microRNAs(miRNAs)are a class of non-coding functional small RNA composed of 21e23 nucleotides,having multiple associations with liver fibrosis.Fibrosis-associated miRNAs are roughly classified into pro-fibrosis or ant...microRNAs(miRNAs)are a class of non-coding functional small RNA composed of 21e23 nucleotides,having multiple associations with liver fibrosis.Fibrosis-associated miRNAs are roughly classified into pro-fibrosis or anti-fibrosis types.The former is capable of activating hepatic stellate cells(HSCs)by modulating pro-fibrotic signaling pathways,mainly including TGF-b/SMAD,WNT/b-catenin,and Hedgehog;the latter is responsible for maintenance of the quiescent phenotype of normal HSCs,phenotypic reversion of activated HSCs(aHSCs),inhibition of HSCs proliferation and suppression of the extracellular matrix-associated gene expression.Moreover,several miRNAs are involved in regulation of liver fibrosis via alternative mechanisms,such as interacting between hepatocytes and other liver cells via exosomes and increasing autophagy of aHSCs.Thus,understanding the role of these miRNAs may provide new avenues for the development of novel interventions against hepatic fibrosis.展开更多
Manure management is the primary source of greenhouse gas （GHG） emissions from pig farming, which in turn accounts for 18% of the total global GHG emissions from the livestock industry. In this review, GHG emissions...Manure management is the primary source of greenhouse gas （GHG） emissions from pig farming, which in turn accounts for 18% of the total global GHG emissions from the livestock industry. In this review, GHG emissions （N20 and CH4 emissions in particular） from individual pig manure （PGM） management practices （European practises in particular） are systematically analyzed and discussed. These manure management practices include manure storage, land application, solid/liquid separation, anaerobic digestion, composting and aerobic wastewater treatment. The potential reduction in net GHG emissions by changing and optimising these techniques is assessed. This review also identifies key research gaps in the literature including the effect of straw covering of liquid PGM storages, the effect of solid/liquid separation, and the effect of dry anaerobic digestion on net GHG emissions from PGM management. In addition to identifying these research gaps, several recommendations including the need to standardize units used to report GHG emissions, to account ~br indirect N20 emissions, and to include a broader research scope by conducting detailed life cycle assessment are also discussed. Overall, anaerobic digestion and compositing to liquid and solid fractions are best PGM management practices with respect to their high GHG mitigation potential.展开更多
The digital transformation in agriculture introduces new challenges in terms of data,knowledge and technology adoption due to critical interoperability issues,and also challenges regarding the identification of the mo...The digital transformation in agriculture introduces new challenges in terms of data,knowledge and technology adoption due to critical interoperability issues,and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.DEMETER(Building an Interoperable,Data-Driven,Innovative and Sustainable European Agri-Food Sector)addresses these challenges by providing an overarching solution that integrates various heterogeneous hardware and software resources(e.g.,devices,networks,platforms)and enables the seamless sharing of data and knowledge throughout the agri-food chain.This paper introduces the main concepts of DEMETER and its reference architecture to address the data sharing and interoperability needs of farmers,which is validated via two rounds of 20 large-scale pilots along the DEMETER lifecycle.This paper elaborates on the two pilots carried out in region of Murcia in Spain,which target the arable crops sector and demonstrate the benefits of the deployed DEMETER reference architecture.展开更多
This study assessed the effects of reducing hydraulic retention times （HRTs） from 21 days to 10.5 days when anaerobically co-digesting pig manure and food waste. Continuously stirred tank reactors of 3.75 L working ...This study assessed the effects of reducing hydraulic retention times （HRTs） from 21 days to 10.5 days when anaerobically co-digesting pig manure and food waste. Continuously stirred tank reactors of 3.75 L working volume were operated in triplicate at 42℃. Digester HRT was progressively decreased from 21 to 15 days to 10.5 days, with an associated increase in organic loading rate （OLR） from 3.1 kg volatile solids （VS）·m^-3.day^-1 to 5.1 kg VS·m^3.day-1 to 7.25 kg VS.m^-3·day^-1. Reducing HRT from 21 days to 15 days caused a decrease in specific methane yields and VS removal rates. Operation at a HRT of 10.5 days initially resulted in the accumulation of isobutyric acid in each reactor. High throughput 16S rRNA gene sequencing revealed that this increase coincided with a shift in acidogenic bacterial populations, which most likely resulted in the increased isobutyric acid concentrations. This may in turn have caused the increase in relative abundance of Clocamonaceae bacteria, which syntrophically degrade non-acetate volatile fatty acids （VFAs） into H2 and CO2. This, along with the increase in abundance of other syntrophic VFA oxidizers, such as Spiorchatetes, suggests that VFA oxidation plays a role in digester operation at low HRTs. Reducing the HRT to below 21 days compromised the ability of the anaerobic digestion system to reduce enteric indicator organism counts below regulatory limits.展开更多
In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occu...In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.展开更多
Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local...Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local features while ignoring global features.In this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize sentiment analysis of short texts.First,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network.Second,this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block,and then sending the combined features into the final classifier.Last,a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.展开更多
Globally,the Rising Plate Meter(RPM)is a device used to measure compressed sward height,to enable estimation of herbage mass.Despite improved farm management practices aided by a variety of technological advances,the ...Globally,the Rising Plate Meter(RPM)is a device used to measure compressed sward height,to enable estimation of herbage mass.Despite improved farm management practices aided by a variety of technological advances,the standard design of a RPM has remained relatively unchanged.Recently,however,a RPM utilising a micro-sonic sensor,with digital data capture capability via a Bluetooth communications link to a smart device application,has been developed.Here,we assess the comparable ability of both a standard cumulative ratchet counter RPM and the micro-sonic sensor RPM,to accurately and precisely measure fixed heights.Moreover,as correct allocation of grazing area requires accurate geolocation positioning,we assess the associated GPS technology.The micro-sonic sensor RPM was significantly more accurate for height capture than the cumulative ratchet counter RPM.Overall,across all heights,the cumulative ratchet counter RPM underestimated height by 7.68±0.06mm(mean±SE).Alternatively,the micro-sonic sensor RPM overestimated height by 0.18±0.08 mm.In relation to a practical applications,these discrepancies can result in an under-and overestimation of dry matter yield by 13.71%and 0.32%kilograms per hectare,respectively.The performance of the on-board GPS did not significantly differ from that of a tertiary device.Overall,the wireless technology,integrated mapping,and decision support tools offered by the innovative micro-sonic sensor RPM provides for a highly efficacious grassland management tool.展开更多
We describe modeling the solid-state dye laser with the microcavity size comparable to light wavelength. Certain symmetry in the allocation of gain material leads to depletion of odd longitudinal modes that, in turn, ...We describe modeling the solid-state dye laser with the microcavity size comparable to light wavelength. Certain symmetry in the allocation of gain material leads to depletion of odd longitudinal modes that, in turn, increases the tunability range of the microlaser. We provide simple physical explanation for the modeling results.展开更多
This paper presents a new method for extract three-dimensional （3D） discrete spherical Fourier descriptors based on surface curvature voxels for pollen particle recognition. In order to reduce the high amount of pol...This paper presents a new method for extract three-dimensional （3D） discrete spherical Fourier descriptors based on surface curvature voxels for pollen particle recognition. In order to reduce the high amount of pollen information and noise disturbance, the geometric normalized curvature voxels with the principal curvedness are first extracted to represent the intrinsic pollen volumetric data. Then the curvature voxels are decomposed into radial and angular components with spherical harmonic transform in spherical coordinates. Finally the 3D discrete Fourier transform is applied to the decomposed curvature voxels to obtain the 3D spherical Fourier descriptors for pollen recognition. Experimental results show that the presented descriptors are invariant to different pollen particle geometric transformations, such as pose change and spatial rotation, and can obtain high recognition accuracy and speed simultaneously.展开更多
With the development of information technology,cloud computing technology has brought many conveniences to all aspects of work and life.With the continuous promotion,popularization and vigorous development of e-govern...With the development of information technology,cloud computing technology has brought many conveniences to all aspects of work and life.With the continuous promotion,popularization and vigorous development of e-government and e-commerce,the number of documents in electronic form is getting larger and larger.Electronic document is an indispensable main tool and real record of e-government and business activities.How to scientifically and effectively manage electronic documents?This is an important issue faced by governments and enterprises in improving management efficiency,protecting state secrets or business secrets,and reducing management costs.This paper discusses the application of cloud computing technology in the construction of electronic file management system,proposes an architecture of electronic file management system based on cloud computing,and makes a more detailed discussion on key technologies and implementation.The electronic file management system is built on the cloud architecture to enable users to upload,download,share,set security roles,audit,and retrieve files based on multiple modes.An electronic file management system based on cloud computing can make full use of cloud storage,cloud security,and cloud computing technologies to achieve unified,reliable,and secure management of electronic files.展开更多
Over the past 10 years,lightning disaster has caused a large number of casualties and considerable economic loss worldwide.Lightning poses a huge threat to various industries.In an attempt to reduce the risk of lightn...Over the past 10 years,lightning disaster has caused a large number of casualties and considerable economic loss worldwide.Lightning poses a huge threat to various industries.In an attempt to reduce the risk of lightning-caused disaster,many scholars have carried out in-depth research on lightning.However,these studies focus primarily on the lightning itself and other meteorological elements are ignored.In addition,the methods for assessing the risk of lightning disaster fail to give detailed attention to regional features(lightning disaster risk).This paper proposes a grid-based risk assessment method based on data from multiple sources.First,this paper considers the impact of lightning,the population density,the economy,and geographical environment data on the occurrence of lightning disasters;Second,this paper solves the problem of imbalanced lightning disaster data in geographic grid samples based on the K-means clustering algorithm;Third,the method calculates the feature of lightning disaster in each small field with the help of neural network structure,and the calculation results are then visually reflected in a zoning map by the Jenks natural breaks algorithm.The experimental results show that our method can solve the problem of imbalanced lightning disaster data,and offer 81%accuracy in lightning disaster risk assessment.展开更多
基金supported by grants from the National Natural Science Foundation of China(No.32072889,U1703104)Key R&D Program of Zhejiang Province(China)(No.2019C02052)Scientific Research and Development Talent Fund of Zhejiang Agriculture and Forestry University,China(No.2021LFR038).
文摘microRNAs(miRNAs)are a class of non-coding functional small RNA composed of 21e23 nucleotides,having multiple associations with liver fibrosis.Fibrosis-associated miRNAs are roughly classified into pro-fibrosis or anti-fibrosis types.The former is capable of activating hepatic stellate cells(HSCs)by modulating pro-fibrotic signaling pathways,mainly including TGF-b/SMAD,WNT/b-catenin,and Hedgehog;the latter is responsible for maintenance of the quiescent phenotype of normal HSCs,phenotypic reversion of activated HSCs(aHSCs),inhibition of HSCs proliferation and suppression of the extracellular matrix-associated gene expression.Moreover,several miRNAs are involved in regulation of liver fibrosis via alternative mechanisms,such as interacting between hepatocytes and other liver cells via exosomes and increasing autophagy of aHSCs.Thus,understanding the role of these miRNAs may provide new avenues for the development of novel interventions against hepatic fibrosis.
文摘Manure management is the primary source of greenhouse gas （GHG） emissions from pig farming, which in turn accounts for 18% of the total global GHG emissions from the livestock industry. In this review, GHG emissions （N20 and CH4 emissions in particular） from individual pig manure （PGM） management practices （European practises in particular） are systematically analyzed and discussed. These manure management practices include manure storage, land application, solid/liquid separation, anaerobic digestion, composting and aerobic wastewater treatment. The potential reduction in net GHG emissions by changing and optimising these techniques is assessed. This review also identifies key research gaps in the literature including the effect of straw covering of liquid PGM storages, the effect of solid/liquid separation, and the effect of dry anaerobic digestion on net GHG emissions from PGM management. In addition to identifying these research gaps, several recommendations including the need to standardize units used to report GHG emissions, to account ~br indirect N20 emissions, and to include a broader research scope by conducting detailed life cycle assessment are also discussed. Overall, anaerobic digestion and compositing to liquid and solid fractions are best PGM management practices with respect to their high GHG mitigation potential.
基金based on work carried out under the H2020 DEMETER project (Grant Agreement No 857202)that is funded by the European Commission under H2020-EU.2.1.1 (DT-ICT-08-2019).
文摘The digital transformation in agriculture introduces new challenges in terms of data,knowledge and technology adoption due to critical interoperability issues,and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.DEMETER(Building an Interoperable,Data-Driven,Innovative and Sustainable European Agri-Food Sector)addresses these challenges by providing an overarching solution that integrates various heterogeneous hardware and software resources(e.g.,devices,networks,platforms)and enables the seamless sharing of data and knowledge throughout the agri-food chain.This paper introduces the main concepts of DEMETER and its reference architecture to address the data sharing and interoperability needs of farmers,which is validated via two rounds of 20 large-scale pilots along the DEMETER lifecycle.This paper elaborates on the two pilots carried out in region of Murcia in Spain,which target the arable crops sector and demonstrate the benefits of the deployed DEMETER reference architecture.
文摘This study assessed the effects of reducing hydraulic retention times （HRTs） from 21 days to 10.5 days when anaerobically co-digesting pig manure and food waste. Continuously stirred tank reactors of 3.75 L working volume were operated in triplicate at 42℃. Digester HRT was progressively decreased from 21 to 15 days to 10.5 days, with an associated increase in organic loading rate （OLR） from 3.1 kg volatile solids （VS）·m^-3.day^-1 to 5.1 kg VS·m^3.day-1 to 7.25 kg VS.m^-3·day^-1. Reducing HRT from 21 days to 15 days caused a decrease in specific methane yields and VS removal rates. Operation at a HRT of 10.5 days initially resulted in the accumulation of isobutyric acid in each reactor. High throughput 16S rRNA gene sequencing revealed that this increase coincided with a shift in acidogenic bacterial populations, which most likely resulted in the increased isobutyric acid concentrations. This may in turn have caused the increase in relative abundance of Clocamonaceae bacteria, which syntrophically degrade non-acetate volatile fatty acids （VFAs） into H2 and CO2. This, along with the increase in abundance of other syntrophic VFA oxidizers, such as Spiorchatetes, suggests that VFA oxidation plays a role in digester operation at low HRTs. Reducing the HRT to below 21 days compromised the ability of the anaerobic digestion system to reduce enteric indicator organism counts below regulatory limits.
基金Dr. Steve Jones, Scientific Advisor of the Canon Foundation for Scientific Research (7200 The Quorum, Oxford Business Park, Oxford OX4 2JZ, England). Canon Foundation for Scientific Research funded the UPC 2013 tuition fees of the corresponding author during her writing this article
文摘In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
基金This work was supported by the National Key R&D Program of China under Grant Number 2018YFB1003205by the National Natural Science Foundation of China under Grant Numbers U1836208,U1536206,U1836110,61602253,and 61672294+3 种基金by the Startup Foundation for Introducing Talent of NUIST(1441102001002)by the Jiangsu Basic Research Programs-Natural Science Foundation under Grant Number BK20181407by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local features while ignoring global features.In this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize sentiment analysis of short texts.First,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network.Second,this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block,and then sending the combined features into the final classifier.Last,a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.
文摘Globally,the Rising Plate Meter(RPM)is a device used to measure compressed sward height,to enable estimation of herbage mass.Despite improved farm management practices aided by a variety of technological advances,the standard design of a RPM has remained relatively unchanged.Recently,however,a RPM utilising a micro-sonic sensor,with digital data capture capability via a Bluetooth communications link to a smart device application,has been developed.Here,we assess the comparable ability of both a standard cumulative ratchet counter RPM and the micro-sonic sensor RPM,to accurately and precisely measure fixed heights.Moreover,as correct allocation of grazing area requires accurate geolocation positioning,we assess the associated GPS technology.The micro-sonic sensor RPM was significantly more accurate for height capture than the cumulative ratchet counter RPM.Overall,across all heights,the cumulative ratchet counter RPM underestimated height by 7.68±0.06mm(mean±SE).Alternatively,the micro-sonic sensor RPM overestimated height by 0.18±0.08 mm.In relation to a practical applications,these discrepancies can result in an under-and overestimation of dry matter yield by 13.71%and 0.32%kilograms per hectare,respectively.The performance of the on-board GPS did not significantly differ from that of a tertiary device.Overall,the wireless technology,integrated mapping,and decision support tools offered by the innovative micro-sonic sensor RPM provides for a highly efficacious grassland management tool.
文摘We describe modeling the solid-state dye laser with the microcavity size comparable to light wavelength. Certain symmetry in the allocation of gain material leads to depletion of odd longitudinal modes that, in turn, increases the tunability range of the microlaser. We provide simple physical explanation for the modeling results.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60472061)the Natural Science Foundation of Jiangsu Province,China (Grant No. BK20090149)the Natural Science Foundation of Higher Education Institutions of Jiangsu Province,China (Grant No. 08KJD520019).
文摘This paper presents a new method for extract three-dimensional （3D） discrete spherical Fourier descriptors based on surface curvature voxels for pollen particle recognition. In order to reduce the high amount of pollen information and noise disturbance, the geometric normalized curvature voxels with the principal curvedness are first extracted to represent the intrinsic pollen volumetric data. Then the curvature voxels are decomposed into radial and angular components with spherical harmonic transform in spherical coordinates. Finally the 3D discrete Fourier transform is applied to the decomposed curvature voxels to obtain the 3D spherical Fourier descriptors for pollen recognition. Experimental results show that the presented descriptors are invariant to different pollen particle geometric transformations, such as pose change and spatial rotation, and can obtain high recognition accuracy and speed simultaneously.
基金research Grants from the National Social Science Foundation of China(Grant No.18FTQ005).The author of the grant is Shi Jin.The URL of the sponsor site is http://www.npopss-cn.gov.cn/.
文摘With the development of information technology,cloud computing technology has brought many conveniences to all aspects of work and life.With the continuous promotion,popularization and vigorous development of e-government and e-commerce,the number of documents in electronic form is getting larger and larger.Electronic document is an indispensable main tool and real record of e-government and business activities.How to scientifically and effectively manage electronic documents?This is an important issue faced by governments and enterprises in improving management efficiency,protecting state secrets or business secrets,and reducing management costs.This paper discusses the application of cloud computing technology in the construction of electronic file management system,proposes an architecture of electronic file management system based on cloud computing,and makes a more detailed discussion on key technologies and implementation.The electronic file management system is built on the cloud architecture to enable users to upload,download,share,set security roles,audit,and retrieve files based on multiple modes.An electronic file management system based on cloud computing can make full use of cloud storage,cloud security,and cloud computing technologies to achieve unified,reliable,and secure management of electronic files.
基金the National Key R&D Program of China under grant number 2018YFB1003205by the National Natural Science Foundation of China under grant number U1836208,U1536206,U1836110,61602253 and 61672294+3 种基金by the Startup Foundation for Introducing Talent of NUIST(1441102001002)by the Jiangsu Basic Research Programs-Natural Science Foundation under grant number BK20181407by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Postgraduate Research and Innovation Plan Project in Jiangsu Province under grant number KYCX20_0934 and by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Over the past 10 years,lightning disaster has caused a large number of casualties and considerable economic loss worldwide.Lightning poses a huge threat to various industries.In an attempt to reduce the risk of lightning-caused disaster,many scholars have carried out in-depth research on lightning.However,these studies focus primarily on the lightning itself and other meteorological elements are ignored.In addition,the methods for assessing the risk of lightning disaster fail to give detailed attention to regional features(lightning disaster risk).This paper proposes a grid-based risk assessment method based on data from multiple sources.First,this paper considers the impact of lightning,the population density,the economy,and geographical environment data on the occurrence of lightning disasters;Second,this paper solves the problem of imbalanced lightning disaster data in geographic grid samples based on the K-means clustering algorithm;Third,the method calculates the feature of lightning disaster in each small field with the help of neural network structure,and the calculation results are then visually reflected in a zoning map by the Jenks natural breaks algorithm.The experimental results show that our method can solve the problem of imbalanced lightning disaster data,and offer 81%accuracy in lightning disaster risk assessment.
