Testing the accuracy of 3D automatic landmarking via genome-wide association studies

Various advances in 3D automatic phenotyping and landmark-based geometric morphometric methods have been made. While it is generally accepted that automatic landmarking compromises the capture of the biological variation, no studies have directly tested the actual impact of such landmarking approaches in analyses requiring a large number of specimens and for which the precision of phenotyping is crucial to extract an actual biological signal adequately. Here, we use a recently developed 3D atlas-based automatic landmarking method to test its accuracy in detecting QTLs associated with craniofacial development of the house mouse skull and lower jaws for a large number of specimens (circa 700) that were previously phenotyped via a semi-automatic landmarking method complemented with manual adjustment. We compare both landmarking methods with univariate and multivariate mapping of the skull and the lower jaws. We find that most significant SNPs and QTLs are not recovered based on the data derived from the automatic landmarking method. Our results thus confirm the notion that information is lost in the automated landmarking procedure although somewhat dependent on the analyzed structure. The automatic method seems to capture certain types of structures slightly better, such as lower jaws whose shape is almost entirely summarized by its outline and could be assimilated as a 2D flat object. By contrast, the more apparent 3D features exhibited by a structure such as the skull are not adequately captured by the automatic method. We conclude that using 3D atlas-based automatic landmarking methods requires careful consideration of the experimental question.

MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record

Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation. However, effective use of EHR data for research and clinical decision support is often hampered by a lack of reliable disease labels. To compile gold-standard labels, researchers often rely on clinical experts to develop rule-based phenotyping algorithms from billing codes and other surrogate features. This process is tedious and error-prone due to recall and observer biases in how codes and measures are selected, and some phenotypes are incompletely captured by a handful of surrogate features. To address this challenge, we present a novel automatic phenotyping model called MixEHR-Guided (MixEHR-G), a multimodal hierarchical Bayesian topic model that efficiently models the EHR generative process by identifying latent phenotype structure in the data. Unlike existing topic modeling algorithms wherein, the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1.3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a phenotype label annotation task, and it can accurately estimate relative phenotype prevalence functions without gold-standard phenotype information. Altogether, MixEHR-G is an important step towards building an interpretable and automated phenotyping system using EHR data.

Precision separation process of sunflower seeds

According to the requirements for the technological processes of purification and separation of the seed mixture to obtain the sunflower seed material of the parent components (varietal purity – 98,0-99,9%) for all parts of the breeding and seed production process, a rational precision technological scheme of the separation processes has been developed, which includes automation of technical processes of separation means. In order to increase the efficiency of the sunflower breeding and seed-growing process, a device for automatic seed phenotyping has been added to the developed technological line, which can significantly intensify and shorten the breeding process and improve the design of the breeding program through bioinformatic data analysis and seed sorting. Functional dependencies are established and methods of automated control of precision mechanized process of seed separation are developed on the basis of coordination of its mode and technological parameters. Tape device for automatic phenotyping of sunflower seed material according to its morphological and marker features have been developed. The device are configured for high accuracy of individual measurement of the geometric dimensions of sunflower seeds with determination of their shape and color and provide low complexity and high technological implementation of the phenotyping process (determination, identification and separation) of seeds.

Automatic Phenotyping of Tomatoes in Production Greenhouses Using Robotics and Computer Vision: From Theory to Practice

High-throughput phenotyping is playing an increasingly important role in many areas of agriculture. Breeders will use it to obtain values for the traits of interest so that they can estimate genetic value and select promising varieties; growers may be interested in having predictions of yield well in advance of the actual harvest. In most phenotyping applications, image analysis plays an important role, drastically reducing the dependence on manual labor while being non-destructive. An automatic phenotyping system combines a reliable acquisition system, a high-performance segmentation algorithm for detecting fruits in individual images, and a registration algorithm that brings the images (and the corresponding detected plants or plant components) into a coherent spatial reference frame. Recently, significant advances have been made in the fields of robotics, image registration, and especially image segmentation, which each individually have improved the prospect of developing a fully integrated automatic phenotyping system. However, so far no complete phenotyping systems have been reported for routine use in a production environment. This work catalogs the outstanding issues that remain to be resolved by describing a prototype phenotyping system for a production tomato greenhouse, for many reasons a challenging environment.

Testing the accuracy of 3D automatic landmarking via genome-wide association studies

Background: Various advances in 3D automatic phenotyping and particularly in landmark-based geometric morphometric methods have been made, but only a few studies have tested the reliability of such automatic procedures in morphometric analyses. It is generally accepted that automatic landmarking compromises the capture of the actual biological variation, and this not only affects its performance to effectively detect differences among sample means but also the structure of covariance matrices. However, no studies have directly tested the actual impact of such landmarking approaches in analyses requiring a large number of specimens and for which the precision of phenotyping is crucial to capture an actual biological signal adequately. Results: Here, we use a recently developed 3D atlas-based automatic landmarking method to test its accuracy in detecting QTLs associated with craniofacial development of the house mouse skull and lower jaws for a large number of specimens (circa 700) that were previously phenotyped via a semi-automatic landmarking method complemented with manual adjustment. We compare both landmarking methods with univariate and multivariate mapping of the skull and the lower jaws. In the univariate mapping, the automatic approach failed to recover the same SNPs and found only 1 out of 17 previously identified QTLs for the skull, but found one new QTL. Similarly, for the lower jaws, the automatic approach failed to recover the same SNPs but found 2 neighbouring SNPs for 1 out of 8 previously identified QTLs. For centroid size, the same general results were recovered by the automatic method for both the skull and lower jaws, with the same peak SNP being found for the lower jaws. In the multivariate mapping, the automatic approach did not detect the same markers nor QTLs having their regions overlapping with the ones identified with the semi-automatic procedure for the skull, while the same marker, which is also the peak SNP and sole QTL, was recovered by the automatic pipeline for lower jaws. Conclusion: Our results confirm the notion that information is lost in the automated landmarking procedure but somewhat dependent on the analyzed structure. The automatic method seems to capture certain types of structures slightly better, such as lower jaws whose shape is almost entirely summarized by its outline and could be assimilated as a 2D flat object. By contrast, the more apparent 3D features exhibited by a structure such as the skull are not adequately captured by the automatic method. We conclude that using 3D atlas-based automatic landmarking methods requires careful consideration of the experimental question and the cautious interpretation of their results.

Automatic 3D dense phenotyping provides reliable and accurate shape quantification of the human mandible

Automatic craniomaxillofacial (CMF) three dimensional (3D) dense phenotyping promises quantification of the complete CMF shape compared to the limiting use of sparse landmarks in classical phenotyping. This study assesses the accuracy and reliability of this new approach on the human mandible. Classic and automatic phenotyping techniques were applied on 30 unaltered and 20 operated human mandibles. Seven observers indicated 26 anatomical landmarks on each mandible three times. All mandibles were subjected to three rounds of automatic phenotyping using Meshmonk. The toolbox performed non-rigid surface registration of a template mandibular mesh consisting of 17,415 quasi landmarks on each target mandible and the quasi landmarks corresponding to the 26 anatomical locations of interest were identified. Repeated-measures reliability was assessed using root mean square (RMS) distances of repeated landmark indications to their centroid. Automatic phenotyping showed very low RMS distances confirming excellent repeated-measures reliability. The average Euclidean distance between manual and corresponding automatic landmarks was 1.40 mm for the unaltered and 1.76 mm for the operated sample. Centroid sizes from the automatic and manual shape configurations were highly similar with intraclass correlation coefficients (ICC) of > 0.99. Reproducibility coefficients for centroid size were < 2 mm, accounting for < 1% of the total variability of the centroid size of the mandibles in this sample. ICC’s for the multivariate set of 325 interlandmark distances were all > 0.90 indicating again high similarity between shapes quantified by classic or automatic phenotyping. Combined, these findings established high accuracy and repeated-measures reliability of the automatic approach. 3D dense CMF phenotyping of the human mandible using the Meshmonk toolbox introduces a novel improvement in quantifying CMF shape.

Automatic Phenotyping Test of Sunflower Seeds

The development of automated precision technologies for the phenotyping test of seeds by a complex of functional features in the selection process of sunflower is relevant and promising. The task of developing a device for the automatic phenotyping test of seeds and the algorithm for finding and isolating seeds based on color information was set. Research was conducted on a stand, which consisted of the following elements: Video Microscope Camera 1080 P 16MP HDMI USB manufactured by Eakins, a set of LEDs of three types (red, green, blue) and a personal computer.The results of experimental studies of the process of automatic phenotyping test of seeds of different sunflower varieties allowed us to establish an average error of determining the geometric dimensions of sunflower seeds (length L and width B) – 0.06 mm. The histograms of the color distribution of sunflower seeds in the RGB color space with different illumination are established. As a result of the analysis of the obtained histograms of the color distribution of sunflower seeds in the RGB color space it is established that in the case of color homogeneity, the discreteness of the channels with red illumination is most clearly seen.A device for automatic phenotyping test of seeds has been developed, which preserves the accuracy of individual measurement of the geometric dimensions of sunflower seeds, determining their shape and color, which corresponds to modern measuring tools, and provides low complexity and high technological implementation of the phenotyping test procedure (determination, ascertaining and identification) material, according to its morphological and marker features.

Automatic phenotyping of electronical health record: PheVis algorithm

ABSTRACTElectronic Health Records (EHRs) often lack reliable annotation of patient medical conditions. Yu et al. recently proposed PheNorm, an automated unsupervised algorithm to identify patient medical conditions from EHR data. PheVis extends PheNorm at the visit resolution. PheVis combines diagnosis codes together with medical concepts extracted from medical notes, incorporating past history in a machine learning approach to provide an interpretable “white box” predictor of the occurrence probability for a given medical condition at each visit. PheVis is applied to two real-world use-cases using the datawarehouse of the University Hospital of Bordeaux: i) rheumatoid arthritis, a chronic condition; ii) tuberculosis, an acute condition (cross-validated AUROC were respectively 0.948 [0.945 ; 0.950] and 0.987 [0.983 ; 0.990]). PheVis performs well for chronic conditions, though absence of exclusion of past medical history by natural language processing tools limits its performance in French for acute conditions.

TECHNICAL AND TECHNOLOGICAL MAINTENANCE OF PRECISION SEPARATION OF SEED SUNFLOWER MATERIAL

To obtain homogeneous genetic seed of sunflower parent components, which by varietal and sowing qualities, must have a varietal purity of 99.6-99.9%, it is necessary to ensure their precise (exact) separation according to the morphological and physico-mechanical properties in the complex. Based on the necessary requirements for the technological processes of cleaning and separation of seed mixture, a rational precision technological line has been developed for the processes of separation of seed material of sunflower, which includes automation of technical equipment. Also, to increase the efficiency of the sunflower breeding process, a device for automatic phenotyping of seeds has been added to the developed production line, which makes it possible to significantly intensify and reduce the selection process and improve the design of the crossing program due to bioinformative data analysis and sorting of seeds. As a result of the analysis of technological methods for the separation of sunflower seed material and their technical support, it has been established that the main trends in the development of precision seed cleaning equipment are the creation of adaptive control systems that allow dynamic optimization of operating modes of the working bodies without operator intervention.