Dock extraction from China’s Gaofen-2 multispectral imagery based on region-line primitive association analyses

2018 ◽  
Vol 40 (10) ◽  
pp. 3878-3899
Author(s):  
Jie Wang ◽  
Jiru Huang ◽  
Min Wang ◽  
Dongping Ming
Keyword(s):  
Multispectral Imagery ◽  
Association Analyses ◽  
Region Line

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Genome-Wide Analysis of Sex Disparities in the Genetic Architecture of Lung and Colorectal Cancers

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 686
Author(s):  
Alireza Nazarian ◽  
Alexander M. Kulminski
Keyword(s):  
Nucleotide Polymorphisms ◽  
Genetic Associations ◽  
Significance Level ◽  
Association Analyses ◽  
Complex Disorders ◽  
Specific Effects ◽  
Genome Wide ◽  
Genetic Mechanisms ◽  
Sex Disparities ◽  
Almost All

Almost all complex disorders have manifested epidemiological and clinical sex disparities which might partially arise from sex-specific genetic mechanisms. Addressing such differences can be important from a precision medicine perspective which aims to make medical interventions more personalized and effective. We investigated sex-specific genetic associations with colorectal (CRCa) and lung (LCa) cancers using genome-wide single-nucleotide polymorphisms (SNPs) data from three independent datasets. The genome-wide association analyses revealed that 33 SNPs were associated with CRCa/LCa at P < 5.0 × 10−6 neither males or females. Of these, 26 SNPs had sex-specific effects as their effect sizes were statistically different between the two sexes at a Bonferroni-adjusted significance level of 0.0015. None had proxy SNPs within their ±1 Mb regions and the closest genes to 32 SNPs were not previously associated with the corresponding cancers. The pathway enrichment analyses demonstrated the associations of 35 pathways with CRCa or LCa which were mostly implicated in immune system responses, cell cycle, and chromosome stability. The significant pathways were mostly enriched in either males or females. Our findings provided novel insights into the potential sex-specific genetic heterogeneity of CRCa and LCa at SNP and pathway levels.

scholarly journals Polymorphism Detection of GDF9 Gene and Its Association with Litter Size in Luzhong Mutton Sheep (Ovis aries)

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 571
Author(s):  
Fengyan Wang ◽  
Mingxing Chu ◽  
Linxiang Pan ◽  
Xiangyu Wang ◽  
Xiaoyun He ◽  
...  
Keyword(s):  
Litter Size ◽  
Tertiary Structure ◽  
Novel Mutation ◽  
Major Gene ◽  
Ovis Aries ◽  
Nucleotide Polymorphisms ◽  
Economic Traits ◽  
Coding Region ◽  
Association Analyses ◽  
Gdf9 Gene

Litter size is one of the most important economic traits in sheep. GDF9 and BMPR1B are major genes affecting the litter size of sheep. In this study, the whole coding region of GDF9 was sequenced and all the SNPs (single nucleotide polymorphisms) were determined in Luzhong mutton ewes. The FecB mutation was genotyped using the Sequenom MassARRAY®SNP assay technology. Then, the association analyses between polymorphic loci of GDF9 gene, FecB, and litter size were performed using a general linear model procedure. The results showed that eight SNPs were detected in GDF9 of Luzhong mutton sheep, including one novel mutation (g.41769606 T > G). The g.41768501A > G, g.41768485 G > A in GDF9 and FecB were significantly associated with litter size in Luzhong mutton ewes. The g.41768485 G > A is a missense mutation in the mature GDF9 protein region and is predicted to affect the tertiary structure of the protein. The results preliminarily demonstrated that GDF9 was a major gene affecting the fecundity of Luzhong mutton sheep and the two loci g.41768501A > G and g.41768485 G > A may be potential genetic markers for improving litter size.

scholarly journals Birthweight DNA methylation signatures in infant saliva

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chiara Moccia ◽  
Maja Popovic ◽  
Elena Isaevska ◽  
Valentina Fiano ◽  
Morena Trevisan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cord Blood ◽  
Gestational Age ◽  
Low Birthweight ◽  
Continuous Variable ◽  
Linear Regression Models ◽  
Association Analyses ◽  
Cpg Sites ◽  
Adverse Health Outcomes ◽  
The Look

Abstract Background Low birthweight has been repeatedly associated with long-term adverse health outcomes and many non-communicable diseases. Our aim was to look-up cord blood birthweight-associated CpG sites identified by the PACE Consortium in infant saliva, and to explore saliva-specific DNA methylation signatures of birthweight. Methods DNA methylation was assessed using Infinium HumanMethylation450K array in 135 saliva samples collected from children of the NINFEA birth cohort at an average age of 10.8 (range 7–17) months. The association analyses between birthweight and DNA methylation variations were carried out using robust linear regression models both in the exploratory EWAS analyses and in the look-up of the PACE findings in infant saliva. Results None of the cord blood birthweight-associated CpGs identified by the PACE Consortium was associated with birthweight when analysed in infant saliva. In saliva EWAS analyses, considering a false discovery rate p-values < 0.05, birthweight as continuous variable was associated with DNA methylation in 44 CpG sites; being born small for gestational age (SGA, lower 10th percentile of birthweight for gestational age according to WHO reference charts) was associated with DNA methylation in 44 CpGs, with only one overlapping CpG between the two analyses. Despite no overlap with PACE results at the CpG level, two of the top saliva birthweight CpGs mapped at genes associated with birthweight with the same direction of the effect also in the PACE Consortium (MACROD1 and RPTOR). Conclusion Our study provides an indication of the birthweight and SGA epigenetic salivary signatures in children around 10 months of age. DNA methylation signatures in cord blood may not be comparable with saliva DNA methylation signatures at about 10 months of age, suggesting that the birthweight epigenetic marks are likely time and tissue specific.

scholarly journals Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions

2020 ◽  
Vol 12 (24) ◽  
pp. 4190
Author(s):  
Siyamthanda Gxokwe ◽  
Timothy Dube ◽  
Dominic Mazvimavi
Keyword(s):  
Remote Sensing ◽  
Arid Regions ◽  
Remote Sensing Data ◽  
Sub Saharan Africa ◽  
Future Research ◽  
Multispectral Imagery ◽  
Aquatic Life ◽  
Sensing Data ◽  
Spatial Coverage ◽  
Semi Arid

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales.

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