Towards a classification of organic enrichment in marine sediments based on biogeochemical indicators

Shallow marine sediments can be remotely classified by the spatial correlation properties of their seismic reflection signatures provided one uses a highly repetitive broadband acoustic source. A classification scheme defined by three spatial coherence parameters is shown capable of automatically differentiating between several formations of unconsolidated sediments in a limited area of offshore Newfoundland. The consistency and generality of the technique are explored and comparisons with standard echogram interpretation are made.

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Streptomyces chumphonensis sp. nov., isolated from marine sediments

INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY ◽

10.1099/ijs.0.062992-0 ◽

2014 ◽

Vol 64 (Pt_8) ◽

pp. 2605-2610 ◽

Cited By ~ 17

Author(s):

Wongsakorn Phongsopitanun ◽

Chitti Thawai ◽

Khanit Suwanborirux ◽

Takuji Kudo ◽

Moriya Ohkuma ◽

...

Keyword(s):

Marine Sediments ◽

Type Species ◽

Sequence Similarity ◽

Diaminopimelic Acid ◽

Rrna Gene ◽

Content Type ◽

Phenotypic Differences ◽

Link Type ◽

Phosphatidylinositol Mannosides

Two actinomycete strains, KK1-2T and CPB4-7, were isolated from marine sediments collected in Chumphon province, Thailand. Chumphon province, Thailand. Their taxonomic positions were determined using a polyphasic approach. The morphological, cultural and chemotaxonomic characteristics of these isolates were consistent with the classification of the strains as representing a member of the genus Streptomyces . They contained ll-diaminopimelic acid in their cell wall peptidoglycan; the whole-cell sugars were ribose and glucose. The predominant menaquinones were MK9-(H6) and MK9-(H8). The major polar lipids were phosphatidylethanolamine, phosphatidylinositol, diphosphatidylglycerol, phosphatidylglycerol and phosphatidylinositol mannosides. The predominant cellular fatty acids were anteiso-C15 : 0, iso-C16 : 0 and iso-C15 : 0. On the basis of 16S rRNA gene sequence similarity studies, these isolates were determined to be closely related to Streptomyces xinghaiensis JCM 16958T (98.2 %), Streptomyces rimosus subsp. paromomycinus JCM 4541T (98.1 %), Streptomyces sclerotialus JCM 4828T (98.1 %) and Streptomyces flocculus JCM 4476T (98.0 %). The G+C contents of the genomic DNA of strains KK1-2T and CPB4-7 were 73.3 and 74.2 mol%, respectively. They could be clearly distinguished from the related type strains by a low DNA–DNA relatedness and phenotypic differences. On the basis of these results, these strains represent a novel species of the genus Streptomyces , for which the name Streptomyces chumphonensis sp. nov. (type strain KK1-2T = JCM 18522T = TISTR 2106T = PCU 330T) is proposed.

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First-order machine learning based detection and classification of foraminifera in marine sediments from Arctic environments

10.5194/egusphere-egu2020-7606 ◽

2020 ◽

Author(s):

Steffen Aagaard-Sørensen ◽

Thomas Haugland Johansen ◽

Juho Junttila

Keyword(s):

Marine Sediments ◽

Barents Sea ◽

Size Fraction ◽

Ct Scanning ◽

Common Species ◽

Training Data ◽

Fine Tuning ◽

The Arctic ◽

Machine Leaning

Foraminifera are microscopic single-celled organisms, ubiquitous to the marine realm, that construct shells during their life cycle. The shells, in general, fossilize well in the sediment and they are diagnosable due to inter-species morphology and ornamentation variability. Classifying and counting foraminiferal shells is an important tool in assessing and reconstructing past and present environmental, oceanographic and climatological conditions. However, the present day manual identification procedure, performed with a microscope and a needle/brush, is a very time consuming. Circumventing this manual procedure, using machine leaning, promises to dramatically lower the time consumption related to generating foraminiferal data records.The first step towards that end is developing a deep learning model that can detect and classify microscopic foraminifera from 2D digital microscope pictures. The work is based on a VGG16 model implementation that has been pre trained on the ImageNet dataset and employing transfer learning techniques to adapt the model to the foraminifera task. The 2D photographic training data input was constructed by combining objects representative of and extracted from Arctic marine sediments (100&#181;m-1mm size fraction) from the Barents Sea region. Four object groups, including 1) calcareous and 2) agglutinated benthic foraminifera, 3) planktic foraminifera and 4) sediments were used in the training data construction. With the initial set-up the algorithms were able to identify adherence to one of the four groups correctly ~90% of the time and with further fine-tuning and refinement reaching 98% correct identifications.The second step is to use machine leaning for classification of individual benthic calcareous foraminiferal species within the sediment. The work will focus on the 20 most common species that comprise ca. &#8805; 80% of the total benthic calcareous foraminiferal fauna in the Arctic. The training of the algorithms will be done using targeted species-specific 2D photographic and 3D CT scanning data in addition to potentially using hyperspectral imaging.

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