Search results for: neural stem cells

Authors: Shamill Amedot Udonwa, Phyllis S. Y. Chong, Lim S. L. Julia, Wee-Joo Chng

Abstract:

In this paper, we investigated how PRL-3 expression in H929 and U266 cells affects the efficacy of drug treatment. H929 and U266 cells were treated with Bortezomib (BTZ) of different concentrations, and it was observed that H929 cells were resistant to BTZ, while U266 cells were not viable. Investigations into how BTZ targets these cells were conducted, and it was observed that BTZ affects the PARP-Caspase3 pathway as well as PRL-3-Leo1 pathways. These pathways regulate cell proliferation and cell cycle, respectively. Hence, we are able to show the mechanism of how BTZ affects cells and also the role PRL-3 plays on downstream oncogenes such as cyclin-D1 and c-MYC. More importantly, this investigation into PRL-3 in BTZ resistance will be highly applicable in the future as the first clinical trials of PRL-3 antibody (PRL3-zumab) are ongoing at the National University Hospital, Singapore (NUHS). This would mean that understanding the mechanism of resistance through PRL-3, which has yet to be studied, will demonstrate the potential of PRL-3 in developing novel strategies to improve the treatment of MM.

Keywords: drug resistance, hematology, multiple myeloma, oncogene

Procedia PDF Downloads 115

Authors: Nurul Amziah Md Yunus, Izhal Abdul Halin, Nasri Sulaiman, Noor Faezah Ismail, Nik Hasniza Nik Aman

Abstract:

Nanotechnology has become the world attention in various applications including the solar cells devices due to the uniqueness and benefits of achieving low cost and better performances of devices. Recently, thin film solar cells such as cadmium telluride (CdTe), copper-indium-gallium-diSelenide (CIGS), copper-zinc-tin-sulphide (CZTS), and dye-sensitized solar cells (DSSC) enhanced by nanotechnology have attracted much attention. Thus, a compilation of nanotechnology devices giving the progress in the solar cells has been presented. It is much related to nanoparticles or nanocrystallines, carbon nanotubes, and nanowires or nanorods structures.

Keywords: nanotechnology, nanocrystalline, nanowires, carbon nanotubes, nanorods, thin film solar cells

Procedia PDF Downloads 593

Authors: Kairat Uteulin, Azhar Iskakova, Serik Mukhambetzhanov, Bayan Yesbolayeva, Gabit Bari, Aslan Zheksenbai, Kabyl Zhambakin, Chingis Dzhabykbayev, Vladimir Piven, Izbasar Rakhimbaiev

Abstract:

To explore the potential for in vitro rapid regeneration of Russian dandelion (Taraxacum kok-saghyz Rodin), different concentrations of 6-Benzylaminopurine (BAP), 2,4-Dichlorophenoxyacetic acid (2.4-D) and BAP combined with Indole-3-acetic acid (IAA) were evaluated for their effects on the induction of somatic embryos from leaf, seed stem and root explants. Different explants were cultured on MS medium supplemented with various concentrations (0, 0.5, 1, 1.5, 2, 2.5 and 3 mg/l) of each kind of hormone. Callus induction percentage, fresh weight, color and texture of the callus were assessed after 14 and 28 days of culture. The optimum medium for the proliferation of embryogenic calli from leaf and root explants was MS supplemented with 2.5 mg/L BAP and 0.5 mg/L 2.4-D. Concentrations of 2.5 mg/L BAP and 1.5 mg/L IAA also had a remarkable effect on root and stem explants. The best concentration to produce callus from stem explants was 0.5 mg/L BAP and 1 mg/L IAA. Results of mean comparison showed that BAP and 2.4-D were more effective on different explants than BAP and IAA. Results of the double staining method proved that somatic embryogenesis occurred in the most concentrations of BAP and 2.4-D. Under microscopic observations, the different developmental stages of the embryos (globular, heart, torpedo and cotyledonary) were revealed together in callus cells, indicating that the most tested hormone combinations were effective for somatic embryogenesis formation in this species. Seed explants formed torpedo and cotyledonary stages faster than leaf and root explants in the most combinations. Most calli from seed explants were cream colored and friable, while calli were compact and light green from leaf and root explants. Some combinations gave direct regeneration and (3 mg/L BAP and 2 mg/L IAA) in seed explants and (0.5 mg/L BAP and 2.5 mg/L IAA) in leaf explants had the highest number of shoots with average of 21 and 27 shoots per callus. The developed protocol established the production of different callus types from seed, leaf, and root explants and plant regeneration through somatic embryogenesis.

Keywords: taraxacum kok-saghyz Rodin, callus, somatic embryogenesis

Procedia PDF Downloads 345

Authors: Ramin Amirmardfar

Abstract:

Throughout Necessary oxygen should be supplied for all cells of a mammal at any moment through blood to make it possible remain alive all cells the mammal’s body. In case a mammal’s bulk is large, there is a farther distance between cells in different tissues and mammals’ heart. Therefore red blood cells in bulky mammal’s body should be capable of conveying oxygen to farther distances. To make it practical, oxygen should be glued red blood cells tenaciously. In other words, cohesion strength of oxygen to red blood cell of bulky mammal’s blood should be much more than the same of small mammal’s blood. In mammal’s bodies, the controlling factor of amount of cohesion of oxygen to red blood cell, are organic phosphates (like DPG). The less DPG in red blood cells of a mammal, the more cohesion of oxygen to red blood cell (at the same rate). As much as oxygen is glued more tenacious to red blood cells, oxygen could been carried to farther distance and as much as oxygen could be conveyed to farther points of heart, bulk of mammal could be larger at the same rate.

Keywords: mammals size, animals size, organic phosphates, DPG, red blood cell, metabolism

Procedia PDF Downloads 330

Authors: Kang-Hyun Leem, Myung-Gyou Kim, Hye Kyung Kim

Abstract:

The prickly pear cactus (Opuntia ficus-indica) has a global distribution and have been used for medicinal benefits such as artherosclerosis, diabetes, gastritis, and hyperglycemia. However, very little information is currently available for their mechanism. The prikly pear variety Opuntia ficus-indica var. Saboten (OFS) is widely cultivated in Cheju Island, southwestern region of Korea, and used as a functional food. Present study investigated the effects of OFS on pancreatic β-cell function using pancreatic islet β cells (HIT cell). Alpha-glucosidase inhibition, glucose uptake, insulin secretion, insulin sensitivity, and pancreatic β cell proliferation were determined. The inhibitory effect of ethanol extract of OFS stem on α-glucosidase enzyme was measured in a cell free system. Glucose uptake was determined using fluorescent glucose analogue, 2-NBDG. Insulin secretion was measured by ELISA assay. Cell proliferation was measured by MTT assay. Ethanol extracts of OFS dose-dependently inhibited α-glucosidase activity as well as glucose uptake. Insulinotrophic effect of OFS extract was observed at high glucose media in pancreatic β-islet cells. Furthermore, pancreatic β cell regeneration was also observed.These results suggest that OFS mediates the antidiabetic activity mainly via α-glucosidase inhibition, glucose uptake, and improved insulin sensitivity.

Keywords: prickly pear cactus, Opuntia ficus-indica var. Saboten, pancreatic islet HIT cells, α-glucosidase, glucose uptake, insulinotrophic

Procedia PDF Downloads 441

Authors: Alisan Kayabolen, Dilek Keskin, Ferit Avcu, Andac Aykan, Fatih Zor, Aysen Tezcaner

Abstract:

Adipose tissue engineering is a promising field for regeneration of soft tissue defects. However, only very thin implants can be used in vivo since vascularization is still a problem for thick implants. Another problem is finding a biocompatible scaffold with good mechanical properties. In this study, the aim is to develop a thick vascularized adipose tissue that will integrate with the host, and perform its in vitro and in vivo characterizations. For this purpose, a hydrogel of decellularized adipose tissue (DAT) and fibroin was produced, and both endothelial cells and adipocytes that were differentiated from adipose derived stem cells were encapsulated in this hydrogel. Mixing DAT with fibroin allowed rapid gel formation by vortexing. It also provided to adjust mechanical strength by changing fibroin to DAT ratio. Based on compression tests, gels of DAT/fibroin ratio with similar mechanical properties to adipose tissue was selected for cell culture experiments. In vitro characterizations showed that DAT is not cytotoxic; on the contrary, it has many natural ECM components which provide biocompatibility and bioactivity. Subcutaneous implantation of hydrogels resulted with no immunogenic reaction or infection. Moreover, localized empty hydrogels gelled successfully around host vessel with required shape. Implantations of cell encapsulated hydrogels and histological analyses are under study. It is expected that endothelial cells inside the hydrogel will form a capillary network and they will bind to the host vessel passing through hydrogel.

Keywords: adipose tissue engineering, decellularization, encapsulation, hydrogel, vascularization

Procedia PDF Downloads 507

Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

Abstract:

It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

Procedia PDF Downloads 288

Authors: A. N. Gornostaeva, P. I. Bobyleva, E. R. Andreeva, L. B. Buravkova

Abstract:

Multipotent mesenchymal stromal cells (MSCs) possess immunomodulatory properties. The effect of MSCs on the crucial cellular immunity compartment – T-cells is of a special interest. It is known that MSC tissue niche and expected milieu of their interaction with T- cells are characterized by low oxygen concentration, whereas the in vitro experiments usually are carried out at a much higher ambient oxygen (20%). We firstly evaluated immunomodulatory effects of MSCs on T-cells at tissue-related oxygen (5%) after interaction implied cell-to-cell contacts and paracrine factors only. It turned out that MSCs under reduced oxygen can effectively suppress the activation and proliferation of PHA-stimulated T-cells and can provoke decrease in the production of proinflammatory and increase in anti-inflammatory cytokines. In hypoxia some effects were amplified (inhibition of proliferation, anti-inflammatory cytokine profile shift). This impact was more evident after direct cell-to-cell interaction; lack of intercellular contacts could revoke the potentiating effect of hypoxia.

Keywords: MSCs, T-cells, activation, low oxygen, cell-to-cell interaction, immunosuppression

Procedia PDF Downloads 356

Authors: Chu Liu

Abstract:

With the development of society and the changes of the times, the requirements for the cultivation of learners are different. In the 21st century, STEM education has become a boom in the development of education in various countries, aiming to improve the comprehensive ability of learners in science, technology, engineering, and mathematics. The rise of robot education provides an effective way for STEM education to cultivate computational thinking ability, interdisciplinary ability, problem-solving ability, and teamwork ability. Although robot education has been developed in China for several years, it still lacks a standard curriculum system. This article uses programming software as a platform, through the research and analysis of 'Basic Education Information Technology Curriculum Standards (2012 Edition)', combines with the actual learning situation of learners, tries to conduct teaching project design research, and aims at providing references for the teaching ideas and method of robot education courses. In contemporary society, technological advances increasingly require creativity. Innovative comprehensive talents urgently need a radical and effective education reform to keep up with social changes. So in this context, robot teaching design can be used for students. The tendency of creativity to influence is worth to be verified.

Keywords: STEM education, robot teaching, primary and secondary school students, tendency of creativity

Procedia PDF Downloads 95

Authors: Oladapo Tolulope Ibitoye

Abstract:

Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

Procedia PDF Downloads 45

Authors: Kyungbae Pi, Kibeom Lee, Yongil Kim, Eun-Jung Lee

Abstract:

Abnormal adipocyte growth, in terms of increased cell numbers and increased cell differentiation, is considered to be a major pathological feature of obesity. Thus, the inhibition of preadipocyte mitogenesis and differentiation could help prevent and suppress obesity. The aim of this study was to assess whether extracts from Weissella koreensis 521 cells isolated from kimchi could exert anti-adipogenic effects in 3T3-L1 cells (fat cells). Differentiating 3T3-L1 cells were treated with W. koreensis 521 cell extracts (W. koreensis 521_CE), and cell viability was assessed by MTT assays. At concentrations below 0.2 mg/ml, W. koreensis 521_CE did not exert any cytotoxic effect in 3T3-L1 cells. However, treatment with W. koreensis 521_CE significantly inhibited adipocyte differentiation, as assessed by morphological analysis and Oil Red O staining of fat. W. koreensis 521_CE treatment (0.2 mg/ml) also reduced lipid accumulation by 24% in fully differentiated 3T3-L1 adipocytes. These findings collectively indicate that Weissella koreensis 521 may help prevent obesity.

Keywords: Weissella koreensis 521, 3T3-L1 cells, adipocyte differentiation, obesity

Procedia PDF Downloads 227

Authors: S. Neelima, P. S. Subramanyam

Abstract:

The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

Procedia PDF Downloads 399

Authors: Han Bing, Yin Zhenjie

Abstract:

In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

Procedia PDF Downloads 274

Authors: Gisele Ragusa, Lilian Leung

Abstract:

There are essential needs for science, technology, engineering, and math (STEM) workforces nationally. This important need underscores the necessity of increasing numbers of students attending both two-year community colleges and universities, thereby enabling and supporting a larger pool of students to enter the workforce. The greatest number of students in STEM programs attend public higher education institutions, with an even larger majority beginning their academic experiences enrolled in two-year public colleges. Accordingly, this research explores the impact of experiences and academic support practices on two-year (community) college students’ academic achievement in STEM majors with a focus on supporting students who are the first in their families to attend college. This research is a result of three years of iterative trials of differing supports to improve such students’ academic success with a cross-student comparative research methodological structure involving peer-to-peer and faculty academic supports. Results of this research indicate that background experiences and a combination of peer-to-peer and faculty-led academic support practices, including supplementary instruction, peer mentoring, and study skills support, significantly improve students’ academic success in STEM majors. These results confirm the needs that first-generation students have in navigating their college careers and what can be effective in supporting them.

Keywords: higher education policy, student support, two-year colleges, STEM achievement

Procedia PDF Downloads 55

Authors: Jie Zhao, Meng Su

Abstract:

Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

Procedia PDF Downloads 55

Authors: Abdolreza Memari

Abstract:

In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: viscosity, Iranian crude oil, radial based, neural network, roller ball method, KHAN model

Procedia PDF Downloads 463

Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

Abstract:

Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

Procedia PDF Downloads 571

Authors: Youssef A. Yakoub, Ramy M. Shaaban

Abstract:

Covid-19 has changed and affected the whole world dramatically in a new way that the entire world, even scientists, have not imagined before. The educational institutions around the world have been fighting since Covid-19 hit the world last December to keep the educational process unchanged for all students. E-learning was a must for almost all US universities during the pandemic. It was specifically more challenging to use eLearning instead of regular classes among students who take practical education. The aim of this study is to examine the perception of STEM students towards using eLearning instead of traditional methods during their practical study. Focus groups of STEM students studying at a western Pennsylavian, mid-size university were interviewed. Semi-structured interviews were designed to get an insight on students’ perception towards the alternative educational methods they used in the past seven months. Using convenient sampling, four students were chosen from different STEM fields: science of physics, technology, electrical engineering, and mathematics. The interview was primarily about the extent to which these students were satisfied, and their educational needs were met through distance education during the pandemic. The interviewed students were generally able to do a satisfactory performance during their virtual classes, but they were not satisfied enough with the learning methods. The main challenges they faced included the inability to have real practical experience, insufficient materials posted by the faculty, and some technical problems associated with their study. However, they reported they were satisfied with the simulation programs they had. They reported these simulations provided them with a good alternative to their traditional practical education. In conclusion, this study highlighted the challenges students face during the pandemic. It also highlighted the various learning tools students see as good alternatives to their traditional education.

Keywords: eLearning, STEM education, COVID-19 crisis, online practical training

Procedia PDF Downloads 108

Authors: Oluwaponmile David Alao

Abstract:

Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

Procedia PDF Downloads 623

Authors: Dong-An Wang

Abstract:

All kinds of microspheres have been extensively employed as carriers for drug, gene and therapeutic cell delivery. Most therapeutic cell delivery microspheres rely on a two-step methodology: fabrication of microspheres and subsequent seeding of cells onto them. In this study, we have developed a novel one-step cell encapsulation technique using a convenient and instant water-in-oil single emulsion approach to form cell-encapsulated gelatin microspheres. This technology is adopted for hyaline cartilage tissue engineering, in which autologous chondrocytes are used as therapeutic cells. Cell viability was maintained throughout and after the microsphere formation (75-100 µm diameters) process that avoids involvement of any covalent bonding reactions or exposure to any further chemicals. Further encapsulation of cell-laden microspheres in alginate gels were performed under 4°C via a prompt process. Upon the formation of alginate constructs, they were immediately relocated into CO2 incubator where the temperature was maintained at 37°C; under this temperature, the cell-laden gelatin microspheres dissolved within hours to yield similarly sized cavities and the chondrocytes were therefore suspended within the cavities inside the alginate gel bulk. Hence, the gelatin cell-laden microspheres served two roles: as cell delivery vehicles which can be removable through temperature curing, and as porogens within an alginate hydrogel construct to provide living space for cell growth and tissue development as well as better permeability for mutual diffusions. These cell-laden microspheres, namely “temperature-cured dissolvable gelatin microsphere based cell carriers” (tDGMCs), were further encapsulated in a chondrocyte-laden alginate scaffold system and analyzed by WST-1, gene expression analyses, biochemical assays, histology and immunochemistry stains. The positive results consistently demonstrated the promise of tDGMC technology in delivering these non-anchorage dependent cells (chondrocytes). It can be further conveniently translated into delivery of other non-anchorage dependent cell species, including stem cells, progenitors or iPS cells, for regeneration of tissues in internal organs, such as engineered hepatogenesis or pancreatic regeneration.

Keywords: biomaterials, tissue engineering, microsphere, hydrogel, porogen, anchorage dependence

Procedia PDF Downloads 363

Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

Abstract:

The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

Procedia PDF Downloads 14

Authors: Vinai K. Singh

Abstract:

In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

Procedia PDF Downloads 108

Authors: Luristya Nur Mahfut, Nada Mawarda Rilek, Ameiga Cautsarina Putri, Mujaroh Khotimah

Abstract:

The use of bioetanol from lignocellulosic material has begone to be developed. In Indonesia the most abundant lignocellulosic material is stem of palm which contain 32.22% of cellulose. Indonesia produces approximatelly 300.375.000 tons of stem of palm each year. To produce bioetanol from lignocellulosic material, the first process is pretreatment. But, until now the method of lignocellulosic pretretament is uneffective. This is related to the particle size and the method of pretreatment of less than optimal so that led to an overhaul of the lignin insufficient, consequently increased levels of cellulose was not significant resulting in low yield of bioetanol. To solve the problem, this research was implemented by using the process of pretreatment method ultasonifikasi in order to produce higher pulp with nano-sized particles that will obtain higher of yield ethanol from stem of palm. Research methods used in this research is the RAK that is composed of one factor which is the frequency ultrasonic waves with three varians, they are 30 kHz, 40 kHz, 50 kHz, and use constant variable is concentration of NaOH. The analysis conducted in this research is the influence of the frequency of the wave to increase levels of cellulose and change size on the scale of nanometers on pretreatment process by using the PSA methods (Particle Size Analyzer), and a Cheason. For the analysis of the results, data, and best treatment using ANOVA and test BNT with confidence interval 5%. The best treatment was obtained by combination X3 (frequency of sonication 50 kHz) and lignin (19,6%) cellulose (59,49%) and hemicellulose (11,8%) with particle size 385,2nm (18,8%).

Keywords: bioethanol, pretreatment, stem of palm, cellulosa

Procedia PDF Downloads 303

Authors: Saimir Heta, Ilma Robo, Dhimiter Papakozma, Eduart Kapaj, Vera Ostreni

Abstract:

Introduction: The biocompatible materials applied to the implant surfaces are the target of recent literature studies. Methodologies: Modification of implant surfaces in different ways such as application of additional ions, surface microstructure change, surface or laser ultrasound alteration, or application of various substances such as recombinant proteins are among the most affected by articles published in the literature. The study is of review type with the main aim of finding the different ways that the mesenchymal cell reaction to these materials is, according to the literature, in the same percentage positive to the osteointegration process. Results: It is emphasized in the literature that implant success as a key evaluation key has more to implement implant treatment protocol ranging from dental health amenity and subsequent of the choice of implant type depending on the alveolar shape of the ridge level. Conclusions: Osteointegration is a procedure that should initially be physiologically independent of the type of implant pile material. With this physiological process, it can not "boast" for implant success or implantation depending on the brand of the selected implant, as the breadth of synthetic or natural materials that promote osteointegration is relatively large.

Keywords: mesenchymal cells, implants, review, biocompatible materials

Procedia PDF Downloads 65

Authors: Hong Li, Tingxian Li, Xudong Wang, Qinghuo Lin

Abstract:

Excess-iron (Fe) stresses involved in legume symbiotic nitrogen fixation are not understood. Our objectives were to investigate the tolerance of yardlong-bean plants to soil excess-Fe stress and antagonistic effects of organic amendments and rhizobial inoculants on plant root nodulation and stem ureide formation. The study was conducted in the tropical Hainan Island during 2012-2013. The soil was strongly acidic (pH 5.3±0.4) and highly variable in Fe concentrations(596±79 mg/kg). The treatments were arranged in a split-plot design with three blocks. The treatment effects were significant on root nodulation, stem ureide, amino acids, plant N/Fe accumulation and bean yields (P<0.05). The yardlong-bean stem allantoin, amino acids and nitrate concentrations and relative ureide % declined with high soil Fe concentrations (>300 mg/kg). It was concluded that the co-variance of excess Fe stress could inhibit legume symbiotic N fixation efficiency. Organic amendments and rhizobial inoculants could help improve crop tolerance to excess Fe stress.

Keywords: atmospheric N fixation, root nodulation, soil Fe co-variance, stem ureide, yardlong-bean plants

Procedia PDF Downloads 250

Authors: Xiaohan Bookman, Xiaoyan Zhu

Abstract:

The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions. As this paper defines two processes in Feed-Forward Neural Network, our limited condition is the abstracted features of samples which are worked out in the abstraction process. And the final outputs are the probability distribution for different clusters in the clustering process. As Entropy-Based Principle is considered into the feed-forward neural network, a clustering method is born. We have conducted some experiments on six open UCI data sets, comparing with a few baselines and applied purity as the measurement. The results illustrate that our method outperforms all the other baselines that are most popular clustering methods.

Keywords: feed-forward neural network, clustering, max-entropy principle, probabilistic models

Procedia PDF Downloads 411

Authors: Sadiq J. Abou-Loukh

Abstract:

The present work deals with implementation of Petri nets, which own the perfect ability of modeling, are used to establish a fault diagnosis model. Fault diagnosis of a control system received considerable attention in the last decades. The formalism of representing neural networks based on Petri nets has been presented. Neural Petri Net (NPN) reasoning model is investigated and developed for the fault diagnosis process of electric control system. The proposed NPN has the characteristics of easy establishment and high efficiency, and fault status within the system can be described clearly when compared with traditional testing methods. The proposed system is tested and the simulation results are given. The implementation explains the advantages of using NPN method and can be used as a guide for different online applications.

Keywords: petri net, neural petri net, electric control system, fault diagnosis

Procedia PDF Downloads 443

Authors: Nazir Amir

Abstract:

Teaching approaches to present science and mathematics content amongst academically-low-achieving students may need to be different than approaches that are adopted for the more academically-inclined students, primarily due to the different learning needs and learning styles of these students. In crafting out lessons to motivate and engage these students, teachers need to consider the backgrounds of these students and have a good understanding of their interests so that lessons can be presented in ways that appeal to them, and made relevant not just to the world around them, but also to their personal experiences. This presentation highlights how the author worked with a Professional Learning Community (PLC) of teachers in crafting out fun and feasible classroom teaching approaches to present science and mathematics content in ways that are made Relevant, Appealing, and Personal (RAP) to groups of academically-low-achieving students in Singapore. Feedback from the students and observations from their work suggest that they were engaged through the RAP-modes of instruction, and were able to appreciate the role of science and mathematics through a variety of low-cost design-based STEM (Science, Technology, Engineering, and Mathematics) activities. Such results imply that teachers teaching academically-low-achieving students, and those in under-resourced communities, could consider infusing RAP-infused instructions into their lessons in getting students develop positive attitudes towards STEM subjects.

Keywords: STEM Education, STEAM Education, Curriculum Instruction, Academically At-Risk Students, Singapore

Procedia PDF Downloads 265

Authors: Leo Nnamdi Ozurumba-Dwight

Abstract:

Single-cell genomic analytical systems have proved to be a platform to isolate bulk cells into selected single cells for genomic, proteomic, and related metabolomic studies. This is enabling systematic investigations of the level of heterogeneity in a diverse and wide pool of cell populations. Single cell technologies, embracing techniques such as high parameter flow cytometry, single-cell sequencing, and high-resolution images are playing vital roles in these investigations on messenger ribonucleic acid (mRNA) molecules and related gene expressions in tracking the nature and course of disease conditions. This entails targeted molecular investigations on unit cells that help us understand cell behavoiur and expressions, which can be examined for their health implications on the health state of patients. One of the vital good sides of single-cell RNA sequencing (scRNA seq) is its probing capacity to detect deranged or abnormal cell populations present within homogenously perceived pooled cells, which would have evaded cursory screening on the pooled cell populations of biological samples obtained as part of diagnostic procedures. Despite conduction of just single-cell transcriptome analysis, scRNAseq now permits comparison of the transcriptome of the individual cells, which can be evaluated for gene expressional patterns that depict areas of heterogeneity with pharmaceutical drug discovery and clinical treatment applications. It is vital to strictly work through the tools of investigations from wet lab to bioinformatics and computational tooled analyses. In the precise steps for scRNAseq, it is critical to do thorough and effective isolation of viable single cells from the tissues of interest using dependable techniques (such as FACS) before proceeding to lysis, as this enhances the appropriate picking of quality mRNA molecules for subsequent sequencing (such as by the use of Polymerase Chain Reaction machine). Interestingly, scRNAseq can be deployed to analyze various types of biological samples such as embryos, nervous systems, tumour cells, stem cells, lymphocytes, and haematopoietic cells. In haematopoietic cells, it can be used to stratify acute myeloid leukemia patterns in patients, sorting them out into cohorts that enable re-modeling of treatment regimens based on stratified presentations. In immunotherapy, it can furnish specialist clinician-immunologist with tools to re-model treatment for each patient, an attribute of precision medicine. Finally, the good predictive attribute of scRNAseq can help reduce the cost of treatment for patients, thus attracting more patients who would have otherwise been discouraged from seeking quality clinical consultation help due to perceived high cost. This is a positive paradigm shift for patients’ attitudes primed towards seeking treatment.

Keywords: immunotherapy, transcriptome, re-modeling, mRNA, scRNA-seq

Procedia PDF Downloads 145

Authors: Yasser A. Ahmed, Juleen M Dickson, Evan S. Krystofiak, Julie A. Oliver

Abstract:

Cancer cells urgently need folate to support their rapid division. Folate receptors (FR) are over-expressed on a wide range of tumor cells, including breast cancer cells. FR are distributed over the entire surface of cancer cells, but are polarized to the apical surface of normal cells. Targeting of cancer cells using specific surface molecules such as folate receptors may be one of the strategies used to kill cancer cells without hurting the neighing normal cells. The aim of the current study was to try a method of SEM detecting FR in a murine breast cancer cell model (4T1 cells) using secondary antibody conjugated to gold or gold-coated magnetite nanoparticles. 4T1 cells were suspended in RPMI medium witth FR antibody and incubated with secondary antibody for fluorescence microscopy. The cells were cultured on 30mm Thermanox coverslips for 18 hours, labeled with FR antibody then incubated with secondary antibody conjugated to gold or gold-coated magnetite nanoparticles and processed to scanning electron microscopy (SEM) analysis. The fluorescence microscopy study showed strong punctate FR expression on 4T1 cell membrane. With SEM, the labeling with gold or gold-coated magnetite conjugates showed a similar pattern. Specific labeling occurred in nanoparticle clusters, which are clearly visualized in backscattered electron images. The 4T1 tumor cell model may be useful for the development of FR-targeted tumor therapy using gold-coated magnetite nano-particles.

Keywords: cancer cell, nanoparticles, cell culture, SEM

Procedia PDF Downloads 710