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| 2. |
- Luo, Jiawei, et al.
(författare)
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Online Sparse DOA Estimation Based on Sub–Aperture Recursive LASSO for TDM–MIMO Radar
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:9
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Tidskriftsartikel (refereegranskat)abstract
- The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple– output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high– computational complexity when dealing with massive TDM–MIMO observations, due to high– dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from O( K3) to O( K2). Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method.
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| 3. |
- Long, Feiwu, et al.
(författare)
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The impact of immunoglobulin G N-glycosylation level on COVID-19 outcome: evidence from a Mendelian randomization study
- 2023
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Ingår i: Frontiers in Immunology. - : FRONTIERS MEDIA SA. - 1664-3224. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochrans Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels.
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| 4. |
- Shi, Tingting, et al.
(författare)
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The super-pangenome of Populus unveils genomic facets for its adaptation and diversification in widespread forest trees
- 2024
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Ingår i: Molecular Plant. - : Elsevier. - 1674-2052 .- 1752-9867. ; 17:5, s. 725-746
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the underlying mechanisms and links between genome evolution and adaptive innovations stands as a key goal in evolutionary studies. Poplars, among the world's most widely distributed and cultivated trees, exhibit extensive phenotypic diversity and environmental adaptability. In this study, we present a genus-level super-pangenome comprising 19 Populus genomes, revealing the likely pivotal role of private genes in facilitating local environmental and climate adaptation. Through the integration of pangenomes with transcriptomes, methylomes, and chromatin accessibility mapping, we unveil that the evolutionary trajectories of pangenes and duplicated genes are closely linked to local genomic landscapes of regulatory and epigenetic architectures, notably CG methylation in gene-body regions. Further comparative genomic analyses have enabled the identification of 142 202 structural variants across species that intersect with a significant number of genes and contribute substantially to both phenotypic and adaptive divergence. We have experimentally validated a ∼180-bp presence/absence variant affecting the expression of the CUC2 gene, crucial for leaf serration formation. Finally, we developed a user-friendly web-based tool encompassing the multi-omics resources associated with the Populus super-pangenome (http://www.populus-superpangenome.com). Together, the present pioneering super-pangenome resource in forest trees not only aids in the advancement of breeding efforts of this globally important tree genus but also offers valuable insights into potential avenues for comprehending tree biology.
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| 5. |
- Zhang, Yongchao, et al.
(författare)
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High-Throughput Hyperparameter-Free Sparse Source Location for Massive TDM-MIMO Radar : Algorithm and FPGA Implementation
- 2023
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892. ; 61
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Tidskriftsartikel (refereegranskat)abstract
- The sparse iterative covariance-based estimation (SPICE) algorithm is promising for hyperparameter-free sparse source location for time-division-multiplexing multiple-input-multiple-output (TDM-MIMO) radar systems, with well-documented merits in resolution enhancement and sidelobe suppression. Regrettably, the method typically requires a large number of iterations to converge, each requiring high-dimensional matrix operations, rendering the existing batch SPICE method impractical and expensive to implement in hardware when dealing with massive TDM-MIMO observations. In order to enable real-time processing, this article presents a subaperture-recursive (SAR) SPICE method, allowing for recursively refining the location parameters for each received (RX) block observation that becomes available sequentially in time. The proposed method not only offers the same benefits as the batch SPICE method but also allows for computationally efficient online processing, without the need for high-dimensional matrix operations, notably reducing the required hardware resources as well as processing time. We further present a high-throughput architecture for the resulting method on an XCZU15EG-FFVB1156 field-programmable gate array (FPGA). In combination with simulation results, we demonstrate the effectiveness through experimental data measured by a cascaded MIMO radar system with 12 transmit (TX) and 16 receive (RX) antennas, demonstrating that the computational time of resolving closely spaced sources on 256 predefined grid points can be processed in merely 12 ms.
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| 6. |
- Zhang, Yongchao, et al.
(författare)
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Online Sparse Reconstruction for Scanning Radar Using Beam-Updating q-SPICE
- 2022
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - 1545-598X. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- The generalized sparse iterative covariance-based estimation ( $q$ -SPICE) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this letter, we strive to alleviate this problem, deriving a beam-updating $q$ -SPICE algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The resulting method is a regularized extension of the current online $q$ -SPICE implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current online $q$ -SPICE. Our experimental assessment, conducted using both simulated and real data, demonstrates the advantage of the beam-updating $q$ -SPICE method in the task of sparse reconstruction for scanning radar.
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| 7. |
- Zhang, Yongchao, et al.
(författare)
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Sparse source location for real aperture radar using generalized sparse covariance fitting
- 2017
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Ingår i: 2017 IEEE Radar Conference, RadarConf 2017. - 9781467388238 ; , s. 1069-1074
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Konferensbidrag (refereegranskat)abstract
- Source location for real aperture radar (RAR) has raised many concerns in the fields of ground-based monitoring for aircrafts and vessels. Notably, the resolution of RAR in azimuth is constrained by the antenna beam width, which results in low degree of location accuracy. In this paper, we exploit the inherent sparseness of the target distributions to formulate a superresolution methodology to locate the observed sources. Making use of a recently developed generalized sparse covariance fitting technique, we show that the resulting estimator enjoys improved resolution and higher location accuracy as compared with the RAR system and other recent superresolution algorithms.
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| 8. |
- Zhang, Yongchao, et al.
(författare)
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Wideband Sparse Reconstruction for Scanning Radar
- 2018
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892. ; 56:10, s. 6055-6068
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Tidskriftsartikel (refereegranskat)abstract
- Recently, the generalized sparse iterative covariance-based estimation algorithm was extended to allow for varying norm constraints in scanning radar applications. In this paper, further to this development, we introduce a wideband dictionary framework which can provide a computationally efficient estimation of sparse signals. The technique is formed by initially introducing a coarse grid dictionary constructed from integrating elements, spanning bands of the considered parameter space. After forming estimates of the initially activated bands, these are retained and refined, whereas nonactivated bands are discarded from the further optimization, resulting in a smaller and zoomed dictionary with a finer grid. Implementing this scheme allows for reliable sparse signal reconstruction, at a much lower computational cost as compared to directly forming a larger dictionary spanning the whole parameter space. Simulation and real data processing results demonstrate that the proposed wideband estimator offers significant computational savings, without noticeable loss of performance.
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| 9. |
- Liu, Yulin, et al.
(författare)
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Soil type and temperature determine soil respiration seasonal dynamics in dairy grassland
- 2024
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Ingår i: Soil Ecology Letters. - : Springer Nature. - 2662-2289. ; 6:4
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Tidskriftsartikel (refereegranskat)abstract
- Soil respiration rates (Rs) were measured in New Zealand dairy grassland.Both season and soil type significantly affected Rs.Soil temperature and soil type dominated overall Rs.Soil respiration (Rs), the CO2 release from root respiration and microbial metabolism, affects global soil carbon storage and cycling. Only few studies have looked at Rs in the southern hemisphere, especially regarding the interaction between soil type and environmental factors on Rs in dairy grassland. We investigated the relationship between Rs and soil temperature (Ts), soil water content (SWC), soil type, and other environmental factors based on summer and winter measurements at four sites in New Zealand. Across sites, soil respiration rates ranged from 0.29 to 14.58 with a mean of 5.38 +/- 0.13 (mean +/- standard error) mu mol CO2 m-2 s-1. Mean summer R s was 86.5% higher than mean winter Rs, largely driven by organic/gley and pumice soils while ultic soils showed very little seasonal temperature sensitivity. Overall mean Rs in organic/gley soils was 108.0% higher than that in ultic soils. The high Rs rate observed in organic/gley was likely due to high soil organic matter content, while low Rs in ultic and pallic soils resulted from high clay content and low hydraulic conductance. Soil temperature drove overall Rs. Our findings indicate that soil type and soil temperature together best explain Rs. This implies that a mere classification of land use type may be insufficient for global C models and should be supplemented with soil type information, at least locally.
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| 10. |
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| 11. |
- Xu, Cheng, et al.
(författare)
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Prognostic value and biological function of LRRN4 in colorectal cancer
- 2022
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Ingår i: Cancer Cell International. - : BMC. - 1475-2867. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Several nervous and nerve-related biomarkers have been detected in colorectal cancer (CRC) and can contribute to the progression of CRC. However, the role of leucine-rich repeat neuronal 4 (LRRN4), a recently identified neurogenic marker, in CRC remains unclear. Methods We examined the expression and clinical outcomes of LRRN4 in CRC from TCGA-COREAD mRNA-sequencing datasets and immunohistochemistry in a Chinese cohort. Furthermore, colony formation, flow cytometry, wound healing assays and mouse xenograft models were used to investigate the biological significance of LRRN4 in CRC cell lines with LRRN4 knockdown or overexpression in vitro and in vivo. In addition, weighted coexpression network analysis, DAVID and western blot analysis were used to explore the potential molecular mechanism. Results We provide the first evidence that LRRN4 expression, at both the mRNA and protein levels, was remarkably high in CRC compared to controls and positively correlated with the clinical outcome of CRC patients. Specifically, LRRN4 was an independent prognostic factor for progression-free survival and overall survival in CRC patients. Further functional experiments showed that LRRN4 promoted cell proliferation, cell DNA synthesis and cell migration and inhibited apoptosis. Knockdown of LRRN4 can correspondingly decrease these effects in vitro and can significantly suppress the growth of xenografts. Several biological functions and signaling pathways were regulated by LRRN4, including proteoglycans in cancer, glutamatergic synapse, Ras, MAPK and PI3K. LRRN4 knockdown resulted in downregulation of Akt, p-Akt, ERK1/2 and p-ERK1/2, the downstream of the Ras/MAPK signaling pathway, overexpression of LRRN4 leaded to the upregulation of these proteins. Conclusions Our results suggest that LRRN4 could be a biological and molecular determinant to stratify CRC patients into distinct risk categories, and mechanistically, this is likely attributable to LRRN4 regulating several malignant phenotypes of neoplastic cells via RAS/MAPK signal pathways.
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| 12. |
- Zhang, Yongchao, et al.
(författare)
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Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting
- 2019
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Ingår i: IGARSS 2019 : 2019 IEEE International Geoscience and Remote Sensing Symposium - 2019 IEEE International Geoscience and Remote Sensing Symposium. - 9781538691557 - 9781538691540 ; , s. 8562-8565
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Konferensbidrag (refereegranskat)abstract
- Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The simulation results verify the superior performance of the resulting estimator as compared to other recent SRR imaging methods.
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