ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

Space Shuttle interactive meteorological data system study

Although focused toward the operational meteorological support review and definition of an operational meteorological interactive data display systems (MIDDS) requirements for the Space Meteorology Support Group at NASA/Johnson Space Center, the total operational meteorological support requirements and a systems concept for the MIDDS network integration of NASA and Air Force elements to support the National Space Transportation System are also addressed

Assessing microscope image focus quality with deep learning

Background Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. Results We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Conclusions Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler

Domestication alone does not lead to inequality: intergenerational wealth transmission among horticulturalists

We present empirical measures of wealth inequality and its intergenerational transmission among four horticulturalist populations. Wealth is construed broadly as embodied somatic and neural capital, including body size, fertility and cultural knowledge, material capital such as land and household wealth, and relational capital in the form of coalitional support and field labor. Wealth inequality is moderate for most forms of wealth, and intergenerational wealth transmission is low for material resources and moderate for embodied and relational wealth. Our analysis suggests that domestication alone does not transform social structure; rather, the presence of scarce, defensible resources may be required before inequality and wealth transmission patterns resemble the familiar pattern in more complex societies. Land ownership based on usufruct and low‐intensity cultivation, especially in the context of other economic activities such as hunting and fishing, is associated with more egalitarian wealth distributions as found among hunter‐gatherers

Living with kin in lowland horticultural societies

Postmarital residence patterns in traditional human societies figure prominently in models of hominid social evolution with arguments for patrilocal human bands similar in structure to female-dispersal systems in other African apes. However, considerable flexibility in hunter-gatherer cultures has led to their characterization as primarily multilocal. Horticulturalists are associated with larger, more sedentary social groups with more political inequality and intergroup conflict and may therefore provide additional insights into evolved human social structures. We analyze coresidence patterns of primary kin for 34 New World horticultural societies (6,833 adults living in 243 residential groupings) to show more uxorilocality (women live with more kin) than found for hunter-gatherers. Our findings further point to the uniqueness of human social structures and to considerable variation that is not fully described by traditional postmarital residence typologies. Sex biases in coresident kin can vary according to the scale of analysis (household vs. house cluster vs. village) and change across the life span, with women often living with more kin later in life. Headmen in large villages live with more close kin, primarily siblings, than do nonheadmen. Importantly, human marriage exchange and residence patterns create meta-group social structures, with alliances extending across multiple villages often united in competition against other large alliances at scales unparalleled by other species. © 2012 by The Wenner-Gren Foundation for Anthropological Research. All rights reserved

Small-scale societies exhibit fundamental variation in the role of intentions in moral judgment

Intent and mitigating circumstances play a central role in moral and legal assessments in large-scale industrialized societies. Although these features of moral assessment are widely assumed to be universal, to date, they have only been studied in a narrow range of societies. We show that there is substantial cross-cultural variation among eight traditional small-scale societies (ranging from hunter-gatherer to pastoralist to horticulturalist) and two Western societies (one urban, one rural) in the extent to which intent and mitigating circumstances influence moral judgments. Although participants in all societies took such factors into account to some degree, they did so to very different extents, varying in both the types of considerations taken into account and the types of violations to which such considerations were applied. The particular patterns of assessment characteristic of large-scale industrialized societies may thus reflect relatively recently culturally evolved norms rather than inherent features of human moral judgment