Human anucleate platelets cannot be directly modified using traditional genetic approaches. Instead, studies of platelet gene function depend on alternative models. Megakaryocytes (the nucleated ...precursor to platelets) are the nearest cell to platelets in origin, structure, and function. However, achieving consistent genetic modifications in primary megakaryocytes has been challenging, and the functional effects of induced gene deletions on human megakaryocytes for even well-characterized platelet genes (eg, ITGA2B) are unknown. Here we present a rapid and systematic approach to screen genes for platelet functions in CD34+ cell-derived megakaryocytes called CRIMSON (CRISPR-edited megakaryocytes for rapid screening of platelet gene functions). By using CRISPR/Cas9, we achieved efficient nonviral gene editing of a panel of platelet genes in megakaryocytes without compromising megakaryopoiesis. Gene editing induced loss of protein in up to 95% of cells for platelet function genes GP6, RASGRP2, and ITGA2B; for the immune receptor component B2M; and for COMMD7, which was previously associated with cardiovascular disease and platelet function. Gene deletions affected several select responses to platelet agonists in megakaryocytes in a manner largely consistent with those expected for platelets. Deletion of B2M did not significantly affect platelet-like responses, whereas deletion of ITGA2B abolished agonist-induced integrin activation and spreading on fibrinogen without affecting the translocation of P-selectin. Deletion of GP6 abrogated responses to collagen receptor agonists but not thrombin. Deletion of RASGRP2 impaired functional responses to adenosine 5′-diphosphate (ADP), thrombin, and collagen receptor agonists. Deletion of COMMD7 significantly impaired multiple responses to platelet agonists. Together, our data recommend CRIMSON for rapid evaluation of platelet gene phenotype associations.
•An approach called CRIMSON uses CRISPR-edited megakaryocytes for rapid assessment of genes associated with platelet function.•CRIMSON defines platelet-like responses in megakaryocytes after deleting platelet genes ITGA2B, RASGRP2, GP6, B2M, and novel gene COMMD7.
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Autonomous driving systems are set to become a reality in transport systems and, so, maximum acceptance is being sought among users. Currently, the most advanced architectures require driver ...intervention when functional system failures or critical sensor operations take place, presenting problems related to driver state, distractions, fatigue, and other factors that prevent safe control. Therefore, this work presents a redundant, accurate, robust, and scalable LiDAR odometry system with fail-aware system features that can allow other systems to perform a safe stop manoeuvre without driver mediation. All odometry systems have drift error, making it difficult to use them for localisation tasks over extended periods. For this reason, the paper presents an accurate LiDAR odometry system with a fail-aware indicator. This indicator estimates a time window in which the system manages the localisation tasks appropriately. The odometry error is minimised by applying a dynamic 6-DoF model and fusing measures based on the Iterative Closest Points (ICP), environment feature extraction, and Singular Value Decomposition (SVD) methods. The obtained results are promising for two reasons: First, in the KITTI odometry data set, the ranking achieved by the proposed method is twelfth, considering only LiDAR-based methods, where its translation and rotation errors are 1.00 % and 0.0041 deg/m, respectively. Second, the encouraging results of the fail-aware indicator demonstrate the safety of the proposed LiDAR odometry system. The results depict that, in order to achieve an accurate odometry system, complex models and measurement fusion techniques must be used to improve its behaviour. Furthermore, if an odometry system is to be used for redundant localisation features, it must integrate a fail-aware indicator for use in a safe manner.
•Custom Convolutional Neural Networks improve WiFi localisation performance.•Reduction of the localisation error compared to traditional methods.•Great generalisation ability and adaptation to motion ...conditions.•Highly scalable, able to achieve real-time localisation in bigger environments.
Different technologies have been proposed to provide indoor localisation: magnetic field, Bluetooth, WiFi, etc. Among them, WiFi is the one with the highest availability and highest accuracy. This fact allows for an ubiquitous accurate localisation available for almost any environment and any device. However, WiFi-based localisation is still an open problem.
In this article, we propose a new WiFi-based indoor localisation system that takes advantage of the great ability of Convolutional Neural Networks in classification problems. Three different approaches were used to achieve this goal: a custom architecture called WiFiNet, designed and trained specifically to solve this problem, and the most popular pre-trained networks using both transfer learning and feature extraction.
Results indicate that WiFiNet is as a great approach for indoor localisation in a medium-sized environment (30 positions and 113 access points) as it reduces the mean localisation error (33%) and the processing time when compared with state-of-the-art WiFi indoor localisation algorithms such as SVM.
TAMs constitute a large fraction of infiltrating immune cells in melanoma tissues, but their significance for clinical outcomes remains unclear. We explored diverse TAM parameters in clinically ...relevant primary cutaneous melanoma samples, including density, location, size, and polarization marker expression; in addition, because cytokine production is a hallmark of macrophages function, we measured CCL20, TNF, and VEGFA intracellular cytokines by single-cell multiparametric confocal microscopy. The Kaplan–Meier method was used to analyze correlation with melanoma-specific disease-free survival and overall survival. No significant correlations with clinical parameters were observed for TAM density, morphology, or location. Significantly, higher contents of the intracellular cytokines CCL20, TNF, and VEGFA were quantified in TAMs infiltrating metastasizing compared to non-metastasizing skin primary melanomas (p < 0.001). To mechanistically explore cytokine up-regulation, we performed in vitro studies with melanoma-conditioned macrophages, using RNA-seq to explore involved pathways and specific inhibitors. We show that p53 and NF-κB coregulate CCL20, TNF, and VEGFA in melanoma-conditioned macrophages. These results delineate a clinically relevant pro-oncogenic cytokine profile of TAMs with prognostic significance in primary melanomas and point to the combined therapeutic targeting of NF-kB/p53 pathways to control the deviation of TAMs in melanoma.
This work presents a novel method for predicting vehicle trajectories in highway scenarios using efficient bird’s eye view representations and convolutional neural networks. Vehicle positions, motion ...histories, road configuration, and vehicle interactions are easily included in the prediction model using basic visual representations. The U-net model has been selected as the prediction kernel to generate future visual representations of the scene using an image-to-image regression approach. A method has been implemented to extract vehicle positions from the generated graphical representations to achieve subpixel resolution. The method has been trained and evaluated using the PREVENTION dataset, an on-board sensor dataset. Different network configurations and scene representations have been evaluated. This study found that U-net with 6 depth levels using a linear terminal layer and a Gaussian representation of the vehicles is the best performing configuration. The use of lane markings was found to produce no improvement in prediction performance. The average prediction error is 0.47 and 0.38 meters and the final prediction error is 0.76 and 0.53 meters for longitudinal and lateral coordinates, respectively, for a predicted trajectory length of 2.0 seconds. The prediction error is up to 50% lower compared to the baseline method.
Introducción: Actualmente existe controversia respecto a los beneficios de realizar linfadenectomía en pacientes de melanoma con una biopsia selectiva de ganglio centinela (BSGC) positiva. La carga ...tumoral > 1 mm se ha propuesto como el parámetro mas relevante asociado a una linfadenectomía positiva y un deterioro de la supervivencia libre de enfermedad. Material y métodos: Se analizaron los datos de 119 pacientes de melanoma con BSGC positiva atendidos en el periodo entre Junio de 1997 y Junio de 2017. Los pacientes se clasificaron según la carga tumoral en dos grupos: ≤ 1 mm and > 1 mm. Resultados: La linfadenectomía resultó positiva en sólo 6 (10%) pacientes con una carga tumoral ≤ 1 mm, y en 23 (37.7%) pacientes con carga tumoral > 1 mm (p < 0.001). En análisis univariante, la carga tumoral fue el único factor predictivo de linfadenectomía positiva (OR 5.24 (1.94-14.13)). En análisis multivariante, la carga tumoral fue la única variable independiente de supervivencia específica de melanoma (SEM). Conclusion: Aunque la realización de linfadenectomía debe individualizarse en cada caso, la carga tumoral > 1 mm puede ser un factor predictivo de la presencia de ganglios no centinelas positivos en piezas de linfadenectomía, y un factor pronostico independiente importante para la SEM.
This paper introduces a novel method of lane-change and lane-keeping detection and prediction of surrounding vehicles based on Convolutional Neural Network (CNN) classification approach. Context, ...interaction, vehicle trajectories, and scene appearance are efficiently combined into a single RGB image that is fed as input for the classification model. Several state-of-the-art classification-CNN models of varying complexity are evaluated to find out the most suitable one in terms of anticipation and prediction. The model has been trained and evaluated using the PREVENTION dataset, a specific dataset oriented to vehicle maneuver and trajectory prediction. The proposed model can be trained and used to detect lane changes as soon as they are observed, and to predict them before the lane change maneuver is initiated. Concurrently, a study on human performance in predicting lane-change maneuvers using visual inputs has been conducted, so as to establish a solid benchmark for comparison. The empirical study reveals that humans are able to detect the 83.9% of lane changes on average 1.66 seconds in advance. The proposed automated maneuver detection model increases anticipation by 0.43 seconds and accuracy by 2.5% compared to human results, while the maneuver prediction model increases anticipation by 1.03 seconds with an accuracy decrease of only 0.5%.
This paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban ...environments are more complex and those models are not suitable for inner city intersections or other urban situations. The approach presented in this paper generates a model based on the information provided by a digital navigation map and a vision-based sensing module. On the one hand, the digital map includes data about the road type (residential, highway, intersection, etc.), road shape, number of lanes, and other context information such as vegetation areas, parking slots, and railways. On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. The high-level interpretation module is designed to learn the best set of parameters of a function that maps all the available features to the actual parametric model of the urban road, using a weighted F-score as a cost function to be optimized. We show that the presented approach eases the maintenance of digital maps using crowd-sourcing, due to the small number of data to send, and adds important context information to traditional road detection systems.
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