anjiang Reservoir].

Xin'anjiang Reservoir is the largest reservoir in eastern China, with a surface area of 580 km2 and a mean depth of 30 m. It is in an oligotrophic or mesotrophic state at present. This study measured carbon dioxide (CO2) emissions from the upstream river, the reservoir's main body, and the river downstream of the Xin'anjiang Reservoir to investigate the spatial and seasonal variability of CO2 emissions from the water surface using static floating chambers and gas chromatography. Results showed that the downstream river had, significantly, the highest CO2 emission flux[(1535.00±1447.46) mg·(m2·h)-1], followed by the upstream river[(120.39±135.41) mg·(m2·h)-1]. The reservoir's main body had the lowest flux[(36.65-61.94) mg·(m2·h)-1]. The high CO2 emission flux in the downstream river was probably influenced by turbulence during the discharge periods, which would allow the dissolved CO2 in the hypolimnion before the dam to be released to the atmosphere in the watercourse of the downstream river. However, the CO2 emission flux decreased with distance to the dam, likely because of the drop in strength of the turbulence. Moreover, there was an obvious alternation between CO2 source and CO2 sink in the main body of the reservoir, with CO2 sources in autumn and winter and CO2 sinks in spring and summer. The maximum and minimum CO2 emission values occurred in winter and spring, respectively. Such variability in the CO2 emissions was probably influenced by the bloom of alga in spring and summer, because dissolved CO2 in the water was absorbed by the respiration of alga. However, hydrologic conditions were unstable in the upstream river due to a fast water flow, so alga was difficult to bloom there, and a CO2 source was observed throughout the year, except during April and August. The measurement of the flux from the upstream river, main body, and downstream river required a long period for the investigation of greenhouse gas emissions to avoid underestimating the total CO2 emission from a hydroelectric reservoir system.