Search results for: dopaminergic neurons

Authors: Yuko Ishiwaka, Tomohiro Yoshida, Tadateru Itoh

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Human makes preparation called readiness potential unconsciously (RP) before awareness of their own decision. For example, when recognizing a button and pressing the button, the RP peaks are observed 200 ms before the initiation of the movement. It has been known that the preparatory movements are acquired before actual movements, but it has not been still well understood how humans can obtain the RP during their growth. On the proposition of why the brain must respond earlier, we assume that humans have to adopt the dangerous environment to survive and then obtain the behavior to cover the various time lags distributed in the body. Without RP, humans cannot take action quickly to avoid dangerous situations. In taking action, the brain makes decisions, and signals are transmitted through the Spinal Cord to the muscles to the body moves according to the laws of physics. Our research focuses on the time lag of the neuron signal transmitting from a brain to muscle via a spinal cord. This time lag is one of the essential factors for readiness potential. We propose a firing pattern controller model of a motor nerve system considered innervation and jitter, which produces time lag. In our simulation, we adopt innervation and jitter in our proposed muscle-skeleton model, because these two factors can create infinitesimal time lag. Q10 Hodgkin Huxley model to calculate action potentials is also adopted because the refractory period produces a more significant time lag for continuous firing. Keeping constant power of muscle requires cooperation firing of motor neurons because a refractory period stifles the continuous firing of a neuron. One more factor in producing time lag is slow or fast-twitch. The Expanded Hill Type model is adopted to calculate power and time lag. We will simulate our model of muscle skeleton model by controlling the firing pattern and discuss the relationship between the time lag of physics and neurons. For our discussion, we analyze the time lag with our simulation for knee bending. The law of inertia caused the most influential time lag. The next most crucial time lag was the time to generate the action potential induced by innervation and jitter. In our simulation, the time lag at the beginning of the knee movement is 202ms to 203.5ms. It means that readiness potential should be prepared more than 200ms before decision making.

Keywords: firing patterns, innervation, jitter, motor nerve system, readiness potential

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Authors: Anjani Kumar Tiwari, Neelam Kumari, Anil Mishra

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The family of phosphodiesterases (PDEs) plays a critical role in control of the level, localization, and duration of intracellular 3’-5’-cyclic adenosine monophosphate (cAMP) and 3’-5’-cyclic guanosine monophosphate (cGMP) signals by specifically hydrolyzing these cyclic nucleotides. As the involvement of cyclic nucleotide second messengers in cell signaling and homeostasis is established, the regulation of these pathways in the brain by various PDE isoforms is an area of considerable interest, as they are involved in nearly all brain functions and in the etiology of neuropsychiatric diseases. The PDE10A isoform, isolated from different species and characterized regarding structure and function, has received much attention in recent years, particularly in the context of schizophrenia and Huntington’s disease, which are both related to a role of PDE10A in the regulation of striatal dopaminergic neurotransmission. Quinazoline analogue 1-(4-methoxyphenyl)-6,7-dimethoxyquinazoline, was evaluated as specific PET marker for phosphodiesterase (PDE) 10A. Here, we report the radiosynthesis of [11C]2 and the in vitro and in vivo evaluation of [11C]2 as a potential positron emission tomography (PET) radiotracer for imaging PDE10A in the central nervous system (CNS). The radiosynthesis of [11C]2 was achieved by O-methylation of the corresponding des-methyl precursor with [11C]methyl iodide. [11C]2 was obtained with ∼50% radiochemical yield. PET imaging studies in rat brain displayed initial specific uptake with very rapid clearance of [11C]2 from brain. Though [11C]2 is not an ideal radioligand for clinical imaging of PDE10A in the CNS. Modified analogue of quinazoline having a higher potency for inhibiting PDE10A and improved pharmacokinetic properties will be necessary for imaging this enzyme with PET.

Keywords: PDE10A, PET, radiotracer, quinazoline

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Authors: M. Khouil, N. Saber, M. Mestari

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In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: collision identification, fixed time, convex polyhedra, neural network, AMAXNET

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Authors: Zongyan Li, Matt Best

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This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated.

Keywords: correlation analysis, F-ratio, levenberg-marquardt, MSE, NARX, neural network, optimisation

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Authors: Yayun Hsu, Henry Horng-Shing Lu

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This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate cross talk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds since biological information is inherently included in the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.

Keywords: brainbow, 3D imaging, image segmentation, neuron morphology, biological data mining, non-parametric learning

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Authors: M. Khouil, N. Saber, M. Mestari

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This study aimed to propose, a different architecture of a Path Planning using the NECMOP. where several nonlinear objective functions must be optimized in a conflicting situation. The ability to detect and avoid collision is very important for mobile intelligent machines. However, many artificial vision systems are not yet able to quickly and cheaply extract the wealth information. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons linear and threshold logic, which simplified the actual implementation of all the networks proposed. This article represents a comprehensive algorithm that determine through the AMAXNET network a measure (a mini-maximum point) in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: path planning, collision detection, convex polyhedron, neural network

Procedia PDF Downloads 421

Authors: Jon-Chao Hong, Tsai-Ru Fan, Shih-Min Hsu

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Virtual reality (VR) can generate mirror neurons that facilitate learners to transfer virtual skills to a real environment in skill training, and most studies approved the positive effect of applying in many domains. However, rare studies have focused on the experiential values of participants from a gender perspective. To address this issue, the present study used a VR program named kitchen assistant training, focusing on cutting vegetables and invited 400 students to practice for 20 minutes. Useful data from 367 were subjected to statistical analysis. The results indicated that male participants. From the comparison of average, it seems that females perceived higher than males in learning effectiveness. Expectedly, the VR-Cutting vegetables can be used for pre-training of real vegetable cutting.

Keywords: exponential value, facilitate learning, gender difference, virtual reality

Procedia PDF Downloads 78

Authors: Ravid Inbar

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CaMKII (calcium-calmodulin dependent protein kinase II) makes up 2% of the protein in our brain and has a critical role in memory formation and long-term potentiation of neurons. Despite this, research has yet to uncover the role of one of the domains on the activation of this kinase. The following proposes to express the protein without the hub domain in E. coli, leaving only the kinase and regulatory segment of the protein. Next, a series of kinase assays will be conducted to elucidate the role the hub domain plays on CaMKII sensitivity to calcium/calmodulin activation. The hub domain may be important for activation; however, it may also be a variety of domains working together to influence protein activation and not the hub alone. Characterization of a protein is critical to the future understanding of the protein's function, as well as for producing pharmacological targets in cases of patients with diseases.

Keywords: CaMKII, hub domain, kinase assays, kinase + reg seg

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Sandy Bao, Yankang Bao

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The understanding of information that is being processed in the brain, especially in the lateral geniculate nucleus (LGN), has been proven challenging for modern neuroscience and for researchers with a focus on how neurons process signals and images. In this paper, we are proposing a method to image process different colors within different layers of LGN, that is, green information in layers 4 & 6 and red & blue in layers 3 & 5 based on the surface dimension of layers. We take into consideration the images in LGN and visual cortex, and that the edge detected information from the visual cortex needs to be considered in order to return back to the layers of LGN, along with the image in LGN to form the new image, which will provide an improved image that is clearer, sharper, and making it easier to identify objects in the image. Matrix Laboratory (MATLAB) simulation is performed, and results show that the clarity of the output image has significant improvement.

Keywords: lateral geniculate nucleus, matrix laboratory, neuroscience, visual cortex

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Authors: Leila Noori, Rosario Barone, Francesca Rappa, Antonella Marino Gammazza, Alessandra Maria Vitale, Giuseppe Donato Mangano, Giusy Sentiero, Filippo Macaluso, Kathryn H. Myburgh, Francesco Cappello, Federica Scalia

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The neuromuscular system controls, directs, and allows movement of the body through the action of neural circuits, which include motor neurons, sensory neurons, and skeletal muscle fibers. Protein homeostasis of the involved cytotypes appears crucial to maintain the correct and prolonged functions of the neuromuscular system, and both neuronal cells and skeletal muscle fibers express significant quantities of protein chaperones, the molecular machinery responsible to maintain the protein turnover. Genetic mutations or defective post-translational modifications of molecular chaperones (i.e., genetic or acquired chaperonopathies) may lead to neuromuscular disorders called as neurochaperonopathies. The limited knowledge of the effects of the defective chaperones on skeletal muscle fibers and neurons impedes the progression of therapeutic approaches. A distinct genetic variation of CCT5 gene encoding for the subunit 5 of the chaperonin CCT (Chaperonin Containing TCP1; also known as TRiC, TCP1 Ring Complex) was recently described associated with severe distal motor neuropathy by our team. In this study, we investigated the histopathological abnormalities of the skeletal muscle biopsy of the pediatric patient affected by the mutation Leu224Val in the CCT5 subunit. We provide molecular and structural features of the diseased skeletal muscle tissue that we believe may be useful to identify undiagnosed cases of this rare genetic disorder. We investigated the histological abnormalities of the affected tissue via hematoxylin and eosin staining. Then we used immunofluorescence and qPCR techniques to explore the expression and distribution of CCT5 in diseased and healthy skeletal muscle tissue. Immunofluorescence and immunohistochemistry assays were performed to study the sarcomeric and structural proteins of skeletal muscle, including actin, myosin, tubulin, troponin-T, telethonin, and titin. We performed Western blot to examine the protein expression of CCT5 and some heat shock proteins, Hsp90, Hsp60, Hsp27, and α-B crystallin, along with the main client proteins of the CCT5, actin, and tubulin. Our findings revealed muscular atrophy, abnormal morphology, and different sizes of muscle fibers in affected tissue. The swollen nuclei and wide interfiber spaces were seen. Expression of CCT5 had been decreased and showed a different distribution pattern in the affected tissue. Altered expression, distribution, and bandage pattern were detected by confocal microscopy for the interested muscular proteins in tissue from the patient compared to the healthy control. Protein levels of the studied Hsps normally located at the Z-disk were reduced. Western blot results showed increased levels of the actin and tubulin proteins in the diseased skeletal muscle biopsy compared to healthy tissue. Chaperones must be expressed at high levels in skeletal muscle to counteract various stressors such as mechanical, oxidative, and thermal crises; therefore, it seems relevant that defects of molecular chaperones may result in damaged skeletal muscle fibers. So far, several chaperones or cochaperones involved in neuromuscular disorders have been defined. Our study shows that alteration of the CCT5 subunit is associated with the damaged structure of skeletal muscle fibers and alterations of chaperone system components and paves the way to explore possible alternative substrates of chaperonin CCT. However, further studies are underway to investigate the CCT mechanisms of action to design applicable therapeutic strategies.

Keywords: molecular chaperones, neurochaperonopathy, neuromuscular system, protein homeostasis

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Authors: Maha J. Balgoon, Gehan A. Raouf, Safaa Y. Qusti, Soad S. Ali

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The main cause of Alzheimer disease (AD) was believed to be mainly due to the accumulation of free radicals owing to oxidative stress (OS) in brain tissue. The mechanism of the neurotoxicity of Aluminum chloride (AlCl3) induced AD in hippocampus Albino wister rat brain tissue, the curative & the protective effects of Lipidium sativum group (LS) water extract were assessed after 8 weeks by attenuated total reflection spectroscopy ATR-IR and histologically by light microscope. ATR-IR results revealed that the membrane phospholipid undergo free radical attacks, mediated by AlCl3, primary affects the polyunsaturated fatty acids indicated by the increased of the olefinic -C=CH sub-band area around 3012 cm-1 from the curve fitting analysis. The narrowing in the half band width(HBW) of the sνCH2 sub-band around 2852 cm-1 due to Al intoxication indicates the presence of trans form fatty acids rather than gauch rotomer. The degradation of hydrocarbon chain to shorter chain length, increasing in membrane fluidity, disorder and decreasing in lipid polarity in AlCl3 group were indicated by the detected changes in certain calculated area ratios compared to the control. Administration of LS was greatly improved these parameters compared to the AlCl3 group. Al influences the Aβ aggregation and plaque formation, which in turn interferes to and disrupts the membrane structure. The results also showed a marked increase in the β-parallel and antiparallel structure, that characterize the Aβ formation in Al-induced AD hippocampal brain tissue, indicated by the detected increase in both amide I sub-bands around 1674, 1692 cm-1. This drastic increase in Aβ formation was greatly reduced in the curative and protective groups compared to the AlCl3 group and approaches nearly the control values. These results were supported too by the light microscope. AlCl3 group showed significant marked degenerative changes in hippocampal neurons. Most cells appeared small, shrieked and deformed. Interestingly, the administration of LS in curative and protective groups markedly decreases the amount of degenerated cells compared to the non-treated group. Also the intensity of congo red stained cells was decreased. Hippocampal neurons looked more/or less similar to those of control. This study showed a promising therapeutic effect of Lipidium sativum group (LS) on AD rat model that seriously overcome the signs of oxidative stress on membrane lipid and restore the protein misfolding.

Keywords: aluminum chloride, alzheimer disease, ATR-IR, Lipidium sativum

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Authors: Urvi Panwar, Kanchan Mishra, Kanjaksha Ghosh, ShankerLal Kothari

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Background: Day-by-day the increasing prevalence of neurodegenerative diseases have become a global issue to manage them by medical sciences. The adult neural stem cells are rare and require an invasive and painful procedure to obtain it from central nervous system. Mesenchymal stem cell (MSCs) therapies have shown remarkable application in treatment of various cell injuries and cell loss. MSCs can be derived from various sources like adult tissues, human bone marrow, umbilical cord blood and cord tissue. MSCs have similar proliferation and differentiation capability, but the human umbilical cord-derived mesenchymal stem cells (hUCMSCs) are proved to be more beneficial with respect to cell procurement, differentiation to other cells, preservation, and transplantation. Material and method: Human umbilical cord is easily obtainable and non-controversial comparative to bone marrow and other adult tissues. The umbilical cord can be collected after delivery of baby, and its tissue can be cultured using explant culture method. Cell culture medium such as DMEMF12+10% FBS and DMEMF12+Neural growth factors (bFGF, human noggin, B27) with antibiotics (Streptomycin/Gentamycin) were used to culture and differentiate mesenchymal stem cells into neural cells, respectively. The characterisations of MSCs were done with Flow Cytometer for surface markers CD90, CD73 and CD105 and colony forming unit assay. The differentiated various neural cells will be characterised by fluorescence markers for neurons, astrocytes, and oligodendrocytes; quantitative PCR for genes Nestin and NeuroD1 and Western blotting technique for gap43 protein. Result and discussion: The high quality and number of MSCs were isolated from human umbilical cord via explant culture method. The obtained MSCs were differentiated into neural cells like neurons, astrocytes and oligodendrocytes. The differentiated neural cells can be used to treat neural injuries and neural cell loss by delivering cells by non-invasive administration via cerebrospinal fluid (CSF) or blood. Moreover, the MSCs can also be directly delivered to different injured sites where they differentiate into neural cells. Therefore, human umbilical cord is demonstrated to be an inexpensive and easily available source for MSCs. Moreover, the hUCMSCs can be a potential source for neural cell therapies and neural cell regeneration for neural cell injuries and neural cell loss. This new way of research will be helpful to treat and manage neural cell damages and neurodegenerative diseases like Alzheimer and Parkinson. Still the study has a long way to go but it is a promising approach for many neural disorders for which at present no satisfactory management is available.

Keywords: bone marrow, cell therapy, explant culture method, flow cytometer, human umbilical cord, mesenchymal stem cells, neurodegenerative diseases, neuroprotective, regeneration

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Authors: Stylianos Kampakis

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This paper presents a new algorithm for neural networks called “Input Dropout with Aggressive Re-weighting” (IDAR) aimed specifically at datasets with many useless features. IDAR combines two techniques (dropout of input neurons and aggressive re weighting) in order to eliminate the influence of noisy features. The technique can be seen as a generalization of dropout. The algorithm is tested on two different benchmark data sets: a noisy version of the iris dataset and the MADELON data set. Its performance is compared against three other popular techniques for dealing with useless features: L2 regularization, LASSO and random forests. The results demonstrate that IDAR can be an effective technique for handling data sets with many useless features.

Keywords: neural networks, feature selection, regularization, aggressive reweighting

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Authors: Mahdi Akhbardeh

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Synchronization of neuronal firing is a hallmark of several neurological diseases. Recently, stimulation techniques have been developed which make it possible to desynchronize oscillatory neuronal activity in a mild and effective way, without suppressing the neurons' firing. As yet, these techniques are being used to establish demand-controlled deep brain stimulation (DBS) techniques for the therapy of movement disorders like severe Parkinson's disease or essential tremor. We here present a first conceptualization suggesting that the nucleus accumbens is a promising target for the standard, that is, permanent high-frequency, DBS in patients with severe and chronic obsessive-compulsive disorder (OCD). In addition, we explain how demand-controlled DBS techniques may be applied to the therapy of OCD in those cases that are refractory to behavioral therapies and pharmacological treatment.

Keywords: stereotactic neurosurgery, deep brain stimulation, obsessive-compulsive disorder, phase resetting

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Amin Alvani

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Multiple sclerosis (MS) is a chronic, often disabling disease in which the immune system attacks the myelin sheath of neurons in the central nervous system. The present study aimed to investigate the Relationship between behavioral inhibition/approach system (BIS-BAS) and perceived stress (PS) whit control white blood cell (WBC). 60 MS patients (male=36.7, female=63.3%; age range=15-65 participated in the study and completed the demographic questionnaire, the count blood cell (CBC) test, the behavioral Activation and behavioral inhibition scale (BIS-BAS), and the perceived stress Questionnaire (PSS-14). The results revealed that Between of BAS-reward responsiveness (BAS-DR) subscale and PS, in more than MS patient (BIS), there are increase WBC.

Keywords: behavioral inhibition/approach system, perceived stress, white blood cell, multiple sclerosis

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Authors: Thomas E. Portegys

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An animal behavior problem is presented in the form of a nest-building task that involves two cooperating virtual birds, a male and female. The female builds a nest into which she lays an egg. The male's job is to forage in a forest for food for both himself and the female. In addition, the male must fetch stones from a nearby desert for the female to use as nesting material. The task is completed when the nest is built, and an egg is laid in it. A goal-seeking neural network and a recurrent neural network were trained and tested with little success. The goal-seeking network was then enhanced with “place cells”, allowing the birds to spatially navigate the world, building the nest while keeping themselves fed. Place cells are neurons in the hippocampus that map space.

Keywords: artificial animal intelligence, artificial life, goal-seeking neural network, nest-building, place cells, spatial navigation

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Authors: W. Soufi, F. Boukli Hacene, S. Ghalem

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Alzheimer's disease (AD) is a neurodegenerative disease that leads to a progressive and permanent deterioration of nerve cells. This disease is progressively accompanied by an intellectual deterioration leading to psychological manifestations and behavioral disorders that lead to a loss of autonomy. It is the most frequent of degenerative dementia. Alzheimer's disease (AD), which affects a growing number of people, has become a major public health problem in a few years. In the context of the study of the mechanisms governing the evolution of AD disease, we have found that natural flavonoids are good acetylcholinesterase inhibitors that reduce the rate of ßA secretion in neurons. This work is to study the inhibition of acetylcholinesterase (AChE) which is an enzyme involved in Alzheimer's disease, by methods of molecular modeling. These results will probably help in the development of an effective therapeutic tool in the fight against the development of Alzheimer's disease. Our goal of the research is to study the inhibition of acetylcholinesterase (AChE) by molecular modeling methods.

Keywords: Alzheimer's disease, acetylcholinesterase, flavonoids, molecular modeling

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Authors: Isaac Cassar, Trevor Davis, Yi-Kai Lo, Wentai Liu

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Retinal prostheses have been successful in eliciting visual responses in implanted subjects. As these prostheses progress, one of their major limitations is the need for increased resolution. As an alternative to increasing the number of electrodes, virtual electrodes may be used to increase the effective resolution of current electrode arrays. This paper presents a virtual electrode technique based upon time-offsets between stimuli. Two adjacent electrodes are stimulated with identical pulses with too short of pulse widths to activate a neuron, but one has a time offset of one pulse width. A virtual electrode of twice the pulse width was then shown to appear in the center, with a total width capable of activating a neuron. This can be used in retinal implants by stimulating electrodes with pulse widths short enough to not elicit responses in neurons, but with their combined pulse width adequate to activate a neuron in between them.

Keywords: electrical stimulation, neuroprosthesis, retinal implant, retinal prosthesis, virtual electrode

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Authors: Akanksha Bhatnagar, Sandhya Kortegare, Felice Elefant

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Context: Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by progressive cognitive decline and memory loss. The cause of AD is not fully understood, but it is thought to be caused by a combination of genetic, environmental, and lifestyle factors. One of the hallmarks of AD is the loss of neurons in the hippocampus, a brain region that is important for memory and learning. This loss of neurons is thought to be caused by a decrease in histone acetylation, which is a process that regulates gene expression. Research Aim: The research aim of the study was to develop mall molecule compounds that can enhance the activity of Tip60, a histone acetyltransferase that is important for memory and learning. Methodology/Analysis: The researchers used in silico structural modeling and a pharmacophore-based virtual screening approach to design and synthesize small molecule compounds strongly predicted to target and enhance Tip60’s HAT activity. The compounds were then tested in vitro and in vivo to assess their ability to enhance Tip60 activity and rescue cognitive deficits in AD models. Findings: The researchers found that several of the compounds were able to enhance Tip60 activity and rescue cognitive deficits in AD models. The compounds were also developed to cross the blood-brain barrier, which is an important factor for the development of potential AD therapeutics. Theoretical Importance: The findings of this study suggest that Tip60 HAT activators have the potential to be developed as therapeutic agents for AD. The compounds are specific to Tip60, which suggests that they may have fewer side effects than other HDAC inhibitors. Additionally, the compounds are able to cross the blood-brain barrier, which is a major hurdle for the development of AD therapeutics. Data Collection: The study collected data from a variety of sources, including in vitro assays and animal models. The in vitro assays assessed the ability of compounds to enhance Tip60 activity using histone acetyltransferase (HAT) enzyme assays and chromatin immunoprecipitation assays. Animal models were used to assess the ability of the compounds to rescue cognitive deficits in AD models using a variety of behavioral tests, including locomotor ability, sensory learning, and recognition tasks. The human clinical trials will be used to assess the safety and efficacy of the compounds in humans. Questions: The question addressed by this study was whether Tip60 HAT activators could be developed as therapeutic agents for AD. Conclusions: The findings of this study suggest that Tip60 HAT activators have the potential to be developed as therapeutic agents for AD. The compounds are specific to Tip60, which suggests that they may have fewer side effects than other HDAC inhibitors. Additionally, the compounds are able to cross the blood-brain barrier, which is a major hurdle for the development of AD therapeutics. Further research is needed to confirm the safety and efficacy of these compounds in humans.

Keywords: Alzheimer's disease, cognition, neuroepigenetics, drug discovery

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Authors: Laidi Maamar, Hanini Salah

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The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

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Authors: Gül Fatma Türker, Hatice Akman

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Portable muscle stimulators for real-time applications has first introduced by Liberson in 1961. Now these systems has been advanced. In this study, FES (Functional Electrical Stimulator) and NMES (Neuromuscular Electrostimulator) systems are analyzed through their hardware and their quality of life improvements for foot drop patients. FES and NMES systems are used for people whose leg muscles and leg neural connections are healty but not able to walk properly because of their injured central nervous system like spinal cord injuries. These systems are used to stimulate neurons or muscles by getting information from other movements and programming these stimulations to get natural walk and it is accepted as a rehabilitation method for the correction of drop foot. This systems support person to approach natural form of walking. Foot drop is characterized by steppage gait. It is a gait abnormality. This systems helps to person for plantar and dorse reflection movements which are hard to done for foot drop patients.

Keywords: FES, foot drop, NMES, stimulator

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Ilker Ali Ozkan

Abstract:

In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

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Authors: Reihane Mehrparvar

Abstract:

Human age could exceed further by altering gene expression through food intaking, although as a consequence of recent century eating patterns, human life-span getting shorter by emerging irregulating in autophagy mechanism, insulin, leptin, gut microbiota which are important etiological factors of type-2 diabetes, obesity, infertility, cancer, metabolic and autoimmune diseases. However, restricted calorie intake and vigorous exercise might be beneficial for losing weight and metabolic regulation in a short period but could not be implementable in the long term as a way of life. Therefore, the lack of a dietary program that is compatible with the genes of the body is essential. Sweet and high-glycemic-index (HGI) foods were associated with type-2 diabetes and cancer morbidity. The neuropsychological perspective characterizes the inclination of sweet and HGI-food consumption as addictive behavior; hence this process engages preference of gut microbiota, neural node, and dopaminergic functions. Moreover, meal composition is not the only factor that affects body hemostasis. In this narrative review, it is believed to attempt to investigate how the body responded to different food intakes and represent an accurate model based on current evidence. Eating frequently and ingesting unassimilable protein and carbohydrates may not be compatible with human genes and could cause impairments in the self-renovation mechanism. This trajectory indicates our body is more adapted to starvation and eating animal meat and marrow. Here has been recommended a model that takes into account three important factors: frequent eating, meal composition, and circadian rhythm, which may offer a promising intervention for obesity, inflammation, cardiovascular, autoimmune disorder, type-2 diabetes, insulin resistance, infertility, and cancer through intensifying autophagy-mechanism and eliminate medical costs.

Keywords: metabolic disease, anti-aging, type-2 diabetes, autophagy

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Authors: M. Testa, L. Peotta, S. Giusiano, B. Lazzolino, U. Manera, A. Canosa, M. Grassano, F. Palumbo, A. Bombaci, S. Cabras, F. Di Pede, L. Solero, E. Matteoni, C. Moglia, A. Calvo, A. Chio

Abstract:

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease of adulthood, which primarily affects the central nervous system and is characterized by progressive bilateral degeneration of motor neurons. The degeneration processes in ALS extend far beyond the neurons of the motor system, and affects cognition, behaviour and language. To outline the prevalence of language deficits in an ALS cohort and explore their profile along with demographic and neuropsychological data. A full neuropsychological battery and language assessment was administered to 56 ALS patients. Neuropsychological assessment included tests of executive functioning, verbal fluency, social cognition and memory. Language was assessed using tests for verbal comprehension, production and pragmatics. Patients were cognitively classified following the Revised Consensus Criteria and divided in three groups showing different levels of language deficits: group 1 - no language deficit; group 2 - one language deficit; group 3 - two or more language deficits. Chi-square for independence and non-parametric measures to compare groups were applied. Nearly half of ALS-CN patients (48%) reported one language test under the clinical cut-off, and only 13% of patents classified as ALS-CI showed no language deficits, while the rest 87% of ALS-CI reported two or more language deficits. ALS-BI and ALS-CBI cases all reported two or more language deficits. Deficits in production and in comprehension appeared more frequent in ALS-CI patients (p=0.011, p=0.003 respectively), with a higher percentage of comprehension deficits (83%). Nearly all ALS-CI reported at least one deficit in pragmatic abilities (96%) and all ALS-BI and ALS-CBI patients showed pragmatic deficits. Males showed higher percentage of pragmatic deficits (97%, p=0.007). No significant differences in language deficits have been found between bulbar and spinal onset. Months from onset and level of impairment at testing (ALS-FRS total score) were not significantly different between levels and type of language impairment. Age and education were significantly higher for cases showing no deficits in comprehension and pragmatics and in the group showing no language deficits. Comparing performances at neuropsychological tests among the three levels of language deficits, no significant differences in neuropsychological performances were found between group 1 and 2; compared to group 1, group 3 appeared to decay specifically on executive testing, verbal/visuospatial learning, and social cognition. Compared to group 2, group 3 showed worse performances specifically in tests of working memory and attention. Language deficits have found to be spread in our sample, encompassing verbal comprehension, production and pragmatics. Our study reveals that also cognitive intact patients (ALS-CN) showed at least one language deficit in 48% of cases. Pragmatic domain is the most compromised (84% of the total sample), present in nearly all ALS-CI (96%), likely due to the influence of executive impairment. Lower age and higher education seem to preserve comprehension, pragmatics and presence of language deficits. Finally, executive functions, verbal/visuospatial learning and social cognition differentiate the group with no language deficits from the group with a clinical language impairment (group 3), while attention and working memory differentiate the group with one language deficit from the clinical impaired group.

Keywords: amyotrophic lateral sclerosis, language assessment, neuropsychological assessment, language deficit

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Authors: Cristiano Giordani, Marco Diociaiuti, Cecilia Bombelli, Laura Zanetti-Polzi, Marcello Belfiore, Raoul Fioravanti, Gianfranco Macchia

Abstract:

We investigated induced functional effects by evaluating Ca2+-influx in liposomes and cell viability in HT22-DIFF neurons. Only solutions rich in unstructured Prefibrillar-Oligomers (PFOs) were able, in the presence of Monosialoganglioside-GM1 (GM1), to induce Ca2+-influx and were also neurotoxic, suggesting a correlation between the two phenomena. Thus, in the presence of GM1, we investigated the protein conformation and liposome modification due to the interaction. Circular Dichroism showed that GM1 fostered the formation of β-structures and Energy Filtered-Transmission Electron Microscopy that PFOs formed “amyloid-channels” as reported for Aβ. We speculate that electrostatic forces occurring between the positive PFOs and negative GM1 drive the initial binding, while the hydrophobic profile of the flexible PFO is responsible for the subsequent pore formation. Conversely, the rigid β-structured mature/fibers (MFs) and proto-fibers (PFs) were unable to induce membrane damage and Ca2+- influx.

Keywords: amyloid proteins, neurotoxicity, lipid-rafts, GM1

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Authors: S. Mousavian, A. Abedianpour, A. Khanmohammadi, S. Hematian, Gh. Eidi Veisi

Abstract:

Ionic liquids are finding a wide range of applications from reaction media to separations and materials processing. In these applications, Vapor–Liquid equilibrium (VLE) is the most important one. VLE for six systems at 353 K and activity coefficients at infinite dilution 〖(γ〗_i^∞) for various solutes (alkanes, alkenes, cycloalkanes, cycloalkenes, aromatics, alcohols, ketones, esters, ethers, and water) in the ionic liquids (1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide [EMIM][BTI], 1-hexyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide [HMIM][BTI], 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide [OMIM][BTI], and 1-butyl-1-methylpyrrolidinium bis (trifluoromethylsulfonyl) imide [BMPYR][BTI]) have been used to train neural networks in the temperature range from (303 to 333) K. Densities of the ionic liquids, Hildebrant constant of substances, and temperature were selected as input of neural networks. The networks with different hidden layers were examined. Networks with seven neurons in one hidden layer have minimum error and good agreement with experimental data.

Keywords: ionic liquid, neural networks, VLE, dilute solution

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