Erratum to: Detection of Essential Changes in Spatio-Temporal Processes with Applications to Camera Based Quality Control

2015 ◽  
pp. E1-E1
Author(s):  
Ewaryst Rafajłowicz
Keyword(s):  
Quality Control ◽  
Spatio Temporal

On the potential and challenges of using machine-learning for automated quality control of environmental sensor data

2020 ◽  
Author(s):  
Lennart Schmidt ◽  
Hannes Mollenhauer ◽  
Corinna Rebmann ◽  
David Schäfer ◽  
Antje Claussnitzer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Ground Truth ◽  
Sensor Data ◽  
Small Scale ◽  
Ground Truth Data ◽  
Starting Point ◽  
Environmental Sensor ◽  
Spatio Temporal ◽  
Automated Quality Control

<p>With more and more data being gathered from environmental sensor networks, the importance of automated quality-control (QC) routines to provide usable data in near-real time is becoming increasingly apparent. Machine-learning (ML) algorithms exhibit a high potential to this respect as they are able to exploit the spatio-temporal relation of multiple sensors to identify anomalies while allowing for non-linear functional relations in the data. In this study, we evaluate the potential of ML for automated QC on two spatio-temporal datasets at different spatial scales: One is a dataset of atmospheric variables at 53 stations across Northern Germany. The second dataset contains timeseries of soil moisture and temperature at 40 sensors at a small-scale measurement plot.</p><p>Furthermore, we investigate strategies to tackle three challenges that are commonly present when applying ML for QC: 1) As sensors might drop out, the ML models have to be designed to be robust against missing values in the input data. We address this by comparing different data imputation methods, coupled with a binary representation of whether a value is missing or not. 2) Quality flags that mark erroneous data points to serve as ground truth for model training might not be available. And 3) There is no guarantee that the system under study is stationary, which might render the outputs of a trained model useless in the future. To address 2) and 3), we frame the problem both as a supervised and unsupervised learning problem. Here, the use of unsupervised ML-models can be beneficial as they do not require ground truth data and can thus be retrained more easily should the system be subject to significant changes. In this presentation, we discuss the performance, advantages and drawbacks of the proposed strategies to tackle the aforementioned challenges. Thus, we provide a starting point for researchers in the largely untouched field of ML application for automated quality control of environmental sensor data.</p>

scholarly journals Spatio-temporal changes in the heatwaves and coldwaves in Spain (1950-2018): Influence of the East Atlantic pattern

2021 ◽  
Vol 25 (3) ◽  
pp. 168-183
Author(s):  
David Espín-Sánchez ◽  
Carmelo Conesa-García
Keyword(s):  
Quality Control ◽  
Iberian Peninsula ◽  
Altitudinal Gradient ◽  
Global Index ◽  
Data Quality Control ◽  
Climatic Region ◽  
East Atlantic ◽  
Duration Magnitude ◽  
Weather Stations ◽  
Spatio Temporal

The Iberian Peninsula has a complex orography, which determines an important altitudinal gradient and alternation of valleys and mountains, and periodic cold/warm advections air. In the present investigation the evolution of the characteristics of heatwaves (HWs) and coldwaves (CWs) (number of events, frequency, duration, magnitude, and amplitude) was analyzed. A total of 28 homogeneous-period weather stations (1950-2018), grouped into six regions (cluster). After submitting the meteorological series to a process of homogenization and data quality control, various ET-SCI indices were estimated in order to obtain evolution trends in each climatic region. In all cases, there was an increase, often significant, in the recurrence of HW events (0.3 / 10 yrs) as well as a decrease in CW events (-0.2 / 10 yrs). In addition, the evolution of the above indices and anomalies was correlated with the evolution of the global index of the East Atlantic (EAi).

scholarly journals ReOBS: a new approach to synthesize long-term multi-variable dataset and application to the SIRTA supersite

2018 ◽  
Vol 10 (2) ◽  
pp. 919-940 ◽  
Author(s):  
Marjolaine Chiriaco ◽  
Jean-Charles Dupont ◽  
Sophie Bastin ◽  
Jordi Badosa ◽  
Julio Lopez ◽  
...  
Keyword(s):  
Quality Control ◽  
Control Treatment ◽  
New Approach ◽  
Precise Information ◽  
Single File ◽  
Scientific Approach ◽  
Temporal Averaging ◽  
Atmospheric Observations ◽  
Spatio Temporal

Abstract. A scientific approach is presented to aggregate and harmonize a set of 60 geophysical variables at hourly timescale over a decade, and to allow multiannual and multi-variable studies combining atmospheric dynamics and thermodynamics, radiation, clouds and aerosols from ground-based observations. Many datasets from ground-based observations are currently in use worldwide. They are very valuable because they contain complete and precise information due to their spatio-temporal co-localization over more than a decade. These datasets, in particular the synergy between different type of observations, are under-used because of their complexity and diversity due to calibration, quality control, treatment, format, temporal averaging, metadata, etc. Two main results are presented in this article: (1) a set of methods available for the community to robustly and reliably process ground-based data at an hourly timescale over a decade is described and (2) a single netCDF file is provided based on the SIRTA supersite observations. This file contains approximately 60 geophysical variables (atmospheric and in ground) hourly averaged over a decade for the longest variables. The netCDF file is available and easy to use for the community. In this article, observations are “re-analyzed”. The prefix “re” refers to six main steps: calibration, quality control, treatment, hourly averaging, homogenization of the formats and associated metadata, as well as expertise on more than a decade of observations. In contrast, previous studies (i) took only some of these six steps into account for each variable, (ii) did not aggregate all variables together in a single file and (iii) did not offer an hourly resolution for about 60 variables over a decade (for the longest variables). The approach described in this article can be applied to different supersites and to additional variables. The main implication of this work is that complex atmospheric observations are made readily available for scientists who are non-experts in measurements. The dataset from SIRTA observations can be downloaded at http://sirta.ipsl.fr/reobs.html (last access: April 2017) (Downloads tab, no password required) under https://doi.org/10.14768/4F63BAD4-E6AF-4101-AD5A-61D4A34620DE.

scholarly journals ReOBS: a new approach to synthetize long-term multi-variable dataset and application to the SIRTA supersite

2018 ◽  
Author(s):  
Marjolaine Chiriaco ◽  
Jean-Charles Dupont ◽  
Sophie Bastin ◽  
Jordi Badosa ◽  
Julio Lopez ◽  
...  
Keyword(s):  
Quality Control ◽  
Control Treatment ◽  
New Approach ◽  
Precise Information ◽  
Single File ◽  
Scientific Approach ◽  
Temporal Averaging ◽  
Atmospheric Observations ◽  
Spatio Temporal

Abstract. A scientific approach is presented to aggregate and harmonize a set of sixty geophysical variables at hourly scale over a decade, and to allow multiannual and multi-variables studies combining atmospheric dynamics and thermodynamics, radiation, clouds and aerosols, from ground-based observations. Many datasets from ground-based observations are currently in use worldwide. They are very valuable because they contain complete and precise information due to their spatio-temporal co-localization over more than a decade. These dataset, in particular the synergy between different type of observations, are under-used because of their complexity and diversity due to calibration, quality control, treatment, format, temporal averaging, metadata, etc. Two main results are presented in this article: (1) a set of methods available for the community to robustly and reliably process ground-based data at a hourly time scale over a decade is described, and (2) a single netCDF file is provided based on the SIRTA supersite observations. This file contains approximately sixty geophysical variables (atmospheric and in-ground) hourly averaged over a decade for the longest variables. The netCDF file is available and easy to use for the community. In this article, observations are "re-analyzed". The prefix "re" refers to six main steps: calibration, quality control, treatment, hourly averaging, homogenization of the formats and associated metadata, and expertise on more than ten years of observations. In contrast, previous studies (i) took only some of these six steps into account for each variable, (ii) did not aggregate all variables together in a single file, and (iii) did not offer an hourly resolution for about sixty variables over a decade (for the longest variables). The approach described in this article can be applied to different supersites and to additional variables. The main implication of this work is that complex atmospheric observations are made readily available for scientists that are non-experts in measurements. Dataset from SIRTA observations can be downloaded on http://sirta.ipsl.fr/reobs.html (tab download, no password required) under DOI http://dx.doi.org/10.14768/4F63BAD4- E6AF-4101-AD5A-61D4A34620DE .

scholarly journals Quantitative and spatio-temporal features of protein aggregation in Escherichia coli and consequences on protein quality control and cellular ageing

2010 ◽  
Vol 29 (5) ◽  
pp. 910-923 ◽  
Author(s):  
Juliane Winkler ◽  
Anja Seybert ◽  
Lars König ◽  
Sabine Pruggnaller ◽  
Uta Haselmann ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Quality Control ◽  
Protein Aggregation ◽  
Protein Quality ◽  
Protein Quality Control ◽  
Temporal Features ◽  
Cellular Ageing ◽  
Spatio Temporal

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.

Elucidating cyclic AMP signaling in subcellular domains with optogenetic tools and fluorescent biosensors

2019 ◽  
Vol 47 (6) ◽  
pp. 1733-1747 ◽  
Author(s):  
Christina Klausen ◽  
Fabian Kaiser ◽  
Birthe Stüven ◽  
Jan N. Hansen ◽  
Dagmar Wachten
Keyword(s):  
Cyclic Amp ◽  
Adenosine Monophosphate ◽  
Adenylyl Cyclases ◽  
Temporal Precision ◽  
Fluorescent Biosensors ◽  
Subcellular Domains ◽  
Spatio Temporal ◽  
Hydrolysis Of ◽  
Shed Light ◽  
Cyclic Amp Signaling

The second messenger 3′,5′-cyclic nucleoside adenosine monophosphate (cAMP) plays a key role in signal transduction across prokaryotes and eukaryotes. Cyclic AMP signaling is compartmentalized into microdomains to fulfil specific functions. To define the function of cAMP within these microdomains, signaling needs to be analyzed with spatio-temporal precision. To this end, optogenetic approaches and genetically encoded fluorescent biosensors are particularly well suited. Synthesis and hydrolysis of cAMP can be directly manipulated by photoactivated adenylyl cyclases (PACs) and light-regulated phosphodiesterases (PDEs), respectively. In addition, many biosensors have been designed to spatially and temporarily resolve cAMP dynamics in the cell. This review provides an overview about optogenetic tools and biosensors to shed light on the subcellular organization of cAMP signaling.

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