Photoacoustic Quantification of Tissue Oxygenation Using Conditional Invertible Neural Networks.

Intelligent systems in interventional healthcare depend on the reliable perception of the environment. In this context, photoacoustic tomography (PAT) has emerged as a non-invasive, functional imaging modality with great clinical potential. Current research focuses on converting the high-dimensional, not human-interpretable spectral data into the underlying functional information, specifically the blood oxygenation.

Efficient Photoacoustic Image Synthesis with Deep Learning.

Photoacoustic imaging potentially allows for the real-time visualization of functional human tissue parameters such as oxygenation but is subject to a challenging underlying quantification problem. While in silico studies have revealed the great potential of deep learning (DL) methodology in solving this problem, the inherent lack of an efficient gold standard method for model training and validation remains a grand challenge. This work investigates whether DL can be leveraged to accurately and efficiently simulate photon propagation in biological tissue, enabling photoacoustic image synthesis.

Low-Density Reference Fingerprinting SNP Dataset of CIMMYT Maize Lines for Quality Control and Genetic Diversity Analyses.

CIMMYT maize lines (CMLs), which represent the tropical maize germplasm, are freely available worldwide. All currently released 615 CMLs and fourteen temperate maize inbred lines were genotyped with 180 kompetitive allele-specific PCR single nucleotide polymorphisms to develop a reference fingerprinting SNP dataset that can be used to perform quality control (QC) and genetic diversity analyses. The QC analysis identified 25 CMLs with purity, identity, or mislabeling issues.

Photoacoustic image synthesis with generative adversarial networks.

Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the optical inverse problem with supervised machine learning were hampered by the absence of labeled reference data. While this bottleneck has been tackled by simulating training data, the domain gap between real and simulated images remains an unsolved challenge.

Basal cell carcinoma invasion of an ear piercing.

Basal cell carcinoma (BCC) development in the context of a piercing is a rare phenomenon, reported in the literature in only six instances. We present a 55-year-old woman with nodular BCC involving her auricular piercing and extending clinically onto the posteroinferior right ear lobule and right post-auricular crease. Histological analysis revealed spread of the BCC through the piercing onto the anterior lobule, with evidence that the cancer utilized the piercing as a low resistance pathway for this microscopic invasion.

SIMPA: an open-source toolkit for simulation and image processing for photonics and acoustics.

Significance: Optical and acoustic imaging techniques enable noninvasive visualisation of structural and functional properties of tissue. The quantification of measurements, however, remains challenging due to the inverse problems that must be solved. Emerging data-driven approaches are promising, but they rely heavily on the presence of high-quality simulations across a range of wavelengths due to the lack of ground truth knowledge of tissue acoustical and optical properties in realistic settings.

Semantic segmentation of multispectral photoacoustic images using deep learning.

Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability.

Unilateral Breast Skin Changes in Patient With Hidradenitis Suppurativa: Malignancy Versus Lymphedema.

Hidradenitis suppurativa (HS) is an inflammatory skin disorder typically affecting the groin, inframammary folds, and axillae. HS is characterized by the development of boils, abscesses, fistulas, and sinus tracts. Due to the inflammatory destruction of lymph vessels, patients with long-standing HS may develop lymphedema.

Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage.

To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments.

Extreme winter weather: A force to be remembered in dermatopathology.

It is important for the dermatopathologist to be adept in differentiating tissue artifacts from normal tissue variants and pathologies. Numerous tissue artifacts have been described to date; however, once we are familiar with the common artifacts that appear in our practice, we may not immediately recognize other confounders. For example, dermatopathologists in more temperate regions of the country may not be familiar with freezing artifact.

Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern.

Purpose: Photoacoustic tomography (PAT) is a novel imaging technique that can spatially resolve both morphological and functional tissue properties, such as vessel topology and tissue oxygenation. While this capacity makes PAT a promising modality for the diagnosis, treatment, and follow-up of various diseases, a current drawback is the limited field of view provided by the conventionally applied 2D probes.

Methods: In this paper, we present a novel approach to 3D reconstruction of PAT data (Tattoo tomography) that does not require an external tracking system and can smoothly be integrated into clinical workflows.

Deep learning for biomedical photoacoustic imaging: A review.

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI.

Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program.

Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a "test-half-predict-half approach." Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times.

Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints.

Undomesticated wild species, crop wild relatives, and landraces represent sources of variation for wheat improvement to address challenges from climate change and the growing human population. Here, we study 56,342 domesticated hexaploid, 18,946 domesticated tetraploid and 3,903 crop wild relatives in a massive-scale genotyping and diversity analysis. Using DArTseq technology, we identify more than 300,000 high-quality SNPs and SilicoDArT markers and align them to three reference maps: the IWGSC RefSeq v1.

Strategies for Effective Use of Genomic Information in Crop Breeding Programs Serving Africa and South Asia.

Much of the world's population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world.

Empirical Comparison of Tropical Maize Hybrids Selected Through Genomic and Phenotypic Selections.

Genomic selection predicts the genomic estimated breeding values (GEBVs) of individuals not previously phenotyped. Several studies have investigated the accuracy of genomic predictions in maize but there is little empirical evidence on the practical performance of lines selected based on phenotype in comparison with those selected solely on GEBVs in advanced testcross yield trials. The main objectives of this study were to (1) empirically compare the performance of tropical maize hybrids selected through phenotypic selection (PS) and genomic selection (GS) under well-watered (WW) and managed drought stress (WS) conditions in Kenya, and (2) compare the cost-benefit analysis of GS and PS.

Cost-benefit analysis of a population-based education program on the wise use of antibiotics.

Objective: In 2005, the Do Bugs Need Drugs (DBND) program was imported to British Columbia (BC) from Alberta with the goal of reducing unnecessary antibiotic use in the community. The objective of this study was to estimate the impact of the program on antibiotic-associated costs and cost-benefit.

Methods: We used data on antibiotic prescription and costs from BC PharmaNet for the period of 1996 to 2014.

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants.

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale.

Systems genetic and pharmacological analysis identifies candidate genes underlying mechanosensation in the von Frey test.

Mechanical sensitivity is commonly affected in chronic pain and other neurological disorders. To discover mechanisms of individual differences in punctate mechanosensation, we performed quantitative trait locus (QTL) mapping of the response to von Frey monofilament stimulation in BXD recombinant inbred (BXD) mice. Significant loci were detected on mouse chromosome (Chr) 5 and 15, indicating the location of underlying polymorphisms that cause heritable variation in von Frey response.