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Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color representations and inter-scanner color variation in the WSIs. Although pathologists can accommodate a range of color variation during their evaluation of WSIs, color variability can degrade the performance of computational applications in digital pathology. In particular, color variability can compromise the generalization of artificial intelligence applications to large volumes of data from diverse sources. To address these challenges, we developed a process that includes two modules: (1) assessing the color reproducibility of our scanners and the color variation among them and (2) applying color correction to WSIs to minimize the color deviation and variation. Our process ensures consistent color reproduction across WSI scanners and enhances color homogeneity in WSIs, and its flexibility enables easy integration as a post-processing step following scanning by WSI scanners of different models and from different manufacturers., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Mei-Lan Chu, Xing-Yue M Ge, Jeffrey Eastham, Trung Nguyen, Reina N Fuji, Ruth Sullivan and Daniel Ruderman are employees of Genentech, Inc. and are Roche stockholders.

Background: Colorectal cancer (CRC) mortality is principally due to metastatic disease, with the most frequent organ of metastasis being the liver. Biochemical and mechanical factors residing in the tumor microenvironment are considered to play a pivotal role in metastatic growth and response to therapy. However, it is difficult to study the tumor microenvironment systematically owing to a lack of fully controlled model systems that can be investigated in rigorous detail., Results: We present a quantitative imaging dataset of CRC cell growth dynamics influenced by in vivo-mimicking conditions. They consist of tumor cells grown in various biochemical and biomechanical microenvironmental contexts. These contexts include varying oxygen and drug concentrations, and growth on conventional stiff plastic, softer matrices, and bioengineered acellular liver extracellular matrix. Growth rate analyses under these conditions were performed via the cell phenotype digitizer (CellPD)., Conclusions: Our data indicate that the growth of highly aggressive HCT116 cells is affected by oxygen, substrate stiffness, and liver extracellular matrix. In addition, hypoxia has a protective effect against oxaliplatin-induced cytotoxicity on plastic and liver extracellular matrix. This expansive dataset of CRC cell growth measurements under in situ relevant environmental perturbations provides insights into critical tumor microenvironment features contributing to metastatic seeding and tumor growth. Such insights are essential to dynamical modeling and understanding the multicellular tumor-stroma dynamics that contribute to metastatic colonization. It also establishes a benchmark dataset for training and testing data-driven dynamical models of cancer cell lines and therapeutic response in a variety of microenvironmental conditions., (© The Author(s) 2021. Published by Oxford University Press GigaScience.)

Small molecules that target the androgen receptor (AR) are the mainstay of therapy for lethal castration-resistant prostate cancer (CRPC), yet existing drugs lose their efficacy during continued treatment. This evolution of resistance is due to heterogenous mechanisms which include AR mutations causing the identical drug to activate instead of inhibit the receptor. Understanding in molecular detail the paradoxical phenomenon wherein an AR antagonist is transformed into an agonist by structural mutations in the target receptor is thus of paramount importance. Herein, we describe a reciprocal paradox: opposing antagonist and agonist AR regulation determined uniquely by enantiomeric forms of the same drug structure. The antiandrogen BMS-641988, which has ( R )-chirality at C-5 encompasses a previously uncharacterized ( S )-stereoisomer that is, surprisingly, a potent agonist of AR, as demonstrated by transcriptional assays supported by cell imaging studies. This duality was reproduced in a series of novel compounds derived from the BMS-641988 scaffold. Coupled with in silico modeling studies, the results inform an AR model that explains the switch from potent antagonist to high-affinity agonist in terms of C-5 substituent steric interactions with helix 12 of the ligand binding site. They imply strategies to overcome AR drug resistance and demonstrate that insufficient enantiopurity in this class of AR antagonist can confound efforts to correlate structure with function., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Although researchers have found support for a relationship between temperature and violence and evidence of temporal patterns in violent crime, research on homicide shows less consistent results and no research on mass murder has been conducted. We address this by examining predictive factors in multi-victim shootings (those with four or more victims, including injured), a more general crime category than mass murder, but one with likely similar predictive factors. We used data from the Gun Violence Archive to understand the relationship between multi-victim shootings and temperature as well as other extrinsic factors. To avoid the confound between season and temperature, we employed temperature anomaly (the difference between actual and expected temperature) as a predictor of daily shooting rate. Using a generalized linear model for the daily count of multi-victim shootings in the U.S., we found that these events are significantly more frequent on weekends, some major holidays, hotter seasons, and when the temperature is higher than usual. Like other crimes, rates of multi-victim shooting vary systematically.

For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular marker used for prognosis and treatment decisions. During clinical management, ERS is determined by pathologists from immunohistochemistry (IHC) staining of biopsied tissue for the targeted receptor, which highlights the presence of cellular surface antigens. This is an expensive, time-consuming process which introduces discordance in results due to variability in IHC preparation and pathologist subjectivity. In contrast, hematoxylin and eosin (H&E) staining-which highlights cellular morphology-is quick, less expensive, and less variable in preparation. Here we show that machine learning can determine molecular marker status, as assessed by hormone receptors, directly from cellular morphology. We develop a multiple instance learning-based deep neural network that determines ERS from H&E-stained whole slide images (WSI). Our algorithm-trained strictly with WSI-level annotations-is accurate on a varied, multi-country dataset of 3,474 patients, achieving an area under the curve (AUC) of 0.92 for sensitivity and specificity. Our approach has the potential to augment clinicians' capabilities in cancer prognosis and theragnosis by harnessing biological signals imperceptible to the human eye.

Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features that predict the clinical subtypes of breast cancer, as assessed by immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2). Translating deep learning to this and related problems in histopathology presents a challenge due to the lack of large, well-annotated data sets, which are typically required for the algorithms to learn statistically significant discriminatory patterns. To overcome this limitation, we introduce the concept of "tissue fingerprints," which leverages large, unannotated datasets in a label-free manner to learn H&E features that can distinguish one patient from another. The hypothesis is that training the algorithm to learn the morphological differences between patients will implicitly teach it about the biologic variation between them. Following this training internship, we used the features the network learned, which we call "fingerprints," to predict ER, PR, and Her2 status in two datasets. Despite the discovery dataset being relatively small by the standards of the machine learning community (n = 939), fingerprints enabled the determination of ER, PR, and Her2 status from whole slide H&E images with 0.89 AUC (ER), 0.81 AUC (PR), and 0.79 AUC (Her2) on a large, independent test set (n = 2531). Tissue fingerprints are concise but meaningful histopathologic image representations that capture biological information and may enable machine learning algorithms that go beyond the traditional ER/PR/Her2 clinical groupings by directly predicting theragnosis.

Objective: Cell-free DNA (cfDNA) is an attractive cancer biomarker, as it is thought to reflect a component of the underlying genetic makeup of the tumor and is readily accessible in serial fashion. Because chemotherapy regimens are expected to act rapidly on cancer and cfDNA is cleared from the blood within minutes, we hypothesized that cfDNA would reflect immediate effects of treatment. Here, we developed a method for monitoring long cfDNA fragments, and report dynamic changes in response to cytotoxic chemotherapy., Results: Peripheral blood was obtained from 15 patients with metastatic castration-resistant prostate cancer (CRPC) immediately before and after cytotoxic chemotherapy infusion. cfDNA was extracted and quantified for long interspersed nuclear elements (LINE1; 297 bp) using qPCR. Targeted deep sequencing was performed to quantify the frequency of mutations in exon 8 of the androgen receptor (AR), a mutational hotspot region in CRPC. Single nucleotide mutations in AR exon 8 were found in 6 subjects (6/15 = 40%). Analytical variability was minimized by pooling independent PCR reactions for each library. In 5 patients, tumor-derived long cfDNA levels were found to change immediately after infusion. Detailed analysis of one subject suggests that cytotoxic chemotherapy can produce rapidly observable effects on cfDNA.

In this pilot study, we introduce a machine learning framework to identify relationships between cancer tissue morphology and hormone receptor pathway activation in breast cancer pathology hematoxylin and eosin (H&E)-stained samples. As a proof-of-concept, we focus on predicting clinical estrogen receptor (ER) status-defined as greater than one percent of cells positive for estrogen receptor by immunohistochemistry staining-from spatial arrangement of nuclear features. Our learning pipeline segments nuclei from H&E images, extracts their position, shape and orientation descriptors, and then passes them to a deep neural network to predict ER status. After training on 57 tissue cores of invasive ductal carcinoma (IDC), our pipeline predicted ER status in an independent test set of patient samples (AUC ROC = 0.72, 95%CI = 0.55-0.89, n  = 56). This proof of concept shows that machine-derived descriptors of morphologic histology patterns can be correlated to signaling pathway status. Unlike other deep learning approaches to pathology, our system uses deep neural networks to learn spatial relationships between pre-defined biological features, which improves the interpretability of the system and sheds light on the features the neural network uses to predict ER status. Future studies will correlate morphometry to quantitative measures of estrogen receptor status and, ultimately response to hormonal therapy., Competing Interests: The authors declare no competing interests.

Cancer proteomics is the manifestation of relevant biological processes in cancer development. Thus, it reflects the activities of tumor cells, host-tumor interactions, and systemic responses to cancer therapy. To understand the causal effects of tumorigenesis or therapeutic intervention, longitudinal studies are greatly needed. However, most of the conventional mouse experiments are unlikely to accommodate frequent collection of serum samples with a large enough volume for multiple protein assays towards single-object analysis. Here, we present a technique based on magneto-nanosensors to longitudinally monitor the protein profiles in individual mice of lymphoma models using a small volume of a sample for multiplex assays. Methods: Drug-sensitive and -resistant cancer cell lines were used to develop the mouse models that render different outcomes upon the drug treatment. Two groups of mice were inoculated with each cell line, and treated with either cyclophosphamide or vehicle solution. Serum samples taken longitudinally from each mouse in the groups were measured with 6-plex magneto-nanosensor cytokine assays. To find the origin of IL-6, experiments were performed using IL-6 knock-out mice. Results: The differences in serum IL-6 and GCSF levels between the drug-treated and untreated groups were revealed by the magneto-nanosensor measurement on individual mice. Using the multiplex assays and mouse models, we found that IL-6 is secreted by the host in the presence of tumor cells upon the drug treatment. Conclusion: The multiplex magneto-nanosensor assays enable longitudinal proteomic studies on mouse tumor models to understand tumor development and therapy mechanisms more precisely within a single biological object., Competing Interests: Competing Interests: S.X.W. has related patents or patent applications assigned to Stanford University and out-licensed for potential commercialization. S.X.W. has stock or stock options in MagArray, Inc., which has licensed relevant patents from Stanford University for commercialization of magneto-nanosensor chips.

Cells display broad heterogeneity across multiple phenotypic features, including motility, morphology, and cell signaling. Live-cell imaging techniques are beginning to capture the importance and interdependence of these phenomena. However, existing image analysis pipelines often fail to capture the intricate changes that occur in small subpopulations, either due to poor segmentation protocols or cell tracking errors. Here we report a pipeline designed to image and track single-cell dynamic phenotypes in heterogeneous cell populations. We provide step-by-step instructions for three phenotypically different cell lines across two time scales as well as recommendations for adaptation to custom data sets. Our protocols include steps for quality control that can be used to filter out erroneous tracks and improve assessment of heterogeneity. We demonstrate possible phenotypic readouts including motility, nuclear receptor translocation, and mitosis.

Each immunoglobulin isotype has unique immune effector functions. The contribution of these functions in the elimination of pathogens and tumors can be determined by monitoring quantitative temporal changes in isotype levels. Here, we developed a novel technique using magneto-nanosensors based on the effect of giant magnetoresistance (GMR) for longitudinal monitoring of total and antigen-specific isotype levels with high precision, using as little as 1 nL of serum. Combining in vitro serologic measurements with in vivo imaging techniques, we investigated the role of the antibody response in the regression of firefly luciferase (FL)-labeled lymphoma cells in spleen, kidney, and lymph nodes in a syngeneic Burkitt's lymphoma mouse model. Regression status was determined by whole body bioluminescent imaging (BLI). The magneto-nanosensors revealed that anti-FL IgG2a and total IgG2a were elevated and sustained in regression mice compared to non-regression mice (p < 0.05). This platform shows promise for monitoring immunotherapy, vaccination, and autoimmunity.

Heavy alcohol consumption is considered an important public health issue in the United States as over 88 000 people die every year from alcohol-related causes. Research is being conducted to understand the etiology of alcohol consumption and to develop strategies to decrease high-risk consumption and its consequences, but there are still important gaps in determining the main factors that influence the consumption behaviors throughout the drinking event. There is a need for methodologies that allow us not only to identify such factors but also to have a comprehensive understanding of how they are connected and how they affect the dynamical evolution of a drinking event. In this paper, we use previous empirical findings from laboratory and field studies to build a mathematical model of the blood alcohol concentration dynamics in individuals that are in drinking events. We characterize these dynamics as the result of the interaction between a decision-making system and the metabolic process for alcohol. We provide a model of the metabolic process for arbitrary alcohol intake patterns and a characterization of the mechanisms that drive the decision-making process of a drinker during the drinking event. We use computational simulations and Lyapunov stability theory to analyze the effects of the parameters of the model on the blood alcohol concentration dynamics that are characterized. Also, we propose a methodology to inform the model using data collected in situ and to make estimations that provide additional information to the analysis. We show how this model allows us to analyze and predict previously observed behaviors, to design new approaches for the collection of data that improves the construction of the model, and help with the design of interventions.

Because proteomics experiments are so complex they can readily fail, and do so without clear cause. Using standard experimental design techniques and incorporating quality control can greatly increase the chances of success. This chapter introduces the relevant concepts and provides examples specific to proteomic workflows. Applying these notions to design successful proteomics experiments is straightforward. It can help identify failure causes and greatly increase the likelihood of inter-laboratory reproducibility.

Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.

Background: The genetic origins of chemotherapy resistance are well established; however, the role of epigenetics in drug resistance is less well understood. To investigate mechanisms of drug resistance, we performed systematic genetic, epigenetic, and transcriptomic analyses of an alkylating agent-sensitive murine lymphoma cell line and a series of resistant lines derived by drug dose escalation., Methods: Dose escalation of the alkylating agent mafosfamide was used to create a series of increasingly drug-resistant mouse Burkitt's lymphoma cell lines. Whole genome sequencing, DNA microarrays, reduced representation bisulfite sequencing, and chromatin immunoprecipitation sequencing were used to identify alterations in DNA sequence, mRNA expression, CpG methylation, and H3K27me3 occupancy, respectively, that were associated with increased resistance., Results: Our data suggest that acquired resistance cannot be explained by genetic alterations. Based on integration of transcriptional profiles with transcription factor binding data, we hypothesize that resistance is driven by epigenetic plasticity. We observed that the resistant cells had H3K27me3 and DNA methylation profiles distinct from those of the parental lines. Moreover, we observed DNA methylation changes in the promoters of genes regulated by E2a and members of the polycomb repressor complex 2 (PRC2) and differentially expressed genes were enriched for targets of E2a. The integrative analysis considering H3K27me3 further supported a role for PRC2 in mediating resistance. By integrating our results with data from the Immunological Genome Project (Immgen.org), we showed that these transcriptional changes track the B-cell maturation axis., Conclusions: Our data suggest a novel mechanism of drug resistance in which E2a and PRC2 drive changes in the B-cell epigenome; these alterations attenuate alkylating agent treatment-induced apoptosis.

The androgen receptor (AR) pathway plays a central role in prostate cancer (PCa) growth and progression and is a validated therapeutic target. In response to ligand binding AR translocates to the nucleus, though the molecular mechanism is not well understood. We therefore developed multimodal Image Correlation Spectroscopy (mICS) to measure anisotropic molecular motion across a live cell. We applied mICS to AR translocation dynamics to reveal its multimodal motion. By integrating fluorescence imaging methods we observed evidence for diffusion, confined movement, and binding of AR within both the cytoplasm and nucleus of PCa cells. Our findings suggest that in presence of cytoplasmic diffusion, the probability of AR crossing the nuclear membrane is an important factor in determining the AR distribution between cytoplasm and the nucleus, independent of functional microtubule transport. These findings may have implications for the future design of novel therapeutics targeting the AR pathway in PCa.

Topic: This column describes how public partners can help incentivize participation in training. Specifically, a state mental health agency and its implementation center applied financial and nonfinancial incentives to encourage participation in training and implementation supports., Purpose: Although training is not sufficient to change practice, it is a necessary first step in implementing evidence-based treatments. Finding ways to incentivize participation, particularly strategies with minimal resource involvement, is important for the psychiatric rehabilitation workforce and cash-strapped public systems., Sources Used: This description draws from published material and experiences from New York State., Conclusions and Implications for Practice: Engaging public partners to incentivize training can significantly increase participation in training. Incentive programs exist that do not require additional funding-an important consideration, given the fiscal climate for most public payers., ((c) 2016 APA, all rights reserved).)

This column focuses on use of learning collaboratives by the Center for Practice Innovations to help programs implement the evidence-based individual placement and support model of supported employment in New York State. These learning collaboratives use fidelity and performance indicator data to drive the development of program-specific individualized quality improvement plans. As of 2014, 59 (69%) of 86 eligible programs have joined the initiative. Programs are achieving employment outcomes for consumers on par with national benchmarks, along with improved fidelity.

Background: Substance abuse is the fastest growing health concern for older adults. Heavy drinking among older persons is associated with an increased risk of health consequences such as diabetes, cognitive impairment, sleep issues, and depression. It is important to note, however, the prevalence estimates of alcohol use among older adults are often based on inconsistent methodology. To address these potential methodological shortcomings, this study examines drinking patterns among low-income older adults using both self-report and unobtrusive methods., Methods: The study was conducted in a low-income residential senior center in the United States. A total of 174 participants, aged 60 years or older, completed 2 self-administered cross-sectional surveys. A bogus recycling program was implemented to assess the amount of alcohol consumed by residents. Logistic regression analysis was utilized to model predictors of drinking status and to determine predictors of 3 category Alcohol Use and Disorders Identification Test scores. Bivariate associations that predicted associations with alcohol use were included in the final multivariate model. Alcohol containers collected from recycling were converted to standard drink estimates in order to calculate the capital consumption of residents., Results: About 40% of respondents reported consuming alcohol and 25% reported drinking at least once a week. On average, a total of 1,079 drinks were consumed per month. There were 3 significant predictors of drinking status: age, education, and diagnosis of diabetes. Additionally, there appears to be an increase in recycled alcohol containers coinciding with the time residents received their social security checks., Conclusions: Overall, the combination of self-report and unobtrusive measures of alcohol consumption has potential to highlight different aspects of drinking behavior with a population living in a single dwelling such as a senior center apartment complex, residential hall, and the like., (Copyright © 2014 by the Research Society on Alcoholism.)

This column describes the Center for Practice Innovations (CPI), which was created in 2007 by the New York State Office of Mental Health and the Department of Psychiatry at Columbia University. CPI uses innovative approaches to build stakeholder collaborations, develop and maintain practitioners' expertise, and build agency infrastructures that support implementing and sustaining evidence-based practices. CPI's five core initiatives provide training in co-occurring mental and substance use disorders, assertive community treatment, supported employment and education, wellness self-management, and treatment of first-episode psychosis. Central to CPI's activities are award-winning training modules, statewide learning collaboratives, and use of a learning management system.

first distinguished "simple" from "complex" cells in visual cortex and proposed a processing hierarchy in which rows of LGN cells are pooled to drive oriented simple cell subunits, which are pooled in turn to drive complex cells. Although parsimonious and highly influential, the pure hierarchical model has since been challenged by results indicating that many complex cells receive excitatory monosynaptic input from LGN cells or do not depend on simple cell input. Alternative accounts of complex cell orientation tuning remain scant, however, and the function of monosynaptic LGN contacts onto complex cell dendrites remains unknown. We have used a biophysically detailed compartmental model to investigate whether nonlinear integration of LGN synaptic inputs within the dendrites of individual pyramidal cells could contribute to complex-cell receptive field structure. We show that an isolated cortical neuron with "active" dendrites, driven only by excitatory inputs from overlapping ON- and OFF-center LGN subfields, can produce clear phase-invariant orientation tuning-a hallmark response characteristic of a complex cell. The tuning is shown to depend critically both on the spatial arrangement of LGN synaptic contacts across the complex cell dendritic tree, established by a Hebbian developmental principle, and on the physiological efficacy of excitatory voltage-dependent dendritic ion channels. We conclude that unoriented LGN inputs to a complex cell could contribute in a significant way to its orientation tuning, acting in concert with oriented inputs to the same cell provided by simple cells or other complex cells. As such, our model provides a novel, experimentally testable hypothesis regarding the basis of orientation tuning in the complex cell population, and more generally underscores the potential importance of nonlinear intradendritic subunit processing in cortical neurophysiology.

One of the most robust qualities of our visual world is the scale invariance of natural images. Not only has scaling been found in different visual environments, but the phenomenon also appears to be calibration-independent. This paper proposes a simple property of natural images which explains this robustness: they are collages of regions corresponding to statistically independent "objects". Evidence is provided for these objects having a power-law distribution of sizes within images, from which follows scaling in natural images. It is commonly suggested that scaling instead results from edges, each with power spectrum 1/k2. This hypothesis is refuted by example.