scholarly journals Computational processing and error reduction strategies for standardized quantitative data in biological networks

FEBS Journal ◽  
2005 ◽  
Vol 272 (24) ◽  
pp. 6400-6411 ◽  
Author(s):  
Marcel Schilling ◽  
Thomas Maiwald ◽  
Sebastian Bohl ◽  
Markus Kollmann ◽  
Clemens Kreutz ◽  
...  
Keyword(s):  
Biological Networks ◽  
Quantitative Data ◽  
Error Reduction ◽  
Reduction Strategies

The Basis of Navigation Across Species

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Cody A. Freas ◽  
Ken Cheng
Keyword(s):  
Large Scale ◽  
Error Reduction ◽  
Annual Review ◽  
Publication Date ◽  
Additional Component ◽  
Oscillatory Systems ◽  
Neural Architecture ◽  
Wide Range ◽  
Reduction Strategies ◽  
Cycling Behavior

Animals navigate a wide range of distances, from a few millimeters to globe-spanning journeys of thousands of kilometers. Despite this array of navigational challenges, similar principles underlie these behaviors across species. Here, we focus on the navigational strategies and supporting mechanisms in four well-known systems: the large-scale migratory behaviors of sea turtles and lepidopterans as well as navigation on a smaller scale by rats and solitarily foraging ants. In lepidopterans, rats, and ants we also discuss the current understanding of the neural architecture which supports navigation. The orientation and navigational behaviors of these animals are defined in terms of behavioral error-reduction strategies reliant on multiple goal-directed servomechanisms. We conclude by proposing to incorporate an additional component into this system: the observation that servomechanisms operate on oscillatory systems of cycling behavior. These oscillators and servomechanisms comprise the basis for directed orientation and navigational behaviors. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Creating, generating and comparing random network models with NetworkRandomizer

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2524 ◽  
Author(s):  
Gabriele Tosadori ◽  
Ivan Bestvina ◽  
Fausto Spoto ◽  
Carlo Laudanna ◽  
Giovanni Scardoni
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Quantitative Data ◽  
Random Network ◽  
Network Models ◽  
Information Network ◽  
Statistical Tool ◽  
New Techniques ◽  
High Throughput Data ◽  
Multiplication Algorithm

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Error Reduction in Surgical Pathology

2006 ◽  
Vol 130 (5) ◽  
pp. 630-632
Author(s):  
Raouf E. Nakhleh
Keyword(s):  
Information Access ◽  
Error Reduction ◽  
Significant Error ◽  
Surgical Pathology ◽  
Complex Nature ◽  
Tight Coupling ◽  
Multiple Factors ◽  
Ample Opportunity ◽  
Reduction Strategies ◽  
Culture Strategies

Abstract Context.—Because of its complex nature, surgical pathology practice is inherently error prone. Currently, there is pressure to reduce errors in medicine, including pathology. Objective.—To review factors that contribute to errors and to discuss error-reduction strategies. Design.—Literature review. Results.—Multiple factors contribute to errors in medicine, including variable input, complexity, inconsistency, tight coupling, human intervention, time constraints, and a hierarchical culture. Strategies that may reduce errors include reducing reliance on memory, improving information access, error-proofing processes, decreasing reliance on vigilance, standardizing tasks and language, reducing the number of handoffs, simplifying processes, adjusting work schedules and environment, providing adequate training, and placing the correct people in the correct jobs. Conclusions.—Surgical pathology is a complex system with ample opportunity for error. Significant error reduction is unlikely to occur without a sustained comprehensive program of quality control and quality assurance. Incremental adoption of information technology and automation along with improved training in patient safety and quality management can help reduce errors.

The Institute of Medicine's Report on Medical Error

2000 ◽  
Vol 124 (11) ◽  
pp. 1674-1678 ◽  
Author(s):  
Ronald L. Sirota
Keyword(s):  
United States ◽  
Patient Safety ◽  
Medical Error ◽  
Relevant Literature ◽  
The United States ◽  
Error Reduction ◽  
High Rate ◽  
Institute Of Medicine ◽  
The Past ◽  
Reduction Strategies

Abstract Context.—During the past several years, more attention has been focused on the topics of medical error and patient safety than in the past. At the end of 1999, the Institute of Medicine (IOM) published a seminal report concerning medical error in the United States; this report will have sweeping implications for all disciplines of medicine, including pathology. Objective.—To review the major findings of the IOM report on medical error and to discuss their implications for the field of pathology. Methods.—Review of the IOM report on medical error and discussion of other relevant literature on medical error. Results.—The IOM report on medical error highlights an unacceptable rate of medical error in the United States and mandates a 50% reduction in medical error during the next 5 years. It recommends regulatory solutions to this problem, as well as organizational approaches to error reduction. It proposes both mandatory and voluntary systems for reporting of medical error. The report suggests that systems should be examined for latent flaws and that individual culpability for error should not be overemphasized. The report recommends that error-reduction strategies that have been applied to other industries should be studied and that known concepts of error reduction should be applied to medicine. Strategies that the IOM suggests can be applied to pathology. Conclusions.—Medical error occurs at an unacceptably high rate. Recommendations made in the IOM report on medical error and patient safety should be applied to the practice of pathology.

scholarly journals Medication errors in a pediatric anesthesia setting: Incidence, etiologies, and error reduction strategies

2018 ◽  
Vol 49 ◽  
pp. 107-111 ◽  
Author(s):  
Izabela C. Leahy ◽  
Meghan Lavoie ◽  
David Zurakowski ◽  
Amanda W. Baier ◽  
Robert M. Brustowicz
Keyword(s):  
Medication Errors ◽  
Pediatric Anesthesia ◽  
Error Reduction ◽  
Reduction Strategies

scholarly journals Creating, generating and comparing random network models with NetworkRandomizer

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2524 ◽  
Author(s):  
Gabriele Tosadori ◽  
Ivan Bestvina ◽  
Fausto Spoto ◽  
Carlo Laudanna ◽  
Giovanni Scardoni
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Quantitative Data ◽  
Random Network ◽  
Network Models ◽  
Information Network ◽  
Statistical Tool ◽  
New Techniques ◽  
High Throughput Data ◽  
Multiplication Algorithm

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

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