The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used vegetation indices for monitoring ecosystem dynamics and modeling biosphere processes. However, global NDVI products ...are usually provided with relatively coarse spatial resolutions that lack important spatial details. Producing NDVI time-series data with high spatiotemporal resolution is indispensable for monitoring land surfaces and ecosystem changes, especially in spatiotemporally heterogeneous areas. The Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method was developed in this study to fill this need. In accord with the distinctive characteristics of NDVIs with large data variance and high spatial autocorrelation compared with raw reflectance bands, the IFSDAF method first produces a time-dependent increment with linear unmixing and a space-dependent increment via thin plate spline interpolation. It then makes a final prediction by optimal integration of these two increments with the constrained least squares method. Moreover, the IFSDAF was developed with the capacity to use all available finer-scaled images, including those partly contaminated by clouds. NDVI images with coarse spatial resolution (MODIS) and fine spatial resolution (Landsat and Sentinel) in areas with great spatial heterogeneity and significant land cover changes were used to test the performance of the IFSDAF method. The root mean square error and relative root mean square error of predicted relative to observed results were 0.0884 and 22.12%, respectively, in heterogeneous areas, and 0.0546 and 25.77%, respectively, in areas of land-cover change. These promising results demonstrated the strength and robustness of the IFSDAF method in providing reliable NDVI datasets with high spatial and temporal resolution to support research on land surface processes. The efficiency of the proposed IFSDAF method can be greatly improved by using only the space-dependent increment. This simplification will make IFSDAF a feasible method for monitoring global vegetation.
•IFSDAF was proposed to fuse NDVI data with different resolutions.•IFSDAF performs well in areas with great spatial heterogeneity.•IFSDAF can capture land cover changes in the fused image.•IFSDAF optimizes the combination of temporal and spatial information.•IFSDAF can use partially cloud contaminated fine-resolution images as input.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The tradeoff in remote sensing instruments that balances the spatial resolution and temporal frequency limits our capacity to monitor spatial and temporal dynamics effectively. The spatiotemporal ...data fusion technique is considered as a cost-effective way to obtain remote sensing data with both high spatial resolution and high temporal frequency, by blending observations from multiple sensors with different advantages or characteristics. In this paper, we develop the spatial and temporal nonlocal filter-based fusion model (STNLFFM) to enhance the prediction capacity and accuracy, especially for complex changed landscapes. The STNLFFM method provides a new transformation relationship between the fine-resolution reflectance images acquired from the same sensor at different dates with the help of coarse-resolution reflectance data, and makes full use of the high degree of spatiotemporal redundancy in the remote sensing image sequence to produce the final prediction. The proposed method was tested over both the Coleambally Irrigation Area study site and the Lower Gwydir Catchment study site. The results show that the proposed method can provide a more accurate and robust prediction, especially for heterogeneous landscapes and temporally dynamic areas.
Pervasiveness of ubiquitous computing advances the manufacturing scheme into a ubiquitous manufacturing era which poses significant challenges on sensing technology and system reliability. To improve ...manufacturing system reliability, this paper presents a new virtual tool wear sensing technique based on multisensory data fusion and artificial intelligence model for tool condition monitoring. It infers the difficult-to-measure tool wear parameters (e.g. tool wear width) by fusing in-process multisensory data (e.g. force, vibration, etc.) with dimension reduction technique and support vector regression model. Different state-of-the-art dimension reduction techniques including kernel principal component analysis, locally linear embedding, isometric feature mapping, and minimum redundancy maximum relevant method have been investigated for feature fusion in a virtual sensing model, and the kernel principal component analysis performs best in terms of sensing accuracy. The effectiveness of the developed virtual tool wear sensing technique is experimentally validated in a set of machining tool run-to-failure tests on a computer numerical control milling machine. The results show that the estimated tool wear width through virtual sensing is comparable to that measured offline by a microscope instrument in terms of accuracy, moreover, in a more cost-effective manner.
•A multi-sensory fusion based virtual tool wear sensing model is developed.•KPCA performs best among the dimension reduction techniques investigated.•The virtual tool wear sensing model is validated using a machining tool life test.•The performance of the model is comparable to the costly offline instrumentation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Cooperative learning for multiview analysis Ding, Daisy Yi; Li, Shuangning; Narasimhan, Balasubramanian ...
Proceedings of the National Academy of Sciences - PNAS,
09/2022, Volume:
119, Issue:
38
Journal Article
Peer reviewed
Open access
We propose a method for supervised learning with multiple sets of features ("views"). The multiview problem is especially important in biology and medicine, where "-omics" data, such as genomics, ...proteomics, and radiomics, are measured on a common set of samples. "Cooperative learning" combines the usual squared-error loss of predictions with an "agreement" penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. One version of our fitting procedure is modular, where one can choose different fitting mechanisms (e.g., lasso, random forests, boosting, or neural networks) appropriate for different data views. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty, yielding feature sparsity. The method can be especially powerful when the different data views share some underlying relationship in their signals that can be exploited to boost the signals. We show that cooperative learning achieves higher predictive accuracy on simulated data and real multiomics examples of labor-onset prediction. By leveraging aligned signals and allowing flexible fitting mechanisms for different modalities, cooperative learning offers a powerful approach to multiomics data fusion.
In this paper, a magnetic resonance imaging (MRI) oriented novel attention-based glioma grading network (AGGN) is proposed. By applying the dual-domain attention mechanism, both channel and spatial ...information can be considered to assign weights, which benefits highlighting the key modalities and locations in the feature maps. Multi-branch convolution and pooling operations are applied in a multi-scale feature extraction module to separately obtain shallow and deep features on each modality, and a multi-modal information fusion module is adopted to sufficiently merge low-level detailed and high-level semantic features, which promotes the synergistic interaction among different modality information. The proposed AGGN is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the proposed AGGN in comparison to other advanced models, which also presents high generalization ability and strong robustness. In addition, even without the manually labeled tumor masks, AGGN can present considerable performance as other state-of-the-art algorithms, which alleviates the excessive reliance on supervised information in the end-to-end learning paradigm.
•The proposed AGGN can alleviate the reliance on manually labeled tumor masks.•Dual-domain attention is useful for selecting the modality and location of MRI.•Multi-modal and multi-scale learning benefits analyzing brain MRI comprehensively.•Effective fusion methods enhance the presentation ability of robust features.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Dynamic ocean management, or management that uses near real-time data to guide the spatial distribution of commercial activities, is an emerging approach to balance ocean resource use and ...conservation. Employing a wide range of data types, dynamic ocean management can be used to meet multiple objectives—for example, managing target quota, bycatch reduction, and reducing interactions with species of conservation concern. Here, we present several prominent examples of dynamic ocean management that highlight the utility, achievements, challenges, and potential of this approach. Regulatory frameworks and incentive structures, stakeholder participation, and technological applications that align with user capabilities are identified as key ingredients to support successful implementation. By addressing the variability inherent in ocean systems, dynamic ocean management represents a new approach to tackle the pressing challenges of managing a f luid and complex environment.
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BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Loaded into ARGONAUTE(AGO) proteins, eukaryotic micro(mi)RNAs regulate gene expression via cleavage, translational repression, and/or accelerated decay of sequence‐complementary target transcripts. ...Despite their importance in development, cell identity maintenance and stress responses, how individual miRNAs contribute to spatial gene regulation within the complex cell mosaics formed in tissues/organs has remained inaccessible in any organism to date. We have developed a non‐invasive methodology to examine, at single‐cell‐type resolution, the AGO‐loading and activity patterns of entire miRNA cohorts in intact organs, applied here to the Arabidopsis root tip. A dual miRNAome–targetome analytical interface allowing intuitive data integration/visualization was developed as the basis for in‐depth investigations via single‐cell‐type experimentation. These uncovered an array of so far speculative or hitherto unknown types of spatial miRNA‐mediated gene regulation schemes, including via widespread cell‐to‐cell movement between contiguous layers of distinct identities. This study provides the proof of principle that minimally invasive, genome‐scale analysis of miRNA activities within and between single‐cell types of whole organs is achievable.
Synopsis
Spatial coordination of miRNA activity at a genome‐wide level within and across the various cell types of an intact organ remains largely unexplored. The current study tackles this question in the Arabidopsis root tip and offers novel insights into miRNA‐mediated gene regulation in a complex organ.
Exhaustive, cell‐type‐specific repertoires of functional miRNAs (“miRNAomes”) where acquired via cell‐type‐specific AGO1 immunoprecipitation.
Cell‐type‐specific translatomes obtained in WT versus miRNA‐deficient roots allowed identification of cell‐type‐specific repertoires of miRNA target transcripts (“targetomes”).
miRNAomes and targetomes were then integrated into the miRoot browser, allowing user‐friendly data interrogation.
miRoot coupled to experimentation uncovered new modes of plant miRNA action, including via unexpectedly widespread, functional cell‐to‐cell movement of miRNA between root layers.
Genome‐wide analysis of cell type‐specific miRNA loading and activity patterns in the Arabidopsis root tip provides novel insights into miRNA‐mediated gene regulation in a complex organ.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Research in several fields now requires the analysis of data sets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas ...(TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such data sets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data and provides new directions for the visual exploration of joint and individual structures. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene—miRNA associations and provides better characterization of tumor types. Data and software are available at https://genome.unc.edu/jive/.
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BFBNIB, INZLJ, NMLJ, NUK, PNG, UL, UM, UPUK, ZRSKP
Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory ...and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm/yr (6.56×10^4 cu.km/yr) to 617.1 mm/yr (6.87×10^4 cu.km/yr). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm/sq.yr with a significance level of p<0.05 and 0.38 mm yr−2 with a significance level of p<0.05, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm/sq.yr, p>0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm/yr. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET.
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