Search results for: deep brain stimulation

Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

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Authors: Tapas Karmaker

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Centuries ago, we came to know about ‘Golden Ratio’ also known as Golden Angle. The idea of this research is based on this theme. Researcher perceives ‘The Golden Ratio’ in terms of harmony, meaning that every single item in the universe follows a harmonic behavior. In case of human being, brain responses easily and quickly to this harmony to help memorization. In this theory, harmony means a link. This study has been carried out on a segment of school students and a segment of common people for a period of three years from 2003 to 2006. The research in this respect intended to determine the impact of harmony in the brain of these people. It has been found that students and common people can increase their memorization capacity as much as 70 times more by applying this method. This method works faster and better between age of 8 and 30 years. This result was achieved through tests to assess memorizing capacity by using tools like words, rhymes, texts, math and drawings. The research concludes that this harmonic method can be applied for improving the capacity of learning languages, for the better quality of lifestyle, or any other terms of life as well as in professional activity.

Keywords: language, education, golden brain, learning, teaching

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Authors: Paula Andrea Segura Delgado, Martha Helena Ramírez-Bahena

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Research on how the brain learns has a transcendental application in the educational context. It is crucial for teacher training to understand the nature of brain changes and their direct influence on learning processes. This communication is based on a literature review focused on neuroscience, neuroeducation, and the impact of digital technology on the human brain. Information was gathered from both English and Spanish language sources, using online journals, books and reports. The general objective was to analyze the role of neuroscience knowledge in enriching our understanding of the learning process. In fact, the authors have focused on the impact of digital technology on the human brain as well as its influence in the field of education..Neuroscience knowledge can contribute significantly to improving the training of educators and therefore educational practices. Education as an instrument of change and school as an agent of socialization, it is necessary to understand what it aims to transform: the human brain. Understanding the functioning of the human brain has important repercussions on education: this elucidates cognitive skills, psychological processes and elements that influence the learning process (memory, executive functions, emotions and the circadian cycle); helps identify psychological and neurological deficits that can impede learning processes (dyslexia, autism, hyperactivity); It allows creating environments that promote brain development and contribute to the advancement of brain capabilities in alignment with the stages of neurobiological development. The digital age presents diverse opportunities to every social environment. The frequent use of digital technology (DT) has had a significant and abrupt impact on both the cognitive abilities and physico-chemical properties of the brain, significantly influencing educational processes. Hence, educational community, with the insights from advances in neuroscience, aspire to identify the positive and negative effects of digital technology on the human brain. This knowledge helps ensure the alignment of teacher training and practices with these findings. The knowledge of neuroscience enables teachers to develop teaching methods that are aligned with the way the brain works. For example, neuroscience research has shown that digital technology is having a significant impact on the human brain (addition, anxiety, high levels of dopamine, circadian cycle disorder, decrease in attention, memory, concentration, problems with their social relationships). Therefore, it is important to understand the nature of these changes, their impact on the learning process, and how educators should effectively adapt their approaches based on these brain's changes.

Keywords: digital technology, learn process, neuroscience knowledge, neuroeducation, training proffesors

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Authors: M.R Asghari Bejestani, Gh. R. Mohammad Khani, V.R. Nafisi

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Imagined speech recognition is one of the most interesting approaches to BCI development and a lot of works have been done in this area. Many different experiments have been designed and hundreds of combinations of feature extraction methods and classifiers have been examined. Reported classification accuracies range from the chance level to more than 90%. Based on non-stationary nature of brain signals, we have introduced 3 classification modes according to time difference in inter and intra-class samples. The modes can explain the diversity of reported results and predict the range of expected classification accuracies from the brain signal accusation procedure. In this paper, a few samples are illustrated by inspecting results of some previous works.

Keywords: brain computer interface, silent talk, imagined speech, classification, signal processing

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Authors: B. L. Avanthi Isaka, P. G. Ranjith

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The development of technically-sound enhanced geothermal systems (EGSs) is identified as a viable solution for world growing energy demand with immense potential, low carbon dioxide emission and importantly, as an environmentally friendly option for renewable energy production. The use of supercritical carbon dioxide (ScCO₂) as the working fluid in EGSs by replacing traditional water-based method is promising due to multiple advantages prevail in ScCO₂-injection for underground reservoir stimulation. The evolution of reservoir stimulation using ScCO₂ and the understanding of the flow behavior of a ScCO₂-stimulated geothermal reservoir is vital in applying ScCO₂-EGSs as a replacement for water-based EGSs. The study is therefore aimed to investigate the flow behavior of a ScCO₂-fractured rock medium at in-situ stress and temperature conditions. A series of permeability tests were conducted for ScCO₂ fractured Harcourt granite rock specimens at 90ºC, under varying confining pressures from 5–60 MPa using the high-pressure and high-temperature tri-axial set up which can simulate deep geological conditions. The permeability of the ScCO₂-fractured rock specimens was compared with that of water-fractured rock specimens. The results show that the permeability of the ScCO₂-fractured rock specimens is one order higher than that of water-fractured rock specimens and the permeability exhibits a non-linear reduction with increasing confining pressure due to the stress-induced fracture closure. Further, the enhanced permeability of the ScCO₂-induced fracture with multiple secondary branches was explained by exploring the CT images of the rock specimens. However, a single plain fracture was induced under water-based fracturing.

Keywords: supercritical carbon dioxide, fracture permeability, granite, enhanced geothermal systems

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Authors: T. O. Ikpesu, I. Tongo, A. Ariyo

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This study was conducted to assess the effects of an organophosphate pesticide glyphosate formulation on neurological enzymes in the brain, liver and serum of juvenile Clarias gariepinus, and also to examine the antidotal prospect of Garcinia kola seeds extract. The fish divided into five groups were exposed to different treatments of glyphosate formulation and Garcinia kola seeds extract. Acetyl cholinesterase activities in the brain, liver and serum of the fish were estimated in the experimental and control fishes on day -7, 14, 21 and of 28 by spectrophotometrical methods. The enzyme was significantly (p < 0.05) inhibited in glyphosate formulation test. The inhibition percentages of AChE ranged for the brain, liver and serum between 40.7–59.4%, 50-57% and 27.5–51.3%, respectively. The aberrated parameters were recovered in G. kola seeds extract treated aquaria, and was dose and time dependent. The present study demonstrated that in vivo glyphosate formulation exposure caused AChE inhibition in the brain, liver and the serum. The brain tissue, however, might be suggested as a good indicator tissue for aquatic pollutants exposure in the fish and G. kola seeds extract has shown to be a good remedy for neurology restoration in a noxious circumstance. The findings has shown that xenobiotics could be eliminated from aquatic organisms, especially fish, and could be put into practice in areas at risk of pollutants. This approach can reduce the risks of biomagnification of poison in sea food. Hence, formulation of this plant extracts into capsule should be encouraged and supported.

Keywords: glyphosate, Clarias gariepinus, brain, Garcinia kola, acetyl cholinesterase, enzymes

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Somojit Saha, Rohit K. Chatterjee, Sarit K. Das, Avijit Kar

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A new force field is designed for propagation of the parametric contour into deep narrow cortical fold in the application of knowledge based reconstruction of cerebral cortex from MR image of brain. Designing of this force field is highly inspired by the Generalized Gradient Vector Flow (GGVF) model and markedly differs in manipulation of image information in order to determine the direction of propagation of the contour. While GGVF uses edge map as its main driving force, the newly designed force field uses the map of distance between zero valued pixels and their nearest non-zero valued pixel as its main driving force. Hence, it is called Zero-Non-Zero Distance (ZNZD) force field. The objective of this force field is forceful propagation of the contour beyond spurious convergence due to partial volume effect (PVE) in to narrow sulcal fold. Being function of the corresponding non-zero pixel value, the force field has got an inherent property to determine spuriousness of the edge automatically. It is effectively applied along with some morphological processing in the application of cortical reconstruction to breach the hindrance of PVE in narrow sulci where conventional GGVF fails.

Keywords: deformable model, external force field, partial volume effect, cortical reconstruction, MR image of brain

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Authors: Amedeo D'Angiulli, Kylie Schibli

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Traditionally, children’s brain development research has emphasized the limitative aspects of disability and impairment, electing as an explanatory model the classical clinical notions of brain lesion or functional deficit. In contrast, Positive Educational Neuroscience (PEN) is a new approach that emphasizes strengths and human flourishing related to the brain by exploring how learning practices have the potential to enhance neurocognitive flexibility through neuroplastic overcompensation. This mini-review provides an overview of PEN and shows how it links to the concept of neurocognitive flexibility. We provide examples of how the present concept of neurocognitive flexibility can be applied to special education by exploring examples of neuroplasticity in the learning domain, including: (1) learning to draw in congenitally totally blind children, and (2) music training in children from disadvantaged neighborhoods. PEN encourages educators to focus on children’s strengths by recognizing the brain’s capacity for positive change and to incorporate activities that support children’s individual development.

Keywords: neurocognitive development, positive educational neuroscience, sociocultural approach, special education

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Authors: Mir Jalil Razavi, Tianming Liu, Xianqiao Wang

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Cortical folding, characterized by convex gyri and concave sulci, has an intrinsic relationship to the brain’s functional organization. Understanding the mechanism of the brain’s convoluted patterns can provide useful clues into normal and pathological brain function. During the development, the cerebral cortex experiences a noticeable expansion in volume and surface area accompanied by tremendous tissue folding which may be attributed to many possible factors. Despite decades of endeavors, the fundamental mechanism and key regulators of this crucial process remain incompletely understood. Therefore, to taking even a small role in unraveling of brain folding mystery, we present a mechanical model to find mechanism of 3-hinges formation in a growing brain that it has not been addressed before. A 3-hinge is defined as a gyral region where three gyral crests (hinge-lines) join. The reasons that how and why brain prefers to develop 3-hinges have not been answered very well. Therefore, we offer a theoretical and computational explanation to mechanism of 3-hinges formation in a growing brain and validate it by experimental observations. In theoretical approach, the dynamic behavior of brain tissue is examined and described with the aid of a large strain and nonlinear constitutive model. Derived constitute model is used in the computational model to define material behavior. Since the theoretical approach cannot predict the evolution of cortical complex convolution after instability, non-linear finite element models are employed to study the 3-hinges formation and secondary morphological folds of the developing brain. Three-dimensional (3D) finite element analyses on a multi-layer soft tissue model which mimics a small piece of the brain are performed to investigate the fundamental mechanism of consistent hinge formation in the cortical folding. Results show that after certain amount growth of cortex, mechanical model starts to be unstable and then by formation of creases enters to a new configuration with lower strain energy. By further growth of the model, formed shallow creases start to form convoluted patterns and then develop 3-hinge patterns. Simulation results related to 3-hinges in models show good agreement with experimental observations from macaque, chimpanzee and human brain images. These results have great potential to reveal fundamental principles of brain architecture and to produce a unified theoretical framework that convincingly explains the intrinsic relationship between cortical folding and 3-hinges formation. This achieved fundamental understanding of the intrinsic relationship between cortical folding and 3-hinges formation would potentially shed new insights into the diagnosis of many brain disorders such as schizophrenia, autism, lissencephaly and polymicrogyria.

Keywords: brain, cortical folding, finite element, three hinge

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Authors: Mannan Malik Muhammad Naeem, Jeong Myung Yung

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Functional near infrared spectroscopy (fNIRS) has a favorable position in non-invasive brain imaging techniques. The concentration change of oxygenated hemoglobin and de-oxygenated hemoglobin during particular cognitive activity is the basis for this neuro-imaging modality. Two wavelengths of near-infrared light can be used with modified Beer-Lambert law to explain the indirect status of neuronal activity inside brain. The temporal resolution of fNIRS is very good for real-time brain computer-interface applications. The portability, low cost and an acceptable temporal resolution of fNIRS put it on a better position in neuro-imaging modalities. In this study, an optimization model for impulse response function has been used to estimate/predict initial dip using fNIRS data. In addition, the activity strength parameter related to motor based cognitive task has been analyzed. We found an initial dip that remains around 200-300 millisecond and better localize neural activity.

Keywords: fNIRS, brain-computer interface, optimization algorithm, adaptive signal processing

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Authors: Musa D. Aliyu, Ouahid Harireche, Colin D. Hills

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The incapability of supercritical C02 to transport and dissolve mineral species from the geothermal reservoir to the fracture apertures and other important parameters in heat mining makes it an attractive substance for Heat extraction from hot dry rock. In other words, the thermodynamic efficiency of hot dry rock (HDR) reservoirs also increases if supercritical C02 is circulated at excess temperatures of 3740C without the drawbacks connected with silica dissolution. Studies have shown that circulation of supercritical C02 in homogenous geothermal reservoirs is quite encouraging; in comparison to that of the water. This paper aims at investigating the aforementioned processes in the case of the heterogeneous geothermal reservoir located at the Soultz site (France). The MultiPhysics finite element package COMSOL with an interface of coupling different processes encountered in the geothermal reservoir stimulation is used. A fully coupled numerical model is developed to study the thermal and hydraulic processes in order to predict the long-term operation of the basic reservoir parameters that give optimum energy production. The results reveal that the temperature of the SCC02 at the production outlet is higher than that of water in long-term stimulation; as the temperature is an essential ingredient in rating the energy production. It is also observed that the mass flow rate of the SCC02 is far more favourable compared to that of water.

Keywords: FEM, HDR, heterogeneous reservoir, stimulation, supercritical C02

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Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

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As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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Authors: Sumera, Sanila Amber, Fatima Javed Mirza, Amjad Ali, Saadia Zahid

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Glioblastoma multiforme (GBM) is a widely spread and fatal primary brain tumor with an increased risk of relapse in spite of aggressive treatment. The current procedures for GBM diagnosis include invasive procedures i.e. resection or biopsy, to acquire tumor mass. Implementation of negligibly invasive tests as a potential diagnostic technique and biofluid-based monitoring of GBM stresses on discovering biomarkers in CSF and blood. Therefore, we performed a comprehensive in silico analysis to identify potential circulating biomarkers for GBM. Initially, six gene and protein databases were utilized to mine brain-specific proteins. The resulting proteins were filtered using a channel of five tools to predict the secretory proteins. Subsequently, the expression profile of the secreted proteins was verified in the brain and blood using two databases. Additional verification of the resulting proteins was done using Plasma Proteome Database (PPD) to confirm their presence in blood. The final set of proteins was searched in literature for their relationship with GBM, keeping a special emphasis on secretome proteome. 2145 proteins were firstly mined as brain-specific, out of which 69 proteins were identified as secretory in nature. Verification of expression profile in brain and blood eliminated 58 proteins from the 69 proteins, providing a final list of 11 proteins. Further verification of these 11 proteins further eliminated 2 proteins, giving a final set of nine secretory proteins i.e. OPCML, NPTX1, LGI1, CNTN2, LY6H, SLIT1, CREG2, GDF1 and SERPINI1. Out of these 9 proteins, 7 were found to be linked to GBM, whereas 2 proteins are not investigated in GBM so far. We propose that these secretory proteins can serve as potential circulating biomarker signatures of GBM and will facilitate the development of minimally invasive diagnostic methods and novel therapeutic interventions for GBM.

Keywords: glioblastoma multiforme, secretory proteins, brain secretome, biomarkers

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Authors: Simeon Mayala, Ida Herdlevær, Jonas Bull Haugsøen, Shamundeeswari Anandan, Sonia Gavasso, Morten Brun

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In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the standard gold segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient.

Keywords: brain tumor, brain tumor segmentation, minimum spanning tree, segmentation, image processing

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Authors: Sulaiman B. Ali Alharbi, Bassam H. Mashat, Naif Abdullah Al-Harbi, Milton Wainwright, Abeer S. Aloufi, Sulamain Alnaimat

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Bismuth salicylate was found to inhibit the growth of a range of bacteria and yeast, Candida albican. In general the growth of bacteria did not result in the increase in bismuth solubilisation, in contrast, bismuth solubilisation increased following the growth of C. albicans. A significant increase in the biomass (dry weight) of Aspergillus niger and Aspergillus oryzae occurred in vitro when these fungi were grown in the presence of bismuth salicylate. Biomass increase occurred over a range of bismuth compound additions, which in the case of A. oryzae was associated with the increase in the solubilisation of the insoluble bismuth compounds.

Keywords: bacterial inhibition, fungal growth stimulation, medical uses of bismuth, yeast inhibition

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Authors: Azeddine El-Hassouny, Chandrashekhar Meshram, Geraldin Nanfack

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In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32.

Keywords: label noise, deep learning, discrete latent variable, variational inference, MNIST, CIFAR32

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Authors: Y. Melis, E. Apicella, E. Dozio, L. Mendolicchio

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Introduction: Feeding and Eating Disorders (FED) represent the psychiatric pathology with the highest mortality rate and one of the major disorders with the highest psychiatric and clinical comorbidity. The vagus nerve represents one of the main components of the sympathetic and parasympathetic nervous system and is involved in important neurophysiological functions. In FED, there is a spectrum of symptoms which with TaVNS (Transcutaneous Auricular Vagus Nerve Stimulation) therapy, is possible to have a therapeutic efficacy. Materials and Methods: Sample subjects are composed of 15 female subjects aged > 18 ± 51. Admitted to a psychiatry community having been diagnosed according to DSM-5: anorexia nervosa (AN) (N= 9), bulimia nervosa (BN) (N= 5), binge eating disorder (BED) (N= 1). The protocol included 9 weeks of Ta-VNS stimulation at a frequency of 1.5-3.5 mA for 4 hours per day. The variables detected are the following: Heart Rate Variability (HRV), Hamilton Depression Rating Scale (HAMD-HDRS-17), Body Mass Index (BMI), Beck Anxiety Index (BAI). Results: Data analysis showed statistically significant differences between recording times (p > 0.05) in HAM-D (t0 = 18.28 ± 5.31; t4 = 9.14 ± 7.15), in BAI (t0 = 24.7 ± 10.99; t4 = 13.8 ± 7.0). The reported values show how during (T0-T4) the treatment there is a decay of the degree in the depressive state, in the state of anxiety, and an improvement in the value of BMI. In particular, the BMI in the AN-BN sub-sample had a minimum gain of 5% and a maximum of 11%. The analysis of HRV did not show a clear change among subjects, thus confirming the discordance of the activity of the sympathetic and parasympathetic nervous system in FED. Conclusions: Although the sample does not possess a relevant value to determine long-term efficacy of Ta-VNS or on a larger population, this study reports how the application of neuro-stimulation in FED may become a further approach therapeutic. Indeed, substantial improvements are highlighted in the results and confirmed hypotheses proposed by the study.

Keywords: feeding and eating disorders, neurostimulation, anxiety disorders, depression

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Authors: Kseniya Gladun

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Tactile stimulation of a dorsal side of the wrist can have a strong impact on our attitude toward physical objects such as pleasant and unpleasant impact. This study explored different aspects of tactile perception to investigate atypical touch sensitivity in children with autism spectrum disorder (ASD). This study included 40 children with ASD and 40 healthy children aged 5 to 9 years. We recorded rsEEG (sampling rate of 250 Hz) during 20 min using EEG amplifier “Encephalan” (Medicom MTD, Taganrog, Russian Federation) with 19 AgCl electrodes placed according to the International 10–20 System. The electrodes placed on the left, and right mastoids served as joint references under unipolar montage. The registration of EEG v19 assignments was carried out: frontal (Fp1-Fp2; F3-F4), temporal anterior (T3-T4), temporal posterior (T5-T6), parietal (P3-P4), occipital (O1-O2). Subjects were passively touched by 4 types of tactile stimuli on the left wrist. Our stimuli were presented with a velocity of about 3–5 cm per sec. The stimuli materials and procedure were chosen for being the most "pleasant," "rough," "prickly" and "recognizable". Type of tactile stimulation: Soft cosmetic brush - "pleasant" , Rough shoe brush - "rough", Wartenberg pin wheel roller - "prickly", and the cognitive tactile stimulation included letters by finger (most of the patient’s name ) "recognizable". To designate the moments of the stimuli onset-offset, we marked the moment when the moment of the touch began and ended; the stimulation was manual, and synchronization was not precise enough for event-related measures. EEG epochs were cleaned from eye movements by ICA-based algorithm in EEGLAB plugin for MatLab 7.11.0 (Mathwork Inc.). Muscle artifacts were cut out by manual data inspection. The response to tactile stimuli was significantly different in the group of children with ASD and healthy children, which was also depended on type of tactile stimuli and the severity of ASD. Amplitude of Alpha rhythm increased in parietal region to response for only pleasant stimulus, for another type of stimulus ("rough," "thorny", "recognizable") distinction of amplitude was not observed. Correlation dimension D2 was higher in healthy children compared to children with ASD (main effect ANOVA). In ASD group D2 was lower for pleasant and unpleasant compared to the background in the right parietal area. Hilbert transform changes in the frequency of the theta rhythm found only for a rough tactile stimulation compared with healthy participants only in the right parietal area. Children with autism spectrum disorders and healthy children were responded to tactile stimulation differently with specific frequency distribution alpha and theta band in the right parietal area. Thus, our data supports the hypothesis that rsEEG may serve as a sensitive index of altered neural activity caused by ASD. Children with autism have difficulty in distinguishing the emotional stimuli ("pleasant," "rough," "prickly" and "recognizable").

Keywords: autism, tactile stimulation, Hilbert transform, pediatric electroencephalography

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Authors: Shelly Sharma, Pooja Chadha

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Humans are exposed to pesticides and insecticides either directly or indirectly. Exposure to these pesticides may lead to acute toxicity to mammals and non-target organisms. Chlorpyrifos (CPF) is a broad spectrum organophosphate pesticide widely used in various countries of the world. The aim of the present study was to assess the toxicity associated with chlorpyrifos exposure and possible mitigating effect of cow urine against genotoxic and toxic effects in rat brain induced by chlorpyrifos. For this purpose LD50 was determined and rats were orally administered with 1/8th of LD50 (19mg/kg b.wt). Brain samples were taken after 24hrs, 48hrs and 72hrs of treatment. A significant increase in the % tail DNA was observed along with the increase in MDA levels of brain tissues in chlorpyrifos treated groups as compared to control. Cow urine treated groups show decrease in DNA damage and MDA levels as compared to CPF treated group. The study indicates that cow urine has ameliorative potential against neurotoxicity and genotoxicity induced by CPF. Cow urine is considered rich in vitamin A, E and volatile fatty acids which provide antioxidant potential to it. Thus, it can be used as a genoprotective agent.

Keywords: comet assay, brain, cow urine, genotoxicity, toxicity

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Authors: Raheela Akhtar, Zafar Iqbal Chaudhary, Yongqun Oliver He, Muhammad Younus, Aftab Ahmad Anjum

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Brucella abortus is an intracellular pathogen affecting macrophages. Macrophages release some components such as lysozymes (LZ), reactive oxygen species (ROS) and reactive nitrite intermediates (RNI) which are important tools against intracellular survival of Brucella. The antibrucella activity of bovine and murine macrophages was compared following stimulation with Brucella abortus lipopolysaccharides. Our results revealed that murine macrophages were ten times more potent to produce antibrucella components than bovine macrophages. The differential production of these components explained the differential Brucella killing ability of these species that was measured in terms of intramacrophagic survival of Brucella in murine and bovine macrophages.

Keywords: bovine macrophages, Brucella abortus, cell stimulation, cytokines, Murine macrophages

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Zahra F. Al-Khateeb, Shenel Shekerzade, Hasna Boumenar, Siân M. Henson, Jordi L. Tremoleda, A. T. Michael-Titus

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Traumatic brain injury (TBI) is the leading cause of disability and death in young adults and also increases the risk ofneurodegeneration. The mechanisms linking moderate to severe TBI to neurodegeneration are not known. It has been proposed that cellular senescence inductionpost-injury could amplify neuroinflammation and induce long-term changes. The impact of these processes after injury to an immature brain has not been characterised yet. We carried out a controlled cortical impact injury (CCI) in juvenile 1 month-old male CD1 mice. Animals were anesthetised and received a unilateral CCI injury. The sham group received anaesthesia and had a craniotomy. A naïve group had no intervention. The brain tissue was analysed at 5 days and 35 days post-injury using immunohistochemistry and markers for microglia, astrocytes, and senescence. Compared tonaïve animals, injured mice showed an increased microglial and astrocytic reaction early post-injury, as reflected in Iba1 and GFAP markers, respectively; the GFAP increase persisted in the later phase. The senescence analysis showed a significant increase inγH2AX-53BP1 nuclear foci, 8-oxoguanine, p19ARF, p16INK4a, and p53 expression in naïve vs. sham groups and naïve vs. CCI groups, at 5 dpi. At 35 days, the difference was no longer statistically significant in all markers. The injury induced a decrease p21 expression vs. the naïve group, at 35 dpi. These results indicate the induction of a complex senescence response after immature brain injury. Some changes occur early and may reflect the activation/proliferation of non-neuronal cells post-injury that had been hindered, whereas changes such as p21 downregulation may reflect a delayed response and pro-repair processes.

Keywords: cellular senescence, traumatic brain injury, brain injury, controlled cortical impact

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Authors: Ashkan Shafee, Ahmad Fahimifar

Abstract:

There is a growing demand for construction of high-rise buildings and infrastructures in large cities, which sometimes require deep excavations in the vicinity of pile foundations. In this study, a two-dimensional finite element analysis is used to gain insight into the response of pile groups adjacent to deep excavations in sand. The numerical code was verified by available experimental works, and a parametric study was performed on different working load combinations, excavation depth and supporting system. The results show that the simple two-dimensional plane strain model can accurately simulate the excavation induced changes on adjacent pile groups. It was found that further excavation than pile toe level and also inclined loading on adjacent pile group can severely affect the serviceability of the foundation.

Keywords: deep excavation, inclined loading, lateral deformation, pile group

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Authors: Harry G. Segal

Abstract:

The idea of the “self” has been essential ever since the early modern period in western culture, especially since the development of psychotherapy, but advances in neuroscience and cognitive theory challenge traditional notions of the self. More specifically, neuroanatomists have found no location of “the self” in the brain; instead, consciousness has been posited to be a rapid combination of perception, memory, anticipation of future events, and judgment. In this paper, a theoretical model is presented to address these neuroanatomical findings and to revise the historical understanding of “selfhood” in the practice of psychotherapy.

Keywords: the self, psychotherapy, the self and the brain

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Authors: W. Swiderski

Abstract:

In the paper introduced the diagnostic technique making possible the research of internal structures in composite materials reinforced fibres using in different applications. The main reason of damages in structures of these materials is the changing distribution of load in constructions in the lifetime. Appearing defect is largely complicated because of the appearance of disturbing of continuity of reinforced fibres, binder cracks and loss of fibres adhesiveness from binders. Defect in composite materials is usually more complicated than in metals. At present, infrared thermography is the most effective method in non-destructive testing composite. One of IR thermography methods used in non-destructive evaluation is vibrothermography. The vibrothermography is not a new non-destructive method, but the new solution in this test is use ultrasonic waves to thermal stimulation of materials. In this paper, both modelling and experimental results which illustrate the advantages and limitations of ultrasonic IR thermography in inspecting composite materials will be presented. The ThermoSon computer program for computing 3D dynamic temperature distribuions in anisotropic layered solids with subsurface defects subject to ulrasonic stimulation was used to optimise heating parameters in the detection of subsurface defects in composite materials. The program allows for the analysis of transient heat conduction and ultrasonic wave propagation phenomena in solids. The experiments at MIAT were fulfilled by means of FLIR SC 7600 IR camera. Ultrasonic stimulation was performed with the frequency from 15 kHz to 30 kHz with maximum power up to 2 kW.

Keywords: composite material, ultrasonic, infrared thermography, non-destructive testing

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Authors: Ana Lucia Molina

Abstract:

Introduction: Fine motor skills refer to the use of the hands and coordination of the small muscles that control the fingers. A deficiency in fine motor skills is as important as a change in global movements, as fine motor skills directly affect activities of daily living. Fine movements are involved in some functions, such as motor control of the extremities, sensitivity, strength and tonus of the hands. A growing interest in the effects of non-invasive neuromodulation, such as transcranial stimulation technologies, through transcranial magnetic stimulation (TMS), has been observed in the scientific literature, with promising results in fine motor rehabilitation, as it provides modulation of the corresponding cortical activity in the area primary motor skills of the hands in both hemispheres (according to the International System 10-20, corresponding to C3 and C4). Objectives: to carry out a literature review about the effects of TMS on the cortical motor area corresponding to hand motricity. Methodology: This is a bibliographic survey carried out between October 2022 and March 2023 at Pubmed, Google Scholar, Lillacs and Virtual Health Library (BVS), with a national and international database. Some books on neuromodulation were included. Results: 28 articles and 5 books were initially found, and after reading the abstracts, only 14 articles and 3 books were selected, with publication dates between 2008 and 2022, to compose the literature review since it suited the purpose of this study. Conclusion: TMS has shown promising results in the treatment of fine motor rehabilitation, such as improving coordination, muscle strength and range of motion of the hands, being a complementary technique to existing treatments and thus providing more potent results for manual skills in activities of daily living. It is important to emphasize the need for more specific studies on the application of TMS for the treatment of manual disorders, which describe the uniqueness of each movement.

Keywords: transcranial magnetic stimulation, fine motor skills, motor rehabilitation, non-invasive neuromodulation

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

Abstract:

Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Asmaa El Harat, Toumi Hicham, Youssef Baddi

Abstract:

This survey delves into the complex realm of Internet of Things (IoT) security, highlighting the urgent need for effective cybersecurity measures as IoT devices become increasingly common. It explores a wide array of cyber threats targeting IoT devices and focuses on mitigating these attacks through the combined use of deep learning and machine learning algorithms, as well as edge and cloud computing paradigms. The survey starts with an overview of the IoT landscape and the various types of attacks that IoT devices face. It then reviews key machine learning and deep learning algorithms employed in IoT cybersecurity, providing a detailed comparison to assist in selecting the most suitable algorithms. Finally, the survey provides valuable insights for cybersecurity professionals and researchers aiming to enhance security in the intricate world of IoT.

Keywords: internet of things (IoT), cybersecurity, machine learning, deep learning

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Authors: M. Ghasemi, F. Eskandari, B. Hamzehei, A. R. Arshi

Abstract:

Bioelectric activity of nervous cells might be changed causing by various factors. This alteration can lead to unforeseen circumstances in other organs of the body. Therefore, the purpose of this study was to model a single neuron and its behavior under an initial stimulation. This study was developed based on cable theory by means of the Bond Graph method. The numerical values of the parameters were derived from empirical studies of cellular electrophysiology experiments. Initial excitation was applied through square current functions, and the resulted action potential was estimated along the neuron. The results revealed that the model was developed in this research adapted with the results of experimental studies and demonstrated the electrical behavior of nervous cells properly.

Keywords: bond graph, stimulation, nervous cells, modeling

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