Unmixing grain-size distributions in lake sediments: a new method of endmember modeling using hierarchical clustering

2017 ◽  
Vol 89 (1) ◽  
pp. 365-373 ◽  
Author(s):  
Xiaonan Zhang ◽  
Aifeng Zhou ◽  
Xin Wang ◽  
Mu Song ◽  
Yongtao Zhao ◽  
...  
Keyword(s):  
Grain Size ◽  
Hierarchical Clustering ◽  
Average Distance ◽  
Sedimentary Environment ◽  
Junggar Basin ◽  
Transport Processes ◽  
Sediment Provenance ◽  
Statistical Parameters ◽  
Data Set ◽  
Wide Range

AbstractThe grain-size distribution (GSD) of sediments provides information on sediment provenance, transport processes, and the sedimentary environment. Although a wide range of statistical parameters have been applied to summarize GSDs, most are directed at only parts of the distribution, which limits the amount of environmental information that can be retrieved. Endmember modeling provides a flexible method for unmixing GSDs; however, the calculation of the exact number of endmembers and geologically meaningful endmember spectra remain unresolved using existing modeling methods. Here we present the methodology hierarchical clustering endmember modeling analysis (CEMMA) for unmixing the GSDs of sediments. Within the CEMMA framework, the number of endmembers can be inferred from agglomeration coefficients, and the grain-size spectra of endmembers are defined on the basis of the average distance between the samples in the clusters. After objectively defining grain-size endmembers, we use a least squares algorithm to calculate the fractions of each GSD endmember that contributes to individual samples. To test the CEMMA method, we use a grain-size data set from a sediment core from Wulungu Lake in the Junggar Basin in China, and find that application of the CEMMA methodology yields geologically and mathematically meaningful results. We conclude that CEMMA is a rapid and flexible approach for analyzing the GSDs of sediments.

Sedimentary environment prediction of grain-size data based on machine learning approach

2020 ◽  
Vol 8 (3) ◽  
pp. SL71-SL78
Author(s):  
Qiao Su ◽  
Yanhui Zhu ◽  
Fang Hu ◽  
Xingyong Xu
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Prediction Model ◽  
Sedimentary Environment ◽  
Machine Learning Algorithms ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Data Set ◽  
Sedimentary Environments ◽  
Size Data

Grain size is one of the most important records for sedimentary environment, and researchers have made remarkable progress in the interpretation of sedimentary environments by grain size analysis in the past few decades. However, these advances often depend on the personal experience of the scholars and combination with other methods used together. Here, we constructed a prediction model using the K-nearest neighbors algorithm, one of the machine learning methods, which can predict the sedimentary environments of one core through a known core. Compared to the results of other studies based on the comprehensive data set of grain size and four other indicators, this model achieved a high precision value only using the grain size data. We have also compared our prediction model with other mainstream machine learning algorithms, and the experimental results of six evaluation metrics shed light on that this prediction model can achieve the higher precision. The main errors of the model reflect the length of the conversation area of sedimentary environment, which is controlled by the sedimentary dynamics. This model can provide a quick comparison method of the cores in a similar environment; thus, it may point out the preliminary guidance for further study.

scholarly journals A synthetic map of the northwest European Shelf sedimentary environment for applications in marine science

2017 ◽  
Author(s):  
Robert J. Wilson ◽  
Douglas C. Speirs ◽  
Alessandro Sabatino ◽  
Michael R. Heath
Keyword(s):  
Grain Size ◽  
North Sea ◽  
Sedimentary Environment ◽  
International Investment ◽  
Marine Policy ◽  
Continuous Variables ◽  
Seabed Sediment ◽  
The North ◽  
Wide Range ◽  
The North Sea

Abstract. Seabed sediment mapping is important for a wide range of marine policy, planning and scientific issues, and there has been considerable national and international investment around the world in the collation and synthesis of sediment data sets. However, in Europe at least, much of this effort has been directed towards seabed classification and mapping of discrete habitats. Scientific users often have to resort to reverse-engineering these classifications to recover continuous variables such as mud content and median grain size that are required for many ecological and biophysical studies. Here we present a new set of 0.125 by 0.125° resolution synthetic maps of continuous properties of the northwest European sedimentary environment, extending from the Bay of Biscay to the northern limits of the North Sea and the Faroe Islands. The maps are a blend of gridded survey data, and statistically modelled values based on distributions of bed shear stress due to tidal currents and waves, and bathymetric properties. Recent work has shown that statistical models can predict sediment composition in British waters and the North Sea with high accuracy, and here we extend this to the entire shelf and to the mapping of other key seabed parameters. The maps include percentage compositions of mud, sand and gravel; porosity and permeability; median grain size of the whole-sediment and of the sand and the gravel fractions; carbon and nitrogen content of sediments; percentage of seabed area covered by rock; mean and maximum depth-averaged tidal velocity and wave-orbital velocity at the seabed; and mean monthly natural disturbance rates. A number of applications for these maps exist, including species distribution modelling and the more accurate representation of seafloor biogeochemistry in ecosystem models. The data products are available from doi:10.15129/07bc686e-a354-40de-8c08-372ced7aad64.

scholarly journals Pervasive influence of fine silt sediments on the radiocarbon age of bulk sedimentary OC, alkenones and GDGTs via hydrodynamic mineral sorting processes in continental margins.

2020 ◽  
Author(s):  
Blanca Ausin ◽  
Elena Bruni ◽  
Negar Haghipour ◽  
Caroline Welte ◽  
Hannah Gies ◽  
...  
Keyword(s):  
Grain Size ◽  
Continental Margin ◽  
Planktonic Foraminifera ◽  
Transport Processes ◽  
Fine Silt ◽  
Sediment Grain Size ◽  
Size Fractions ◽  
Definitive Evidence ◽  
Wide Range ◽  
Grain Size Fractions

<p>Since Ohkouchi et al. (2002) pioneering work, compound specific radiocarbon (<sup>14</sup>C) dating has been largely used to explore <sup>14</sup>C age discrepancies between co-deposited sedimentary components in a wide range of depositional settings. Older <sup>14</sup>C ages of bulk organic carbon (OC) and alkenones relative to co-deposited planktonic foraminifera have been mainly attributed to lateral sediment transport processes by means of organic matter (OM)-mineral associations.</p><p>Definitive evidence for this hypothesis requires in-depth investigations at the mineral grain-size level. Here, we examine the radiocarbon signatures of OC and two molecular biomarkers widely used as paleothermometers (i.e., alkenones and glycerol diakyl glycerol tetraether (GDGTs)) associated to discrete sediment grain-size fractions collected from a range of continental margin settings. Our results evidence the pervasive influence of hydrodynamically-driven sorting processes on the OM content and composition of continental margin sediments, manifested in the <sup>14</sup>C age variability of OC, alkenones and GDGTs residing in bulk sediments corresponding grain-size fractions. We find that OC and both, alkenones and GDGTs, preferentially reside within the fine silt fraction, which accounts for a substantial fraction of the bulk sediment mass. Therefore, fine silt exerts a strong influence on the <sup>14</sup>C ages of these three components in bulk sediments. Given the propensity to resuspension and advection of fine silt under strong currents, the extent of its impact on the paleotemperature signal recorded by alkenones and GDGTs is also assessed. </p><p> </p><p>Ohkouchi, N., Eglinton, T.I., Keigwin, L.D., Hayes, J.M., 2002. Spatial and Temporal Offsets Between Proxy Records in a Sediment Drift. Science 298, 1224-1227.</p>

scholarly journals A synthetic map of the north-west European Shelf sedimentary environment for applications in marine science

2018 ◽  
Vol 10 (1) ◽  
pp. 109-130 ◽  
Author(s):  
Robert J. Wilson ◽  
Douglas C. Speirs ◽  
Alessandro Sabatino ◽  
Michael R. Heath
Keyword(s):  
Grain Size ◽  
North Sea ◽  
Sedimentary Environment ◽  
Marine Policy ◽  
Seabed Sediment ◽  
North West ◽  
The North ◽  
Wide Range ◽  
The North Sea ◽  
West European

Abstract. Seabed sediment mapping is important for a wide range of marine policy, planning and scientific issues, and there has been considerable national and international investment around the world in the collation and synthesis of sediment datasets. However, in Europe at least, much of this effort has been directed towards seabed classification and mapping of discrete habitats. Scientific users often have to resort to reverse engineering these classifications to recover continuous variables, such as mud content and median grain size, that are required for many ecological and biophysical studies. Here we present a new set of 0.125∘ by 0.125∘ resolution synthetic maps of continuous properties of the north-west European sedimentary environment, extending from the Bay of Biscay to the northern limits of the North Sea and the Faroe Islands. The maps are a blend of gridded survey data, statistically modelled values based on distributions of bed shear stress due to tidal currents and waves, and bathymetric properties. Recent work has shown that statistical models can predict sediment composition in British waters and the North Sea with high accuracy, and here we extend this to the entire shelf and to the mapping of other key seabed parameters. The maps include percentage compositions of mud, sand and gravel; porosity and permeability; median grain size of the whole sediment and of the sand and the gravel fractions; carbon and nitrogen content of sediments; percentage of seabed area covered by rock; mean and maximum depth-averaged tidal velocity and wave orbital velocity at the seabed; and mean monthly natural disturbance rates. A number of applications for these maps exist, including species distribution modelling and the more accurate representation of sea-floor biogeochemistry in ecosystem models. The data products are available from https://doi.org/10.15129/1e27b806-1eae-494d-83b5-a5f4792c46fc.

scholarly journals The Chemical Composition and Surface Texture of Transparent Heavy Minerals from Core LQ24 in the Changjiang Delta

Minerals ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 454 ◽  
Author(s):  
Yue ◽  
Yue ◽  
Panwar ◽  
Zhang ◽  
Jin
Keyword(s):  
Grain Size ◽  
Surface Texture ◽  
Chemical Weathering ◽  
Environmental Changes ◽  
Sedimentary Environment ◽  
Heavy Minerals ◽  
Sediment Provenance ◽  
Geochemical Data ◽  
Geochemical Composition ◽  
Changjiang Delta

The assessment of textural and compositional modifications of detrital sediments is required to reconstruct past source to sink dynamics. The Changjiang Delta is an ideal location to study the sedimentary environment from the Pliocene to Quaternary transition. In the present study, we aim to decipher the response of heavy minerals to mechanical wear and chemical weathering since the Pliocene. With the application of a scanning electron microscope and an electron probe, the geochemistry and surface texture of different heavy minerals (amphibole, epidote, and tourmaline groups) with grain-size fractions of 32–63 µm and 63–125 µm were studied. The result shows that the surface texture of unstable minerals (amphibole, epidote) changed under strong chemical weathering in the Pliocene sediments. By contrast, unstable minerals of the Pleistocene sediments are relatively fresh and similar to those of the modern Changjiang sediment. The stable mineral tourmaline does not exhibit morphology changes in different chemical weathering conditions. No effect of grain size on geochemical composition is noticed. The single minerals of very fine sand and coarse silt show similar geochemical and morphological features. The integration of mineralogy, geochemical data, and grain size parameters yield a more precise understanding of the physical and chemical response of heavy minerals to different weathering conditions. The outcome of the study is also helpful in deciphering sediment provenance changes and environmental changes in the Changjiang basin.

The Effect of Grain-Boundary Diffusion on the Oxidation of Low-Chromium Steels

2005 ◽  
Vol 237-240 ◽  
pp. 946-951 ◽  
Author(s):  
Ulrich Krupp ◽  
V.B. Trindade ◽  
Peter Schmidt ◽  
Hans Jürgen Christ ◽  
U. Buschmann ◽  
...  
Keyword(s):  
Grain Size ◽  
Grain Boundary ◽  
Boundary Diffusion ◽  
Grain Boundary Diffusion ◽  
Transport Processes ◽  
Gradual Transition ◽  
Oxide Growth ◽  
Low Alloy Steels ◽  
Data Set ◽  
Cr Steels

Even though the oxidation behavior of steels is generally considered as to be widely understood, a closer look reveals some open questions, e.g. regarding the influence of the substrate grain size on the overall oxidation kinetics. At temperatures below 570°C the main constituent of the oxide scale formed on top of low alloy steels is magnetite. As shown by gold marker experiments it grows outward and inward at the same time, the latter exhibiting a gradual transition to the more stable spinel compound FeCr2O4. As indicated by intergranular-oxidation attack below the superficial scale, inward oxide growth seems to be driven by oxygen transport along the grain boundaries serving as fast diffusion paths. This is supported by thermogravimetric oxidation tests in air on low-Cr steels with varying grain size: The smaller the grains the higher the oxidation rate. Recently, a numerical model for the diffusive transport processes based on the finite-difference approach has been developed, which distinguishes between fast grain-boundary diffusion and bulk diffusion. Qualitatively, it is capable to predict the relationship between substrate grain size and inward oxide growth kinetics. Together with the thermodynamic tool ChemApp and in combination with a data set for the Fe-Cr-O system the mechanism-based simulation of the overall oxidation process of low-Cr steels is possible.

Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

2019 ◽  
Vol 16 (7) ◽  
pp. 808-817 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Aromatase Inhibitors ◽  
Direct Synthesis ◽  
3D Qsar ◽  
Chemotherapeutic Agents ◽  
Modeling Tools ◽  
Data Set ◽  
Wide Range ◽  
Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

HCVS: Pinpointing Chromatin States Through Hierarchical Clustering and Visualization Scheme

2019 ◽  
Vol 14 (2) ◽  
pp. 148-156
Author(s):  
Nighat Noureen ◽  
Sahar Fazal ◽  
Muhammad Abdul Qadir ◽  
Muhammad Tanvir Afzal
Keyword(s):  
Hierarchical Clustering ◽  
Real Data ◽  
Cell Types ◽  
Computational Scheme ◽  
Data Set ◽  
Chromatin States ◽  
Functional Regions ◽  
Visualization Strategy ◽  
Hidden States ◽  
Next Generation Sequencing Ngs

Background: Specific combinations of Histone Modifications (HMs) contributing towards histone code hypothesis lead to various biological functions. HMs combinations have been utilized by various studies to divide the genome into different regions. These study regions have been classified as chromatin states. Mostly Hidden Markov Model (HMM) based techniques have been utilized for this purpose. In case of chromatin studies, data from Next Generation Sequencing (NGS) platforms is being used. Chromatin states based on histone modification combinatorics are annotated by mapping them to functional regions of the genome. The number of states being predicted so far by the HMM tools have been justified biologically till now. Objective: The present study aimed at providing a computational scheme to identify the underlying hidden states in the data under consideration. </P><P> Methods: We proposed a computational scheme HCVS based on hierarchical clustering and visualization strategy in order to achieve the objective of study. Results: We tested our proposed scheme on a real data set of nine cell types comprising of nine chromatin marks. The approach successfully identified the state numbers for various possibilities. The results have been compared with one of the existing models as well which showed quite good correlation. Conclusion: The HCVS model not only helps in deciding the optimal state numbers for a particular data but it also justifies the results biologically thereby correlating the computational and biological aspects.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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