Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient’s cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Drivers of change and pressures on aquatic ecosystems : guidance on indicators and methods to assess drivers and pressures

The AQUACROSS project, funded under the EU’s Horizon 2020 Research and Innovation Programme, seeks to improve the management of aquatic ecosystems, thereby supporting the achievement of the EU 2020 Biodiversity Strategy and the Strategic Plan for Biodiversity 2011-2020. According to the structure of the AQUACROSS project, Work Package (WP) 4 builds on and forms part of the Assessment Framework (AF) developed in WP3. Task 4.1 builds the basis for the analysis of drivers of change and pressures on aquatic ecosystems (WP4) and should provide guidance for the analyses performed within the case studies. Hence, this Deliverable (D4.1) aims at the AQUCROSS consortia partners, in order to help guide their work going forward under Task 4.2. Within this deliverable, the following objectives are addressed: Conceptualise how drivers, pressures and environmental states are interwoven across the aquatic realms and in relation to complex social-ecological systems Define the role of drivers that directly or indirectly act on different levels, the interacting effects of these drivers, related human activities and the resulting pressures along the freshwater-marine continuum Deepen the understanding of the Driver-Pressure-State (D-P-S) part of the AF by exploring the existing qualitative and quantitative approaches of D-P-S assessment systems Identify the most suitable set of pressure-sensitive indicators, including indicators for ecosystem state Propose integrative indicators especially for newly emerging drivers and pressures based on currently used cost-effective indicator

Discretization reaction-diffusion models with finite difference method

Discretization model is a continuous model transformation procedure to model discrete. Discretization is done using advanced finite difference method, by analogy differential equations using limit rules, with different equations using the different between discrete time points. The model used in this paper is a model of reaction-diffusion (Turing) that represents the diffusion of fluid in the cells that cause the cells to move. Finite difference method is a numerical method that can be used to solve partial differential equations. Methods used explicit finite difference scheme developed for the time difference and central difference for the space to complete the reactiondiffusion equation (Turing). Based on the numerical solution obtained then the amount of domain growth does not affect the stability of reaction-diffusion models (Turing)

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Women in Science 2017

Ever since its 1967 start, SURF has been a cornerstone of Smith’s science education. Women in Science 2017 summarizes research done by Smith College’s SURF Program participants during the summer of 2017. 151 students participated in SURF (144 hosted on campus and nearby eld sites), supervised by 58 faculty mentor-advisors drawn from the Clark Science Center and connected to its eighteen science, mathematics, and engineering departments and programs and associated centers and units. At summer’s end, SURF participants summarized their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1006/thumbnail.jp

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes