Search results for: neural progentor cells

Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Madhu Gupta, Vikas Sharma, Suresh P. Vyas

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CD13 receptors are abundantly overexpressed in tumor cells as well as in neovasculature. The CD13 receptors were selected as a targeted site and polymeric nanoparticles (NPs) as a targeted delivery system. By combining these, a cyclic NGR (cNGR) peptide ligand was coupled on the terminal end of polyethylene glycol-b-poly(lactic-co-glycolic acid) (PEG-b-PLGA) and prepared the dual targeted-NPs (cNGR-PEG-PTX-NPs) to enhance the intracellular delivery of anticancer drug to tumor cells and tumor endothelial cells via ligand-receptor interaction. In-vitro cytotoxicity studies confirmed that the presence of cNGR enhanced the cytotoxic efficiency by 2.8 folds in Human Umbilical Vein Endothelial (HUVEC) cells, while cytotoxicity was improved by 2.6 folds in human fibrosarcoma (HT-1080) cells as compared to non-specific stealth NPs. Compared with other tested NPs, cNGR-PEG-PTX-NPs revealed more cytotoxicity by inducing more apoptosis and higher intracellular uptake. The tumor volume inhibition rate was 59.7% in case of cNGR-PEG-PTX-NPs that was comparatively more with other formulations, indicating that cNGR-PEG-PTX-NPs could more effectively inhibit tumor growth. As a consequence, the cNGR-PEG-PTX-NPs play a key role in enhancing tumor therapeutic efficiency for treatment of CD13 receptor specific solid tumor.

Keywords: cyclic NGR, CD13 receptor, targeted polymeric NPs, solid tumor, intracellular delivery

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Authors: Oriahi Love Ndidi

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High concentration photovoltaic promises a more efficient, higher power output than traditional photovoltaic modules. One of the driving forces of this high system efficiency has been the continuous improvement of III-V multi-junction solar cell efficiencies. Multi-junction solar cells built from III-V semiconductors are being evaluated globally in concentrated photovoltaic systems designed to supplement electricity generation for utility companies. The high efficiency of this III-V multi-junction concentrator cells, with demonstrated efficiency over 40 percent since 2006, strongly reduces the cost of concentrated photovoltaic systems, and makes III-V multi-junction cells the technology of choice for most concentrator systems today.

Keywords: cost of multi-junction solar cell, efficiency, photovoltaic systems, reliability

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Authors: Radia Kamel

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Considerable progress has been made in the development of bulk-heterojunction organic solar cells (OSCs) based on a blend of p-type and n-type organic semiconductors. To optimize the interfacial properties between the active layer and the electrode, a cathode buffer layer (CBL) is introduced. This layer can reduce the leakage current, increasing the open-circuit voltage and the fill factor while improving the OSC stability. In this work, the performance of PM6:Y6 OSC with 1-Chloronaphthalene as an additive is examined. To accomplish this, three CBLs PNDIT-F3N-Br, ZrAcac, and PDINO, are compared using the conventional configuration. The device with PNDIT-F3N-Br as CBL exhibits the highest power conversion efficiency of 16.04%. The results demonstrate that modifying the cathode buffer layer is crucial for achieving high-performance OSCs.

Keywords: bulk heterojunction, cathode buffer layer, efficiency, organic solar cells

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Authors: Taejung Lho, Jonghwan Lee

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Most of the photovoltaic (PV) module manufacturers have recently interested in reducing the manufacturing cost. One of available solution is the use of the thin photovoltaic cell because of reducing of raw material cost. Thin PV cells, however, are damaged large deformation which causes possible microcracks inside PV cell, leading to failure problem. In this paper, deformation characteristics by heat conduction in soldering process of PV cells are analyzed through ANSYS software tool. They have been tested for different PV cell thickness and soldering temperature profile. Accordingly optimal soldering process to minimize the deformation of PV cell has been suggested.

Keywords: photovoltaic (PV) cell, infrared(IR) lamp soldering, optimal soldering temperature profile, deformation, temperature distribution, 3D scanner, ANSYS

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Authors: Khadija Refouh

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Culture bound-expressions have been a bottleneck for Natural Language Processing (NLP) and comprehension, especially in the case of machine translation (MT). In the last decade, the field of machine translation has greatly advanced. Neural machine translation NMT has recently achieved considerable development in the quality of translation that outperformed previous traditional translation systems in many language pairs. Neural machine translation NMT is an Artificial Intelligence AI and deep neural networks applied to language processing. Despite this development, there remain some serious challenges that face neural machine translation NMT when translating culture bounded-expressions, especially for low resources language pairs such as Arabic-English and Arabic-French, which is not the case with well-established language pairs such as English-French. Machine translation of opaque idioms from English into French are likely to be more accurate than translating them from English into Arabic. For example, Google Translate Application translated the sentence “What a bad weather! It runs cats and dogs.” to “يا له من طقس سيء! تمطر القطط والكلاب” into the target language Arabic which is an inaccurate literal translation. The translation of the same sentence into the target language French was “Quel mauvais temps! Il pleut des cordes.” where Google Translate Application used the accurate French corresponding idioms. This paper aims to perform NMT experiments towards better translation of opaque idioms using high quality clean multilingual corpus. This Corpus will be collected analytically from human generated idiom translation. AutoML translation, a Google Neural Machine Translation Platform, is used as a custom translation model to improve the translation of opaque idioms. The automatic evaluation of the custom model will be compared to the Google NMT using Bilingual Evaluation Understudy Score BLEU. BLEU is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Human evaluation is integrated to test the reliability of the Blue Score. The researcher will examine syntactical, lexical, and semantic features using Halliday's functional theory.

Keywords: multilingual corpora, natural language processing (NLP), neural machine translation (NMT), opaque idioms

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Authors: John Lachapelle

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Introduction: Neural stimulation is increasingly targeted toward treatment of back pain, PTSD, Parkinson’s disease, and for sensory perception. Sensory recording during stimulation is important in order to examine neural response to stimulation. Most neural amplifiers (headstages) focus on noise efficiency factor (NEF). Conversely, neural headstages need to handle artifacts from several sources including power lines, movement (EMG), and neural stimulation itself. In this work a layered approach to artifact rejection is used to reduce corruption of the neural ENG signal by 60dBv, resulting in recovery of sensory signals in rats and primates that would previously not be possible. Methods: The approach combines analog techniques to reduce and handle unwanted signal amplitudes. The methods include optimized (1) sensory electrode placement, (2) amplifier configuration, and (3) artifact blanking when necessary. The techniques together are like concentric moats protecting a castle; only the wanted neural signal can penetrate. There are two conditions in which the headstage operates: unwanted artifact < 50mV, linear operation, and artifact > 50mV, fast-settle gain reduction signal limiting (covered in more detail in a separate paper). Unwanted Signals at the headstage input: Consider: (a) EMG signals are by nature < 10mV. (b) 60 Hz power line signals may be > 50mV with poor electrode cable conditions; with careful routing much of the signal is common to both reference and active electrode and rejected in the differential amplifier with <50mV remaining. (c) An unwanted (to the neural recorder) stimulation signal is attenuated from stimulation to sensory electrode. The voltage seen at the sensory electrode can be modeled Φ_m=I_o/4πσr. For a 1 mA stimulation signal, with 1 cm spacing between electrodes, the signal is <20mV at the headstage. Headstage ASIC design: The front end ASIC design is designed to produce < 1% THD at 50mV input; 50 times higher than typical headstage ASICs, with no increase in noise floor. This requires careful balance of amplifier stages in the headstage ASIC, as well as consideration of the electrodes effect on noise. The ASIC is designed to allow extremely small signal extraction on low impedance (< 10kohm) electrodes with configuration of the headstage ASIC noise floor to < 700nV/rt-Hz. Smaller high impedance electrodes (> 100kohm) are typically located closer to neural sources and transduce higher amplitude signals (> 10uV); the ASIC low-power mode conserves power with 2uV/rt-Hz noise. Findings: The enhanced neural processing ASIC has been compared with a commercial neural recording amplifier IC. Chronically implanted primates at MGH demonstrated the presence of commercial neural amplifier saturation as a result of large environmental artifacts. The enhanced artifact suppression headstage ASIC, in the same setup, was able to recover and process the wanted neural signal separately from the suppressed unwanted artifacts. Separately, the enhanced artifact suppression headstage ASIC was able to separate sensory neural signals from unwanted artifacts in mouse-implanted peripheral intrafascicular electrodes. Conclusion: Optimizing headstage ASICs allow observation of neural signals in the presence of large artifacts that will be present in real-life implanted applications, and are targeted toward human implantation in the DARPA HAPTIX program.

Keywords: ASIC, biosensors, biomedical signal processing, biomedical sensors

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Authors: M. Hosseinnezhad, K. Gharanjig

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Natural dyes, extracted from black carrot and bramble, were utilized as photosensitizers to prepare dye-sensitized solar cells (DSSCs). Spectrophotometric studies of the natural dyes in solution and on a titanium dioxide substrate were carried out in order to assess changes in the status of the dyes. The results show that the bathochromic shift is seen on the photo-electrode substrate. The chemical binding of the natural dyes at the surface photo-electrode were increased by the chelating effect of the Ti(IV) ions. The cyclic voltammetry results showed that all extracts are suitable to be performed in DSSCs. Finally, photochemical performance and stability of DSSCs based on natural dyes were studied. The DSSCs sensitized by black carrot extract have been reported to achieve up to Jsc=1.17 mAcm^-2, Voc= 0.55 V, FF= 0.52, η=0.34%, whereas Bramble extract can obtain up to Jsc=2.24 mAcm^-2, Voc= 0.54 V, FF= 0.57, η=0.71%. The power conversion efficiency was obtained from the mixed dyes in DSSCs. The power conversion efficiency of dye-sensitized solar cells using mixed Black carrot and Bramble dye is the average of the their efficiency in single DSSCs.

Keywords: anthocyanin, dye-sensitized solar cells, green energy, optical materials

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Authors: Arzu Karahan, Loriano Ballarin, Baruch Rinkevich

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Marine invertebrates, the highly diverse phyla of multicellular organisms, represent phenomena that are either not found or highly restricted in the vertebrates. These include phenomena like budding, fission, a fusion of ramets, and high regeneration power, such as the ability to create whole new organisms from either tiny parental fragment, many of which are controlled by totipotent, pluripotent, and multipotent stem cells. Thus, there is very much that can be learned from these organisms on the practical and evolutionary levels, further resembling Darwin's words, “It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change”. The ‘stem cell’ notion highlights a cell that has the ability to continuously divide and differentiate into various progenitors and daughter cells. In vertebrates, adult stem cells are rare cells defined as lineage-restricted (multipotent at best) with tissue or organ-specific activities that are located in defined niches and further regulate the machinery of homeostasis, repair, and regeneration. They are usually categorized by their morphology, tissue of origin, plasticity, and potency. The above description not always holds when comparing the vertebrates with marine invertebrates’ stem cells that display wider ranges of plasticity and diversity at the taxonomic and the cellular levels. While marine/aquatic invertebrates stem cells (MISC) have recently raised more scientific interest, the know-how is still behind the attraction they deserve. MISC, not only are highly potent but, in many cases, are abundant (e.g., 1/3 of the entire animal cells), do not locate in permanent niches, participates in delayed-aging and whole-body regeneration phenomena, the knowledge of which can be clinically relevant. Moreover, they have massive hidden potential for the discovery of new bioactive molecules that can be used for human health (antitumor, antimicrobial) and biotechnology. The MARISTEM COST action (Stem Cells of Marine/Aquatic Invertebrates: From Basic Research to Innovative Applications) aims to connect the European fragmented MISC community. Under this scientific umbrella, the action conceptualizes the idea for adult stem cells that do not share many properties with the vertebrates’ stem cells, organizes meetings, summer schools, and workshops, stimulating young researchers, supplying technical and adviser support via short-term scientific studies, making new bridges between the MISC community and biomedical disciplines.

Keywords: aquatic/marine invertebrates, adult stem cell, regeneration, cell cultures, bioactive molecules

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Authors: X. S. Wu, H. Jia, P. H. Qian, Z. Zhang, H. L. Cai, F. M. Zhang

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A bipolar resistance switching behaviour is detected for a Ti/TiO2-x/Au memristor device, which is fabricated by a masked designed magnetic sputtering. The current dependence of voltage indicates the curve changes slowly and continuously. When voltage pulses are applied to the device, the set and reset processes maintains linearity, which is used to simulate the synapses. We argue that the conduction mechanism of the device is from the oxygen vacancy channel model, and the resistance of the device change slowly due to the reaction between the titanium electrode and the intermediate layer and the existence of a large number of oxygen vacancies in the intermediate layer. Then, Hopfield neural network is constructed to simulate the behaviour of neural network in image processing, and the accuracy rate is more than 98%. This shows that titanium dioxide memristor has a broad application prospect in high performance neural network simulation.

Keywords: memristor fabrication, neuromorphic computing, bionic synaptic application, TiO₂-based

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Authors: Danilo López, Edwin Rivas, Leyla López

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This paper presents the development of an algorithm that predicts the arrival of a secondary user (SU) to a base station (BS) in a cognitive network based on infrastructure, requesting a Best Effort (BE) or Real Time (RT) type of service with a determined bandwidth (BW) implementing neural networks. The algorithm dynamically uses a neural network construction technique using the geometric pyramid topology and trains a Multilayer Perceptron Neural Networks (MLPNN) based on the historical arrival of an SU to estimate future applications. This will allow efficiently managing the information in the BS, since it precedes the arrival of the SUs in the stage of selection of the best channel in CRN. As a result, the software application determines the probability of arrival at a future time point and calculates the performance metrics to measure the effectiveness of the predictions made.

Keywords: cognitive radio, base station, best effort, MLPNN, prediction, real time

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Authors: Qian Liu, Steve Furber

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To explore how the brain may recognize objects in its general,accurate and energy-efficient manner, this paper proposes the use of a neuromorphic hardware system formed from a Dynamic Video Sensor~(DVS) silicon retina in concert with the SpiNNaker real-time Spiking Neural Network~(SNN) simulator. As a first step in the exploration on this platform a recognition system for dynamic hand postures is developed, enabling the study of the methods used in the visual pathways of the brain. Inspired by the behaviours of the primary visual cortex, Convolutional Neural Networks (CNNs) are modeled using both linear perceptrons and spiking Leaky Integrate-and-Fire (LIF) neurons. In this study's largest configuration using these approaches, a network of 74,210 neurons and 15,216,512 synapses is created and operated in real-time using 290 SpiNNaker processor cores in parallel and with 93.0% accuracy. A smaller network using only 1/10th of the resources is also created, again operating in real-time, and it is able to recognize the postures with an accuracy of around 86.4% -only 6.6% lower than the much larger system. The recognition rate of the smaller network developed on this neuromorphic system is sufficient for a successful hand posture recognition system, and demonstrates a much-improved cost to performance trade-off in its approach.

Keywords: spiking neural network (SNN), convolutional neural network (CNN), posture recognition, neuromorphic system

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Authors: M. Yoneda

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In this paper, a lateral walking design force per person is proposed and compared with Imperial College test results. Numerical simulations considering the proposed walking design force which is incorporated into the neural-oscillator model are carried out placing much emphasis on the synchronization (the lock-in phenomenon) for a pedestrian bridge model with the span length of 50 m. Numerical analyses are also conducted for an existing pedestrian suspension bridge. As compared with full scale measurements for this suspension bridge, it is confirmed that the analytical method based on the neural-oscillator model might be one of the useful ways to explain the synchronization (the lock-in phenomenon) of pedestrians being on the bridge.

Keywords: pedestrian bridge, human-induced lateral vibration, neural-oscillator, full scale measurement, dynamic response analysis

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Authors: Izzatul Ulfana, Angga Pratomo Cahyadi, Rimayanti, Widjiati

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Freezing oocyte could cause temperature stress. Temperature stress triggers cell damage. Insulin Transferrin Selenium (ITS) and Heat Shock Protein 70 (HSP70) had been used to prevent damage to the oocyte after freezing. ITS and HSP70 could cause the difference protective effect. The aim of this research was to obtain an effective cryoprotectant for freezing local goat oocyte in cumulus cells change. The research began by collecting the ovary from a local slaughterhouse in Indonesia, aspiration follicle, in vitro maturation and the freezing had been used vitrification method. Examination of the morphology cells by native staining method. Data on the calculation morphology oocyte analyzed by Kruskall-Wallis Test. After the Kruskall-Wallis Test which indicated significance, followed by Mann-Whitney Test to compare between treatment groups. As a result, cryoprotectant ITS has the best culumus cells after warming

Keywords: Insulin Transferrin Selenium, Heat Shock Protein 70, cryoprotectant, vitrification

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Authors: Agatha Bebbington, Terry Tetley, Kathryn Hadler

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Background: Astronauts will set foot on the Moon later this decade, and are at high risk of lunar dust inhalation. Freshly-fractured lunar dust produces reactive oxygen species in solution, which are known to cause cellular damage and inflammation. Cytotoxicity and inflammatory mediator release was measured in pulmonary alveolar epithelial cells (cells that line the gas-exchange zone of the lung) exposed to a lunar dust simulant, LMS-1. It was hypothesised that freshly-fractured LMS-1 would result in increased cytotoxicity and inflammatory mediator release, owing to the angular morphology and high reactivity of fractured particles. Methods: A human alveolar epithelial type 1-like cell line (TT1) and a human macrophage-like cell line (THP-1) were exposed to 0-200μg/ml of unground, aged-ground, and freshly-ground LMS-1 (screened at <22μm). Cell viability, cytotoxicity, and inflammatory mediator release (IL-6, IL-8) were assessed using MMT, LDH, and ELISA assays, respectively. LMS-1 particles were characterised for their size, surface area, and morphology before and after grinding. Results: Exposure to LMS-1 particles did not result in overt cytotoxicity in either TT1 epithelial cells or THP-1 macrophage-like cells. A dose-dependent increase in IL-8 release was observed in TT1 cells, whereas THP-1 cell exposure, even at low particle concentrations, resulted in increased IL-8 release. Both cytotoxic and pro-inflammatory responses were most marked and significantly greater in TT1 and THP-1 cells exposed to freshly-fractured LMS-1. Discussion: LMS-1 is a novel lunar dust simulant; this is the first study to determine its toxicological effects on respiratory cells in vitro. An increased inflammatory response in TT1 and THP-1 cells exposed to ground LMS-1 suggests that low particle size, increased surface area, and angularity likely contribute to toxicity. Conclusions: Evenlow levels of exposure to LMS-1 could result in alveolar inflammation. This may have pathological consequences for astronauts exposed to lunar dust on future long-duration missions. Future research should test the effect of low-dose, intermittent lunar dust exposure on the respiratory system.

Keywords: lunar dust, LMS-1, lunar dust simulant, long-duration space travel, lunar dust toxicity

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

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Authors: Gyaltsen Dakpa, K. J. Senthil Kumar, Nai-Wen Tsao, Sheng-Yang Wang

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Context: Coronavirus disease 2019 (COVID-19) is a severe respiratory illness caused by the SARS-CoV-2 virus. One of the hallmarks of COVID-19 is a change in metabolism, which can lead to increased severity and mortality. The mechanism of SARS-CoV-2-mediated perturbations of metabolic pathways has yet to be fully understood. Research Aim: This study aimed to investigate the metabolic alteration caused by SARS-CoV-2 spike protein in Phorbol 12-myristate 13-acetate (PMA)-induced human monocytes (THP-1) and to examine the regulatory effect of natural compounds like Antcins A on SARS-CoV-2 spike protein-induced metabolic alteration. Methodology: The study used a combination of proton nuclear magnetic resonance (1H-NMR) and MetaboAnalyst 5.0 software. THP-1 cells were treated with SARS-CoV-2 spike protein or control, and the metabolomic profiles of the cells were compared. Antcin A was also added to the cells to assess its regulatory effect on SARS-CoV-2 spike protein-induced metabolic alteration. Findings: The study results showed that treatment with SARS-CoV-2 spike protein significantly altered the metabolomic profiles of THP-1 cells. Eight metabolites, including glycerol-phosphocholine, glycine, canadine, sarcosine, phosphoenolpyruvic acid, glutamine, glutamate, and N, N-dimethylglycine, were significantly different between control and spike-protein treatment groups. Antcin A significantly reversed the changes in these metabolites. In addition, treatment with antacid A significantly inhibited SARS-CoV-2 spike protein-mediated up-regulation of TLR-4 and ACE2 receptors. Theoretical Importance The findings of this study suggest that SARS-CoV-2 spike protein can cause significant metabolic alterations in THP-1 cells. Antcin A, a natural compound, has the potential to reverse these metabolic alterations and may be a potential candidate for developing preventive or therapeutic agents for COVID-19. Data Collection: The data for this study was collected from THP-1 cells that were treated with SARS-CoV-2 spike protein or a control. The metabolomic profiles of the cells were then compared using 1H-NMR and MetaboAnalyst 5.0 software. Analysis Procedures: The metabolomic profiles of the THP-1 cells were analyzed using 1H-NMR and MetaboAnalyst 5.0 software. The software was used to identify and quantify the cells' metabolites and compare the control and spike-protein treatment groups. Questions Addressed: The question addressed by this study was whether SARS-CoV-2 spike protein could cause metabolic alterations in THP-1 cells and whether Antcin A can reverse these alterations. Conclusion: The findings of this study suggest that SARS-CoV-2 spike protein can cause significant metabolic alterations in THP-1 cells. Antcin A, a natural compound, has the potential to reverse these metabolic alterations and may be a potential candidate for developing preventive or therapeutic agents for COVID-19.

Keywords: SARS-CoV-2-spike, ¹H-NMR, metabolomics, antcin-A, taiwanofungus camphoratus

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Authors: Giuffrida Graziella, Pennisi Stefania, Coppa Federica, Iannello Giulia, Cartelli Simone, Lo Faro Riccardo, Ferruggia Greta, Brundo Maria Violetta

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Cladode chemical composition may vary according to soil factors, cultivation season, and plant age. The primary metabolites of cladodes are water, carbohydrates, and proteins. The carbohydrates in cladodes are divided into two types: structural and storage. Polysaccharides from Opuntia ficus‐indica (L.) Mill plants build molecular networks with the capacity to retain water; thus, they act as mucoprotective agents. Mucilage is the main polysaccharide of cladodes; it contains polymers of β‐d‐galacturonic acid bound in positions (1–4) and traces of R‐linked l‐rhamnose (1-2). Mucilage regulates both the cell water content during prolonged drought and the calcium flux in the plant cells. The in vitro analysis of keratinocytes in monolayer, through the scratch-wound-healing assay, provided promising results. After 48 hours of exposure, the wound scratch was almost completely closed in cells treated with cladode extract. After 72 hours, the treated cells reached complete confluence, while in the untreated cells (negative control) the confluence was reached after 96 hours. We also added a positive control group of cells treated with colchicine, which inhibited wound closure for a more comprehensive analysis.

Keywords: cladodes, metabolites, polysaccharide, scratch-wound-healing assay

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Authors: Yu-Ling Tsai, Chih-Jung Chang, Chia-Chen Lu, Eric Wu, Chuan-Sheng Lin, Tzu-Lung Lin, Hsin-Chih Lai

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It is urgent that novel anti-obesity measures that are safe, effective and widely available are developed for counteracting the rapidly growing obesity epidemics. In the present study, we show that a probiotic bacterium Parabacteroides goldsteinii screened through culture under the high molecular weight polysaccharides prepared from two iconic medicinal fungi, the Ganoderma lucidum and the Hirsutella sinensis, reduced body weight by ca. 20% in high-fat diet (HFD)-fed mice. The bacterium also decreased intestinal permeability, metabolic endotoxemia, inflammation and insulin resistance. Notably, oral administration of live, but not high temperature-killed, P. goldsteinii to HFD fed mice considerably reduces weight gain and obesity-associated metabolic disorders. A three months feeding of the mice with P. goldsteinii did not show any aberrant side effects, indicating the safety of this bacterium. Transcriptome analysis indicated that P. goldsteinii enhances immunity in resting dendritic cells, but reduces inflammation in lipopolysaccharide (LPS)-induced dendritic cells. On top, Naïve T-cells were skewed towards regulatory T-cells after encountering with dendritic cells (DCs) pretreated with P. goldsteinii. These results indicated P. goldsteinii showed anti-inflammatory effects and can work as a potential probiotic ameliorating obesogenicity and related metabolic syndromes.

Keywords: Parabacteroides goldsteinii, gut microbiome, obesity, immune modulation

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Authors: Jiangxia Han, Liang Xue, Keda Chen

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Despite the significant progress over the last decades in reservoir simulation using numerical discretization, meshing is complex. Moreover, the high degree of freedom of the space-time flow field makes the solution process very time-consuming. Therefore, we present Physics-Informed Convolutional Neural Networks(PICNN) as a hybrid scientific theory and data method for reservoir modeling. Besides labeled data, the model is driven by the scientific theories of the underlying problem, such as governing equations, boundary conditions, and initial conditions. PICNN integrates governing equations and boundary conditions into the network architecture in the form of a customized convolution kernel. The loss function is composed of data matching, initial conditions, and other measurable prior knowledge. By customizing the convolution kernel and minimizing the loss function, the neural network parameters not only fit the data but also honor the governing equation. The PICNN provides a methodology to model and history-match flow and transport problems in porous media. Numerical results demonstrate that the proposed PICNN can provide an accurate physical solution from a limited dataset. We show how this method can be applied in the context of a forward simulation for continuous problems. Furthermore, several complex scenarios are tested, including the existence of data noise, different work schedules, and different good patterns.

Keywords: convolutional neural networks, deep learning, flow and transport in porous media, physics-informed neural networks, reservoir simulation

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Authors: Lamyaa Gamal El-Deen Taha, Ashraf Sharawi

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Planet image is a new data source from planet lab. This research is concerned with the assessment of Planet image for land cover mapping. Two pixel based classifiers and one subpixel based classifier were compared. Firstly, rectification of Planet image was performed. Secondly, a comparison between minimum distance, maximum likelihood and neural network classifications for classification of Planet image was performed. Thirdly, the overall accuracy of classification and kappa coefficient were calculated. Results indicate that neural network classification is best followed by maximum likelihood classifier then minimum distance classification for land cover mapping.

Keywords: planet image, land cover mapping, rectification, neural network classification, multilayer perceptron, soft classifiers, hard classifiers

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Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Fatma Zuhrotun Nisa, Aprilina Ratriany, Agus Wijanarka

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Background: Cassava leaves are widely used by the people of Indonesia as a vegetable and treat various diseases, including anticancer believed as food. However, not much research on the anticancer activity of cassava leaves, especially in colon cancer. Objectives: the aim of this study is to investigate anti-cancer activity of cassava leaves (Manihot esculanta C.) against colon cancer (WiDr) cells in vitro. Methods: effect of crude aqueous extract of leaves of cassava and cassava leaves boiled tested in colon cancer cells widr. Determination of Anticancer uses the MTT method with parameters such as the percentage of deaths. Results: raw cassava leaf water extract gave IC50 of 63.1 mg / ml. While the water extract of boiled cassava leaves gave IC50 of 79.4 mg/ml. However, there is no difference anticancer activity of raw cassava leaves or cancer (p> 0.05). Conclusion: Cassava leaves contain a variety of compounds that have previously been reported to have anticancer activity. Linamarin, β-carotene, vitamin C, and fiber were thought to affect the IC50 cassava leaf extract against colon cancer cells WiDr.

Keywords: boiled cassava leaves, cassava leaves raw, anticancer activity, colon cancer, IC50

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Authors: Nadine Wiesmann, Melanie Viel, Christoph Buhr, Rachel Tanner, Wolfgang Tremel, Juergen Brieger

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Recidivation of tumors and the development of resistances against the classical anti-tumor approaches represent a major challenge we face when treating cancer. In order to master this challenge, we are in desperate need of new treatment options beyond the beaten tracks. Zinc oxide nanoparticles (ZnO NPs) represent such an innovative approach. Zinc oxide is characterized by a high level of biocompatibility, concurrently ZnO NPs are able to exert anti-tumor effects. By concentration of the nanoparticles at the tumor site, tumor cells can specifically be exposed to the nanoparticles while low zinc concentrations at off-target sites are tolerated well and can be excreted easily. We evaluated the toxicity of ZnO NPs in vitro with the help of immortalized tumor cell lines and primary cells stemming from healthy tissue. Additionally, the Chorioallantoic Membrane Assay (CAM Assay) was employed to gain insights into the in vivo behavior of the nanoparticles. We could show that ZnO NPs interact with tumor cells as nanoparticulate matter. Furthermore, the extensive release of zinc ions from the nanoparticles nearby and within the tumor cells results in overload with zinc. Beyond that, ZnO NPs were found to further the generation of reactive oxygen species (ROS). We were able to show that tumor cells were more prone to the toxic effects of ZnO NPs at intermediate concentrations compared to fibroblasts. With the help of ZnO NPs covered by a silica shell in which FITC dye was incorporated, we were able to track ZnO NPs within tumor cells as well as within a whole organism in the CAM assay after injection into the bloodstream. Depending on the applied concentrations, selective tumor cell killing seems feasible. Furthermore, the combinational treatment of tumor cells with radiotherapy and ZnO NPs shows promising results. Still, further investigations are needed to gain a better understanding of the interaction between ZnO NPs and the human body to be able to pave the way for their application as an innovative anti-tumor agent in the clinics.

Keywords: metal oxide nanoparticles, nanomedicine, overcome resistances against classical treatment options, zinc oxide nanoparticles

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Authors: Yao-Hong Tsai

Abstract:

Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: unmanned aerial vehicle, object tracking, deep learning, collision avoidance

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Authors: M. P. Nanda Kumar, K. Dheeraj

Abstract:

The design of a feedback controller, so as to minimize a given performance criterion, for a general non-linear dynamical system is difficult; if not impossible. But for a large class of non-linear dynamical systems, the open loop control that minimizes a performance criterion can be obtained using calculus of variations and Pontryagin’s minimum principle. In this paper, the open loop optimal trajectories, that minimizes a given performance measure, is used to train the neural network whose inputs are state variables of non-linear dynamical systems and the open loop optimal control as the desired output. This trained neural network is used as the feedback controller. In other words, attempts are made here to solve the “inverse optimal control problem” by using the state and control trajectories that are optimal in an open loop sense.

Keywords: inverse optimal control, radial basis function, neural network, controller design

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Authors: Min Jindong, Wang Mingxia

Abstract:

With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

Procedia PDF Downloads 278

Authors: Alaa A. Zaky, Bandar Alfaifi, Saleh Alyahya, Alkistis Kontou, Panos Kotsampopoulos

Abstract:

In this work, we present for the first time the grid-integration of 3rd generation perovskite solar cells (PSCs) based on nanotechnology in fabrication. The effect of this penetration is analyzed in normal, fault and islanding cases of operation under different irradiation conditions using the power hardware in the loop (PHIL) methodology. The PHL method allows the PSCs connection to the electric grid which is simulated in the real-time digital simulator (RTDS), for laboratory validation of the PSCs behavior under conditions very close to real.

Keywords: perovskite solar cells, power hardware in the loop, real-time digital simulator, smart grid

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Authors: Jiaqi Huang, Yuheng Wang

Abstract:

Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks.

Keywords: automated theorem proving, premises selection, deep learning, interpreting deep learning

Procedia PDF Downloads 183

Authors: Dibyakanta Seth, Hari Niwas Mishra, Sankar Chandra Deka

Abstract:

Microencapsulation is an established method of protecting bacteria from the adverse conditions. An improved extrusion spraying technique was used to encapsulate mixed bacteria culture of S. thermophilus and L. bulgaricus using sodium alginate as the coating material. The effect of nozzle air pressure (200, 300, 400 and 500 kPa), sodium alginate concentration (1%, 1.5%, 2%, 2.5% and 3% w/v), different concentration of calcium chloride (0.1, 0.2, 1 M) and initial cell loads (10⁷, 10⁸, 10⁹ cfu/ml) on the viability of encapsulated bacteria were investigated. With the increase in air pressure the size of microcapsules decreased, however the effect was non-significant. There was no significant difference (p > 0.05) in the viability of encapsulated cells when the concentration of calcium chloride was increased. Increased level of sodium alginate significantly increased the survival ratio of encapsulated bacteria (P < 0.01). Encapsulation with 3% alginate was treated as optimum since a higher concentration of alginate increased the gel strength of the solution and thus was difficult to spray. Under optimal conditions 3% alginate, 10⁹ cfu/ml cell load, 20 min hardening time in 0.1 M CaCl2 and 400 kPa nozzle air pressure, the viability of bacteria cells was maximum compared to the free cells. The microcapsules made at the optimal condition when mixed with yoghurt and subjected to spray drying at 148°C, the survival ratio was 2.48×10⁻¹ for S. thermophilus and 7.26×10⁻¹ for L. bulgaricus. In contrast, the survival ratio of free cells of S. thermophilus and L. bulgaricus were 2.36×10⁻³ and 8.27×10⁻³, respectively. This study showed a decline in viable cells count of about 0.5 log over a period of 7 weeks while there was a decline of about 1 log in cultures which were incorporated as free cells in yoghurt. Microencapsulation provided better protection at higher acidity compared to free cells. This study demonstrated that microencapsulation of yoghurt culture in sodium alginate is an effective technique of protection against extreme drying conditions.

Keywords: extrusion, microencapsulation, spray drying, sweetened yoghurt

Procedia PDF Downloads 253