Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution predi...Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution prediction of RNA transcripts.These methods usually only offer a partial analysis of ST data,neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk.Here,we present eMCI,an explainable multimodal correlation integration model based on deep neural network framework.eMCI leverages the fusion of scRNA-seq and ST data using different spot–cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level.First,eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets.Second,eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication,by employing an attribution algorithm to dissect the visual input.Especially,eMCI has been applied to 3 cross-species datasets,including zebrafish melanomas,soybean nodule maturation,and human embryonic lung,which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context.In summary,eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference.This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell–cell communication specificity.展开更多
Conventional scan-to-scan integration correlation （SIC） algorithms can detect small and stationary targets. However, they are ineffective in detecting small and fast-moving targets. This paper presents an improved S...Conventional scan-to-scan integration correlation （SIC） algorithms can detect small and stationary targets. However, they are ineffective in detecting small and fast-moving targets. This paper presents an improved SIC algorithm together with clutter suppression measures that remove or decrease sea clutter. The algorithm divides the scan-to-scan integration （SI） into two branches, one provides optimum clutter attenuation by means of SI weighting while the other ensures that targets are detected even if they are fast-moving. Sea clutter suppression can lower detection thre-sholds and, at the same time, increase signal-to-clutter ratio. Simulation results show that the proposed approach greatly improves the detection capability for warship radar.展开更多
For the first time, the diagnosis idea based on a correlation integral isproposed, which regard's the correlation integral as a feature set. The correlation dimension iscontained in the double-log curve of the cor...For the first time, the diagnosis idea based on a correlation integral isproposed, which regard's the correlation integral as a feature set. The correlation dimension iscontained in the double-log curve of the correlation integral to scale, so extracting featuresdirectly from the correlation integral can avoid the bottleneck problem of determining the range ofnon-scale length. Several features extracted from the correlation integral are better than thesingle feature of the correlation dimension when describing the signal. It is obvious that thismethod utilizes more information of the signal than does the correlation dimension. The diagnosisexamples verify that this method is more accurate and more effective.展开更多
In this paper,we focus on the BDS test,which is a nonparametric test of independence.Specifically,the null hypothesis H0 of it is that {u_(t)} is i.i.d.(independent and identically distributed),where {u_(t)} is a rand...In this paper,we focus on the BDS test,which is a nonparametric test of independence.Specifically,the null hypothesis H0 of it is that {u_(t)} is i.i.d.(independent and identically distributed),where {u_(t)} is a random sequence.The BDS test is widely used in economics and finance,but it has a weakness that cannot be ignored:over-rejecting H0 even if the length T of {u_(t)} is as large as(100;2000).To improve the over-rejection problem of BDS test,considering that the correlation integral is the foundation of this test,we not only accurately describe the expectation of the correlation integral under H_(0),but also calculate all terms of the asymptotic variance of the correlation integral whose order is O(T^(-1))and O(T^(-2)),which is essential to improve the finite sample performance of BDS test.Based on this,we propose a revised BDS(RBDS)test and prove its asymptotic normality under H0.The RBDS test not only inherits all the advantages of BDS test,but also effectively corrects the over-rejection problem of it,which can be fully confirmed by the simulation results we presented.Moreover,based on the simulation results,we find that similar to BDS test,RBDS test would also be affected by the parameter estimations of the ARCH-type model,resulting in size distortion,but this phenomenon can be alleviated by the logarithmic transformation preprocessing of the estimate residuals of the model.Besides,through some actual datasets that have been demonstrated to fit well with ARCH-type models,we also compared the performance of BDS test and RBDS test in evaluating the goodness-of-fit of the model in empirical problem,and the results reflect that,under the same condition,the performance of the RBDS test is more encouraging.展开更多
In order to explore the effects of chemical substances changes in damaged masson pine （Pinus massoniana） needles on population dynamics of Dendroli- mus kikuchii, D. kikuchii larvae were reat~ with P. massoniana nee...In order to explore the effects of chemical substances changes in damaged masson pine （Pinus massoniana） needles on population dynamics of Dendroli- mus kikuchii, D. kikuchii larvae were reat~ with P. massoniana needles with different damage degrees （mild, moderate and severe）, and its population parame- ters and contents of nutrients and secondary substances in damaged P. massoniana needles were measured, and the integrated correlation coefficient was adopted for data analysis. The results showed that with the damage degree aggravating, flavones in needles increased accordingly, while contents of soluble sugars, polysaeeha- rides and proteins decreased. The average developmental duration and mortality of D. kikuchii larvae increased with the damage degree increasing. No significant correlation was found between the changes in contents of tannins or total phenols and the developmental duration or mortality of each instar larvae. There were signif- icant direct and integrated correlations between contents of nutrients and secondary substances of P. massoniana needles and the developmental duration or mortality of each instar larvae except the 6＇h instar larvae. With the damage degree increasing, all parameters of D. kikuchii population including body weight of the 7~ instar larvae, average feeding capacity of larvae, pupal weight, pupation rate, female ratio and fecundity decreased. No significant correlation was found between the changes in contents of tannins or total phenols and population parameters of D. kikuchii larvae. The results suggest that the contents of nutrients and secondary sub- stances in P. ,mssoniana needles dramatically influenced the population parameters of D. kikuchii, and the importance from high to low successively was soluble sugars 〉 proteins 〉 polysaccharides 〉 flavones. Contents of tannins and total phenols seemed to have little influence.展开更多
On April 25, 2015, Nepal was struck by the MW7.8 Gorkha earthquake followed by an intense aftershock sequence. It was one of the most destructive earthquakes in the Himalayan arc, causing more than 8900 fatalities. In...On April 25, 2015, Nepal was struck by the MW7.8 Gorkha earthquake followed by an intense aftershock sequence. It was one of the most destructive earthquakes in the Himalayan arc, causing more than 8900 fatalities. In this study, we analyzed the dataset (429 events, magnitude of completeness (Mc) ≥ 4.2 local magnitude) of the first 45 days after the Gorkha earthquake to estimate the seismicity parameters b-value, D-value, and p-value. We used the maximum likelihood method to estimate the b-value and Omori-Utsu parameters, whereas the correlation integral method was applied to estimate the fractal dimension (D-value). The analysis was carried out using running and sliding window techniques. The lowest b-value (0.57 ± 0.04) and the highest D-value (1.65 ± 0.02) were computed at the time of the Gorkha earthquake, after which the b-value significantly increased to a maximum of 1.57. It again dropped to 0.93 at the time of the major aftershock on May 12, 2015. The D-value showed an initial quick drop and then decreased in a wavy pattern until the end of the study period, indicating the clustering and scattering of earthquakes in a fault region. The b-value contour map identified the eastern part of the study area as a high stress region (b = ~0.8), implying that the stress shifted to that region. The D-value contour map reveals that the seismogenic structure shifted from linear to planar in the region. The rate of aftershock decay (p = 0.86 ± 0.04) for a short period reflects that the level of stress decreased rapidly. This study helps to understand the level of stress and seismicity pattern of a region, which could be useful for aftershock studies.展开更多
Throughout scientific research, the state space reconstruction that embeds a non-linear time series is the first and necessary step for characterizing and predicting the behavior of a complex system. This requires to ...Throughout scientific research, the state space reconstruction that embeds a non-linear time series is the first and necessary step for characterizing and predicting the behavior of a complex system. This requires to choose appropriate values of time delay T and embedding dimension dE. Three methods are applied and discussed on nonlinear time series provided by the Rössler attractor equations set: Cao’s method, the C-C method developed by Kim et al. and the C-C-1 method developed by Cai et al. A way to fix a parameter necessary to implement the last method is given. Focus has been put on small size and/or noisy time series. The reconstruction quality is measured by using a criterion based on the transformation smoothness.展开更多
We propose a novel method of slice image reconstruction with controllable spatial filtering by using the correlation of periodic delta-function arrays （PDFAs） with elemental images in computational integral imaging....We propose a novel method of slice image reconstruction with controllable spatial filtering by using the correlation of periodic delta-function arrays （PDFAs） with elemental images in computational integral imaging. The multiple PDFAs, whose spatial periods correspond to object＇s depths with the elemental image array （EIA）, can generate a set of spatially filtered EIAs for multiple object depths compared with the conventional method for the depth of a single object. We analyze a controllable spatial filtering effect by the proposed method. To show the feasibility of the proposed method, we carry out preliminary experiments for multiple objects and present the results.展开更多
基金supported by the National Key R&D Program of China(Nos.2023YFF1204700 and 2022YFF1202100)the National Natural Science Foundation of China(Nos.12371485,62201150,T2341022,62172164,12322119,and 12271180)the Natural Science Foundation of Guangdong Province of China(Nos.2022A1515110759,2023A1515110558,and 2024A1515011797).
文摘Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution prediction of RNA transcripts.These methods usually only offer a partial analysis of ST data,neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk.Here,we present eMCI,an explainable multimodal correlation integration model based on deep neural network framework.eMCI leverages the fusion of scRNA-seq and ST data using different spot–cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level.First,eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets.Second,eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication,by employing an attribution algorithm to dissect the visual input.Especially,eMCI has been applied to 3 cross-species datasets,including zebrafish melanomas,soybean nodule maturation,and human embryonic lung,which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context.In summary,eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference.This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell–cell communication specificity.
文摘Conventional scan-to-scan integration correlation （SIC） algorithms can detect small and stationary targets. However, they are ineffective in detecting small and fast-moving targets. This paper presents an improved SIC algorithm together with clutter suppression measures that remove or decrease sea clutter. The algorithm divides the scan-to-scan integration （SI） into two branches, one provides optimum clutter attenuation by means of SI weighting while the other ensures that targets are detected even if they are fast-moving. Sea clutter suppression can lower detection thre-sholds and, at the same time, increase signal-to-clutter ratio. Simulation results show that the proposed approach greatly improves the detection capability for warship radar.
文摘For the first time, the diagnosis idea based on a correlation integral isproposed, which regard's the correlation integral as a feature set. The correlation dimension iscontained in the double-log curve of the correlation integral to scale, so extracting featuresdirectly from the correlation integral can avoid the bottleneck problem of determining the range ofnon-scale length. Several features extracted from the correlation integral are better than thesingle feature of the correlation dimension when describing the signal. It is obvious that thismethod utilizes more information of the signal than does the correlation dimension. The diagnosisexamples verify that this method is more accurate and more effective.
基金the National Natural Science Foundation of China(12271536,12171198)First Class Discipline of Zhejiang-A(Zhejiang University of Finance and Economics-Statistics)(10344921011/003).
文摘In this paper,we focus on the BDS test,which is a nonparametric test of independence.Specifically,the null hypothesis H0 of it is that {u_(t)} is i.i.d.(independent and identically distributed),where {u_(t)} is a random sequence.The BDS test is widely used in economics and finance,but it has a weakness that cannot be ignored:over-rejecting H0 even if the length T of {u_(t)} is as large as(100;2000).To improve the over-rejection problem of BDS test,considering that the correlation integral is the foundation of this test,we not only accurately describe the expectation of the correlation integral under H_(0),but also calculate all terms of the asymptotic variance of the correlation integral whose order is O(T^(-1))and O(T^(-2)),which is essential to improve the finite sample performance of BDS test.Based on this,we propose a revised BDS(RBDS)test and prove its asymptotic normality under H0.The RBDS test not only inherits all the advantages of BDS test,but also effectively corrects the over-rejection problem of it,which can be fully confirmed by the simulation results we presented.Moreover,based on the simulation results,we find that similar to BDS test,RBDS test would also be affected by the parameter estimations of the ARCH-type model,resulting in size distortion,but this phenomenon can be alleviated by the logarithmic transformation preprocessing of the estimate residuals of the model.Besides,through some actual datasets that have been demonstrated to fit well with ARCH-type models,we also compared the performance of BDS test and RBDS test in evaluating the goodness-of-fit of the model in empirical problem,and the results reflect that,under the same condition,the performance of the RBDS test is more encouraging.
基金Supported by Special Major Project of Science and Technology Department of Fujian Province(2006NZ0001-2)Key Project of Forest Seedlings of Forestry Department of Fujian Province(2003-07)
文摘In order to explore the effects of chemical substances changes in damaged masson pine （Pinus massoniana） needles on population dynamics of Dendroli- mus kikuchii, D. kikuchii larvae were reat~ with P. massoniana needles with different damage degrees （mild, moderate and severe）, and its population parame- ters and contents of nutrients and secondary substances in damaged P. massoniana needles were measured, and the integrated correlation coefficient was adopted for data analysis. The results showed that with the damage degree aggravating, flavones in needles increased accordingly, while contents of soluble sugars, polysaeeha- rides and proteins decreased. The average developmental duration and mortality of D. kikuchii larvae increased with the damage degree increasing. No significant correlation was found between the changes in contents of tannins or total phenols and the developmental duration or mortality of each instar larvae. There were signif- icant direct and integrated correlations between contents of nutrients and secondary substances of P. massoniana needles and the developmental duration or mortality of each instar larvae except the 6＇h instar larvae. With the damage degree increasing, all parameters of D. kikuchii population including body weight of the 7~ instar larvae, average feeding capacity of larvae, pupal weight, pupation rate, female ratio and fecundity decreased. No significant correlation was found between the changes in contents of tannins or total phenols and population parameters of D. kikuchii larvae. The results suggest that the contents of nutrients and secondary sub- stances in P. ,mssoniana needles dramatically influenced the population parameters of D. kikuchii, and the importance from high to low successively was soluble sugars 〉 proteins 〉 polysaccharides 〉 flavones. Contents of tannins and total phenols seemed to have little influence.
文摘On April 25, 2015, Nepal was struck by the MW7.8 Gorkha earthquake followed by an intense aftershock sequence. It was one of the most destructive earthquakes in the Himalayan arc, causing more than 8900 fatalities. In this study, we analyzed the dataset (429 events, magnitude of completeness (Mc) ≥ 4.2 local magnitude) of the first 45 days after the Gorkha earthquake to estimate the seismicity parameters b-value, D-value, and p-value. We used the maximum likelihood method to estimate the b-value and Omori-Utsu parameters, whereas the correlation integral method was applied to estimate the fractal dimension (D-value). The analysis was carried out using running and sliding window techniques. The lowest b-value (0.57 ± 0.04) and the highest D-value (1.65 ± 0.02) were computed at the time of the Gorkha earthquake, after which the b-value significantly increased to a maximum of 1.57. It again dropped to 0.93 at the time of the major aftershock on May 12, 2015. The D-value showed an initial quick drop and then decreased in a wavy pattern until the end of the study period, indicating the clustering and scattering of earthquakes in a fault region. The b-value contour map identified the eastern part of the study area as a high stress region (b = ~0.8), implying that the stress shifted to that region. The D-value contour map reveals that the seismogenic structure shifted from linear to planar in the region. The rate of aftershock decay (p = 0.86 ± 0.04) for a short period reflects that the level of stress decreased rapidly. This study helps to understand the level of stress and seismicity pattern of a region, which could be useful for aftershock studies.
文摘Throughout scientific research, the state space reconstruction that embeds a non-linear time series is the first and necessary step for characterizing and predicting the behavior of a complex system. This requires to choose appropriate values of time delay T and embedding dimension dE. Three methods are applied and discussed on nonlinear time series provided by the Rössler attractor equations set: Cao’s method, the C-C method developed by Kim et al. and the C-C-1 method developed by Cai et al. A way to fix a parameter necessary to implement the last method is given. Focus has been put on small size and/or noisy time series. The reconstruction quality is measured by using a criterion based on the transformation smoothness.
基金supported by the information technology(IT)research and development program of MKE/KEIT(10041682Development of High-Definition 3D Image Processing Technologies Using Advanced Integral Imaging with Improved Depth Range)
文摘We propose a novel method of slice image reconstruction with controllable spatial filtering by using the correlation of periodic delta-function arrays （PDFAs） with elemental images in computational integral imaging. The multiple PDFAs, whose spatial periods correspond to object＇s depths with the elemental image array （EIA）, can generate a set of spatially filtered EIAs for multiple object depths compared with the conventional method for the depth of a single object. We analyze a controllable spatial filtering effect by the proposed method. To show the feasibility of the proposed method, we carry out preliminary experiments for multiple objects and present the results.
