Cerenkov Luminescence Tomography(CLT)is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes.However,due to severe ill-posed inverse problem,obtaining accur...Cerenkov Luminescence Tomography(CLT)is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes.However,due to severe ill-posed inverse problem,obtaining accurate reconstruction results is still a challenge for traditional model-based methods.The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source,which effectively improves the performance of CLT reconstruction.However,the previously proposed deep learning-based methods cannot work well when the order of input is disarranged.In this paper,a novel 3D graph convolution-based residual network,GCR-Net,is proposed,which can obtain a robust and accurate reconstruction result from the photon intensity of the surface.Additionally,it is proved that the network is insensitive to the order of input.The performance of this method was evaluated with numerical simulations and in vivo experiments.The results demonstrated that compared with the existing methods,the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing threedimensional information.展开更多
The succession of fungal community,trophic mode and metabolic characteristics were evaluated in 60 days composting of swine manure by high-throughput sequencing,FUNGuild and Biolog method,respectively.The result showe...The succession of fungal community,trophic mode and metabolic characteristics were evaluated in 60 days composting of swine manure by high-throughput sequencing,FUNGuild and Biolog method,respectively.The result showed that the ftingal community diversity reached to the highest level(76 OTUs)in the thermophilic phase of composting,then sustained decline to 15 OTUs after incubation.There were 10 fungal function groups in the raw swine manure.Pathotroph-saprotroph fungi reached to 15.91%on Day-10but disappeared on Day-60.Dung saprotroph-undefined saprotroph fungi grown from 0.19%to 52.39%during the treatment.The ftmgal community had more functional groups but the lower substrate degradation rates in the thermophilic phase.The fungal communities on Day-0 and Day-60 had the highest degradation rates of amino acids and polymers,respectively.Redundancy analysis showed that ORP(49.6%),VS/Ash(45.3%)and moisture(39.2%)were the main influence factors on the succession of fungal community in the swine manure composting process.展开更多
基金National Key Research and Development Program of China (2019YFC1521102)National Natural Science Foundation of China (61701403,61806164,62101439,61906154)+4 种基金China Postdoctoral Science Foundation (2018M643719)Natural Science Foundation of Shaanxi Province (2020JQ-601)Young Talent Support Program of the Shaanxi Association for Science and Technology (20190107)Key Research and Development Program of Shaanxi Province (2019GY-215,2021ZDLSF06-04)Major research and development project of Qinghai (2020-SF-143).
文摘Cerenkov Luminescence Tomography(CLT)is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes.However,due to severe ill-posed inverse problem,obtaining accurate reconstruction results is still a challenge for traditional model-based methods.The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source,which effectively improves the performance of CLT reconstruction.However,the previously proposed deep learning-based methods cannot work well when the order of input is disarranged.In this paper,a novel 3D graph convolution-based residual network,GCR-Net,is proposed,which can obtain a robust and accurate reconstruction result from the photon intensity of the surface.Additionally,it is proved that the network is insensitive to the order of input.The performance of this method was evaluated with numerical simulations and in vivo experiments.The results demonstrated that compared with the existing methods,the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing threedimensional information.
基金This research was financially supported by the National Natural Science Foundation of China(Nos.51778180 and 51761145031)Fundamental Research Funds for the Central Universities(HIT.NSRIF.2017057)+1 种基金Postdoctoral Science Special Foundation of Heilongjiang(LBH-TZ0510)Heilongjiang Postdoctoral Funds for scientific research initiation(LBH-Q16110).
文摘The succession of fungal community,trophic mode and metabolic characteristics were evaluated in 60 days composting of swine manure by high-throughput sequencing,FUNGuild and Biolog method,respectively.The result showed that the ftingal community diversity reached to the highest level(76 OTUs)in the thermophilic phase of composting,then sustained decline to 15 OTUs after incubation.There were 10 fungal function groups in the raw swine manure.Pathotroph-saprotroph fungi reached to 15.91%on Day-10but disappeared on Day-60.Dung saprotroph-undefined saprotroph fungi grown from 0.19%to 52.39%during the treatment.The ftmgal community had more functional groups but the lower substrate degradation rates in the thermophilic phase.The fungal communities on Day-0 and Day-60 had the highest degradation rates of amino acids and polymers,respectively.Redundancy analysis showed that ORP(49.6%),VS/Ash(45.3%)and moisture(39.2%)were the main influence factors on the succession of fungal community in the swine manure composting process.
