Rapid and Low-Cost Methodology To Obtain an Updated Compositional Model of Produced Fluids Using Separator Discharge Data Analysis

AbstractModern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven, parameter-free, and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling making user-defined aggregation obsolete while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human-gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbial ecologists gain insights into the structure and functioning of the underlying ecosystem of interest.

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Assisting Pottery Restoration Procedures with Digital Technologies

International Journal of Computational Methods in Heritage Science ◽

10.4018/ijcmhs.2019010102 ◽

2019 ◽

Vol 3 (1) ◽

pp. 20-32 ◽

Cited By ~ 2

Author(s):

Ioannis Kalasarinis ◽

Anestis Koutsoudis

Keyword(s):

Data Analysis ◽

Open Source Software ◽

Shape Matching ◽

Low Cost ◽

Digital Technologies ◽

Hellenistic Period ◽

Ancient Greek ◽

3D Data ◽

Wide Range ◽

3D Digitisation

The fragmentary nature of pottery is considered a common place. Conservators are requested to apply a proper restoration solution by taking under consideration a wide range of morphological features and physicochemical properties that derive from the artefact itself. In this work, the authors discuss on a low-cost pottery-oriented restoration pipeline that is based on the exploitation of technologies such as 3D digitisation, data analysis, processing and printing. The pipeline uses low-cost commercial and open source software tools and on the authors' previously published 3D pose normalisation algorithm that was initially designed for 3D vessel shape matching. The authors objectively evaluate the pipeline by applying it on two ancient Greek vessels of the Hellenistic period. The authors describe in detail the involved procedures such as the photogrammetric 3D digitisation, the 3D data analysis and processing, the 3D printing procedures and the synthetic shreds post processing. They quantify the pipeline's applicability and efficiency in terms of cost, knowledge overhead and other aspects related to restoration tasks.

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Fluctuating light experiments and semi-automated plant phenotyping enabled by self-built growth racks and simple upgrades to the IMAGING-PAM

Plant Methods ◽

10.1186/s13007-019-0546-1 ◽

2019 ◽

Vol 15 (1) ◽

Cited By ~ 1

Author(s):

Dominik Schneider ◽

Laura S. Lopez ◽

Meng Li ◽

Joseph D. Crawford ◽

Helmut Kirchhoff ◽

...

Keyword(s):

Data Analysis ◽

Low Cost ◽

Growth Conditions ◽

Research Field ◽

Constant Light ◽

Plant Phenotyping ◽

Natural Light ◽

Light Conditions ◽

Fluctuating Light ◽

Science Labs

Abstract Background Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. Results We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5, confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. Conclusions With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.

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Fast-GBS v2.0: an analysis toolkit for genotyping-by-sequencing data

Genome ◽

10.1139/gen-2020-0077 ◽

2020 ◽

Vol 63 (11) ◽

pp. 577-581

Author(s):

Davoud Torkamaneh ◽

Jérôme Laroche ◽

François Belzile

Keyword(s):

Data Analysis ◽

Missing Data ◽

Low Cost ◽

Genotyping By Sequencing ◽

Data Imputation ◽

Sequencing Data ◽

Missing Data Imputation ◽

Analysis Process ◽

Computational Resources ◽

Analysis Platform

Genotyping-by-sequencing (GBS) is a rapid, flexible, low-cost, and robust genotyping method that simultaneously discovers variants and calls genotypes within a broad range of samples. These characteristics make GBS an excellent tool for many applications and research questions from conservation biology to functional genomics in both model and non-model species. Continued improvement of GBS relies on a more comprehensive understanding of data analysis, development of fast and efficient bioinformatics pipelines, accurate missing data imputation, and active post-release support. Here, we present the second generation of Fast-GBS (v2.0) that offers several new options (e.g., processing paired-end reads and imputation of missing data) and features (e.g., summary statistics of genotypes) to improve the GBS data analysis process. The performance assessment analysis showed that Fast-GBS v2.0 outperformed other available analytical pipelines, such as GBS-SNP-CROP and Gb-eaSy. Fast-GBS v2.0 provides an analysis platform that can be run with different types of sequencing data, modest computational resources, and allows for missing-data imputation for various species in different contexts.

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Incumbent responses to low-cost airline entry and exit: A spatial autoregressive panel data analysis

Research in Transportation Economics ◽

10.1016/j.retrec.2009.01.004 ◽

2008 ◽

Vol 24 (1) ◽

pp. 15-24 ◽

Cited By ~ 23

Author(s):

Bogdan Daraban ◽

Gary M. Fournier

Keyword(s):

Data Analysis ◽

Panel Data ◽

Low Cost ◽

Panel Data Analysis ◽

Entry And Exit ◽

Spatial Autoregressive

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Flight tests and flight data analysis - teaching aerospace engineering students

Global Journal of Information Technology Emerging Technologies ◽

10.18844/gjit.v6i2.939 ◽

2016 ◽

Vol 6 (2) ◽

pp. 136

Author(s):

Grzegorz Kopecki

Keyword(s):

Mathematical Model ◽

Data Analysis ◽

Engineering Students ◽

Low Cost ◽

Aerospace Engineering ◽

Flight Tests ◽

High Class ◽

Flight Data ◽

Aircraft Flight ◽

The Mathematical Model

The ability to carry out in-flight tests and to analyse the flight data registered is, in the case of aerospace engineering students, a vital aspect of education. Since aircraft flight tests are very expensive, frequently the funds allocated to them in the process of education are insufficient. The aim of this article is to present a relatively low-cost method of training students to carry out flight tests and to analyse flight data. The method relies on three consecutive steps. At first, simulation tests relying on the mathematical model of an aircraft are carried out. During these simulations, students analyse aircraft behaviour. Next, flight data registered during previously held in-flight tests are analysed. Finally, flight tests are performed by students. As a result, having mastered the ability to analyse real flight data, the students trained will become high-class specialists being able to conduct flight tests and analyse flight data.

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Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain

Water Science & Technology ◽

10.2166/wst.2016.002 ◽

2016 ◽

Vol 73 (7) ◽

pp. 1756-1767 ◽

Cited By ~ 5

Author(s):

G. Astray ◽

B. Soto ◽

D. Lopez ◽

M. A. Iglesias ◽

J. C. Mejuto

Keyword(s):

Data Analysis ◽

Prediction Models ◽

Good Tool ◽

Discharge Data ◽

Coefficient Of Correlation ◽

Hand Temperature ◽

Artificial Neural ◽

Input Variables ◽

Western Spain ◽

North Western

Transit data analysis and artificial neural networks (ANNs) have proven to be a useful tool for characterizing and modelling non-linear hydrological processes. In this paper, these methods have been used to characterize and to predict the discharge of Lor River (North Western Spain), 1, 2 and 3 days ahead. Transit data analyses show a coefficient of correlation of 0.53 for a lag between precipitation and discharge of 1 day. On the other hand, temperature and discharge has a negative coefficient of correlation (−0.43) for a delay of 19 days. The ANNs developed provide a good result for the validation period, with R2 between 0.92 and 0.80. Furthermore, these prediction models have been tested with discharge data from a period 16 years later. Results of this testing period also show a good correlation, with R2 between 0.91 and 0.64. Overall, results indicate that ANNs are a good tool to predict river discharge with a small number of input variables.

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