Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model

Many interesting natural phenomena are sparsely distributed and discrete. Locating the hotspots of such sparsely distributed phenomena is often difficult because their density gradient is likely to be very noisy. We present a novel approach to this search problem, where we model the co-occurrence relations between a robot's observations with a Bayesian nonparametric topic model. This approach makes it possible to produce a robust estimate of the spatial distribution of the target, even in the absence of direct target observations. We apply the proposed approach to the problem of finding the spatial locations of the hotspots of a specific phytoplankton taxon in the ocean. We use classified image data from Imaging FlowCytobot (IFCB), which automatically measures individual microscopic cells and colonies of cells. Given these individual taxon-specific observations, we learn a phytoplankton community model that characterizes the co-occurrence relations between taxa. We present experiments with simulated robot missions drawn from real observation data collected during a research cruise traversing the US Atlantic coast. Our results show that the proposed approach outperforms nearest neighbor and k-means based methods for predicting the spatial distribution of hotspots from in-situ observations.Comment: To appear in ICRA 2017, Singapor

Informatics solutions for large ocean optics datasets

Ocean Optics XXI, Glasgow, Scotland October 8-12 2012Lack of observations that span the wide range of critical space and time scales continues to limit many aspects of oceanography. As ocean observatories and observing networks mature, the role for optical technologies and approaches in helping to overcome this limitation continues to grow. As a result the quantity and complexity of data produced is increasing at a pace that threatens to overwhelm the capacity of individual researchers who must cope with large high-resolution datasets, complex, multi-stage analyses, and the challenges of preserving sufficient metadata and provenance information to ensure reproducibility and avoid costly reprocessing or data loss. We have developed approaches to address these new challenges in the context of a case study involving very large numbers (~1 billion) of images collected at coastal observatories by Imaging FlowCytobot, an automated submersible flow cytometer that produces high resolution images of plankton and other microscopic particles at rates up to 10 Hz for months to years. By developing partnerships amongst oceanographers generating and using such data and computer scientists focused on improving science outcomes, we have prototyped a replicable system. It provides simple and ubiquitous access to observational data and products via web services in standard formats; accelerates image processing by enabling algorithms developed with desktop applications to be rapidly deployed and evaluated on shared, high-performance servers; and improves data integrity by replacing error-prone manual data management processes with generalized, automated services. The informatics system is currently in operation for multiple Imaging FlowCytobot datasets and being tested with other types of ocean imagery.This research was supported by grants from the Gordon and Betty Moore Foundation, NSF, NASA, and ONR (NOPP)

Distance maps to estimate cell volume from two-dimensional plankton images

Author Posting. © Association for the Sciences of Limnology and Oceanography, 2012. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography: Methods 10 (2012): 278-288, doi:10.4319/lom.2012.10.278.We describe and evaluate an algorithm that uses a distance map to automatically calculate the biovolume of a planktonic organism from its two-dimensional boundary. Compared with existing approaches, this algorithm dramatically increases the speed and accuracy of biomass estimates from plankton images, and is thus especially suited for use with automated cell imaging technologies that produce large quantities of data. The algorithm operates on a two-dimensional image processed to identify organism boundaries. First, the distance of each interior pixel to the nearest boundary is calculated; next these same distances are assumed to apply for projection in the third dimension; and finally the resulting volume is adjusted by a multiplicative factor assuming locally circular cross-sections in the third dimension. Other cross-sectional shape factors can be applied as needed. In this way, the simple, computationally efficient, volume calculation can be refined to include taxon-specific shape information if available. We show that compared to traditional manual microscopic analysis, the distance map algorithm is unbiased and accurate (mean difference = -0.25%, standard deviation = 17%) for a range of cell morphologies, including those with concave boundaries that deviate from simple geometric shapes and whose volumes are not well represented by a solid of revolution around a single axis. Automated calculation of cell volumes can now be implemented with a combination of this new distance map algorithm for complex shapes and the solid of revolution approach for simple shapes, with an automated decision criterion to choose the appropriate approach for each image.This research was supported by grants (to HMS) from the Gordon and Betty Moore Foundation and NASA’s Ocean Biology and Biogeochemistry program, and a Woods Hole Oceanographic Institution Summer Student Fellow award (to EAM)

Meeting Impacts of Two Types of EMS Anonymity and Initial Difference in Opinions

A laboratoryexperiment was conducted to study the effects of two types of anonymityin an electronic meeting system (EMS) setting (source anonymity: participants know who their group members are but do not know the source of any comment, and participant anonymity:, participants do not know who their group members are), initial difference in opinions, and their interaction on participation and satisfaction. Results suggest that the effects of participant anonymityshould not be considered as similar in nature to but stronger than those of source anonymity. The extent to which source and participant anonymitymake a group salient to its members is proposed as a crucial determinant of the effects of source and participant anonymity

Odpowiedzialność za przestępstwo zgwałcenia w krajach muzułmańskich

The crime of rape is a frequent subject matter for the doctrine. This article aims at presenting the complex analysis of current penal regulations in the law of Islam, basing on selected countries, such as Egypt, the United Arab Emirates, Pakistan and Turkey. The legal situation of women in the culture of Muslim countries, as the most sufficient factor affecting the number of sexual crimes in those countries, is set as the foundation for the analysis in this paper.Przestępstwo zgwałcenia jest częstym przedmiotem zainteresowania zarówno przedstawicieli prawa karnego, jak i kryminologii. Niniejszy artykuł ma na celu dogłębną analizę zagadnień związanych z odpowiedzialnością za przestępstwo zgwałcenia w prawie islamu na przykładzie systemów prawnych Egiptu, Zjednoczonych Emiratów Arabskich, Pakistanu oraz Turcji. Punktem wyjścia do analizy stanu prawnego poszczególnych krajów jest analiza sytuacji prawnej kobiety w kulturze krajów muzułmańskich, ponieważ jest to najistotniejszy czynnik wpływający na wysoką liczbę przestępstw seksualnych popełnianych w tych krajach

A fluorescence-activated cell sorting subsystem for the Imaging FlowCytobot

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Limnology and Oceanography: Methods 15 (2017): 94–102, doi:10.1002/lom3.10145.Recent advances in plankton ecology have brought to light the importance of variability within populations and have suggested that cell-to-cell differences may influence ecosystem-level processes such as species succession and bloom dynamics. Flow cytometric cell sorting has been used to capture individual plankton cells from natural water samples to investigate variability at the single cell level, but the crude taxonomic resolution afforded by the fluorescence and light scattering measurements of conventional flow cytometers necessitates sorting and analyzing many cells that may not be of interest. Addition of imaging to flow cytometry improves classification capability considerably: Imaging FlowCytobot, which has been deployed at the Martha's Vineyard Coastal Observatory since 2006, allows classification of many kinds of nano- and microplankton to the genus or even species level. We present in this paper a modified bench-top Imaging FlowCytobot (IFCB-Sorter) with the capability to sort both single cells and colonies of phytoplankton and microzooplankton from seawater samples. The cells (or subsets selected based on their images) can then be cultured for further manipulation or processed for analyses such as nucleic acid sequencing. The sorting is carried out in two steps: a fluorescence signal triggers imaging and diversion of the sample flow into a commercially available “catcher tube,” and then a solenoid-based flow control system isolates each sorted cell along with 20 μL of fluid.NSF Grant Number: OCE-11300140; WHOI internal support; NSERC through a Post-Graduate Masters awar