Search results for: neural signal recording

Authors: Delphine Morales, Florian Lombart, Agathe Truchot, Pauline Maire, Pascale Vigneron, Antoine Galmiche, Catherine Lok, Muriel Vayssade

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Targeted therapy molecules are used as a first-line treatment for metastatic melanoma with B-Raf mutation. Nevertheless, these molecules can cause side effects to patients and are efficient on 50 to 60 % of them. Indeed, melanoma cell sensitivity to targeted therapy molecules is dependent on tumor microenvironment (cell-cell and cell-extracellular matrix interactions). To better unravel factors modulating cell sensitivity to B-Raf inhibitor, we have developed and compared several melanoma models: from metastatic melanoma cells cultured as monolayer (2D) to a co-culture in a 3D dermal equivalent. Cell response was studied in different melanoma cell lines such as SK-MEL-28 (mutant B-Raf (V600E), sensitive to Vemurafenib), SK-MEL-3 (mutant B-Raf (V600E), resistant to Vemurafenib) and a primary culture of dermal human fibroblasts (HDFn). Assays have initially been performed in a monolayer cell culture (2D), then a second time on a 3D dermal equivalent (dermal human fibroblasts embedded in a collagen gel). All cell lines were treated with Vemurafenib (a B-Raf inhibitor) for 48 hours at various concentrations. Cell sensitivity to treatment was assessed under various aspects: Cell proliferation (cell counting, EdU incorporation, MTS assay), MAPK signaling pathway analysis (Western-Blotting), Apoptosis (TUNEL), Cytokine release (IL-6, IL-1α, HGF, TGF-β, TNF-α) upon Vemurafenib treatment (ELISA) and histology for 3D models. In 2D configuration, the inhibitory effect of Vemurafenib on cell proliferation was confirmed on SK-MEL-28 cells (IC50=0.5 µM), and not on the SK-MEL-3 cell line. No apoptotic signal was detected in SK-MEL-28-treated cells, suggesting a cytostatic effect of the Vemurafenib rather than a cytotoxic one. The inhibition of SK-MEL-28 cell proliferation upon treatment was correlated with a strong expression decrease of phosphorylated proteins involved in the MAPK pathway (ERK, MEK, and AKT/PKB). Vemurafenib (from 5 µM to 10 µM) also slowed down HDFn proliferation, whatever cell culture configuration (monolayer or 3D dermal equivalent). SK-MEL-28 cells cultured in the dermal equivalent were still sensitive to high Vemurafenib concentrations. To better characterize all cell population impacts (melanoma cells, dermal fibroblasts) on Vemurafenib efficacy, cytokine release is being studied in 2D and 3D models. We have successfully developed and validated a relevant 3D model, mimicking cutaneous metastatic melanoma and tumor microenvironment. This 3D melanoma model will become more complex by adding a third cell population, keratinocytes, allowing us to characterize the epidermis influence on the melanoma cell sensitivity to Vemurafenib. In the long run, the establishment of more relevant 3D melanoma models with patients’ cells might be useful for personalized therapy development. The authors would like to thank the Picardie region and the European Regional Development Fund (ERDF) 2014/2020 for the funding of this work and Oise committee of "La ligue contre le cancer".

Keywords: 3D human skin model, melanoma, tissue engineering, vemurafenib efficiency

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Authors: Barbara Behre, Verolien Cauberghe, Dieneke Van de Sompel

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Sustainable consumption is on the rise, yet it has still not entered the mainstream in several industries, such as the fashion industry. In online fashion contexts, sustainability cues have been used to signal the sustainable benefits of certain garments to promote sustainable consumption. These sustainable cues may focus on the ecological or social dimension of sustainability. Since sustainability, in general, relates to distant, abstract benefits, the current study aims to examine if and how psychological distance may mediate the effects of exposure to different sustainability cues on consumption outcomes. Following the framework of Construal Level Theory of Psychological Distance, reduced psychological distance renders the construal level more concrete, which may influence attitudes and subsequent behavior in situations like fashion shopping. Most studies investigated sustainability as a composite, failing to differentiate between ecological and societal aspects of sustainability. The few studies examining sustainability more in detail uncovered that environmental sustainability is rather perceived in abstract cognitive construal, whereas social sustainability is linked to concrete construal. However, the construal level affiliation of the sustainability dimensions likely is not universally applicable to different domains and stages of consumption, which further suggest a need to clarify the relationships between environmental and social sustainability dimensions and the construal level of psychological distance within fashion brand consumption. While psychological distance and construal level have been examined in the context of sustainability, these studies yielded mixed results. The inconsistent findings of past studies might be due to the context-dependence of psychological distance as inducing construal differently in diverse situations. Especially in a hedonic consumption context like online fashion shopping, the role of visual processing of information could determine behavioural outcomes as linked to situational construal. Given the influence of the mode of processing on psychological distance and construal level, the current study examines the moderating role of verbal versus non-verbal presentation of the sustainability cues. In a 3 (environmental sustainability vs. social sustainability vs. control) x 2 (non-verbal message vs. verbal message) between subjects experiment, the present study thus examines how consumers evaluate sustainable brands in online shopping contexts in terms of psychological distance and construal level, as well as the impact on brand attitudes and buying intentions. The results among 246 participants verify the differential impact of the sustainability dimensions on fashion brand purchase intent as mediated by construal level and perceived psychological distance. The ecological sustainability cue is perceived as more concrete, which might be explained by consumer bias induced by the predominance of pro-environmental sustainability messages. The verbal versus non-verbal presentation of the sustainability cue neither had a significant influence on distance perceptions and construal level nor on buying intentions. This study offers valuable contributions to the sustainable consumption literature, as well as a theoretical basis for construal-level framing as applied in sustainable fashion branding.

Keywords: construal level theory, environmental vs social sustainability, online fashion shopping, sustainable fashion

Procedia PDF Downloads 82

Authors: Felipe M. de Freitas, Ícaro V. Soares, Lucas L. L. Fortes, Sandro T. M. Gonçalves, Úrsula D. C. Resende

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There is a dispersed energy in Radio Frequencies (RF) that can be reused to power electronics circuits such as: sensors, actuators, identification devices, among other systems, without wire connections or a battery supply requirement. In this context, there are different types of energy harvesting systems, including rectennas, coil systems, graphene and new materials. A secondary step of an energy harvesting system is the rectification of the collected signal which may be carried out, for example, by the combination of one or more Schottky diodes connected in series or shunt. In the case of a rectenna-based system, for instance, the diode used must be able to receive low power signals at ultra-high frequencies. Therefore, it is required low values of series resistance, junction capacitance and potential barrier voltage. Due to this low-power condition, voltage multiplier configurations are used such as voltage doublers or modified bridge converters. Lowpass filter (LPF) at the input, DC output filter, and a resistive load are also commonly used in the rectifier design. The electronic circuits projects are commonly analyzed through simulation in SPICE (Simulation Program with Integrated Circuit Emphasis) environment. Despite the remarkable potential of SPICE-based simulators for complex circuit modeling and analysis of quasi-static electromagnetic fields interaction, i.e., at low frequency, these simulators are limited and they cannot model properly applications of microwave hybrid circuits in which there are both, lumped elements as well as distributed elements. This work proposes, therefore, the electromagnetic modelling of electronic components in order to create models that satisfy the needs for simulations of circuits in ultra-high frequencies, with application in rectifiers coupled to antennas, as in energy harvesting systems, that is, in rectennas. For this purpose, the numerical method FDTD (Finite-Difference Time-Domain) is applied and SPICE computational tools are used for comparison. In the present work, initially the Ampere-Maxwell equation is applied to the equations of current density and electric field within the FDTD method and its circuital relation with the voltage drop in the modeled component for the case of lumped parameter using the FDTD (Lumped-Element Finite-Difference Time-Domain) proposed in for the passive components and the one proposed in for the diode. Next, a rectifier is built with the essential requirements for operating rectenna energy harvesting systems and the FDTD results are compared with experimental measurements.

Keywords: energy harvesting system, LE-FDTD, rectenna, rectifier, wireless power systems

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Authors: Ekaterina Zvorykina

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Nowadays, Arctic and Antarctic regions are distinguished to be one of the most important strategic resources for global development. Nonetheless, living conditions in Arctic regions still demand certain improvements. As soon as the region is rarely populated, one of the main points of interest is health accommodation of the people, who migrate to Arctic region for permanent and shift work. At Arctic and Antarctic latitudes, personnel face polar day and polar night conditions during the time of the year. It means that they are deprived of natural sunlight in winter season and have continuous daylight in summer. Firstly, the change in light intensity during 24-hours period due to migration affects circadian rhythms. Moreover, the controlled artificial light in winter is also an issue. The results of the recent studies on night shift medical professionals, who were exposed to permanent artificial light, have already demonstrated higher risks in cancer, depression, Alzheimer disease. Moreover, people exposed to frequent time zones change are also subjected to higher risks of heart attack and cancer. Thus, our main goals are to understand how high latitude work and living conditions can affect human health and how it can be prevented. In our study, we analyze molecular and cellular factors, which play important role in circadian rhythm change and distinguish main risk groups in people, migrating to high latitudes. The main well-studied index of circadian timing is melatonin or its metabolite 6-sulfatoxymelatonin. In low light intensity melatonin synthesis is disturbed and as a result human organism requires more time for sleep, which is still disregarded when it comes to working time organization. Lack of melatonin also causes shortage in serotonin production, which leads to higher depression risk. Melatonin is also known to inhibit oncogenes and increase apoptosis level in cells, the main factors for tumor growth, as well as circadian clock genes (for example Per2). Thus, people who work in high latitudes can be distinguished as a risk group for cancer diseases and demand more attention. Clock/Clock genes, known to be one of the main circadian clock regulators, decrease sensitivity of hypothalamus to estrogen and decrease glucose sensibility, which leads to premature aging and oestrous cycle disruption. Permanent light exposure also leads to accumulation superoxide dismutase and oxidative stress, which is one of the main factors for early dementia and Alzheimer disease. We propose a new screening system adjusted for people, migrating from middle to high latitudes and accommodation therapy. Screening is focused on melatonin and estrogen levels, sleep deprivation and neural disorders, depression level, cancer risks and heart and vascular disorders. Accommodation therapy includes different types artificial light exposure, additional melatonin and neuroprotectors. Preventive procedures can lead to increase of migration intensity to high latitudes and, as a result, the prosperity of Arctic region.

Keywords: circadian rhythm, high latitudes, melatonin, neuroprotectors

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Authors: Antonio Ruiz Gonzalez, Kwang-Leong Choy

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The measurement of electrolytes has a high value in the clinical routine. Ions are present in all body fluids with variable concentrations and are involved in multiple pathologies such as heart failures and chronic kidney disease. In the case of dissolved potassium, although a high concentration in the blood (hyperkalemia) is relatively uncommon in the general population, it is one of the most frequent acute electrolyte abnormalities. In recent years, the integration of thin films technologies in this field has allowed the development of highly sensitive biosensors with ultra-low limits of detection for the assessment of metals in liquid samples. However, despite the current efforts in the miniaturization of sensitive devices and their integration into portable systems, only a limited number of successful examples used commercially can be found. This fact can be attributed to a high cost involved in their production and the sustained degradation of the electrodes over time, which causes a signal drift in the measurements. Thus, there is an unmet necessity for the development of low-cost and robust sensors for the real-time monitoring of analyte concentrations in patients to allow the early detection and diagnosis of diseases. This paper reports a thin film ion-selective sensor for the evaluation of potassium ions in aqueous samples. As an alternative for this fabrication method, aerosol assisted chemical vapor deposition (AACVD), was applied due to cost-effectivity and fine control over the film deposition. Such a technique does not require vacuum and is suitable for the coating of large surface areas and structures with complex geometries. This approach allowed the fabrication of highly homogeneous surfaces with well-defined microstructures onto 50 nm thin gold layers. The degradative processes of the ubiquitously employed poly (vinyl chloride) membranes in contact with an electrolyte solution were studied, including the polymer leaching process, mechanical desorption of nanoparticles and chemical degradation over time. Rational design of a protective coating based on an organosilicon material in combination with cellulose to improve the long-term stability of the sensors was then carried out, showing an improvement in the performance after 5 weeks. The antifouling properties of such coating were assessed using a cutting-edge quartz microbalance sensor, allowing the quantification of the adsorbed proteins in the nanogram range. A correlation between the microstructural properties of the films with the surface energy and biomolecules adhesion was then found and used to optimize the protective film.

Keywords: hyperkalemia, drift, AACVD, organosilicon

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Authors: Nilki Upathissa, Torin Wirasinghe

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This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users.

Keywords: recommendation systems, hybrid recommendation systems, machine learning, data science, long short-term memory, recurrent neural network

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Authors: Ayten Ekin Meşe, Selahattin Şentürk, Melike Duvanoğlu

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Petroleum refineries are a highly complex process industry with continuous production and high operating costs. Physical separation of crude oil starts with the crude oil distillation unit, continues with various conversion and purification units, and passes through many stages until obtaining the final product. To meet the desired product specification, process parameters are strictly followed. To be able to ensure the quality of distillates, routine analyses are performed in quality control laboratories based on appropriate international standards such as American Society for Testing and Materials (ASTM) standard methods and European Standard (EN) methods. The cut point of distillates in the crude distillation unit is very crucial for the efficiency of the upcoming processes. In order to maximize the process efficiency, the determination of the quality of distillates should be as fast as possible, reliable, and cost-effective. In this sense, an alternative study was carried out on the crude oil distillation unit that serves the entire refinery process. In this work, studies were conducted with three different crude oil distillates which are Light Straight Run Naphtha (LSRN), Heavy Straight Run Naphtha (HSRN), and Kerosene. These products are named after separation by the number of carbons it contains. LSRN consists of five to six carbon-containing hydrocarbons, HSRN consist of six to ten, and kerosene consists of sixteen to twenty-two carbon-containing hydrocarbons. Physical properties of three different crude distillation unit products (LSRN, HSRN, and Kerosene) were determined using Near-Infrared Spectroscopy with multivariate calibration. The absorbance spectra of the petroleum samples were obtained in the range from 10000 cm⁻¹ to 4000 cm⁻¹, employing a quartz transmittance flow through cell with a 2 mm light path and a resolution of 2 cm⁻¹. A total of 400 samples were collected for each petroleum sample for almost four years. Several different crude oil grades were processed during sample collection times. Extended Multiplicative Signal Correction (EMSC) and Savitzky-Golay (SG) preprocessing techniques were applied to FT-NIR spectra of samples to eliminate baseline shifts and suppress unwanted variation. Two different multivariate calibration approaches (Partial Least Squares Regression, PLS and Genetic Inverse Least Squares, GILS) and an ensemble model were applied to preprocessed FT-NIR spectra. Predictive performance of each multivariate calibration technique and preprocessing techniques were compared, and the best models were chosen according to the reproducibility of ASTM reference methods. This work demonstrates the developed models can be used for routine analysis instead of conventional analytical methods with over 90% accuracy.

Keywords: crude distillation unit, multivariate calibration, near infrared spectroscopy, data preprocessing, refinery

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Adae Amoako, Daniel Montero, Peter Murray, George Pujalte

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We present a case of a 42-year-old, right-handed Caucasian male who presented to a medical orthopedics clinic with left wrist pain. The patient indicated that the pain started two months prior to the visit. He could only remember helping a friend move furniture prior to the onset of pain. Examination of the left wrist showed limited extension compared to the right. There was clicking with flexion and extension of the wrist on the dorsal aspect. Mild tenderness was noticed over the distal radioulnar joint. There was ulnar and radial deviation on provocation. Initial 4-view x-rays of the left wrist showed mild radiocarpal and scapho-trapezium-trapezoid (ST-T) osteoarthritis, with subchondral cysts seen in the lunate and scaphoid, with no obvious fractures. The patient was initially put in a wrist brace and diclofenac topical gel was prescribed for pain control, as a patient could not take non-steroidal anti-inflammatory drugs (NSAIDs) due to gastritis. Despite diclofenac topical gel use and bracing, symptoms remained, and a steroid injection with 1 mL of lidocaine with 10 mg of triamcinolone acetonide was performed under fluoroscopy. He obtained some relief but after 3 months, the injection had to be repeated. On 2-month follow up after the initial evaluation, symptoms persisted. Magnetic resonance imaging (MRI) was obtained which showed an abnormal T1 hypodense signal involving the proximal pole of the scaphoid and articular collapse proximally of the scaphoid, with marked irregularity of the overlying cartilage, suggesting a remote injury, findings consistent with avascular necrosis of the proximal pole of the scaphoid. A month after that, the patient had the left proximal pole of the scaphoid debrided and an intercompartmental supraretinacular artery vascularized. Pedicle bone graft reconstruction of the proximal pole of the left scaphoid was done. A non-vascularized autograft from the left radius was also applied. He was put in a thumb spica cast with the interphalangeal joint free for 6 weeks. On 6-week follow-up after surgery, the patient was healing well and could make a composite fist with his left hand. The diagnosis of Preiser’s disease is primarily based on radiological findings. Due to the fact that necrosis happens over a period of time, most AVNs are diagnosed at the late stages of the disease. There appear to be no specific guidelines on the management AVN of the scaphoid. In the past, immobilization and arthroscopic debridement had been used. Radial osteotomy has also been tried. Vascularized bone grafts have also been used to treat Preiser’s disease. In our patient, we used three of these treatment modalities, starting with conservative management with topical NSAIDS and immobilization, then debridement with vascularized bone grafts.

Keywords: wrist pain, avascular necrosis of the scaphoid, Preiser’s disease, vascularized bone grafts

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Authors: Busra Gundede, Ozal Mutlu, Nagihan Gulsoy

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Background: Over the years, material research has led to the development of numerous metals and alloys for using in biomedical applications. One of the major tasks of biomaterial research is the functionalization of the material surface to improve the biocompatibility according to a specific application. 316L and 316L alloys are excellent for various bio-applications. This research was investigated the effect of titanium (Ti), niobium (Nb), and zirconium (Zr) additives on injection molded austenitic grade 316L stainless steels in vitro biocompatibility. For this purpose, cytotoxic tests were performed to evaluate the potential biocompatibility of the specimens. Materials and Methods: 3T3 fibroblast were cultivated in DMEM supplemented with 10% fetal bovine serum and %1 penicillin-streptomycin at 37°C with 5% CO2 and 95%humidity. Trypsin/EDTA solution was used to remove cells from the culture flask. Cells were reseeded at a density of 1×105cell in 25T flasks. The medium change took place every 3 days. The trypan blue assay was used to determine cell viability. Cell viability is calculated as the number of viable cells divided by the total number of cells within the grids on the cell counter machine counted the number of blue staining cells and the number of total cells. Cell viability should be at least 95% for healthy log-phase cultures. MTT assay was assessed for 96-hours. Cells were cultivated in 6-well flask within 5 ml DMEM and incubated as same conditions. 0,5mg/ml MTT was added for 4-hours and then acid-isoprohanol was added for solubilize to formazan crystals. Cell morphology after 96h was investigated by SEM. The medium was removed, samples were washed with 0.15 M PBS buffer and fixed for 12h at 4- 8°C with %2,5 gluteraldehyte. Samples were treated with 1% osmium tetroxide. Samples were then dehydrated and dried, mounted on appropriate stubs with colloidal silver and sputter-coated with gold. Images were collected using a scanning electron microscope. ROS assay is a cell viability test for in vitro studies. Cells were grown for 96h, ROS solution added on cells in 6 well plate flask and incubated for 1h. Fluorescence signal indicates ROS generation by cells. Results: Trypan Blue exclusion assay results were 96%, 92%, 95%, 90%, 91% for negative control group, 316L, 316L-Ti, 316L-Nb and 316L-Zr, respectively. Results were found nearly similar to each other when compared with control group. Cell viability from MTT analysis was found to be 100%, 108%, 103%, 107%, and 105% for the control group, 316L, 316L-Ti, 316L-Nb and 316L-Zr, respectively. Fluorescence microscopy analysis indicated that all test groups were same as the control group in ROS assay. SEM images demonstrated that the attachment of 3T3 cells on biomaterials. Conclusion: We, therefore, concluded that Ti, Nb and Zr additives improved physical properties of 316L stainless. In our in vitro experiments showed that these new additives did not modify the cytocompatibility of stainless steel and these additives on 316L might be useful for biomedical applications.

Keywords: 316L stainles steel, biocompatibility, cell culture, Ti, Nb, Zr

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Authors: Ibtissem Hosni, Lilia Bennaceur Farah, Saber Mohamed Naceur

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In order to assess the water availability in the soil, it is crucial to have information about soil distributed moisture content; this parameter helps to understand the effect of humidity on the exchange between soil, plant cover and atmosphere in addition to fully understanding the surface processes and the hydrological cycle. On the other hand, aerodynamic roughness length is a surface parameter that scales the vertical profile of the horizontal component of the wind speed and characterizes the surface ability to absorb the momentum of the airflow. In numerous applications of the surface hydrology and meteorology, aerodynamic roughness length is an important parameter for estimating momentum, heat and mass exchange between the soil surface and atmosphere. It is important on this side, to consider the atmosphere factors impact in general, and the natural erosion in particular, in the process of soil evolution and its characterization and prediction of its physical parameters. The study of the induced movements by the wind over soil vegetated surface, either spaced plants or plant cover, is motivated by significant research efforts in agronomy and biology. The known major problem in this side concerns crop damage by wind, which presents a booming field of research. Obviously, most models of soil surface require information about the aerodynamic roughness length and its temporal and spatial variability. We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat algorithm to describe natural surface roughness. We have introduced multi-layer aspect of the humidity of the soil surface, to take into account a volume component in the problem of backscattering radar signal. As humidity increases, the dielectric constant of the soil-water mixture increases and this change is detected by microwave sensors. Nevertheless, many existing models in the field of radar imagery, cannot be applied directly on areas covered with vegetation due to the vegetation backscattering. Thus, the radar response corresponds to the combined signature of the vegetation layer and the layer of soil surface. Therefore, the key issue of the numerical estimation of soil moisture is to separate the two contributions and calculate both scattering behaviors of the two layers by defining the scattering of the vegetation and the soil blow. This paper presents a synergistic methodology, and it is for estimating roughness and soil moisture from C-band radar measurements. The methodology adequately represents a microwave/optical model which has been used to calculate the scattering behavior of the aerodynamic vegetation-covered area by defining the scattering of the vegetation and the soil below.

Keywords: aerodynamic, bi-dimensional, vegetation, synergistic

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Authors: Mebruka Mohammed, Daniel Bisrat, Asfaw Debella, Tarekegn Birhanu

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Lack of quality control standards for medicinal plants and their preparations is considered major barrier to their integration in to effective primary health care in Ethiopia. Poor quality herbal preparations led to countless adverse reactions extending to death. Denial of penetration for the Ethiopian medicinal plants in to the world’s booming herbal market is also another significant loss resulting from absence of herbal quality control system. Thus, in the present study, Gnidia stenophylla Gilg (popular antimalarial plant of south eastern Ethiopia), is standardized and a full monograph is produced that can serve as a guideline in quality control of the crude drug. Morphologically, the roots are found to be cylindrical and tapering towards the end. It has a hard, corky and friable touch with saddle brown color externally and it is relatively smooth and pale brown internally. It has got characteristic pungent odor and very bitter taste. Microscopically it has showed lignified xylem vessels, wider medullary rays with some calcium oxalate crystals, reddish brown secondary metabolite contents and slender shaped long fibres. Physicochemical standards quantified and resulted: foreign matter (5.25%), moisture content (6.69%), total ash (40.80%), acid insoluble ash (8.00%), water soluble ash (2.30%), alcohol soluble extractive (15.27%), water soluble extractive (10.98%), foaming index (100.01 ml/g), swelling index (7.60 ml/g). Phytochemically: Phenols, flavonoids, steroids, tannins and saponins were detected in the root extract; TLC and HPLC fingerprints were produced and an analytical marker was also tentatively characterized as 3-(3,4-dihydro-3,5-dihydroxy-2-(4-hydroxy-5-methylhex-1-en-2-yl)-7-methoxy-4-oxo-2H-chromen-8-yl)-5-hydroxy-2-(4-hydroxyphenyl)-7-methoxy-4H-chromen-4-one. Residue wise pesticides (i.e. DDT, DDE, g-BHC) and radiochemical levels fall below the WHO limit while Heavy metals (i.e. Co, Ni, Cr, Pb, and Cu), total aerobic count and fungal load lie way above the WHO limit. In conclusion, the result can be taken as signal that employing non standardized medicinal plants could cause many health risks of the Ethiopian people and Africans’ at large (as 80% of inhabitants in the continent depends on it for primary health care). Therefore, following a more universal approach to herbal quality by adopting the WHO guidelines and developing monographs using the various quality parameters is inevitable to minimize quality breach and promote effective herbal drug usage.

Keywords: Gnidia stenophylla Gilg, standardization/monograph, pharmacognostic, residue/impurity, quality

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Authors: Debashis Nath

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Drawn from multiple reanalysis datasets, an increasing trend and westward shift in the number of Potential Vorticity (PV) intrusion events over the Pacific are evident. The increased frequency can be linked to a long-term trend in upper tropospheric (UT, 200 hPa) equatorial westerly wind and subtropical jets (STJ) during boreal winter to spring. These may be resulting from anomalous warming and cooling over the western Pacific warm pool and the tropical eastern Pacific, respectively. The intrusions brought dry and ozone rich air of stratospheric origin deep into the tropics. In the tropical UT, interannual ozone variability is mainly related to convection associated with El Niño/Southern Oscillation. Zonal mean stratospheric overturning circulation organizes the transport of ozone rich air poleward and downward to the high and midlatitudes leading there to higher ozone concentration. In addition to these well described mechanisms, we observe a long-term increasing trend in ozone flux over the northern hemispheric outer tropical (10–25°N) central Pacific that results from equatorward transport and downward mixing from the midlatitude UT and lower stratosphere (LS) during PV intrusions. This increase in tropospheric ozone flux over the Pacific Ocean may affect the radiative processes and changes the budget of atmospheric hydroxyl radicals. The results demonstrate a long-term increase in outer tropical Pacific PV intrusions linked with the strengthening of the upper tropospheric equatorial westerlies and weakening of the STJ. Zonal variation in SST, characterized by gradual warming in the western Pacific–warm pool and cooling in the central–eastern Pacific, is associated with the strengthening of the Pacific Walker circulation. In the Western Pacific enhanced convective activity leads to precipitation, and the latent heat released in the process strengthens the Pacific Walker circulation. However, it is linked with the trend in global mean temperature, which is related to the emerging anthropogenic greenhouse signal and negative phase of PDO. On the other hand, the central-eastern Pacific cooling trend is linked to the weakening of the central–eastern Pacific Hadley circulation. It suppresses the convective activity due to sinking air motion and imports less angular momentum to the STJ leading to a weakened STJ. While, more PV intrusions result from this weaker STJ on its equatorward side; significantly increase the stratosphere-troposphere exchange processes on the longer timescale. This plays an important role in determining the atmospheric composition, particularly of tropospheric ozone, in the northern outer tropical central Pacific. It may lead to more ozone of stratospheric origin in the LT and even in the marine boundary, which may act as harmful pollutants and affect the radiative processes by changing the global budgets of atmospheric hydroxyl radicals.

Keywords: PV intrusion, westerly duct, ozone, Central Pacific

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Authors: Norihiro Kato, Yuriko Takayama

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Some gram-negative bacteria enable the simultaneous activation of gene expression involved in N-acylhomoserine lactone (AHL) dependent cell-to-cell communication system. Such regulatory system for the bacterial group behavior is termed as quorum sensing (QS) because a diffusible AHL signal can accumulate around the cell during the increase of the cell density and trigger activation of the sequential QS process. By blocking the QS, the expression of diverse genes related to infection, antibiotic production, and biofilm formation is inhibited. Conditioning of QS by regulation of the DNA-receptor-AHL interaction is a potential target for enhancing host defenses against pathogenicity. We focused on engineered application of transcriptional regulator SpnR produced in opportunistic human pathogen Serratia marcescens. The SpnR can interact with AHL signals at an N-terminal domain and also with a promoter region of a QS target gene at a C-terminal domain. As the initial process of the QS activation, the SpnR forms a complex with the AHL to enhance the expression of pig cluster; the SpnR normally acts as an activator for the expression of the QS-dependent gene. In this research, we attempt to artificially control QS by changing the role of SpnR. The QS-dependent prodigiosin production is expected to inhibit by externally added SpnR in the culture broth of AS-1 strain because the AHL concentration was kept below the threshold by AHL-SpnR complex formation. Maltose-binding protein (MBP)-tagged SpnR (MBP-SpnR) was overexpressed in Escherichia coli and purified using an affinity chromatography equipped with an amylose resin column. The specific interaction between AHL and MBP-SpnR was demonstrated by quartz crystal microbalance (QCM) sensor. AHL with amino end-group was coupled with COOH-terminated self-assembled monolayer prepared on a gold electrode of 27-MHz quartz crystal sensor using water-soluble carbodiimide. After the injection of MBP-SpnR into a cup-type sensor cell filled with the buffer solution, time course of resonant frequency change (ΔFs) was determined. A decrease of ΔFs clearly showed the uptake of MBP-SpnR onto the AHL-immobilized electrode. Furthermore, no binding affinity was observed after the heat-inactivation of MBP-SpnR at 80ºC. These results suggest that MBP-SpnR possesses a specific affinity for AHL. MBP-SpnR was added to the culture medium as an AHL trap to study inhibitory effects on intracellularly accumulated prodigiosin. With approximately 2 µM MBP-SpnR, the amount of prodigiosin induced was half that of the control without any additives. In conclusion, the function of SpnR could be switched by adding it to the cell culture. Exogenously added MBP-SpnR possesses high affinity for AHL derived from cells and acts as an inhibitor of AHL-mediated QS.

Keywords: intracellular signaling, microbial biotechnology, quorum sensing, transcriptional regulator

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

Procedia PDF Downloads 127

Authors: Lee Yan Nian

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Safety investigation is different from an administrative investigation in that the former is conducted by an independent agency and the purpose of such investigation is to prevent accidents in the future and not to apportion blame or determine liability. Before October 2018, Taiwan railway occurrences were investigated by local supervisory authority. Characteristics of this kind of investigation are that enforcement actions, such as administrative penalty, are usually imposed on those persons or units involved in occurrence. On October 21, 2018, due to a Taiwan Railway accident, which caused 18 fatalities and injured another 267, establishing an agency to independently investigate this catastrophic railway accident was quickly decided. The Taiwan Transportation Safety Board (TTSB) was then established on August 1, 2019 to take charge of investigating major aviation, marine, railway and highway occurrences. The objective of this study is to assess the effectiveness of safety investigations conducted by the TTSB. In this study, the major railway occurrence investigation reports published by the TTSB are used for modeling and analysis. According to the classification of railway occurrences investigated by the TTSB, accident types of Taiwan railway occurrences can be categorized into: derailment, fire, Signal Passed at Danger and others. A Causal Factor Analysis System (CFAS) developed by the TTSB is used to identify the influencing causal factors and their causal relationships in the investigation reports. All terminologies used in the CFAS are equivalent to the Human Factors Analysis and Classification System (HFACS) terminologies, except for “Technical Events” which was added to classify causal factors resulting from mechanical failure. Accordingly, the Bayesian network structure of each occurrence category is established based on the identified causal factors in the CFAS. In the Bayesian networks, the prior probabilities of identified causal factors are obtained from the number of times in the investigation reports. Conditional Probability Table of each parent node is determined from domain experts’ experience and judgement. The resulting networks are quantitatively assessed under different scenarios to evaluate their forward predictions and backward diagnostic capabilities. Finally, the established Bayesian network of derailment is assessed using investigation reports of the same accident which was investigated by the TTSB and the local supervisory authority respectively. Based on the assessment results, findings of the administrative investigation is more closely tied to errors of front line personnel than to organizational related factors. Safety investigation can identify not only unsafe acts of individual but also in-depth causal factors of organizational influences. The results show that the proposed methodology can identify differences between safety investigation and administrative investigation. Therefore, effective intervention strategies in associated areas can be better addressed for safety improvement and future accident prevention through safety investigation.

Keywords: administrative investigation, bayesian network, causal factor analysis system, safety investigation

Procedia PDF Downloads 93

Authors: M. I. Tyletc, O. I. Podgornya, T. G. Shaposhnikova, S. V. Shabelnikov, A. G. Mittenberg, M. A. Daugavet

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Ascidiacea is the most numerous class of the Tunicata subtype. These chordates' distinctive feature of the anatomical structure is a tunic consisting of cellulose fibrils, protein molecules, and single cells. The mechanisms of the tunic formation are not known in detail; tunic formation could be used as the model system for studying the interaction of cells with the extracellular matrix. Our model species is the ascidian Styela rustica, which is prevalent in benthic communities of the White Sea. As previously shown, the tunic formation involves morula blood cells, which contain the major 48 kDa protein p48. P48 participation in the tunic formation was proved using antibodies against the protein. The nature of the protein and its function remains unknown. The current research aims to determine the amino acid sequence of p48, as well as to clarify its role in the tunic formation. The peptides that make up the p48 amino acid sequence were determined by mass spectrometry. A search for peptides in protein sequence databases identified sequences homologous to p48 in Styela clava, Styela plicata, and Styela canopus. Based on sequence alignment, their level of similarity was determined as 81-87%. The correspondent sequence of ascidian Styela canopus was used for further analysis. The Styela rustica p48 sequence begins with a signal peptide, which could indicate that the protein is secretory. This is consistent with experimentally obtained data: the contents of morula cells secreted in the tunic matrix. The isoelectric point of p48 is 9.77, which is consistent with the experimental results of acid electrophoresis of morula cell proteins. However, the molecular weight of the amino acid sequence of ascidian Styela canopus is 103 kDa, so p48 of Styela rustica is a shorter homolog. The search for conservative functional domains revealed the presence of two Ca-binding EGF-like domains, thrombospondin (TSP1) and tyrosinase domains. The p48 peptides determined by mass spectrometry fall into the region of the sequence corresponding to the last two domains and have amino acid substitutions as compared to Styela canopus homolog. The tyrosinase domain (pfam00264) is known to be part of the phenoloxidase enzyme, which participates in melanization processes and the immune response. The thrombospondin domain (smart00209) interacts with a wide range of proteins, and is involved in several biological processes, including coagulation, cell adhesion, modulation of intercellular and cell-matrix interactions, angiogenesis, wound healing and tissue remodeling. It can be assumed that the tyrosinase domain in p48 plays the role of the phenoloxidase enzyme, and TSP1 provides a link between the extracellular matrix and cell surface receptors, and may also be responsible for the repair of the tunic. The results obtained are consistent with experimental data on p48. The domain organization of protein suggests that p48 is an enzyme involved in the tunic tunning and is an important regulator of the organization of the extracellular matrix.

Keywords: ascidian, p48, thrombospondin, tyrosinase, tunic, tunning

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Authors: Lucia Billeci, Gennaro Tartarisco, Maurizio Varanini

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Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health.

Keywords: fetal electrocardiography, fetal QRS detection, independent component analysis (ICA), optimization, wearable

Procedia PDF Downloads 262

Authors: Marcel Schulze, Behrem Aslan, Silke Lux, Alexandra Philipsen

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Objective: Patients with Attention Deficit/Hyperactivity Disorder (ADHD) often report that they are being flooded by sensory impressions. Studies investigating sensory processing show hypersensitivity for sensory inputs across the senses in children and adults with ADHD. Especially the auditory modality is affected by deficient acoustical inhibition and modulation of signals. While studying unimodal signal-processing is relevant and well-suited in a controlled laboratory environment, everyday life situations occur multimodal. A complex interplay of the senses is necessary to form a unified percept. In order to achieve this, the unimodal sensory modalities are bound together in a process called multisensory integration (MI). In the current study we investigate MI in an adult ADHD sample using the McGurk-effect – a well-known illusion where incongruent speech like phonemes lead in case of successful integration to a new perceived phoneme via late top-down attentional allocation . In ADHD neuronal dysregulation at rest e.g., aberrant within or between network functional connectivity may also account for difficulties in integrating across the senses. Therefore, the current study includes resting-state functional connectivity to investigate a possible relation of deficient network connectivity and the ability of stimulus integration. Method: Twenty-five ADHD patients (6 females, age: 30.08 (SD:9,3) years) and twenty-four healthy controls (9 females; age: 26.88 (SD: 6.3) years) were recruited. MI was examined using the McGurk effect, where - in case of successful MI - incongruent speech-like phonemes between visual and auditory modality are leading to a perception of a new phoneme. Mann-Whitney-U test was applied to assess statistical differences between groups. Echo-planar imaging-resting-state functional MRI was acquired on a 3.0 Tesla Siemens Magnetom MR scanner. A seed-to-voxel analysis was realized using the CONN toolbox. Results: Susceptibility to McGurk was significantly lowered for ADHD patients (ADHDMdn:5.83%, ControlsMdn:44.2%, U= 160.5, p=0.022, r=-0.34). When ADHD patients integrated phonemes, reaction times were significantly longer (ADHDMdn:1260ms, ControlsMdn:582ms, U=41.0, p<.000, r= -0.56). In functional connectivity medio temporal gyrus (seed) was negatively associated with primary auditory cortex, inferior frontal gyrus, precentral gyrus, and fusiform gyrus. Conclusion: MI seems to be deficient for ADHD patients for stimuli that need top-down attentional allocation. This finding is supported by stronger functional connectivity from unimodal sensory areas to polymodal, MI convergence zones for complex stimuli in ADHD patients.

Keywords: attention-deficit hyperactivity disorder, audiovisual integration, McGurk-effect, resting-state functional connectivity

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Authors: Shahira M. Ezzat, Marwa I. Ezzat, Mona M. Okba, Esther T. Menze, Ashraf B. Abdel-Naim, Shahnas O. Mohamed

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Background: In order to decrease the burden of the high cost of synthetic drugs, it is important to focus on phytopharmaceuticals. The aim of our study was to search for the mechanism of ginger (Zingiber officinale Roscoe) anti-inflammatory potential and to correlate it to its biophytochemicals. Methods: Various extracts viz. water, 50%, 70%, 80%, and 90% ethanol were prepared from ginger rhizomes. Fractionation of the aqueous extract (AE) was accomplished using Diaion HP-20. In vitro anti-inflammatory activity of the different extracts and isolated compounds was evaluated by protein denaturation inhibition, membrane stabilization, protease inhibition, and anti-lipoxygenase assays. In vivo anti-inflammatory activity of AE was estimated by assessment of rat paw oedema after carrageenan injection. Prostaglandin E2 (PGE2), certain inflammation markers (TNF-α, IL-6, IL-1α, IL-1β, INFr, MCP-1MIP, RANTES, and Nox) levels and MPO activity in the paw edema exudates were measured. Total antioxidant capacity (TAC) was also determined. Histopathological alterations of paw tissues were scored. Results: All the tested extracts showed significant (p < 0.1) anti-inflammatory activities. The highest percentage of heat induced albumin denaturation (66%) was exhibited by the 50% ethanol (250 μg/ml). The 70 and 90% ethanol extracts (500 μg/ml) were more potent as membrane stabilizers (34.5 and 37%, respectively) than diclofenac (33%). The 80 and 90% ethanol extracts (500 μg/ml) showed maximum protease inhibition (56%). The strongest anti-lipoxygenase activity was observed for the AE. It showed more significant lipoxygenase inhibition activity than that of diclofenac (58% and 52%, respectively) at the same concentration (125 μg/ml). Fractionation of AE yielded four main fractions (Fr I-IV) which showed significant in vitro anti-inflammatory. Purification of Fr-III and IV led to the isolation of 6-poradol (G1), 6-shogaol (G2); methyl 6- gingerol (G3), 5-gingerol (G4), 6-gingerol (G5), 8-gingerol (G6), 10-gingerol (G7), and 1-dehydro-6-gingerol (G8). G2 (62.5 ug/ml), G1 (250 ug/ml), and G8 (250 ug/ml) exhibited potent anti-inflammatory activity in all studied assays, while G4 and G5 exhibited moderate activity. In vivo administration of AE ameliorated rat paw oedema in a dose-dependent manner. AE (at 200 mg/kg) showed significant reduction (60%) of PGE2 production. The AE at different doses (at 25-200 mg/kg) showed significant reduction in inflammatory markers except for IL-1α. AE (at 25 mg/kg) is superior to indomethacin in reduction of IL-1β. Treatment of animals with the AE (100, 200 mg/kg) or indomethacin (10 mg/kg) showed significant reduction in TNF-α, IL-6, MCP-1, and RANTES levels, and MPO activity by about (31, 57 and 32% ) (65, 60 and 57%) (27, 41 and 28%) (23, 32 and 23%) (66, 67 and 67%) respectively. AE at 100 and 200 mg/kg was equipotent to indomethacin in reduction of NOₓ level and in increasing the TAC. Histopathological examination revealed very few inflammatory cells infiltration and oedema after administration of AE (200 mg/kg) prior to carrageenan. Conclusion: Ginger anti-inflammatory activity is mediated by inhibiting macrophage and neutrophils activation as well as negatively affecting monocyte and leukocyte migration. Moreover, it produced dose-dependent decrease in pro-inflammatory cytokines and chemokines and replenished the total antioxidant capacity. We strongly recommend future investigations of ginger in the potential signal transduction pathways.

Keywords: anti-lipoxygenase activity, inflammatory markers, 1-dehydro-6-gingerol, 6-shogaol

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Authors: Yuriko Takayama, Norihiro Kato

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Quorum sensing (QS) is a cell-to-cell communication system in bacteria to regulate expression of target genes. In gram-negative bacteria, activation on QS is controlled by a concentration increase of N-acylhomoserine lactone (AHL), which can diffuse in and out of the cell. Effective control of QS is expected to avoid virulence factor production in infectious pathogens, biofilm formation, and antibiotic production because various cell functions in gram-negative bacteria are controlled by AHL-mediated QS. In this research, we applied cyclodextrins (CDs) as artificial hosts for the AHL signal to reduce the AHL concentration in the culture broth below its threshold for QS activation. The AHL-receptor complex induced under the high AHL concentration activates transcription of the QS-target gene. Accordingly, artificial reduction of the AHL concentration is one of the effective strategies to inhibit the QS. A hydrophobic cavity of the CD can interact with the acyl-chain of the AHL due to hydrophobic interaction in aqueous media. We studied N-hexanoylhomoserine lactone (C6HSL)-mediated QS in Serratia marcescens; accumulation of C6HSL is responsible for regulation of the expression of pig cluster. Inhibitory effects of added CDs on QS were demonstrated by determination of prodigiosin amount inside cells after reaching stationary phase, because production of prodigiosin depends on the C6HSL-mediated QS. By adding approximately 6 wt% hydroxypropyl-β-CD (HP-β-CD) in Luria-Bertani (LB) medium prior to inoculation of S. maecescens AS-1, the intracellularly accumulated prodigiosin was drastically reduced to 7-10%, which was determined after the extraction of prodigiosin in acidified ethanol. The AHL retention ability of HP-β-CD was also demonstrated by Chromobacterium violacuem CV026 bioassay. The CV026 strain is an AHL-synthase defective mutant that activates QS solely by adding AHLs from outside of cells. A purple pigment violacein is induced by activation of the AHL-mediated QS. We demonstrated that the violacein production was effectively suppressed when the C6HSL standard solution was spotted on a LB agar plate dispersing CV026 cells and HP-β-CD. Physico-chemical analysis was performed to study the affinity between the immobilized CD and added C6HSL using a quartz crystal microbalance (QCM) sensor. The COOH-terminated self-assembled monolayer was prepared on a gold electrode of 27-MHz AT-cut quartz crystal. Mono(6-deoxy-6-N, N-diethylamino)-β-CD was immobilized on the electrode using water-soluble carbodiimide. The C6HSL interaction with the β-CD cavity was studied by injecting the C6HSL solution to a cup-type sensor cell filled with buffer solution. A decrement of resonant frequency (ΔFs) clearly showed the effective C6HSL complexation with immobilized β-CD and its stability constant for MBP-SpnR-C6HSL complex was on the order of 102 M-1. The CD has high potential for engineered control of QS because it is safe for human use.

Keywords: acylhomoserine lactone, cyclodextrin, intracellular signaling, quorum sensing

Procedia PDF Downloads 216

Authors: Emanuela Chiarella, Annamaria Aloisio, Nisticò Clelia, Maria Mesuraca

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ZNF521 is a stem cell-associated transcription co-factor, that plays a crucial role in the homeostatic regulation of the stem cell compartment in the hematopoietic, osteo-adipogenic, and neural system. In normal hematopoiesis, primary human CD34+ hematopoietic stem cells display typically a high expression of ZNF521, while its mRNA levels rapidly decrease when these progenitors progress towards erythroid, granulocytic, or B-lymphoid differentiation. However, most acute myeloid leukemias (AMLs) and leukemia-initiating cells keep high ZNF521 expression. In particular, AMLs are often characterized by chromosomal translocations involving the Mixed Lineage Leukemia (MLL) gene, which MLL gene includes a variety of fusion oncogenes arisen from genes normally required during hematopoietic development; once they are fused, they promote epigenetic and transcription factor dysregulation. The chromosomal translocation t(9;11)(p21-22;q23), fusing the MLL gene with AF9 gene, results in a monocytic immune phenotype with an aggressive course, frequent relapses, and a short survival time. To better understand the dysfunctional transcriptional networks related to genetic aberrations, AML gene expression profile datasets were queried for ZNF521 expression and its correlations with specific gene rearrangements and mutations. The results showed that ZNF521 mRNA levels are associated with specific genetic aberrations: the highest expression levels were observed in AMLs involving t(11q23) MLL rearrangements in two distinct datasets (MILE and den Boer); elevated ZNF521 mRNA expression levels were also revealed in AMLs with t(7;12) or with internal rearrangements of chromosome 16. On the contrary, relatively low ZNF521 expression levels seemed to be associated with the t(8;21) translocation, that in turn is correlated with the AML1-ETO fusion gene or the t(15;17) translocation and in AMLs with FLT3-ITD, NPM1, or CEBPα double mutations. Invitro, we found that the enforced co-expression of ZNF521 in cord blood-derived CD34+ cells induced a significant proliferative advantage, improving MLL-AF9 effects on the induction of proliferation and the expansion of leukemic progenitor cells. Transcriptome profiling of CD34+ cells transduced with either MLL-AF9, ZNF521, or a combination of the two transgenes highlighted specific sets of up- or down-regulated genes that are involved in the leukemic phenotype, including those encoding transcription factors, epigenetic modulators, and cell cycle regulators as well as those engaged in the transport or uptake of nutrients. These data enhance the functional cooperation between ZNF521 and MA9, resulting in the development, maintenance, and clonal expansion of leukemic cells. Finally, silencing of ZNF521 in MLL-AF9-transformed primary CD34+ cells inhibited their proliferation and led to their extinction, as well as ZNF521 silencing in the MLL-AF9+ THP-1 cell line resulted in an impairment of their growth and clonogenicity. Taken together, our data highlight ZNF521 role in the control of self-renewal and in the immature compartment of malignant hematopoiesis, which, by altering the gene expression landscape, contributes to the development and/or maintenance of AML acting in concert with the MLL-AF9 fusion oncogene.

Keywords: AML, human zinc finger protein 521 (hZNF521), mixed lineage leukemia gene (MLL) AF9 (MLLT3 or LTG9), cord blood-derived hematopoietic stem cells (CB-CD34+)

Procedia PDF Downloads 84

Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

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Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

Procedia PDF Downloads 156

Authors: A. Abdouni, C. Thieulin, M. Djaghloul, R. Vargiolu, H. Zahouani

Abstract:

A decline in the main sensory modalities (vision, hearing, taste, and smell) is well reported to occur with advancing age, it is expected a similar change to occur with touch sensation and perception. In this study, we have focused on the touch sensations highlighting ageing and gender influences with in vivo systems. The touch process can be divided into two main phases: The first phase is the first contact between the finger and the object, during this contact, an adhesive force has been created which is the needed force to permit an initial movement of the finger. In the second phase, the finger mechanical properties with their surface topography play an important role in the obtained sensation. In order to understand the age and gender effects on the touch sense, we develop different ideas and systems for each phase. To better characterize the contact, the mechanical properties and the surface topography of human finger, in vivo studies on the pulp of 40 subjects (20 of each gender) of four age groups of 26±3, 35+-3, 45+-2 and 58±6 have been performed. To understand the first touch phase a classical indentation system has been adapted to measure the finger contact properties. The normal force load, the indentation speed, the contact time, the penetration depth and the indenter geometry have been optimized. The penetration depth of a glass indenter is recorded as a function of the applied normal force. Main assessed parameter is the adhesive force F_ad. For the second phase, first, an innovative approach is proposed to characterize the dynamic finger mechanical properties. A contactless indentation test inspired from the techniques used in ophthalmology has been used. The test principle is to blow an air blast to the finger and measure the caused deformation by a linear laser. The advantage of this test is the real observation of the skin free return without any outside influence. Main obtained parameters are the wave propagation speed and the Young's modulus E. Second, negative silicon replicas of subject’s fingerprint have been analyzed by a probe laser defocusing. A laser diode transmits a light beam on the surface to be measured, and the reflected signal is returned to a set of four photodiodes. This technology allows reconstructing three-dimensional images. In order to study the age and gender effects on the roughness properties, a multi-scale characterization of roughness has been realized by applying continuous wavelet transform. After determining the decomposition of the surface, the method consists of quantifying the arithmetic mean of surface topographic at each scale SMA. Significant differences of the main parameters are shown with ageing and gender. The comparison between men and women groups reveals that the adhesive force is higher for women. The results of mechanical properties show a Young’s modulus higher for women and also increasing with age. The roughness analysis shows a significant difference in function of age and gender.

Keywords: ageing, finger, gender, touch

Procedia PDF Downloads 244

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Moustafa Osman Mohammed

Abstract:

This paper introduces Luhmann’s autopoietic social systems starting with the original concept of autopoiesis by biologists and scientists, including the modification of general systems based on socialized medicine. A specific type of autopoietic system is explained in the three existing groups of the ecological phenomena: interaction, social and medical sciences. This hypothesis model, nevertheless, has a nonlinear interaction with its natural environment ‘interactional cycle’ for the exchange of photon energy with molecular without any changes in topology. The external forces in the systems environment might be concomitant with the natural fluctuations’ influence (e.g. radioactive radiation, electromagnetic waves). The cantilever sensor deploys insights to the future chip processor for prevention of social metabolic systems. Thus, the circuits with resonant electric and optical properties are prototyped on board as an intra–chip inter–chip transmission for producing electromagnetic energy approximately ranges from 1.7 mA at 3.3 V to service the detection in locomotion with the least significant power losses. Nowadays, therapeutic systems are assimilated materials from embryonic stem cells to aggregate multiple functions of the vessels nature de-cellular structure for replenishment. While, the interior actuators deploy base-pair complementarity of nucleotides for the symmetric arrangement in particular bacterial nanonetworks of the sequence cycle creating double-stranded DNA strings. The DNA strands must be sequenced, assembled, and decoded in order to reconstruct the original source reliably. The design of exterior actuators have the ability in sensing different variations in the corresponding patterns regarding beat-to-beat heart rate variability (HRV) for spatial autocorrelation of molecular communication, which consists of human electromagnetic, piezoelectric, electrostatic and electrothermal energy to monitor and transfer the dynamic changes of all the cantilevers simultaneously in real-time workspace with high precision. A prototype-enabled dynamic energy sensor has been investigated in the laboratory for inclusion of nanoscale devices in the architecture with a fuzzy logic control for detection of thermal and electrostatic changes with optoelectronic devices to interpret uncertainty associated with signal interference. Ultimately, the controversial aspect of molecular frictional properties is adjusted to each other and forms its unique spatial structure modules for providing the environment mutual contribution in the investigation of mass temperature changes due to pathogenic archival architecture of clusters.

Keywords: autopoiesis, nanoparticles, quantum photonics, portable energy, photonic structure, photodynamic therapeutic system

Procedia PDF Downloads 101

Authors: Chien-Ho Lin, Ho-Ching Yang, Barbara Knowlton, Shin-Leh Huang, Ming-Chang Chiang

Abstract:

Practicing motor tasks arranged in an interleaved order (interleaved practice, or IP) generally leads to better learning than practicing tasks in a repetitive order (repetitive practice, or RP), an example of how desirable difficulty during practice benefits learning. Greater difficulty during practice, e.g. IP, is associated with greater brain activity measured by higher blood-oxygen-level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) in the sensorimotor areas of the brain. In this study resting-state fMRI was applied to investigate whether increase in resting-state brain connectivity immediately after practice predicts the benefit of desirable difficulty to motor learning. 26 healthy adults (11M/15F, age = 23.3±1.3 years) practiced two sets of three sequences arranged in a repetitive or an interleaved order over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day, fMRI data were acquired in a resting state after practice. The resting-state fMRI data was decomposed using a group-level spatial independent component analysis (ICA), yielding 9 independent components (IC) matched to the precuneus network, primary visual networks (two ICs, denoted by I and II respectively), sensorimotor networks (two ICs, denoted by I and II respectively), the right and the left frontoparietal networks, occipito-temporal network, and the frontal network. A weighted resting-state functional connectivity (wRSFC) was then defined to incorporate information from within- and between-network brain connectivity. The within-network functional connectivity between a voxel and an IC was gauged by a z-score derived from the Fisher transformation of the IC map. The between-network connectivity was derived from the cross-correlation of time courses across all possible pairs of ICs, leading to a symmetric nc x nc matrix of cross-correlation coefficients, denoted by C = (pᵢⱼ). Here pᵢⱼ is the extremum of cross-correlation between ICs i and j; nc = 9 is the number of ICs. This component-wise cross-correlation matrix C was then projected to the voxel space, with the weights for each voxel set to the z-score that represents the above within-network functional connectivity. The wRSFC map incorporates the global characteristics of brain networks measured by the between-network connectivity, and the spatial information contained in the IC maps measured by the within-network connectivity. Pearson correlation analysis revealed that greater IP-minus-RP difference in wRSFC was positively correlated with the RP-minus-IP difference in the response time on Day 5, particularly in brain regions crucial for motor learning, such as the right dorsolateral prefrontal cortex (DLPFC), and the right premotor and supplementary motor cortices. This indicates that enhanced resting brain connectivity during the early phase of memory consolidation is associated with enhanced learning following interleaved practice, and as such wRSFC could be applied as a biomarker that measures the beneficial effects of desirable difficulty on motor sequence learning.

Keywords: desirable difficulty, functional magnetic resonance imaging, independent component analysis, resting-state networks

Procedia PDF Downloads 183

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 21

Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

Abstract:

Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

Procedia PDF Downloads 110