Geographic Information Systems (GIS) as a Tool for Positive Identification from Frontal Sinus Radiographs

2021 ◽  
Author(s):  
Jenna Watson
Keyword(s):  
Frontal Sinus ◽  
Similarity Search ◽  
Comparison Method ◽  
Error Rates ◽  
Sufficient Information ◽  
Analysis Tool ◽  
Positive Identification ◽  
True Match ◽  
Area And Perimeter ◽  
User Friendly

Frontal sinus radiographs are frequently used to identify human remains. However, the method of visually comparing antemortem (AM) to postmortem (PM) cranial radiographs has been criticized for being a subjective approach that relies on practitioner experience, training, and judgment rather than on objective, quantifiable procedures with published error rates. The objective of this study was to explore the use of ArcMap and its spatial analysis tool, Similarity Search, as a quantifiable, reliable, and reproducible method for identifying frontal sinus matches from cranial radiographs. Using cranial radiographs of 100 individuals from the William M. Bass DonatedSkeletal Collection, the frontal sinuses were digitized to create two-dimensional polygons. Similarity Search was evaluated on its ability to identify the correct AM radiograph using three variables: the number of scallops and the area and perimeter values of the polygons. Using all three variables, Similarity Search correctly identified the true match AM polygon in 58% of the male groups and in 62% of the female groups. These results indicate that ArcMap can be used with frontal sinus radiographs. However, further analysis of the three variables revealed that scallop number did not provide sufficient information about frontal sinus shape to increase the accuracy of Similarity Search, and area and perimeter only captured the size of the frontal sinus polygons, not shape. This research is a first step in developing a user-friendly, quantifiable frontal sinus comparison method for the purpose of positive identification.

scholarly journals Development of a User-Friendly Pipeline for Mutational Analyses of HIV Using Ultra-Accurate Maximum-Depth Sequencing

Viruses ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1338
Author(s):  
Morgan E. Meissner ◽  
Emily J. Julik ◽  
Jonathan P. Badalamenti ◽  
William G. Arndt ◽  
Lauren J. Mills ◽  
...  
Keyword(s):  
Error Rates ◽  
Maximum Depth ◽  
Sequencing Data ◽  
Background Error ◽  
High Background ◽  
Immunodeficiency Virus ◽  
User Friendly ◽  
Viral Mutagenesis ◽  
Hiv 1

Human immunodeficiency virus type 2 (HIV-2) accumulates fewer mutations during replication than HIV type 1 (HIV-1). Advanced studies of HIV-2 mutagenesis, however, have historically been confounded by high background error rates in traditional next-generation sequencing techniques. In this study, we describe the adaptation of the previously described maximum-depth sequencing (MDS) technique to studies of both HIV-1 and HIV-2 for the ultra-accurate characterization of viral mutagenesis. We also present the development of a user-friendly Galaxy workflow for the bioinformatic analyses of sequencing data generated using the MDS technique, designed to improve replicability and accessibility to molecular virologists. This adapted MDS technique and analysis pipeline were validated by comparisons with previously published analyses of the frequency and spectra of mutations in HIV-1 and HIV-2 and is readily expandable to studies of viral mutation across the genomes of both viruses. Using this novel sequencing pipeline, we observed that the background error rate was reduced 100-fold over standard Illumina error rates, and 10-fold over traditional unique molecular identifier (UMI)-based sequencing. This technical advancement will allow for the exploration of novel and previously unrecognized sources of viral mutagenesis in both HIV-1 and HIV-2, which will expand our understanding of retroviral diversity and evolution.

scholarly journals Content analytics for curriculum review: A learning analytics use case for exploration of learner context

2020 ◽  
Author(s):  
Leah P. Macfadyen
Keyword(s):  
Text Analysis ◽  
Student Writing ◽  
Analysis Tool ◽  
Use Case ◽  
Curriculum Analysis ◽  
Curriculum Review ◽  
Curriculum Renewal ◽  
Text Content ◽  
User Friendly ◽  
Traditional Approaches

Curriculum analysis is a core component of curriculum renewal. Traditional approaches to curriculum analysis are manual, slow and subjective, but some studies have suggested that text analysis might usefully be employed for exploration of curriculum. This concise paper outlines a pilot use case of content analytics to support curriculum review and analysis. I have co-opted Quantext – a relatively user-friendly text analysis tool designed to help educators explore student writing – for analysis of the text content of the 17 courses in our online master’s program. Quantext computed descriptive metrics and readability indices for each course and identified top keywords and ngrams per course. Compilation and comparison of these revealed frequent curricular topics and networks of thematic relationships between courses, in ways that both individual educators and curriculum committees can interpret and use for decision-making. Future Quantext features will allow even more sophisticated identification of curricular gaps and redundancies.

Increasing Reading Fluency in Elementary Students with Low Vision through Repeated Readings

1998 ◽  
Vol 92 (5) ◽  
pp. 276-292 ◽  
Author(s):  
C.A. Layton ◽  
A.J. Koenig
Keyword(s):  
Elementary Students ◽  
Reading Fluency ◽  
Low Vision ◽  
Error Rates ◽  
Repeated Readings ◽  
User Friendly

The purpose of this study was to explore a user-friendly method to increase the reading fluency of four elementary students with low vision. An analysis of the effects of repeated readings on the students’ reading rates, error rates, and comprehension found that the intervention was successful in improving all four students’ reading fluency and did not adversely affect their error rates or comprehension. The results from generalized readings indicated that the students’ improved reading rates were generalized to classroom reading.

scholarly journals B-COURSE: A WEB-BASED TOOL FOR BAYESIAN AND CAUSAL DATA ANALYSIS

2002 ◽  
Vol 11 (03) ◽  
pp. 369-387 ◽  
Author(s):  
PETRI MYLLYMÄKI ◽  
TOMI SILANDER ◽  
HENRY TIRRI ◽  
PEKKA URONEN
Keyword(s):  
Data Analysis ◽  
Bayesian Network ◽  
Network Models ◽  
Causal Modeling ◽  
Analysis Tool ◽  
Support Material ◽  
Online Data ◽  
Web Based ◽  
User Friendly ◽  
Bayesian Network Models

B-Course is a free web-based online data analysis tool, which allows the users to analyze their data for multivariate probabilistic dependencies. These dependencies are represented as Bayesian network models. In addition to this, B-Course also offers facilities for inferring certain type of causal dependencies from the data. The software uses a novel "tutorial stylerdquo; user-friendly interface which intertwines the steps in the data analysis with support material that gives an informal introduction to the Bayesian approach adopted. Although the analysis methods, modeling assumptions and restrictions are totally transparent to the user, this transparency is not achieved at the expense of analysis power: with the restrictions stated in the support material, B-Course is a powerful analysis tool exploiting several theoretically elaborate results developed recently in the fields of Bayesian and causal modeling. B-Course can be used with most web-browsers (even Lynx), and the facilities include features such as automatic missing data handling and discretization, a flexible graphical interface for probabilistic inference on the constructed Bayesian network models (for Java enabled browsers), automatic prettyHyphen;printed layout for the networks, exportation of the models, and analysis of the importance of the derived dependencies. In this paper we discuss both the theoretical design principles underlying the B-Course tool, and the pragmatic methods adopted in the implementation of the software.

scholarly journals POTATO: An automated pipeline for batch analysis of optical tweezers data

2021 ◽  
Author(s):  
Stefan Buck ◽  
Lukas Pekarek ◽  
Neva Caliskan
Keyword(s):  
Data Analysis ◽  
Optical Tweezers ◽  
Single Molecule ◽  
Molecular Motors ◽  
Analysis Tool ◽  
Recent Developments ◽  
Protein Nucleic Acid ◽  
Programming Knowledge ◽  
Nucleic Acid Interactions ◽  
User Friendly

Optical tweezers is a single-molecule technique that allows probing of intra- and intermolecular interactions that govern complex biological processes involving molecular motors, protein-nucleic acid interactions and protein/RNA folding. Recent developments in instrumentation eased and accelerated optical tweezers data acquisition, but analysis of the data remains challenging. Here, to enable high-throughput data analysis, we developed an automated python-based analysis pipeline called POTATO (Practical Optical Tweezers Analysis TOol). POTATO automatically processes the high-frequency raw data generated by force-ramp experiments and identifies (un)folding events using predefined parameters. After segmentation of the force-distance trajectories at the identified (un)folding events, sections of the curve can be fitted independently to worm-like chain and freely-jointed chain models, and the work applied on the molecule can be calculated by numerical integration. Furthermore, the tool allows plotting of constant force data and fitting of the Gaussian distance distribution over time. All these features are wrapped in a user-friendly graphical interface (https://github.com/REMI-HIRI/POTATO), which allows researchers without programming knowledge to perform sophisticated data analysis.

scholarly journals CEDAR, an online resource for the reporting and exploration of complexome profiling data

2020 ◽  
Author(s):  
Joeri van Strien ◽  
Alexander Haupt ◽  
Uwe Schulte ◽  
Hans-Peter Braun ◽  
Alfredo Cabrero-Orefice ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Complexes ◽  
Mass Spectrometry Data ◽  
Analysis Tool ◽  
Data Resource ◽  
Online Resource ◽  
Comprehensive Information ◽  
Central Resource ◽  
Rest Api ◽  
User Friendly

Complexome profiling is an emerging 'omics approach that systematically interrogates the composition of protein complexes (the complexome) of a sample, by combining biochemical separation of native protein complexes with mass-spectrometry based quantitation proteomics. The resulting fractionation profiles hold comprehensive information on the abundance and composition of the complexome, and have a high potential for reuse by experimental and computational researchers. However, the lack of a central resource that provides access to these data, reported with adequate descriptions and an analysis tool, has limited their reuse. Therefore, we established the ComplexomE profiling DAta Resource (CEDAR, www3.cmbi.umcn.nl/cedar/), an openly accessible database for depositing and exploring mass spectrometry data from complexome profiling studies. Compatibility and reusability of the data is ensured by a standardized data and reporting format containing the "minimum information required for a complexome profiling experiment" (MIACE). The data can be accessed through a user-friendly web interface, as well as programmatically using the REST API portal. Additionally, all complexome profiles available on CEDAR can be inspected directly on the website with the profile viewer tool that allows the detection of correlated profile sand inference of potential complexes. In conclusion, CEDAR is a unique,growing and invaluable resource for the study of protein complex composition and dynamics across biological systems.

scholarly journals cmvdrg - An R package for Human Cytomegalovirus antiviral Drug Resistance Genotyping

2020 ◽  
Author(s):  
Oscar J Charles ◽  
Cristina Venturini ◽  
Judith Breuer
Keyword(s):  
Drug Resistance ◽  
Open Source ◽  
Human Cytomegalovirus ◽  
Antiviral Drug ◽  
R Package ◽  
Effective Vaccine ◽  
Analysis Tool ◽  
Resistance Mutations ◽  
Drug Resistance Genotyping ◽  
User Friendly

AbstractThe prevention and treatment of HCMV infection is based on the utilization of antiviral therapies as HCMV lacks an effective vaccine. The rise of drug resistance is therefore an increasing patient threat. We identified the need for an open source and comprehensive HCMV resistance mutations database, to support the research community in this area. Here we present “Cytomegalovirus Drug Resistance Genotyping” (cmvdrg), a freely available database contained within an easily accessible R package, which provides a succinct extraction of literature material in the form of a text file database. Additionally, cmvdrg includes methods for calling resistance in common sequencing files and an optional user-friendly web interface.AvailabilityThe cmvdrg package is freely available under the GNU GPL v3 license at https://github.com/ucl-pathgenomics/cmvdrg,One Sentence SummaryCurrently data regarding Human Cytomegalovirus resistant mutations are contained in unconnected literature sources, here we present an exhaustive open source database and analysis tool for the community.

scholarly journals The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms

Cosmetics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 67
Author(s):  
Perry Xiao ◽  
Xu Zhang ◽  
Wei Pan ◽  
Xiang Ou ◽  
Christos Bontozoglou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Imaging Techniques ◽  
Learning Algorithms ◽  
Research Work ◽  
Software Tool ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Skin Imaging ◽  
User Friendly

We present our latest research work on the development of a skin image analysis tool by using machine-learning algorithms. Skin imaging is very import in skin research. Over the years, we have used and developed different types of skin imaging techniques. As the number of skin images and the type of skin images increase, there is a need of a dedicated skin image analysis tool. In this paper, we report the development of such software tool by using the latest MATLAB App Designer. It is simple, user friendly and yet powerful. We intend to make it available on GitHub, so that others can benefit from the software. This is an ongoing project; we are reporting here what we have achieved so far, and more functions will be added to the software in the future.

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