scholarly journals GIS-based spatio-temporal and geostatistical analysis of groundwater parameters of Lahore region Pakistan and their source characterization

2021 ◽  
Vol 80 (21) ◽  
Author(s):  
Sadia Ismail ◽  
M. Farooq Ahmed
Keyword(s):  
Geostatistical Analysis ◽  
Source Characterization ◽  
Spatio Temporal

scholarly journals Spatio-temporal distribution patterns of Plutella xylostella (Lepidoptera: Plutellidae) in a fine-scale agricultural landscape based on geostatistical analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian-Yu Li ◽  
Yan-Ting Chen ◽  
Meng-Zhu Shi ◽  
Jian-Wei Li ◽  
Rui-Bin Xu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Plutella Xylostella ◽  
Agricultural Landscape ◽  
Seasonal Pattern ◽  
Distribution Patterns ◽  
Geostatistical Analysis ◽  
Distribution Model ◽  
Detailed Knowledge ◽  
Fine Scale ◽  
Spatio Temporal

AbstractA detailed knowledge on the spatial distribution of pests is crucial for predicting population outbreaks or developing control strategies and sustainable management plans. The diamondback moth, Plutella xylostella, is one of the most destructive pests of cruciferous crops worldwide. Despite the abundant research on the species’s ecology, little is known about the spatio-temporal pattern of P. xylostella in an agricultural landscape. Therefore, in this study, the spatial distribution of P. xylostella was characterized to assess the effect of landscape elements in a fine-scale agricultural landscape by geostatistical analysis. The P. xylostella adults captured by pheromone-baited traps showed a seasonal pattern of population fluctuation from October 2015 to September 2017, with a marked peak in spring, suggesting that mild temperatures, 15–25 °C, are favorable for P. xylostella. Geostatistics (GS) correlograms fitted with spherical and Gaussian models showed an aggregated distribution in 21 of the 47 cases interpolation contour maps. This result highlighted that spatial distribution of P. xylostella was not limited to the Brassica vegetable field, but presence was the highest there. Nevertheless, population aggregations also showed a seasonal variation associated with the growing stage of host plants. GS model analysis showed higher abundances in cruciferous fields than in any other patches of the landscape, indicating a strong host plant dependency. We demonstrate that Brassica vegetables distribution and growth stage, have dominant impacts on the spatial distribution of P. xylostella in a fine-scale landscape. This work clarified the spatio-temporal dynamic and distribution patterns of P. xylostella in an agricultural landscape, and the distribution model developed by geostatistical analysis can provide a scientific basis for precise targeting and localized control of P. xylostella.

GIS-based Spatio-Temporal and Geostatistical Analysis of Groundwater Parameters of Lahore Region Pakistan and their Source Characterization

2021 ◽  
Author(s):  
Sadia Ismail ◽  
M Farooq Ahmed
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Water Quality Index ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Large Data ◽  
Quality Parameters ◽  
Source Characterization ◽  
Data Set ◽  
Spatio Temporal

Abstract Assessment of groundwater quality is critical, especially in the areas where it is continuously deteriorating due to unplanned industrial growth. This study utilizes GIS-based spatio-temporal and geostatistics tools to characterize the groundwater quality parameters of Lahore region. For this purpose, a large data set of the groundwater quality parameters (for a period of 2005–2016) was obtained from the deep unconfined aquifers. GIS-based water quality index (WQI) and entropy water quality index (EWQI) models were prepared using 15 water quality parameters pH (power of hydrogen), TDS (Total dissolve solids), EC (Electrical conductivity), TH (Total hardness), Ca2+ (Calcium), Mg2+ (Magnesium), Na+ (Sodium), K+ (Potassium), Cl− (Chloride), As (Arsenic), F (Fluoride), Fe (Iron), HCO3− (Bicarbonate), NO3− (Nitrate), and SO42− (Sulfate). The data analysis exhibits that 12% of the groundwater samples fell within the category of poor quality that helped to identify the permanent epicenters of deteriorating water quality index in the study area. As per the entropy theory, Fe, NO3−, K, F, SO42− and As, are the major physicochemical parameters those influence groundwater quality. The spatio-temporal analysis of the large data set revealed an extreme behavior in pH values along the Hudiara drain, and overall high arsenic concentration levels in most of the study area. The geochemical analysis shows that the groundwater chemistry is strongly influence by subsurface soil water interaction. The research highlights the significance of using GIS-based spatio-temporal and geostatistical tools to analyze the large data sets of physicochemical parameters at regional level for the detailed source characterization studies.

Source characterization and spatio–temporal evolution of the metal pollution in the sediments of the Basque estuaries (Bay of Biscay)

2013 ◽  
Vol 66 (1-2) ◽  
pp. 25-38 ◽  
Author(s):  
Irati Legorburu ◽  
José Germán Rodríguez ◽  
Ángel Borja ◽  
Iratxe Menchaca ◽  
Oihana Solaun ◽  
...  
Keyword(s):  
Metal Pollution ◽  
Temporal Evolution ◽  
Bay Of Biscay ◽  
Source Characterization ◽  
Spatio Temporal

SciKit-GStat: A scipy flavored geostatistical analysis toolbox written in Python

2020 ◽  
Author(s):  
Mirko Mälicke
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Building Blocks ◽  
Geostatistical Analysis ◽  
Geostatistical Methods ◽  
R Programming Language ◽  
The Past ◽  
Spatio Temporal ◽  
R Programming ◽  
Python Programming

<p><span>Geostatistical and spatio-temporal methods and applications have made major advances during the past decades. New data sources became available and more powerful and available computer systems fostered the development of more sophisticated analysis frameworks. However, the building blocks for these developments, geostatistical packages available in a multitude of programming languages, have not experienced the same attention. Although there are some examples, like the gstat package available for the R programming language, that are used as a de-facto standard for geostatistical analysis, many languages are still missing such implementations. During the past decade, the Python programming language has gained a lot of visibility and became an integral part of many geoscientist’s tool belts. Unfortunately, Python is missing a standard library for geostatistics. This leads to a new technical implementation of geostatistical methods with almost any new publication that uses Python. Thus, reproducing results and reusing codes is often cumbersome and can be error-prone.</span></p><p><span>During the past three years I developed scikit-gstat, a scipy flavored geostatistical toolbox written in Python to tackle these challenges. Scipy flavored means, that it uses classes, interfaces and implementation rules from the very popular scipy package for scientific Python, to make scikit-gstat fit into existing analysis workflows as seamlessly as possible. Scikit-gstat is open source and hosted on Github. It is well documented and well covered by unit tests. The tutorials made available along with the code are styled as lecture notes and </span><span>are</span><span> open </span><span>to everyone</span><span>. The package is extensible, to make it as easy as possible for other researchers to build new models on top, even without experience in Python. Additionally, scikit-gstat has an interface to the scikit-learn package, which makes it usable in existing data analysis workflows that involve machine learning. During the development of scikit-gstat a few other geostatistical packages evolved, namely pykrige for Kriging and gstools mainly for geostatistical simulations and random field generations. Due to overlap and to reduce development efforts, the author has made effort to implement interfaces to these libraries. This way, scikit-gstat understands other developments not as competing solutions, but as parts of an evolving geostatistical framework in Python that should be more streamlined in the future.</span></p>

E3 ubiquitin ligases

2005 ◽  
Vol 41 ◽  
pp. 15-30 ◽  
Author(s):  
Helen C. Ardley ◽  
Philip A. Robinson
Keyword(s):  
Protein Complexes ◽  
Loss Of Function ◽  
Direct Role ◽  
Cellular Processes ◽  
Substrate Protein ◽  
Protein Ubiquitination ◽  
C Terminus ◽  
Full Complement ◽  
Spatio Temporal ◽  
Eukaryotic Organisms

The selectivity of the ubiquitin–26 S proteasome system (UPS) for a particular substrate protein relies on the interaction between a ubiquitin-conjugating enzyme (E2, of which a cell contains relatively few) and a ubiquitin–protein ligase (E3, of which there are possibly hundreds). Post-translational modifications of the protein substrate, such as phosphorylation or hydroxylation, are often required prior to its selection. In this way, the precise spatio-temporal targeting and degradation of a given substrate can be achieved. The E3s are a large, diverse group of proteins, characterized by one of several defining motifs. These include a HECT (homologous to E6-associated protein C-terminus), RING (really interesting new gene) or U-box (a modified RING motif without the full complement of Zn2+-binding ligands) domain. Whereas HECT E3s have a direct role in catalysis during ubiquitination, RING and U-box E3s facilitate protein ubiquitination. These latter two E3 types act as adaptor-like molecules. They bring an E2 and a substrate into sufficiently close proximity to promote the substrate's ubiquitination. Although many RING-type E3s, such as MDM2 (murine double minute clone 2 oncoprotein) and c-Cbl, can apparently act alone, others are found as components of much larger multi-protein complexes, such as the anaphase-promoting complex. Taken together, these multifaceted properties and interactions enable E3s to provide a powerful, and specific, mechanism for protein clearance within all cells of eukaryotic organisms. The importance of E3s is highlighted by the number of normal cellular processes they regulate, and the number of diseases associated with their loss of function or inappropriate targeting.

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