FairSubset: A tool to choose representative subsets of data for use with replicates or groups of different sample sizes

Automated hippocampal segmentation algorithms evaluated in stroke patients

Deep learning segmentation algorithms can produce reproducible results in a matter of seconds. However, their application to more complex datasets is uncertain and may fail in the presence of severe structural abnormalities-such as those commonly seen in stroke patients. In this investigation, six recent, deep learning-based hippocampal segmentation algorithms were tested on 641 stroke patients of a multicentric, open-source dataset ATLAS 2.0. The comparisons of the volumes showed that the methods are not interchangeable with concordance correlation coefficients from 0.266 to 0.816. While the segmentation algorithms demonstrated an overall good performance (volumetric similarity [VS] 0.816 to 0.972, DICE score 0.786 to 0.921, and Hausdorff distance [HD] 2.69 to 6.34), no single out-performing algorithm was identified: FastSurfer performed best in VS, QuickNat in DICE and average HD, and Hippodeep in HD. Segmentation performance was significantly lower for ipsilesional segmentation, with a decrease in performance as a function of lesion size due to the pathology-based domain shift. Only QuickNat showed a more robust performance in volumetric similarity. Even though there are many pre-trained segmentation methods, it is important to be aware of the possible decrease in performance for the segmentation results on the lesion side due to the pathology-based domain shift. The segmentation algorithm should be selected based on the research question and the evaluation parameter needed. More research is needed to improve current hippocampal segmentation methods.

Combination of Autophagy Selective Therapeutics With Doxil: An Assessment of Pathological Toxicity

Background: Combination therapy of targeted drugs in cancer treatment is a field in constant flux, with research balancing side effects with efficacy. Efficacy from combination therapy is improved either through synthetic lethality or through prevention of recurrent clones. Previous research has shown (hydroxy-)chloroquine is insufficient to disrupt autophagy in tumors. Hence, either combinations or novel autophagy agents are desired. In vivo studies of ovarian cancer have revealed that chloroquine can be combined with up to four other autophagy drugs to suppress ovarian cancer growth. While cancer efficacy is now established for the autophagy drug combination, it is unclear what toxicities may require monitoring in human trials. Additive toxicity with chemotherapy is also unknown.

Methods: To address toxicity in more depth than previous weight-monitoring studies, biochemical and histopathology studies were performed. Mouse groups were treated with autophagy drugs for 2 weeks, with or without the chemotherapy Doxil. After the last dose, mice were processed for blood biochemistry, white blood cell markers, and histopathology.

Results: Data from a comprehensive blood biochemistry panel, flow cytometric measurements of blood cell markers, and histopathology are herein reported. While Doxil presented clear bone marrow and immunologic toxicity, autophagy drugs were overall less toxic and more variable in their presentation of potential toxicities. Only minor additive effects of autophagy drugs with Doxil were observed.

Conclusion: Combinations of autophagy drugs may be considered for therapy in human oncology trials, with possible side effects to monitor informed by these murine pre-clinical data.

An Exploratory Analysis of the Internal Structure of Test Through a Multimethods Exploratory Approach of the ASQ:SE in Brazil

Background  A wide range of exploratory methods is available in psychometrics as means of gathering insight on existing data and on the process of establishing the number and nature of an internal structure factor of a test. Exploratory factor analysis (EFA) and principal component analysis (PCA) remain well-established techniques despite their different theoretical perspectives. Network analysis (NA) has recently gained popularity together with such algorithms as the Next Eigenvalue Sufficiency Test. These analyses link statistics and psychology, but their results tend to vary, leading to an open methodological debate on statistical assumptions of psychometric analyses and the extent to which results that are generated with these analyses align with the theoretical basis that underlies an instrument. The current study uses a previously published data set from the Ages & Stages Questionnaires: Social-Emotional to explore, show, and discuss several exploratory analyses of its internal structure. To a lesser degree, this study furthers the ongoing debate on the interface between theoretical and methodological perspectives in psychometrics.

Methods  From a sample of 22,331 sixty-month-old children, 500 participants were randomly selected. Pearson and polychoric correlation matrices were compared and used as inputs in the psychometric analyses. The number of factors was determined via well-known rules of thumb, including the parallel analysis and the Hull method. Multidimensional solutions were rotated via oblique methods. R and Factor software were used, the codes for which are publicly available at https://luisfca.shinyapps.io/psychometrics_asq_se/ .

Results  Solutions from one to eight dimensions were suggested. Polychoric correlation overcame Pearson correlation, but nonconvergence issues were detected. The Hull method achieved a unidimensional structure. PCA and EFA achieved similar results. Conversely, six clusters were suggested via NA.

Conclusion  The statistical outcomes for determining the factor structure of an assessment diverged, varying from one to eight domains, which allowed for different interpretations of the results. Methodological implications are further discussed.

Single-cell analysis of copy-number alterations in serous ovarian cancer reveals substantial heterogeneity in both low- and high-grade tumors

Unusually high aneuploidy is a hallmark of epithelial serous ovarian cancer (SOC). Previous analyses have focused on aneuploidy on average across all tumor cells. With the expansion of single-cell sequencing technologies, however, an analysis of copy number heterogeneity cell-to-cell is now technically feasible. Here, we describe an analysis of single-cell RNA sequencing (scRNA-seq) data to infer arm-level aneuploidy in individual serous ovarian cancer cells. By first clustering high-quality sequenced epithelial versus non-epithelial cells, high-confidence tumor cell populations were identified. InferCNV was used to predict segmented copy-number alterations (CNAs), which were then used to determine arm-level aneuploidy at the single-cell level. Control comparisons of normal cells to normal cells showed zero arm-level aneuploidy, whereas a median of four aneuploid events were detectable in cancer cells. A heterogeneity analysis of high-grade tumor cells compared to low-grade tumor cells showed similar levels of cell-to-cell variation between cancer grades. Metastatic tumors potentially showed selection pressure with reduced cell-to-cell variation compared to cells from primary tumors. Minor cell populations with CNAs similar to metastatic cells were identified within the matched primary tumors. Taken together, these results provide a minimum estimate for single-cell aneuploidy in serous ovarian cancer and demonstrate the utility of single-cell sequencing for CNA analysis.

Fundamentals of Microscopy

The light (or optical) microscope is the icon of science. The aphorism "seeing is believing" is often quoted in scientific papers involving microscopy. Unlike many scientific instruments, the light microscope will deliver an image however badly it is set up. Fluorescence microscopy is a widely used research tool across all disciplines of biological and biomedical science. Most universities and research institutions have microscopes, including confocal microscopes. This introductory paper in a series detailing advanced light microscopy techniques explains the foundations of both electron and light microscopy for biologists and life scientists working with the mouse. An explanation is given of how an image is formed. A description is given of how to set up a light microscope, whether it be a brightfield light microscope on the laboratory bench, a widefield fluorescence microscope, or a confocal microscope. These explanations are accompanied by operational protocols. A full explanation on how to set up and adjust a microscope according to the principles of Köhler illumination is given. The importance of Nyquist sampling is discussed. Guidelines are given on how to choose the best microscope to image the particular sample or slide preparation that you are working with. These are the basic principles of microscopy that a researcher must have an understanding of when operating core bioimaging facility instruments, in order to collect high-quality images. © 2020 The Authors. Basic Protocol 1: Setting up Köhler illumination for a brightfield microscope Basic Protocol 2: Aligning the fluorescence bulb and setting up Köhler illumination for a widefield fluorescence microscope Basic Protocol 3: Generic protocol for operating a confocal microscope.