A fully automated end-to-end process for fluorescence microscopy images of yeast cells:From segmentation to detection and classification

In recent years, an enormous amount of fluorescence microscopy images were collected in high-throughput lab settings. Analyzing and extracting relevant information from all images in a short time is almost impossible. Detecting tiny individual cell compartments is one of many challenges faced by biologists. This paper aims at solving this problem by building an end-to-end process that employs methods from the deep learning field to automatically segment, detect and classify cell compartments of fluorescence microscopy images of yeast cells. With this intention we used Mask R-CNN to automatically segment and label a large amount of yeast cell data, and YOLOv4 to automatically detect and classify individual yeast cell compartments from these images. This fully automated end-to-end process is intended to be integrated into an interactive e-Science server in the PerICo1 project, which can be used by biologists with minimized human effort in training and operation to complete their various classification tasks. In addition, we evaluated the detection and classification performance of state-of-the-art YOLOv4 on data from the NOP1pr-GFP-SWAT yeast-cell data library. Experimental results show that by dividing original images into 4 quadrants YOLOv4 outputs good detection and classification results with an F1-score of 98% in terms of accuracy and speed, which is optimally suited for the native resolution of the microscope and current GPU memory sizes. Although the application domain is optical microscopy in yeast cells, the method is also applicable to multiple-cell images in medical application

Biografo: An integrated tool for forensic writer identification

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-20125-2_17The design and performance of a practical integrated tool for writer identification in forensic scenarios is presented. The tool has been designed to help forensic examiners along the complete identification process: from the data acquisition to the recognition itself, as well as with the management of large writer-related databases. The application has been implemented using JavaScript running over a relational database which provides the whole system with some very desirable and unique characteristics such as the possibility to perform all type of queries (e.g., find individuals with some very discriminative character, find a specific document, display all the samples corresponding to one writer, etc.), or a complete control over the set of parameters we want to use in a specific recognition task (e.g., users in the database to be used as control set, set of characters to be used in the identification, size of the ranked list we want as final result, etc.). The identification performance of the tool is evaluated on a real-case forensic database showing some very promising results.This work has been partially supported by the Spanish Dirección General de la Guardia Civil, and projects Contexts (S2009/TIC-1485) from CAM, Bio-Challenge (TEC2009-11186) from Spanish MICINN, BBfor2 (ITN-2008-238803) from the European Commision, and Cátedra UAM-Telefónica

One-vs-One classification for deep neural networks

For performing multi-class classification, deep neural networks almost always employ a One-vs-All (OvA) classification scheme with as many output units as there are classes in a dataset. The problem of this approach is that each output unit requires a complex decision boundary to separate examples from one class from all other examples. In this paper, we propose a novel One-vs-One (OvO) classification scheme for deep neural networks that trains each output unit to distinguish between a specific pair of classes. This method increases the number of output units compared to the One-vs-All classification scheme but makes learning correct decision boundaries much easier. In addition to changing the neural network architecture, we changed the loss function, created a code matrix to transform the one-hot encoding to a new label encoding, and changed the method for classifying examples. To analyze the advantages of the proposed method, we compared the One-vs-One and One-vs-All classification methods on three plant recognition datasets (including a novel dataset that we created) and a dataset with images of different monkey species using two deep architectures. The two deep convolutional neural network (CNN) architectures, Inception-V3 and ResNet-50, are trained from scratch or pre-trained weights. The results show that the One-vs-One classification method outperforms the One-vs-All method on all four datasets when training the CNNs from scratch. However, when using the two classification schemes for fine-tuning pre-trained CNNs, the One-vs-All method leads to the best performances, which is presumably because the CNNs had been pre-trained using the One-vs-All scheme

Target and (Astro-)WISE technologies - Data federations and its applications

After its first implementation in 2003 the Astro-WISE technology has been rolled out in several European countries and is used for the production of the KiDS survey data. In the multi-disciplinary Target initiative this technology, nicknamed WISE technology, has been further applied to a large number of projects. Here, we highlight the data handling of other astronomical applications, such as VLT-MUSE and LOFAR, together with some non-astronomical applications such as the medical projects Lifelines and GLIMPS, the MONK handwritten text recognition system, and business applications, by amongst others, the Target Holding. We describe some of the most important lessons learned and describe the application of the data-centric WISE type of approach to the Science Ground Segment of the Euclid satellite.Comment: 9 pages, 5 figures, Proceedngs IAU Symposium No 325 Astroinformatics 201

A HPMT based set-up to characterize scintillating crystals

We have developed a fully automatic measurement set-up, capable of measuring light yields arising from scintillating crystals in a linear range of about four orders of magnitude. The photodetector is a Hybrid Photomultiplier Tube especially developed to optimize linear range and photon detection. Crystal and photodetector are temperature controlled by a closed water circuit, as this is essential when measuring low light yield scintillating crystals with a marked temperature dependence of their light yield. Gamma sources can be placed either on top or on the side of the crystal. In this latter case, the source can be automatically moved by a computer-controlled step motor to provide a uniformity profile of the light yield along the crystal. Tagged and not-tagged operation modes are possible. The whole set-up is computer-controlled in an effort to provide fast and reliable measurements, to characterize many crystals per day. This is important for the quality control of the Lead Tungstate crystals that will be applied in the electromagnetic calorimeter of the CMS-detector at the LHC at CERN

CentroidNetV2:A hybrid deep neural network for small-object segmentation and counting

This paper presents CentroidNetV2, a novel hybrid Convolutional Neural Network (CNN) that has been specifically designed to segment and count many small and connected object instances. This complete redesign of the original CentroidNet uses a CNN backbone to regress a field of centroid-voting vectors and border-voting vectors. The segmentation masks of the individual object instances are produced by decoding centroid votes and border votes. A loss function that combines cross-entropy loss and Euclidean-distance loss achieves high quality centroids and borders of object instances. Several backbones and loss functions are tested on three different datasets ranging from precision agriculture to microbiology and pathology. CentroidNetV2 is compared to the state-of-the art networks You Only Look Once Version 3 (YOLOv3) and Mask Recurrent Convolutional Neural Network (MRCNN). On two out of three datasets CentroidNetV2 achieves the highest F1 score and on all three datasets CentroidNetV2 achieves the highest recall. CentroidNetV2 demonstrates the best ability to detect small objects although the best segmentation masks for larger objects are produced by MRCNN. (c) 2020 Elsevier B.V. All rights reserved

Increased mortality with delayed and missed switch to second-line antiretroviral therapy in South Africa

Background. After failure of first-line antiretroviral therapy (ART) in the public sector, delayed or missed second-line ART switch is linked with poor outcomes in patients with advanced HIV. Setting: We investigated delayed or missed second-line ART switch following confirmed virologic failure in the largest private sector HIV cohort in Africa. Methods. We included HIV-infected adults with confirmed virologic failure after six months of non-nucleoside reverse-transcriptase inhibitor-based ART. We estimated the effect of timing of switch on the hazard of death using inverse probability of treatment weighting of marginal structural models. We adjusted for time-dependent confounding of CD4 count, viral load, and visit frequency. Results. 5748 patients (53% female) with confirmed virologic failure met inclusion criteria; the median age was 40 (interquartile range [IQR]: 35 - 47), advanced HIV was present in 48% and the prior duration of NNRTI-based ART was 1083 days (IQR: 665-1770). Median time to confirmation of virologic failure and to second-line switch was 196 (IQR: 136-316) and 220 days (IQR: 65-542), respectively. Switching to second-line ART after confirmed failure compared to remaining on first-line ART reduced risk of subsequent death [aHR: 0.47 (95% CI: 0.36-0.63)]. Compared to patients who experienced delayed switch, those switched immediately had a lower risk of death, regardless of CD4 cell count. Conclusions. Delayed or missed switch to second-line ART after confirmed first-line ART failure is common in the South African private sector and associated with mortality. Novel interventions to minimize switch delay should be tested and not limited to those with advanced disease at treatment failure

Explorations, Vol. 2, No. 1

Cover: The painting reproduced on the cover is a 22” by 30” acrylic on paper entitled Passage-10, by James Linehan, Assistant Professor of Art at the University of Maine at Orono, where he teaches painting. ©James Linehan, 1985. Articles include: Polyunsaturated Fats: are they killing us? by Linda J. Kling Where are the Dreamers: aspirations of Maine\u27s rural high school students, by Robert A. Cobb, Walter G. McIntire, and Philip A. Pratt Elsewhere in Education: a research sampler \u22Physical Education and Handicapped Children, Stephen A. Butterfield School Climate and Teacher Efficacy, Theodore Coadarci The Principal Principle, Gordon A. Donaldson, Jr. Assessing Leadership, Ronald L. Sparkes Malnutrition in Maine, by Richard A. Cook Hypertension: aging and intellect, by Merrill F. Elias and Michael Robbins From Campus to Public Schools A Ceiling on Shelter, by Peggy K. Schomaker From the Dispatch Case: Control of Cell Growth at the Level of the Genetic Code, by R.D. Blake In the Spring issue of EXPLORATIONS: The sure but silent force in American foreign policy in post World War II Japan—Harry F. Ker