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In this paper, we study sharp bound on higher-dimensional Lebesgue product space for Hardy operator on Heisenberg group, the constants of sharp bounds are obtained. In addition, we also give the boundedness for weighted Hardy operator and weighted Cesaro operator

Delay differential equation is an important field in applied mathematics since it concerns more situations than the ordinary differential equation. Moreover, it makes the equations more applicable to the object's movement in real life. My project is the lecture note on the delay differential equation provides a basic introduction to the delay differential equation, its application in real life, improving the ordinary differential equation, the primary method and definition for solving the delay differential equation and the use of the way in the ordinary differential equation to estimate the periodic solution to the delay differential equation.

Supplemental material, sj-docx-1-jbm-10.1177_17246008211032689 for The prognostic significance of microRNA-221 in hepatocellular carcinoma: An updated meta-analysis by Wenfeng Liu, Keshu Hu, Feng Zhang, Shenxin Lu, Rongxin Chen, Zhenggang Ren and Xin Yin in The International Journal of Biological Markers

Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, and when they are, it is often at a national scale, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries. Our study reveals three key findings: First, we find that the overall potential losses to fisheries is higher than potential losses to agriculture, but there is substantial within-country variability. Second, while more than two-thirds of locations will bear a double burden of potential losses to both fisheries and agriculture simultaneously, mitigation could reduce the proportion of places facing a double burden. Third, lower socioeconomic status communities are more likely to experience potential impacts than higher socioeconomic status communities.

Objective. To analyze the application of screening questionnaire on chronic obstructive pulmonary disease (COPD) and pulmonary function test on dust-exposed migrant workers.Methods. According to 149 eligible objects of study selected in medical outreach, the real situation on them threaten by COPD and influential elements are analyzed in depth.Results. 73 workers are suspected to be suffered COPD by pulmonary function test. Th e detection rate (62.6%)of suspected COPD in the population with total score of more than 5 is significantly higher than that of the population with total score of less than 5 (14.3%). Th e result has the statistical significance (X2=28.19, pConclusion. By using questionnaires and pulmonary function tests, the early diagnosis and intervention of COPD are available.

Climate change has great impacts on hydrological processes worldwide. The Tibetan Plateau (TP), the "Water Tower” of Asia, poses significant influences on Asian climate and is also one of the most sensitive areas to climate change. Therefore, it is of importance to investigate the plausible future hydrological regimes in the TP based on the climate scenarios provided by General Circulation Models (GCMs). In this study, the Variable Infiltration Capacity model was coupled with Shuffled Complex Evolution developed at the University of Arizona to explore the responses of hydrological processes to climate change in the Lhasa River basin, the tributary of the Yarlung Zangbo River in the southern TP. A downscaling framework based on Automatic Statistical Downscaling was used to generate the future climate data from two GCMs (Echam5 and Miroc3.2_Medres) under three scenarios (A1B, A2 and B1) for the period of 2046-2065. Results show increases for both air temperature and annual precipitation in the future climate. Evaporation, runoff and streamflow will experience a rising trend, whereas spring snow cover will reduce dramatically. These changes present significant spatial and temporal variations. The alteration of hydrological processes may challenge the local water resource management. This study is helpful for policy makers to tackle climate change related issues in terms of mitigation and adaptation.

Increasing temperature trends are expected to impact yields of major field crops by affecting various plant processes, such as phenology, growth, and evapotranspiration. However, future projections typically do not consider the effects of agronomic adaptation in farming practices. We use an ensemble of seven Global Gridded Crop Models to quantify the impacts and adaptation potential of field crops under increasing temperature up to 6 K, accounting for model uncertainty. We find that without adaptation, the dominant effect of temperature increase is to shorten the growing period and to reduce grain yields and production. We then test the potential of two agronomic measures to combat warming‐induced yield reduction: (i) use of cultivars with adjusted phenology to regain the reference growing period duration and (ii) conversion of rainfed systems to irrigated ones in order to alleviate the negative temperature effects that are mediated by crop evapotranspiration. We find that cultivar adaptation can fully compensate global production losses up to 2 K of temperature increase, with larger potentials in continental and temperate regions. Irrigation could also compensate production losses, but its potential is highest in arid regions, where irrigation expansion would be constrained by water scarcity. Moreover, we discuss that irrigation is not a true adaptation measure but rather an intensification strategy, as it equally increases production under any temperature level. In the tropics, even when introducing both adapted cultivars and irrigation, crop production declines already at moderate warming, making adaptation particularly challenging in these areas., Plain Language Summary: Global warming affects yields of grain crops, which are at the base of human diets. We use crop models to quantify its impacts on global crop production and to assess how adaptation could compensate for the adverse effects. We find that up to 2 K of increased temperature production can be maintained at the current level by using new cultivars, selected to maintain current growing period length under warming. Irrigation, as another management strategy, is shown to have the potential to increase yields in dry regions if water is available. However, models do not indicate that irrigation reduces the crops' sensitivity to warming. We find large differences in the yield response to warming and adaptation across climatic regions. While continental and temperate regions may benefit from higher temperatures but also show sizable adaptation potentials, tropical and arid regions show largest temperature impacts and smaller adaptation potentials. After all, these two crop management options appear effective to balance the effects of moderate warming but cannot fully compensate impacts above 2 K of warming., Key Points: Without agronomic adaptation, the dominant effect of temperature increase is to shorten growing periods and to reduce yields and production. Adaptation via cultivars that maintain current growing periods under warming can compensate global production losses up to 2 K. Irrigation would act as intensification rather than true adaptation, as it hardly affects the sensitivity of crop yields to warming., Early Postdoctoral Mobility Fellowship http://dx.doi.org/10.13039/501100001711, 7th Framework Programme Early http://dx.doi.org/10.13039/100013273, MACMIT project, BioNex Project, University of Chicago Center for Robust Decision‐making on Climate and Energy Policy http://dx.doi.org/10.13039/100006445

The invention provides a frame level feature aggregation method for video target detection, and relates to the technical field of computer vision. The invention provides a frame-level feature aggregation method for video target detection. The method comprises the following steps: firstly, extracting deep features from a single-frame image through a feature network; extracting an inter-frame optical flow by using an optical flow network FlowNet; aligning the frame-level features of the adjacent frames to the current frame based on the optical flow to realize frame-level feature propagation; finally, calculating the scaling cosine similarity weight through the mapping network and the weight scaling network, and using the scaling cosine similarity weight to aggregate the multi-frame featuresto generate the aggregated features. According to the frame-level feature aggregation method for video target detection provided by the invention, the weight distribution is more reasonable, and the aggregated features are input into the video target detection network, so that video detection in complex scenes such as motion blur, low pixels, lens zooming and shielding can have a better detectioneffect and robustness.

A series of ursolic acid (UA), oleanolic acid (OA) and 18β-glycyrrhetinic acid (GA) derivatives were synthesized by introducing a range of substituted aromatic side-chains at the C-2 position after the hydroxyl group at C-3 position was oxidized. Their antibacterial activities were evaluated in vitro against a panel of four Staphylococcus strains. The results revealed that the introduction of aromatic side-chains at the C-2 position of GA led to the discovery of potent triterpenoid derivatives for inhibition of both drug sensitive and resistant S. aureus, while the other two series derivatives of UA and OA showed no significant antibacterial activity even at high concentrations. In particular, GA derivative showed good potency against all four strains of Staphylococcus (MIC = 1.25 - 5 μmol/L) with acceptable pharmacokinetics properties and low cytotoxicity in vitro. Molecular docking was also performed using S. aureus DNA gyrase structure to rationalize the observed antibacterial activity. Therefore, this series of GA derivatives have strong potential for the development of a new type of triterpenoid antibacterial agent.

©2020. The Authors. The extent and impact of climate-related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter-Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross-category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia.

Graph convolutional networks (GCNs) have been widely used for representation learning on graph data, which can capture structural patterns on a graph via specifically designed convolution and readout operations. In many graph classification applications, GCN-based approaches have outperformed traditional methods. However, most of the existing GCNs are inefficient to preserve local information of graphs -- a limitation that is especially problematic for graph classification. In this work, we propose a locality-preserving dense GCN with graph context-aware node representations. Specifically, our proposed model incorporates a local node feature reconstruction module to preserve initial node features into node representations, which is realized via a simple but effective encoder-decoder mechanism. To capture local structural patterns in neighbourhoods representing different ranges of locality, dense connectivity is introduced to connect each convolutional layer and its corresponding readout with all previous convolutional layers. To enhance node representativeness, the output of each convolutional layer is concatenated with the output of the previous layer's readout to form a global context-aware node representation. In addition, a self-attention module is introduced to aggregate layer-wise representations to form the final representation. Experiments on benchmark datasets demonstrate the superiority of the proposed model over state-of-the-art methods in terms of classification accuracy., 18 pages

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase II. The GGCMI Phase II experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological mean yield response without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

International audience

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

Thallium (Tl) is a non-essential, highly toxic element having Tl(I) and Tl(III) two redox states. Tl(III) is more toxic to organisms than Tl(I) but is less of attention. The objective of this study was to investigate the reason of Tl(III) instability in the presence of plant. The content of Tl(III) was rapidly reduced from 100% to 16.1% in Tl solution cultivating rice seedlings while content of Tl(I) was increased from 0 to 83.8% within 8 h. High performance liquid chromatography (HPLC) analysis showed that organic acids exudates in rice roots caused the pH decline in rhizosphere, but only oxalate was the specific organic acid in root exudates under Tl stress. The anion channel inhibitor, nifiumic acid (NIF), could efficiently inhibit the oxalate exudation but had no effect on oxalate content in root cytosol compared with Tl treatment. The exogenous oxalate, malate and citrate significantly contributed the reduction of Tl(III) to Tl(I). It was concluded that the contact of Tl(III) with organic acids in plant cells or in root exudates promoted the instability of Tl(III) with plants. Meanwhile, the presence of DTPA could effectively restrain the conversion of Tl(III) to Tl(I). This study provided better understanding of the basic biochemical mechanism for the reduction of Tl(III) in contact with plants. It will contribute to controlling relevant factors to stabilize Tl(III) for further toxic assessment on organism and to studying Tl(III) behavior in ecological system.

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives., Design Type(s)modeling and simulation objective • data integration objective • software variation design • time series designMeasurement Type(s)cultivated environmentTechnology Type(s)computational modeling techniqueFactor Type(s)crop • atmospheric weather • atmospheric wind speed • atmospheric water vapour • atmospheric carbon dioxide • agricultural environmental material • agricultural featureSample Characteristic(s)land Machine-accessible metadata file describing the reported data (ISA-Tab format)

The traditional SIR model cannot fully reflect the regularity of information propagation in social networks. In this paper, the advertising spreading model which is applied to social networks is established, and the corresponding dynamic evolution equations are given. Meanwhile, due to that there is no unified evaluation criteria for the validity of spreading models currently, the application of AEI, the advertising effectiveness index to evaluate and analyze the effectiveness of spreading models is put forward in this paper. The experiment results demonstrate that the model proposed in this paper can correctly reflect the trend of advertising spreading in social network, and accurately describe the spreading process, and the validity of the model is also verified in this paper.

A sesame straw-like MgFe2O4/N-doped hollow porous carbon nanofiber (MFO/NPCNF) composite was first designed and synthesized successfully by electrostatic spinning and spatial confinement strategy. Spherical MgFe2O4 nanoparticles about 15 nm are in-situ spatially confined grown and well dispersed in the porous and hollow structure of NPCNF by the interaction between ultrafine MgFe2O4 nanoparticles and N-doped site, which can not only improve the conductivity, promote the penetration of electrolyte, accelerate the transfer of lithium ions and enhance the reaction kinetics, but also buffer the volume expansion and improve the cycling capacity and stability. Owing to the superior structure design, the MFO/NPCNF anode shows a high cycle capacity of 1205.8 mAh g−1 after 250 cycles at 0.5 A g−1 and excellent rate performance with 359.5 mAh g−1 at a current density up to 10 A g−1, which is near to the theoretical specific capacity of commercial graphite.

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase II experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase II experimental protocol and its simulation data archive. Twelve crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (``CTWN') for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase II archive. For example, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that indicates yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions, but is largest in high-latitude regions where crops may be grown in the future.

SF6 gas is widely used in various power equipment. In this paper, a baseline correction algorithm based on spectral detection method based on optimal wavelet basis is proposed. The algorithm selects the optimal wavelet base, removes the strong spectrum information by threshold method, eliminates the influence of continuum fitting, and performs the least squares fitting on the residual signal to obtain the continuum, and removes the continuum from the original spectrum to obtain the required Line spectrum. The accuracy of the quantitative analysis of the line spectrum intensity after processing is greatly improved, and the baseline is effectively estimated and corrected, and the fitting accuracy is higher than the traditional iterative wavelet baseline correction algorithm.

Staphylococcus aureus is an opportunistic pathogen and one of the major bacteria responsible for community-acquired and nosocomial infections for which the treatment is complicated by the emergence of multidrug resistant strains. Its adaptation to multiple growth conditions, which contributes to its virulence, is under the control of numerous factors including regulatory RNAs, often called sRNAs for small RNAs. We performed an accurate survey of all sRNAs from the model strain HG003, a NCTC8325 derivative commonly used for S. aureus genetic regulation studies. It is a complex task, essential to accomplish molecular and functional studies. We found about 50 bona fide sRNAs, a number much lower than previously reported.As most sRNAs contribute to the "fine-tuning" of gene expression, sRNA-dependent phenotypes are generally difficult to detect. However, sRNA-mediated phenotypes may emerge as dominant traits after a few generations under selective pressure. We set up an experimental strategy to evaluate the fitness of S. aureus sRNA mutants within a population of sRNA mutants. A library of eighty HG003 mutants with sRNA genes replaced by specific DNA “barcode” sequences to allow the identification of each mutant was constructed. The mutant library was grown in different conditions, DNA barcodes were PCR and counted by DNA-seq. Mutants diminishing or accumulating during a stress condition provide information on sRNA functions. The use of specific primers allows multiplexing 50 experimental conditions in one DNA-seq library. We questioned whether sRNAs could affect the antibiotic resistance in S. aureus. Here, we present data using the above-described method with the sRNA mutant library tested in the presence of 10 antibiotics (targeting the cell wall and cell membrane, inhibiting the biosynthesis of protein, interrupting DNA replication and RNA synthesis of bacteria). Several mutants were affected by the tested growth conditions. For examples, the proportion of mutant sau6836 increases dramatically in vancomycin and reduces in flucloxacillin, cloxacillin and cefazolin. The proportion of RNAIII-agr mutant increases progressively in the presence of gentamicin, linezolid and clindamycin. The proportion of 6S RNA mutant decreases significantly in the presence of rifampicin. Interestingly, the 6S RNA and the rifampicin are targeting the RNA polymerase. The sRNA RsaA targets a regulator of autolytic activities and its corresponding mutant is remarkably reduced in the presence of ciprofloxacin. These examples illustrate the power of fitness experiments to identify phenotypes and reveals that several sRNAs contribute to modulate the resistance to antibiotics., Staphylococcus aureus est un agent pathogène opportuniste responsable d’infections communautaires et nosocomiales pour lesquelles les traitements sont compliqués du fait de l'émergence de souches multi-résistantes. L’adaptation rapide de S. aureus à de multiples conditions de croissance contribue à sa virulence ; elle dépend de nombreux facteurs incluant la régulation des petits ARN (ARNrég). Nous avons réalisé une étude précise de tous les petits ARN de la souche modèle HG003, un dérivé de la souche NCTC8325 couramment utilisé pour les études de régulation génétique chez S. aureus. C'est une tâche complexe qui est essentielle pour réaliser des études moléculaires et fonctionnelles. Nous avons trouvé environ 50 authentiques (bona fide) petits ARN, un nombre beaucoup plus faible que précédemment rapporté. Comme la plupart des ARNrég contribuent à une « régulation fine » de l'expression génique, les phénotypes dépendants des ARNrég sont généralement difficiles à détecter. Cependant, ces phénotypes peuvent apparaître comme un caractère important après plusieurs générations sous une pression sélective. Nous avons développé une stratégie expérimentale pour mesurer l’évolution de la quantité de mutants d’ARNrég dans une population de mutants de S. aureus. Nous avons construit une collection de quatre-vingts mutants d’ARNrég dans la souche HG003. Chaque gène d’ARNrég est remplacé par une séquence d'ADN « code-barres » spécifique pour l’identification des mutants. La bibliothèque de mutants est cultivée dans différentes conditions de croissance, les codes-barres sont amplifiés par PCR et comptés par séquençage massif. Nous pouvons ainsi déterminer les mutants qui diminuent ou s'accumulent pendant une condition de stress et inférer une fonction à certains ARNrég. L'utilisation d'amorces spécifiques permet de multiplexer 50 conditions expérimentales. Nous nous sommes posés la question suivante : les ARNrég de S. aureus participent-t-ils à la résistance aux antibiotiques ? Dans ce mémoire, nous présentons des données en utilisant la méthode décrite ci-dessus. La bibliothèque de mutants d’ARNrég a été testée en présence de 10 antibiotiques ciblant les enveloppes, la synthèse des protéines, la réplication de l'ADN ou la synthèse de l'ARN. Plusieurs mutants sont affectés par les conditions de croissance testées. Par exemple, la proportion du mutant sau6836 augmente considérablement en présence de vancomycine et est réduite en présence de flucloxacilline, cloxacilline ou céfazoline. La proportion du mutant ARNIII-agr augmente progressivement en présence de gentamicine, de linézolide et de clindamycine. La proportion de mutant d'ARN 6S diminue significativement en présence de rifampicine. Il est important de noter que l'ARN 6S et la rifampicine ciblent l'ARN polymérase. L’ARNrég RsaA est un régulateur des autolysines dont l’absence affecte la survie en présence de ciprofloxacine. Ces exemples illustrent la puissance des expériences de compétition pour identifier les phénotypes dépendants des ARNrég et révèlent que plusieurs ARNrég contribuent à moduler la résistance aux antibiotiques.

Aiming at a large number of useless signaling messages generated by Session Initiation Protocol (SIP) in IP networks, the problem of SIP flooding attacks caused by normal call requests cannot be established by users, and a SIP flooding attack detection method based on convolutional neural network is proposed. This method uses a window to perform convolution calculation on the input data, and judges whether there is a SIP message flood attack in the network based on the result of the calculation. Experimental results show that the proposed method can effectively detect SIP flooding attacks in IMS networks.

© 2018 The Author(s). Published by IOP Publishing Ltd. Following the adoption of the Paris Agreement, there has been an increasing interest in quantifying impacts at discrete levels of global mean temperature (GMT) increase such as 1.5 °C and 2 °C above pre-industrial levels. Consequences of anthropogenic greenhouse gas emissions on agricultural productivity have direct and immediate relevance for human societies. Future crop yields will be affected by anthropogenic climate change as well as direct effects of emissions such as CO 2 fertilization. At the same time, the climate sensitivity to future emissions is uncertain. Here we investigate the sensitivity of future crop yield projections with a set of global gridded crop models for four major staple crops at 1.5 °C and 2 °C warming above pre-industrial levels, as well as at different CO 2 levels determined by similar probabilities to lead to 1.5 °C and 2 °C, using climate forcing data from the Half a degree Additional warming, Prognosis and Projected Impacts project. For the same CO 2 forcing, we find consistent negative effects of half a degree warming on productivity in most world regions. Increasing CO 2 concentrations consistent with these warming levels have potentially stronger but highly uncertain effects than 0.5 °C warming increments. Half a degree warming will also lead to more extreme low yields, in particular over tropical regions. Our results indicate that GMT change alone is insufficient to determine future impacts on crop productivity. ispartof: ENVIRONMENTAL RESEARCH LETTERS vol:13 issue:6 status: published

Bacterial regulatory RNAs have been extensively studied for over a decade, and are progressively being integrated into the complex genetic regulatory network. Transcriptomic arrays, recent deep-sequencing data and bioinformatics suggest that bacterial genomes produce hundreds of regulatory RNAs. However, while some have been authenticated, the existence of the others varies according to strains and growth conditions, and their detection fluctuates with the methodologies used for data acquisition and interpretation. For example, several small RNA (sRNA) candidates are now known to be parts of UTR transcripts. Accurate annotation of regulatory RNAs is a complex task essential for molecular and functional studies. We defined bona fide sRNAs as those that (i) likely act in trans and (ii) are not expressed from the opposite strand of a coding gene. Using published data and our own RNA-seq data, we reviewed hundreds of Staphylococcus aureus putative regulatory RNAs using the DETR'PROK computational pipeline and visual inspection of expression data, addressing the question of which transcriptional signals correspond to sRNAs. We conclude that the model strain HG003, a NCTC8325 derivative commonly used for S. aureus genetic regulation studies, has only about 50 bona fide sRNAs, indicating that these RNAs are less numerous than commonly stated. Among them, about half are associated to the S. aureus sp. core genome and a quarter are possibly expressed in other Staphylococci. We hypothesize on their features and regulation using bioinformatic approaches.

HYPOTHESIS AND THEORY ARTICLE; International audience; Bacterial regulatory RNAs have been extensively studied for over a decade, and are progressively being integrated into the complex genetic regulatory network. Transcriptomic arrays, recent deep-sequencing data and bioinformatics suggest that bacterial genomes produce hundreds of regulatory RNAs. However, while some have been authenticated, the existence of the others varies according to strains and growth conditions, and their detection fluctuates with the methodologies used for data acquisition and interpretation. For example, several small RNA (sRNA) candidates are now known to be parts of UTR transcripts. Accurate annotation of regulatory RNAs is a complex task essential for molecular and functional studies. We definedbona fidesRNAs as those that (i) likely act intransand (ii) are not expressed from the opposite strand of a coding gene. Using published data and our own RNA-seq data, we reviewed hundreds ofStaphylococcus aureusputative regulatory RNAs using the DETR'PROK computational pipeline and visual inspection of expression data, addressing the question of which transcriptional signals correspond to sRNAs. We conclude that the model strain HG003, a NCTC8325 derivative commonly used forS. aureusgenetic regulation studies, has only about 50bona fidesRNAs, indicating that these RNAs are less numerous than commonly stated. Among them, about half are associated to theS. aureussp. core genome and a quarter are possibly expressed in otherStaphylococci. We hypothesize on their features and regulation using bioinformatic approaches.

Bacterial regulatory RNAs have been extensively studied for over a decade, and are progressively being integrated into the complex genetic regulatory network. Transcriptomic arrays, recent deep-sequencing data and bioinformatics suggest that bacterial genomes produce hundreds of regulatory RNAs. However, while some have been authenticated, the existence of the others varies according to strains and growth conditions, and their detection fluctuates with the methodologies used for data acquisition and interpretation. For example, several small RNA (sRNA) candidates are now known to be parts of UTR transcripts. Accurate annotation of regulatory RNAs is a complex task essential for molecular and functional studies. We defined bona fide sRNAs as those that (i) likely act in trans and (ii) are not expressed from the opposite strand of a coding gene. Using published data and our own RNA-seq data, we reviewed hundreds of Staphylococcus aureus putative regulatory RNAs using the DETR'PROK computational pipeline and visual inspection of expression data, addressing the question of which transcriptional signals correspond to sRNAs. We conclude that the model strain HG003, a NCTC8325 derivative commonly used for S. aureus genetic regulation studies, has only about 50 bona fide sRNAs, indicating that these RNAs are less numerous than commonly stated. Among them, about half are associated to the S. aureus sp. core genome and a quarter are possibly expressed in other Staphylococci. We hypothesize on their features and regulation using bioinformatic approaches.

The effect of starting raw materials on the dielectric, ferroelectric and electro-shape-memory properties of 0.5 mol% Mn3+ doped (Pb40Sr60)TiO3 ceramics was systematically studied. SEM observation showed that the ceramics fabricated from Pb3O4, SrCO3, TiO2 and MnCO3 possessed the most homogeneous microstructure and the largest grain size. As the macroscopic consequence, such ceramics exhibited large dielectric property and well-developed double hysteresis loop. More interestingly, it showed large digital-like recoverable electrostrain of 0.11 % under the electric field of 3 kV/mm, which can rival the widely used linear piezoelectricity of the hard PZT ceramics. Defect distribution within the large and homogenous grains was analyzed to explore the microscopic origin of the simultaneous domain switching behavior and the digital-like strain properties. Such feature indicated the importance of material processing on the on/off-characterized electromechanical applications.

For the identification of the dynamics of the Vermont Yankee BWR with the reactor noise, different parametric models have been tested. The widely used ARMA model is unable to identify the nonlinearity in the noise data. A systematic method by using the NARMA model, which takes advantage of both the ANN and ARMA, is developed. Comparisons are made between the identification results with ARMA and NARMA model. The advantages of identification with NARMA model over ARMA model are demonstrated. The linear-kernels of the identified NARMA models are extracted so that the natural frequency, damping ratio and time constants of the BWR are obtained. The values of those characteristics are well corresponded with the eigenvalues calculated by the differential equations of the Vermont Yankee BWR. The damping ratio with negative value is found to be a criterion for the existence of limit-cycle, which can be seen from the impulse response on the (X t , X t-1 ) plane, in stable nonlinear system.

A new optimizing objective (three-dimensional power distribution control) for the load-following problem in nuclear reactor is presented. To realize such an objective, a combination of Harmonic Synthesis Method and Nodal Method has been practiced on the numeric calculation for optimizing problem on the 200 MW Heating Reactor, in which the control rods serve as the only control variables. In contrast, most of the load-following problems have been solved under a one-dimensional neutron model. And it seems that such a model is not suitable for the reactor core whose power was controlled only by control rods. In this case, the control rod causes a strong absorption of neutron in a local area thus makes the radial power distribution play a much more important role in the load-following process. As the 3-dimensional model is used and the corresponding method is performed, both of the total power level and power distribution are controlled well. For example, the power peak-factor is lower about 4% than that with one-dimensional method.

In most of PWRs, the ex-core ion-chambers are the sole real-time sensors to respond to in-core power and its axial offset. However, the calibration coefficient of the ion-chambers depends on the (3D) power distribution and varies with the burn-up. People expect to know the variance in distribution caused by burn-up directly from the signals of ion-chambers. This expectation is not realized as yet, because an ion-chamber almost only responds to its nearest fuel assemblies. The authors then developed a two-step method for burn-up characteristic extraction: the harmonics synthesis method and harmonics’ burn-up grouping. Using the extracted burn-up characteristics, the relationship between the readings of the ex-core ion-chambers and the in-core 3D power distribution is set up. Through the simulation on the heating reactor, the method of burn-up characteristic extraction is verified under engineering conditions. It is possible to on-line extract the variance caused by burn-up in 3D power distribution.

Lambda(λ)-modes are very useful for many reactor physics analysis applications. Since modern nodal methods are known to be fast and efficient, it is suggested here - for reasons of accuracy and saving computation time - to calculate the λ-modes by such modern nodal techniques instead of using finite difference methods. Because there is a lack of a precedent, we demonstrate here the computation and usage of λ-modes of nodal equations by selecting the power iteration procedure with a simplified orthogonalization technique to calculate the λ-modes by the Nodal Green's Function Method (NGFM). The concept of nodal adjoint equation is introduced and the NGFM adjoint equation has been solved for the first time. At last, the physical meaning and usage of lamda-modes of nodal equations is discussed. Numerical results have demonstrated the success of this calculation procedure as well as its excellence performance, that is accuracy, time-saving and consistency with results obtained by analytical and finite difference methods.

This paper investigates the dynamic response of offshore wind turbine systems subjected joint wind and wave loads. Relying on the finite element model, Kane’s equation is adopted to consider the rotation of blades. Besides, the generator-torque control and blade-pitch control are taken into consideration. Wind and wave loads are generated by the physical random models. The aerodynamic loads on blades are calculated by the Blade Element Momentum (BEM) theory, and the wave loads are calculated by the linear theory of wave. The dynamic response of the NREL-5MW wind turbine system is carried out. This study provides a basis for the global reliability analysis of offshore wind turbines.

F, Cl, S and P were determined, using electron microprobe, in magmatic inclusions trapped within minerals and glass mesostasis from Wudalianchi volcanic rocks. The initial volcanic magma from Wudalianchi corresponds to the basanitic magma crystallized near the surface ( pressure < 91 Mpa ). The potential H2O content of this magma is in the range 2 — 4 wt. %. The initial composition of volcanic magmas varies regularly from early to late volcanic events. From the Middle Pleistocene to the recent eruptions (1719 – 1721 yr.), the basicity of volcanic magma tends to increase, as reflected by an increase in MgO and CaO contents and by a progressive decrease in SiO2 and K2O contents. Meanwhile. from early (Q2 ) to late (Q3) episodic eruptions of the Middle Pleistocene, the initial concentrations of chlorine in volcanic magmas range from 1430 – 1930 ppm to 1700 ppm and decrease to 700 — 970 ppm for the first episodic eruption during the Holocene (Q41). The chlorine concentrations of volcanic magmas of recent eruption (Q42) are increased again to 2600 – 2870 ppm. A parallel evolution trend for phosphorus and chlorine concentrations in magmas has been certified: 1500 – 5970 ppm (Q2)→ 3500 – 4210 ppm (Q3)→ 1100– 3500 ppm (Q41)→ 6800– 7900 ppm (Q42). The fluorine contents of volcanic magmas, from early to late volcanic events, show the same trend: 770 – 2470 ppm → 200–700 ppm → 700 – 800 ppm.

Following the adoption of the Paris Agreement, there has been an increasing interest in quantifying impacts at discrete levels of global mean temperature (GMT) increase such as 1.5 °C and 2 °C above pre-industrial levels. Consequences of anthropogenic greenhouse gas emissions on agricultural productivity have direct and immediate relevance for human societies. Future crop yields will be affected by anthropogenic climate change as well as direct effects of emissions such as CO2 fertilization. At the same time, the climate sensitivity to future emissions is uncertain. Here we investigate the sensitivity of future crop yield projections with a set of global gridded crop models for four major staple crops at 1.5 °C and 2 °C warming above pre-industrial levels, as well as at different CO2 levels determined by similar probabilities to lead to 1.5 °C and 2 °C, using climate forcing data from the Half a degree Additional warming, Prognosis and Projected Impacts project. For the same CO2 forcing, we find consistent negative effects of half a degree warming on productivity in most world regions. Increasing CO2 concentrations consistent with these warming levels have potentially stronger but highly uncertain effects than 0.5 °C warming increments. Half a degree warming will also lead to more extreme low yields, in particular over tropical regions. Our results indicate that GMT change alone is insufficient to determine future impacts on crop productivity., Environmental Research Letters, 13 (6), ISSN:1748-9326, ISSN:1748-9318

Agricultural production must increase to feed a growing and wealthier population, as well as to satisfy increasing demands for biomaterials and biomass-based energy. At the same time, deforestation and land-use change need to be minimized in order to preserve biodiversity and maintain carbon stores in vegetation and soils. Consequently, agricultural land use needs to be intensified in order to increase food production per unit area of land. Here we use simulations of AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) phase 1 to assess implications of input-driven intensification (water, nutrients) on crop yield and yield stability, which is an important aspect in food security. We find region- and crop-specific responses for the simulated period 1980–2009 with broadly increasing yield variability under additional nitrogen inputs and stabilizing yields under additional water inputs (irrigation), reflecting current patterns of water and nutrient limitation. The different models of the GGCMI ensemble show similar response patterns, but model differences warrant further research on management assumptions, such as variety selection and soil management, and inputs as well as on model implementation of different soil and plant processes, such as on heat stress, and parameters. Higher variability in crop productivity under higher fertilizer input will require adequate buffer mechanisms in trade and distribution/storage networks to avoid food price volatility.

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

Increasing temperature trends are expected to impact yields of major field crops by affecting various plant processes, such as phenology, growth, and evapotranspiration. However, future projections typically do not consider the effects of agronomic adaptation in farming practices. We use an ensemble of seven Global Gridded Crop Models to quantify the impacts and adaptation potential of field crops under increasing temperature up to 6 K, accounting for model uncertainty. We find that without adaptation, the dominant effect of temperature increase is to shorten the growing period and to reduce grain yields and production. We then test the potential of two agronomic measures to combat warming‐induced yield reduction: (i) use of cultivars with adjusted phenology to regain the reference growing period duration and (ii) conversion of rainfed systems to irrigated ones in order to alleviate the negative temperature effects that are mediated by crop evapotranspiration. We find that cultivar adaptation can fully compensate global production losses up to 2 K of temperature increase, with larger potentials in continental and temperate regions. Irrigation could also compensate production losses, but its potential is highest in arid regions, where irrigation expansion would be constrained by water scarcity. Moreover, we discuss that irrigation is not a true adaptation measure but rather an intensification strategy, as it equally increases production under any temperature level. In the tropics, even when introducing both adapted cultivars and irrigation, crop production declines already at moderate warming, making adaptation particularly challenging in these areas., Plain Language Summary: Global warming affects yields of grain crops, which are at the base of human diets. We use crop models to quantify its impacts on global crop production and to assess how adaptation could compensate for the adverse effects. We find that up to 2 K of increased temperature production can be maintained at the current level by using new cultivars, selected to maintain current growing period length under warming. Irrigation, as another management strategy, is shown to have the potential to increase yields in dry regions if water is available. However, models do not indicate that irrigation reduces the crops' sensitivity to warming. We find large differences in the yield response to warming and adaptation across climatic regions. While continental and temperate regions may benefit from higher temperatures but also show sizable adaptation potentials, tropical and arid regions show largest temperature impacts and smaller adaptation potentials. After all, these two crop management options appear effective to balance the effects of moderate warming but cannot fully compensate impacts above 2 K of warming., Key Points: Without agronomic adaptation, the dominant effect of temperature increase is to shorten growing periods and to reduce yields and production. Adaptation via cultivars that maintain current growing periods under warming can compensate global production losses up to 2 K. Irrigation would act as intensification rather than true adaptation, as it hardly affects the sensitivity of crop yields to warming., Early Postdoctoral Mobility Fellowship http://dx.doi.org/10.13039/501100001711, 7th Framework Programme Early http://dx.doi.org/10.13039/100013273, MACMIT project, BioNex Project, University of Chicago Center for Robust Decision‐making on Climate and Energy Policy http://dx.doi.org/10.13039/100006445

Following the adoption of the Paris Agreement, there has been an increasing interest in quantifying impacts at discrete levels of global mean temperature (GMT) increase such as 1.5 °C and 2 °C above pre-industrial levels. Consequences of anthropogenic greenhouse gas emissions on agricultural productivity have direct and immediate relevance for human societies. Future crop yields will be affected by anthropogenic climate change as well as direct effects of emissions such as CO2 fertilization. At the same time, the climate sensitivity to future emissions is uncertain. Here we investigate the sensitivity of future crop yield projections with a set of global gridded crop models for four major staple crops at 1.5 °C and 2 °C warming above pre-industrial levels, as well as at different CO2 levels determined by similar probabilities to lead to 1.5 °C and 2 °C, using climate forcing data from the Half a degree Additional warming, Prognosis and Projected Impacts project. For the same CO2 forcing, we find consistent negative effects of half a degree warming on productivity in most world regions. Increasing CO2 concentrations consistent with these warming levels have potentially stronger but highly uncertain effects than 0.5 °C warming increments. Half a degree warming will also lead to more extreme low yields, in particular over tropical regions. Our results indicate that GMT change alone is insufficient to determine future impacts on crop productivity.

The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia., Earth's Future, 8 (12), ISSN:2328-4277

Aims: High intensity interval training (HIIT) improves mitochondrial characteristics. This study compared the impact of two workload-matched high intensity interval training (HIIT) protocols with different work:recovery ratios on regulatory factors related to mitochondrial biogenesis in the soleus muscle of diabetic rats. Materials and methods: Twenty-four Wistar rats were randomly divided into four equal-sized groups: non-diabetic control, diabetic control (DC), diabetic with long recovery exercise [4–5 × 2-min running at 80%–90% of the maximum speed reached with 2-min of recovery at 40% of the maximum speed reached (DHIIT1:1)], and diabetic with short recovery exercise (5–6 × 2-min running at 80%–90% of the maximum speed reached with 1-min of recovery at 30% of the maximum speed reached [DHIIT2:1]). Both HIIT protocols were completed five times/week for 4 weeks while maintaining equal running distances in each session. Results: Gene and protein expressions of PGC-1α, p53, and citrate synthase of the muscles increased significantly following DHIIT1:1 and DHIIT2:1 compared to DC (p ˂ 0.05). Most parameters, except for PGC-1α protein (p = 0.597), were significantly higher in DHIIT2:1 than in DHIIT1:1 (p ˂ 0.05). Both DHIIT groups showed significant increases in maximum speed with larger increases in DHIIT2:1 compared with DHIIT1:1. Conclusion: Our findings indicate that both HIIT protocols can potently up-regulate gene and protein expression of PGC-1α, p53, and CS. However, DHIIT2:1 has superior effects compared with DHIIT1:1 in improving mitochondrial adaptive responses in diabetic rats., Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe; 794