Class Distribution Estimation in Imprecise Domains Based on Supervised Learning

cap. 9- pp. 187-202a cuantificación -o estimación de proporciones- desempeña un papel importante en muchos problemas prácticos de clasificación. Por un lado, una máquina que clasifica automáticamente un elemento en un grupo de clases predefinidas, tomará decisiones subóptimas, si la distribución de clases en el dominio de prueba (real) difiere de la que se asume en el aprendizaje. La estimación de la nueva distribución de clases es necesaria para adaptar el clasificador a las nuevas condiciones operativas. Por otro lado, hay algunos dominios reales donde la propia tarea de cuantificación es el objetivo principal. Algunos campos, como el control de calidad, el marketing directo, el estudio de tendencias o algunas tareas de reconocimiento textual, requieren métodos que puedan estimar de forma fiable, la proporción de elementos dentro de cada categoría, sin ninguna preocupación acerca de cómo cada elemento ha sido clasificado individualmente. Describimos varias técnicas de cuantificación que se basan en el aprendizaje supervisado y proporcionan estas estimaciones basadas en: a) la matriz de confusión del clasificador, b) las estimaciones de probabilidad posteriores y c) las medidas de divergencia distribucional. Ilustramos estas técnicas, así como su robustez contra el rendimiento del clasificador base, en un entorno práctico de control de calidad seminal donde el objetivo final es cuantificar la proporción de espermatozoides con acrosoma dañado/intacto

DeepHistoClass: A novel strategy for confident classification of immunohistochemistry images using Deep Learning

© 2021 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.mcpro.2021.100140A multitude of efforts worldwide aim to create a single cell reference map of the human body, for fundamental understanding of human health, molecular medicine and targeted treatment. Antibody-based proteomics using immunohistochemistry (IHC) has proven to be an excellent technology for integration with large-scale single cell transcriptomics datasets. The golden standard for evaluation of IHC staining patterns is manual annotation, which is expensive and may lead to subjective errors. Artificial intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. Here, the aim was to present a reliable and comprehensive framework for automated annotation of IHC images. We developed a multi-label classification of 7,848 complex IHC images of human testis corresponding to 2,794 unique proteins, generated as part of the Human Protein Atlas (HPA) project. Manual annotation data for eight different cell types was generated as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric; DeepHistoClass (DHC) confidence score; the average diagnostic performance improved from 86.9% to 96.3%. This metric not only reveals which images are reliably classified by the model, but can also be utilized for identification of manual annotation errors. The proposed streamlined workflow can be developed further for other tissue types in health and disease, and has important implications for digital pathology initiatives or large-scale protein mapping efforts such as the HPA project.Knut and Alice Wallenberg Foundation

Regulation of Mother-to-Offspring Transmission of mtDNA Heteroplasmy

mtDNA is present in multiple copies in each cell derived from the expansions of those in the oocyte. Heteroplasmy, more than one mtDNA variant, may be generated by mutagenesis, paternal mtDNA leakage, and novel medical technologies aiming to prevent inheritance of mtDNA-linked diseases. Heteroplasmy phenotypic impact remains poorly understood. Mouse studies led to contradictory models of random drift or haplotype selection for mother-tooffspring transmission of mtDNA heteroplasmy. Here, we show that mtDNA heteroplasmy affects embryo metabolism, cell fitness, and induced pluripotent stem cell (iPSC) generation. Thus, genetic and pharmacological interventions affecting oxidative phosphorylation (OXPHOS) modify competition among mtDNA haplotypes during oocyte development and/or at early embryonic stages. We show that heteroplasmy behavior can fall on a spectrum from random drift to strong selection, depending on mito-nuclear interactions and metabolic factors. Understanding heteroplasmy dynamics and its mechanisms provide novel knowledge of a fundamental biological process and enhance our ability to mitigate risks in clinical applications affecting mtDNA transmission.Peer reviewe

Assessment of DNA structure and integrity in the human spermatozoon

Male fertility is routinely assessed by basic semen analysis, but this is poorly predictive of fertility outcome. One promising advanced diagnostic is assessing sperm DNA damage, but there is a lack of method standardisation, clinical thresholds and comparison data for different assays. The objective of this study was to validate standardised methods for the assessment of sperm DNA quality using TUNEL, Acridine orange (AO) and Chromomycin A3 (CMA3) in slide-based assays; and assess prognostic value for fertility and miscarriage. The data obtained reveals that using differing optimal mounting solution for different assays is key to reliable results. The use of DNA fragmentation inductors such as DNase and hydrogen peroxide in donor samples confirmed and validated the use of AO and TUNEL for detecting DNA damaged cells. A novel semi-automated computer-based scoring system for fluorescence microscopy images was devised and compared with visual operator results for intra-assay variability of AO and TUNEL assays. This system allowed objective and consistent results free of operator subjectivity. The assessment of TUNEL, AO and CMA3 values in a subset of patients from the HABSelect trial showed no correlation between the assays corroborating that different assays measure different aspects of DNA quality. The number of patient samples assessed were insufficiently powered to draw firm conclusions related to clinical outcome, but we believe they are useful in making a case for further investigations in the field

When the machine does not know measuring uncertainty in deep learning models of medical images

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRecently, Deep learning (DL), which involves powerful black box predictors, has outperformed human experts in several medical diagnostic problems. However, these methods focus exclusively on improving the accuracy of point predictions without assessing their outputs’ quality and ignore the asymmetric cost involved in different types of misclassification errors. Neural networks also do not deliver confidence in predictions and suffer from over and under confidence, i.e. are not well calibrated. Knowing how much confidence there is in a prediction is essential for gaining clinicians’ trust in the technology. Calibrated uncertainty quantification is a challenging problem as no ground truth is available. To address this, we make two observations: (i) cost-sensitive deep neural networks with Dropweights models better quantify calibrated predictive uncertainty, and (ii) estimated uncertainty with point predictions in Deep Ensembles Bayesian Neural Networks with DropWeights can lead to a more informed decision and improve prediction quality. This dissertation focuses on quantifying uncertainty using concepts from cost-sensitive neural networks, calibration of confidence, and Dropweights ensemble method. First, we show how to improve predictive uncertainty by deep ensembles of neural networks with Dropweights learning an approximate distribution over its weights in medical image segmentation and its application in active learning. Second, we use the Jackknife resampling technique to correct bias in quantified uncertainty in image classification and propose metrics to measure uncertainty performance. The third part of the thesis is motivated by the discrepancy between the model predictive error and the objective in quantified uncertainty when costs for misclassification errors or unbalanced datasets are asymmetric. We develop cost-sensitive modifications of the neural networks in disease detection and propose metrics to measure the quality of quantified uncertainty. Finally, we leverage an adaptive binning strategy to measure uncertainty calibration error that directly corresponds to estimated uncertainty performance and address problematic evaluation methods. We evaluate the effectiveness of the tools on nuclei images segmentation, multi-class Brain MRI image classification, multi-level cell type-specific protein expression prediction in ImmunoHistoChemistry (IHC) images and cost-sensitive classification for Covid-19 detection from X-Rays and CT image dataset. Our approach is thoroughly validated by measuring the quality of uncertainty. It produces an equally good or better result and paves the way for the future that addresses the practical problems at the intersection of deep learning and Bayesian decision theory. In conclusion, our study highlights the opportunities and challenges of the application of estimated uncertainty in deep learning models of medical images, representing the confidence of the model’s prediction, and the uncertainty quality metrics show a significant improvement when using Deep Ensembles Bayesian Neural Networks with DropWeights

Application of timed artificial insemination in synchronized dairy and beef cows using sexed and non-sexed semen

In beef and dairy cattle, using sexed semen improves genetic progress and increases the proportion of desired gender calves following artificial insemination (AI). Oestrus detection is a limiting factor for maximum reproductive efficiency in dairy and beef cows. Many intrinsic and extrinsic factors, however, can hinder oestrous synchronization. Advanced sperm analyses in cattle provide accurate results on sperm quality. The best measure of sperm quality, according to the computer assisted sperm analysis (CASA) system, is the highest sperm motility or velocity. Therefore, the present study aimed to assess the application of timed artificial insemination (TAI) in synchronized dairy and beef cows using sexed semen in emerging farmer’s cattle herds of Gauteng province. A total of two hundred and thirty three cows (dairy; n = 136 and beef; n = 97) were selected from the emerging cattle herds. All cows were selected based on; age (3 to ≥ 7 years), body condition score (BCS) of ≤ 2.5, 3 and ≥ 3.5 (1 to 5 scale), not pregnant (excluding heifers), parity (1 to ≥ 5th), negative to contagious abortion, 90 days postpartum and lactation status (lactating or dry) and assigned to a 9-day Ovsynch + controlled intravaginal drug release (CIDR) and TAI protocol. In brief, on any given day throughout the oestrous cycle (Day 0) the cows received an insertion of CIDR device into the vagina, with intramuscular (i.m.) administration of Estradiol benzoate® (EB). On Day 8, i.m. administration of prostaglandin (PGF2α), with adhesive tail-head heat mount detectors (HMD) and CIDR was removed. On Day 9, i.m. administration of EB. The TAI was performed by the same inseminator 55 hours following CIDR removal using frozen-thawed X-sexed or non-sexed semen from 8 bulls (4 Holstein Friesian and 4 Angus), 2 sexed and 2 non-sexed sperm from each cattle type (dairy and beef) were supplied by American breeders service (ABS) Global, South Africa. At AI, oestrus behaviour was assessed by activation of the HMD colour either as are red (oestrus/activated patch) or white (no oestrus/ not activated patch). The GameteTek Cryo-Mobile laboratory truck was used during thawing of semen straws and sperm quality parameters (sperm motility, velocity, morphology and membrane integrity) were immediately assessed before AI. Pregnancy diagnosis was performed on Days 35, 65 and 95 following TAI with the aid of a transrectal ultrasound scanner and transrectal palpation. The calving date and calf sex were recorded at calving. All data were analyzed by general linear model (GLM) procedure of Statistical Analysis System (SAS, 9.3.3). Analysis of variance (ANOVA) was tested to compare treatment means for semen quality (sexed vs non-sexed semen), bull (n = 8) and treatment × bull as a fixed effect. Chi-square test was used to determine significant differences for equal proportions. Differences between the variables were considered to be statistically significant at P 0.05). The average sperm membrane integrity percentage was recorded among dairy (sexed; 51.0% and non-sexed; 64.1%) and beef (sexed; 52.3% and non-sexed; 71.2%) bulls. The proportion of pregnancy was high in dairy (sexed; 41.4% and non-sexed; 48.5%) compared with beef (sexed; 38.0% and non-sexed; 37.0 %) cows (P < 0.05). In conclusion, the acceptable oestrus synchronization expression and conception rates of dairy and beef cows were achieved. It is recommended that sexed semen can be successfully utilized through advanced reproductive biotechnologies in an organized emerging cattle farming system.Agriculture and  Animal HealthM. Sc. (Agriculture

Regulation of mother-to-offspring transmission of mtDNA heteroplasmy

mtDNA is present in multiple copies in each cell derived from the expansions of those in the oocyte. Heteroplasmy, more than one mtDNA variant, may be generated by mutagenesis, paternal mtDNA leakage, and novel medical technologies aiming to prevent inheritance of mtDNA-linked diseases. Heteroplasmy phenotypic impact remains poorly understood. Mouse studies led to contradictory models of random drift or haplotype selection for mother-to-offspring transmission of mtDNA heteroplasmy. Here, we show that mtDNA heteroplasmy affects embryo metabolism, cell fitness, and induced pluripotent stem cell (iPSC) generation. Thus, genetic and pharmacological interventions affecting oxidative phosphorylation (OXPHOS) modify competition among mtDNA haplotypes during oocyte development and/or at early embryonic stages. We show that heteroplasmy behavior can fall on a spectrum from random drift to strong selection, depending on mito-nuclear interactions and metabolic factors. Understanding heteroplasmy dynamics and its mechanisms provide novel knowledge of a fundamental biological process and enhance our ability to mitigate risks in clinical applications affecting mtDNA transmission

Recent Advances in Pharmaceutical Sciences VIII

This E-book is the eighth volume of a series that compiles contributions from different areas of the multidisciplinary field of Pharmaceutical Sciences. The E-book consists of 7 chapters that cover the areas of organic chemistry, health and environmental management, plant physiology, food science, toxicology, botany, parasitology, physiology, biochemistry and molecular biology, microbiology, and pharmacology