Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification

Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L1-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD) from normal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods

Informacijski servisni sustav za poljoprivredni IoT

Internet of Things (IoT) was faced with some difficulties which contained mass data management, various standards of object identification, data fusion of multiple sources, business data management and information service providing. In China, some safety monitoring systems of agricultural product always adopt centralized system architecture in which the data is stored concentratively. These systems could not be connected with or accessed by each other. This paper proposed an information system of agriculture Internet of Things based on distributed architecture. A distributed information service system based on IoT-Information Service, Object Naming Service, Discovery Service is designed to provide public information service including of capturing, standardizing, managing and querying of massive business data of agriculture production. A coding scheme for agricultural product, business location and logistic unit is provided for data identification. A business event model of agriculture IoT is presented for business data management. The whole system realizes the tracking and tracing of agricultural products, and quality monitoring of agriculture production. The implementation of this information service system is introduced.Internet stvari suočen je s poteškoćama poput upravljanja s velikom količinom podataka, različitim standardnima identifikacije objekata, fuzije podataka iz više izvora, upravljanja poslovnim podatcima i pružanje informacijskih usluga. Sigurnosno nadgledanje poljoprivrednih proizvoda u Kini uvijek podliježe centraliziranoj arhitekturi gdje su podatci koncentrirani na jednom mjestu. Takvi sustavi ne mogu biti povezani jedni s drugim te jedan drugome ne mogu pristupati. U ovome radu predložen je informacijski sustav za poljoprivredni internet stvari temeljen na distribuiranoj arhitekturi. Distribuirani informacijski servisni sustav baziran na IoT (Internet stvari), sustav za imenovanje objekata i sustav za otkrivanje omogućuju javni informacijski servis uključujući prikupljanje, standardizaciju, upravljanje i ispitivanje velikih količina podataka o poljoprivrednim proizvodima. Prikazana je shema kodiranja za poljopoprivredne proizvode, poslovne lokacije i logističke jedinice za identifikaciju podataka. Poslovni model doga.aja za poljoprivredni IoT je prezentiran za upravljanje poslovnim podatcima. Cjelokupni sustav omogućuje praćenje poljoprivrednih proizvoda te nadgledanje njihove kvalitete. Rad tako.er daje uvid u implementaciju informacijskog servisnog sustava

Robust multi-atlas label propagation by deep sparse representation

Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional flat and shallow dictionary to a deep multi-layer structure, where the top layer (label-specific dictionaries) consists of groups of representative atlas patches and the subsequent layers (residual dictionaries) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures, compared to other counterpart label fusion methods

Using computed tomography angiography and computational fluid dynamics to study aortic coarctation in different arch morphologies

ObjectiveTo study the differences in computed tomography angiography (CTA) imaging of gothic arches, crenel arches, and romanesque arches in children with Aortic Coarctation (CoA), and to apply computational fluid dynamics (CFD) to study hemodynamic changes in CoA children with gothic arch aorta.MethodsThe case data and CTA data of children diagnosed with CoA (95 cases) in our hospital were retrospectively collected, and the morphology of the aortic arch in the children was defined as gothic arch (n = 27), crenel arch (n = 25) and romanesque arch (n = 43). The three groups were compared with D1/AOA, D2/AOA, D3/AOA, D4/AOA, D5/AOA, and AAO-DAO angle, TAO-DAO angle, and aortic arch height to width ratio (A/T). Computational fluid dynamics was applied to assess hemodynamic changes in children with gothic arches.ResultsThere were no significant differences between D1/AOA and D2/AOA among gothic arch, crenel arch, and romanesque arch (P > 0.05). The differences in D3/AOA, D4/AOA, and D5/AOA among the three groups were statistically significant (P < 0.05), D4/AOA, D5/AOA of the gothic arch group were smaller than the crenel arch group, and the D3/AOA and D5/AOA of the gothic arch group were smaller than the romanesque arch group (P < 0.05). The difference in AAO-DAO angle among the three groups was statistically significant (P < 0.05), and the AAO-DAO angle of gothic arch was smaller than that of romanesque arch and crenel arch group (P < 0.05). There was no significant difference in the TAO-DAO angle between the three groups (P > 0.05). The difference in A/T values among the three groups was statistically significant (P < 0.05), and the A/T values: gothic arch > romanesque arch > crenel arch (P < 0.05). The CFD calculation of children with gothic arch showed that the pressure drop between the distal stenosis and the descending aorta was 58 mmHg, and the flow rate at the isthmus and descending aorta was high and turbulent.ConclusionGothic aortic arch is common in CoA, it may put adverse effects on the development of the aortic isthmus and descending aorta, and its A/T value and AAO-DAO angle are high. CFD could assess hemodynamic changes in CoA

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6−1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers

Global Asymptotic Stability of Stochastic Neural Networks with Time-Varying Delays

Abstract-This paper is concerned with asymptotic stability of stochastic neural networks with time-varying delay. Distinct difference from other analytical approach lies in "linearization" of neural network model, by which the considered neural network model is transformed into a linear time-variant system. A sufficient condition is derived such that for all admissible disturbance, the considered neural network is asymptotic stability in the mean square. The stability criterion is formulated by means of the feasibility of a LMI, which can be easily checked in practice. Finally, a numerical example is given to illustrate the effectiveness of the developed method

Spatial-temporal data-augmentation-based functional brain network analysis for brain disorders identification

IntroductionDue to the lack of devices and the difficulty of gathering patients, the small sample size is one of the most challenging problems in functional brain network (FBN) analysis. Previous studies have attempted to solve this problem of sample limitation through data augmentation methods, such as sample transformation and noise addition. However, these methods ignore the unique spatial-temporal information of functional magnetic resonance imaging (fMRI) data, which is essential for FBN analysis.MethodsTo address this issue, we propose a spatial-temporal data-augmentation-based classification (STDAC) scheme that can fuse the spatial-temporal information, increase the samples, while improving the classification performance. Firstly, we propose a spatial augmentation module utilizing the spatial prior knowledge, which was ignored by previous augmentation methods. Secondly, we design a temporal augmentation module by random discontinuous sampling period, which can generate more samples than former approaches. Finally, a tensor fusion method is used to combine the features from the above two modules, which can make efficient use of spatial-temporal information of fMRI simultaneously. Besides, we apply our scheme to different types of classifiers to verify the generalization performance. To evaluate the effectiveness of our proposed scheme, we conduct extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and REST-meta-MDD Project (MDD) dataset.ResultsExperimental results show that the proposed scheme achieves superior classification accuracy (ADNI: 82.942%, MDD: 63.406%) and feature interpretation on the benchmark datasets.DiscussionThe proposed STDAC scheme, utilizing both spatial and temporal information, can generate more diverse samples than former augmentation methods for brain disorder classification and analysis

A Delay-Dependent Stability Criterion for Neural Networks with Interval Time-varying Delays

Abstract-This paper presents a new result of stability analysis for neural networks with interval time-varying delays. A less conservative stability criterion is established by constructing a new Lyapunov-Krasovskii functional and introducing some free weighting matrices. Numerical examples show that the proposed criterion is effective and is an improvement over some existing results in the literature

Analysis on fatigue crack growth laws for crumb rubber modified (CRM) asphalt mixture

In recent years, crumb rubber has been applied widely in asphalt pavement, and many researchers have indicated that crumb rubber modified (CRM) asphalt mixture is an environmentally friendly material. In this study, the notched semi-circular bending (SCB) test was employed to study the fatigue cracking property for CRM asphalt mixture. Then the cracking growth length was obtained by image processing technology, and its correlation with the fatigue number was established and studied in this paper. Concurrently, the influence of gradation type, asphalt content, test temperature, stress ratio, loading frequency, rubber powder concentration and rubber powder size on CRM asphalt mixtures\u27 fatigue life and crack growth laws were investigated by this method. The results indicated that the gap-graded CRM asphalt mixture had a longer fatigue life and a lower crack growth rate than the continuous graded mixtures Moreover, at the optimum asphalt content, the fatigue life was much longer and the crack growth rate was much lower at smaller loading times with higher loading frequency at the CRM asphalt mixture concentration of 20% using the smaller 80 mesh fine crumb rubber size. © 2013 Elsevier Ltd. All rights reserved

Effect of warm mixture asphalt (WMA) additives on high failure temperature properties for crumb rubber modified (CRM) binders

This paper investigates the effect of three warm mixture asphalt (WMA) additives on the high temperature rheological properties of both unaged and rolling thin film oven (RTFO) aged crumb rubber modified (CRM) binders. The WMA additives used in this study include Sasobit, RH and Advera. The ambient 40-mesh tire rubber with the concentrations of 10%, 15%, 20%, and 25% by the weight of asphalt binder, respectively, was used in this study. Dynamic shear rheometer (DSR) was employed to measure the complex modulus (G) and phase angle (δ) of CRM binders at various testing temperatures. The statistical analysis of variance (ANOVA) was applied to quantify the effects of WMA additives on the CRM binders\u27 rutting resistance properties. It was found in this study that, the three WMA additives could all improve the CRM binders\u27 resistance to rutting, and greatly improved high-temperature portion of the performance grade (PG) of CRM binders. It is found that Sasobit had the most remarkable effect on G of both unaged and RTFO-aged CRM binders, RH only had significant effect on G of RTFO-aged binders, Advera\u27s effect was indistinctive. Furthermore, WMA additives\u27 effect on δ was not conclusive. © 2012 Elsevier Ltd. All rights reserved