scholarly journals Inconsistencies in C. elegans behavioural annotation

2016 ◽  
Author(s):  
Balazs Szigeti ◽  
Thomas Stone ◽  
Barbara Webb
Keyword(s):  
Online Survey ◽  
Expert Judgement ◽  
High Quality ◽  
Data Set ◽  
Psychophysical Procedure ◽  
C Elegans ◽  
Detection Algorithms ◽  
Fixed Set ◽  
Expert Annotation ◽  
Automated Methods

High quality behavioural annotation is a key component to link genes to behaviour, yet relatively little attention has been paid to check the consistency of various automated methods and expert judgement. In this paper we investigate the consistency of annotation for the ‘Omega turn’ of C. elegans, which is a frequently used behavioural assay for this animal. First the output of four Omega detection algorithms are examined for the same data set, and shown to have relative low consistency, with F-scores around 0.5. Consistency of expert annotation is then analysed, based on an online survey combining two methods: participants judged a fixed set of predetermined clips; and an adaptive psychophysical procedure was used to estimate individual's threshold for Omega turn detection. This survey also revealed a substantial lack of consistency in decisions and thresholds. Such inconsistency makes cross-publication comparison difficult and raises issues of reproducibility.

scholarly journals Text Mining of Hazard and Operability Analysis Reports Based on Active Learning

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1178
Author(s):  
Zhenhua Wang ◽  
Beike Zhang ◽  
Dong Gao
Keyword(s):  
Active Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Chemical System ◽  
Chemical Safety ◽  
High Quality ◽  
Data Set ◽  
Final Model ◽  
Named Entity ◽  
Sampling Algorithms

In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize the hidden dangers in the system, and be of great significance to improve the safety of the whole chemical system. However, due to the standardization and professionalism of chemical safety analysis text, it is difficult to improve the performance of traditional models. To solve this problem, in this study, an improved method based on active learning is proposed, and three novel sampling algorithms are designed, Variation of Token Entropy (VTE), HAZOP Confusion Entropy (HCE) and Amplification of Least Confidence (ALC), which improve the ability of the model to understand HAZOP text. In this method, a part of data is used to establish the initial model. The sampling algorithm is then used to select high-quality samples from the data set. Finally, these high-quality samples are used to retrain the whole model to obtain the final model. The experimental results show that the performance of the VTE, HCE, and ALC algorithms are better than that of random sampling algorithms. In addition, compared with other methods, the performance of the traditional model is improved effectively by the method proposed in this paper, which proves that the method is reliable and advanced.

scholarly journals Fractional Order Processing of Satellite Images

2021 ◽  
Vol 11 (11) ◽  
pp. 5288
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Rui Melicio
Keyword(s):  
Edge Detection ◽  
Fractional Order ◽  
High Performance ◽  
Satellite Images ◽  
Automatic Detection ◽  
Automatic Processing ◽  
Order Processing ◽  
Detection Algorithms ◽  
Grey Scale ◽  
Fixed Set

Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

scholarly journals Mask R-CNN Based C. Elegans Detection with a DIY Microscope

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 257
Author(s):  
Sebastian Fudickar ◽  
Eike Jannik Nustede ◽  
Eike Dreyer ◽  
Julia Bornhorst
Keyword(s):  
Cell Biology ◽  
High Throughput Screening ◽  
Low Cost ◽  
Image Acquisition ◽  
Rapid Development ◽  
Model Organism ◽  
Large Data ◽  
Data Set ◽  
C Elegans ◽  
Do It Yourself

Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an [email protected] IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906.

scholarly journals What the Public Think about Social Services: A Report from Scotland

2019 ◽  
Vol 50 (4) ◽  
pp. 1146-1166
Author(s):  
Trish McCulloch ◽  
Stephen Webb
Keyword(s):  
Social Services ◽  
Public Perception ◽  
Online Survey ◽  
Public Perceptions ◽  
Data Set ◽  
Service Workers ◽  
The Public ◽  
Welfare Institutions ◽  
Nationally Representative ◽  
Two Stages

Abstract This article reports on findings of a government-funded research project which set out to understand what the public think about social services in Scotland. The authors were particularly keen to examine issues of legitimacy, trust and licence to operate for social services as they are framed in public perceptions. Drawing on a national online survey of 2,505 nationally representative adults, the findings provide the first and largest empirical data set on public perceptions of social services in Scotland. Data analysis occurred in two stages and employed descriptive statistical measurement and cross-tabulation analysis. The findings indicate that, overall, people in Scotland are positive about social services and the value of their impact on society. Furthermore, they believe that social services perform a valuable public role. These findings are significant for debates surrounding social services and suggest that the Scottish public has a more positive view of social services than social service workers and welfare institutions typically perceive. The findings demonstrate the need to develop a more theoretically rich understanding of the relationships between public perception, legitimacy and social licence in social services, including attention to co-productive models of engagement.

scholarly journals Louisiana Medicaid access for treatment and care for hepatitis C virus (LA-MATCH) project: A cross-sectional study protocol

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257437
Author(s):  
Hasheemah Afaneh ◽  
Susanne Straif-Bourgeois ◽  
Evrim Oral ◽  
Ashley Wennerstrom ◽  
Olivia Sugarman ◽  
...  
Keyword(s):  
Hepatitis C ◽  
Online Survey ◽  
Cross Sectional Study ◽  
Fixed Cost ◽  
Sectional Study ◽  
Hcv Treatment ◽  
Cross Sectional ◽  
Data Set ◽  
Direct Acting Antiviral ◽  
Louisiana State University

Introduction This article presents the Louisiana Hepatitis C Elimination Program’s evaluation protocol underway at the Louisiana State University Health Sciences Center–New Orleans. With the availability of direct-acting antiviral (DAA) agents, the elimination of Hepatitis C (HCV) has become a possibility. The HCV Elimination Program was initiated by the Louisiana Department of Health (LDH) Office of Public Health (OPH), LDH Bureau of Health Services Financing (Medicaid), and the Louisiana Department of Public Safety and Corrections (DPSC) to provide HCV treatment through an innovative pricing arrangement with Asegua Therapeutics, whereby a fixed cost is set for a supply of treatment over five years. Materials and methods A cross-sectional study design will be used. Data will be gathered from two sources: 1) an online survey administered via REDCap to a sample of Medicaid members who are receiving HCV treatment, and 2) a de-identified data set that includes both Medicaid claims data and OPH surveillance data procured via a Data Use Agreement between LSUHSC-NO and Louisiana Medicaid. Discussion The evaluation will contribute to an understanding of the scope and reach of this innovative treatment model, and as a result, an understanding of areas for improvement. Further, this evaluation may provide insight for other states considering similar contracting mechanisms and programs.

scholarly journals Portrait of a Russian Homeschooler’s Family: Results of an Online Poll

Discourse ◽  
2021 ◽  
Vol 6 (6) ◽  
pp. 87-98
Author(s):  
M. N. Yashina
Keyword(s):  
Online Survey ◽  
Primary School Children ◽  
Family Education ◽  
Data Set ◽  
Number Of Children ◽  
Random Samples ◽  
The Social ◽  
The Family ◽  
Total Data ◽  
High Level

Introduction. The practice of obtaining family education has become a fashionable trend in our country in recent years. Despite the growing popularity among the population, we have not yet received enough scientific understanding of this form of training. The purpose of the article is to describe the social portrait of families who have chosen a family form of education for a child. The scientific novelty of the work is due to the empirical data presented in it, which have a dynamic nature of observing the studied object.Methodology and sources. The methodological basis for the study was the conflict approach and the principles of a radical humanistic approach to education in the interpretation of I. Illich. The empirical basis of the study is the results of three surveys of parents o f c hildren f rom 6 t o 1 8 y ears o ld w ho are o n f amily e ducation. S urveys w ere implemented from 2016 to 2020, according to the same methodology and tools. To collect data, a questionnaire for an online survey was developed, which was distributed on social networks, mainly in VKontakte communities dedicated to family education. The total data set includes 443 respondents.Results and discussion. According to surveys, children in family education grow up in full families, where the parent's ode has a high level of education, the mother, as a rule, does not work or has the possibility of a free schedule and is a teacher for the child. The main source of income in the family is the father. The total income of the family, which averages from 40 to 60 thousand rubles per month. In the family, most often two children, one of whom is in family education. Family education is mainly provided with primary school children.Conclusion. The peculiarity of studying family education not only in our country, but also in the world is the lack of accurate statistics on the number of children of homeschoolers. In this regard, only non-random samples are possible in the implementation. The portrait of Russian homeschoolers differs from American ones, in particular in the level at which family education is implemented, the place of residence of families, and their income.

scholarly journals Online Grocery Shopping: a Boom, Hype, or Black Ice?

2017 ◽  
Author(s):  
Stefan Bongard
Keyword(s):  
Online Survey ◽  
Business Models ◽  
Economic Research ◽  
Grocery Shopping ◽  
Sustainable Business ◽  
Data Set ◽  
Convenience Sample ◽  
Survey Tool ◽  
Grocery Industry ◽  
Food And Drink

Buying groceries online is no longer a novel phenomenon: recent studies (2016) show that in Germany, approximately 30 percent of potential buyers have already purchased groceries online. Together with the latest grocery shopping services from the online giant Amazon (e.g. Amazon Fresh and Amazon go), this growing sector of online food and drink retail comprises an attractive field for economic research. General research objectives in this field investigate sustainable business models, planning of logistics structures, and changes in buyer behaviour. The purpose of this present study was to analyze buyer behavior in the field of online food retail based on a process design derived from principles of Quality Management. A convenience sample of 822 valid data records was collected from November– December 2016 using a sophisticated online survey tool. The data set contains responses from 256 individuals who had already bought groceries online, while the rest of the respondents had not previously purchased groceries online. The study strongly underscores the great potential of online retail grocery industry, while also detailing the potential risks associated with this business model, such as low profit margins and packaging issues.

Alternative Clustering

2017 ◽  
pp. 1-12 ◽  
Author(s):  
Avinash Navlani ◽  
V. B. Gupta
Keyword(s):  
Research Problem ◽  
Research Community ◽  
Data Sets ◽  
Complex Data ◽  
High Quality ◽  
Data Set ◽  
Alternative Clustering ◽  
Complex Data Sets ◽  
Data Objects ◽  
Community Clustering

In the last couple of decades, clustering has become a very crucial research problem in the data mining research community. Clustering refers to the partitioning of data objects such as records and documents into groups or clusters of similar characteristics. Clustering is unsupervised learning, because of unsupervised nature there is no unique solution for all problems. Most of the time complex data sets require explanation in multiple clustering sets. All the Traditional clustering approaches generate single clustering. There is more than one pattern in a dataset; each of patterns can be interesting in from different perspectives. Alternative clustering intends to find all unlike groupings of the data set such that each grouping has high quality and distinct from each other. This chapter gives you an overall view of alternative clustering; it's various approaches, related work, comparing with various confusing related terms like subspace, multi-view, and ensemble clustering, applications, issues, and challenges.

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