Risk factors and predictive model of cerebral edema after road traffic accidents-related traumatic brain injury.

PURPOSE: Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries. METHODS: This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q1, Q3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve. RESULTS: According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval (CI): 2.08 - 25.42, p = 0.002), 2.85 (95% CI: 1.11 - 7.31, p = 0.030), 2.62 (95% CI: 1.12 - 6.13, p = 0.027), 2.44 (95% CI: 1.25 - 4.76, p = 0.009), and 1.5 (95% CI: 1.10 - 2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ2 = 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ2 = 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively. CONCLUSION: Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

Investigating neuropathological changes and underlying neurobiological mechanisms in the early stages of primary blast-induced traumatic brain injury: Insights from a rat model.

The utilization of explosives and chemicals has resulted in a rise in blast-induced traumatic brain injury (bTBI) in recent times. However, there is a dearth of diagnostic biomarkers and therapeutic targets for bTBI due to a limited understanding of biological mechanisms, particularly in the early stages. The objective of this study was to examine the early neuropathological characteristics and underlying biological mechanisms of primary bTBI. A total of 83 Sprague Dawley rats were employed, with their heads subjected to a blast shockwave of peak overpressure ranging from 172 to 421 kPa in the GI, GII, and GIII groups within a closed shock tube, while the body was shielded. Neuromotor dysfunctions, morphological changes, and neuropathological alterations were detected through modified neurologic severity scores, brain water content analysis, MRI scans, histological, TUNEL, and caspase-3 immunohistochemical staining. In addition, label-free quantitative (LFQ)-proteomics was utilized to investigate the biological mechanisms associated with the observed neuropathology. Notably, no evident damage was discernible in the GII and GI groups, whereas mild brain injury was observed in the GIII group. Neuropathological features of bTBI were characterized by morphologic changes, including neuronal injury and apoptosis, cerebral edema, and cerebrovascular injury in the shockwave's path. Subsequently, 3153 proteins were identified and quantified in the GIII group, with subsequent enriched neurological responses consistent with pathological findings. Further analysis revealed that signaling pathways such as relaxin signaling, hippo signaling, gap junction, chemokine signaling, and sphingolipid signaling, as well as hub proteins including Prkacb, Adcy5, and various G-protein subunits (Gnai2, Gnai3, Gnao1, Gnb1, Gnb2, Gnb4, and Gnb5), were closely associated with the observed neuropathology. The expression of hub proteins was confirmed via Western blotting. Accordingly, this study proposes signaling pathways and key proteins that exhibit sensitivity to brain injury and are correlated with the early pathologies of bTBI. Furthermore, it highlights the significance of G-protein subunits in bTBI pathophysiology, thereby establishing a theoretical foundation for early diagnosis and treatment strategies for primary bTBI.

A Survey on Autism Care, Diagnosis, and Intervention Based on Mobile Apps Focusing on Usability and Software Design.

This article presents a systematic review on autism care, diagnosis, and intervention based on mobile apps running on smartphones and tablets. Here, the term "intervention" means a carefully planned set of activities with the objective of improving autism symptoms. We guide our review on related studies using five research questions. First, who benefits the most from these mobile apps? Second, what are the primary purposes of these mobile apps? Third, what mechanisms have been incorporated in these mobiles apps to improve usability? Fourth, what guidelines have been used in the design and implementation of these mobile apps? Fifth, what theories and frameworks have been used as the foundation for these mobile apps to ensure the intervention effectiveness? As can be seen from these research questions, we focus on the usability and software development of the mobile apps. Informed by the findings of these research questions, we propose a taxonomy for the mobile apps and their users. The mobile apps can be categorized into autism support apps, educational apps, teacher training apps, parental support apps, and data collection apps. The individuals with autism spectrum disorder (ASD) are the primary users of the first two categories of apps. Teachers of children with ASD are the primary users of the teacher training apps. Parents are the primary users of the parental support apps, while individuals with ASD are usually the primary users of the data collection apps and clinicians and autism researchers are the beneficiaries. Gamification, virtual reality, and autism-specific mechanisms have been used to improve the usability of the apps. User-centered design is the most popular approach for mobile app development. Augmentative and alternative communication, video modeling, and various behavior change practices have been used as the theoretical foundation for intervention efficacy.

A Hybrid Deep Transfer Learning Model With Kernel Metric for COVID-19 Pneumonia Classification Using Chest CT Images.

Coronavirus disease-2019 (COVID-19) as a new pneumonia which is extremely infectious, the classification of this coronavirus is essential to effectively control the development of the epidemic. Pathological changes in the chest computed tomography (CT) scans are often used as one of the diagnostic criteria of COVID-19. Meanwhile, deep learning-based transfer learning is currently an effective strategy for computer-aided diagnosis (CAD). To further improve the performance of deep transfer learning model used for COVID-19 classification with CT images, in this article, we propose a hybrid model combined with a semi-supervised domain adaption model and extreme learning machine (ELM) classifier, and the application of a novel multikernel correntropy induced loss function in transfer learning is also presented. The proposed model is evaluated on open-source datasets. The experimental results are compared to some baseline models to verify the effectiveness, while adopting accuracy, precision, recall, F1 score and area under curve (AUC) as the evaluation metrics. Experimental results show that the proposed method improves the performance of original model and is more suitable for CT images analysis.

Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts.

Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals.

The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. Traditional EEG recognition models largely depend on artificial experience and are of weak generalization ability. To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which composes of three convolutional blocks and three fully connected layers. Thereinto, each convolutional block consists of five types of layers: convolutional layer, batch normalization layer, nonlinear activation layer, dropout layer, and max-pooling layer. Model performance is evaluated on the University of Bonn dataset, which achieves the accuracy of 97.63%∼99.52% in the two-class classification problem, 96.73%∼98.06% in the three-class EEG classification problem, and 93.55% in classifying the complicated five-class problem.

Altered Patterns of Phase Position Connectivity in Default Mode Subnetwork of Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment.

Alzheimer's disease (AD), which most commonly occurs in the elder, is a chronic neurodegenerative disease with no agreed drugs or treatment protocols at present. Amnestic mild cognitive impairment (aMCI), earlier than AD onset and later than subjective cognitive decline (SCD) onset, has a serious probability of converting into AD. The SCD, which can last for decades, subjectively complains of decline impairment in memory. Distinct altered patterns of default mode network (DMN) subnetworks connected to the whole brain are perceived as prominent hallmarks of the early stages of AD. Nevertheless, the aberrant phase position connectivity (PPC) connected to the whole brain in DMN subnetworks remains unknown. Here, we hypothesized that there exist distinct variations of PPC in DMN subnetworks connected to the whole brain for patients with SCD and aMCI, which might be acted as discriminatory neuroimaging biomarkers. We recruited 27 healthy controls (HC), 20 SCD and 28 aMCI subjects, respectively, to explore aberrant patterns of PPC in DMN subnetworks connected to the whole brain. In anterior DMN (aDMN), SCD group exhibited aberrant PPC in the regions of right superior cerebellum lobule (SCL), right superior frontal gyrus of medial part (SFGMP), and left fusiform gyrus (FG) in comparison of HC group, by contrast, no prominent difference was found in aMCI group. It is important to note that aMCI group showed increased PPC in the right SFGMP in comparison with SCD group. For posterior DMN (pDMN), SCD group showed decreased PPC in the left superior parietal lobule (SPL) and right superior frontal gyrus (SFG) compared to HC group. It is noteworthy that aMCI group showed decreased PPC in the left middle frontal gyrus of orbital part (MFGOP) and right SFG compared to HC group, yet increased PPC was found in the left superior temporal gyrus of temporal pole (STGTP). Additionally, aMCI group exhibited decreased PPC in the left MFGOP compared to SCD group. Collectively, our results have shown that the aberrant regions of PPC observed in DMN are related to cognitive function, and it might also be served as impressible neuroimaging biomarkers for timely intervention before AD occurs.

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction.

Kernel adaptive filtering (KAF) is an effective nonlinear learning algorithm, which has been widely used in time series prediction. The traditional KAF is based on the stochastic gradient descent (SGD) method, which has slow convergence speed and low filtering accuracy. Hence, a kernel conjugate gradient (KCG) algorithm has been proposed with low computational complexity, while achieving comparable performance to some KAF algorithms, e.g., the kernel recursive least squares (KRLS). However, the robust learning performance is unsatisfactory, when using KCG. Meanwhile, correntropy as a local similarity measure defined in kernel space, can address large outliers in robust signal processing. On the basis of correntropy, the mixture correntropy is developed, which uses the mixture of two Gaussian functions as a kernel function to further improve the learning performance. Accordingly, this article proposes a novel KCG algorithm, named the kernel mixture correntropy conjugate gradient (KMCCG), with the help of the mixture correntropy criterion (MCC). The proposed algorithm has less computational complexity and can achieve better performance in non-Gaussian noise environments. To further control the growing radial basis function (RBF) network in this algorithm, we also use a simple sparsification criterion based on the angle between elements in the reproducing kernel Hilbert space (RKHS). The prediction simulation results on a synthetic chaotic time series and a real benchmark dataset show that the proposed algorithm can achieve better computational performance. In addition, the proposed algorithm is also successfully applied to the practical tasks of malware prediction in the field of malware analysis. The results demonstrate that our proposed algorithm not only has a short training time, but also can achieve high prediction accuracy.

Simulation of pH-Regulated Electrokinetic Ion Transport in Nanopores with Polyelectrolyte Brushes.

Nanopores with polyelectrolyte brushes in a pHregulated ion channel have been simulated. Electrokinetic ion transport was studied taking account of the effects of pH, presence of multiple ionic species and bulk ionic concentration. A continuum-based model that composed of coupled Poisson-Nernst- Planck equations for the ionic mass transport with electric potential, and Navier-Stokes and Brinkman equations for the fluid flow, were employed. Our results demonstrate that the bulk ionic concentration and pH together can greatly influence the charge of the polyelectrolyte brushes, the distribution of electric potential in the nanopore and also the fluid flow is sensitive to the pH variations.

Comparative Study of Pore Formation Energy by High Intensity, Nanosecond Electrical Pulse.

Nanosecond, high intensity electric pulses create nanopores in the cell membrane. Pore formation energy is probed by taking account of the strain energy based on the continuum model. Maxwell stress acting on the cell membrane is included in the 3D model calculation as well as the effect of membrane curvature. In addition, comparison between cylindrical and toroidal pores were made to explore the difference of strain energy and force over the pores at a range of radii. Through the analyses the transmembrane potential were kept constant in order to obtain a transient response in that the electric pulse has a ultrashort duration and pore-evolving process is rapid as well. Our results demonstrate that under the same circumstances toroidal pores have higher strain energy than cylindrical pores due to the surface area and volume of the pore shape.

Ionic Conduction in Biological Nanopores Created by Ultrashort9 High-Intensity Pulses.

Ultrashort, high-intensity electric pulses open nanopores in biological cell membranes. Ion transport in nanopore is analyzed using a numerical method that couples the Nernst-Planck equations for ionic concentrations, the Poisson equation for the electric potential, and Navier-Stokes equations for the fluid flow. Roles of the applied bias, pore size, as well as the surface charge lining the membrane are comprehensively examined through I-V characteristics, conductance variations of the pore. Our results show that the surface charge distribution has an impact on the ionic conduction due to mutual electrostatic force interference. In addition, a larger pore would conduct a larger ionic current thus being more conductive on the condition of the same bias applied, which would suggest a bias-dependent expansion of pores.

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm.

Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through the use of swarm intelligence paradigm, a solution approach is accordingly proposed by employing an efficient Jaya algorithm, called PA-Jaya, to deal with the power allocation problem in cognitive OFDM radio networks for IoT. Because of the algorithm-specific parameter-free feature in the proposed PA-Jaya algorithm, a satisfactory computational performance could be achieved in the handling of this problem. For this optimization problem with some constraints, the simulation results show that compared with some popular algorithms, the efficiency of spectrum utilization could be further improved by using PA-Jaya algorithm with faster convergence speed, while maximizing the total transmission rate.

The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control.

In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into a continuous time-delaying system. Then we analyze the exponential stability and asymptotic stability of the equilibrium points for this model. By constructing a suitable Lyapunov function, using the Lyapunov stability theorem and some inequality techniques, some sufficient criteria for ensuring the stability of equilibrium points are obtained. Finally, numerical examples are given to demonstrate the effectiveness of our results.

Dependability enhancing mechanisms for integrated clinical environments.

In this article, we present a set of lightweight mechanisms to enhance the dependability of a safety-critical real-time distributed system referred to as an integrated clinical environment (ICE). In an ICE, medical devices are interconnected and work together with the help of a supervisory computer system to enhance patient safety during clinical operations. Inevitably, there are strong dependability requirements on the ICE. We introduce a set of mechanisms that essentially make the supervisor component a trusted computing base, which can withstand common hardware failures and malicious attacks. The mechanisms rely on the replication of the supervisor component and employ only one input-exchange phase into the critical path of the operation of the ICE. Our analysis shows that the runtime latency overhead is much lower than that of traditional approaches.

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme.

Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE) in ELM is replaced by the half-quadratic optimization technique. Simulation results show that our proposed scheme outperforms other traditional localization schemes.

High-throughput state-machine replication using software transactional memory.

State-machine replication is a common way of constructing general purpose fault tolerance systems. To ensure replica consistency, requests must be executed sequentially according to some total order at all non-faulty replicas. Unfortunately, this could severely limit the system throughput. This issue has been partially addressed by identifying non-conflicting requests based on application semantics and executing these requests concurrently. However, identifying and tracking non-conflicting requests require intimate knowledge of application design and implementation, and a custom fault tolerance solution developed for one application cannot be easily adopted by other applications. Software transactional memory offers a new way of constructing concurrent programs. In this article, we present the mechanisms needed to retrofit existing concurrency control algorithms designed for software transactional memory for state-machine replication. The main benefit for using software transactional memory in state-machine replication is that general purpose concurrency control mechanisms can be designed without deep knowledge of application semantics. As such, new fault tolerance systems based on state-machine replications with excellent throughput can be easily designed and maintained. In this article, we introduce three different concurrency control mechanisms for state-machine replication using software transactional memory, namely, ordered strong strict two-phase locking, conventional timestamp-based multiversion concurrency control, and speculative timestamp-based multiversion concurrency control. Our experiments show that speculative timestamp-based multiversion concurrency control mechanism has the best performance in all types of workload, the conventional timestamp-based multiversion concurrency control offers the worst performance due to high abort rate in the presence of even moderate contention between transactions. The ordered strong strict two-phase locking mechanism offers the simplest solution with excellent performance in low contention workload, and fairly good performance in high contention workload.