Classifying structural alterations of the cytoskeleton by spectrum enhancement and descriptor fusion.

A classifier capable of ranking structural alterations of the cytoskeleton is developed. Images of cytoskeletal microtubules obtained from the epifluorescence microscopy of primary culture rat hepatocytes are analyzed. Morphological descriptors are extracted by contour and mass fractal analysis, direct methods, and spectrum enhancement. All methods are designed and tuned to make the extracted morphological descriptors insensitive to absolute fluorescence intensities. Spectrum enhancement is a nonlinear filter that involves spatial differentiation of the gray-scale image followed by conversion of power spectral density to the logarithmic scale and averaging over arcs in the reciprocal domain. Enhanced spectra exhibit local maxima that correspond to the structured microtubule bundles of a normal cytoskeleton. Descriptor fusion for classification is achieved by means of multivariate analysis. The classifier is trained by image sets representing normal ("negative control") microtubules and those altered by exposure to a fungicide at the highest dose of the experiment design. Some sensitivity and validation tests, including discriminant functions analysis, are applied to the classifier. The latter is applied to recognize images of microtubules not used in the training stage and comes from treatments at lower concentrations and shorter times. As a result, structural alterations are ranked and structural recovery after treatment is quantified. The method has potential use in quantitative, morphology-based tests on the cytoskeleton treated either by anticancer drugs or by cytotoxic agents

Towards productizing AI/ML Models: An industry perspective from data scientists

The transition from AI/ML models to production-ready AI-based systems is a challenge for both data scientists and software engineers. In this paper, we report the results of a workshop conducted in a consulting company to understand how this transition is perceived by practitioners. Starting from the need for making AI experiments reproducible, the main themes that emerged are related to the use of the Jupyter Notebook as the primary prototyping tool, and the lack of support for software engineering best practices as well as data science specific functionalities

Negative Echo in the Density Evolution of Ultracold Fermionic Gases

We predict a nonequilibrium critical phenomenon in the space-time density evolution of a fermionic gas above the temperature of transition into the superfluid phase. On the BCS side of the BEC-BCS crossover, the evolution of a localized density disturbance exhibits a negative echo at the point of the initial inhomogeneity. Approaching the BEC side, this effect competes with the slow spreading of the density of bosonic molecules. However, even here the echo dominates for large enough times. This effect may be used as an experimental tool to locate the position of the transition.Comment: 4 pages, 2 figure

Assessment of the response of hepatocellular carcinoma to interventional radiology treatments

According to Barcelona Clinic Liver Cancer (BCLC) guidelines, interventional radiology procedures are valuable treatment options for many hepatocellular carcinomas (HCCs) that are not amenable to resection or transplantation. Accurate assessment of the efficacy of therapies at earlier stages enables completion of treatment, optimal follow-up and to prevent potentially unnecessary treatments, side effects and costly failure. The goal of this review is to summarize and describe the radiological strategies that have been proposed to predict survival and to stratify HCC responses after interventional radiology therapies. New techniques currently in development are also described

State-of-practice on as-is modelling of industrial facilities

90% of the time needed for the conversion from point clouds to 3D models of industrial facilities is spent on geometric modelling due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. Our previous work has successfully identified the most frequent industrial objects which are in descending order: electrical conduit, straight pipes, circular hollow sections, elbows, channels, solid bars, I-beams, angles, flanges and valves. We modelled those on a state-of-theart software, EdgeWise and then evaluated the performance of this software for pipeline and structural modelling. The modelling of pipelines is summarized in three basic steps: (a) automated extraction of cylinders, (b) their semantic classification and (c) manual extraction and editing of pipes. The results showed that cylinders are modelled with 75 % recall and 62 % precision on average. We discovered that pipes, electrical conduit and circular hollow sections require 80 % of the Total Modelling Hours (TMH) of the 10 most frequent IOs to build the plant model. TMH was then compared to modelling hours in Revit and showed that 67 % of pipe modelling time is saved by EdgeWise. This paper is the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving i