Search results for: neural tube defects

Authors: Shankha Sanyal, Archi Banerjee, Sayan Biswas, Sourya Sengupta, Sayan Nag, Ranjan Sengupta, Dipak Ghosh

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The term ‘gestalt’ has been widely used in the field of psychology which defined the perception of human mind to group any object not in part but as a 'unified' whole. Music, in general, is polyphonic - i.e. a combination of a number of pure tones (frequencies) mixed together in a manner that sounds harmonious. The study of human brain response due to different frequency groups of the acoustic signal can give us an excellent insight regarding the neural and functional architecture of brain functions. Hence, the study of music cognition using neuro-biosensors is becoming a rapidly emerging field of research. In this work, we have tried to analyze the effect of different frequency bands of music on the various frequency rhythms of human brain obtained from EEG data. Four widely popular Rabindrasangeet clips were subjected to Wavelet Transform method for extracting five resonant frequency bands from the original music signal. These frequency bands were initially analyzed with Detrended/Adaptive Fractal analysis (DFA/AFA) methods. A listening test was conducted on a pool of 100 respondents to assess the frequency band in which the music becomes non-recognizable. Next, these resonant frequency bands were presented to 20 subjects as auditory stimulus and EEG signals recorded simultaneously in 19 different locations of the brain. The recorded EEG signals were noise cleaned and subjected again to DFA/AFA technique on the alpha, theta and gamma frequency range. Thus, we obtained the scaling exponents from the two methods in alpha, theta and gamma EEG rhythms corresponding to different frequency bands of music. From the analysis of music signal, it is seen that loss of recognition is proportional to the loss of long range correlation in the signal. From the EEG signal analysis, we obtain frequency specific arousal based response in different lobes of brain as well as in specific EEG bands corresponding to musical stimuli. In this way, we look to identify a specific frequency band beyond which the music becomes non-recognizable and below which in spite of the absence of other bands the music is perceivable to the audience. This revelation can be of immense importance when it comes to the field of cognitive music therapy and researchers of creativity.

Keywords: AFA, DFA, EEG, gestalt in music, Hurst exponent

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Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

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Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya

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Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.

Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa

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Authors: Sara Tokhi Arab, Rozo Noguchi, Tofeal Ahamed

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Drought is a natural disaster that occurs in a region due to a lack of precipitation and high temperatures over a continuous period or in a single season as a consequence of climate change. Precipitation deficits and prolonged high temperatures mostly affect the agricultural sector, water resources, socioeconomics, and the environment. Consequently, it causes agricultural product loss, food shortage, famines, migration, and natural resources degradation in a region. Agriculture is the first sector affected by drought. Therefore, it is important to develop an agricultural drought risk and loss assessment to mitigate the drought impact in the agriculture sector. In this context, the main purpose of this study was to assess yield loss using composite drought indices in the drought-affected vineyards. In this study, the CDI was developed for the years 2016 to 2020 by comprising five indices: the vegetation condition index (VCI), temperature condition index (TCI), deviation of NDVI from the long-term mean (NDVI DEV), normalized difference moisture index (NDMI) and precipitation condition index (PCI). Moreover, the quantitative principal component analysis (PCA) approach was used to assign a weight for each input parameter, and then the weights of all the indices were combined into one composite drought index. Finally, Bayesian regularized artificial neural networks (BRANNs) were used to evaluate the yield variation in each affected vineyard. The composite drought index result indicated the moderate to severe droughts were observed across the Kabul Province during 2016 and 2018. Moreover, the results showed that there was no vineyard in extreme drought conditions. Therefore, we only considered the severe and moderated condition. According to the BRANNs results R=0.87 and R=0.94 in severe drought conditions for the years of 2016 and 2018 and the R= 0.85 and R=0.91 in moderate drought conditions for the years of 2016 and 2018, respectively. In the Kabul Province within the two years drought periods, there was a significate deficit in the vineyards. According to the findings, 2018 had the highest rate of loss almost -7 ton/ha. However, in 2016 the loss rates were about – 1.2 ton/ha. This research will support stakeholders to identify drought affect vineyards and support farmers during severe drought.

Keywords: grapes, composite drought index, yield loss, satellite remote sensing

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Authors: Mohammad Khazaei, Reza Rostami, Hasan Gharayagh Zandi, Rouhollah Basatnia, Mahbubeh Ghayour Najafabadi

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Objective: Methylphenidate has been demonstrated to enhance attention and cognitive processes, and placebo treatments have also been found to improve attention and cognitive processes. Additionally, methylphenidate may have positive effects on motion perception and sports performance. Nevertheless, additional research is needed to fully comprehend the neural mechanisms underlying the effects of methylphenidate and placebo on cognitive and motor functions. Methods: In this randomized controlled trial, 18 young semi-professional basketball players aged 18-23 years were randomly and equally assigned to either a Ritalin or Placebo group. The participants performed 20 consecutive free throws; their scores were recorded on a 0-3 scale. The participants’ brain activity was recorded using electroencephalography (EEG) for 5 minutes seated with their eyes closed. The Ritalin group received a 10 mg dose of methylphenidate, while the Placebo group received a 10mg dose of placebo. The EEG was obtained 90 minutes after the drug was administere Results: There was no significant difference in the absolute power of brain waves between the pre-test and post-tests in the Placebo group. However, in the Ritalin group, a significant difference in the absolute power of brain waves was observed in the Theta band (5-6 Hz) and Beta band (21-30 Hz) between pre- and post-tests in Fp2, F8, and Fp1. In these areas, the absolute power of Beta waves was higher during the post-test than during the pre-test. The Placebo group showed a more significant difference in free throw scores than the Ritalin group. Conclusions: In conclusion, these results suggest that Ritalin effect on brain activity in areas associated with attention and cognitive processes, as well as improve basketball free throws. However, there was no significant placebo effect on brain activity performance, but it significantly affected the improvement of free throws. Further research is needed to fully understand the effects of methylphenidate and placebo on cognitive and motor functions.

Keywords: methylphenidate, placebo effect, electroencephalography, basketball free throw

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Authors: Martin Cvek, Miroslav Mrlik, Michal Sedlacik, Tomas Plachy

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Magnetorheological elastomers (MREs) are multi-phase composite materials containing micron-sized ferromagnetic particles dispersed in an elastomeric matrix. Their properties such as modulus, damping, magneto-striction, and electrical conductivity can be controlled by an external magnetic field and/or pressure. These features of the MREs are used in the development of damping devices, shock attenuators, artificial muscles, sensors or active elements of electric circuits. However, imperfections on the particle/matrix interfaces result in the lower performance of the MREs when compared with theoretical values. Moreover, magnetic particles are susceptible to corrosion agents such as acid rains or sea humidity. Therefore, the modification of particles is an effective tool for the improvement of MRE performance due to enhanced compatibility between particles and matrix as well as improvements of their thermo-oxidation and chemical stability. In this study, the carbonyl iron (CI) particles were controllably modified with poly(trimethylsilyloxyethyl methacrylate) (PHEMATMS) nano-grafts to develop magnetic core–shell structures exhibiting proper wetting with various elastomeric matrices resulting in improved performance within a frame of rheological, magneto-piezoresistance, pressure-piezoresistance, or radio-absorbing properties. The desired molecular weight of PHEMATMS nano-grafts was precisely tailored using surface-initiated atom transfer radical polymerization (ATRP). The CI particles were firstly functionalized using a 3-aminopropyltriethoxysilane agent, followed by esterification reaction with α-bromoisobutyryl bromide. The ATRP was performed in the anisole medium using ethyl α-bromoisobutyrate as a macroinitiator, N, N´, N´´, N´´-pentamethyldiethylenetriamine as a ligand, and copper bromide as an initiator. To explore the effect PHEMATMS molecular weights on final properties, two variants of core-shell structures with different nano-graft lengths were synthesized, while the reaction kinetics were designed through proper reactant feed ratios and polymerization times. The PHEMATMS nano-grafts were characterized by nuclear magnetic resonance and gel permeation chromatography proving information to their monomer conversions, molecular chain lengths, and low polydispersity indexes (1.28 and 1.35) as the results of the executed ATRP. The successful modifications were confirmed via Fourier transform infrared- and energy-dispersive spectroscopies while expected wavenumber outputs and element presences, respectively, of constituted PHEMATMS nano-grafts, were occurring in the spectra. The surface morphology of bare CI and their PHEMATMS-grafted analogues was further studied by scanning electron microscopy, and the thicknesses of grafted polymeric layers were directly observed by transmission electron microscopy. The contact angles as a measure of particle/matrix compatibility were investigated employing the static sessile drop method. The PHEMATMS nano-grafts enhanced compatibility of hydrophilic CI with low-surface-energy hydrophobic polymer matrix in terms of their wettability and dispersibility in an elastomeric matrix. Thus, the presence of possible defects at the particle/matrix interface is reduced, and higher performance of modified MREs is expected.

Keywords: atom transfer radical polymerization, core-shell, particle modification, wettability

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Authors: Narin Ozturk, Xiao-Mei Li, Sylvie Giachetti, Francis Levi, Alper Okyar

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P-glycoprotein (P-gp, MDR1, ABCB1) is a transmembrane protein acting as an ATP-dependent efflux pump and functions as a biological barrier by extruding drugs and xenobiotics out of cells in healthy tissues especially in intestines, liver and brain as well as in tumor cells. The circadian timing system controls a variety of biological functions in mammals including xenobiotic metabolism and detoxification, proliferation and cell cycle events, and may affect pharmacokinetics, toxicity and efficacy of drugs. Selective mTOR (mammalian target of rapamycin) inhibitor everolimus is an immunosuppressant and anticancer drug that is active against many cancers, and its pharmacokinetics depend on P-gp. The aim of this study was to investigate the dosing time-dependent toxicity of everolimus with respect to the intestinal P-gp expression rhythms in mdr1a::Luc mice using Real Time-Biolumicorder (RT-BIO) System. Mdr1a::Luc male mice were synchronized with 12 h of Light and 12 h of Dark (LD12:12, with Zeitgeber Time 0 – ZT0 – corresponding Light onset). After 1-week baseline recordings, everolimus (5 mg/kg/day x 14 days) was administered orally at ZT1-resting period- and ZT13-activity period- to mdr1a::Luc mice singly housed in an innovative monitoring device, Real Time-Biolumicorder units which let us monitor real-time and long-term gene expression in freely moving mice. D-luciferin (1.5 mg/mL) was dissolved in drinking water. Mouse intestinal mdr1a::Luc oscillation profile reflecting P-gp gene expression and locomotor activity pattern were recorded every minute with the photomultiplier tube and infrared sensor respectively. General behavior and clinical signs were monitored, and body weight was measured every day as an index of toxicity. Drug-induced body weight change was expressed relative to body weight on the initial treatment day. Statistical significance of differences between groups was validated with ANOVA. Circadian rhythms were validated with Cosinor Analysis. Everolimus toxicity changed as a function of drug timing, which was least following dosing at ZT13, near the onset of the activity span in male mice. Mean body weight loss was nearly twice as large in mice treated with 5 mg/kg everolimus at ZT1 as compared to ZT13 (8.9% vs. 5.4%; ANOVA, p < 0.001). Based on the body weight loss and clinical signs upon everolimus treatment, tolerability for the drug was best following dosing at ZT13. Both rest-activity and mdr1a::Luc expression displayed stable 24-h periodic rhythms before everolimus and in both vehicle-treated controls. Real-time bioluminescence pattern of mdr1a revealed a circadian rhythm with a 24-h period with an acrophase at ZT16 (Cosinor, p < 0.001). Mdr1a expression remained rhythmic in everolimus-treated mice, whereas down-regulation was observed in P-gp expression in 2 of 4 mice. The study identified the circadian pattern of intestinal P-gp expression with an unprecedented precision. The circadian timing depending on the P-gp expression rhythms may play a crucial role in the tolerability/toxicity of everolimus. The circadian changes in mdr1a genes deserve further studies regarding their relevance for in vitro and in vivo chronotolerance of mdr1a-transported anticancer drugs. Chronotherapy with P-gp-effluxed anticancer drugs could then be applied according to their rhythmic patterns in host and tumor to jointly maximize treatment efficacy and minimize toxicity.

Keywords: circadian rhythm, chronotoxicity, everolimus, mdr1a::Luc mice, p-glycoprotein

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Authors: Sarayu Vanga, Jorge Galeano-Cabral, Kaya Wei

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Cerebral hypoxia refers to a condition in which there is a decrease in oxygen supply to the brain. Patients suffering from this condition experience a decrease in their body temperature. While there isn't any cure to treat cerebral hypoxia as of date, certain procedures are utilized to help aid in the treatment of the condition. Regulating the body temperature is an example of one of those procedures. Hypoxia is well known to reduce the body temperature of mammals, although the neural origins of this response remain uncertain. In order to speed recovery from this condition, it is necessary to maintain a stable body temperature. In this study, we present an approach to regulating body temperature for patients who suffer from cerebral hypoxia or other similar conditions. After a thorough literature study, we propose the use of thermoelectric blankets, which are temperature-controlled thermal blankets based on thermoelectric devices. These blankets are capable of heating up and cooling down the patient to stabilize body temperature. This feature is possible through the reversible effect that thermoelectric devices offer while behaving as a thermal sensor, and it is an effective way to stabilize temperature. Thermoelectricity is the direct conversion of thermal to electrical energy and vice versa. This effect is now known as the Seebeck effect, and it is characterized by the Seebeck coefficient. In such a configuration, the device has cooling and heating sides with temperatures that can be interchanged by simply switching the direction of the current input in the system. This design integrates various aspects, including a humidifier, ventilation machine, IV-administered medication, air conditioning, circulation device, and a body temperature regulation system. The proposed design includes thermocouples that will trigger the blanket to increase or decrease a set temperature through a medical temperature sensor. Additionally, the proposed design allows an efficient way to control fluctuations in body temperature while being cost-friendly, with an expected cost of 150 dollars. We are currently working on developing a prototype of the design to collect thermal and electrical data under different conditions and also intend to perform an optimization analysis to improve the design even further. While this proposal was developed for treating cerebral hypoxia, it can also aid in the treatment of other related conditions, as fluctuations in body temperature appear to be a common symptom that patients have for many illnesses.

Keywords: body temperature regulation, cerebral hypoxia, thermoelectric, blanket design

Procedia PDF Downloads 146

Authors: Shan Zhang, Peter Suechting

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Predicting trade patterns is critical to decision-making in public and private domains, especially in the current context of trade disputes among major economies. In the past, U.S. recycling has relied heavily on strong demand for recyclable materials overseas. However, starting in 2017, a series of new recycling policies (bans and higher inspection standards) was enacted by multiple countries that were the primary importers of recyclables from the U.S. prior to that point. As the global trade flow of recycling shifts, some new importers, mostly developing countries in South and Southeast Asia, have been overwhelmed by the sheer quantities of scrap materials they have received. As the leading exporter of recyclable materials, the U.S. now has a pressing need to build its recycling industry domestically. With respect to the global trade in scrap materials used for recycling, the interest in this paper is (1) predicting how the export of recyclable materials from the U.S. might vary over time, and (2) predicting how international trade flows for recyclables might change in the future. Focusing on three major recyclable materials with a history of trade, this study uses data-driven and machine learning (ML) algorithms---supervised (shrinkage and tree methods) and unsupervised (neural network method)---to decipher the international trade pattern of recycling. Forecasting the potential trade values of recyclables in the future could help importing countries, to which those materials will shift next, to prepare related trade policies. Such policies can assist policymakers in minimizing negative environmental externalities and in finding the optimal amount of recyclables needed by each country. Such forecasts can also help exporting countries, like the U.S understand the importance of healthy domestic recycling industry. The preliminary result suggests that gravity models---in addition to particular selection macroeconomic predictor variables--are appropriate predictors of the total export value of recyclables. With the inclusion of variables measuring aspects of the political conditions (trade tariffs and bans), predictions show that recyclable materials are shifting from more policy-restricted countries to less policy-restricted countries in international recycling trade. Those countries also tend to have high manufacturing activities as a percentage of their GDP.

Keywords: environmental economics, machine learning, recycling, international trade

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Authors: Kareem Abdelwahed, Ahmed Salama, Ahmed Rabie, Ahmed Hamdy, Waleed Abdelfattah, Ahmed Abd El-Rahman

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Conventional vapor-compression refrigeration systems rely on typical refrigerants, such as CFC, HCFC and ammonia. Despite of their suitable thermodynamic properties and their stability in the atmosphere, their corresponding global warming potential and ozone depletion potential raise concerns about their usage. Thus, the need for new refrigeration systems, which are environment-friendly, inexpensive and simple in construction, has strongly motivated the development of thermoacoustic energy conversion systems. A thermoacoustic refrigerator (TAR) is a device that is mainly consisting of a resonator, a stack and two heat exchangers. Typically, the resonator is a long circular tube, made of copper or steel and filled with Helium as a the working gas, while the stack has short and relatively low thermal conductivity ceramic parallel plates aligned with the direction of the prevailing resonant wave. Typically, the resonator of a standing-wave refrigerator has one end closed and is bounded by the acoustic driver at the other end enabling the propagation of half-wavelength acoustic excitation. The hot and cold heat exchangers are made of copper to allow for efficient heat transfer between the working gas and the external heat source and sink respectively. TARs are interesting because they have no moving parts, unlike conventional refrigerators, and almost no environmental impact exists as they rely on the conversion of acoustic and heat energies. Their fabrication process is rather simpler and sizes span wide variety of length scales. The viscous and thermal interactions between the stack plates, heat exchangers' plates and the working gas significantly affect the flow field within the plates' channels, and the energy flux density at the plates' surfaces, respectively. Here, the design, the manufacture and the testing of a compact refrigeration system that is based on the thermoacoustic energy-conversion technology is reported. A 1-D linear acoustic model is carefully and specifically developed, which is followed by building the hardware and testing procedures. The system consists of two harmonically-oscillating pistons driven by a simple 1-HP rotary drive mechanism operating at a frequency of 42Hz -hereby, replacing typical expensive linear motors and loudspeakers-, and a thermoacoustic stack within which the energy conversion of sound into heat is taken place. Air at ambient conditions is used as the working gas while the amplitude of the driver's displacement reaches 19 mm. The 30-cm-long stack is a simple porous ceramic material having 100 square channels per square inch. During operation, both oscillating-gas pressure and solid-stack temperature are recorded for further analysis. Measurements show a maximum temperature difference of about 27 degrees between the stack hot and cold ends with a Carnot coefficient of performance of 11 and estimated cooling capacity of five Watts, when operating at ambient conditions. A dynamic pressure of 7-kPa-amplitude is recorded, yielding a drive ratio of 7% approximately, and found in a good agreement with theoretical prediction. The system behavior is clearly non-linear and significant non-linear loss mechanisms are evident. This work helps understanding the operation principles of thermoacoustic refrigerators and presents a keystone towards developing commercial thermoacoustic refrigerator units.

Keywords: refrigeration system, rotary drive mechanism, standing-wave, thermoacoustic refrigerator

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Authors: Yanjun Chen, Chuanping Hu, Jie Shao, Lin Mei, Chongyang Zhang

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Traditional image classification is based on encoding scheme (e.g. Fisher Vector, Vector of Locally Aggregated Descriptor) with low-level image features (e.g. SIFT, HoG). Compared to these low-level local features, deep convolutional features obtained at the mid-level layer of convolutional neural networks (CNN) have richer information but lack of geometric invariance. For scene classification, there are scattered objects with different size, category, layout, number and so on. It is crucial to find the distinctive objects in scene as well as their co-occurrence relationship. In this paper, we propose a method to take advantage of both deep convolutional features and the traditional encoding scheme while taking object-centric and scene-centric information into consideration. First, to exploit the object-centric and scene-centric information, two CNNs that trained on ImageNet and Places dataset separately are used as the pre-trained models to extract deep convolutional features at multiple scales. This produces dense local activations. By analyzing the performance of different CNNs at multiple scales, it is found that each CNN works better in different scale ranges. A scale-wise CNN adaption is reasonable since objects in scene are at its own specific scale. Second, a fisher kernel is applied to aggregate a global representation at each scale and then to merge into a single vector by using a post-processing method called scale-wise normalization. The essence of Fisher Vector lies on the accumulation of the first and second order differences. Hence, the scale-wise normalization followed by average pooling would balance the influence of each scale since different amount of features are extracted. Third, the Fisher vector representation based on the deep convolutional features is followed by a linear Supported Vector Machine, which is a simple yet efficient way to classify the scene categories. Experimental results show that the scale-specific feature extraction and normalization with CNNs trained on object-centric and scene-centric datasets can boost the results from 74.03% up to 79.43% on MIT Indoor67 when only two scales are used (compared to results at single scale). The result is comparable to state-of-art performance which proves that the representation can be applied to other visual recognition tasks.

Keywords: deep convolutional features, Fisher Vector, multiple scales, scale-specific normalization

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: C. Bel, J. Nevado, F. Ciceri, M. Ropacki, T. Hoffmann, P. Lapunzina, C. Buesa

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Background: The Phelan-McDermid syndrome (PMS) is a genetic disorder caused by the deletion of the terminal region of chromosome 22 or mutation of the SHANK3 gene. Shank3 disruption in mice leads to dysfunction of synaptic transmission, which can be restored by epigenetic regulation with both Lysine Specific Demethylase 1 (LSD1) inhibitors. PMS subjects result in a variable degree of intellectual disability, delay or absence of speech, autistic spectrum disorders symptoms, low muscle tone, motor delays and epilepsy. Vafidemstat is an LSD1 inhibitor in Phase II clinical development with a well-established and favorable safety profile, and data supporting the restoration of memory and cognition defects as well as reduction of agitation and aggression in several animal models and clinical studies. Therefore, vafidemstat has the potential to become a first-in-class precision medicine approach to treat PMS patients. Aims: The goal of this research is to perform an observational trial to psychometrically characterize individuals carrying deletions in SHANK3 and build a foundation for subsequent precision psychiatry clinical trials with vafidemstat. Methodology: This study is characterizing the clinical profile of 20 to 40 subjects, > 16-year-old, with genotypically confirmed PMS diagnosis. Subjects will complete a battery of neuropsychological scales, including the Repetitive Behavior Questionnaire (RBQ), Vineland Adaptive Behavior Scales, Escala de Observación para el Diagnostico del Autismo (Autism Diagnostic Observational Scale) (ADOS)-2, the Battelle Developmental Inventory and the Behavior Problems Inventory (BPI). Results: By March 2021, 19 patients have been enrolled. Unsupervised hierarchical clustering of the results obtained so far identifies 3 groups of patients, characterized by different profiles of cognitive and behavioral scores. The first cluster is characterized by low Battelle age, high ADOS and low Vineland, RBQ and BPI scores. Low Vineland, RBQ and BPI scores are also detected in the second cluster, which in contrast has high Battelle age and low ADOS scores. The third cluster is somewhat in the middle for the Battelle, Vineland and ADOS scores while displaying the highest levels of aggression (high BPI) and repeated behaviors (high RBQ). In line with the observation that female patients are generally affected by milder forms of autistic symptoms, no male patients are present in the second cluster. Dividing the results by gender highlights that male patients in the third cluster are characterized by a higher frequency of aggression, whereas female patients from the same cluster display a tendency toward higher repetitive behavior. Finally, statistically significant differences in deletion sizes are detected comparing the three clusters (also after correcting for gender), and deletion size appears to be positively correlated with ADOS and negatively correlated with Vineland A and C scores. No correlation is detected between deletion size and the BPI and RBQ scores. Conclusions: Precision medicine may open a new way to understand and treat Central Nervous System disorders. Epigenetic dysregulation has been proposed to be an important mechanism in the pathogenesis of schizophrenia and autism. Vafidemstat holds exciting therapeutic potential in PMS, and this study will provide data regarding the optimal endpoints for a future clinical study to explore vafidemstat ability to treat shank3-associated psychiatric disorders.

Keywords: autism, epigenetics, LSD1, personalized medicine

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Authors: Badriah Alayidi, Emad Alyahya

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Background: Most people with Multiple Sclerosis (MS) report worsening balance as the condition progresses. Poor balance control is also well known to be a significant risk factor for both falling and fear of falling. The increased risk of falls with disease progression thus makes balance control an essential target of gait rehabilitation amongst people with MS. Intervention programs have developed various methods to improve balance control, and accumulating evidence suggests that exercise programs may help people with MS improve their balance. Among these methods, virtual reality (VR) is growing in popularity as a balance-training technique owing to its potential benefits, including better compliance and greater user happiness. However, it is not clear if a VR environment will induce different balance control mechanisms in MS as compared to healthy individuals or traditional environments. Therefore, this study aims to examine how individuals with MS control their balance in a VR setting. Methodology: The proposed study takes an empirical approach to estimate and determine the role of balance response in persons with MS using a VR environment. It will use primary data collected through patient observations, physiological and biomechanical evaluation of balance, and data analysis. Results: The preliminary systematic review and meta-analysis indicated that there was variability in terms of the outcome assessing balance response in people with MS. The preliminary results of these assessments have the potential to provide essential indicators of the progression of MS and contribute to the individualization of treatment and evaluation of the interventions’ effectiveness. The literature describes patients who have had the opportunity to experiment in VR settings and then used what they have learned in the real world, suggesting that this VR setting could be more appealing than conditional settings. The findings of the proposed study will be beneficial in estimating and determining the effect of VR on balance control in persons with MS. In previous studies, VR was shown to be an interesting approach to neurological rehabilitation, but more data are needed to support this approach in MS. Conclusions: The proposed study enables an assessment of balance and evaluations of a variety of physiological implications related to neural activity as well as biomechanical implications related to movement analysis.

Keywords: multiple sclerosis, virtual reality, postural control, balance

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Authors: Man-Yun Liu, Emily Chia-Yu Su

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Alzheimer's disease (AD) is the public health crisis of the 21st century. AD is a degenerative brain disease and the most common cause of dementia, a costly disease on the healthcare system. Unfortunately, the cause of AD is poorly understood, furthermore; the treatments of AD so far can only alleviate symptoms rather cure or stop the progress of the disease. Currently, there are several ways to diagnose AD; medical imaging can be used to distinguish between AD, other dementias, and early onset AD, and cerebrospinal fluid (CSF). Compared with other diagnostic tools, blood (plasma) test has advantages as an approach to population-based disease screening because it is simpler, less invasive also cost effective. In our study, we used blood biomarkers dataset of The Alzheimer’s disease Neuroimaging Initiative (ADNI) which was funded by National Institutes of Health (NIH) to do data analysis and develop a prediction model. We used independent analysis of datasets to identify plasma protein biomarkers predicting early onset AD. Firstly, to compare the basic demographic statistics between the cohorts, we used SAS Enterprise Guide to do data preprocessing and statistical analysis. Secondly, we used logistic regression, neural network, decision tree to validate biomarkers by SAS Enterprise Miner. This study generated data from ADNI, contained 146 blood biomarkers from 566 participants. Participants include cognitive normal (healthy), mild cognitive impairment (MCI), and patient suffered Alzheimer’s disease (AD). Participants’ samples were separated into two groups, healthy and MCI, healthy and AD, respectively. We used the two groups to compare important biomarkers of AD and MCI. In preprocessing, we used a t-test to filter 41/47 features between the two groups (healthy and AD, healthy and MCI) before using machine learning algorithms. Then we have built model with 4 machine learning methods, the best AUC of two groups separately are 0.991/0.709. We want to stress the importance that the simple, less invasive, common blood (plasma) test may also early diagnose AD. As our opinion, the result will provide evidence that blood-based biomarkers might be an alternative diagnostics tool before further examination with CSF and medical imaging. A comprehensive study on the differences in blood-based biomarkers between AD patients and healthy subjects is warranted. Early detection of AD progression will allow physicians the opportunity for early intervention and treatment.

Keywords: Alzheimer's disease, blood-based biomarkers, diagnostics, early detection, machine learning

Procedia PDF Downloads 316

Authors: Brennan Lodge

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Amidst a complex regulatory landscape, Retrieval Augmented Generation (RAG) emerges as a transformative tool for Governance Risk and Compliance (GRC) officers. This paper details the application of RAG in synthesizing Large Language Models (LLMs) with external knowledge bases, offering GRC professionals an advanced means to adapt to rapid changes in compliance requirements. While the development for standalone LLM’s (Large Language Models) is exciting, such models do have their downsides. LLM’s cannot easily expand or revise their memory, and they can’t straightforwardly provide insight into their predictions, and may produce “hallucinations.” Leveraging a pre-trained seq2seq transformer and a dense vector index of domain-specific data, this approach integrates real-time data retrieval into the generative process, enabling gap analysis and the dynamic generation of compliance and risk management content. We delve into the mechanics of RAG, focusing on its dual structure that pairs parametric knowledge contained within the transformer model with non-parametric data extracted from an updatable corpus. This hybrid model enhances decision-making through context-rich insights, drawing from the most current and relevant information, thereby enabling GRC officers to maintain a proactive compliance stance. Our methodology aligns with the latest advances in neural network fine-tuning, providing a granular, token-level application of retrieved information to inform and generate compliance narratives. By employing RAG, we exhibit a scalable solution that can adapt to novel regulatory challenges and cybersecurity threats, offering GRC officers a robust, predictive tool that augments their expertise. The granular application of RAG’s dual structure not only improves compliance and risk management protocols but also informs the development of compliance narratives with pinpoint accuracy. It underscores AI’s emerging role in strategic risk mitigation and proactive policy formation, positioning GRC officers to anticipate and navigate the complexities of regulatory evolution confidently.

Keywords: cybersecurity, gen AI, retrieval augmented generation, cybersecurity defense strategies

Procedia PDF Downloads 86

Authors: Glauco M. M. M. Lustosa, João Paulo C. Costa, Sonia M. Zanetti, Mario Cilense, Leinig Antônio Perazolli, Maria Aparecida Zaghete

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The contamination of the environment has been one of the biggest problems of our time, mostly due to developments of many industries. SnO2 is an n-type semiconductor with band gap about 3.5 eV and has its electrical conductivity dependent of type and amount of modifiers agents added into matrix ceramic during synthesis process, allowing applications as sensing of gaseous pollutants on ambient. The chemical synthesis by polymeric precursor method consists in a complexation reaction between tin ion and citric acid at 90 °C/2 hours and subsequently addition of ethyleneglycol for polymerization at 130 °C/2 hours. It also prepared polymeric resin of zinc, cobalt and niobium ions. Stoichiometric amounts of the solutions were mixed to obtain the systems (Zn, Nb)-SnO2 and (Co, Nb) SnO2 . The metal immobilization reduces its segregation during the calcination resulting in a crystalline oxide with high chemical homogeneity. The resin was pre-calcined at 300 °C/1 hour, milled in Atritor Mill at 500 rpm/1 hour, and then calcined at 600 °C/2 hours. X-Ray Diffraction (XDR) indicated formation of SnO2 -rutile phase (JCPDS card nº 41-1445). The characterization by Scanning Electron Microscope of High Resolution showed spherical ceramic powder nanostructured with 10-20 nm of diameter. 20 mg of SnO2 -based powder was kept in 20 ml of isopropyl alcohol and then taken to an electrophoretic deposition (EPD) system. The EPD method allows control the thickness films through the voltage or current applied in the electrophoretic cell and by the time used for deposition of ceramics particles. This procedure obtains films in a short time with low costs, bringing prospects for a new generation of smaller size devices with easy integration technology. In this research, films were obtained in an alumina substrate with interdigital electrodes after applying 2 kV during 5 and 10 minutes in cells containing alcoholic suspension of (Zn, Nb)-SnO2 and (Co, Nb) SnO2 of powders, forming a sensing layer. The substrate has designed integrated micro hotplates that provide an instantaneous and precise temperature control capability when a voltage is applied. The films were sintered at 900 and 1000 °C in a microwave oven of 770 W, adapted by the research group itself with a temperature controller. This sintering is a fast process with homogeneous heating rate which promotes controlled growth of grain size and also the diffusion of modifiers agents, inducing the creation of intrinsic defects which will change the electrical characteristics of SnO2 -based powders. This study has successfully demonstrated a microfabricated system with an integrated micro-hotplate for detection of CO and NO2 gas at different concentrations and temperature, with self-heating SnO2 - based nanoparticles films, being suitable for both industrial process monitoring and detection of low concentrations in buildings/residences in order to safeguard human health. The results indicate the possibility for development of gas sensors devices with low power consumption for integration in portable electronic equipment with fast analysis. Acknowledgments The authors thanks to the LMA-IQ for providing the FEG-SEM images, and the financial support of this project by the Brazilian research funding agencies CNPq, FAPESP 2014/11314-9 and CEPID/CDMF- FAPESP 2013/07296-2.

Keywords: chemical synthesis, electrophoretic deposition, self-heating, gas sensor

Procedia PDF Downloads 269

Authors: A. Schander, T. Tessmann, H. Stemmann, S. Strokov, A. Kreiter, W. Lang

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For the chronic application of neural prostheses and other brain-computer interfaces, long-term stable microelectrodes for electrical stimulation are essential. In recent years many developments were done to investigate different appropriate materials for these electrodes. One of these materials is the electrical conductive polymer poly(3,4-ethylenedioxythiophene) (PEDOT), which has lower impedance and higher charge injection capacity compared to noble metals like gold and platinum. However the long-term stability of this polymer is still unclear. Thus this paper reports on the in-vitro evaluation of the long-term stability of PEDOT coated gold microelectrodes. For this purpose a highly flexible electrocorticography (ECoG) electrode array, based on the polymer polyimide, is used. This array consists of circular gold electrodes with a diameter of 560 µm (0.25 mm2). In total 25 electrodes of this array were coated simultaneously with the polymer PEDOT:PSS in a cleanroom environment using a galvanostatic electropolymerization process. After the coating the array is additionally sterilized using a steam sterilization process (121°C, 1 bar, 20.5 min) to simulate autoclaving prior to the implantation of such an electrode array. The long-term measurements were performed in phosphate-buffered saline solution (PBS, pH 7.4) at the constant body temperature of 37°C. For the in-vitro electrical stimulation a one channel bipolar current stimulator is used. The stimulation protocol consists of a bipolar current amplitude of 5 mA (cathodal phase first), a pulse duration of 100 µs per phase, a pulse pause of 50 µs and a frequency of 1 kHz. A PEDOT:PSS coated gold electrode with an area of 1 cm2 serves as the counter electrode. The electrical stimulation is performed continuously with a total amount of 86.4 million bipolar current pulses per day. The condition of the PEDOT coated electrodes is monitored in between with electrical impedance spectroscopy measurements. The results of this study demonstrate that the PEDOT coated electrodes are stable for more than 3.6 billion bipolar current pulses. Also the unstimulated electrodes show currently no degradation after the time period of 5 months. These results indicate an appropriate long-term stability of this electrode coating for chronic recording and electrical stimulation. The long-term measurements are still continuing to investigate the life limit of this electrode coating.

Keywords: chronic recording, electrical stimulation, long-term stability, microelectrodes, PEDOT

Procedia PDF Downloads 581

Authors: Matthew Baucum

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With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.

Keywords: FMRI, machine learning, meta-analysis, text analysis

Procedia PDF Downloads 440

Authors: Farhana Alam Bhuiyan, Saad Khan, Tanveer Hassan, Jhalok Ranjon Talukder, Syeda Farjana Ahmed, Rahil Roodsaz, Els Rommes, Sabina Faiz Rashid

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Breaking the shame’ is a 3 year (2015-2018) qualitative implementation research project which investigates several aspects of sexual and reproductive health and rights (SRHR) issues for adolescents living in Bangladesh. Scope of learning SRHR issues for adolescents is limited here due to cultural and religious taboos. This study adds to the ongoing discussions around adolescent’s SRHR needs and aims to, 1) understand the overall SRHR needs of urban and rural unmarried female and male adolescents and the challenges they face, 2) explore existing gaps in the content of SRHR curriculum and 3) finally, addresses some critical knowledge gaps by developing and implementing innovative SRHR educational materials. 18 in-depth interviews (IDIs) and 10 focus-group discussions (FGDs) with boys and 21 IDIs and 14 FGDs with girls of ages 13-19, from both urban and rural setting took place. Curriculum materials from two leading organizations, Unite for Body Rights (UBR) Alliance Bangladesh and BRAC Adolescent Development Program (ADP) were also reviewed, with discussions with 12 key program staff. This paper critically analyses the relevance of some of the SRHR topics that are covered, the challenges with existing pedagogic approaches and key sexuality issues that are not covered in the content, but are important for adolescents. Adolescents asked for content and guidance on a number of topics which remain missing from the core curriculum, such as emotional coping mechanisms particularly in relationships, bullying, impact of exposure to porn, and sexual performance anxiety. Other core areas of concern were effects of masturbation, condom use, sexual desire and orientation, which are mentioned in the content, but never discussed properly, resulting in confusion. Due to lack of open discussion around sexuality, porn becomes a source of information for the adolescents. For these reasons, several myths and misconceptions regarding SRHR issues like body, sexuality, agency, and gender roles still persist. The pedagogical approach is very didactic, and teachers felt uncomfortable to have discussions on certain SRHR topics due to cultural taboos or shame and stigma. Certain topics are favored- such as family planning, menstruation- and presented with an emphasis on biology and risk. Rigid formal teaching style, hierarchical power relations between students and most teachers discourage questions and frank conversations. Pedagogy approaches within classrooms play a critical role in the sharing of knowledge. The paper also describes the pilot approaches to implementing new content in SRHR curriculum. After a review of findings, three areas were selected as critically important, 1) myths and misconceptions 2) emotional management challenges, and 3) how to use condom, that have come up from adolescents. Technology centric educational materials such as web page based information platform and you tube videos are opted for which allow adolescents to bypass gatekeepers and learn facts and information from a legitimate educational site. In the era of social media, when information is always a click away, adolescents need sources that are reliable and not overwhelming. The research aims to ensure that adolescents learn and apply knowledge effectively, through creating the new materials and making it accessible to adolescents.

Keywords: adolescents, Bangladesh, media, sexuality education, unmet needs

Procedia PDF Downloads 219

Authors: Bahar Hazal Yalçınkaya, Bayram Yılmaz, Mustafa Özilgen

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Brain information transmission in the neuronal network occurs in the form of electrical signals. Neural work transmits information between the neurons or neurons and target cells by moving charged particles in a voltage field; a fraction of the energy utilized in this process is dissipated via entropy generation. Exergy loss and entropy generation models demonstrate the inefficiencies of the communication along the dendritic trees. In this study, neurons of 4 different animals were analyzed with one dimensional cable model with N=6 identical dendritic trees and M=3 order of symmetrical branching. Each branch symmetrically bifurcates in accordance with the 3/2 power law in an infinitely long cylinder with the usual core conductor assumptions, where membrane potential is conserved in the core conductor at all branching points. In the model, exergy loss and entropy generation rates are calculated for each branch of equivalent cylinders of electrotonic length (L) ranging from 0.1 to 1.5 for four different dendritic branches, input branch (BI), and sister branch (BS) and two cousin branches (BC-1 & BC-2). Thermodynamic analysis with the data coming from two different cat motoneuron studies show that in both experiments nearly the same amount of exergy is lost while generating nearly the same amount of entropy. Guinea pig vagal motoneuron loses twofold more exergy compared to the cat models and the squid exergy loss and entropy generation were nearly tenfold compared to the guinea pig vagal motoneuron model. Thermodynamic analysis show that the dissipated energy in the dendritic tress is directly proportional with the electrotonic length, exergy loss and entropy generation. Entropy generation and exergy loss show variability not only between the vertebrate and invertebrates but also within the same class. Concurrently, single action potential Na⁺ ion load, metabolic energy utilization and its thermodynamic aspect contributed for squid giant axon and mammalian motoneuron model. Energy demand is supplied to the neurons in the form of Adenosine triphosphate (ATP). Exergy destruction and entropy generation upon ATP hydrolysis are calculated. ATP utilization, exergy destruction and entropy generation showed differences in each model depending on the variations in the ion transport along the channels.

Keywords: ATP utilization, entropy generation, exergy loss, neuronal information transmittance

Procedia PDF Downloads 388

Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

Procedia PDF Downloads 279

Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

Procedia PDF Downloads 121

Authors: Marcos Perez, Christian Dumont, Olivier Nodin, Sebastien Nouveau

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Nickel superalloys are used to manufacture high-temperature rotary engine parts such as high-pressure disks in gas turbine engines. High strength at high operating temperatures is required due to the levels of stress and heat the disk must withstand. Therefore it is necessary parts made from materials that can maintain mechanical strength at high temperatures whilst remain comparatively low in cost. A manufacturing process referred to as the triple melt process has made the production of cast and wrought (C&W) nickel superalloys possible. This means that the balance of cost and performance at high temperature may be optimized. AD730TM is a newly developed Ni-based superalloy for turbine disk applications, with reported superior service properties around 700°C when compared to Inconel 718 and several other alloys. The cast ingot is converted into billet during either cogging process or open die forging. The semi-finished billet is then further processed into its final geometry by forging, heat treating, and machining. Conventional ingot-to-billet conversion is an expensive and complex operation, requiring a significant amount of steps to break up the coarse as-cast structure and interdendritic regions. Due to the size of conventional ingots, it is difficult to achieve a uniformly high level of strain for recrystallization, resulting in non-recrystallized regions that retain large unrecrystallized grains. Non-uniform grain distributions will also affect the ultrasonic inspectability response, which is used to find defects in the final component. The main aim is to analyze the recrystallization behavior and microstructural evolution of AD730 at subsolvus temperatures from a semi-finished product (billet) under conditions representative of both cogging and hot forging operations. Special attention to the presence of large unrecrystallized grains was paid. Double truncated cones (DTCs) were hot forged at subsolvus temperatures in hydraulic press, followed by air cooling. SEM and EBSD analysis were conducted in the as-received (billet) and the as-forged conditions. AD730 from billet alloy presents a complex microstructure characterized by a mixture of several constituents. Large unrecrystallized grains present a substructure characterized by large misorientation gradients with the formation of medium to high angle boundaries in their interior, especially close to the grain boundaries, denoting inhomogeneous strain distribution. A fine distribution of intragranular precipitates was found in their interior, playing a key role on strain distribution and subsequent recrystallization behaviour during hot forging. Continuous dynamic recrystallization (CDRX) mechanism was found to be operating in the large unrecrystallized grains, promoting the formation intragranular DRX grains and the gradual recrystallization of these grains. Evidences that hetero-epitaxial recrystallization mechanism is operating in AD730 billet material were found. Coherent γ-shells around primary γ’ precipitates were found. However, no significant contribution to the overall recrystallization during hot forging was found. By contrast, strain presents the strongest effect on the microstructural evolution of AD730, increasing the recrystallization fraction and refining the structure. Regions with low level of deformation (ε ≤ 0.6) were translated into large fractions of unrecrystallized structures (strain accumulation). The presence of undissolved secondary γ’ precipitates (pinning effect), prior to hot forging operations, could explain these results.

Keywords: AD730 alloy, continuous dynamic recrystallization, hot forging, γ’ precipitates

Procedia PDF Downloads 194

Authors: Valentina Marutyan

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Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

Procedia PDF Downloads 67

Authors: Evan Lowhorn, Rocio Alba-Flores

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Traditional classification Convolutional Neural Networks (CNN) attempt to classify an image in its entirety. This becomes problematic when trying to perform classification with a drone’s camera in real-time due to unpredictable backgrounds. Object detectors with bounding boxes can be used to isolate individuals and other items, but the original backgrounds remain within these boxes. These basic detectors have been regularly used to determine what type of object an item is, such as “person” or “dog.” Recent advancement in computer vision, particularly with human imaging, is keypoint detection. Human keypoint detection goes beyond bounding boxes to fully isolate humans and plot points, or Regions of Interest (ROI), on their bodies within an image. ROIs can include shoulders, elbows, knees, heads, etc. These points can then be related to each other and used in deep learning methods such as pose estimation. For drone control based on human motions, poses, or signals using the onboard camera, it is important to have a simple method for pilot identification among multiple individuals while also giving the pilot fine control options for the drone. To achieve this, the OpenPose keypoint detection network was used with body and hand keypoint detection enabled. OpenPose supports the ability to combine multiple keypoint detection methods in real-time with a single network. Body keypoint detection allows simple poses to act as the pilot identifier. The hand keypoint detection with ROIs for each finger can then offer a greater variety of signal options for the pilot once identified. For this work, the individual must raise their non-control arm to be identified as the operator and send commands with the hand on their other arm. The drone ignores all other individuals in the onboard camera feed until the current operator lowers their non-control arm. When another individual wish to operate the drone, they simply raise their arm once the current operator relinquishes control, and then they can begin controlling the drone with their other hand. This is all performed mid-flight with no landing or script editing required. When using a desktop with a discrete NVIDIA GPU, the drone’s 2.4 GHz Wi-Fi connection combined with OpenPose restrictions to only body and hand allows this control method to perform as intended while maintaining the responsiveness required for practical use.

Keywords: computer vision, drone control, keypoint detection, openpose

Procedia PDF Downloads 178

Authors: Alexander E. Hramov, Vadim V. Grubov, Alexey A. Koronovskii, Maria K. Kurovskaуa, Anastasija E. Runnova

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The perception of visual signals in the brain was among the first issues discussed in terms of multistability which has been introduced to provide mechanisms for information processing in biological neural systems. In this work the influence of the cognitive noise on the visual perception of multistable pictures has been investigated. The study includes an experiment with the bistable Necker cube illusion and the theoretical background explaining the obtained experimental results. In our experiments Necker cubes with different wireframe contrast were demonstrated repeatedly to different people and the probability of the choice of one of the cubes projection was calculated for each picture. The Necker cube was placed at the middle of a computer screen as black lines on a white background. The contrast of the three middle lines centered in the left middle corner was used as one of the control parameter. Between two successive demonstrations of Necker cubes another picture was shown to distract attention and to make a perception of next Necker cube more independent from the previous one. Eleven subjects, male and female, of the ages 20 through 45 were studied. The choice of the Necker cube projection was detected with the Electroencephalograph-recorder Encephalan-EEGR-19/26, Medicom MTD. To treat the experimental results we carried out theoretical consideration using the simplest double-well potential model with the presence of noise that led to the Fokker-Planck equation for the probability density of the stochastic process. At the first time an analytical solution for the probability of the selection of one of the Necker cube projection for different values of wireframe contrast have been obtained. Furthermore, having used the results of the experimental measurements with the help of the method of least squares we have calculated the value of the parameter corresponding to the cognitive noise of the person being studied. The range of cognitive noise parameter values for studied subjects turned to be [0.08; 0.55]. It should be noted, that experimental results have a good reproducibility, the same person being studied repeatedly another day produces very similar data with very close levels of cognitive noise. We found an excellent agreement between analytically deduced probability and the results obtained in the experiment. A good qualitative agreement between theoretical and experimental results indicates that even such a simple model allows simulating brain cognitive dynamics and estimating important cognitive characteristic of the brain, such as brain noise.

Keywords: bistability, brain, noise, perception, stochastic processes

Procedia PDF Downloads 440

Authors: Kun Hua O., Dong Woon Kim, Won Hyung Lee

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Introduction: Neuropathic pain induced by spinal or peripheral nerve injury is highly resistant to common painkillers, nerve blocks, and other pain management approaches. Recently, several new therapeutic drug candidates have been developed to control neuropathic pain. In this study, we used the spinal nerve L5 ligation (SNL) model to investigate the ability of intrathecal or intravenous Evans blue to decrease pain behavior and to study the relationship between Evans blue and the neural structure of pain transmission. Method: Neuropathic pain (allodynia) of the left hind paw was induced by unilateral SNL in Sprague-Dawley rats(n=10) in each group. Evans blue (5, 15, 50μg/10μl) or phosphate buffer saline(PBS,10μl) was injected intrathecally at 3days post-ligation or intravenously(1mg/200 μl) 3days and 5days post-ligation . Mechanical sensitivity was assessed using Von Frey filaments at 3 days post-ligation and at 2 hours, days 1, 2, 3, 5,7 after intrathecal Evans blue injection, and on days 2, 4, 7, and 11 at 14 days after intravenous injection. In the intrathecal group, microglia and glutaminergic neurons in the dorsal horn and VNUT(vesicular nucleotide transporter) in the dorsal root ganglia were tested to evaluate co-staining with Evans blue. The experimental procedures were performed in accordance with the animal care guideline of the Korean Academy of Medical Science(Animal ethic committee of Chungnam National University Hospital: CNUH-014-A0005-1). Results: Tight ligation of the L5 spinal nerve induced allodynia in the left hind paw 3 days post-ligation. Intrathecal Evans blue most significantly(P＜0.001) alleviated allodynia at 2 days after intrathecal, but not an intravenous injection. Glutaminergic neurons in the dorsal horn and VNUT in the dorsal root ganglia were co-stained with Evans blue. On the other hand, microglia in the dorsal horn were partially co-stained with Evans blue. Conclusion: We confirmed that Evans blue might have an analgesic effect through the central nervous system, not another system in neuropathic pain of the SNL animal model. These results suggest Evans blue may be a potential new drug for the treatment of chronic pain. This research was supported by the National Research Foundation of Korea (NRF-2020R1A2C100757512), funded by the Ministry of Education.

Keywords: neuropathic pain, Evas blue, intrathecal, intravenous

Procedia PDF Downloads 89

Authors: Michele M. Mahr

Abstract:

The purpose of this study is to educate, inform, and facilitate scholarly conversation and discussion regarding the implementation of mindfulness techniques when working with individuals with substance use disorder (SUD) or addictive behaviors in mental health. Mindfulness can be recognized as the present moment, non-judgmental awareness, initiated by concentrated attention that is non-reactive and as openheartedly as possible. Individuals with SUD or addiction typically are challenged with triggers, environmental situations, cravings, or social pressures which may deter them from remaining abstinent from their drug of choice or addictive behavior. Also, mindfulness is recognized as one of the cognitive and behavioral treatment approaches and is both a physical and mental practice that encompasses individuals to become aware of internal situations and experiences with undivided attention. That said, mindfulness may be an effective strategy for individuals to employ during these experiences. This study will reveal how mental health practitioners and addiction counselors may find mindfulness to be an essential component of increasing wellness when working with individuals seeking mental health treatment. To this end, mindfulness is simply the ability individuals have to know what is actually happening as it is occurring and what they are experiencing at the moment. In the context of substance abuse and addiction, individuals may employ breathing techniques, meditation, and cognitive restructuring of the mind to become aware of present moment experiences. Furthermore, the notion of mindfulness has been directly connected to the development of neuropathways. The creation of the neural pathways then leads to creating thoughts which leads to developing new coping strategies and adaptive behaviors. Mindfulness strategies can assist individuals in connecting the mind with the body, allowing the individual to remain centered and focused. All of these mentioned above are vital components to recovery during substance abuse and addiction treatment. There are a variety of therapeutic modalities applying the key components of mindfulness, such as Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Cognitive Therapy for depression (MBCT). This study will provide an overview of both MBSR and MBCT in relation to treating individuals with substance abuse and addiction. The author will also provide strategies for readers to employ when working with clients. Lastly, the author will create and foster a safe space for discussion and engaging conversation among participants to ask questions, share perspectives, and be educated on the numerous benefits of mindfulness within wellness.

Keywords: mindfulness, wellness, substance abuse, mental health

Procedia PDF Downloads 65

Authors: Hamzeh Alipour, Masoumeh Bagheri, Abbasali Raz, Javad Dadgar Pakdel, Kourosh Azizi, Aboozar Soltani, Mohammad Djaefar Moemenbellah-Fard

Abstract:

Background: Maggot debridement therapy is an appropriate, effective, and controlled method using sterilized larvae of Luciliasericata (L.sericata) to treat wounds. Netrin-A is an enzyme in the Laminins family which secreted from salivary gland of L.sericata with a central role in neural regeneration and angiogenesis. This study aimed to production of new recombinant Netrin-A protein of Luciliasericata larvae by baculovirus expression vector system (BEVS) in SF9. Material and methods: In the first step, gene structure was subjected to the in silico studies, which were include determination of Antibacterial activity, Prion formation risk, homology modeling, Molecular docking analysis, and Optimization of recombinant protein. In the second step, the Netrin-A gene was cloned and amplified in pTG19 vector. After digestion with BamH1 and EcoR1 restriction enzymes, it was cloned in pFastBac HTA vector. It was then transformed into DH10Bac competent cells, and the recombinant Bacmid was subsequently transfected into insect Sf9 cells. The expressed recombinant Netrin-A was thus purified in the Ni-NTA agarose. This protein evaluation was done using SDS-PAGE and western blot, respectively. Finally, its concentration was calculated with the Bradford assay method. Results: The Bacmid vector structure with Netrin-A was successfully constructed and then expressed as Netrin-A protein in the Sf9 cell lane. The molecular weight of this protein was 52 kDa with 404 amino acids. In the in silico studies, fortunately, we predicted that recombinant LSNetrin-A have Antibacterial activity and without any prion formation risk.This molecule hasa high binding affinity to the Neogenin and a lower affinity to the DCC-specific receptors. Signal peptide located between amino acids 24 and 25. The concentration of Netrin-A recombinant protein was calculated to be 48.8 μg/ml. it was confirmed that the characterized gene in our previous study codes L. sericata Netrin-A enzyme. Conclusions: Successful generation of the recombinant Netrin-A, a secreted protein in L.sericata salivary glands, and because Luciliasericata larvae are used in larval therapy. Therefore, the findings of the present study could be useful to researchers in future studies on wound healing.

Keywords: blowfly, BEVS, gene, immature insect, recombinant protein, Sf9

Procedia PDF Downloads 85