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  • 1
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    DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation (2023)
    In:  Remote Sensing of Environment
    Details
    Publication Date: 2023-07-24
    Description: Global navigation satellite system reflectometry (GNSS-R) has shown a capability in recent years to be applied as a novel remote sensing technique to retrieve ocean wind speeds. The combination of GNSS-R observable delay-Doppler maps (DDMs) and deep learning algorithms provides the possibility to build an end-to-end pipeline for improving wind speed estimations. Recent studies have proven that data-driven approaches can be applied to generate enhanced estimation products. However, these are usually trained with convolutional neural networks (CNNs), which include inductive bias throughout the models. The inbuilt translation equivariance in CNNs represents an inexactitude for the feature extraction on DDMs. To address this issue, we propose Transformer-based models, named DDM-Former and DDM-Sequence-Former (DDM-Seq-Former), to exploit delay-Doppler correlation within and between DDMs, respectively. The advantages of our methods over conventional retrieval algorithms and other deep learning-based approaches are presented based on the Cyclone GNSS (CYGNSS) version 3.0 dataset. In addition, a comprehensive study on the attention mechanism for our models is demonstrated. The proposed DDM-Former yields the best overall performance with a root mean square error (RMSE) of and a bias of over the nine months test period. Moreover, with an RMSE of and a bias of , the proposed DDM-Seq-Former can promisingly improve the estimations in the cases with wind speeds higher than . There are still opportunities for further enhancements in creating more robust models that could perform well in all wind regimes given a non-uniform wind distribution. We will make our code publicly available.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 2
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    DDM-Former: Global Ocean Wind Speed Retrieval with Transformer Networks (2023)
    In:  Proceedings
    Details
    Publication Date: 2024-04-11
    Description: As a novel remote sensing technique, GNSS reflectometry (GNSS-R) opens a new era of retrieving Earth surface param- eters. Several studies employ the combination of deep learn- ing and GNSS-R observable delay-Doppler maps (DDMs) to generate ocean wind speed estimation. Unlike these methods that often use convolutional neural networks (CNNs) with in- ductive bias, we proposed a Transformer-based model, named DDM-Former, to exploit fine-grained delay-Doppler correla- tion independently. Our model is evaluated on the Cyclone GNSS (CYGNSS) version 3.0 dataset and shown to outper- form the other retrieval methods.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 3
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    Deep learning in spaceborne GNSS Reflectometry for Ocean Remote Sensing (2023)
    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Details
    Publication Date: 2023-12-11
    Description: GNSS Reflectometry (GNSS-R), referring to exploiting the GNSS signal of opportunity reflected off the Earth surface, has emerged as a novel remote sensing technique for monitoring geophysical parameters. The Cyclone GNSS (CYGNSS), launched on December 15th, 2016, is a constellation of eight microsatellites with cost-effected receivers, fully dedicated to the GNSS-R applications, and can track reflected signals from multiple GNSS satellites. Compared with traditional optical and radar remote sensing, GNSS-R can provide massive datasets with global coverage and improved temporal resolution, which offers unique potential for characterizing the complex Earth system.With the increase of GNSS-R observation data volume, deep learning techniques show their strong capability in retrieving ocean surface wind speed by extracting features from the Delay-Doppler Maps (DDMs). Furthermore, it is shown that deep learning models significantly improve the quality of existing GNSS-R wind speed products. The model achieves an overall RMSE of 1.31 m/s compared with the ERA5 reanalysis data and leads to an improvement of 28% in comparison to the operational retrieval algorithm based on the empirical geophysical model functions (GMFs).However, some known geophysical parameters, such as precipitation, are theorized to be impacting the reflected signals, altering the pattern of the DDMs, and consequently biasing the retrievals. The correction of such bias is not trivial because of its nonlinear dependency on various environmental and technical parameters. Therefore, we explore how deep learning-based fusion on additional precipitation data can correct the bias and further investigate the potential of deep learning models to retrieve precipitation.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 4
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    Can we use deep learning models to identify the functionality of plastics from space? (2023)
    In:  International Journal of Applied Earth Observation and Geoinformation
    Details
    Publication Date: 2024-01-17
    Description: Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 5
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    Function Assignment of Plastics based on Hyperspectral Satellite Images and High-Resolution Data Using Deep Learning Algorithms (2023)
    In:  Proceedings
    Details
    Publication Date: 2024-05-10
    Description: Plastic pollution is becoming an increasingly prominent problem and the function of plastics determines whether they need to be recycled or not. In order to explore the possibility of using satellite imagery to classify the functionality of plastics, this study proposes a two-stage workflow: firstly, a classification map is obtained based on hyperspectral satellite imagery to generate plastic types, and then using these identified plastic coverage areas, a deep learning algorithm is used to assign functionality to these classified plastic areas based on sentinel-2 imagery. By comparing five leading-edge image classification models, classification accuracies of up to 74% were achieved, demonstrating the feasibility of using deep learning models trained on satellite images to identify plastic features.
    Type: info:eu-repo/semantics/conferenceObject
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  • 6
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    Allosite: a method for predicting allosteric sites (2013)
    Oxford University Press
    Details
    Publication Date: 2013-08-28
    Description: Motivation: The use of allosteric modulators as preferred therapeutic agents against classic orthosteric ligands has colossal advantages, including higher specificity, fewer side effects and lower toxicity. Therefore, the computational prediction of allosteric sites in proteins is receiving increased attention in the field of drug discovery. Allosite is a newly developed automatic tool for the prediction of allosteric sites in proteins of interest and is now available through a web server. Availability: The Allosite server and tutorials are freely available at http://mdl.shsmu.edu.cn/AST Contact: jian.zhang@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 7
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    ASD v2.0: updated content and novel features focusing on allosteric regulation (2013)
    Oxford University Press
    Details
    Publication Date: 2013-12-29
    Description: Allostery is the most direct and efficient way for regulation of biological macromolecule function and is induced by the binding of a ligand at an allosteric site topographically distinct from the orthosteric site. AlloSteric Database (ASD, http://mdl.shsmu.edu.cn/ASD ) has been developed to provide comprehensive information on allostery. Owing to the inherent high receptor selectivity and lower target-based toxicity, allosteric regulation is expected to assume a more prominent role in drug discovery and bioengineering, leading to the rapid growth of allosteric findings. In this updated version, ASD v2.0 has expanded to 1286 allosteric proteins, 565 allosteric diseases and 22 008 allosteric modulators. A total of 907 allosteric site-modulator structural complexes and 〉200 structural pairs of orthosteric/allosteric sites in the allosteric proteins were constructed for researchers to develop allosteric site and pathway tools in response to community demands. Up-to-date allosteric pathways were manually curated in the updated version. In addition, both the front-end and the back-end of ASD have been redesigned and enhanced to allow more efficient access. Taken together, these updates are useful for facilitating the investigation of allosteric mechanisms, allosteric target identification and allosteric drug discovery.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 8
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    Neurokinin-1 Receptor-mediated Glioma Cell Migration [Signal Transduction] (2013)
    The American Society for Biochemistry and Molecular Biology (ASBMB)
    Details
    Publication Date: 2013-01-05
    Description: Neurokinin-1 receptor (NK1R) occurs naturally on human glioblastomas. Its activation mediates glioma cell proliferation. However, it is unknown whether NK1R is directly involved in tumor cell migration. In this study, we found human hemokinin-1 (hHK-1), via NK1R, dose-dependently promoted the migration of U-251 and U-87 cells. In addition, we showed that hHK-1 enhanced the activity of MMP-2 and the expression of MMP-2 and MT1-matrix metalloproteinase (MMP), which were responsible for cell migration, because neutralizing the MMPs with antibodies decreased cell migration. The involved mechanisms were then investigated. In U-251, hHK-1 induced significant calcium efflux; phospholipase C inhibitor U-73122 reduced the calcium mobilization, the up-regulation of MMP-2 and MT1-MMP, and the cell migration induced by hHK-1, which meant the migration effect of NK1R was mainly mediated through the Gq-PLC pathway. We further demonstrated that hHK-1 boosted rapid phosphorylation of ERK, JNK, and Akt; inhibition of ERK and Akt effectively reduced MMP-2 induction by hHK-1. Meanwhile, inhibition of ERK, JNK, and Akt reduced the MT1-MMP induction. hHK-1 stimulated significant phosphorylation of p65 and c-JUN in U-251. Reporter gene assays indicated hHK-1 enhanced both AP-1 and NF-κB activity; inhibition of ERK, JNK, and Akt dose-dependently suppressed the NF-κB activity; only the inhibition of ERK significantly suppressed the AP-1 activity. Treatment with specific inhibitors for AP-1 or NF-κB strongly blocked the MMP up-regulation by hHK-1. Taken together, our data suggested NK1R was a potential regulator of human glioma cell migration by the up-regulation of MMP-2 and MT1-MMP.
    Print ISSN: 0021-9258
    Electronic ISSN: 1083-351X
    Topics: Biology , Chemistry and Pharmacology
    Published by The American Society for Biochemistry and Molecular Biology (ASBMB)
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