A brief introduction to computer-intensive methods, with a view towards applications in spatial statistics and stereology

Past, Current, and Future: Application of Image Analysis in Small Molecule Pharmaceutical Development

The often-perceived limitations of image analysis have for many years impeded the widespread application of such systems as first line characterisation tools. Image analysis has, however, undergone a notable resurgence in the pharmaceutical industry fuelled by developments system capabilities and the desire of scientists to characterize the morphological nature of their particles more adequately. The importance of particle shape as well as size is now widely acknowledged. With the increasing use of modelling and simulations, and ongoing developments though the integration of machine learning and artificial intelligence, the utility of image analysis is increasing significantly driven by the richness of the data obtained. Such datasets provide means to circumvent the requirement to rely on less informative descriptors and enable the move towards the use of whole distributions. Combining the improved particle size and shape measurement and description with advances in modelling and simulations is enabling improved means to elucidate the link between particle and bulk powder properties. In addition to improved capabilities to describe input materials, approaches to characterize single components within multicomponent systems are providing scientists means to understand how their material may change during manufacture thus providing a means to link the behaviour of final dosage forms with the particle properties at the point of action. The aim is to provide an overview of image analysis and update readers with innovations and capabilities to other methods in the small molecule arena. We will also describe the use of AI for the improved analysis using image analysis.

Basic quantitative morphological methods applied to the central nervous system

Generating numbers has become an almost inevitable task associated with studies of the morphology of the nervous system. Numbers serve a desire for clarity and objectivity in the presentation of results and are a prerequisite for the statistical evaluation of experimental outcomes. Clarity, objectivity, and statistics make demands on the quality of the numbers that are not met by many methods. This review provides a refresher of problems associated with generating numbers that describe the nervous system in terms of the volumes, surfaces, lengths, and numbers of its components. An important aim is to provide comprehensible descriptions of the methods that address these problems. Collectively known as design-based stereology, these methods share two features critical to their application. First, they are firmly based in mathematics and its proofs. Second and critically underemphasized, an understanding of their mathematical background is not necessary for their informed and productive application. Understanding and applying estimators of volume, surface, length or number does not require more of an organizational mastermind than an immunohistochemical protocol. And when it comes to calculations, square roots are the gravest challenges to overcome. Sampling strategies that are combined with stereological probes are efficient and allow a rational assessment if the numbers that have been generated are "good enough." Much may be unfamiliar, but very little is difficult. These methods can no longer be scapegoats for discrepant results but faithfully produce numbers on the material that is assessed. They also faithfully reflect problems that associated with the histological material and the anatomically informed decisions needed to generate numbers that are not only valid in theory. It is within reach to generate practically useful numbers that must integrate with qualitative knowledge to understand the function of neural systems.

Detection of Phenotypic Alterations Using High-Content Analysis of Whole-Slide Images

Tumors exhibit spatial heterogeneity, as manifested in immunohistochemistry (IHC) staining patterns. Current IHC quantification methods lose information by reducing this heterogeneity in each whole-slide image (WSI) or in selective fields of view to a single staining index. The aim of this study was to investigate the sensitivity of an IHC quantification method that uses this heterogeneity to reliably compare IHC staining patterns. We virtually partitioned WSIs by a grid of square tiles, and computed the staining index distributions to quantify heterogeneities. We used samples from these distributions as inputs to non-parametric statistical comparisons. We applied our grid method to fixed tumor samples from 26 tumors obtained from a double-blind preclinical study of a patient-derived orthotopic xenograft model of pediatric neuroblastoma in CD1 nude mice. We compared the results of our grid method to the results based on whole-slide indices, the current practice. We show that our grid method reliably detects phenotypic alterations that other tests based on whole-slide indices fail to detect. Based on robustness and increased sensitivity of statistical inference, we conclude that our method of whole-slide grid quantification is superior to existing whole-slide quantification techniques.

Dynamics of memory-guided choice behavior in Drosophila

Memory retrieval requires both accuracy and speed. Olfactory learning of the fruit fly Drosophila melanogaster serves as a powerful model system to identify molecular and neuronal substrates of memory and memory-guided behavior. The behavioral expression of olfactory memory has traditionally been tested as a conditioned odor response in a simple T-maze, which measures the result, but not the speed, of odor choice. Here, we developed multiplexed T-mazes that allow video recording of the choice behavior. Automatic fly counting in each arm of the maze visualizes choice dynamics. Using this setup, we show that the transient blockade of serotonergic neurons slows down the choice, while leaving the eventual choice intact. In contrast, activation of the same neurons impairs the eventual performance leaving the choice speed unchanged. Our new apparatus contributes to elucidating how the speed and the accuracy of memory retrieval are implemented in the fly brain.

Block bootstrap methods for the estimation of the intensity of a spatial point process with confidence bounds

This paper deals with the estimation of the intensity of a planar point process on the basis of a single point pattern, observed in a rectangular window. If the model assumptions of stationarity and isotropy hold, the method of block bootstrapping can be used to estimate the intensity of the process with confidence bounds. The results of two variants of block bootstrapping are compared with a parametric approximation based on the assumption of a Gaussian distribution of the numbers of points in deterministic subwindows of the original pattern. The studies were performed on patterns obtained by simulation of well-known point process models (Poisson process, two Matérn cluster processes, Matérn hardcore process, Strauss hardcore process). They were also performed on real histopathological data (point patterns of capillary profiles of 12 cases of prostatic cancer). The methods are presented as worked examples on two cases, where we illustrate their use as a check on stationarity (homogeneity) of a point process with respect to different fields of vision. The paper concludes with various methodological discussions and suggests possible extensions of the block bootstrap approach to other fields of spatial statistics.