scholarly journals Fast and pervasive diagenetic isotope exchange in foraminifera tests is species-dependent

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Deyanira Cisneros-Lazaro ◽  
Arthur Adams ◽  
Jinming Guo ◽  
Sylvain Bernard ◽  
Lukas P. Baumgartner ◽  
...  
Keyword(s):  
Image Analysis ◽  
Organic Matter ◽  
Oxygen Isotope ◽  
Isotope Exchange ◽  
Isotopic Exchange ◽  
Species Level ◽  
Temperature Record ◽  
3 Dimensional ◽  
Bulk Oxygen ◽  
Textural Changes

AbstractOxygen isotope compositions of fossil foraminifera tests are commonly used proxies for ocean paleotemperatures, with reconstructions spanning the last 112 million years. However, the isotopic composition of these calcitic tests can be substantially altered during diagenesis without discernible textural changes. Here, we investigate fluid-mediated isotopic exchange in pristine tests of three modern benthic foraminifera species (Ammonia sp., Haynesina germanica, and Amphistegina lessonii) following immersion into an 18O-enriched artificial seawater at 90 °C for hours to days. Reacted tests remain texturally pristine but their bulk oxygen isotope compositions reveal rapid and species-dependent isotopic exchange with the water. NanoSIMS imaging reveals the 3-dimensional intra-test distributions of 18O-enrichment that correlates with test ultra-structure and associated organic matter. Image analysis is used to quantify species level differences in test ultrastructure, which explains the observed species-dependent rates of isotopic exchange. Consequently, even tests considered texturally pristine for paleo-climatic reconstruction purposes may have experienced substantial isotopic exchange; critical paleo-temperature record re-examination is warranted.

Applications of CCEM to Environmental Health Problems

1985 ◽  
Vol 43 ◽  
pp. 102-103
Author(s):  
R. J. Lee ◽  
J. S. Walker
Keyword(s):  
Electron Microscopy ◽  
Image Analysis ◽  
Environmental Health ◽  
Computer Control ◽  
External Agent ◽  
External Agents ◽  
Computer Controlled ◽  
Primitive State ◽  
Textural Changes ◽  
General Aspects

Electron microscopy (EM), with the advent of computer control and image analysis techniques, is rapidly evolving from an interpretative science into a quantitative technique. Electron microscopy is potentially of value in two general aspects of environmental health: exposure and diagnosis.In diagnosis, electron microscopy is essentially an extension of optical microscopy. The goal is to characterize cellular changes induced by external agents. The external agent could be any foreign material, chemicals, or even stress. The use of electron microscopy as a diagnostic tool is well- developed, but computer-controlled electron microscopy (CCEM) has had only limited impact, mainly because it is fairly new and many institutions lack the resources to acquire the capability. In addition, major contributions to diagnosis will come from CCEM only when image analysis (IA) and processing algorithms are developed which allow the morphological and textural changes recognized by experienced medical practioners to be quantified. The application of IA techniques to compare cellular structure is still in a primitive state.

Transfer Learning for 3‐Dimensional Medical Image Analysis

2021 ◽  
pp. 151-171
Author(s):  
Sanket Singh ◽  
Sarthak Jain ◽  
Akshit Khanna ◽  
Anupam Kumar ◽  
Ashish Sharma
Keyword(s):  
Image Analysis ◽  
Transfer Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
3 Dimensional

Coupled cation and oxygen-isotope exchange between alkali feldspar and aqueous chloride solution

2004 ◽  
Vol 89 (11-12) ◽  
pp. 1822-1825 ◽  
Author(s):  
Theodore C. Labotka ◽  
David R. Cole ◽  
Mostafa Fayek ◽  
Lee R. Riciputi ◽  
Frank J. Stadermann
Keyword(s):  
Oxygen Isotope ◽  
Isotope Exchange ◽  
Chloride Solution ◽  
Alkali Feldspar ◽  
Oxygen Isotope Exchange ◽  
Aqueous Chloride Solution ◽  
Aqueous Chloride

scholarly journals Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 840
Author(s):  
Willem Q. M. van de Koot ◽  
Larissa J. J. van Vliet ◽  
Weilun Chen ◽  
John H. Doonan ◽  
Candida Nibau
Keyword(s):  
Image Analysis ◽  
Organic Matter ◽  
Plant Density ◽  
Important Determinant ◽  
Carbon Sink ◽  
High Accuracy ◽  
Analysis Pipeline ◽  
Colony Density ◽  
Water Table Management ◽  
And Performance

Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.

scholarly journals Assessment of the Content of Dry Matter and Dry Organic Matter in Compost with Neural Modelling Methods

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 307
Author(s):  
Dawid Wojcieszak ◽  
Maciej Zaborowicz ◽  
Jacek Przybył ◽  
Piotr Boniecki ◽  
Aleksander Jędruś
Keyword(s):  
Image Analysis ◽  
Organic Matter ◽  
Dry Matter ◽  
Neural Model ◽  
Original Method ◽  
Neural Models ◽  
Test Error ◽  
Effective Assessment ◽  
Modelling Methods

Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used to analyse composting processes. The scientific problem lets us formulate the research hypothesis: it is possible to identify representative traits of the image of composted material that are necessary to create a neural model supporting the process of assessment of the content of dry matter and dry organic matter in composted material. The effect of the research is the identification of selected features of the composted material and the methods of neural image analysis resulted in a new original method enabling effective assessment of the content of dry matter and dry organic matter. The content of dry matter and dry organic matter can be analysed by means of parameters specifying the colour of compost. The best developed neural models for the assessment of the content of dry matter and dry organic matter in compost are: in visible light RBF 19:19-2-1:1 (test error 0.0922) and MLP 14:14-14-11-1:1 (test error 0.1722), in mixed light RBF 30:30-8-1:1 (test error 0.0764) and MLP 7:7-9-7-1:1 (test error 0.1795). The neural models generated for the compost images taken in mixed light had better qualitative characteristics.

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