Search results for " Extraction"

Direct quantification of cell-free, circulating DNA from unpurified plasma.

2014

Cell-free DNA (cfDNA) in body tissues or fluids is extensively investigated in clinical medicine and other research fields. In this article we provide a direct quantitative real-time PCR (qPCR) as a sensitive tool for the measurement of cfDNA from plasma without previous DNA extraction, which is known to be accompanied by a reduction of DNA yield. The primer sets were designed to amplify a 90 and 222 bp multi-locus L1PA2 sequence. In the first module, cfDNA concentrations in unpurified plasma were compared to cfDNA concentrations in the eluate and the flow-through of the QIAamp DNA Blood Mini Kit and in the eluate of a phenol-chloroform isoamyl (PCI) based DNA extraction, to elucidate the D…

Transesterification of rapeseed oil over acid resins promoted by supercritical carbon dioxide

2011

The methanolysis of rapeseed oil catalyzed by commercial styrene-divinylbenzene macroporous acid resins was performed in a batch reactor at 100-140 °C and 10-46 MPa to study the effect of supercritical carbon dioxide (scCO2) on the performances of the process. Reaction temperatures of 120-140 °C were necessary to obtain high enough yields of fatty acid methyl esters. Upon addition of scCO2 faster transesterification kinetics was obtained also at the lowest investigated operating pressure (10-11 MPa), working in two fluid phase systems. Experiments performed changing the reaction time indicated that most of the esters were formed during the first 3 h. When the pressure was increased at 38-46…

Separation and identification of petroporphyrins extracted from the oil shales of Tarfaya: geochemical study

2002

Abstract Vanadyl and nickel porphyrins were isolated from the oil shales of Tarfaya (Morocco) by extraction followed by column chromatography. The ratios and characteristics of these porphyrin complexes were essentially obtained on the basis of UV–visible and mass spectrometry data. Geochemical information could be drawn from these data. The nature and the contents of the metals coordinated and non-coordinated to porphyrin systems were also determined in this study.

A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition

2019

The number of older people in western countries is constantly increasing. Most of them prefer to live independently and are susceptible to fall incidents. Falls often lead to serious or even fatal injuries which are the leading cause of death for elderlies. To address this problem, it is essential to develop robust fall detection systems. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. We use acceleration and angular velocity data from two public databases to recognize seven different activities, including falls and activities of daily living. From the acceleration and angular velocity data, we extract time- and frequency-do…

On the use of Deep Reinforcement Learning for Visual Tracking: a Survey

2021

This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning. It is showing interesting results in the computer vision field and, recently, it has been applied to the visual tracking problem yielding to the rapid development of novel tracking strategies. After providing an introduction to reinforcement learning, this paper compares recent visual tracking approaches based on deep reinforcement learning. Analysis of the state-of-the-art suggests that reinforcement learning allows modeling varying parts of the tracki…

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

2020

Robust and accurate human activity recognition (HAR) systems are essential to many human-centric services within active assisted living and healthcare facilities. Traditional HAR systems mostly leverage a single sensing modality (e.g., either wearable, vision, or radio frequency sensing) combined with machine learning techniques to recognize human activities. Such unimodal HAR systems do not cope well with real-time changes in the environment. To overcome this limitation, new HAR systems that incorporate multiple sensing modalities are needed. Multiple diverse sensors can provide more accurate and complete information resulting in better recognition of the performed activities. This article…

A Machine Learning Approach for Fall Detection Based on the Instantaneous Doppler Frequency

2019

Modern societies are facing an ageing problem that is accompanied by increasing healthcare costs. A major share of this ever-increasing cost is due to fall-related injuries, which urges the development of fall detection systems. In this context, this paper paves the way for the development of radio-frequency-based fall detection systems, which do not require the user to wear any device and can detect falls without compromising the user's privacy. For the design of such systems, we present an activity simulator that generates the complex path gain of indoor channels in the presence of one person performing three different activities: slow fall, fast fall, and walking. We have developed a mac…

2020

Recommender systems are information software that retrieves relevant items for users from massive sources of data. The variational autoencoder (VAE) has proven to be a promising approach for recommendation systems, as it can explore high-level user-item relations and extract contingencies from the input effectively. However, the previous variants of VAE have so far seen limited application to domain-specific recommendations that require additional side information. Hence, The Ensemble Variational Autoencoder framework for recommendations (EnsVAE) is proposed. This architecture specifies a procedure to transform sub-recommenders’ predicted utility matrix into interest probabilities that allo…

Motif patterns in 2D

2008

AbstractMotif patterns consisting of sequences of intermixed solid and don’t-care characters have been introduced and studied in connection with pattern discovery problems of computational biology and other domains. In order to alleviate the exponential growth of such motifs, notions of maximal saturation and irredundancy have been formulated, whereby more or less compact subsets of the set of all motifs can be extracted, that are capable of expressing all others by suitable combinations. In this paper, we introduce the notion of maximal irredundant motifs in a two-dimensional array and develop initial properties and a combinatorial argument that poses a linear bound on the total number of …

2021

This paper proposes a new method for blind mesh visual quality assessment (MVQA) based on a graph convolutional network. For that, we address the node classification problem to predict the perceived visual quality. First, two matrices representing the 3D mesh are considered: a graph adjacency matrix and a feature matrix. Both matrices are used as input to a shallow graph convolutional network. The network consists of two convolutional layers followed by a max-pooling layer to provide the final feature representation. With this structure, the Softmax classifier predicts the quality score category without the reference mesh’s availability. Experiments are conducted on four publicly available …