Search results for: neural progentor cells

Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Yilei Xue, Yat-Hing Ham, Wenting Qiu, Wan Chan, Stefan Nagl

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Polydopamine (PDA) is a popular material in biological and medical applications due to its excellent biocompatibility, outstanding physicochemical properties, and facile fabrication. In this project, a new sandwich-structured PDA and silver (Ag) hybrid material named PDA-Ag-PDA was synthesized and characterized layer-by-layer, where silver nanoparticles (Ag NPs) are wrapped in PDA coatings, using SEM, AFM, 3D surface metrology, and contact angle meter. The silver loading capacity is positively proportional to the roughness value of the initial PDA film. This designed film was subsequently integrated within a digital microfluidic (DMF) platform coupling with an oxygen sensor layer for on-chip antibacterial assay. The concentration of E. coli was quantified on DMF by real-time monitoring oxygen consumption during E. coli growth with the optical oxygen sensor layer. The PDA-Ag-PDA coating shows an 99.9% reduction in E. coli population under non-nutritive condition with 1-hour treatment and has a strong growth inhibition of E. coliin nutrient LB broth as well. Furthermore, PDA-Ag-PDA film maintaining a low cytotoxicity effect to human cells. After treating with PDA-Ag-PDA film for 24 hours, 82% HEK 293 and 86% HeLa cells were viable. The SERS enhancement factor of PDA-Ag-PDA is estimated to be 1.9 × 104 using Rhodamine 6G (R6G). Multifunctional PDA-Ag-PDA coating provides an alternative platform to conjugate biomolecules and perform biological applications on DMF, in particular, for the adhesive protein and cell study.

Keywords: polydopamine, silver nanoparticles, digital microfluidic, optical sensor, antimicrobial assay, SERS

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Authors: Somnath Mahato, Minarul Islam Sarkar, Luis Guillermo Gerling, Joaquim Puigdollers, Asit Kumar Kar

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Nanostructured Zinc Oxide (ZnO) thin films have been successfully deposited on indium tin oxide (ITO) coated glass substrates by a simple two electrode electrodeposition process at constant potential. The preparative parameters such as deposition time, deposition potential, concentration of solution, bath temperature and pH value of electrolyte have been optimized for deposition of uniform ZnO thin films. X-ray diffraction studies reveal that the prepared ZnO thin films have a high preferential oriented c-axis orientation with compact hexagonal (wurtzite) structure. Surface morphological studies show that the ZnO films are smooth, continuous, uniform without cracks or holes and compact with nanorod-like structure on the top of the surface. Optical properties reveal that films exhibit higher absorbance in the violet region of the optical spectrum; it gradually decreased in the visible range with increases in wavelength and became least at the beginning of NIR region. The photoluminescence spectra shows that the observed peaks are attributed to the various structural defects in the nanostructured ZnO crystal. The microstructural and optical properties suggest that the electrodeposited ZnO thin films are suitable for application in photosensitive devices such as photovoltaic solar cells photoelectrochemical cells and light emitting diodes etc.

Keywords: electrodeposition, microstructure, optical properties, ZnO thin films

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Authors: Juan Jose Filgueira, Daniela Londono-Serna, Liliana Maria Hoyos

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Carnation (Dianthus caryophyllus) is one of the most important products of exportation in the floriculture industry worldwide. Fusariosis is the disease that causes the highest losses on farms, in particular the one produced by Fusarium oxysporum f.sp. dianthi, called vascular wilt. Gene identification and metabolic routes of the genes that participate in the building of the plant response to Fusarium are some of the current targets in the carnation breeding industry. The techniques for the identifying of resistant genes in the plants, is the analysis of the transcriptome obtained during the host-pathogen interaction. In this work, we report the cell transcriptome of different varieties of carnation that present differential response from Fusarium oxysporum f.sp. dianthi attack. The cells of the different hybrids produced in the outbreeding program were cultured in vitro and elicited with the parasite in a dual culture. The isolation and purification of mRNA was achieved by using affinity chromatography Oligo dT columns and the transcriptomes were obtained by using Illumina NGS techniques. A total of 85,669 unigenes were detected in all the transcriptomes analyzed and 31,000 annotations were found in databases, which correspond to 36.2%. The library construction of genic expression techniques used, allowed to recognize the variation in the expression of genes such as Germin-like protein, Glycosyl hydrolase family and Cinnamate 4-hydroxylase. These have been reported in this study for the first time as part of the response mechanism to the presence of Fusarium oxysporum.

Keywords: Carnation, Fusarium, vascular wilt, transcriptome

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Authors: M. Riazat, H. Abdolvand, M. Baniassadi

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In this present work, 3D reconstruction of cathode of SOFC is developed with various volume fractions and porosity. Three Phase Boundary (TPB) of construction of such derived micro structures is calculated. The neural network is used to optimize the porosity and volume fraction of each phase to reach a structure with maximum TPB.

Keywords: fuel cell, solid oxide, TPB, 3D reconstruction

Procedia PDF Downloads 324

Authors: S. Ilahi, S. Almosni, F. Chouchene, M. Perrin, K. Zelazna, N. Yacoubi, R. Kudraweic, P. Rale, L. Lombez, J. F. Guillemoles, O. Durand, C. Cornet

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Great efforts have been dedicated to obtain high quality of GaAsPN. The properties of GaAsPN have played a great part on the development of solar cells devices based in Si substrate. The incorporation of N in GaAsPN that having a band gap around of 1.7 eV is of special interest in view of growing in Si substrate. In fact, post-growth and rapid thermal annealing (RTA) could be an effective way to improve the quality of the layer. Then, the influence of growth conditions and post-growth annealing on optical and thermal parameters is considered. We have used Photothermal deflection spectroscopy PDS to investigate the impact of rapid thermal annealing on thermal and optical properties of GaAsPN. In fact, the principle of the PDS consists to illuminate the sample by a modulated monochromatic light beam. Then, the absorbed energy is converted into heat through the nonradiative recombination process. The generated thermal wave propagates into the sample and surrounding media creating a refractive-index gradient giving rise to the deflection of a laser probe beam skimming the sample surface. The incident light is assumed to be uniform, and only the sample absorbs the light. In conclusion, the results are promising revealing an improvement in absorption coefficient and thermal conductivity.

Keywords: GaAsPN absorber, photothermal defelction technique PDS, photonics on silicon, thermal conductivity

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Authors: Saifullah K., Muhammad Naziruddin Saifullah, Muhammad N.

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The bio control potential and mechanism of P. chlamydosporia against Meloidogyne incognita was evaluated in the present study. Under invitro conditions, P. chlamydosporia was tested for parasitism of eggs and females of M. incognita. The results indicated that this fungus parasitized 87% eggs and 82% females. Culture filtrate (CF) of P. chlamydosporia was tested for its larvicide activity against M. incognita 2nd stage juvenile. The maximum mortality was 97.3% at 100% concentration of the culture filtrate while minimum mortality was 7.3% in 25% concentration after 24 hrs. The result of the pot experiment proved that P. chlamydosporia has reduced the incidence of RKN and improved all tested agronomic growth parameters. The treatment with inoculated M. incognita alone reduced plant height, fresh shoot, and fresh root weight by 44.7%, 29.8%, and 32.8% respectively over uninoculated healthy control. Histopathological studies on the interaction of Pochonia chlamydosporia and Meloidogyne incognita on tomato roots revealed anatomical changes among treatments. Less number of galls with small in size and scarcer abnormalities in the vascular cylinder was observed in plants inoculated with P. chlamydosporia and M. incognita than the plants treated with nematode only. The fungus was seen in in the intercellular spaces of cortical and epidermal cells while the vascular bundles of the plant remain intact, inoculated only with P. chlamydosporia. In the infected roots, many mature females were seen which feed on giant cells. The findings also revealed that control healthy plants were not affected and no histological changes were noted.

Keywords: histopathology, Pochonia chlamydosporia, Meloidogyne incognita, tomato

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Authors: Mohamed H. El-Newehy, Badr M. Thamer, Nasser A. M. Barakat, Mohammad A.Abdelkareem, Salem S. Al-Deyab, Hak Y. Kim

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In this study, the influence of nitrogen doping on the electrocatalytic activity of carbon nanofibers with nickel nanoparticles toward methanol oxidation is introduced. The modified carbon nanofibers have been synthesized from calcination of electrospun nanofiber mats composed of nickel acetate tetrahydrate, poly(vinyl alcohol) and urea in argon atmosphere at 750oC. The utilized physicochemical characterizations indicated that the proposed strategy leads to form carbon nanofibers having nickel nanoparticles and doped by nitrogen. Moreover, due to the high-applied voltage during the electrospinning process, the utilized urea chemically bonds with the polymer matrix, which leads to form nitrogen-doped CNFs after the calcination process. Investigation of the electrocatalytic activity indicated that nitrogen doping NiCNFs strongly enhances the oxidation process of methanol as the current density increases from 52.5 to 198.5 mA/cm2 when the urea content in the original electrospun solution was 4 wt% urea. Moreover, the nanofibrous morphology exhibits distinct impact on the electrocatalytic activity. Also, nitrogen-doping enhanced the stability of the introduced Ni-based electrocatalyst. Overall, the present study introduces effective and simple strategy to modify the electrocatalytic activity of the nickel-based materials.

Keywords: electrospinning, methanol electrooxidation, fuel cells, nitrogen-doping, nickel

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Authors: Feng Huang, Carlos G. Ochoa, Haitao Zhao

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Poyang Lake, the largest freshwater lake in China, is located at the middle-lower reaches of the Yangtze River basin. It has great value in socioeconomic development and is internationally recognized as an important lacustrine and wetland ecosystem with abundant biodiversity. Impacted by ongoing climate change and anthropogenic activities, especially the regulation of the Three Gorges Reservoir since 2003, Poyang Lake has experienced significant water regime evolution, resulting in challenges for the management of water resources and the environment. Quantifying the contribution of hydrologic and topographic changes to water regime alteration is necessary for policymakers to design effective adaption strategies. Long term hydrologic data were collected and the back-propagation neural networks were constructed to simulate the lake water level. The impacts of hydrologic and topographic changes were differentiated through scenario analysis that considered pre-impact and post-impact hydrologic and topographic scenarios. The lake water regime was characterized by hydrologic indicators that describe monthly water level fluctuations, hydrologic features during flood and drought seasons, and frequency and rate of hydrologic variations. The results revealed different contributions of hydrologic and topographic changes to different features of the lake water regime.Noticeable changes were that the water level declined dramatically during the period of reservoir impoundment, and the drought was enhanced during the dry season. The hydrologic and topographic changes exerted a synergistic effect or antagonistic effect on different lake water regime features. The findings provide scientific reference for lacustrine and wetland ecological protection associated with water regime alterations.

Keywords: back-propagation neural network, scenario analysis, water regime, Poyang Lake

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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

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The deer velvet antler is well known for its traditional medicinal value and is widely used in the clinic. It has been considered to possess bone-strengthening activity. The goal of this study was to investigate the anti-osteoporotic effect of deer antler velvet on ovariectomized rats (OVX), and their possible mechanism of the action. In the first step, the in vitro effects of DAE on bone loss were determined. The proliferation, collagen content and alkaline phosphatase (ALP) activity of human osteoblastic MG-63 cells and osteoclastogenesis from bone marrow-derived precursor cells were measured. The in vivo experiment confirmed the positive effect of DAE on bone tissue. 3-month old female Sparague-Dawley rats were either sham operated or OVX, and administered DAE (20 and 100 mg/kg) for 4 weeks. DAE increased MG-63 cell proliferation and ALP activity in a dose-dependent manner. Collagen content was also increased by DAE treatment. However, the effect of DAE on bone resorption was not observed. OVX rats supplemented with DAE showed osteoprotective effects as the bone ALP level was increased and c-terminal telopeptide level was decreased by 100 mg/kg DAE treatment compared with OVX controls. Moreover, the tartrate-resistant acid phosphatase-5b level was also decreased by DAE treatment. The present study suggests that DAE is effective in preventing bone loss in OVX rats, and may be potential therapeutic agents for the treatment of postmenopausal osteoporosis.

Keywords: bone ALP, c-terminal telopeptide, deer antler, osteoporosis, ovariectomy, tartrate-resistant acid phosphatase-5b

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Authors: Tauseef Ahmad, Muhammad Ishaq, Mathew Eapen, Ahyoung Park, Sam Karpiniec, Vanni Caruso, Rajaraman Eri

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Fucoidans are complex, fucose-rich sulfated polymers discovered in brown seaweeds. Fucoidans are popular around the world, particularly in the nutraceutical and pharmaceutical industries, due to their promising medicinal properties. Fucoidans have been shown to have a variety of biological activities, including anti-inflammatory effects. They are known to inhibit inflammatory processes through a variety of mechanisms, including enzyme inhibition and selectin blockade. Inflammation is a part of the complicated biological response of living systems to damaging stimuli, and it plays a role in the pathogenesis of a variety of disorders, including arthritis, inflammatory bowel disease, cancer, and allergies. In the current investigation, various fucoidan extracts from Undaria pinnatifida, Fucus vesiculosus, Macrocystis pyrifera, Ascophyllum nodosum, and Laminaria japonica were assessed for inhibition of pro-inflammatory cytokine production (TNF-α, IL-1β, and IL-6) in LPS induced human macrophage cell line (THP-1) and human peripheral blood mononuclear cells (PBMCs). Furthermore, we also sought to catalogue these extracts based on their anti-inflammatory effects in the current in-vitro cell model. Materials and Methods: To assess the cytotoxicity of fucoidan extracts, MTT (3-[4,5-dimethylthiazol-2-yl]-2,5, -diphenyltetrazolium bromide) cell viability assay was performed. Furthermore, a dose-response for fucoidan extracts was performed in LPS induced THP-1 cells and PBMCs after pre-treatment for 24 hours, and levels of TNF-α, IL-1β, and IL-6 cytokines were measured using Enzyme-Linked Immunosorbent Assay (ELISA). Results: The MTT cell viability assay demonstrated that fucoidan extracts exhibited no evidence of cytotoxicity in THP-1 cells or PBMCs after 48 hours of incubation. The results of the sandwich ELISA revealed that all fucoidan extracts suppressed cytokine production in LPS-stimulated PBMCs and human THP-1 cells in a dose-dependent manner. Notably, at lower concentrations, the lower molecular fucoidan (5-30 kDa) extract from Macrocystis pyrifera was a highly efficient inhibitor of pro-inflammatory cytokines. Fucoidan extracts from all species including Undaria pinnatifida, Fucus vesiculosus, Macrocystis pyrifera, Ascophyllum nodosum, and Laminaria japonica exhibited significant anti-inflammatory effects. These findings on several fucoidan extracts provide insight into strategies for improving their efficacy against inflammation-related diseases. Conclusion: In the current research, we have successfully catalogued several fucoidan extracts based on their efficiency in LPS-induced macrophages and PBMCs in downregulating the key pro-inflammatory cytokines (TNF-, IL-1 and IL-6), which are prospective targets in human inflammatory illnesses. Further research would provide more information on the mechanism of action, allowing it to be tested for therapeutic purposes as an anti-inflammatory medication.

Keywords: fucoidan, PBMCs, THP-1, TNF-α, IL-1β, IL-6, inflammation

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Authors: Tayyaba Bari, Muhammad Hamza Anjum, Samra Kanwal, Fakhera Ikram

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Osteoarthritis, a degenerative condition, affects more than 213 million individuals globally. Since articular cartilage has no or limited vessels, therefore, after deteriorating, it is unable to rejuvenate. Traditional approaches for cartilage repair, like autologous chondrocyte implantation, microfracture and cartilage transplantation are often associated with postoperative complications and lead to further degradation. Decellularized human umbilical cord has gained interest as a viable treatment for cartilage repair. Decellularization removes all cellular contents as well as debris, leaving a biologically active 3D network known as extracellular matrix (ECM). This matrix is biodegradable, non-immunogenic and provides a microenvironment for homeostasis, growth and repair. UC derived bioink function as 3D scaffolding material, not only mediates cell-matrix interactions but also adherence, proliferation and propagation of cells for 3D organoids. This study comprises different physical, chemical and biological approaches to optimize the decellularization of human umbilical cord (UC) tissues followed by the solubilization of these tissues to bioink formation. The decellularization process consisted of two cycles of freeze thaw where the umbilical cord at -20˚C was thawed at room temperature followed by dissection in small sections from 0.5 to 1cm. Similarly decellularization with ionic and non-ionic detergents Sodium dodecyl sulfate (SDS) and Triton-X 100 revealed that both concentrations of SDS i.e 0.1% and 1% were effective in complete removal of cells from the small UC tissues. The results of decellularization was further confirmed by running them on 1% agarose gel. Histological analysis revealed the efficacy of decellularization, which involves paraffin embedded samples of 4μm processed for Hematoxylin-eosin-safran and 4,6-diamidino-2-phenylindole (DAPI). ECM preservation was confirmed by Alcian Blue, and Masson’s trichrome staining on consecutive sections and images were obtained. Sulfated GAG’s content were determined by 1,9-dimethyl-methylene blue (DMMB) assay, similarly collagen quantification was done by hydroxy proline assay. This 3D bioengineered scaffold will provide a typical atmosphere as in the extracellular matrix of the tissue, which would be seeded with the mesenchymal cells to generate the desired 3D ink for in vitro and in vivo cartilage regeneration applications.

Keywords: umbilical cord, 3d printing, bioink, tissue engineering, cartilage regeneration

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Authors: Nada A. El-Shahaw, Anas A.Salem, M. Kobeisy, Hoda M. Shabaan

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Reactive oxygen species (ROS) is collective term both oxygen radical, such superoxide (O₂•), hydroxyl(OH•), peroxyl (RO₂), and hydroperoxyl (HO₂•), and certain non-radical oxidizing agents, such as hydrogen peroxide (H₂O₂), hypochlorous acid (HOCL), and ozone (O₃), that can be convert easily to radical. The importance of antioxidants is shown here punicalagin. Punicalagin is preventing the harmful effect of (ROS) in all cells, specially gonadal cells. So, the aim of study was to investigate effects of punicalagin (PL) on maternal live body weight (MLBW), number of services/conception (NS), conception rate (CR), gestation length (GL), kindling rate (KR), total litter size (TLS), live litter size (LLS), kit weight (KW), progesterone (P4) and estradiol-17 (E2) concentrations at 1st and 2nd pregnancy of young does. A total of 28 healthy virgin does (6 months old) were divided into 2 equal groups. Group I, each doe, was injected IM with 100 ug PL twice/week pre-mating and one time 3 days post-mating. Group II, each doe was injected IM with sterilized water (control). Blood samples were taken at pre-mating, mating, post-mating, throughout pregnancy, and immediately post-kindling for assaying P4 and E2. All does were naturally mated with fertile bucks. Results revealed that PL displayed their significant impacts on MLBW, NS/conception, CR, GL, KR, TLS, LLS, KWs (birth and weaning), P4 and E2 concentrations either at 1ˢᵗ/2ⁿᵈ pregnancy or both of them. Conclusively, PL improved pregnancy outcomes of young do particularly at 2ⁿᵈ pregnancy and could be recommended in rabbit's farms.

Keywords: punicalagin, pregnancy, estradiol-17β, progesterone, does

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, gain

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Isalira Peroba Ramos, Cherley Borba Vieira de Andrade, Grazielle Suhett, Camila Salata, Paulo Cesar Canary, Guilherme Visconde Brasil, Antonio Carlos Campos de Carvalho, Regina Coeli dos Santos Goldenberg

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Background: The therapeutic options for patients with cancer now include increasingly complex combinations of medications, radiation therapy (RT), and surgical intervention. Many of these treatments have important potential adverse cardiac effects and are likely to have significant effects on patient outcomes. Cell therapy appears to be promising for the treatment of chronic and degenerative diseases, including cardiomyopathy induced by RT, as the current therapeutic options are insufficient. Aims: To evaluate the potential of bone marrow mesenchymal cells (BMMCs) in radioinduced cardiac damage Methods: Female Wistar rats, 3 months old (Ethics Committee 054/14), were divided into 2 groups, non-treated irradiated group (IR n=15) and irradiated and BMMC treated (IRT n=10). Echocardiography was performed to evaluate heart function. After euthanasia, 3 months post treatment; the left ventricle was removed and prepared for RT-qPCR (VEGF and Pro Collagen I) and histological (picrosirius) analysis. Results: In both groups, 45 days after irradiation, ejection fraction (EF) was in the normal range for these animals (> 70%). However, the BMMC treated group had EF (83.1%±2.6) while the non-treated IR group showed a significant reduction (76.1%±2.6) in relation to the treated group. In addition, we observed an increase in VEGF gene expression and a decrease in Pro Collagen I in IRT when compared to IR group. We also observed by histology that the collagen deposition was reduced in IRT (10.26%±0.83) when compared to IR group (25.29%±0.96). Conclusions: Treatment with BMMCs was able to prevent ejection fraction reduction and collagen deposition in irradiated animals. The increase of VEGF and the decrease of pro collagen I gene expression might explain, at least in part, the cell therapy benefits. All authors disclose no financial or personal relationships with individuals or organizations that could be perceived to bias their work. Sources of funding: FAPERJ, CAPES, CNPq, MCT.

Keywords: mesenchymal cells, radioation, cardiotoxicity, bone marrow

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Authors: Amina Ben Abla, Sylvie Changotade, Geraldine Rohman, Guilhem Boeuf, Cyrine Dridi, Ahmed Elmarjou, Florence Dufour, Didier Lutomski, Abdellatif Elm’semi

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Understanding cell adhesion and interaction with the extracellular matrix is essential for biomedical and biotechnological applications, including the development of biomaterials. In recent years, numerous biomaterials have emerged and were used in the field of tissue engineering. Nevertheless, the lack of interaction of biomaterials with cells still limits their bio-integration. Thus, the design of bioactive biomaterials to improve cell attachment and proliferation is of growing interest. In this study, bio-functionalized material was developed combining a synthetic polymer scaffold surface with selected domains of type III human fibronectin (FNIII-DOM) to promote cell adhesion and proliferation. Bioadhesive ligand includes cell-binding domains of human fibronectin, a major ECM protein that interacts with a variety of integrins cell-surface receptors, and ECM proteins through specific binding domains were engineered. FNIII-DOM was produced in bacterial system E. coli in 5L fermentor with a high yield level reaching 20mg/L. Bioactivity of the produced fragment was validated by studying cellular adhesion of human cells. The adsorption and immobilization of FNIII-DOM onto the polymer scaffold were evaluated in order to develop an innovative biomaterial.

Keywords: biomaterials, cellular adhesion, fibronectin, tissue engineering

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Authors: Mehrnaz Mostafavi

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Recent advances in single-molecule protein identification (ID) and quantification techniques are poised to revolutionize proteomics, enabling researchers to delve into single-cell proteomics and identify low-abundance proteins crucial for biomedical and clinical research. This paper introduces a different approach to single-molecule protein ID and quantification using tri-color amino acid tags and a plasmonic nanopore device. A comprehensive simulator incorporating various physical phenomena was designed to predict and model the device's behavior under diverse experimental conditions, providing insights into its feasibility and limitations. The study employs a whole-proteome single-molecule identification algorithm based on convolutional neural networks, achieving high accuracies (>90%), particularly in challenging conditions (95–97%). To address potential challenges in clinical samples, where post-translational modifications affecting labeling efficiency, the paper evaluates protein identification accuracy under partial labeling conditions. Solid-state nanopores, capable of processing tens of individual proteins per second, are explored as a platform for this method. Unlike techniques relying solely on ion-current measurements, this approach enables parallel readout using high-density nanopore arrays and multi-pixel single-photon sensors. Convolutional neural networks contribute to the method's versatility and robustness, simplifying calibration procedures and potentially allowing protein ID based on partial reads. The study also discusses the efficacy of the approach in real experimental conditions, resolving functionally similar proteins. The theoretical analysis, protein labeler program, finite difference time domain calculation of plasmonic fields, and simulation of nanopore-based optical sensing are detailed in the methods section. The study anticipates further exploration of temporal distributions of protein translocation dwell-times and the impact on convolutional neural network identification accuracy. Overall, the research presents a promising avenue for advancing single-molecule protein identification and quantification with broad applications in proteomics research. The contributions made in methodology, accuracy, robustness, and technological exploration collectively position this work at the forefront of transformative developments in the field.

Keywords: nano proteomics, nanopore-based optical sensing, deep learning, artificial intelligence

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Authors: Ananya Ananya, Karthik Rao

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Accurate segmentation of knee cartilage in 3-D magnetic resonance (MR) images for quantitative assessment of volume is crucial for studying and diagnosing osteoarthritis (OA) of the knee, one of the major causes of disability in elderly people. Radiologists generally perform this task in slice-by-slice manner taking 15-20 minutes per 3D image, and lead to high inter and intra observer variability. Hence automatic methods for knee cartilage segmentation are desirable and are an active field of research. This paper presents design and experimental evaluation of 2D convolutional neural networks based fully automated methods for knee cartilage segmentation in 3D MRI. The architectures are validated based on 40 test images and 60 training images from SKI10 dataset. The proposed methods segment 2D slices one by one, which are then combined to give segmentation for whole 3D images. Proposed methods are modified versions of U-net and dilated convolutions, consisting of a single step that segments the given image to 5 labels: background, femoral cartilage, tibia cartilage, femoral bone and tibia bone; cartilages being the primary components of interest. U-net consists of a contracting path and an expanding path, to capture context and localization respectively. Dilated convolutions lead to an exponential expansion of receptive field with only a linear increase in a number of parameters. A combination of modified U-net and dilated convolutions has also been explored. These architectures segment one 3D image in 8 – 10 seconds giving average volumetric Dice Score Coefficients (DSC) of 0.950 - 0.962 for femoral cartilage and 0.951 - 0.966 for tibia cartilage, reference being the manual segmentation.

Keywords: convolutional neural networks, dilated convolutions, 3 dimensional, fully automated, knee cartilage, MRI, segmentation, U-net

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Authors: Laura Gantley, Vanessa M. Conn, Stuart Webb, Kirsty Kirk, Marta Gabryelska, Duncan Holds, Brett W. Stringer, Simon J. Conn

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Trinucleotide repeat disorders comprise ~20 severe, inherited human neuromuscular and neurodegenerative disorders, which are a result of an abnormal expansion of repetitive sequences in the DNA. The most common of these, Huntington’s disease, results from the expansion of the CAG repeat region in exon 1 of the HTT gene via an unknown mechanism. Non-coding RNAs have been implicated in the initiation and progression of many diseases; thus, we focus on one circular RNA (circRNA) molecule arising from non-canonical splicing (back splicing) of HTT pre-mRNA. This circRNA and its mouse orthologue were transgenically overexpressed in human cells (SHSY-5Y and HEK293T) and mouse cells (Mb1), respectively. High-content imaging and flow cytometry demonstrated the overexpression of this circRNA reduces cell proliferation, reduces nuclear size independent of cellular size, and alters cell cycle progression. Analysis of protein by western blot and immunofluorescence demonstrated no change to HTT protein levels but altered nuclear-cytoplasmic distribution without impacting the expansion of the HTT repeat region. As these phenotypic and genotypic changes are found in Huntington’s disease patients, these results may suggest that this circRNA may play a functional role in the progression of Huntington’s disease.

Keywords: cell biology, circular RNAs, Huntington’s disease, molecular biology, neurodegenerative disorders

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Authors: Miaomiao Zhang, Sabrina Beckmann, Haluk Ertan, Rocky Chau, Mike Manefield

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Phenazines are a large class of nitrogen-containing aromatic heterocyclic compounds, which are almost exclusively produced by bacteria from diverse genera including Pseudomonas and Streptomyces. Phenazine-1-carboxylic acid (PCA) as one of 'core' phenazines are converted from chorismic acid before modified to other phenazine derivatives in different cells. Phenazines have attracted enormous interests because of their multiple roles on biocontrol, bacterial interaction, biofilm formation and fitness of their producers. However, in spite of ecological importance, degradation as a part of phenazines’ fate only have extremely limited attention now. Here, to isolate PCA-degrading bacteria, 200 mg L-1 PCA was supplied as sole carbon, nitrogen and energy source in minimal mineral medium. Quantitative PCR and Reverse-transcript PCR were employed to study abundance and activity of functional gene MFORT 16269 in PCA degradation, respectively. Intermediates and products of PCA degradation were identified with LC-MS/MS. After enrichment and isolation, a PCA-degrading strain was selected from soil and was designated as Rhodanobacter sp. PCA2 based on full 16S rRNA sequencing. As determined by HPLC, strain PCA2 consumed 200 mg L-1 (836 µM) PCA at a rate of 17.4 µM h-1, accompanying with significant cells yield from 1.92 × 105 to 3.11 × 106 cells per mL. Strain PCA2 was capable of degrading other phenazines as well, including phenazine (4.27 µM h-1), pyocyanin (2.72 µM h-1), neutral red (1.30 µM h-1) and 1-hydroxyphenazine (0.55 µM h-1). Moreover, during the incubation, transcript copies of MFORT 16269 gene increased significantly from 2.13 × 106 to 8.82 × 107 copies mL-1, which was 2.77 times faster than that of the corresponding gene copy number (2.20 × 106 to 3.32 × 107 copies mL-1), indicating that MFORT 16269 gene was activated and played roles on PCA degradation. As analyzed by LC-MS/MS, decarboxylation from the ring structure was determined as the first step of PCA degradation, followed by cleavage of nitrogen-containing ring by dioxygenase which catalyzed phenazine to nitrosobenzene. Subsequently, phenylhydroxylamine was detected after incubation for two days and was then transferred to aniline and catechol. Additionally, genomic and proteomic analyses were also carried out for strain PCA2. Overall, the findings presented here showed that a newly isolated strain Rhodanobacter sp. PCA2 was capable of degrading phenazines through decarboxylation and cleavage of nitrogen-containing ring, during which MFORT 16269 gene was activated and played important roles.

Keywords: decarboxylation, MFORT16269 gene, phenazine-1-carboxylic acid degradation, Rhodanobacter sp. PCA2

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Authors: Mohammadjavad Sotoudeheian

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COVID-19, which began in December 2019, uses the angiotensin-converting enzyme 2 (ACE2) receptor to enter and spread through the cells. ACE2 mRNA is present in almost every organ, including nasopharynx, lung, as well as the brain. Ports of entry of SARS-CoV-2 into the central nervous system (CNS) may include arterial circulation, while viremia is remarkable. However, it is imperious to develop neurological symptoms evaluation CSF analysis in patients with COVID-19, but theoretically, ACE2 receptors are expressed in cerebellar cells and may be a target for SARS-CoV-2 infection in the brain. Recent evidence agrees that SARS-CoV-2 can impact the brain through direct and indirect injury. Two biomarkers for CNS injury, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL) detected in the plasma of patients with COVID-19. NFL, an axonal protein expressed in neurons, is related to axonal neurodegeneration, and GFAP is over-expressed in CNS inflammation. GFAP cytoplasmic accumulation causes Schwan cells to misfunction, so affects myelin generation, reduces neuroskeletal support over NfLs during CNS inflammation, and leads to axonal degeneration. Interleukin-6 (IL-6), which extensively over-express due to interleukin storm during COVID-19 inflammation, regulates gene expression, as well as GFAP through STAT molecular pathway. IL-6 also impresses the phosphoinositide 3-kinase (PI3K)/STAT/smads pathway. The PI3K/ protein kinase B (Akt) pathway is the main modulator upstream of the mammalian target of rapamycin (mTOR), and alterations in this pathway are common in neurodegenerative diseases. Most neurodegenerative diseases show a disruption of autophagic function and display an abnormal increase in protein aggregation that promotes cellular death. Therefore, induction of autophagy has been recommended as a rational approach to help neurons clear abnormal protein aggregates and survive. The mTOR is a major regulator of the autophagic process and is regulated by cellular stressors. The mTORC1 pathway and mTORC2, as complementary and important elements in mTORC1 signaling, have become relevant in the regulation of the autophagic process and cellular survival through the extracellular signal-regulated kinase (ERK) pathway.

Keywords: mTORC1, COVID-19, PI3K, autophagy, neurodegeneration

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Authors: Marwa M. Abu-Serie, Marwa M. Eltarahony

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The search for reliable, effective, and safe nanoparticles (NPs) as a treatment for cancer is a pressing priority. In this study, Cu-NPs were fabricated by Streptomyces cyaneofuscatus through simultaneous bioreduction strategy of copper nitrate salt. The as-prepared Cu-NPs subjected to structural analysis; energy-dispersive X-ray spectroscopy, elemental mapping, X-ray diffraction, transmission electron microscopy, and ζ-potential. These biological synthesized Cu-NPs were mixed with disulfiram (DS), forming a nanocomplex of Cu-DS with a size of ~135 nm. The prepared nanocomplex (nanoCu-DS) exhibited higher anticancer activity than that of free complex of DS-Cu, Cu-NPs, and DS alone. This was illustrated by the lowest IC50 of nanoCu-DS (< 4 µM) against human breast and liver cancer cell lines comparing with DS-Cu, Cu-NPs, and DS (~8, 22.98-33.51 and 11.95-14.86, respectively). Moreover, flow cytometric analysis confirmed that higher apoptosis percentage range of nanoCu-DS-treated in MDA-MB 231, MCF-7, Huh-7, and HepG-2 cells (51.24-65.28%) than free complex of Cu-DS ( < 4.5%). Regarding inhibition potency of liver and breast cancer cell migration, no significant difference was recorded between free and nanocomplex. Furthermore, nanoCu-DS suppressed gene expression of β-catenine, Akt, and NF-κB and upregulated p53 expression (> 3, >15, > 5 and ≥ 3 folds, respectively) more efficiently than free complex (all ~ 1 fold) in MDA-MB 231 and Huh-7 cells. Our finding proved this prepared nano complex has a powerful anticancer activity relative to free complex, thereby offering a promising cancer treatment.

Keywords: biologically prepared Cu-NPs, breast cancer cell lines, liver cancer cell lines, nanoCu- disulfiram

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Authors: Eiji Nakamachi, Ryota Sakiyama, Koji Yamamoto, Yusuke Morita, Hidetoshi Sakamoto

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The regeneration of injured central nerve network caused by the cerebrovascular accidents is difficult, because of poor regeneration capability of central nerve system composed of the brain and the spinal cord. Recently, new regeneration methods such as transplant of nerve cells and supply of nerve nutritional factor were proposed and examined. However, there still remain many problems with the canceration of engrafted cells and so on and it is strongly required to establish an efficacious treating method of a central nerve system. Blackman proposed the electromagnetic stimulation method to enhance the axonal nerve extension. In this study, we try to design and fabricate a new three-dimensional (3D) bio-reactor, which can load a uniform AC magnetic field stimulation on PC12 cells in the extracellular environment for enhancement of an axonal nerve extension and 3D nerve network generation. Simultaneously, we measure the morphology of PC12 cell bodies, axons, and dendrites by the multiphoton excitation fluorescence microscope (MPM) and evaluate the effectiveness of the uniform AC magnetic stimulation to enhance the axonal nerve extension. Firstly, we designed and fabricated the uniform AC magnetic field stimulation bio-reactor. For the AC magnetic stimulation system, we used the laminated silicon steel sheets for a yoke structure of 3D chamber, which had a high magnetic permeability. Next, we adopted the pole piece structure and installed similar specification coils on both sides of the yoke. We searched an optimum pole piece structure using the magnetic field finite element (FE) analyses and the response surface methodology. We confirmed that the optimum 3D chamber structure showed a uniform magnetic flux density in the PC12 cell culture area by using FE analysis. Then, we fabricated the uniform AC magnetic field stimulation bio-reactor by adopting analytically determined specifications, such as the size of chamber and electromagnetic conditions. We confirmed that measurement results of magnetic field in the chamber showed a good agreement with FE results. Secondly, we fabricated a dish, which set inside the uniform AC magnetic field stimulation of bio-reactor. PC12 cells were disseminated with collagen gel and could be 3D cultured in the dish. The collagen gel were poured in the dish. The collagen gel, which had a disk shape of 6 mm diameter and 3mm height, was set on the membrane filter, which was located at 4 mm height from the bottom of dish. The disk was full filled with the culture medium inside the dish. Finally, we evaluated the effectiveness of the uniform AC magnetic field stimulation to enhance the nurve axonal extension. We confirmed that a 6.8 increase in the average axonal extension length of PC12 under the uniform AC magnetic field stimulation at 7 days culture in our bio-reactor, and a 24.7 increase in the maximum axonal extension length. Further, we confirmed that a 60 increase in the number of dendrites of PC12 under the uniform AC magnetic field stimulation. Finally, we confirm the availability of our uniform AC magnetic stimulation bio-reactor for the nerve axonal extension and the nerve network generation.

Keywords: nerve regeneration, axonal extension , PC12 cell, magnetic field, three-dimensional bio-reactor

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Noor Akhmazillah Fauzi, Mohammed Mehdi Farid, Filipa V. Silva

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The aim of this paper is to investigate if different concentration of Manuka honey (as a model food) has a major influence on the inactivation of Saccharomyces cerevisiae (as the testing microorganism) after subjecting it to HPP. Honey samples with different sugar concentrations (20, 30, 40, 50, 60 and 70 °Brix) were prepared aseptically using sterilized distilled water. No dilution of honey was made for the 80 °Brix sample. For the 0 °Brix sample (control), sterilized distilled water was used. Thermal treatment at 55 °C for 10 min (conventionally applied in honey pasteurisation in industry) was carried out for comparison purpose. S. cerevisiae cell numbers in honey samples were established before and after each HPP and thermal treatment. The number of surviving cells was determined after a proper dilution of the untreated and treated samples by the viable plate count method. S. cerevisiae cells, in different honey concentrations (0 to 80 °Brix), subjected to 600 MPa (at ambient temperature) showed an increasing resistance to inactivation with °Brix. A significant correlation (p < 0.05) between cell reduction and °Brix was found. Cell reduction in high pressure-treated samples varied linearly with °Brix (R2 > 0.9), confirming that the baroprotective effect of the food is due to sugar content. This study has practical implications in establishing efficient process design for commercial manufacturing of high sugar food products and on the potential use of HPP for such products.

Keywords: high pressure processing, honey, Saccharomyces cerevisiae, °Brix

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Authors: Kkochorong Park, Keun Cheon Kim, Hyoban Lee, Eun Ju Lee, Bongsoo Kim

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Introduction of biomaterials such as DNA, RNA, proteins is important for many research areas. There are many methods to introduce biomaterials into living organisms like tissue and cells. To introduce biomaterials, several indirect methods including virus‐mediated delivery, chemical reagent (i.e., lipofectamine), electrophoresis have been used. Such methods are passive delivery using an endocytosis process of cell, reducing an efficiency of delivery. Unlike the indirect delivery method, it has been reported that a direct delivery of exogenous biomolecules into nucleus have been more efficient to expression or integration of biomolecules. Nano-sized material is beneficial for detect signal from cell or deliver stimuli/materials into the cell at cellular and molecular levels, due to its similar physical scale. Especially, because 1 dimensional (1D) nanomaterials such as nanotube, nanorod and nanowire with high‐aspect ratio have nanoscale geometry and excellent mechanical, electrical, and chemical properties, they could play an important role in molecular and cellular biology. In this study, by using single crystalline 1D noble metal nanowire, we fabricated nano-sized 1D injector which can successfully interface with living cells and directly deliver biomolecules into several types of cell line (i.e., stem cell, mammalian embryo) without inducing detrimental damages on living cell. This nano-bio technology could be a promising and robust tool for introducing exogenous biomaterials into living organism.

Keywords: DNA, gene delivery, nanoinjector, nanowire

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

Procedia PDF Downloads 131