A New Automated Method for Improved Flood Defense Representation in Large‐Scale Hydraulic Models

Objectives: This study aimed to develop a fully automated human identification method based on a convolutional neural network (CNN) with a large-scale dental panoramic radiograph (DPR) dataset. Methods: In total, 2,760 DPRs from 746 subjects who had 2 to 17 DPRs with various changes in image characteristics due to various dental treatments (tooth extraction, oral surgery, prosthetics, orthodontics, or tooth development) were collected. The test dataset included the latest DPR of each subject (746 images) and the other DPRs (2,014 images) were used for model training. A modified VGG16 model with two fully connected layers was applied for human identification. The proposed model was evaluated with rank-1, –3, and −5 accuracies, running time, and gradient-weighted class activation mapping (Grad-CAM)–applied images. Results: This model had rank-1,–3, and −5 accuracies of 82.84%, 89.14%, and 92.23%, respectively. All rank-1 accuracy values of the proposed model were above 80% regardless of changes in image characteristics. The average running time to train the proposed model was 60.9 sec per epoch, and the prediction time for 746 test DPRs was short (3.2 sec/image). The Grad-CAM technique verified that the model automatically identified humans by focusing on identifiable dental information. Conclusion: The proposed model showed good performance in fully automatic human identification despite differing image characteristics of DPRs acquired from the same patients. Our model is expected to assist in the fast and accurate identification by experts by comparing large amounts of images and proposing identification candidates at high speed.

Download Full-text

Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

Journal of Advanced Transportation ◽

10.1155/2018/8963234 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Raj Bridgelall ◽

Pan Lu ◽

Denver D. Tolliver ◽

Tai Xu

Keyword(s):

Large Scale ◽

Statistical Tests ◽

Poor Quality ◽

Connected Vehicle ◽

Cleaning Method ◽

Mobility Data ◽

Automated Method ◽

Vehicle Data ◽

Chi Squared ◽

Trip Production

On-demand shared mobility services such as Uber and microtransit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.

Download Full-text

A fully-automated method discovers loss of mouse-lethal and human-monogenic disease genes in 58 mammals

Nucleic Acids Research ◽

10.1093/nar/gkaa550 ◽

2020 ◽

Vol 48 (16) ◽

pp. e91-e91

Author(s):

Yatish Turakhia ◽

Heidi I Chen ◽

Amir Marcovitz ◽

Gill Bejerano

Keyword(s):

Evolutionary Biology ◽

Large Scale ◽

Gene Annotation ◽

Monogenic Disease ◽

Disease Genes ◽

Congenital Diseases ◽

Manual Curation ◽

Automated Method ◽

Human Ortholog ◽

Early Mouse

Abstract Gene losses provide an insightful route for studying the morphological and physiological adaptations of species, but their discovery is challenging. Existing genome annotation tools focus on annotating intact genes and do not attempt to distinguish nonfunctional genes from genes missing annotation due to sequencing and assembly artifacts. Previous attempts to annotate gene losses have required significant manual curation, which hampers their scalability for the ever-increasing deluge of newly sequenced genomes. Using extreme sequence erosion (amino acid deletions and substitutions) and sister species support as an unambiguous signature of loss, we developed an automated approach for detecting high-confidence gene loss events across a species tree. Our approach relies solely on gene annotation in a single reference genome, raw assemblies for the remaining species to analyze, and the associated phylogenetic tree for all organisms involved. Using human as reference, we discovered over 400 unique human ortholog erosion events across 58 mammals. This includes dozens of clade-specific losses of genes that result in early mouse lethality or are associated with severe human congenital diseases. Our discoveries yield intriguing potential for translational medical genetics and evolutionary biology, and our approach is readily applicable to large-scale genome sequencing efforts across the tree of life.

Download Full-text

Large-scale analysis of iliopsoas muscle volumes in the UK Biobank

Scientific Reports ◽

10.1038/s41598-020-77351-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Julie A. Fitzpatrick ◽

Nicolas Basty ◽

Madeleine Cule ◽

Yi Liu ◽

Jimmy D. Bell ◽

...

Keyword(s):

Large Scale ◽

Large Population ◽

Psoas Muscle ◽

Magnetic Resonance Images ◽

Muscle Volume ◽

Iliopsoas Muscle ◽

Uk Biobank ◽

Cross Sectional ◽

Automated Method ◽

The Uk

AbstractPsoas muscle measurements are frequently used as markers of sarcopenia and predictors of health. Manually measured cross-sectional areas are most commonly used, but there is a lack of consistency regarding the position of the measurement and manual annotations are not practical for large population studies. We have developed a fully automated method to measure iliopsoas muscle volume (comprised of the psoas and iliacus muscles) using a convolutional neural network. Magnetic resonance images were obtained from the UK Biobank for 5000 participants, balanced for age, gender and BMI. Ninety manual annotations were available for model training and validation. The model showed excellent performance against out-of-sample data (average dice score coefficient of 0.9046 ± 0.0058 for six-fold cross-validation). Iliopsoas muscle volumes were successfully measured in all 5000 participants. Iliopsoas volume was greater in male compared with female subjects. There was a small but significant asymmetry between left and right iliopsoas muscle volumes. We also found that iliopsoas volume was significantly related to height, BMI and age, and that there was an acceleration in muscle volume decrease in men with age. Our method provides a robust technique for measuring iliopsoas muscle volume that can be applied to large cohorts.

Download Full-text

Automated Method for Delineating Harvested Stands Based on Harvester Location Data

Remote Sensing ◽

10.3390/rs12172754 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2754

Author(s):

Timo Melkas ◽

Kirsi Riekki ◽

Juha-Antti Sorsa

Keyword(s):

Forest Inventory ◽

Large Scale ◽

Field Measurements ◽

Detailed Comparison ◽

Satellite System ◽

Comparison Method ◽

Aerial Images ◽

Location Data ◽

Automated Method ◽

Good Stand

The data produced by cut-to-length harvesters provide new large-scale data source for event-based update of national forest stand inventory by Finnish Forest Centre. This study aimed to automate geoprocessing, which generates delineations of operated areas from harvester location data. Automated algorithms were developed and tested with a dataset of 455 harvested objects, recorded during harvestings. In automated stand delineation, the location points are clustered, the stand points are identified and external strip roads are separated. Then, stand polygons are produced. To validate the results, automatic delineations were compared to 57 observed delineations from field measurements and aerial images. A detailed comparison method was developed to study the correspondence. Stand polygonization parameter was adjusted and areal correspondence with 1% error on average was obtained for stands over 0.75 ha. Good stand shape agreement was observed. Overall, the automated method worked well, and the operative stand delineations were found suitable for updating the forest inventory data. To modify the operative stands towards forest inventory stands, a balancing algorithm is introduced to create a solid, unique stand boundary between overlapping stands. This algorithm is beneficial for upkeep of stand networks. In addition, the Global Navigation Satellite System (GNSS) accuracy of the harvesters was examined and estimated numerically.

Download Full-text

An automated method to detect transiting circumbinary planets

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stz2637 ◽

2019 ◽

Vol 490 (1) ◽

pp. 1313-1324 ◽

Cited By ~ 3

Author(s):

Diana Windemuth ◽

Eric Agol ◽

Josh Carter ◽

Eric B Ford ◽

Nader Haghighipour ◽

...

Keyword(s):

Large Scale ◽

Search Algorithm ◽

Simulated Data ◽

Automated Detection ◽

Light Curves ◽

Automated Method ◽

Kepler Mission ◽

Automated Technique ◽

Automated Algorithms ◽

Circumbinary Planets

ABSTRACT To date a dozen transiting ‘Tatooines’ or circumbinary planets (CBPs) have been discovered, by eye, in the data from the Kepler mission; by contrast, thousands of confirmed circumstellar planets orbiting around single stars have been detected using automated algorithms. Automated detection of CBPs is challenging because their transits are strongly aperiodic with irregular profiles. Here, we describe an efficient and automated technique for detecting circumbinary planets that transit their binary hosts in Kepler light curves. Our method accounts for large transit timing variations (TTVs) and transit duration variations (TDVs), induced by binary reflex motion, in two ways: (1) We directly correct for large-scale TTVs and TDVs in the light curves by using Keplerian models to approximate binary and CBP orbits; and (2) We allow additional aperiodicities on the corrected light curves by employing the Quasi-periodic Automated Transit Search algorithm. We demonstrate that our method dramatically improves detection significance using simulated data and two previously identified CBP systems, Kepler-35 and Kepler-64.

Download Full-text

CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

10.7287/peerj.preprints.554v2 ◽

2015 ◽

Cited By ~ 5

Author(s):

Donovan H Parks ◽

Michael Imelfort ◽

Connor T Skennerton ◽

Philip Hugenholtz ◽

Gene W Tyson

Keyword(s):

Large Scale ◽

Single Cells ◽

Metagenomic Data ◽

Marker Genes ◽

Specific Gene ◽

Diverse Range ◽

Automated Method ◽

A Genome ◽

Wide Range

Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. While this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of ‘marker’ genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate, single cell and metagenome derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination, and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.

Download Full-text

A Large-Scale Automated Method for Hepatocyte Isolation: Effects on Proliferation in Culture

Cell Transplantation ◽

10.3727/000000005783983007 ◽

2005 ◽

Vol 14 (5) ◽

pp. 291-299 ◽

Cited By ~ 6

Author(s):

M. J. Nieuwoudt ◽

E. Kreft ◽

B. Olivier ◽

S. Malfeld ◽

J. Vosloo ◽

...

Keyword(s):

Cell Cycle ◽

Flow Cytometry ◽

Cell Viability ◽

Large Scale ◽

Centrifuge Method ◽

Automated Method ◽

Bioartificial Liver Support ◽

Albumin Production ◽

Hepatocyte Isolation ◽

Pyruvate Ratio

Large-scale sterile methods for isolating hepatocytes are desirable for the development of bioartificial liver support systems. In this study the traditional centrifuge method was compared with the use of a Baylor Rapid Autologous Transfusion (BRAT) machine for isolating large quantities of porcine hepatocytes. After isolating hepatocytes, the methods were evaluated in terms of cell viability and yield per liver, proliferation over 7 days, and the effects on the cell cycle using the trypan blue exclusion test, conventional phase-contrast light microscopy, the lactate to pyruvate ratio, the leakage of lactate dehydrogenase (LD) and aspartate aminotransferase (AST), lidocaine clearance, albumin production, and flow cytometry. With the centrifuge method the mean cell viability was 92.5%, while with the BRAT method the viability was 95.9%. The minimal cell yields with the BRAT procedure were 7.3 × 109 for 250-ml centrifuge bowls and 2.8 × 109 for 165-ml bowls, which compares well with that found by other authors. Because the same initial procedures were employed in both methods the total hepatocyte yield per liver was comparable. Flow cytometry confirmed that the proliferation of hepatocytes was facilitated by oxygenation during the isolation procedure. The recovery of hepatocytes in culture following isolation was similar after either method. Daily microscopic investigation indicated that cytoplasmic vacuolization and granularities were present after either procedure and these disappeared following 3–4 days of culturing. Flow cytometry indicated that the hepatocyte cell cycle was similar after either method; at 7 days the profile indicated that the cells were still proliferating. Trends in the lactate to pyruvate ratio and the leakage of LD and AST indicated that the functional polarity of hepatocytes was regained after approximately 3 days. Lidocaine clearance at 4 days indicated that the cytochrome P450 system was active, while significant albumin production was apparent at day 5. The benefit of using BRAT technology in hepatocyte isolation lies in guaranteed sterility, convenience, speed, and the ability to oxygenate media and cell suspensions during the procedure.

Download Full-text

Annual Greenland accumulation rates (2009–2012) from airborne Snow Radar

The Cryosphere Discussions ◽

10.5194/tcd-9-6697-2015 ◽

2015 ◽

Vol 9 (6) ◽

pp. 6697-6731 ◽

Cited By ~ 1

Author(s):

L. S. Koenig ◽

A. Ivanoff ◽

P. M. Alexander ◽

J. A. MacGregor ◽

X. Fettweis ◽

...

Keyword(s):

Large Scale ◽

Climate Model ◽

Regional Climate ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Ice Cores ◽

The Arctic ◽

Accumulation Rates ◽

Surface Mass ◽

Automated Method

Abstract. Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet (GrIS) through increasing surface melt, emphasizing the need to closely monitor surface mass balance (SMB) in order to improve sea-level rise predictions. Here, we quantify accumulation rates, the largest component of GrIS SMB, at a higher spatial resolution than currently available, using Snow Radar stratigraphy. We use a semi-automated method to derive annual-net accumulation rates from airborne Snow Radar data collected by NASA's Operation IceBridge from 2009 to 2012. An initial comparison of the accumulation rates from the Snow Radar and the outputs of a regional climate model (MAR) shows that, in general, the radar-derived accumulation matches closely with MAR in the interior of the ice sheet but MAR estimates are high over the southeast GrIS. Comparing the radar-derived accumulation with contemporaneous ice cores reveals that the radar captures the annual and long-term mean. The radar-derived accumulation rates resolve large-scale patterns across the GrIS with uncertainties of up to 11 %, attributed mostly to uncertainty in the snow/firn density profile.

Download Full-text