Impact of ocean acidification on microzooplankton grazing dynamics

This study examines the potential impacts of projected atmospheric carbon dioxide (pCO2) levels reaching 800 ppm by the end of the century, focusing on ocean acidification effects on marine ecosystems in the coastal areas of Bohai. We investigated how acidification affects the grazing patterns of microzooplankton using dilution techniques and ecophysiological methods. Our findings indicate that acidic conditions shift the phytoplankton community structure, changing dominant species. Elevated CO2 concentrations reduced grazing pressure on phytoplankton, with less steep declines in growth rates at 800 ppm CO2 (spring: 2.43 d−1 vs. 2.16 d−1, summer: −0.46 d−1 vs. −0.73 d−1, autumn: −0.45 d−1 vs. −0.90 d−1) and significant decreases in grazing pressure percentages (%Pp from 0.84 to 0.58 and %Pi from 0.64 to 0.46). Short-term acid exposure significantly increased superoxide dismutase activity in both microplankton (from 0.03 to 0.08 U mg−1, p<0.01) and nanoplankton (from 0.05 to 0.09 U mg−1, p<0.001), indicating an adaptive response to oxidative stress. These results highlight that elevated CO2 levels primarily boost phytoplankton growth by reducing microzooplankton grazing pressure, resulting in higher growth rates and a shift towards smaller-sized phytoplankton, reflecting complex short-term ecological responses to acidification. Further research is needed to understand the long-term effects of ocean acidification on microzooplankton and their role in marine secondary productivity

Phenolic acid-induced phase separation and translation inhibition mediate plant interspecific competition

Phenolic acids (PAs) secreted by donor plants suppress the growth of their susceptible plant neighbours. However, how structurally diverse ensembles of PAs are perceived by plants to mediate interspecific competition remains a mystery. Here we show that a plant stress granule (SG) marker, RNA-BINDING PROTEIN 47B (RBP47B), is a sensor of PAs in Arabidopsis. PAs, including salicylic acid, 4-hydroxybenzoic acid, protocatechuic acid and so on, directly bind RBP47B, promote its phase separation and trigger SG formation accompanied by global translation inhibition. Salicylic acid-induced global translation inhibition depends on RBP47 family members. RBP47s regulate the proteome rather than the absolute quantity of SG. The rbp47 quadruple mutant shows a reduced sensitivity to the inhibitory effect of the PA mixture as well as to that of PA-rich rice when tested in a co-culturing ecosystem. In this Article, we identified the long sought-after PA sensor as RBP47B and illustrated that PA-induced SG-mediated translational inhibition was one of the PA perception mechanisms.This work was supported by funds from the National Natural Science Foundation of China (31970641); the State Key Laboratory for Protein and Plant Gene Research, School of Life Sciences, Peking University, Center for Life Sciences; the USDA National Institute of Food and Agriculture, Hatch project 3808 to W.W.; the National Natural Science Foundation of China (31970283); Beijing Nova Program of Science and Technology (Z191100001119027); Capital Normal University and State Key Laboratory for Protein and Plant Gene Research, School of Life Sciences, Peking University, to M.Z.; the European Commission Marie Curie-IEF reSGulating-702473 to E.G.B.; Natural Science Foundation of Fujian Province (2020J01546) to J.L.; Knut and Alice Wallenberg Foundation and Swedish Research Council VR to P.V.B.; International Postdoctoral Exchange Fellowship Program and Postdoctoral Fellowship of Center for Life Sciences, and National Natural Science Foundation of China (3220050423) to Z.X.; and the Postdoctoral Fellowship of Center for Life Sciences to S.Z., Y.L. and C.C.Peer reviewe

Optimal Power Allocation in Spatial Modulation Systems

In spatial modulation (SM) systems, when channel state information is available at the transmitter, adaptive power allocation can be exploited to enhance the SM performance. In this paper, optimal power allocation in SM systems is considered from an information-theoretical view. First, as there is no closed-form expression of the SM capacity, the instantaneous mutual information is formulated in an analytic form for SM systems with two transmit antennas. Then, two power-allocation algorithms are proposed to maximize the SM mutual information. One is a sub-optimal power-allocation algorithm that is established in closed form to maximize the upper bound of SM mutual information. The other is an iterative algorithm for optimal power allocation that is proposed to achieve the maximum mutual information of SM systems while dramatically reducing the operational complexity compared with global computer searching. To evaluate the performance of the proposed power-allocation algorithms, numerical and simulation results are reported in terms of outage capacity and ergodic capacity, which demonstrates that the proposed power-allocation algorithms significantly improve the SM performance, compared with conventional equal-power allocation

An Efficient Group-Based Replica Placement Policy for Large-Scale Geospatial 3D Raster Data on Hadoop

Geospatial three-dimensional (3D) raster data have been widely used for simple representations and analysis, such as geological models, spatio-temporal satellite data, hyperspectral images, and climate data. With the increasing requirements of resolution and accuracy, the amount of geospatial 3D raster data has grown exponentially. In recent years, the processing of large raster data using Hadoop has gained popularity. However, data uploaded to Hadoop are randomly distributed onto datanodes without consideration of the spatial characteristics. As a result, the direct processing of geospatial 3D raster data produces a massive network data exchange among the datanodes and degrades the performance of the cluster. To address this problem, we propose an efficient group-based replica placement policy for large-scale geospatial 3D raster data, aiming to optimize the locations of the replicas in the cluster to reduce the network overhead. An overlapped group scheme was designed for three replicas of each file. The data in each group were placed in the same datanode, and different colocation patterns for three replicas were implemented to further reduce the communication between groups. The experimental results show that our approach significantly reduces the network overhead during data acquisition for 3D raster data in the Hadoop cluster, and maintains the Hadoop replica placement requirements

An Efficient Group-Based Replica Placement Policy for Large-Scale Geospatial 3D Raster Data on Hadoop

Geospatial three-dimensional (3D) raster data have been widely used for simple representations and analysis, such as geological models, spatio-temporal satellite data, hyperspectral images, and climate data. With the increasing requirements of resolution and accuracy, the amount of geospatial 3D raster data has grown exponentially. In recent years, the processing of large raster data using Hadoop has gained popularity. However, data uploaded to Hadoop are randomly distributed onto datanodes without consideration of the spatial characteristics. As a result, the direct processing of geospatial 3D raster data produces a massive network data exchange among the datanodes and degrades the performance of the cluster. To address this problem, we propose an efficient group-based replica placement policy for large-scale geospatial 3D raster data, aiming to optimize the locations of the replicas in the cluster to reduce the network overhead. An overlapped group scheme was designed for three replicas of each file. The data in each group were placed in the same datanode, and different colocation patterns for three replicas were implemented to further reduce the communication between groups. The experimental results show that our approach significantly reduces the network overhead during data acquisition for 3D raster data in the Hadoop cluster, and maintains the Hadoop replica placement requirements

Identification and analysis of key circRNAs in the mouse embryonic ovary provides insight into primordial follicle development

Abstract Background CircRNAs are a class of noncoding RNAs with tissue- and development-specific expression characteristics. In many mammals, primordial follicle development begins in the embryonic stage. However, the study of circRNAs in primordial follicle development in mice has not been reported. Results In this study, ovaries were collected from mouse foetuses at 15.5 days post coitus (dpc) and 17.5 dpc, which are two key stages of primordial follicle development. A total of 4785 circRNAs were obtained by using RNA-seq. Of these, 83 differentially expressed circRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that these differential circRNAs were mainly involved in the regulation of reproductive development. Through qRT-PCR, back-splice sequence detection and enzyme digestion protection experiments, we found that circ-009346, circ-014674, circ-017054 and circ-008296 were indeed circular. Furthermore, circ-009346, circ-014674 and circ-017054 were identified as three key circRNAs by analysing their expression in the ovaries of mice at different developmental stages. The circRNA-miRNA-mRNA interaction network was constructed and validated for target miRNA and mRNA using qRT-PCR. The interacting genes circ-009346, circ-014674, and circ-017054 were subjected to KEGG enrichment analysis. We found that circ-014674 may participate in the assembly and reserve of primordial follicles through oestrogen and the Janus kinase (JAK) signal transducer and activator of transcription (STAT) signalling pathway (JAK-SATA). Circ-009346 and circ-017054 may have similar functions and are involved in the activation and growth of primordial follicles through the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) signalling pathways. Conclusions Based on our findings, three circRNAs associated with primordial follicle development were identified, and their potential mechanisms of regulating primordial follicle development were revealed. These findings will help us better understand the molecular mechanism of circRNAs in primordial follicles and provide important references and targets for the development of primordial follicles

Effect of Purpureocillium lilacinum on inter-root soil microbial community and metabolism of tobacco

Abstract Background Numerous chemical pesticides have been used in agricultural production to combat crop diseases and pests. Despite ensuring certain economic advantages, they have also resulted in issues like environmental contamination, declining soil quality, and pesticide residues. Because biological control is environmentally friendly and difficult to acquire resistance to, it has been used in practice recently. Methods In this study, we isolated the endophytic fungus Purpureocillium lilacinum from Portulaca oleracea L., which was previously found to have inhibitory effects on soil pathogens in tobacco fields. To find out how the biocontrol agent P. lilacinum affects soil microorganisms and plant metabolism in tobacco cultivation, we used amplicon sequencing technology and gas chromatography-mass spectrometry to look at the structure of soil microbial communities and the networks of interactions between microorganisms and metabolites in the inter-rhizosphere soil of tobacco fields treated with different amounts of P. lilacinum. Results The findings showed that there was a trend toward less microbial diversity among inter-root microorganisms as solid-state fermentation (SSF) products of P. lilacinum increased; however, submerged fermentation (SmF) had no discernible impact on microbial diversity when compared to the direct use of SSF. Additionally, the relationship between inter-root fungi and volatile compounds in tobacco leaves was dominated by a negative correlation. Conclusions The result demonstrated that P. lilacinum’s antagonistic interaction in the inter-rhizosphere microbial community was dominant and valuable for biopesticide application. P. lilacinum can work more effectively on tobacco roots by using SSF products. P. lilacinum’s opposition to fungal colonies may enhance the volatile chemicals in tobacco leaves. These provide some implications for the biocontrol application of P. lilacinum

An Evaluation of the Influence of Meteorological Factors and a Pollutant Emission Inventory on PM_2.5 Prediction in the Beijing–Tianjin–Hebei Region Based on a Deep Learning Method

In this study, a Long Short-Term Memory (LSTM) network approach is employed to evaluate the prediction performance of PM2.5 in the Beijing–Tianjin–Hebei region (BTH). The proposed method is evaluated using the hourly air quality datasets from the China National Environmental Monitoring Center, European Center for Medium-range Weather Forecasts ERA5 (ECMWF-ERA5), and Multi-resolution Emission Inventory for China (MEIC) for the years 2016 and 2017. The predicted PM2.5 concentrations demonstrate a strong correlation with the observed values (R2 = 0.871–0.940) in the air quality dataset. Furthermore, the model exhibited the best performance in situations of heavy pollution (PM2.5 > 150 μg/m3) and during the winter season, with respective R2 values of 0.689 and 0.915. In addition, the influence of ECMWF-ERA5’s hourly meteorological factors was assessed, and the results revealed regional heterogeneity on a large scale. Further evaluation was conducted by analyzing the chemical components of the MEIC inventory on the prediction performance. We concluded that the same temporal profile may not be suitable for addressing emission inventories in a large area with a deep learning method