Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks

Funding Information: This work was funded by Fundac¸ão para a Ciência e Tecnologia, under the projects InfoCent-IoT (PTDC/EEI-TEL/30433/2017), CoSHARE (PTDC/EEI-TEL/30709/2017), and Grant UIDB/50008/2020.This work presents an effective tool to predict the future trajectories of vehicles when its current and previous locations are known. We propose a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) prediction scheme due to its adequacy to learn from sequential data. To fully learn the vehicles' mobility patterns, during the training process we use a dataset that contains real traces of 442 taxis running in the city of Porto, Portugal, during a full year. From experimental results, we observe that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time is evaluated for a distinct number of prior locations considered in the prediction process. The results exhibit a prediction performance higher than 89%, showing the effectiveness of the proposed LSTM network.authorsversionpublishe

The Effect of a Linear Tuning between the Antigenic Stimulations of CD4(+) T Cells and CD4(+) Tregs

We study the equilibria of an Ordinary Differencial Equation (ODE) system where CD4+ effector or helper T cells and Regulatory T cells (Tregs) are present. T cells trigger an immune response in the presence of their specific antigen. Regulatory T cells (Tregs) play a role in limiting auto-immune diseases due to their immune-suppressive ability. Here, we present explicit exact formulas that give the relationship between the concentration of T cells, the concentration of Tregs, and the antigenic stimulation of T cells, when the system is at equilibria, stable or unstable. We found a parameter region of bistability, limited by two thresholds of antigenic stimulation of T cells (hysteresis). Moreover, there are values of the slope parameter of the tuning for which an isola-center bifurcation appears, and, for some other values, there is a transcritical bifurcation. We also present time evolutions of the ODE system

Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI

BACKGROUND: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists. METHODS: An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area's signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed. RESULTS: The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p < 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient. CONCLUSIONS: The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment.publishersversionpublishe

Abnormalities in autonomic function in obese boys at-risk for insulin resistance and obstructive sleep apnea.

Study objectivesCurrent evidence in adults suggests that, independent of obesity, obstructive sleep apnea (OSA) can lead to autonomic dysfunction and impaired glucose metabolism, but these relationships are less clear in children. The purpose of this study was to investigate the associations among OSA, glucose metabolism, and daytime autonomic function in obese pediatric subjects.MethodsTwenty-three obese boys participated in: overnight polysomnography; a frequently sampled intravenous glucose tolerance test; and recordings of spontaneous cardiorespiratory data in both the supine (baseline) and standing (sympathetic stimulus) postures.ResultsBaseline systolic blood pressure and reactivity of low-frequency heart rate variability to postural stress correlated with insulin resistance, increased fasting glucose, and reduced beta-cell function, but not OSA severity. Baroreflex sensitivity reactivity was reduced with sleep fragmentation, but only for subjects with low insulin sensitivity and/or low first-phase insulin response to glucose.ConclusionsThese findings suggest that vascular sympathetic activity impairment is more strongly affected by metabolic dysfunction than by OSA severity, while blunted vagal autonomic function associated with sleep fragmentation in OSA is enhanced when metabolic dysfunction is also present

A review of assessment methods for river hydromorphology

The work leading to this paper has received funding for the EU’s FP7 under Grant Agreement No. 282656 (REFORM

Effect of particle size on silver nanoparticle deposition onto dielectric barrier discharge (DBD) plasma functionalized polyamide fabric

The effect on the deposition of three different size silver nanoparticles (AgNPs) onto a polyamide 6,6 (PA) fabric pre-treated using air dielectric barrier discharge (DBD) plasma was investigated. The SEM, EDS, and XPS analysis confirm that the smaller is the diameter of AgNPs, the higher the amount of adsorbed NPs on the PA. The DBD treatment on PA induces a threefold increase in Ag adsorption. The result confirms a dual effect on the wettability of the plasma treated PA substrate. AgNPs slightly enhance hydrophobicity of the PA surface and, at the same time, protect it against the plasma aging effect. The effect on the deposition of three different size silver nanoparticles (AgNPs) onto a Polyamide 6,6 (PA) fabric pre-treated using air dielectric barrier discharge (DBD) plasma was investigated. The smaller is the size, the higher the loaded AgNPs. The DBD treatment induces a threefold increase in Ag adsorption. AgNPs enhance hydrophobicity of the PA surface and reduce the plasma aging effect.Fundação para a Ciência e a Tecnologia (FCT

Stanniocalcin 1 effects on the renal gluconeogenesis pathway in rat and fish

The mammalian kidney contributes significantly to glucose homeostasis through gluconeogenesis. Considering that stanniocalcin 1 (STC1) regulates ATP production, is synthesized and acts in different cell types of the nephron, the present study hypothesized that STC1 may be implicated in the regulation of gluconeogenesis in the vertebrate kidney. Human STC1 strongly reduced gluconeogenesis from C-14-glutamine in rat renal medulla (MD) slices but not in renal cortex (CX), nor from C-14-lactic acid. Total PEPCK activity was markedly reduced by hSTC1 in MD but not in CX. Pck2 (mitochondrial PEPCK isoform) was down-regulated by hSTC1 in MD but not in CX. In fish (Dicentrarchus labrax) kidney slices, both STC1-A and -B isoforms decreased gluconeogenesis from C-14-acid lactic, while STC1-A increased gluconeogenesis from C-14-glutamine. Overall, our results demonstrate a role for STC1 in the control of glucose synthesis via renal gluconeogenesis in mammals and suggest that it may have a similar role in teleost fishes. (C) 2015 Elsevier Ireland Ltd. All rights reserved.Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) Brazil; Foundation for Science and Technology of Portugal [PTDC/MAR/121279/2010]; bilateral programme CAPES (Brazil)/GRICES (Portugal) CAPES/GRICES [215/08]info:eu-repo/semantics/publishedVersio

A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

<p>Abstract</p> <p>Background</p> <p>The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from microarray experiments, is critical to advance our understanding of complex biological processes. In this context, biclustering algorithms have been recognized as an important tool for the discovery of local expression patterns, which are crucial to unravel potential regulatory mechanisms. Although most formulations of the biclustering problem are NP-hard, when working with time series expression data the interesting biclusters can be restricted to those with contiguous columns. This restriction leads to a tractable problem and enables the design of efficient biclustering algorithms able to identify all maximal contiguous column coherent biclusters.</p> <p>Methods</p> <p>In this work, we propose <it>e</it>-CCC-Biclustering, a biclustering algorithm that finds and reports all maximal contiguous column coherent biclusters with approximate expression patterns in time polynomial in the size of the time series gene expression matrix. This polynomial time complexity is achieved by manipulating a discretized version of the original matrix using efficient string processing techniques. We also propose extensions to deal with missing values, discover anticorrelated and scaled expression patterns, and different ways to compute the errors allowed in the expression patterns. We propose a scoring criterion combining the statistical significance of expression patterns with a similarity measure between overlapping biclusters.</p> <p>Results</p> <p>We present results in real data showing the effectiveness of <it>e</it>-CCC-Biclustering and its relevance in the discovery of regulatory modules describing the transcriptomic expression patterns occurring in <it>Saccharomyces cerevisiae </it>in response to heat stress. In particular, the results show the advantage of considering approximate patterns when compared to state of the art methods that require exact matching of gene expression time series.</p> <p>Discussion</p> <p>The identification of co-regulated genes, involved in specific biological processes, remains one of the main avenues open to researchers studying gene regulatory networks. The ability of the proposed methodology to efficiently identify sets of genes with similar expression patterns is shown to be instrumental in the discovery of relevant biological phenomena, leading to more convincing evidence of specific regulatory mechanisms.</p> <p>Availability</p> <p>A prototype implementation of the algorithm coded in Java together with the dataset and examples used in the paper is available in <url>http://kdbio.inesc-id.pt/software/e-ccc-biclustering</url>.</p