699 research outputs found
AI-powered transmitted light microscopy for functional analysis of live cells
Transmitted light microscopy can readily visualize the morphology of living cells. Here, we introduce artificial-intelligence-powered transmitted light microscopy (AIM) for subcellular structure identification and labeling-free functional analysis of live cells. AIM provides accurate images of subcellular organelles; allows identification of cellular and functional characteristics (cell type, viability, and maturation stage); and facilitates live cell tracking and multimodality analysis of immune cells in their native form without labeling
Capacity planning of prisons in the Netherlands
In this paper we describe a decision support system developed to help in assessing the need for various type of prison cells. In particular we predict the probability that a criminal has to be sent home because of a shortage of cells. The problem is modelled through a queueing network with blocking after service. We focus in particular on the new analytical method to solve
this network
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The relationship between regulation and competition policy for network utilities
Should regulation of potentially competitive elements of network utilities be left with sector regulators or solely subject to normal competition laws? Britain evolved licenses for network activities overseen by regulators while the EU places more emphasis on making sector regulation consistent with competition law. The paper discusses the appropriateness of the competition law approach for telecoms and electricity. Post-modern utilities like telecoms, in which facilities-based competition is possible, lend themselves to the approach laid out in the Communications Directives, and its application to mobile call termination is discussed. Electricity, where collective dominance is more likely, does not fit comfortably into this approach. Instead, licence conditions retain advantages where it may be necessary to modify market rules in a timely and well-informed manner, as exemplified by the English Electricity Pool
Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection
Detecting and segmenting individual cells from microscopy images is critical
to various life science applications. Traditional cell segmentation tools are
often ill-suited for applications in brightfield microscopy due to poor
contrast and intensity heterogeneity, and only a small subset are applicable to
segment cells in a cluster. In this regard, we introduce a novel supervised
technique for cell segmentation in a multi-task learning paradigm. A
combination of a multi-task loss, based on the region and cell boundary
detection, is employed for an improved prediction efficiency of the network.
The learning problem is posed in a novel min-max framework which enables
adaptive estimation of the hyper-parameters in an automatic fashion. The region
and cell boundary predictions are combined via morphological operations and
active contour model to segment individual cells.
The proposed methodology is particularly suited to segment touching cells
from brightfield microscopy images without manual interventions.
Quantitatively, we observe an overall Dice score of 0.93 on the validation set,
which is an improvement of over 15.9% on a recent unsupervised method, and
outperforms the popular supervised U-net algorithm by at least on
average
Artificial Intelligence Algorithms for Eye Banking
Eye banking plays a critical role in modern medicine by providing cornea tissues for transplantation to restore vision for millions of people worldwide. The evaluation of corneal endothelium is done by measuring the corneal endothelial cell density (ECD). Unfortunately, the current system to measure ECD is manual, time-consuming, and error prone. Furthermore, the impact of social behaviors and biological conditions on corneal endothelium and corneal transplant success is largely unexplored. To overcome these challenges, this dissertation aims to develop tools for corneal endothelial image and data analysis that enhance the efficiency and quality of the cornea transplants.
In the first study, an image processing algorithm is developed to analyze corneal endothelial images captured by a Konan CellChek specular microscope. The algorithm successfully identifies the region of interest, filters the image, and employs stochastic watershed segmentation to determine cell boundaries and evaluate endothelial cell density (ECD). The proposed algorithm achieves a high correlation with manual counts (R2 = 0.98) and has an average analysis time of 2.5 seconds.
In the second study, a deep learning-based cell segmentation algorithm called Mobile-CellNet is proposed to estimate ECD. This technique addresses the limitations of classical algorithms and creates a more robust and highly efficient algorithm. The approach achieves a mean absolute error of 4.06% for ECD on the test set, similar to U-Net but with significantly fewer floating-point operations and parameters.
The third study explores the correlation between alcohol abuse and corneal endothelial morphology in a donor pool of 5,624 individuals. Multivariable regression analysis shows that alcohol abuse is associated with a reduction in endothelial cell density, an increase in the coefficient of variation, and a decrease in percent hexagonality.
These studies highlight the potential of big data and artificial algorithms in accurately and efficiently analyzing corneal images and donor medical data to improve the efficiency of eye banking and patient outcomes. By automating the analysis of corneal images and exploring the impact of social behaviors and biological conditions on corneal endothelial morphology, we can enhance the quality and availability of cornea transplants and ultimately improve the lives of millions of people worldwide
Corporate strategy in the UK credit card market: The case of Barclaycard
The case study is concerned with how a long-standing market leader maintains a position of advantage and develops its business in a fast- moving industry undergoing significant change. There are many different strategic options open to Barclaycard, but which will be most suitable? Will all the options be acceptable, not only in terms of the likely risk and returns but also to the major stakeholders? Will the options be feasible? The case invites readers to evaluate and compare a range of strategic options and to choose the best way forward for Barclaycard.banks, corporate strategy
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