Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus.

We have investigated the antibacterial, antifungal and cytotoxic activities of two flavonoids isolated from Retama raetam flowers using the disc diffusion and micro-dilution broth methods. The cytotoxic activity was tested against Hep-2 cells using the MTT assay. The compounds licoflavone C (1) and derrone (2) were active against Pseudomonas aeruginosa and Escherichia coli (7.81–15.62 µg/mL) and showed important antifungal activity. Strong antifungal activity against Candida species (7.81 µg/mL) was for example found with compound 2. The tested compounds also showed strong cytotoxicity against Hep-2 cells. These two compounds may be interesting antimicrobial agents to be used against infectious diseases caused by many pathogens.

The chemical composition of the Tamarix boveana volatile oils obtained from the whole aerial part, flowers, leaves and stems by steam distillation was analysed using gas chromatograph (GC)-flame ionization detectors (FID) and GC-MS. Sixty-two components were identified. Hexadecanoic acid (18.14%), docosane (13.34%), germacrene D (7.68%), fenchyl acetate (7.34%), Benzyl benzoate (4.11%) were found to be the major components in the whole aerial parts. This composition differed according to the tested part: 2.4 Nonadienal was the main compound in the flowers (12.13%) while germacrene D was the major component in leaves (31.43%) and hexadecanoic acid in the stems (13.94%). To evaluate in vitro antimicrobial activity, all volatile oils were tested against six Gram-positive and Gram-negative bacteria and four fungi. The T. boveana volatile oils exhibited an interesting antibacterial activity against all strains tested except Pseudomonas aeruginosa but no antifungal activity was detected.

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In this paper, we propose a global approach for speech emotion recognition (SER) system using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD combined with the Teager-Kaiser Energy Operator (TKEO) gives an efficient time-frequency analysis of the non-stationary signals. In this method, each signal is decomposed using EMD into oscillating components called intrinsic mode functions (IMFs). TKEO is used for estimating the time-varying amplitude envelope and instantaneous frequency of a signal that is supposed to be Amplitude Modulation-Frequency Modulation (AM-FM) signal. A subset of the IMFs was selected and used to extract features from speech signal to recognize different emotions. The main contribution of our work is to extract novel features named modulation spectral (MS) features and modulation frequency features (MFF) based on AM-FM modulation model and combined them with cepstral features. It is believed that the combination of all features will improve the performance of the emotion recognition system. Furthermore, we examine the effect of feature selection on SER system performance. For classification task, Support Vecto Machine (SVM) and Recurrent Neural Networks (RNN) are used to distinguish seven basic emotions. Two databases-the Berlin corpus, and the Spanish corpusare used for the experiments. The results evaluated on the Spanish emotional database, using RNN classifier and a combination of all features extracted from the IMFs enhances the performance of the SER system and achieving 91.16 % recognition rate. For the Berlin database, the combination of all features using SVM classifier has 86.22% recognition rate.

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Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-Euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration.