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.

Study on the performance of an up-flow aerated biofilter (UAB) in municipal wastewater treatments

1994 ◽  
Vol 30 (11) ◽  
pp. 25-33 ◽  
Author(s):  
Yoshimasa Watanabe ◽  
Satoshi Okabe ◽  
Tomochika Arata ◽  
Yuji Haruta
Keyword(s):  
Organic Matter ◽  
Suspended Solids ◽  
Municipal Wastewater ◽  
Unique Feature ◽  
Aeration Rate ◽  
Loading Rates ◽  
Hydraulic Loading ◽  
Running Cost ◽  
And Performance ◽  
Activated Sludge Systems

A comprehensive wastewater treatment system that accomplishes oxidation of organic matter, nitrification, and denitrification was developed, and its characteristics and performance were investigated. A municipal wastewater was treated by an up-flow aerated biofilter (UAB), in which biofilms were developed on stainless meshes installed horizontally. This UAB exhibited a great potential ability of oxidation of organic matter, SS stabilization, and nitrification due to a unique aeration mechanism giving high DO concentrations with relatively low aeration rates. Another unique feature of the UAB was that attached biofilms on stainless meshes physically filtered out and/or adsorbed suspended solids in the wastewater in addition to the biological oxidation of organic matter. A stable nitrification could be achieved at HRT=10 hours corresponding to a hydraulic loading of 86 L m−2 d−1 and at a ratio of aeration rate to wastewater flow rate (A/W) of 2, which is considerably low as compared to aeration rates of typical activated sludge systems. This UAB system also could handle relatively high hydraulic loading rates. The UAB used in this study still have enough space to install more stainless meshes so as to reduce hydraulic loading rates resulting in the reduction of HRT and aeration rate, which leads to improvement of the system performance as well as reduction of the running cost.

scholarly journals Machine learning-based classification of mitochondrial morphology in primary neurons and brain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Garrett M. Fogo ◽  
Anthony R. Anzell ◽  
Kathleen J. Maheras ◽  
Sarita Raghunayakula ◽  
Joseph M. Wider ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cortical Neurons ◽  
Mitochondrial Dynamics ◽  
Automated Image Analysis ◽  
Mitochondrial Morphology ◽  
Analysis Pipeline ◽  
Genetic Ablation ◽  
Block Face

AbstractThe mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeostatic mitochondrial profile can change morphologic distribution in response to various stressors. Therefore, it is imperative to develop a method that robustly measures mitochondrial morphology with high accuracy. Here, we developed a semi-automated image analysis pipeline for the quantitation of mitochondrial morphology for both in vitro and in vivo applications. The image analysis pipeline was generated and validated utilizing images of primary cortical neurons from transgenic mice, allowing genetic ablation of key components of mitochondrial dynamics. This analysis pipeline was further extended to evaluate mitochondrial morphology in vivo through immunolabeling of brain sections as well as serial block-face scanning electron microscopy. These data demonstrate a highly specific and sensitive method that accurately classifies distinct physiological and pathological mitochondrial morphologies. Furthermore, this workflow employs the use of readily available, free open-source software designed for high throughput image processing, segmentation, and analysis that is customizable to various biological models.

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.

scholarly journals European survey shows poor association between soil organic matter and crop yields

2020 ◽  
Vol 118 (3) ◽  
pp. 325-334
Author(s):  
Wytse J. Vonk ◽  
Martin K. van Ittersum ◽  
Pytrik Reidsma ◽  
Laura Zavattaro ◽  
Luca Bechini ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Sugar Beet ◽  
Significant Positive Correlation ◽  
Agricultural Land ◽  
Crop Yields ◽  
Carbon Sink ◽  
Significant Negative Correlation ◽  
Positive Correlation ◽  
Farm Survey

AbstractA number of policies proposed to increase soil organic matter (SOM) content in agricultural land as a carbon sink and to enhance soil fertility. Relations between SOM content and crop yields however remain uncertain. In a recent farm survey across six European countries, farmers reported both their crop yields and their SOM content. For four widely grown crops (wheat, grain maize, sugar beet and potato), correlations were explored between reported crop yields and SOM content (N = 1264). To explain observed variability, climate, soil texture, slope, tillage intensity, fertilisation and irrigation were added as co-variables in a linear regression model. No consistent correlations were observed for any of the crop types. For wheat, a significant positive correlation (p < 0.05) was observed between SOM and crop yields in the Continental climate, with yields being on average 263 ± 4 (95% CI) kg ha−1 higher on soils with one percentage point more SOM. In the Atlantic climate, a significant negative correlation was observed for wheat, with yields being on average 75 ± 2 (95%CI) kg ha−1 lower on soils with one percentage point more SOM (p < 0.05). For sugar beet, a significant positive correlation (p < 0.05) between SOM and crop yields was suggested for all climate zones, but this depended on a number of relatively low yield observations. For potatoes and maize, no significant correlations were observed between SOM content and crop yields. These findings indicate the need for a diversified strategy across soil types, crops and climates when seeking farmers’ support to increase SOM.

scholarly journals Immunofluorescence Image Analysis Pipeline for Distinguishing Epithelial-Mesenchymal Transition

2021 ◽  
Vol 120 (3) ◽  
pp. 269a-270a
Author(s):  
Shreyas U. Hirway ◽  
Nadiah T. Hassan ◽  
Michael Sofroniou ◽  
Christopher A. Lemmon ◽  
Seth H. Weinberg
Keyword(s):  
Image Analysis ◽  
Epithelial Mesenchymal Transition ◽  
Analysis Pipeline ◽  
Mesenchymal Transition

scholarly journals Permafrost-Affected Soils of the Russian Arctic and their Carbon Pools

2014 ◽  
Vol 6 (1) ◽  
pp. 619-655
Author(s):  
S. Zubrzycki ◽  
L. Kutzbach ◽  
E.-M. Pfeiffer
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Sink ◽  
Global Climate ◽  
Fundamental Property ◽  
Climate System ◽  
The Arctic ◽  
Quaternary Period ◽  
Permafrost Regions

Abstract. Permafrost-affected soils have accumulated enormous pools of organic matter during the Quaternary Period. The area occupied by these soils amounts to more than 8.6 million km2, which is about 27% of all land areas north of 50° N. Therefore, permafrost-affected soils are considered to be one of the most important cryosphere elements within the climate system. Due to the cryopedogenic processes that form these particular soils and the overlying vegetation that is adapted to the arctic climate, organic matter has accumulated to the present extent of up to 1024 Pg (1 Pg = 1015 g = 1 Gt) of soil organic carbon stored within the uppermost three meters of ground. Considering the observed progressive climate change and the projected polar amplification, permafrost-affected soils will undergo fundamental property changes. Higher turnover and mineralization rates of the organic matter are consequences of these changes, which are expected to result in an increased release of climate-relevant trace gases into the atmosphere. As a result, permafrost regions with their distinctive soils are likely to trigger an important tipping point within the global climate system, with additional political and social implications. The controversy of whether permafrost regions continue accumulating carbon or already function as a carbon source remains open until today. An increased focus on this subject matter, especially in underrepresented Siberian regions, could contribute to a more robust estimation of the soil organic carbon pool of permafrost regions and at the same time improve the understanding of the carbon sink and source functions of permafrost-affected soils.

scholarly journals Biomass and Volume Modeling along with Carbon Concentration Variations of Short-Rotation Poplar Plantations

Forests ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 780 ◽  
Author(s):  
Lihu Dong ◽  
Faris Rafi Almay Widagdo ◽  
Longfei Xie ◽  
Fengri Li
Keyword(s):  
Carbon Sink ◽  
Biomass Conversion ◽  
Model Fitting ◽  
National Forest Inventory ◽  
Model Systems ◽  
Stem Volume ◽  
Crown Length ◽  
Short Rotation ◽  
And Performance ◽  
Volume Models

Short-rotation forestry is of interest to provide biomass for bioenergy and act as a carbon sink to mitigate global warming. The Poplar tree (Populus × xiaohei) is a fast-growing and high-yielding tree species in Northeast China. In this study, a total of 128 Populus × xiaohei trees from the Songnen Plain, Heilongjiang Province, Northeastern China, were harvested. Several available independent variables, such as tree diameter at breast height (D), tree’s total height (H), crown width (CW), and crown length (CL), were differently combined to develop three additive biomass model systems and eight stem volume models for Populus × xiaohei tree. Variance explained within the three additive biomass model systems ranged from 83% to 98%, which was lowest for the foliage models, and highest for the stem biomass models. Similar findings were found in the stem volume models, in which the models explained more than 94% of the variance. The additional predictors, such as H, CL, or CW, evidently enhanced the model fitting and performance for the total and components biomass along with the stem volume models. Furthermore, the biomass conversion and expansion factors (BCEFs) of the root (118.2 kg/m3), stem (380.2 kg/m3), branch (90.7 kg/m3), and foliage (31.2 kg/m3) were also calculated. The carbon concentrations of Populus × xiaohei in root, stem, branch, and foliage components were 45.98%, 47.74%, 48.32%, and 48.46%, respectively. Overall, the newly established models in this study provided complete and comprehensive tools for quantifying the biomass and stem volume of Populus × xiaohei, which might be essential to be specifically utilized in the Chinese National Forest Inventory.

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