Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification

IntroductionDynamic functional connectivity (dFC), which can capture the abnormality of brain activity over time in resting-state functional magnetic resonance imaging (rs-fMRI) data, has a natural advantage in revealing the abnormal mechanism of brain activity in patients with Attention Deficit/Hyperactivity Disorder (ADHD). Several deep learning methods have been proposed to learn dynamic changes from rs-fMRI for FC analysis, and achieved superior performance than those using static FC. However, most existing methods only consider dependencies of two adjacent timestamps, which is limited when the change is related to the course of many timestamps.MethodsIn this paper, we propose a novel Temporal Dependence neural Network (TDNet) for FC representation learning and temporal-dependence relationship tracking from rs-fMRI time series for automated ADHD identification. Specifically, we first partition rs-fMRI time series into a sequence of consecutive and non-overlapping segments. For each segment, we design an FC generation module to learn more discriminative representations to construct dynamic FCs. Then, we employ the Temporal Convolutional Network (TCN) to efficiently capture long-range temporal patterns with dilated convolutions, followed by three fully connected layers for disease prediction.ResultsAs the results, we found that considering the dynamic characteristics of rs-fMRI time series data is beneficial to obtain better diagnostic performance. In addition, dynamic FC networks generated in a data-driven manner are more informative than those constructed by Pearson correlation coefficients.DiscussionWe validate the effectiveness of the proposed approach through extensive experiments on the public ADHD-200 database, and the results demonstrate the superiority of the proposed model over state-of-the-art methods in ADHD identification

Synthesis and Immobilization of Pt Nanoparticles on Amino-Functionalized Halloysite Nanotubes toward Highly Active Catalysts

A simple and effective method for the preparation of platinum nanoparticles (Pt NPs) grown on amino-functionalized halloysite nanotubes (HNTs) was developed. The nanostructures were synthesized through the functionalization of the HNTs, followed by an in situ approach to generate Pt NPs with diameter of approximately 1.5 nm within the entire HNTs. The synthesis process, composition and morphology of the nanostructures were characterized. The results suggest PtNPs/NH2-HNTs nanostructures with ultrafine PtNPs were successfully synthesized by green chemically-reducing H2PtCl6 without the use of surfactant. The nanostructures exhibit promising catalytic properties for reducing potassium hexacyanoferrate(III) to potassium hexacyanoferrate(II). The presented experiment for novel PtNPs/NH2-HNTs nanostructures is quite simple and environmentally benign, permitting it as a potential application in the future field of catalysts

Effects of tropospheric ozone pollution on net primary productivity and carbon storage in terrestrial ecosystems of China

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 112 (2007): D22S09, doi:10.1029/2007JD008521.We investigated the potential effects of elevated ozone (O3) along with climate variability, increasing CO2, and land use change on net primary productivity (NPP) and carbon storage in China's terrestrial ecosystems for the period 1961–2000 with a process-based Dynamic Land Ecosystem Model (DLEM) forced by the gridded data of historical tropospheric O3 and other environmental factors. The simulated results showed that elevated O3 could result in a mean 4.5% reduction in NPP and 0.9% reduction in total carbon storage nationwide from 1961 to 2000. The reduction of carbon storage varied from 0.1 Tg C to 312 Tg C (a decreased rate ranging from 0.2% to 6.9%) among plant functional types. The effects of tropospheric O3 on NPP were strongest in east-central China. Significant reductions in NPP occurred in northeastern and central China where a large proportion of cropland is distributed. The O3 effects on carbon fluxes and storage are dependent upon other environmental factors. Therefore direct and indirect effects of O3, as well as interactive effects with other environmental factors, should be taken into account in order to accurately assess the regional carbon budget in China. The results showed that the adverse influences of increasing O3 concentration across China on NPP could be an important disturbance factor on carbon storage in the near future, and the improvement of air quality in China could enhance the capability of China's terrestrial ecosystems to sequester more atmospheric CO2. Our estimation of O3 impacts on NPP and carbon storage in China, however, must be used with caution because of the limitation of historical tropospheric O3 data and other uncertainties associated with model parameters and field experiments.This research is funded by NASA Interdisciplinary Science Program (NNG04GM39C)

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

Significance: Diffuse correlation spectroscopy (DCS) is a powerful, non-invasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient μ_a and reduced scattering coefficient μ_s^') and scalp and skull thicknesses. Aim: We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light’s temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance. Approach: We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural networks (DCS-NET), for BFi and coherent factor (β) estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30 mm) on BFi and β estimations among DCS-NET, semi-infinite, and three-layer fitting models. Results: DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of μ_a and μ_s^' at a source-detector separation of 30 mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively. Conclusions: DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements

Topological and topological-electronic correlations in amorphous silicon

In this paper, we study several structural models of amorphous silicon, and discuss structural and electronic features common to all. We note spatial correlations between short bonds, and similar correlations between long bonds. Such effects persist under a first principles relaxation of the system and at finite temperature. Next we explore the nature of the band tail states and find the states to possess a filamentary structure. We detail correlations between local geometry and the band tails.Comment: 7 pages, 11 figures, submitted to Journal of Crystalline Solid

A semi-quantitative scattering theory of amorphous materials

It is argued that topological disorder in amorphous solids can be described by local strains related to local reference crystals and local rotations. An intuitive localization criterion is formulated from this point of view. The Inverse Participation Ratio and the location of mobility edges in band tails is directly related to the character of the disorder potential in amorphous solid, the coordination number, the transition integral and the nodes of wave functions of the corresponding reference crystal. The dependence of the decay rate of band tails on temperature and static disorder are derived. \textit{Ab initio} simulations on a-Si and experiments on a-Si:H are compared to these predictions.Comment: 4 pages, 2 figures, will be submitted to Phys. Rev. Let

An Improved TESLA Protocol Based on Queuing Theory and Benaloh-Leichter SSS in WSNs

Broadcast authentication is a fundamental security technology in wireless sensor networks (ab. WSNs). As an authentication protocol, the most widely used in WSN, TESLA protocol, its publication of key is based on a fixed time interval, which may lead to unsatisfactory performance under the unstable network traffic environment. Furthermore, the frequent network communication will cause the delay authentication for some broadcast packets while the infrequent one will increase the overhead of key computation. To solve these problems, this paper improves the traditional TESLA by determining the publication of broadcast key based on the network data flow rather than the fixed time interval. Meanwhile, aiming at the finite length of hash chain and the problem of exhaustion, a self-renewal hash chain based on Benaloh-Leichter secret sharing scheme (SRHC-BL SSS) is designed, which can prolong the lifetime of network. Moreover, by introducing the queue theory model, we demonstrate that our scheme has much lower key consumption than TESLA through simulation evaluations. Finally, we analyze and prove the security and efficiency of the proposed self-renewal hash chain, comparing with other typical schemes