scholarly journals BEES: Bayesian Ensemble Estimation from SAS

2018 ◽  
Author(s):  
Samuel Bowerman ◽  
Joseph E. Curtis ◽  
Joseph Clayton ◽  
Emre H. Brookes ◽  
Jeff Wereszczynski
Keyword(s):  
Significant Risk ◽  
Secondary Data ◽  
Theoretical Models ◽  
Ease Of Use ◽  
Data Sets ◽  
Angle Scattering ◽  
Ensemble Estimation ◽  
Biomolecular Complexes ◽  
Low Dimensional ◽  
Model Structures

1AbstractMany biomolecular complexes exist in a flexible ensemble of states in solution which are necessary to perform their biological function. Small angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules due to their relative ease of use and ability to simultaneously probe the full ensemble of states. However, SAS data is typically low-dimensional and difficult to interpret without the assistance of additional structural models. In theory, experimental SAS curves can be reconstituted from a linear combination of theoretical models, although this procedure carries significant risk of overfitting the inherently low-dimensional SAS data. Previously, we developed a Bayesian-based method for fitting ensembles of model structures to experimental SAS data that rigorously avoids overfitting. However, we have found that these methods can be difficult to incorporate into typical SAS modeling workflows, especially for users that are not experts in computational modeling. To this end, we present the “Bayesian Ensemble Estimation from SAS” (BEES) program. Two forks of BEES are available, the primary one existing as module for the SASSIE webserver and a developmental version that is a standalone python program. BEES allows users to exhaustively sample ensemble models constructed from a library of theoretical states and to interactively analyze and compare each model’s performance. The fitting routine also allows for secondary data sets to be supplied, thereby simultaneously fitting models to both SAS data as well as orthogonal information. The flexible ensemble of K63-linked ubiquitin trimers is presented as an example of BEES’ capabilities.

scholarly journals The Quality of Statistical Data in the Context of User Needs and Expectations

2020 ◽  
pp. 17-29
Author(s):  
S. ZAIETS
Keyword(s):  
User Satisfaction ◽  
Open Data ◽  
Secondary Data ◽  
Visual Presentation ◽  
Statistical Information ◽  
Ease Of Use ◽  
Expert Assessment ◽  
Data Sets ◽  
Satisfaction Index

Meeting the needs and demands of consumers of statistical information requires appropriate tools to systematically determine the potential, strengths and weaknesses of state statistical institutions, as well as the risks associated with this. In this regard, the assessment of the quality of statistical information by data users is one of the key areas of work of the statistical service in modern conditions. The aim of the study is to consider approaches to assessing users' needs for high-quality statistical information in the context of global, national and information challenges of our time. The article explores ways to identify the needs of users of statistical information, summarizes the results of questionnaires, which are an integral part of quality reports. The components of the evaluation of the use of open data of the Open Data Barometer rating are analyzed, based on surveys during state self-assessment, expert assessment, and secondary data. The leading positions and bottlenecks of Ukraine in the implementation of open data sets have been identified. The advantages are considered and proposals for improving the Methodology for calculating the user satisfaction index of statistical information, which is introduced by the State Statistics Service of Ukraine in order to meet the needs and demands of consumers of statistical information, are presented. The experience of other countries on assessing the level of user satisfaction with services, which should be used in a comprehensive assessment of various aspects of the domestic statistical service and various characteristics of statistical information for users, such as understanding materials, visual presentation of information, ease of use, and more, is considered. The results of the study allowed us to provide suggestions on the need to transform the domestic statistical service into a coordinating center for the distribution of verified, processed and standardized data sets available for identification using open catalogs and data lists based on strategic partnerships with data providers, technology providers, scientists, researchers and the media.

scholarly journals The position of the farm holiday in Austrian tourism

2019 ◽  
Vol 4 (1) ◽  
pp. 697-711 ◽  
Author(s):  
Erika Quendler
Keyword(s):  
Empirical Research ◽  
Relative Position ◽  
Secondary Data ◽  
Point Of View ◽  
Local Context ◽  
Supply Side ◽  
Data Sets ◽  
Demand Side ◽  
Tourist Destinations ◽  
Tourist Accommodation

AbstractTourism is vitally important to the Austrian economy. The number of tourist destinations, both farms and other forms of accommodation, in the different regions of Austria is considerably and constantly changing. This paper discusses the position of the ‘farm holiday’ compared to other forms of tourism. Understanding the resilience of farm holidays is especially important but empirical research on this matter remains limited. The term ‘farm holiday’ covers staying overnight on a farm that is actively engaged in agriculture and has a maximum of 10 guest beds. The results reported in this paper are based on an analysis of secondary data from 2000 and 2018 by looking at two types of indicator: (i) accommodation capacity (supply side) and (ii) attractiveness of a destination (demand side). The data sets cover Austria and its NUTS3 regions. The results show the evolution of farm holidays vis-à-vis other forms of tourist accommodation. In the form of a quadrant matrix they also show the relative position of farm holidays regionally. While putting into question the resilience of farm holidays, the data also reveals where farm holidays could act to expand this niche or learn and improve to effect a shift in their respective position relative to the market ‘leaders’. However, there is clearly a need to learn more about farm holidays within the local context. This paper contributes to our knowledge of farm holidays from a regional point of view and tries to elaborate on the need for further research.

Prevalence and risk factors of root resorption of adjacent teeth in maxillary canine impaction, among untreated children and adolescents

2018 ◽  
Vol 41 (5) ◽  
pp. 447-453 ◽  
Author(s):  
Frédéric Rafflenbeul ◽  
Catherine-Isabelle Gros ◽  
François Lefebvre ◽  
Sophie Bahi-Gross ◽  
Raphaëlle Maizeray ◽  
...  
Keyword(s):  
Risk Factors ◽  
Children And Adolescents ◽  
Root Resorption ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Data Sets ◽  
Maxillary Canine ◽  
3D Data ◽  
Potential Risk Factors ◽  
Associated Risk Factors

Summary Objectives The aim of this retrospective study was to assess in maxillary canine impaction cases both the prevalence of root resorption of adjacent teeth among untreated children and adolescents, and its associated risk factors. Subjects and methods Sixty subjects (mean age 12.2 years; SD 1.9; range 8–17 years) with 83 displaced maxillary canines and without any past or ongoing orthodontic treatment were included in this study. The presence of root resorption was evaluated on images from a single cone beam computed tomography (CBCT) unit. Potential risk factors were measured on the CBCT images and on panoramic reconstructions of the 3D data sets. The sample was characterized by descriptive statistics and multiple logistic regressions were performed to predict root resorption. Results Root resorption of at least one adjacent tooth was detected in 67.5 per cent of the affected quadrants. It was found that 55.7 per cent of the lateral incisors, 8.4 per cent of the central incisors, and 19.5 per cent of first premolars were resorbed. Of the detected resorptions, 71.7 per cent were considered slight, 14.9 per cent moderate, and 13.4 per cent severe. Contact between the displaced canine(s) and the adjacent teeth roots was the only identified statistically significant risk factor, all teeth being considered (odds ratio [OR] = 18.7, 95% confidence interval: 2.26–756, P < 0.01). An enlarged canine dental follicle, a peg upper lateral, or an upper lateral agenesis were not significantly associated with root resorption of adjacent teeth, nor were age nor gender. Conclusions Root resorption of adjacent teeth was detected in more than two-thirds of a sample of sixty untreated children and adolescents.

Comparison of iterative desmearing procedures for one-dimensional small-angle scattering data

2011 ◽  
Vol 44 (1) ◽  
pp. 32-42 ◽  
Author(s):  
Thomas Vad ◽  
Wiebke F. C. Sager
Keyword(s):  
Small Angle ◽  
Incident Beam ◽  
Scattering Data ◽  
Data Sets ◽  
Convergence Criteria ◽  
Small Momentum Transfer ◽  
One Dimensional ◽  
Noise Filter ◽  
Large Numbers ◽  
Angle Scattering

Two simple iterative desmearing procedures – the Lake algorithm and the Van Cittert method – have been investigated by introducing different convergence criteria using both synthetic and experimental small-angle neutron scattering data. Implementing appropriate convergence criteria resulted in stable and reliable solutions in correcting resolution errors originating from instrumental smearing,i.e.finite collimation and polychromaticity of the incident beam. Deviations at small momentum transfer for concentrated ensembles of spheres encountered in earlier studies are not observed. Amplification of statistical errors can be reduced by applying a noise filter after desmearing. In most cases investigated, the modified Lake algorithm yields better results with a significantly smaller number of iterations and is, therefore, suitable for automated desmearing of large numbers of data sets.

scholarly journals Novel Tools Supporting Knowledge Translation for Public Health Practice in British Columbia, Canada

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Joanne Stares ◽  
Jenny Sutherland
Keyword(s):  
Public Health ◽  
Health Care ◽  
Air Quality ◽  
Public Health Practice ◽  
Secondary Data ◽  
Data Sets ◽  
Health Practice ◽  
Notifiable Diseases ◽  
Health Practitioners ◽  
Public Health Practitioners

ABSTRACT ObjectivesUnderlying the delivery of services by the universal Canadian health care system are a number of rich secondary administrative health data sets which contain information on persons who are registered for care and details on their contacts with the system. These datasets are powerful sources of information for investigation of non-notifiable diseases and as an adjunct to traditional communicable disease surveillance. However, there are gaps between public health practitioners, access to these data, and access to experts in the use of these secondary data. The data linkage requires in-depth knowledge of these data including usages, limitations and data quality issues and also the skills to extract data to support secondary usage. OLAP reports have been developed to support operation needs but not on advanced analytics reports for surveillance and cohort study. To fill these gaps, we developed a set of web-based modular, parameterized, extraction and reporting tools for the purpose of: 1) decreasing the time and resources necessary to fill general secondary data requests for public health audiences; 2) quickly providing information from descriptive analysis of secondary data to public health practitioners; 3) informing the development of data feeds for continued enhanced surveillance or further data access requests; 4) assisting in preliminary stages of epidemiological investigations of non-notifiable diseases; and, 5) facilitating access to information from secondary data for evidence-based decision making in public health. ApproachWe intend to present these tools by case study of their application to small area analysis of secondary data in the context of air quality concerns. Data sources include individuals registered for health care coverage in BC, hospital separations, physician consultations, chronic disease registries, and drugs dispensation. Data sets contain complete information from 1992. Data were extracted and analyzed to describe the occurrence of health service utilization for cardiovascular and respiratory morbidity. Analysis was undertaken for BC residents in areas identified by local public health as priorities for monitoring. Health outcomes were directly standardized by age and compared to provincial trends by use of the comparative morbidity figure. ResultsResults will include descriptive epidemiological analysis of secondary data relating to respiratory and cardiovascular morbidity in the context of air quality concerns, summary of next steps, as well as an assessment of tool performance. ConclusionsWhere adopted tools such as these can make information from secondary data more accessible to support public health practice, particularly in regions with low analytical or epidemiological capacity.

Research on capital structure determinants: a review and future directions

2017 ◽  
Vol 13 (2) ◽  
pp. 106-132 ◽  
Author(s):  
Satish Kumar ◽  
Sisira Colombage ◽  
Purnima Rao
Keyword(s):  
Capital Structure ◽  
Meta Analysis ◽  
Empirical Studies ◽  
Secondary Data ◽  
Theoretical Models ◽  
Order Theory ◽  
Organizational Level ◽  
Future Research ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper is to study the status of studies on capital structure determinants in the past 40 years. This paper highlights the major gaps in the literature on determinants of capital structure and also aims to raise specific questions for future research. Design/methodology/approach The prominence of research is assessed by studying the year of publication and region, level of economic development, firm size, data collection methods, data analysis techniques and theoretical models of capital structure from the selected papers. The review is based on 167 papers published from 1972 to 2013 in various peer-reviewed journals. The relationship of determinants of capital structure is analyzed with the help of meta-analysis. Findings Major findings show an increase of interest in research on determinants of capital structure of the firms located in emerging markets. However, it is observed that these regions are still under-examined which provides more scope for research both empirical and survey-based studies. Majority of research studies are conducted on large-sized firms by using secondary data and regression-based models for the analysis, whereas studies on small-sized firms are very meager. As majority of the research papers are written only at the organizational level, the impact of leverage on various industries is yet to be examined. The review highlights the major determinants of capital structure and their relationship with leverage. It also reveals the dominance of pecking order theory in explaining capital structure of firms theoretically as well as statistically. Originality/value The paper covers a considerable period of time (1972-2013). Among very few review papers on capital structure research, to the best of authors’ knowledge; this is the first review to identify what is missing in the literature on the determinants of capital structure while offering recommendations for future studies. It also synthesize the findings of empirical studies on determinants of capital structure statistically.

Study of the Impact of Emerging Technologies Across the Value Chain Function of Educational Technology (EdTech) Firms

2022 ◽  
pp. 221-237
Author(s):  
Som Sekhar Bhattacharyya ◽  
Ankita Walke ◽  
Yash Shah
Keyword(s):  
Value Chain ◽  
Emerging Technologies ◽  
Big Data Analytics ◽  
Secondary Data ◽  
Ease Of Use ◽  
Base Case ◽  
Content Creation ◽  
System A ◽  
Improved Performance ◽  
The Impact

Narrative technology has been a prominent feature in educational value creation. Rapid penetration of internet and better digital infrastructure resulted in adoption of emerging technologies in education sector. As business of EdTech platforms soared up, the purpose of this research was to understand the impact of emerging technologies like big data analytics, cloud-based technologies, blockchain, machine learning, artificial intelligence, augmented reality, and virtual reality on various stages of EdTech value chain. This involved content creation, content distribution, and learning plus management system. A secondary data base case study analysis was carried out of EdTech firms in India. The value factors such as cost, accessibility, ease of use, and updated content came out as main attributes impacting acceptance of EdTech platforms. The mentioned emerging technologies impacted the content creation, delivery, evaluation, and feedback stages which resulted in improved performance across these value factors with lesser associated total costs.

Overview of Predictive Modeling Approaches in Health Care Data Mining

2016 ◽  
pp. 73-95 ◽  
Author(s):  
Sunita Soni
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Clinical Information ◽  
Ease Of Use ◽  
Classification Model ◽  
Computational Time ◽  
Data Sets ◽  
Advantages And Disadvantages ◽  
Mining Methods ◽  
Predictive Data Mining

Medical data mining has great potential for exploring the hidden pattern in the data sets of the medical domain. A predictive modeling approach of Data Mining has been systematically applied for the prognosis, diagnosis, and planning for treatment of chronic disease. For example, a classification system can assist the physician to predict if the patient is likely to have a certain disease, or by considering the output of the classification model, the physician can make a better decision on the treatment to be applied to the patient. Once the model is evaluated and verified, it may be embedded within clinical information systems. The objective of this chapter is to extensively study the various predictive data mining methods to evaluate their usage in terms of accuracy, computational time, comprehensibility of the results, ease of use of the algorithm, and advantages and disadvantages to relatively naive medical users. The research has shown that there is not a single best prediction tool, but instead, the best performing algorithm will depend on the features of the dataset to be analyzed.

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