Enzyme entrapment in polyaniline films observed via fluorescence anisotropy and antiquenching

2014 ◽  
Vol 28 (11) ◽  
pp. 1430004
Author(s):  
Louis R. Nemzer ◽  
Marisa McCaffrey ◽  
Arthur J. Epstein
Keyword(s):  
Fluorescence Anisotropy ◽  
Cost Effective ◽  
Hydrophobic Collapse ◽  
Cost Effective Method ◽  
Proposed Model ◽  
Order Of Magnitude ◽  
Polyaniline Films ◽  
Oxidoreductase Enzymes ◽  
Electron Donation ◽  
Phosphate Buffered Saline

The facile entrapment of oxidoreductase enzymes within polyaniline polymer films by inducing hydrophobic collapse using phosphate buffered saline (PBS) has been shown to be a cost-effective method for fabricating organic biosensors. Here, we use fluorescence anisotropy measurements to verify enzyme immobilization and subsequent electron donation to the polymer matrix, both prerequisites for an effective biosensor. Specifically, we measure a three order of magnitude decrease in the ratio of the fluorescence to rotational lifetimes. In addition, the observed fluorescence antiquenching supports the previously proposed model that the polymer chain assumes a severely coiled conformation when exposed to PBS. These results help to empirically reinforce the theoretical basis previously laid out for this biosensing platform.

scholarly journals ORF Capture-Seq: a versatile method for targeted identification of full-length isoforms

2019 ◽  
Author(s):  
Gloria M. Sheynkman ◽  
Katharine S. Tuttle ◽  
Elizabeth Tseng ◽  
Jason G. Underwood ◽  
Liang Yu ◽  
...  
Keyword(s):  
Cost Effective ◽  
Full Length ◽  
Proof Of Concept ◽  
Protein Coding ◽  
Reading Frame ◽  
Cost Effective Method ◽  
Reasonable Cost ◽  
Order Of Magnitude ◽  
Human Isoforms ◽  
Alternative Isoforms

AbstractMost human protein-coding genes are expressed as multiple isoforms. This in turn greatly expands the functional repertoire of the encoded proteome. While at least one reliable open reading frame (ORF) model has been assigned for every gene, the majority of alternative isoforms remains uncharacterized experimentally. This is primarily due to: i) vast differences of overall levels between different isoforms expressed from common genes, and ii) the difficulty of obtaining contiguous full-length ORF sequences. Here, we present ORF Capture-Seq (OCS), a flexible and cost-effective method that addresses both challenges for targeted full-length isoform sequencing applications using collections of cloned ORFs as probes. As proof-of-concept, we show that an OCS pipeline focused on genes coding for transcription factors increases isoform detection by an order of magnitude, compared to unenriched sample. In short, OCS enables rapid discovery of isoforms from custom-selected genes and will allow mapping of the full set of human isoforms at reasonable cost.

scholarly journals An Overview of the Photocatalytic Water Splitting over Suspended Particles

Catalysts ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 60
Author(s):  
Muhammad Amtiaz Nadeem ◽  
Mohd Adnan Khan ◽  
Ahmed Abdeslam Ziani ◽  
Hicham Idriss
Keyword(s):  
Water Splitting ◽  
Large Scale ◽  
Cost Effective ◽  
Synthetic Methods ◽  
Scale Production ◽  
Practical Applications ◽  
Co Catalysts ◽  
Large Scale Production ◽  
Cost Effective Method ◽  
Order Of Magnitude

The conversion of solar to chemical energy is one of the central processes considered in the emerging renewable energy economy. Hydrogen production from water splitting over particulate semiconductor catalysts has often been proposed as a simple and a cost-effective method for large-scale production. In this review, we summarize the basic concepts of the overall water splitting (in the absence of sacrificial agents) using particulate photocatalysts, with a focus on their synthetic methods and the role of the so-called “co-catalysts”. Then, a focus is then given on improving light absorption in which the Z-scheme concept and the overall system efficiency are discussed. A section on reactor design and cost of the overall technology is given, where the possibility of the different technologies to be deployed at a commercial scale and the considerable challenges ahead are discussed. To date, the highest reported efficiency of any of these systems is at least one order of magnitude lower than that deserving consideration for practical applications.

scholarly journals Modeling the effectiveness of olfactory testing to limit SARS-2-CoV transmission

2020 ◽  
Author(s):  
Daniel B. Larremore ◽  
Derek Toomre ◽  
Roy Parker
Keyword(s):  
Low Cost ◽  
Onset Time ◽  
Cost Effective ◽  
Olfactory Dysfunction ◽  
Molecular Tests ◽  
Point Of Entry ◽  
Screening And Surveillance ◽  
Cost Effective Method ◽  
Olfactory Testing ◽  
Order Of Magnitude

AbstractA central problem in the COVID-19 pandemic is that there is not enough testing to prevent infectious spread of SARS-CoV-2, causing surges and lockdowns with human and economic toll. Molecular tests that detect viral RNAs or antigens will be unable to rise to this challenge unless testing capacity increases by at least an order of magnitude while decreasing turnaround times. Here, we evaluate an alternative strategy based on the monitoring of olfactory dysfunction, a symptom identified in 76-83% of SARS-CoV-2 infections—including those that are otherwise asymptomatic—when a standardized olfaction test is used. We model how screening for olfactory dysfunction, with reflexive molecular tests, could be beneficial in reducing community spread of SARS-CoV-2 by varying testing frequency and the prevalence, duration, and onset time of olfactory dysfunction. We find that monitoring olfactory dysfunction could reduce spread via regular screening, and could reduce risk when used at point-of-entry for single-day events. In light of these estimated impacts, and because olfactory tests can be mass produced at low cost and self-administered, we suggest that screening for olfactory dysfunction could be a high impact and cost effective method for broad COVID-19 screening and surveillance.

scholarly journals Using deep learning for Digital Soil Mapping

2018 ◽  
Author(s):  
José Padarian ◽  
Budiman Minasny ◽  
Alex B. McBratney
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Contextual Information ◽  
Cost Effective ◽  
Data Representation ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Cost Effective Method ◽  
Proposed Model

Abstract. Digital soil mapping has been widely used as a cost-effective method for generating soil maps. However, current DSM data representation rarely incorporates contextual information of the landscape. DSM models are usually calibrated using point observations intersected with spatially corresponding point covariates. Here, we demonstrate the use of the convolutional neural network model that incorporates contextual information surrounding an observation to significantly improve the prediction accuracy over conventional DSM models. We describe a convolutional neural network (CNN) model that takes inputs as images of covariates and explores spatial contextual information by finding non-linear local spatial relationships of neighbouring pixels. Unique features of the proposed model include: input represented as 3D stack of images, data augmentation to reduce overfitting, and simultaneously predicting multiple outputs. Using a soil mapping example in Chile, the CNN model was trained to simultaneously predict soil organic carbon at multiples depths across the country. The results showed the CNN model reduced the error by 30 % compared with conventional techniques that only used point information of covariates. In the example of country-wide mapping at 100 m resolution, the neighbourhood size from 3 to 9 pixels is more effective than at a point location and larger neighbourhood sizes. In addition, the CNN model produces less prediction uncertainty and it is able to predict soil carbon at deeper soil layers more accurately. Because the CNN model takes covariate represented as images, it offers a simple and effective framework for future DSM models.

scholarly journals Using deep learning for digital soil mapping

SOIL ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 79-89 ◽  
Author(s):  
José Padarian ◽  
Budiman Minasny ◽  
Alex B. McBratney
Keyword(s):  
Data Augmentation ◽  
Contextual Information ◽  
Cost Effective ◽  
Data Representation ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Layers ◽  
Cost Effective Method ◽  
Proposed Model ◽  
Simultaneous Prediction

Abstract. Digital soil mapping (DSM) has been widely used as a cost-effective method for generating soil maps. However, current DSM data representation rarely incorporates contextual information of the landscape. DSM models are usually calibrated using point observations intersected with spatially corresponding point covariates. Here, we demonstrate the use of the convolutional neural network (CNN) model that incorporates contextual information surrounding an observation to significantly improve the prediction accuracy over conventional DSM models. We describe a CNN model that takes inputs as images of covariates and explores spatial contextual information by finding non-linear local spatial relationships of neighbouring pixels. Unique features of the proposed model include input represented as a 3-D stack of images, data augmentation to reduce overfitting, and the simultaneous prediction of multiple outputs. Using a soil mapping example in Chile, the CNN model was trained to simultaneously predict soil organic carbon at multiples depths across the country. The results showed that, in this study, the CNN model reduced the error by 30 % compared with conventional techniques that only used point information of covariates. In the example of country-wide mapping at 100 m resolution, the neighbourhood size from 3 to 9 pixels is more effective than at a point location and larger neighbourhood sizes. In addition, the CNN model produces less prediction uncertainty and it is able to predict soil carbon at deeper soil layers more accurately. Because the CNN model takes the covariate represented as images, it offers a simple and effective framework for future DSM models.

scholarly journals Modeling the effectiveness of olfactory testing to limit SARS-CoV-2 transmission

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Daniel B. Larremore ◽  
Derek Toomre ◽  
Roy Parker
Keyword(s):  
Low Cost ◽  
Onset Time ◽  
Cost Effective ◽  
Olfactory Dysfunction ◽  
Molecular Tests ◽  
Point Of Entry ◽  
Screening And Surveillance ◽  
Cost Effective Method ◽  
Olfactory Testing ◽  
Order Of Magnitude

AbstractA central problem in the COVID-19 pandemic is that there is not enough testing to prevent infectious spread of SARS-CoV-2, causing surges and lockdowns with human and economic toll. Molecular tests that detect viral RNAs or antigens will be unable to rise to this challenge unless testing capacity increases by at least an order of magnitude while decreasing turnaround times. Here, we evaluate an alternative strategy based on the monitoring of olfactory dysfunction, a symptom identified in 76–83% of SARS-CoV-2 infections—including those with no other symptoms—when a standardized olfaction test is used. We model how screening for olfactory dysfunction, with reflexive molecular tests, could be beneficial in reducing community spread of SARS-CoV-2 by varying testing frequency and the prevalence, duration, and onset time of olfactory dysfunction. We find that monitoring olfactory dysfunction could reduce spread via regular screening, and could reduce risk when used at point-of-entry for single-day events. In light of these estimated impacts, and because olfactory tests can be mass produced at low cost and self-administered, we suggest that screening for olfactory dysfunction could be a high impact and cost-effective method for broad COVID-19 screening and surveillance.

scholarly journals Apple Quality Identification and Classification by Computer Vision Based on Deep Learning

2021 ◽  
Author(s):  
Yanfei Li ◽  
Xianying Feng ◽  
Yandong Liu ◽  
Xingchang Han
Keyword(s):  
Cost Effective ◽  
Support Vector ◽  
Svm Classifier ◽  
Histogram Of Oriented Gradient ◽  
Quality Classification ◽  
Cost Effective Method ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Apple Quality

Abstract This work researched apple quality identification and classification from real images containing complicated disturbance information (background was similar to the surface of the apples). This paper proposed a novel model based on Convolutional Neural Networks (CNN) which aimed at accurate and fast grading of apple quality. The proposed model was trained and validated, with best training and validation accuracy of 99% and 98.98% at 2590th and 3000th step, respectively. Two other methods, which were Google Inception v3 model and traditional imaging process method, were also used for apple quality classification. The greatest training accuracy of the Google Inception v3 model was 92% with 91.2% validation accuracy. The 78.14% accuracy was obtained by traditional method based on histogram of oriented gradient (HOG) and gray level co-occurrence matrix (GLCM) features merging and support vector machine (SVM) classifier. The three models were tested using independent 300 apples testing set, getting accuracy of 95.33%, 91.33%, and 77.67%, respectively. The results showed that the proposed model was more helpful and accurate for classification of apple quality. Furthermore, the training times of three methods were 27, 51, and 287 minutes, respectively. The proposed model can be considered a cost-effective method for fast grading of apple quality.

scholarly journals Badges to Acknowledge Open Practices: A Simple, Low-Cost, Effective Method for Increasing Transparency

2016 ◽  
Author(s):  
Mallory Kidwell ◽  
Ljiljana B. Lazarevic ◽  
Erica Baranski ◽  
Tom Elis Hardwicke ◽  
Sarah Piechowski ◽  
...  
Keyword(s):  
Data Sharing ◽  
Low Cost ◽  
Open Data ◽  
Cost Effective ◽  
Change Over Time ◽  
Psychological Science ◽  
Cost Effective Method ◽  
Order Of Magnitude ◽  
Open Materials ◽  
Over Time

Beginning January 2014, Psychological Science gave authors the opportunity to signal open data and materials if they qualified for badges that accompanied published articles. Before badges, less than 3% of Psychological Science articles reported open data. After badges, 23% reported open data, with an accelerating trend; 39% reported open data in the first half of 2015, an increase of more than an order of magnitude from baseline. There was no change over time in the low rates of data sharing among comparison journals. Moreover, reporting openness does not guarantee openness. When badges were earned, reportedly available data were more likely to be actually available, correct, usable, and complete than when badges were not earned. Open materials also increased to a weaker degree, and there was more variability among comparison journals. Badges are simple, effective signals to promote open practices and improve preservation of data and materials by using independent repositories.

CAD Of Anticorrosive Protection Of Steel Structures

2019 ◽  
pp. 18-23
Keyword(s):  
Protective Coating ◽  
Computer Aided Design ◽  
Steel Structures ◽  
Cost Effective ◽  
Operating Conditions ◽  
Computational Tool ◽  
Protection Method ◽  
Cost Effective Method ◽  
The Cost ◽  
Aided Design

The choice of cost-effective method of anticorrosive protection of steel structures is an urgent and time consuming task, considering the significant number of protection ways, differing from each other in the complex of technological, physical, chemical and economic characteristics. To reduce the complexity of solving this problem, the author proposes a computational tool that can be considered as a subsystem of computer-aided design and used at the stage of variant and detailed design of steel structures. As a criterion of the effectiveness of the anti-corrosion protection method, the cost of the protective coating during the service life is accepted. The analysis of existing methods of steel protection against corrosion is performed, the possibility of their use for the protection of the most common steel structures is established, as well as the estimated period of effective operation of the coating. The developed computational tool makes it possible to choose the best method of protection of steel structures against corrosion, taking into account the operating conditions of the protected structure and the possibility of using a protective coating.

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