Analysis of factors influencing skin reactions to sunscreens, skin whitening products, and deodorants: Results from a large‐scale patch test dataset in China

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

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Factors influencing large-scale micropropagation of Sphagneticola calendulacea (L.) Pruski and clonality assessment using RAPD and ISSR markers

In Vitro Cellular & Developmental Biology - Plant ◽

10.1007/s11627-017-9824-7 ◽

2017 ◽

Vol 53 (3) ◽

pp. 167-177 ◽

Cited By ~ 10

Author(s):

Suprabuddha Kundu ◽

Umme Salma ◽

Md. Nasim Ali ◽

Nirmal Mandal

Keyword(s):

Large Scale ◽

Issr Markers ◽

Factors Influencing ◽

Sphagneticola Calendulacea ◽

Rapd And Issr ◽

Clonality Assessment

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Analysis of Factors Influencing Effective Utilization Coefficient of Irrigation Water in the Manas River Basin

Water ◽

10.3390/w13020189 ◽

2021 ◽

Vol 13 (2) ◽

pp. 189

Author(s):

Lili Yang ◽

Tong Heng ◽

Guang Yang ◽

Xinchen Gu ◽

Jiaxin Wang ◽

...

Keyword(s):

Irrigation Water ◽

Large Scale ◽

Grey Model ◽

Analytic Hierarchy ◽

Effective Utilization ◽

Factors Affecting ◽

Factors Influencing ◽

Utilization Coefficient ◽

Using Data ◽

Effective Use

The factors influencing the effective utilization coefficient of irrigation water are not understood well. It is usually considered that this coefficient is lower in areas with large-scale irrigation. With this background, we analyzed the effective utilization coefficient of irrigation water using the analytic hierarchy process using data from 2014 to 2019 in Shihezi City, Xinjiang. The weights of the influencing factors on the effective utilization coefficient of irrigation water in different irrigation areas were analyzed. Predictions of the coefficient’s values for different years were made by understanding the trends based on the grey model. The results show that the scale of the irrigation area is not the only factor determining the effective utilization coefficient of irrigation water. Irrigation technology, organizational integrity, crop types, water price management, local economic level, and channel seepage prevention are the most critical factors affecting the effective use of irrigation water. The grey model prediction results show that the effective utilization coefficient of farmland irrigation water will continuously increase and reach 0.7204 in 2029. This research can serve as a reference for government authorities to make scientific decisions on water-saving projects in irrigation districts in terms of management, operation, and investment.

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Understanding Factors Influencing Overheating: The UK’s First Large-Scale Domestic Passivhaus Retrofit

Sustainable Ecological Engineering Design ◽

10.1007/978-3-030-44381-8_30 ◽

2020 ◽

pp. 393-410

Author(s):

Dean Myers ◽

Christopher Gorse ◽

David Johnston

Keyword(s):

Large Scale ◽

Factors Influencing

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Evolution of Large‐Scale Factors Influencing Extreme Rainfall over South Western Coast of India

International Journal of Climatology ◽

10.1002/joc.7455 ◽

2021 ◽

Author(s):

Ajil Kottayil ◽

Anu Xavier ◽

Prince Xavier ◽

K Prajwal ◽

K Mohanakumar

Keyword(s):

Large Scale ◽

Extreme Rainfall ◽

Western Coast ◽

Scale Factors ◽

Factors Influencing

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Automated Detection of Paleoenvironmental Proxy, Eucampia Index, in a Microscopic Slide Using a Convolutional Neural Network System

10.21203/rs.3.rs-88945/v1 ◽

2020 ◽

Author(s):

Saki Ishino ◽

Takuya Itaki

Keyword(s):

Southern Ocean ◽

Large Scale ◽

Classification Performance ◽

Automated Detection ◽

Model Verification ◽

Training Dataset ◽

Test Dataset ◽

Counting Error ◽

Index Value ◽

Particle Images

Abstract The Eucampia Index, which is calculated from valve ratio of Antarctic diatom Eucampia ainarctica varieties, has been expected to be a useful indicator of sea ice coverage or/and sea surface temperature variation in the Southern Ocean. To verify the relationship between the index value and the environmental factors, considerable effort is needed to classify and count valves of E. antarctica in a very large number of samples. In this study, to realize automated detection of the Eucampia Index, we constructed a deep-learning (one of the learning methods of artificial intelligence) based models for identifying Eucampia valves from various particles in a diatom slide. The microfossil Classification and Rapid Accumulation Device (miCRAD) system, which can be used for scanning a slide and cropping images of particles automatically, was employed to collect images in training dataset for the model and test dataset for model verification. As a result of classifying particle images in the test dataset by the initial model "Eant_1000px_200616", accuracy was 78.8%. The Eucampia Index value prepared in the test dataset was 0.80, and the value predicted using the developed model from the same dataset was 0.76. The predicted value was in the range of the manual counting error. These results suggest that the classification performance of the model is similar to that of a human expert. This study revealed that a model capable of detecting the ratio of two diatom species can be constructed using the miCRAD system for the first time. The miCRAD system connected with the developed model in this study is capable of automatically classifying particle images at the same time of capturing images so that the system can be applied to a large-scale analysis of the Eucampia index in the Southern Ocean. Depending on the setting of the classification category, similar method is relevant to investigators who have to process a large number of diatom samples such as for detecting specific species for biostratigraphic and paleoenvironmental studies.

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New Approaches for Quantitative Reconstruction of Radiation Dose in Human Blood Cells

Scientific Reports ◽

10.1038/s41598-019-54967-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Shanaz A. Ghandhi ◽

Igor Shuryak ◽

Shad R. Morton ◽

Sally A. Amundson ◽

David J. Brenner

Keyword(s):

Radiation Dose ◽

Large Scale ◽

Mean Squared Error ◽

Data Sets ◽

Reconstruction Method ◽

Dose Reconstruction ◽

Test Dataset ◽

Human Blood Cells ◽

Exposure Levels

AbstractIn the event of a nuclear attack or large-scale radiation event, there would be an urgent need for assessing the dose to which hundreds or thousands of individuals were exposed. Biodosimetry approaches are being developed to address this need, including transcriptomics. Studies have identified many genes with potential for biodosimetry, but, to date most have focused on classification of samples by exposure levels, rather than dose reconstruction. We report here a proof-of-principle study applying new methods to select radiation-responsive genes to generate quantitative, rather than categorical, radiation dose reconstructions based on a blood sample. We used a new normalization method to reduce effects of variability of signal intensity in unirradiated samples across studies; developed a quantitative dose-reconstruction method that is generally under-utilized compared to categorical methods; and combined these to determine a gene set as a reconstructor. Our dose-reconstruction biomarker was trained using two data sets and tested on two independent ones. It was able to reconstruct dose up to 4.5 Gy with root mean squared error (RMSE) of ± 0.35 Gy on a test dataset using the same platform, and up to 6.0 Gy with RMSE of ± 1.74 Gy on a test set using a different platform.

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