scholarly journals MeshMonk: Open-source large-scale intensive 3D phenotyping

2019 ◽  
Author(s):  
Julie D. White ◽  
Alejandra Ortega-Castrillón ◽  
Harold Matthews ◽  
Arslan A. Zaidi ◽  
Omid Ekrami ◽  
...  
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Target Surface ◽  
Transformation Model ◽  
Shape Variation ◽  
3D Images ◽  
Specific Shape ◽  
Genetic Contributions ◽  
Facial Images ◽  
Landmark Position

AbstractIn the post-genomics era, an emphasis has been placed on disentangling ‘genotype-phenotype’ connections so that the biological basis of complex phenotypes can be understood. However, our ability to efficiently and comprehensively characterize phenotypes lags behind our ability to characterize genomes. Here, we report a toolbox for fast and reproducible high-throughput dense phenotyping of 3D images. Given a target image, a rigid registration is first used to orient a template to the target surface, then the template is transformed further to fit the specific shape of the target using a non-rigid transformation model. As validation, we used N = 41 3D facial images registered with MeshMonk and manually landmarked at 19 locations. We demonstrate that the MeshMonk registration is accurate, with 0.62 mm as the average root mean squared error between the manual and automatic placements and no variation in landmark position or centroid size significantly attributable to landmarking method used. Though validated using 19 landmarks for comparison with traditional methods, MeshMonk allows for automatic dense phenotyping, thus facilitating more comprehensive investigations of 3D shape variation. This expansion opens up an exciting avenue of study in assessing genomic and phenomic data to better understand the genetic contributions to complex morphological traits.

scholarly journals Development and Evaluation of Geostatistical Methods for Estimating Weather Related Collisions: A Large-Scale Case Study

2021 ◽  
pp. 036119812110200
Author(s):  
Andy H. Wong ◽  
Tae J. Kwon
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Hot Spot ◽  
High Ratio ◽  
Contributing Factors ◽  
Geostatistical Methods ◽  
Road Users ◽  
Statistical Measures ◽  
Weather Events

Winter driving conditions pose a real hazard to road users with increased chance of collisions during inclement weather events. As such, road authorities strive to service the hazardous roads or collision hot spots by increasing road safety, mobility, and accessibility. One measure of a hot spot would be winter collision statistics. Using the ratio of winter collisions (WC) to all collisions, roads that show a high ratio of WC should be given a high priority for further diagnosis and countermeasure selection. This study presents a unique methodological framework that is built on one of the least explored yet most powerful geostatistical techniques, namely, regression kriging (RK). Unlike other variants of kriging, RK uses auxiliary variables to gain a deeper understanding of contributing factors while also utilizing the spatial autocorrelation structure for predicting WC ratios. The applicability and validity of RK for a large-scale hot spot analysis is evaluated using the northeast quarter of the State of Iowa, spanning five winter seasons from 2013/14 to 2017/18. The findings of the case study assessed via three different statistical measures (mean squared error, root mean square error, and root mean squared standardized error) suggest that RK is very effective for modeling WC ratios, thereby further supporting its robustness and feasibility for a statewide implementation.

scholarly journals Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 195
Author(s):  
Adrian Sergiu Darabant ◽  
Diana Borza ◽  
Radu Danescu
Keyword(s):  
Race And Ethnicity ◽  
Large Scale ◽  
Recognition Rate ◽  
Social Signals ◽  
Image Characteristics ◽  
Race Effect ◽  
Race Ethnicity ◽  
The Impact ◽  
Facial Images ◽  
Ethnicity Classification

The human face holds a privileged position in multi-disciplinary research as it conveys much information—demographical attributes (age, race, gender, ethnicity), social signals, emotion expression, and so forth. Studies have shown that due to the distribution of ethnicity/race in training datasets, biometric algorithms suffer from “cross race effect”—their performance is better on subjects closer to the “country of origin” of the algorithm. The contributions of this paper are two-fold: (a) first, we gathered, annotated and made public a large-scale database of (over 175,000) facial images by automatically crawling the Internet for celebrities’ images belonging to various ethnicity/races, and (b) we trained and compared four state of the art convolutional neural networks on the problem of race and ethnicity classification. To the best of our knowledge, this is the largest, data-balanced, publicly-available face database annotated with race and ethnicity information. We also studied the impact of various face traits and image characteristics on the race/ethnicity deep learning classification methods and compared the obtained results with the ones extracted from psychological studies and anthropomorphic studies. Extensive tests were performed in order to determine the facial features to which the networks are sensitive to. These tests and a recognition rate of 96.64% on the problem of human race classification demonstrate the effectiveness of the proposed solution.

scholarly journals Location-Aided Uplink Transmission for User-Centric Cell-Free Massive MIMO Systems: A Fairness Priority Perspective

2022 ◽  
Author(s):  
Chen Wei ◽  
Kui Xu ◽  
Zhexian Shen ◽  
Xiaochen Xia ◽  
Wei Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Access Point ◽  
Estimation Accuracy ◽  
Minimum Mean Squared Error ◽  
Input Multiple Output ◽  
User Centric ◽  
Assignment Scheme

Abstract In this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme based on a novel proposed metric representing inter-user interference to balance the relationship among the spectral efficiency (SE), user equipment (UE) fairness and complexity, in which the normalized local partial minimum mean-squared error (LP-MMSE) combining is adopted for some APs, while the normalized maximum ratio (MR) combining is adopted for the remaining APs. A new closed-form SE expression using the normalized MR combining is derived and a novel metric to indicate the UE fairness is also proposed. Moreover, the max-min fairness (MMF) power control algorithm is utilized to further ensure uniformly good service to the UEs. Simulation results demonstrate that the channel estimation accuracy of our proposed IC-IB pilot assignment scheme outperforms that of the conventional pilot assignment schemes. Furthermore, although the proposed location-aided uplink combining scheme is not always the best in terms of the per-UE SE, it can provide the more fairness among UEs and can achieve a good trade-off between the average SE and computational complexity.

scholarly journals Dynamic Radiation Effects Induced by Short-Pulsed GeV U-Ion Beams in Graphite and h-BN Targets

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Philipp Bolz ◽  
Philipp Drechsel ◽  
Alexey Prosvetov ◽  
Pascal Simon ◽  
Christina Trautmann ◽  
...  
Keyword(s):  
Boron Nitride ◽  
Young’S Modulus ◽  
Radiation Effects ◽  
Large Scale ◽  
Hexagonal Boron Nitride ◽  
Young's Modulus ◽  
Target Surface ◽  
High Dose ◽  
Velocity Signal ◽  
Isotropic Graphite

Targets of isotropic graphite and hexagonal boron nitride were exposed to short pulses of uranium ions with ∼1 GeV kinetic energy. The deposited power density of ∼3 MW/cm³ generates thermal stress in the samples leading to pressure waves. The velocity of the respective motion of the target surface was measured by laser Doppler vibrometry. The bending modes are identified as the dominant components in the velocity signal recorded as a function of time. With accumulated radiation damage, the bending mode frequency shifts towards higher values. Based on this shift, Young’s modulus of irradiated isotropic graphite is determined by comparison with ANSYS simulations. The increase of Young’s modulus up to 3 times the pristine value for the highest accumulated fluence of 3 × 1013 ions/cm2 is attributed to the beam-induced microstructural evolution into a disordered structure similar to glassy carbon. Young’s modulus values deduced from microindentation measurements are similar, confirming the validity of the method. Beam-induced stress waves remain in the elastic regime, and no large-scale damage can be observed in graphite. Hexagonal boron nitride shows lower radiation resistance. Circular cracks are generated already at low fluences, risking material failure when applied in high-dose environment.

scholarly journals Season-ahead forecasting of water storage and irrigation requirements – an application to the southwest monsoon in India

2018 ◽  
Vol 22 (10) ◽  
pp. 5125-5141 ◽  
Author(s):  
Arun Ravindranath ◽  
Naresh Devineni ◽  
Upmanu Lall ◽  
Paulina Concha Larrauri
Keyword(s):  
Water Stress ◽  
Large Scale ◽  
Agricultural Sector ◽  
Mean Squared Error ◽  
Monsoon Season ◽  
Water Storage ◽  
Skill Score ◽  
Southwest Monsoon ◽  
False Alarms ◽  
K Nearest Neighbors

Abstract. Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years 2001 to 2013 using the identified predictors and a non-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct 9 out of 13 times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions.

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

2019 ◽  
Vol 9 (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.

scholarly journals MoBPS - Modular Breeding Program Simulator

2020 ◽  
Vol 10 (6) ◽  
pp. 1915-1918 ◽  
Author(s):  
Torsten Pook ◽  
Martin Schlather ◽  
Henner Simianer
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
R Package ◽  
Single Step ◽  
Computationally Efficient ◽  
Breeding Programs ◽  
Genetic Impact ◽  
Founder Populations ◽  
Genetic Contributions ◽  
Impact Simulations

The R-package MoBPS provides a computationally efficient and flexible framework to simulate complex breeding programs and compare their economic and genetic impact. Simulations are performed on the base of individuals. MoBPS utilizes a highly efficient implementation with bit-wise data storage and matrix multiplications from the associated R-package miraculix allowing to handle large scale populations. Individual haplotypes are not stored but instead automatically derived based on points of recombination and mutations. The modular structure of MoBPS allows to combine rather coarse simulations, as needed to generate founder populations, with a very detailed modeling of todays’ complex breeding programs, making use of all available biotechnologies. MoBPS provides pre-implemented functions for common breeding practices such as optimum genetic contributions and single-step GBLUP but also allows the user to replace certain steps with personalized and/or self-written solutions.

scholarly journals Laser generated Richtmyer–Meshkov instability and nonlinear wave paradigm in turbulent mixing: I. Central region of Gaussian spot

2016 ◽  
Vol 34 (4) ◽  
pp. 687-704 ◽  
Author(s):  
Stjepan Lugomer
Keyword(s):  
Flow Field ◽  
Turbulent Mixing ◽  
Large Scale ◽  
Pressure Field ◽  
Three Dimensional ◽  
Target Surface ◽  
Kp Equation ◽  
New Paradigm ◽  
Shape Oscillations ◽  
The Individual

AbstractA three-dimensional Richtmyer–Meshkov instability (RMI) was generated on metal target by the laser pulse of Gaussian-like power profile in the semiconfined configuration (SCC). The SCC enables the extended lifetime of a hot vapor/plasma plume above the target surface as well as the fast multiple reshocks. The oscillatory pressure field of the reshocks causes strong bubble shape oscillations giving rise to the complex wave-vortex phenomena. The irregularity of the pressure field causes distortion of the shock wave front observed as deformed waves. In a random flow field the waves solidified around the bubbles form the broken “egg-karton” structure – or the large-scale chaotic web. In the coherent flow field the shape oscillations and collapse of the large bubbles generate nonlinear waves as the line- and the horseshoe-solitons. The line solitons are organized into a polygonal web, while the horseshoe solitons make either the rosette-like web or appear as the individual parabolic-like solitons. The configurations of the line solitons are juxtapositioned with solitons simulated by the Kadomtsev–Petviashvili (KP) equation. For the horseshoe solitons it was mentioned that it can be obtained by the simulation based on the cylindrical KP equation. The line and the horseshoe solitons represent the wave-vortex phenomena in which the fluid accelerated by the shock and exposed to a subsequent series of fast reshocks follows more complex scenario than in the open configuration. The RMI environment in the SCC generates complex fluid dynamics and the new paradigm of wave vortex phenomena in turbulent mixing.

scholarly journals Large-scale localization of touching somas from 3D images using density-peak clustering

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Shenghua Cheng ◽  
Tingwei Quan ◽  
Xiaomao Liu ◽  
Shaoqun Zeng
Keyword(s):  
Large Scale ◽  
Density Peak ◽  
3D Images ◽  
Density Peak Clustering

Spatial and seasonal distribution of rainfall erosivity in Australia

Soil Research ◽  
2002 ◽  
Vol 40 (6) ◽  
pp. 887 ◽  
Author(s):  
Hua Lu ◽  
Bofu Yu
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Seasonal Distribution ◽  
Rainfall Erosivity ◽  
Continental Scale ◽  
R Factor ◽  
Monthly Distribution ◽  
The Mean

Spatially distributed rainfall erosivity and its seasonal distribution are needed to use the revised universal soil loss equation (RUSLE) for erosion risk assessment at large scale. An erosivity model and 20-year daily rainfall data at 0.05° resolution were used to predict the R-factor and its monthly distribution for RUSLE in Australia. Predicted R-factor values were compared with those previously calculated using pluviograph data for 132 sites around Australia. The daily erosivity model was further evaluated for 43 sites where long-term pluviograph data were available. Predicted and calculated monthly distributions of the R-factor were compared for these 43 sites. For the 132 sites where R-factor values were compiled from previous investigations, the model efficiency was 0.81 with root mean squared error (rmse) of 1832 MJ.mm/(ha.h.year), or 47.5% of the mean for the 132 sites. For the additional 43 sites, the coefficient of efficiency was 0.93 with a 12.7 mm rainfall threshold, and 0.94 when all storms were included in the calculations. The rmse was 908 MJ.mm/(ha.h.year), or 28.6% of the mean for the 43 sites with a zero rainfall threshold. The prediction error for monthly distribution of the R-factor was 2.3% with a zero threshold and 2.5% with 12.7�mm threshold. This and previous studies have shown that the daily rainfall erosivity model can be used to accurately predict the R-factor and its seasonal distribution in Australia. Digital maps were produced showing the spatial and seasonal distribution of the R-factor at 0.05° resolution in Australia. These maps have been used to assess rill and sheet erosion rate at the continental scale.

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