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The multi-beam echo sounder system can not only obtain high-precision seabed bathymetry data, but also obtain high-resolution seabed backscatter strength data. A number of studies have applied acoustic remote sensing method to classify seabed sediment type with multi-beam backscatter strength data, and obtained better classification results than the traditional sediment sampling method. However, these studies mainly focus on the single type sediment classification or seabed mixed sediment classification using single beam data, not multi-beam echo sounder data. Based on backscatter strength data by a high-frequency (300 kHz) Simrad EM3000 multi-beam echo sounder and seabed sediment sampling data of Jiaozhou Bay in Qingdao, China, we establish the relation model between seabed backscatter strength and sediment type characteristics after data processing and corrections. The purpose of data processing is to diminish or weaken the influence of local bottom slope and near nadir reflection on backscatter strength data. Processed backscatter strength data through corrections and compensations can better characterize the features of the seabed sediments. Applying the back propagation neural network method based on genetic algorithm, we present a fast and accurate seabed classification method in this paper which could identify not just a single type of sediment-like rock, sand but also mixed types of sediment like sand gravel, clayey silt and sand-silt-clay. [ABSTRACT FROM PUBLISHER]

Background: Biomedical researchers are strongly encouraged to make their research outputs more Findable, Accessible, Interoperable, and Reusable (FAIR). While many biomedical research outputs are more readily accessible through open data efforts, finding relevant outputs remains a significant challenge. Schema.org is a metadata vocabulary standardization project that enables web content creators to make their content more FAIR. Leveraging Schema.org could benefit biomedical research resource providers, but it can be challenging to apply Schema.org standards to biomedical research outputs. We created an online browser-based tool that empowers researchers and repository developers to utilize Schema.org or other biomedical schema projects. Results: Our browser-based tool includes features which can help address many of the barriers towards Schema.org-compliance such as: The ability to easily browse for relevant Schema.org classes, the ability to extend and customize a class to be more suitable for biomedical research outputs, the ability to create data validation to ensure adherence of a research output to a customized class, and the ability to register a custom class to our schema registry enabling others to search and re-use it. We demonstrate the use of our tool with the creation of the Outbreak.info schema—a large multi-class schema for harmonizing various COVID-19 related resources. Conclusions: We have created a browser-based tool to empower biomedical research resource providers to leverage Schema.org classes to make their research outputs more FAIR. [ABSTRACT FROM AUTHOR]

Shore zone information is essential for coastal habitat assessment, environmental hazard monitoring, and resource conservation. However, traditional coastal zone classification mainly relies on in situ measurements and expert knowledge interpretation, which are costly and inefficient. This study classifies a shore zone area using satellite remote sensing data only and investigates the effect of the statistical indicators from Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) information with the Sentinel-2 data-derived spectral variables on the prediction results. Google Earth Engine was used to synthesize long time-series Sentinel-2 images, and different features were calculated for this synthetic image. Then, statistical indicators reflecting the characteristics of the shore zone profile were extracted from ICESat-2. Finally, a random forest algorithm was used to develop characteristics and shore zone classification. Comparing the results with the data measured shows that the proposed method can effectively classify the shore zone; it has an accuracy of 83.61% and a kappa coefficient of 0.81. [ABSTRACT FROM AUTHOR]

Bisphenol S (BPS) has been gradually used in all kinds of productions. Our previous study has demonstrated that BPS increases the obesogenic effects of a high-glucose diet through regulating lipid metabolism in Caenorhabditis elegans (C. elegans). Whether the effects pass on to the next generations remains uncovered. In the present study, C. elegans was selected as the model organism to investigate the effects of BPS on lipid metabolism in multiple generations. Oil Red O staining and triglyceride assays showed that multi-generational exposure to BPS in C. elegans significantly increased the fat accumulation in wild type worms, while not in the daf-16 gene-deficient worms. In addition, BPS affected the expressions of fat-7 and acs-2 in four generations of C. elegans. Furthermore, BPS promotes fat storage in C. elegans of multiple generations by the daf-16/nhr-49 -mediated signaling pathway. BPS induce the fat accumulation in C. elegans depending on the expression of daf-16/nhr-49. [Display omitted] • BPS at the concentration of 0.01 μM induced fat accumulation in multigeneration of C. elegans. • BPS promoted fat synthesis depending on daf-16 and fat-7 in multi-generation of C. elegans. • BPS inhibited the lipid metabolism of C. elegans via nhr-49 and acs-2 in multi-generation. [ABSTRACT FROM AUTHOR]

In recent years, noticeable subsidence depressions have occurred along the coastal zone of the Yellow River Delta. Consistent with these changes, dramatic human modifications within the coastal zone stand out, and the coastline is altered from an undisturbed natural area to an artificial coastline. However, very few studies have attempted to quantitatively analyze the relationship between subsidence depression and human activities. Here, the subsidence characteristics of the different land-use types in the Yellow River Delta are examined, and their spatiotemporal trends are quantified using a long-term satellite-observed time series of 30 years (1984–2017) regarding the land use map in combination with the InSAR-derived vertical ground deformations during three typical periods (P1: 1992–2000, P2: 2007–2010, and P3: 2016–2017). Noticeably, the highest subsidence rates were observed in areas where substantial human activities were observed, such as the subsidence in the salt fields ranging from 13 mm/year to 32 mm/year to 453 mm/year, respectively. Moreover, through the land-use prediction of Land Change Modeler (LCM), it is found that the salt field area will be further expanded in the future. The ecological vulnerability of the Yellow River Delta coastal zone should receive more attention in the future in terms of planning environmental protection strategies. [ABSTRACT FROM AUTHOR]

Shallow-water depth information is essential for ship navigation and fishery farming. However, the accurate acquisition of shallow-water depth has been a challenge for marine mapping. Combining Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data with multispectral data, satellite-derived bathymetry is a promising solution through which to obtain bathymetric information quickly and accurately. This study proposes a photon refraction correction method considering sea-surface undulations to address errors in the underwater photons obtained by the ICESat-2. First, the instantaneous sea surface and beam emission angle are integrated to determine the sea-surface incidence angle. Next, the distance of photon propagation in water is determined using sea-surface undulation and Snell's law. Finally, position correction is performed through geometric relationships. The corrected photons were combined with the multispectral data for bathymetric inversion, and a bathymetric map of the Yongle Atoll area was obtained. A bathymetric chart was created using the corrected photons and the multispectral data in the Yongle Atoll. Comparing the results of different refraction correction methods with the data measured shows that the refraction correction method proposed in this paper can effectively correct bathymetry errors: the root mean square error is 1.48 m and the R2 is 0.86. [ABSTRACT FROM AUTHOR]