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Recently, 3D composites have gained prominence over 2D composites due to their remarkable through-the-thickness reinforcement that enhanced performances of structures subject to multi-directional stress conditions. However, it is at the expense of reduced in-plane properties. 3D composites also increased damage tolerance due to their high impact and delamination resistance properties, which are susceptible in 2D composites. In aeronautics, both are major concerns in primary structures such as lifting and control surfaces due to high vibrational loads from the airflow. Advancements in the aeroelasticity of aircraft structures show an increasing trend in using smart materials with composite structures for improved aeroelastic performance. An example is combining shape memory alloys (SMAs) with composites for improving damping, stiffness, and vibrational characteristics by utilizing stress generation and strain accommodation properties of SMAs in response to temperature and load, respectively. Published works are mostly numerical in nature, and experimental research is noticeably lacking as aeroelasticity is multidisciplinary and involves both structural and aerodynamic methodology. In this work, 3D composites with embedded SMA wires (for improved in-plane properties) are evaluated in terms of flutter performance in wind tunnel flutter testing under low airspeed conditions. Three 3D orthogonal interlock configurations with a different interlocking pattern of yarns with SMA wire were considered. These 3D configurations are layer-to-layer (L2L), through-the-thickness (TT), and a modified interlock (MF) structure that provides the strongest grip to SMA wire than L2L and TT. The effect of SMA positioning, at mid and near to trailing and leading edges of the cantilevered composite plate, on the aeroelastic flutter properties is also investigated. Results showed that activating SMA wires embedded in 3D structures have significantly improved post-flutter properties while there is a decrement in flutter speed and flutter frequency due to increased flexibility of deflected plate in the airflow by SMA-induced stresses. Among 3D structures, L2L with SMA near to trailing edge showed significant improvement in post-flutter properties by decreasing 22.2% in twist limit cycle oscillation (LCO) amplitude while L2L with SMA at mid showed a decrement of 9.5% for bending LCO amplitude. Hence, this work showed that embedding SMA is beneficial for mitigating the post-flutter vibrations but at the consequence of reduced flutter speed and frequency of flexible composite plate.

Microglial cells are brain-specific macrophages that swiftly react to disruptive events in the brain. Microglial activation leads to specific modifications, including proliferation, morphological changes, migration to the site of insult, and changes in gene expression profiles. A change in inflammatory status has been linked to many neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s disease. For this reason, the investigation and quantification of microglial cells is essential for better understanding their role in disease progression as well as for evaluating the cytocompatibility of novel therapeutic approaches for such conditions. In the following study we implemented a machine learning-based approach for the fast and automatized quantification of microglial cells; this tool was compared with manual quantification (ground truth), and with alternative free-ware such as the threshold-based ImageJ and the machine learning-based Ilastik. We first trained the algorithms on brain tissue obtained from rats and non-human primate immunohistochemically labelled for microglia. Subsequently we validated the accuracy of the trained algorithms in a preclinical rodent model of Parkinson’s disease and demonstrated the robustness of the algorithms on tissue obtained from mice, as well as from images provided by three collaborating laboratories. Our results indicate that machine learning algorithms can detect and quantify microglial cells in all the three mammalian species in a precise manner, equipotent to the one observed following manual counting. Using this tool, we were able to detect and quantify small changes between the hemispheres, suggesting the power and reliability of the algorithm. Such a tool will be very useful for investigation of microglial response in disease development, as well as in the investigation of compatible novel therapeutics targeting the brain. As all network weights and labelled training data are made available, together with our step-by-step user guide, we anticipate that many laboratories will implement machine learning-based quantification of microglial cells in their research.

Introduction: With the recent technical advances in brain imaging modalities such as magnetic resonance imaging, positron emission tomography, and functional magnetic resonance imaging (fMRI), researchers' interests have inclined over the years to study brain functions through the analysis of the variations in the statistical dependence among various brain regions. Through its wide use in studying brain connectivity, the low temporal resolution of the fMRI represented by the limited number of samples per second, in addition to its dependence on brain slow hemodynamic changes, makes it of limited capability in studying the fast underlying neural processes during information exchange between brain regions. Materials and Methods: In this article, the high temporal resolution of the electroencephalography (EEG) is utilized to estimate the effective connectivity within the default mode network (DMN). The EEG data are collected from 20 subjects with alcoholism and 25 healthy subjects (controls), and used to obtain the effective connectivity diagram of the DMN using the Partial Directed Coherence algorithm. Results: The resulting effective connectivity diagram within the DMN shows the unidirectional causal effect of each region on the other. The variations in the causal effects within the DMN between controls and alcoholics show clear correlation with the symptoms that are usually associated with alcoholism, such as cognitive and memory impairments, executive control, and attention deficiency. The correlation between the exchanged causal effects within the DMN and symptoms related to alcoholism is discussed and properly analyzed. Conclusion: The establishment of the causal differences between control and alcoholic subjects within the DMN regions provides valuable insight into the mechanism by which alcohol modulates our cognitive and executive functions and creates better possibility for effective treatment of alcohol use disorder.

Major depressive disorder (MDD), which is also known as unipolar depression, is one of the leading sources of functional frailty. MDD is mostly a chronic disorder that requires a long duration of treatment and clinical management. One of the critical issues in MDD treatment is the need for it’s early diagnosis. Conventional tools in MDD diagnosis are based on questionnaires and other forms of psychiatric evaluations. However, the subjective nature of these tools may lead to misleading inferences. Recently, brain electroencephalography (EEG) signals have been used for the quantitative diagnosis of MDD. Nevertheless, a further improvement of the proposed methods in terms of accuracy and clinical utility is required. In this study, EEG signals from 30 MDD and 30 healthy control (HC) are used to estimate the effective connectivity within the brain default mode network (DMN). Then, effective connections between the major six regions of the DMN are used to train and test a three-dimensional (3D) convolutional neural network. Here, connectivity samples generated from half of the subjects are used for training while the rest are used for testing. The results show that the proposed MDD diagnosis algorithm achieved 100% accuracy,sensitivity and specificity in classifying MDD and HC test subjects.

Alcohol Use Disorder (AUD) is a chronic relapsing brain disease characterized by excessive alcohol use, loss of control over alcohol intake, and negative emotional states under no alcohol consumption. The key factor in successful treatment of AUD is the accurate diagnosis for better medical and therapy management. Conventionally, for individuals to be diagnosed with AUD, certain criteria as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM) should be met. However, this process is subjective in nature and could be misleading due to memory problems and dishonesty of some AUD patients. In this paper, an assessment scheme for objective diagnosis of AUD is proposed. For this purpose, EEG recording of 31 healthy controls and 31 AUD patients are used for the calculation of effective connectivity (EC) between the various regions of the brain Default Mode Network (DMN). The EC is estimated using partial directed coherence (PDC) which are then used as input to a 3D Convolutional Neural Network (CNN) for binary classification of AUD cases. Using 5-fold cross validation, the classification of AUD vs. HC effective connectivity matrices using the proposed 3D-CNN gives an accuracy of 87.85 ± 4.64 %. For further validation, 32 and 30 subjects are randomly selected for training and testing, respectively, giving 100% correct classification of all the testing subjects.

Brain connectivity plays a pivotal role in understanding the brain's information processing functions by providing various details including magnitude, direction, and temporal dynamics of inter-neuron connections. While the connectivity may be classified as structural, functional and causal, a complete in-vivo directional analysis is guaranteed by the latter and is referred to as Effective Connectivity (EC). Two most widely used EC techniques are Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) which are based on multivariate autoregressive models. The drawbacks of these techniques include poor frequency resolution and the requirement for experimental approach to determine signal normalization and thresholding techniques in identifying significant connectivities between multivariate sources.In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source.The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively.Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.