Image analysis and machine learning based methods for disease detection in soybeans

Plant phenotyping is important for genetic enhancements and plant biology research. There is a lot of work done to improve yield of crop plants, by selecting good genotypes to cross-breed in an effort to curb diseases or genetic deficiencies in these crops. In order to select these genotypes, one would have to perform phenotyping. Currently, plant phenotyping is based on visual assessment, where a breeder or researcher would have to visually inspect each plant and visually rate them. Visual rating is inefficient and can be inconsistent due to intra-rater repeatability or inter-rater reliability issues leading to incorrect visual scores. Not only that, it is also labor intensive and time consuming. Hence, there is a need to develop new tools amenable to high throughput phenotyping (HTP) for large scale plant genotype assessments. This requirement for high throughput phenotyping is applicable in a variety abiotic and biotic stresses. We developed a HTP framework which utilizes digital images in an effort for disease detection. This framework enabled us to accurately assign disease ratings to soybean plants that were affected by iron deficiency chlorosis (IDC). Utilizing image analysis techniques, we successfully extracted features pertaining to IDC and trained classification models on these features. A hierarchical classifier, based on linear discriminant analysis and support vector machine classifiers, produced the highest accuracy of 96%. Also, this framework was successfully implemented as a cellphone app. We envision to utilize hyperspectral imaging in the future for more accurate disease detection, prior to symptoms being visible

Survey of Bare Active Galactic Nuclei in the local universe (z < 0.2): I. On the origin of Soft-Excess

We analyse a sample of 21 `bare' Seyfert~1 Active Galactic Nuclei (AGNs), a sub-class of Seyfert~1s, with intrinsic absorption $\mathrm{N_{H}} \sim 10^{20}~ \mathrm{cm}^{-2}$, in the local universe (z $<$ 0.2) using {\it XMM-Newton} and {\it Swift}/XRT observations. The luminosities of the primary continuum, the X-ray emission in the 3 to 10 keV energy range and the soft-excess, the excess emission that appears above the low-energy extrapolation of the power-law fit of 3 to 10 keV X-ray spectra, are calculated. Our spectral analysis reveals that the long-term intrinsic luminosities of the soft-excess and the primary continuum are tightly correlated $(L_{PC}\propto L_{SE}^{1.1\pm0.04})$. We also found that the luminosities are correlated for each source. This result suggests that both the primary continuum and soft excess emissions exhibit a dependency on the accretion rate in a similar way.Comment: Accepted for publication in ApJ Supplement Series, 37 pages, 12 figures 5 table

Coronal Properties of Low-Accreting AGNs using Swift, XMM-Newton and NuSTAR Observations

We studied the broadband X-ray spectra of {\it Swift}/BAT selected low-accreting AGNs using the observations from {\it XMM-Newton}, {\it Swift}, and {\it NuSTAR} in the energy range of $0.5-150$~keV. Our sample consists of 30 AGNs with Eddington ratio, $\lambda_{\rm Edd}<10^{-3}$. We extracted several coronal parameters from the spectral modelling, such as the photon index, hot electron plasma temperature, cutoff energy, and optical depth. We tested whether there exists any correlation/anti-correlation among different spectral parameters. We observe that the relation of hot electron temperature with the cutoff energy in the low accretion domain is similar to what is observed in the high accretion domain. We did not observe any correlation between the Eddington ratio and the photon index. We studied the compactness-temperature diagram and found that the cooling process for extremely low-accreting AGNs is complex. The jet luminosity is calculated from the radio flux, and observed to be related to the bolometric luminosity as $L_{\rm jet} \propto L_{\rm bol}^{0.7}$, which is consistent with the standard radio-X-ray correlation.Comment: Accepted for publication in MNRA

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean

Background: Phenotyping is a critical component of plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of plant stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict plant stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field. Results: We investigated 10 different classification approaches, with the best classifier being a hierarchical classifier with a mean per-class accuracy of ~96%. We construct a phenotypically meaningful ‘population canopy graph’, connecting the automatically extracted canopy trait features with plant stress severity rating. We incorporated this image capture → image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy. Conclusion: We expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated stress trait detection and quantification for plant research, breeding and stress scouting applications

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use

Alcohol use and burden for 195 countries and territories, 1990-2016 : a systematic analysis for the Global Burden of Disease Study 2016

Background Alcohol use is a leading risk factor for death and disability, but its overall association with health remains complex given the possible protective effects of moderate alcohol consumption on some conditions. With our comprehensive approach to health accounting within the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we generated improved estimates of alcohol use and alcohol-attributable deaths and disability-adjusted life-years (DALYs) for 195 locations from 1990 to 2016, for both sexes and for 5-year age groups between the ages of 15 years and 95 years and older. Methods Using 694 data sources of individual and population-level alcohol consumption, along with 592 prospective and retrospective studies on the risk of alcohol use, we produced estimates of the prevalence of current drinking, abstention, the distribution of alcohol consumption among current drinkers in standard drinks daily (defined as 10 g of pure ethyl alcohol), and alcohol-attributable deaths and DALYs. We made several methodological improvements compared with previous estimates: first, we adjusted alcohol sales estimates to take into account tourist and unrecorded consumption; second, we did a new meta-analysis of relative risks for 23 health outcomes associated with alcohol use; and third, we developed a new method to quantify the level of alcohol consumption that minimises the overall risk to individual health. Findings Globally, alcohol use was the seventh leading risk factor for both deaths and DALYs in 2016, accounting for 2.2% (95% uncertainty interval [UI] 1.5-3.0) of age-standardised female deaths and 6.8% (5.8-8.0) of age-standardised male deaths. Among the population aged 15-49 years, alcohol use was the leading risk factor globally in 2016, with 3.8% (95% UI 3.2-4-3) of female deaths and 12.2% (10.8-13-6) of male deaths attributable to alcohol use. For the population aged 15-49 years, female attributable DALYs were 2.3% (95% UI 2.0-2.6) and male attributable DALYs were 8.9% (7.8-9.9). The three leading causes of attributable deaths in this age group were tuberculosis (1.4% [95% UI 1. 0-1. 7] of total deaths), road injuries (1.2% [0.7-1.9]), and self-harm (1.1% [0.6-1.5]). For populations aged 50 years and older, cancers accounted for a large proportion of total alcohol-attributable deaths in 2016, constituting 27.1% (95% UI 21.2-33.3) of total alcohol-attributable female deaths and 18.9% (15.3-22.6) of male deaths. The level of alcohol consumption that minimised harm across health outcomes was zero (95% UI 0.0-0.8) standard drinks per week. Interpretation Alcohol use is a leading risk factor for global disease burden and causes substantial health loss. We found that the risk of all-cause mortality, and of cancers specifically, rises with increasing levels of consumption, and the level of consumption that minimises health loss is zero. These results suggest that alcohol control policies might need to be revised worldwide, refocusing on efforts to lower overall population-level consumption.Peer reviewe

Role of plants in anticancer drug discovery

Cancer is one of the major causes of death and the number of new cases, as well as the number of individuals living with cancer, is expanding continuously. Worldwide the alarming rise in mortality rate due to cancer has fuelled the pursuit for effective anticancer agents to combat this disease. Finding novel and efficient compounds of natural origin has been a major point of concern for research in the pharmaceutical sciences. Plants have been seen to possess the potential to be excellent lead structures and to serve as a basis of promising therapeutic agents for cancer treatment. Many successful anti-cancer drugs currently in use or their analogues are plant derived and many more are under clinical trials. This review aims to highlight the invaluable role that plants have played, and continue to play, in the discovery of anticancer agents.We acknowledge the University of Pretoria for Postdoctoral fellowship to J.K. and B.A.M.http://www.elsevier.com/locate/phytolhb2017ChemistryGenetic

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015 : a systematic analysis for the Global Burden of Disease Study 2015

Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography-year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61.7 years (95% uncertainty interval 61.4-61.9) in 1980 to 71.8 years (71.5-72.2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11.3 years (3.7-17.4), to 62.6 years (56.5-70.2). Total deaths increased by 4.1% (2.6-5.6) from 2005 to 2015, rising to 55.8 million (54.9 million to 56.6 million) in 2015, but age-standardised death rates fell by 17.0% (15.8-18.1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14.1% (12.6-16.0) to 39.8 million (39.2 million to 40.5 million) in 2015, whereas age-standardised rates decreased by 13.1% (11.9-14.3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42.1%, 39.1-44.6), malaria (43.1%, 34.7-51.8), neonatal preterm birth complications (29.8%, 24.8-34.9), and maternal disorders (29.1%, 19.3-37.1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000-183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000-532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Copyright (C) The Author(s). Published by Elsevier Ltd.Peer reviewe

Image analysis and machine learning based methods for disease detection in soybeans

Plant phenotyping is important for genetic enhancements and plant biology research. There is a lot of work done to improve yield of crop plants, by selecting good genotypes to cross-breed in an effort to curb diseases or genetic deficiencies in these crops. In order to select these genotypes, one would have to perform phenotyping. Currently, plant phenotyping is based on visual assessment, where a breeder or researcher would have to visually inspect each plant and visually rate them. Visual rating is inefficient and can be inconsistent due to intra-rater repeatability or inter-rater reliability issues leading to incorrect visual scores. Not only that, it is also labor intensive and time consuming. Hence, there is a need to develop new tools amenable to high throughput phenotyping (HTP) for large scale plant genotype assessments. This requirement for high throughput phenotyping is applicable in a variety abiotic and biotic stresses. We developed a HTP framework which utilizes digital images in an effort for disease detection. This framework enabled us to accurately assign disease ratings to soybean plants that were affected by iron deficiency chlorosis (IDC). Utilizing image analysis techniques, we successfully extracted features pertaining to IDC and trained classification models on these features. A hierarchical classifier, based on linear discriminant analysis and support vector machine classifiers, produced the highest accuracy of 96%. Also, this framework was successfully implemented as a cellphone app. We envision to utilize hyperspectral imaging in the future for more accurate disease detection, prior to symptoms being visible.</p

Graph Based Automated Analysis for Plant Root Phenotyping

There is substantial genetic and phenotypic variation for root architecture, which gives opportunity for selection. Root traits have not been used as selection criterion mainly due to the difficulty in measuring them, as well as their quantitative mode of inheritance. Seedling root traits offer an opportunity to study multiple individuals and to enable repeated measurements per year as compared to adult root phenotyping. Currently no strong relationships between seedling and adult root traits have been established with the traits and tools available so far. To enable fast, efficient and accurate trait extraction from images, we developed a new software framework to capture various traits from a single image of seedling roots. This framework is based on the mathematical notion of converting images of roots into an equivalent graph. We used various mathematical algorithms to quantify the data from the images. This allows automated querying of multiple traits simply as graph operations. This framework is furthermore extendable to 3D tomography image data. Also, this framework has been used to analyze corn images to obtain numerical data. Therefore, this software framework is not limited to roots alone, but can also be used to analyze different types of images.</p