Molecular and Cellular Aspects of Rhabdovirus Entry

Rhabdoviruses enter the cell via the endocytic pathway and subsequently fuse with a cellular membrane within the acidic environment of the endosome. Both receptor recognition and membrane fusion are mediated by a single transmembrane viral glycoprotein (G). Fusion is triggered via a low-pH induced structural rearrangement. G is an atypical fusion protein as there is a pH-dependent equilibrium between its pre- and post-fusion conformations. The elucidation of the atomic structures of these two conformations for the vesicular stomatitis virus (VSV) G has revealed that it is different from the previously characterized class I and class II fusion proteins. In this review, the pre- and post-fusion VSV G structures are presented in detail demonstrating that G combines the features of the class I and class II fusion proteins. In addition to these similarities, these G structures also reveal some particularities that expand our understanding of the working of fusion machineries. Combined with data from recent studies that revealed the cellular aspects of the initial stages of rhabdovirus infection, all these data give an integrated view of the entry pathway of rhabdoviruses into their host cell

Characterization and automatic screening of reactive and abnormal neoplastic B lymphoid cells from peripheral blood

The objective was to advance in the automatic, image-based, characterization and recognition of a heterogeneous set of lymphoid cells from peripheral blood, including normal, reactive, and five groups of abnormal lymphocytes: hairy cells, mantle cells, follicular lymphoma, chronic lymphocytic leukemia, and prolymphocytes. Methods: A number of 4389 images from 105 patients were selected by pathologists, based on morphologic visual appearance, from patients whose diagnosis was confirmed by all the remaining complementary tests. Besides geometry, new color and texture features were extracted using six alternative color spaces to obtain rich information to characterize the cell groups. The recognition system was designed using support vector machines trained with the whole image set. Results: In the experimental tests, individual sets of images from 21 new patients were analyzed by the trained recognition system and compared with the true diagnosis. An overall recognition accuracy of 97.67% was achieved when the cell screening was performed into three groups: normal lymphocytes, abnormal lymphoid cells, and reactive lymphocytes. The accuracy of the whole experimental study was 91.23% when considering the further discrimination of the abnormal lymphoid cells into the specific five groups. Conclusion: The excellent automatic screening of the three groups of normal, reactive, and abnormal lymphocytes is useful as it discriminates between malignancy and not malignancy. The discrimination of the five groups of abnormal lymphoid cells is encouraging toward the idea that the system could be an automated image-based screening method to identify blood involvement by a variety of B lymphomas.Preprin

Optimizing morphology through blood cell image analysis

Introduction Morphological review of the peripheral blood smear is still a crucial diagnostic aid as it provides relevant information related to the diagnosis and is important for selection of additional techniques. Nevertheless, the distinctive cytological characteristics of the blood cells are subjective and influenced by the reviewer's interpretation and, because of that, translating subjective morphological examination into objective parameters is a challenge. Methods The use of digital microscopy systems has been extended in the clinical laboratories. As automatic analyzers have some limitations for abnormal or neoplastic cell detection, it is interesting to identify quantitative features through digital image analysis for morphological characteristics of different cells. Result Three main classes of features are used as follows: geometric, color, and texture. Geometric parameters (nucleus/cytoplasmic ratio, cellular area, nucleus perimeter, cytoplasmic profile, RBC proximity, and others) are familiar to pathologists, as they are related to the visual cell patterns. Different color spaces can be used to investigate the rich amount of information that color may offer to describe abnormal lymphoid or blast cells. Texture is related to spatial patterns of color or intensities, which can be visually detected and quantitatively represented using statistical tools. Conclusion This study reviews current and new quantitative features, which can contribute to optimize morphology through blood cell digital image processing techniques.Peer ReviewedPostprint (published version

Evaluation of a new sustainable continuous system for processing bovine leather

The aim of the present work is to evaluate a new sustainable continuous system for processing bovine leather. By means of a prototype described in the international patent WO, 2010/070571 (A2) of the technological centre AIICA, a dehydration process for bovine hides is carried out. What is obtained through this new process is a dehydrated leather with the optimal physical and chemical characteristics that will allow its subsequent tanning by immersion processes in aqueous solutions of chemical products. When compared to existing traditional processes, there are economic and environmental advantages resulting from the use of this new system. More specifically, the new process results in reductions of 30.6% in water use, 50.2% in chemical use and 16.4% in process time. In addition, a reduction of 27.3% in wastewater and a reduction of 47.5% of thermal energy consumption are obtained. However, this new system presents an increase in electricity consumption of 63.03% and an increase in gaseous emissions of 75% due to the use of acetone in the dehydration process and the 0.5% losses of acetone during the process. In order to better assess the environmental impact of this new tanning system, life cycle analysis methodology has been chosen to perform calculations on the Global Warming Potential (CO2 equivalent emissions) and the energy consumption comparing both traditional and new tanning processes

Image processing and machine learning in the morphological analysis of blood cells

Introduction: This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. Methods: The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. Results: There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Conclusion: Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies.Peer ReviewedPostprint (published version

Application of highly carboxylate resins in aqueous emulsion for leather coating avoiding the use of isopropyl alcohol

Today, the first stages of the finishing processes of buffed cattle hides or full loose grain – known as impregnation – are largely carried out with acrylic resins and penetrating agents (typically, a mixture of surfactants and solvents). This application aims to strengthen the partially buffed grain layer bound to the rest of the dermis. To that end, a composition of emulsified acrylic resins is used, as well as a penetrating agent – usually isopropyl alcohol – and water. The process examined consists in the application of acrylic polymers in an aqueous emulsion that, because of their structure, size, and properties no longer require the use of the isopropyl alcohol contained in the penetrating agents. The use of this alcohol causes adverse effects on the health of workers that are exposed to emanations of isopropanol vapors (irritant to eyes, respiratory tract, and skin) in the work environment. Effects from prolonged exposition and inhalation may lead to headaches, dizziness, drowsiness, nausea and, ultimately, to unconsciousness. By using the new polymers developed in this work, the negative environmental effects of the finishing process can be minimized. At the same time, the use of these highly carboxylate resins avoid the exposure to isopropyl alcohol, which is harmful to the health. In addition, the volatile organic compounds (VOC) are reduced without compromising the appearance, performance and fashion requirements that are expected in the final product.Peer ReviewedPostprint (published version

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks

Background and Objectives: Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-hematological diseases. However, the variability in staining protocols across different laboratories can affect the color of images and performance of automatic recognition models. The objective of this work is to develop, train and evaluate a new system for the normalization of color staining of peripheral blood cell images, so that it transforms images from different centers to map the color staining of a reference center (RC) while preserving the structural morphological features. Methods: The system has two modules, GAN1 and GAN2. GAN1 uses the PIX2PIX technique to fade original color images to an adaptive gray, while GAN2 transforms them into RGB normalized images. Both GANs have a similar structure, where the generator is a U-NET convolutional neural network with ResNet and the discriminator is a classifier with ResNet34 structure. Digitally stained images were evaluated using GAN metrics and histograms to assess the ability to modify color without altering cell morphology. The system was also evaluated as a pre-processing tool before cells undergo a classification process. For this purpose, a CNN classifier was designed for three classes: abnormal lymphocytes, blasts and reactive lymphocytes. Results: Training of all GANs and the classifier was performed using RC images, while evaluations were conducted using images from four other centers. Classification tests were performed before and after applying the stain normalization system. The overall accuracy reached a similar value around 96% in both cases for the RC images, indicating the neutrality of the normalization model for the reference images. On the contrary, it was a significant improvement in the classification performance when applying the stain normalization to the other centers. Reactive lymphocytes were the most sensitive to stain normalization, with true positive rates (TPR) increasing from 46.3% - 66% for the original images to 81.2% - 97.2% after digital staining. Abnormal lymphocytes TPR ranged from 31.9% - 95.7% with original images to 83% - 100% with digitally stained images. Blast class showed TPR ranges of 90.3% - 94.4% and 94.4% - 100%, for original and stained images, respectively. Conclusions: The proposed GAN-based normalization staining approach improves the performance of classifiers with multicenter data sets by generating digitally stained images with a quality similar to the original images and adaptability to a reference staining standard. The system requires low computation cost and can help improve the performance of automatic recognition models in clinical settings.This work is part of a research project funded by the Ministry of Science and Innovation of Spain, with reference PID2019-104087RB-I00.Peer ReviewedPostprint (published version

Digital blood image processing and fuzzy clustering for detection and classification of atypical lymphoid B cells

Automated systems for digital peripheral blood (PB) cell analysis operate most effectively in non-pathological samples. The paper deals with the automatic classification of atypical lymphoid cells using digital image processing. The problem has been approached through a 3-step procedure: 1) Watershed segmentation of nucleus, cytoplasm and peripheral cell zone; 2) feature extraction for each region; and 3) classification using fuzzy c-means. The paper has proposed a new methodology that has been able to automatically classify with high precision three types of lymphoid cells: normal, Hairy Cell Leukemia cells and Chronic Lymphocytic Leukemia cells. This methodology, combining human medical expertise with mathematical and engineering tools, may contribute to improve the efficiency of the hematology laboratory.Peer Reviewe

A dataset of microscopic peripheral blood cell images for development of automatic recognition systems

This article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 images of individual normal cells, which were acquired using the analyzer CellaVision DM96 in the Core Laboratory at the Hospital Clinic of Barcelona. The dataset is organized in the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts and platelets or thrombocytes. The size of the images is 360¿×¿363 pixels, in format jpg, and they were annotated by expert clinical pathologists. The images were captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. This high-quality labelled dataset may be used to train and test machine learning and deep learning models to recognize different types of normal peripheral blood cells. To our knowledge, this is the first publicly available set with large numbers of normal peripheral blood cells, so that it is expected to be a canonical dataset for model benchmarking.Peer ReviewedPostprint (published version