Search results for: neural progentor cells

Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: C. Makwana, S. R. Dave

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Arsenic is toxic to almost all living cells. Its contamination in natural sources affects the growth of microorganisms. The presence of arsenic is associated with various human disorders also. The attempt of this sort of study provides information regarding the performance of our isolated microorganisms in the presence of Arsenic, which have ample scope for bioremediation. Six isolates were selected from the polluted sample of industrial zone Vatva, Ahmedabad, Gujarat, India, out of which two were Thermophilic organisms. The thermophilic exopolysaccharide (EPS) producing Bacillus was used for microbial enhance oil recovery (MEOR) and in the bio beneficiation. Inorganic arsenic primarily exists in the form of arsenate or arsenite. This arsenic resistance isolate was capable of transforming As +3 to As+5. This isolate would be useful for arsenic remediation standpoint from aquatic systems. The study revealed that the thermophilic microorganism was growing at 55 degree centigrade showed considerable remediation property. The results on the growth and enzyme catalysis would be discussed in response to Arsenic remediation.

Keywords: aquatic systems, thermophilic, exopolysacchride, arsenic

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Authors: Helen Pushkarskaya, Michael Smithson, Jane E. Joseph, Christine Corbly, Ifat Levy

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Studies of decision making under uncertainty generally focus on imprecise information about outcome probabilities (“ambiguity”). It is not clear, however, whether conflicting information about outcome probabilities affects decision making in the same manner as ambiguity does. Here we combine functional Magnetic Resonance Imaging (fMRI) and a simple gamble design to study this question. In this design, the levels of ambiguity and conflict are parametrically varied, and ambiguity and conflict gambles are matched on both expected value and variance. Behaviorally, participants avoided conflict more than ambiguity, and attitudes toward ambiguity and conflict did not correlate across subjects. Neurally, regional brain activation was differentially modulated by ambiguity level and aversion to ambiguity and by conflict level and aversion to conflict. Activation in the medial prefrontal cortex was correlated with the level of ambiguity and with ambiguity aversion, whereas activation in the ventral striatum was correlated with the level of conflict and with conflict aversion. This novel double dissociation indicates that decision makers process imprecise and conflicting information differently, a finding that has important implications for basic and clinical research.

Keywords: decision making, uncertainty, ambiguity, conflict, fMRI

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Authors: Mingxin Li, Liya Ni

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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

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Authors: Ali Ashjaran

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Graphene is undoubtedly emerging as one of the most promising materials because of its unique combination of superb properties, which opens a way for its exploitation in a wide spectrum of applications ranging from electronics to optics, sensors, and biodevices. In addition, Graphene-based nanomaterials have many promising applications in energy-related areas. Graphene a single layer of carbon atoms, combines several exceptional properties, which makes it uniquely suited as a coating material: transparency, excellent mechanical stability, low chemical reactivity, Optical, impermeability to most gases, flexibility, and very high thermal and electrical conductivity. Graphene is a material that can be utilized in numerous disciplines including, but not limited to: bioengineering, composite materials, energy technology and nanotechnology, biological engineering, optical electronics, ultrafiltration, photovoltaic cells. This review aims to provide an overiew of graphene structure, properties and some applications.

Keywords: graphene, carbon, anti corrosion, optical and electrical properties, sensors

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Authors: Mohammad F. Bostanudin, Tan S. Fei, Azwan M. Lazim

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Toward the development of colloidal systems that are able to enhance permeation across the skin, a material combining the non-toxic and non-immunogenic of dextran with alkylglycerols permeation enhancing property has been designed. To this purpose, a range of butylglyceryl dextrans (DEX-OX4) were synthesized via functionalization with n-butylglycidyl ether and the successful functionalization was confirmed by NMR and FT-IR spectroscopies, along with GPC with a degree of modification in the range 6.3–35.7 %. A reduced viscosity and an increased molecular weight of DEX-OX4 were also recorded when compared to that of the native dextran. DEX-OX4 was further formulated into nanocarriers and loaded with α-arbutin prior to be investigated for their particle size, morphology, stability, loading ability, and release profiles. The resulting nanoparticles were found to be close-to-spherical and relatively stable at pH 5 and 7, with size 180–220 nm (ζ-potential -22 to -25 mV), and a loading degree of 11.7 %. Lack of toxicity at application-relevant concentrations and increased permeation across skin biological membrane model were demonstrated by nanoparticles in-vitro results against immortalized skin human keratinocytes cells (HaCaT).

Keywords: butylglycerols, dextran, nanoparticles, transdermal

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Authors: Christine Li, Amanda Robertson

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Epidermal inclusion cysts occur when epidermal cells are implanted in the dermis following trauma, or surgery. They are a rare cause of an umbilical mass, with very few cases previously reported following abdominal surgery. These lesions can present with a range of symptoms, including palpable mass, pain, redness, or discharge. This paper reports a case of an umbilical epidermal inclusion cyst in a 52-year-old female presenting with a six-week history of a painful, red umbilical lump on a background of two previous diagnostic laparoscopies. Abdominal computed tomography (CT) scans revealed non-specific soft tissue thickening in the umbilical region. This was successfully treated with complete excision of the lesion. Umbilical lumps are a common presentation but can represent a diagnostic challenge. The differential diagnosis should include an epidermal inclusion cyst, particularly in a patient who has had previous abdominal surgery, including laparoscopic surgery.

Keywords: epidermal inclusion cyst, laparoscopy, umbilical mass, umbilicus

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Authors: Eugene Stepanov, Arkadi Ponossov

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Many mathematical models used in biological and other applications are ill-posed. The reason for that is the nature of differential equations, where the nonlinearities are assumed to be step functions, which is done to simplify the analysis. Prominent examples are switched systems arising from gene regulatory networks and neural field equations. This simplification leads, however, to theoretical and numerical complications. In the presentation, it is proposed to apply the theory of hybrid feedback controls to regularize the problem. Roughly speaking, one attaches a finite state control (‘automaton’), which follows the trajectories of the original system and governs its dynamics at the points of ill-posedness. The construction of the automaton is based on the classification of the attractors of the specially designed adjoint dynamical system. This ‘hybridization’ is shown to regularize the original switched system and gives rise to efficient hybrid numerical schemes. Several examples are provided in the presentation, which supports the suggested analysis. The method can be of interest in other applied fields, where differential equations contain step-like nonlinearities.

Keywords: hybrid feedback control, ill-posed problems, singular perturbation analysis, step-like nonlinearities

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

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Authors: Ali Soroush, Mehdi Mirzaei Alavijeh, Touraj Ahmadi Jouybari, Fazel Zinat-Motlagh, Abbas Aghaei, Mari Ataee

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Diabetes is a disease with long cardiovascular, renal, ophthalmic and neural complications. It is prevalent all around the world including Iran, and its prevalence is increasing. The aim of this study was to determine the factors related to self-care behavior based on health belief model among sample of Iranian diabetic patients. This cross-sectional study was conducted among 301 type 2 diabetic patients in Gachsaran, Iran. Data collection was based on an interview and the data were analyzed by SPSS version 20 using ANOVA, t-tests, Pearson correlation, and linear regression statistical tests at 95% significant level. Linear regression analyses showed the health belief model variables accounted for 29% of the variation in self-care behavior; and perceived severity and perceived self-efficacy are more influential predictors on self-care behavior among diabetic patients.

Keywords: diabetes, patients, self-care behaviors, health belief model

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Authors: Marita Pigłowska, Beata Kurc, Maciej Galiński

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The application of carbon materials in the branches of the electrochemical industry shows an increasing tendency each year due to the many interesting properties they possess. These are, among others, a well-developed specific surface, porosity, high sorption capacity, good adsorption properties, low bulk density, electrical conductivity and chemical resistance. All these properties allow for their effective use, among others in supercapacitors, which can store electric charges of the order of 100 F due to carbon electrodes constituting the capacitor plates. Coals (including expanded graphite, carbon black, graphite carbon fibers, activated carbon) are commonly used in electrochemical methods of removing oil derivatives from water after tanker disasters, e.g. phenols and their derivatives by their electrochemical anodic oxidation. Phenol can occupy practically the entire surface of carbon material and leave the water clean of hydrophobic impurities. Regeneration of such electrodes is also not complicated, it is carried out by electrochemical methods consisting in unblocking the pores and reducing resistances, and thus their reactivation for subsequent adsorption processes. Graphite is commonly used as an anode material in lithium-ion cells, while due to the limited capacity it offers (372 mAh g-1), new solutions are sought that meet both capacitive, efficiency and economic criteria. Increasingly, biodegradable materials, green materials, biomass, waste (including agricultural waste) are used in order to reuse them and reduce greenhouse effects and, above all, to meet the biodegradability criterion necessary for the production of lithium-ion cells as chemical power sources. The most common of these materials are cellulose, starch, wheat, rice, and corn waste, e.g. from agricultural, paper and pharmaceutical production. Such products are subjected to appropriate treatments depending on the desired application (including chemical, thermal, electrochemical). Starch is a biodegradable polysaccharide that consists of polymeric units such as amylose and amylopectin that build an ordered (linear) and amorphous (branched) structure of the polymer. Carbon is also used as a catalyst. Elemental carbon has become available in many nano-structured forms representing the hybridization combinations found in the primary carbon allotropes, and the materials can be enriched with a large number of surface functional groups. There are many examples of catalytic applications of coal in the literature, but the development of this field has been hampered by the lack of a conceptual approach combining structure and function and a lack of understanding of material synthesis. In the context of catalytic applications, the integrity of carbon environmental management properties and parameters such as metal conductivity range and bond sequence management should be characterized. Such data, along with surface and textured information, can form the basis for the provision of network support services.

Keywords: carbon materials, catalysis, BET, capacitors, lithium ion cell

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Authors: Bing Chen, Xiang Ni, Eric Li

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With the population ageing, the number of patients suffering from chronic diseases is increasing, among which stroke is a high incidence for the elderly. In addition, there is a gradual increase in the number of patients with orthopedic or neurological conditions such as spinal cord injuries, nerve injuries, and other knee injuries. These diseases are chronic, with high recurrence and complications, and normal walking is difficult for such patients. Nowadays, robotic knee exoskeletons have been developed for individuals with knee impairments. However, the currently available robotic knee exoskeletons are generally developed with heavyweight, which makes the patients uncomfortable to wear, prone to wearing fatigue, shortening the wearing time, and reducing the efficiency of exoskeletons. Some lightweight materials, such as carbon fiber and titanium alloy, have been used for the development of robotic knee exoskeletons. However, this increases the cost of the exoskeletons. This paper illustrates the design of a new ultra-light and ultra-stiff truss type of lattice structure. The lattice structures are arranged in a fan shape, which can fit well with circular arc surfaces such as circular holes, and it can be utilized in the design of rods, brackets, and other parts of a robotic knee exoskeleton to reduce the weight. The metamaterial is formed by continuous arrangement and combination of small truss structure unit cells, which changes the diameter of the pillar section, geometrical size, and relative density of each unit cell. It can be made quickly through additive manufacturing techniques such as metal 3D printing. The unit cell of the truss structure is small, and the machined parts of the robotic knee exoskeleton, such as connectors, rods, and bearing brackets, can be filled and replaced by gradient arrangement and non-uniform distribution. Under the condition of satisfying the mechanical properties of the robotic knee exoskeleton, the weight of the exoskeleton is reduced, and hence, the patient’s wearing fatigue is relaxed, and the wearing time of the exoskeleton is increased. Thus, the efficiency and wearing comfort, and safety of the exoskeleton can be improved. In this paper, a brief description of the hardware design of the prototype of the robotic knee exoskeleton is first presented. Next, the design of the ultra-light and ultra-stiff truss type of lattice structures is proposed, and the mechanical analysis of the single-cell unit is performed by establishing the theoretical model. Additionally, simulations are performed to evaluate the maximum stress-bearing capacity and compressive performance of the uniform arrangement and gradient arrangement of the cells. Finally, the static analysis is performed for the cell-filled rod and the unmodified rod, respectively, and the simulation results demonstrate the effectiveness and feasibility of the designed ultra-light and ultra-stiff truss type of lattice structures. In future studies, experiments will be conducted to further evaluate the performance of the designed lattice structures.

Keywords: additive manufacturing, lattice structures, metamaterial, robotic knee exoskeleton

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Authors: A. Bhartiya, S. Botchway, M. Yusuf, I. Robinson

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Chromatin condensation is maintained by DNA-based proteins and some divalent cations (Mg²⁺, Ca²⁺, etc.). Condensation process during cell division maintains structural and functional organizations of chromosomes by transferring genetic information correctly to daughter cells. Fluorescence Lifetime Imaging (FLIM) technique measures the fluorescence decay of fixed human chromosomes by calculating the lifetime of fluorophores at a pixel x of the arrival of each photon as a function of time delay t, following excitation with a laser pulse. Fixed metaphase human chromosomes were labelled with DNA-binding dye, DAPI and later DAPI fluorescence lifetime measured using multiphoton microscopy. 5 out of 23 pairs of human chromosomes shown shorter lifetime at the centromere region, differentiating proportion of compaction along the length of chromosomes. Different lifetime was observed in a condensed and de-condensed chromosome. It clearly indicates the involvement of divalent cations in the process of condensation.

Keywords: divalent cations, FLIM (Fluorescence Lifetime Imaging), human chromosomes, multiphoton microscopy

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Authors: Priyanka Sharma, Rajeshwar Nath Srivastava

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Hydrogen has a high level of efficacy in suppressing tumour growth. The role of hydrogen in cancer treatment is unclear. This groundbreaking research will focus on the most effective therapeutic approach for osteosarcoma. Recent data reveals that hydrogen, a naturally occurring gaseous chemical, can protect cells from death. However, little is known about the signalling pathways that regulate cardiac cell death and individual apoptosis signalling by H2 and its downstream targets. According to certain research, the anti-tumor effect of H2 released by magnesium-based biomaterials is mediated by the P53-mediated lysosome-mitochondria apoptosis signalling pathway, bolstering the biomaterial's therapeutic potential as a localised anti-tumor treatment. The role of the H2 molecule in the signalling of apoptotic, autophagic, necroptotic, and pyroptotic cell death in Osteosarcoma is discussed in this paper. Potential Hydrogen-based therapy techniques will broaden the treatment horizon for Osteosarcoma.

Keywords: osteosarcoma, metastasis, hhydrogen, therapeutic

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Authors: Gabriele Barrocas, Habon Issa

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The amygdala is one of various brain regions that tend to be pathological in individuals with autism spectrum disorder (ASD). ASD is a prevalent and heterogeneous developmental disorder affecting all ethnic and socioeconomic groups and consists of a broad range of severity, etiology, and behavioral symptoms. Common features of ASD include but are not limited to repetitive behaviors, obsessive interests, and anxiety. Neuroscientists view the amygdala as the core of the neural system that regulates behavioral responses to anxiogenic and threatening stimuli. Despite this consensus, many previous studies and literature reviews on the amygdala’s alterations in individuals with ASD have reported inconsistent findings. In this review, we will address these conflicts by highlighting recent studies which reveal that anatomical and related socio-emotional differences detected between individuals with and without ASD are highly age-dependent. We will specifically discuss studies using functional magnetic resonance imaging (fMRI), structural MRI, and diffusion tensor imaging (DTI) to provide insights into the neuroanatomical substrates of ASD across development, with a focus on amygdala volumes, cell densities, and connectivity.

Keywords: autism, amygdala, development, abnormalities

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Authors: Animesh Pan, Geoffrey D. Bothun

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With the development of nanotechnology, research in multifunctional delivery systems has a new pace and dimension. An incipient challenge is to design an all-in-one delivery system that can be used for multiple purposes, including tumor targeting therapy, radio-frequency (RF-), near-infrared (NIR-), light-, or pH-induced controlled release, photothermal therapy (PTT), photodynamic therapy (PDT), and medical diagnosis. In this regard, various inorganic nanoparticles (NPs) are known to show great potential as the 'functional components' because of their fascinating and tunable physicochemical properties and the possibility of multiple theranostic modalities from individual NPs. Magnetic, luminescent, and plasmonic properties are the three most extensively studied and, more importantly biomedically exploitable properties of inorganic NPs. Although successful attempts of combining any two of them above mentioned functionalities have been made, integrating them in one system has remained challenge. Keeping those in mind, controlled designs of complex colloidal nanoparticle system are one of the most significant challenges in nanoscience and nanotechnology. Therefore, systematic and planned studies providing better revelation are demanded. We report a multifunctional delivery platform-based liposome loaded with drug, iron-oxide magnetic nanoparticles (MNPs), and a gold shell on the surface of liposomes, were synthesized using a lipid with polyelectrolyte (layersomes) templating technique. MNPs and the anti-cancer drug doxorubicin (DOX) were co-encapsulated inside liposomes composed by zwitterionic phophatidylcholine and anionic phosphatidylglycerol using reverse phase evaporation (REV) method. The liposomes were coated with positively charge polyelectrolyte (poly-L-lysine) to enrich the interface with gold anion, exposed to a reducing agent to form a gold nanoshell, and then capped with thio-terminated polyethylene glycol (SH-PEG2000). The core-shell nanostructures were characterized by different techniques like; UV-Vis/NIR scanning spectrophotometer, dynamic light scattering (DLS), transmission electron microscope (TEM). This multifunctional system achieves a variety of functions, such as radiofrequency (RF)-triggered release, chemo-hyperthermia, and NIR laser-triggered for photothermal therapy. Herein, we highlight some of the remaining major design challenges in combination with preliminary studies assessing therapeutic objectives. We demonstrate an efficient loading and delivery system to significant cell death of human cancer cells (A549) with therapeutic capabilities. Coupled with RF and NIR excitation to the doxorubicin-loaded core-shell nanostructure helped in securing targeted and controlled drug release to the cancer cells. The present core-shell multifunctional system with their multimodal imaging and therapeutic capabilities would be eminent candidates for cancer theranostics.

Keywords: cancer thernostics, multifunctional nanostructure, photothermal therapy, radiofrequency targeting

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Authors: Kazuaki Shiraishi, Narumitsu Asai, Tsukasa Kitahara, Sosuke Mieno, Takaharu Kameoka

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The purpose of this research is to facilitate productivity advances in agriculture. To accomplish this, we developed an automatic three-dimensional (3D) recording system for growth of field crops that consists of a number of inexpensive modules: a very low-cost stereo camera, a couple of ZigBee wireless modules, a Raspberry Pi single-board computer, and a third generation (3G) wireless communication module. Our system uses an inexpensive Web stereo camera in order to keep total costs low. However, inexpensive video cameras record low-resolution images that are very noisy. Accordingly, in order to resolve these problems, we adopted a deep learning method. Based on the results of extended period of time operation test conducted without the use of an external power supply, we found that by using Super-Resolution Convolutional Neural Network method, our system could achieve a balance between the competing goals of low-cost and superior performance. Our experimental results showed the effectiveness of our system.

Keywords: 3D plant data, automatic recording, stereo camera, deep learning, image processing

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Authors: Hee Sun (Sunny) Choi, Gerhard Bruyns, Wang Zhang, Sky Cheng, Saijal Sharma

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This research addresses the need for a comprehensive framework that can guide the design and assessment of multi-level public spaces and public realms and their impact on the built environment. The study aims to understand and measure the neural mechanisms involved in this process. By doing so, it can lay the foundation for vertical and volumetric urbanism and ensure consistency and excellence in the field while also supporting scientific research methods for urban design with cognitive neuroscientists. To investigate these aspects, the paper focuses on the neighborhood scale in Hong Kong, specifically examining multi-level public spaces and quasi-public spaces within both commercial and residential complexes. The researchers use predictive Artificial Intelligence (AI) as a methodology to assess and comprehend the applicability of the urban design framework for vertical and volumetric urbanism. The findings aim to identify the factors that contribute to successful public spaces within a vertical living environment, thus introducing a new typology of public spaces.

Keywords: vertical urbanism, scientific research methods, spatial cognition, urban design guideline

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Authors: Redouane Tlemsani, Abdelkader Benyettou

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Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%.

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, computer vision

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Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

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Authors: Elias Nemer, Greg Vines

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Time-frequency transformation and spectral representations of audio signals are commonly used in various machine learning applications. Training networks on frequency features such as the Mel-Spectrogram or Cochleogram have been proven more effective and convenient than training on-time samples. In practical realizations, these features are created on a different processor and/or pre-computed and stored on disk, requiring additional efforts and making it difficult to experiment with different features. In this paper, we provide a PyTorch framework for creating various spectral features as well as time-frequency transformation and time-domain filter-banks using the built-in trainable conv1d() layer. This allows computing these features on the fly as part of a larger network and enabling easier experimentation with various combinations and parameters. Our work extends the work in the literature developed for that end: First, by adding more of these features and also by allowing the possibility of either starting from initialized kernels or training them from random values. The code is written as a template of classes and scripts that users may integrate into their own PyTorch classes or simply use as is and add more layers for various applications.

Keywords: neural networks Mel-Spectrogram, chromagram, cochleogram, discrete Fourrier transform, PyTorch conv1d()

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Authors: Reshma Jolly, Mohammad Shakir, Mohammad Shoeb Khan, Noor E. Iram

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A nanocomposite system integrating hydroxyapatite, chitin and starch (n-HA/CT/ST) has been synthesized via co-precipitation approach at room temperature, addressing the issues of biocompatibility, mechanical strength and cytotoxicity required for Bone tissue engineering. The interactions, crystallite size and surface morphology against n-HA/CT (nano-hydroxyapatite/chitin) nanocomposite have been obtained by correlating and comparing the results of FTIR, SEM, TEM and XRD. The comparative study of the bioactivity of n-HA/CT and n-HA/CT/ST nanocomposites revealed that the incorporation of starch as templating agent improved these properties in n-HA/CT/ST nanocomposite. The rise in thermal stability in n-HA/CT/ST nanocomposite as compared to n-HA/CT has been observed by comparing the TGA results. The comparison of SEM images of both the scaffolds indicated that the addition of ST influenced the surface morphology of n-HA/CT scaffold which appeared to be rougher and porous. The MTT assay on murine fibroblast L929 cells and in-vitro bioactivity of n-HA/CT/ST matrix referred superior non-toxic property of n-HA/CT/ST nanocomposite and higher possibility of osteo-integration in-vivo, respectively.

Keywords: bioactive, chitin, hyroxyapatite, nanocomposite

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Authors: Temesgen Geremew

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ZnO is currently receiving a lot of attention in the semiconductor industry due to its unique characteristics. ZnO is widely used in solar cells, heat-reflecting glasses, optoelectronic bias, and detectors. In this composition, we provide an overview of the ZnO thin flicks' packages, methods of characterization, and implicit operations. They consist of Transmission spectroscopy, Raman spectroscopy, Field emigration surveying electron microscopy, and X-ray diffraction. This review content also demonstrates how ZnO thin flicks function in electrical components for piezoelectric bias, optoelectronics, detectors, and renewable energy sources. Zinc oxide (ZnO) thin films offer a captivating tapestry of possibilities due to their unique blend of electrical, optical, and mechanical properties. This review delves into the realm of their potential applications and future prospects, highlighting the pivotal contributions of research endeavors aimed at tailoring their functionalities.

Keywords: Zinc oxide, raman spectroscopy, thin films, piezoelectric devices

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Authors: Hasan Basri Jumin

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Protoplasts isolated from embryogenic callus of Citrus sinensis were electrically used with mesophyll protoplasts isolated from seedless Citrus relatives. Hybrid of somatic embryos plantlets was obtained after 7 months of culture. Somatic hybrid plants were regenerated into normal seedlings and successfully transferred to soil after strictly acclimatization in the glass pot. The somatic hybrid plants were obtained by screening on the basis of chromosomes count. The number of chromosome of root tip counting revealed plantlets tetraploids (2n = 4x = 36) and the other were diploids (2n = 2x = 18) morphologically resembling the mesophyll parent. This somatic hybrid will be utilized as a possible pollen parent for improving the Citrus sinensis. A complete protoplast-to-plant system of somatic hybrid was developed for Citrus sinensis and Citrus relatives which could facilitate the transfer of nuclear and cytoplasmic genes from this species into cultivated Citrus through protoplast fusion.

Keywords: chromosome, Murraya paniculata, protoplast fusion, somatic hybrid, tetrapoliod

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Authors: Chandra Shekhar Majumder, Shamsul Haque, Khondaker Anower Hossain, Rafiqul Islam

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Introduction: Ocular helminths are parasites that infect the eye or its adnexa. They can be either motile worms or sessile worms that form cysts. These parasites require two hosts for their life cycle, a definite host (usually a human) and an intermediate host (usually an insect). While there have been reports of ocular helminths infecting various structures of the eye, including the anterior chamber and subconjunctival space, there is no previous record of such a case involving the lens. Research Aim: The aim of this case report is to present a rare case of ocular helminth infection in the lens and to contribute to the understanding of this unusual site of infection. Methodology: This study is a case report, presenting the details and findings of an 80-year-old retired policeman who presented with severe pain, redness, and vision loss in the left eye. The examination revealed the presence of a thread-like helminth in the lens. The data for this case report were collected through clinical examination and medical records of the patient. The findings were described and presented in a descriptive manner. No statistical analysis was conducted. Case report: An 80-year-old retired policeman attended the OPD, Faridpur Medical College Hospital with the complaints of severe pain, redness and gross dimness of vision of the left eye for 5 days. He had a history of diabetes mellitus and hypertension for 3 years. On examination, L/E visual acuity was PL only, moderate ciliary congestion, KP 2+, cells 2+ and posterior synechia from 5 to 7 O’clock position was found. Lens was opaque. A thread like helminth was found under the anterior of the lens. The worm was moving and changing its position during examination. On examination of R/E, visual acuity was 6/36 unaided, 6/18 with pinhole. There was lental opacity. Slit-lamp and fundus examination were within normal limit. Patient was admitted in Faridpur Medical College Hospital. Diabetes mellitus was controlled with insulin. ICCE with PI was done on the same day of admission under depomedrol coverage. The helminth was recovered from the lens. It was thread like, about 5 to 6 mm in length, 1 mm in width and pinkish in colour. The patient followed up after 7 days, VA was HM, mild ciliary congestion, few KPs and cells were present. Media was hazy due to vitreous opacity. The worm was sent to the department of Parasitology, NIPSOM, Dhaka for identification. Theoretical Importance: This case report contributes to the existing literature on ocular helminth infections by reporting a unique case involving the lens. It highlights the need for further research to understand the mechanism of entry of helminths in the lens. Conclusion: To the best of our knowledge, this is the first reported case of ocular helminth infection in the lens. The presence of the helminth in the lens raises interesting questions regarding its pathogenesis and entry mechanism. Further study and research are needed to explore these aspects. Ophthalmologists and parasitologists should be aware of the possibility of ocular helminth infections in unusual sites like the lens.

Keywords: helminth, lens, ocular, unusual

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Jessy Mattar, Mohamad Turk, Maurice Nonus, Nikolai Lebovka, Henri El Zakhem, Eugene Vorobiev

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The effects of electro-stimulation of S. cerevisiae cells in colloidal suspension by Pulsed Electric Fields ‎‎(PEF) with electric field strength E = 20 – 2000 V.cm-1 and effective PEF treatment time tPEF = 10^−5 – 1 s were ‎investigated. The applied experimental procedure includes variations in the preliminary fermentation time and ‎electro-stimulation by PEF-treatment. Plate counting was performed.‎ At relatively high electric fields (E ≥ 1000 V.cm-1) and moderate PEF treatment time (tPEF > 100 µs), the ‎extraction of ionic components from yeast was observed by conductivity measurements, which can be related to ‎electroporation of cell membranes. Cell counting revealed a dependency of the colonies’ size on the time of ‎preliminary fermentation tf and the power consumption W, however no dependencies were noticeable by varying the initial yeast concentration in the treated suspensions.‎

Keywords: intensification, yeast, fermentation, electroporation, biotechnology

Procedia PDF Downloads 469

Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

Procedia PDF Downloads 125

Authors: Diwei Lin, Amanda Jh. Tan

Abstract:

We present a very rare case of de novo large cell neuroendocrine carcinoma of the prostate (LCNEC) in an 84-year-old male on a background of high-grade, muscle-invasive transitional cell carcinoma of the bladder. While NE tumours account for 1% to 5% of all cases of prostate cancer and scattered NE cells can be found in 10% to 100% of prostate adenocarcinomas, pure LCNEC of the prostate is extremely rare. Most LCNEC of the prostate is thought to originate by clonal progression under the selection pressure of therapy and refractory to long-term hormonal treatment for adenocarcinoma of the prostate. De novo LCNEC is only described in case reports and is thought to develop via direct malignant transformation. Limited data in the English literature makes it difficult to accurately predict the prognosis of LCNEC of the prostate. However, current evidence suggesting that increasing NE differentiation in prostate adenocarcinoma is associated with a higher stage, high-grade disease, and a worse prognosis.

Keywords: large cell neuroendocrine cancer, prostate cancer, refractory cancer, medical and health sciences

Procedia PDF Downloads 422