3,764 research outputs found

    Exploring early neurodevelopment in infants with or without elevated likelihood for autism spectrum disorder by using functional near-infrared spectroscopy

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    Autism Spectrum Disorder (ASD) is characterized by deficits in social communication and social interaction, along with restricted and repetitive patterns of behaviour, interests, or activities (RRB) (American Psychiatric Association [APA], 2013). ASD is a high-incident, heterogeneous neurodevelopmental disorder (Johnson & Myers, 2007). People with ASD often encounter social and communication difficulties in their daily lives. Early detection of and intervention with ASD children have always been the focus of the research field and play an important role in helping ASD children lead a more normal life. Since the main features of ASD usually appear after the first year and reliable diagnosis of ASD can be made at 24 months of age (Guthrie et al., 2008), investigation of early brain functions and possible pre-behavioural markers is essential to elucidate the early developmental trajectory of ASD. The emergence of functional near-infrared spectroscopy (fNIRS), as a valuable tool for measuring cortical activity, provides a promising approach for studying functional brain development in young infants (Gervain, 2018; Gervain et al., 2011; Zhang & Roeyers, 2019). fNIRS refers to the use of near-infrared spectroscopy (NIRS) for functional neuroimaging purposes, which measures neural activity by observing hemodynamic changes in the brain tissue (Ferrari & Quaresima, 2012; Guo et al., 2013; Scholkmann et al., 2014). Due to its moderate spatiotemporal resolution and other characteristics (e.g., safety, low-cost, portability), fNIRS has become a widely used implementation in neuroscientific research (Gervain, 2014; Lloyd-Fox et al., 2010; Zhang & Roeyers, 2019). fNIRS is a promising alternative method to study the cognitive development of specific populations, especially those for whom the use of other imaging modalities are limited, such as awake infants (Lloyd-Fox et al., 2015), people with psychiatric conditions (e.g., depression) (Schecklmann et al., 2011) or developmental disorders (e.g., ASD and ADHD) (Ehlis et al., 2008; Zhu et al., 2015). To the best of our knowledge, there are only a few studies using fNIRS to examine brain responses underlying language and social processing in EL infants. The main purpose of this doctoral work was to use fNIRS to gain a deeper understanding of the brain responses (including language and social processes) of infants with or without elevated likelihood for ASD

    Exploring brain functions in autism spectrum disorder : a systematic review on functional near-infrared spectroscopy (fNIRS) studies

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    A growing body of research has investigated the functional development of the brain in autism spectrum disorder (ASD). Functional near-infrared spectroscopy (fNIRS) is increasingly being used in this respect. This method has several advantages over other functional neuroimaging techniques in studying brain functions in ASD, including portability, low cost, and availability in naturalistic settings. This article reviews thirty empirical studies, published in the past decade, that used fNIRS in individuals with ASD or in infants with a high risk of developing ASD. These studies investigated either brain activation using multiple tasks (e.g., face processing, joint attention and working memory) or functional organization under a resting-state condition in ASD. The majority of these studies reported atypical brain activation in the prefrontal cortex, inferior frontal gyrus, middle and superior temporal gyrus. Some studies revealed altered functional connectivity, suggesting an inefficient information transfer between brain regions in ASD. Overall, the findings suggest that fNIRS is a promising tool to explore neurodevelopment in ASD from an early age

    Population Density-based Hospital Recommendation with Mobile LBS Big Data

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    The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system

    Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

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    Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.Comment: To appear in IEEE BHI 201

    Electrical Control of Magnetization in Charge-ordered Multiferroic LuFe2O4

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    LuFe2O4 exhibits multiferroicity due to charge order on a frustrated triangular lattice. We find that the magnetization of LuFe2O4 in the multiferroic state can be electrically controlled by applying voltage pulses. Depending on with or without magnetic fields, the magnetization can be electrically switched up or down. We have excluded thermal heating effect and attributed this electrical control of magnetization to an intrinsic magnetoelectric coupling in response to the electrical breakdown of charge ordering. Our findings open up a new route toward electrical control of magnetization.Comment: 14 pages, 5 figure

    A Method Based on Intuitionistic Fuzzy Dependent Aggregation Operators for Supplier Selection

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    Recently, resolving the decision making problem of evaluation and ranking the potential suppliers have become as a key strategic factor for business firms. In this paper, two new intuitionistic fuzzy aggregation operators are developed: dependent intuitionistic fuzzy ordered weighed averaging (DIFOWA) operator and dependent intuitionistic fuzzy hybrid weighed aggregation (DIFHWA) operator. Some of their main properties are studied. A method based on the DIFHWA operator for intuitionistic fuzzy multiple attribute decision making is presented. Finally, an illustrative example concerning supplier selection is given
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