Search results for: neural progentor cells

Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

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Authors: Gałka Aleksandra, Jelińska Justyna, Masiak Albert, Walentukiewicz Krzysztof

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An offline signature is well-known however not the safest way to verify identity. Nowadays, to ensure proper authentication, i.e. in banking systems, multimodal verification is more widely used. In this paper the online signature analysis based on dynamic time warping (DTW) coupled with machine learning approaches has been presented. In our research signatures made with biometric pens were gathered. Signature features as well as their forgeries have been described. For verification of authenticity various methods were used including convolutional neural networks using DTW matrix and multilayer perceptron using sums of DTW matrix paths. System efficiency has been evaluated on signatures and signature forgeries collected on the same day. Results are presented and discussed in this paper.

Keywords: dynamic time warping, handwritten signature verification, feature-based recognition, online signature

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Authors: S. Kozlovski, S.W. Feigelson, R. Alon

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The b2 integrin ligands ICAM-1 ICAM-2 and the endothelial VLA-4 integrin ligand VCAM-1 are constitutively expressed on different lung vessels and on high endothelial venules (HEVs), the main portal for lymphocyte entry from the blood into lung draining lymph nodes. ICAMs are also ubiquitously expressed by many antigen-presenting leukocytes and have been traditionally suggested as critical for the various antigen-specific immune synapses generated by these distinct leukocytes and specific naïve and effector T cells. Loss of both ICAM-1 and ICAM-2 on the lung vasculature reduces the ability to patrol monocytes and Tregs to patrol the lung vasculature at a steady state. Our new findings suggest, however, that in terms of innate leukocyte trafficking into the lung lamina propria, both constitutively expressed and virus-induced vascular VCAM-1 can functionally compensate for the loss of these ICAMs. In a mouse model for influenza infection, neutrophil and NK cell recruitment and clearance of influenza remained normal in mice deficient in both ICAMs. Strikingly, mice deficient in both ICAMs also mounted normal influenza-specific CD8 proliferation and differentiation. In addition, these mice normally combated secondary influenza infection, indicating that the presence of ICAMs on conventional dendritic cells (cDCs) that present viral antigens are not required for immune synapse formation between these APCs and naïve CD8 T cells as previously suggested. Furthermore, long-lasting humoral responses critical for protection from a secondary homosubtypic influenza infection were also normal in mice deficient in both ICAM-1 and ICAM-2. Collectively, our results suggest that the expression of ICAM-1 and ICAM-2 on lung endothelial and epithelial cells, as well as on DCs and B cells, is not critical for the generation of innate or adaptive anti-viral immunity in the lungs. Our findings also suggest that endothelial VCAM-1 can substitute for the functions of vascular ICAMs in leukocyte trafficking into various lung compartments.

Keywords: emigration, ICAM-1, lymph nodes, VCAM-1

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Authors: Amber C. W. Vandepoele, Michael A. Marciano

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Biological analyses frequently focus on single organisms, however many times, the biological sample consists of more than the target organism; for example, human microbiome research targets bacterial DNA, yet most samples consist largely of human DNA. Therefore, there would be an advantage to removing these contaminating organisms. Conversely, some analyses focus on a single organism but would greatly benefit from the additional information regarding the other organismal components of the sample. Forensic analysis is one such example, wherein most forensic casework, human DNA is targeted; however, it typically exists in complex non-pristine sample substrates such as soil or unclean surfaces. These complex samples are commonly comprised of not just human tissue but also microbial and plant life, where these organisms may help gain more forensically relevant information about a specific location or interaction. This project aims to optimize a ‘phylogenetic’ differential extraction method that will separate mammalian, bacterial and plant cells in a mixed sample. This is accomplished through the use of size exclusion separation, whereby the different cell types are separated through multiple filtrations using 5 μm filters. The components are then lysed via differential enzymatic sensitivities among the cells and extracted with minimal contribution from the preceding component. This extraction method will then allow complex DNA samples to be more easily interpreted through non-targeting sequencing since the data will not be skewed toward the smaller and usually more numerous bacterial DNAs. This research project has demonstrated that this ‘phylogenetic’ differential extraction method successfully separated the epithelial and bacterial cells from each other with minimal cell loss. We will take this one step further, showing that when adding the plant cells into the mixture, they will be separated and extracted from the sample. Research is ongoing, and results are pending.

Keywords: DNA isolation, geolocation, non-human, phylogenetic separation

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Authors: Panomporn Wisuthseriwong, Hatairuk Tungkasen, Siyaporn Namsongsan, Chanikarn Srinark, Mayuva Youngsabanant-Areekijseree

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The objective of the present study was to evaluate the morphology of porcine cumulus-oocyte complexes (pCOCs) and follicular fluid during follicular development. The samples were obtained from local slaughterhouses in Nakorn Pathom Province, Thailand. Pigs were classified as either in the follicular phase or luteal phase. Porcine follicles (n = 3,510) were categorized as small (1-3 mm in diameters; n=2,910), medium (4-6 mm in diameters; n=530) and large (7-8 mm in diameters; n=70). Then pCOCs and follicular fluid were collected. Finally, we found that the oocytes can be categorized into intact cumulus cells layer oocyte, multi-cumulus cells layer oocyte, partial cumulus cells layer oocyte, completely denuded oocyte and degenerated oocyte. They showed high percentage of intact and multi-cumulus cells layer oocytes from small follicles (54.68%) medium follicles (69.06%) and large follicles (68.57%), which have high potential to develop into matured oocytes in vitro. Protein composition of the follicular fluid was separated by SDS-PAGE technique. The result shows that the protein molecular weight in the small and medium follicles are 23, 50, 66, 75, 92, 100, 132, 163, 225 and >225 kDa. Meanwhile, protein molecular weight in large follicles are 12, 16, 23, 50, 66, 75, 92, 100, 132, 163, 225 and >225 kDa. All proteins play an important role in promotion and regulation on development, maturation of oocytes and regulation of ovulation. We conclude that the results of discovery can be used porcine secretion proteins for supplement in IVM/IVF technology. Acknowledgements: The project was funded by a grant from Silpakorn University Research and Development Institute (SURDI) and Faculty of Science, Silpakorn University, Thailand.

Keywords: porcine follicles, porcine oocyte, follicular fluid, SDS-PAGE

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

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Authors: Austin Jiang, Richard M. Salmon, Nicholas W. Morrell, Wei Li

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BMP10 is highly expressed in the developing heart and plays essential roles in cardiogenesis. BMP10 deletion in mice results in embryonic lethality due to impaired cardiac development. In adults, BMP10 expression is restricted to the right atrium, though ventricular hypertrophy is accompanied by increased BMP10 expression in a rat hypertension model. However, reports of BMP10 activity in the circulation are inconclusive. In particular it is not known whether in vivo secreted BMP10 is active or whether additional factors are required to achieve its bioactivity. It has been shown that high-affinity binding of the BMP10 prodomain to the mature ligand inhibits BMP10 signaling activity in C2C12 cells, and it was proposed that prodomain-bound BMP10 (pBMP10) complex is latent. In this study, we demonstrated that the BMP10 prodomain did not inhibit BMP10 signaling activity in multiple endothelial cells, and that recombinant human pBMP10 complex, expressed in mammalian cells and purified under native conditions, was fully active. In addition, both BMP10 in human plasma and BMP10 secreted from the mouse right atrium were fully active. Finally, we confirmed that active BMP10 secreted from mouse right atrium was in the prodomain-bound form. Our data suggest that circulating BMP10 in adults is fully active and that the reported vascular quiescence function of BMP10 in vivo is due to the direct activity of pBMP10 and does not require an additional activation step. Moreover, being an active ligand, recombinant pBMP10 may have therapeutic potential as an endothelial-selective BMP ligand, in conditions characterized by loss of BMP9/10 signaling.

Keywords: bone morphogenetic protein 10 (BMP10), endothelial cell, signal transduction, transforming growth factor beta (TGF-B)

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Authors: Wei-Ru Chang, Jenn-Jiang Hwang, Zen-Ting Hsiao, Shu-Feng Lee

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Proton exchange membrane (PEM) fuel cells are widely used in electrical systems as an economical, low-polluting energy source. This study investigates the effects of PEMFC gas nitriding treatment on metal bipolar plates. The test material was SUS304 stainless steel. The study explored five different pretreatment processes, varying the corrosion resistance and electrical conductivity conditions. The most effective process was industrial acid washing, followed by heating to 500 °C. Under the condition, the corrosion current density was 8.695 μA, significantly lower than that of the untreated pretreatment sample flakes, which was measured as 38.351 μA.

Keywords: nitriding, bipolar, 304, corrosion, resistance, pretreatment

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Authors: Nehir Nebioglu

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Chemotherapy is widely used for the treatment of cancer. Doxorubicin is an anti-cancer chemotherapy drug that is classified as an anthracycline antibiotic. Antitumor antibiotics consist of natural products produced by species of the soil fungus Streptomyces. These drugs act in multiple phases of the cell cycle and are known cell-cycle specific. Although DOX is a precious clinical antineoplastic agent, resistance is also a problem that limits its utility besides cardiotoxicity problem. The drug resistance of cancer cells results from multiple factors including individual variation, genetic heterogeneity within a tumor, and cellular evolution. The mechanism of resistance is thought to involve, in particular, ABCB1 (MDR1, Pgp) and ABCC1 (MRP1) as well as other transporters. Several studies on DOX-resistant cell lines have shown that resistance can be overcome by an inhibition of ABCB1, ABCC1, and ABCC2. This study attempts to understand the effects of different concentration levels of glucose treatment and starvation on the proliferation of Doxorubicin resistant cancer cells lines. To understand the effect of starvation, K562/Dox and K562 cell lines were treated with 0, 5 nM, 50 nM, 500 nM, 5 uM and 50 uM Dox concentrations in both starvation and normal medium conditions. In addition to this, to interpret the effect of glucose treatment, different concentrations (0, 1 mM, 5 mM, 25 mM) of glucose were applied to Dox-treated (with 0, 5 nM, 50 nM, 500 nM, 5 uM and 50 uM) K562/Dox and K652 cell lines. All results show significant decreasing in the cell count of K562/Dox, when cells were starved. However, while proliferation of K562/Dox lines decrease is associated with the increasingly applied Dox concentration, K562/Dox starved ones remain at the same proliferation level. Thus, the results imply that an amount of K562/Dox lines gain starvation resistance and remain resistant. Furthermore, for K562/Dox, there is no clear effect of glucose treatment in terms of cell proliferation. In the presence of a moderate level of glucose (5 mM), proliferation increases compared to other concentration of glucose for each different Dox application. On the other hand, a significant increase in cell proliferation in moderate level of glucose is only observed in 5 uM Dox concentration. The moderate concentration level of Dox can be examined in further studies. For the high amount of glucose (25 mM), cell proliferation levels are lower than moderate glucose application. The reason could be high amount of glucose may not be absorbable by cells. Also, in the presence of low amount of glucose, proliferation is decreasing in an orderly manner of increase in Dox concentration. This situation can be explained by the glucose depletion -Warburg effect- in the literature.

Keywords: drug resistance, cancer cells, chemotherapy, doxorubicin

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Authors: B. Bouadjemi, S. Bentata, T. Lantri, Souidi Amel, W.Bensaali, A. Zitouni, Z. Aziz

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We performed first-principle calculations, the full-potential linearized augmented plane wave (FP-LAPW) method is used to calculate structural, optoelectronic and magnetic properties of cubic halide perovskites CsPbX3 (X= F,I). We employed for this study the GGA approach and for exchange is modeled using the modified Becke-Johnson (mBJ) potential to predicting the accurate band gap of these materials. The optical properties (namely: the real and imaginary parts of dielectric functions, optical conductivities and absorption coefficient absorption make this halide perovskites promising materials for solar cells applications.

Keywords: halide perovskites, mBJ, solar cells, FP-LAPW, optoelectronic properties, absorption coefficient

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Authors: Wadha Alqahtani

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In recent times, polymers have seen a great surge in interest in the field of medicine, particularly chemotherapeutics. One recent innovation is the conversion of polymeric materials into “polymeric nanoparticles”. These nanoparticles can be designed and modified to encapsulate and transport drugs selectively to cancer cells, minimizing collateral damage to surrounding healthy tissues, and improve patient quality of life. In this study, we have synthesized pseudo-branched polyester polymers from bio-based small molecules, including sorbitol, glutaric acid and a propargylic acid derivative to further modify the polymer to make it “click-able" with an azide-modified target ligand. Melt polymerization technique was used for this polymerization reaction, using lipase enzyme catalyst NOVO 435. This reaction was conducted between 90- 95 °C for 72 hours. The polymer samples were collected in 24-hour increments for characterization and to monitor reaction progress. The resulting polymer was purified with the help of methanol dissolving and filtering with filter paper then characterized via NMR, GPC, FTIR, DSC, TGA and MALDI-TOF. Following characterization, these polymers were converted to a polymeric nanoparticle drug delivery system using solvent diffusion method, wherein DiI optical dye and chemotherapeutic drug Taxol can be encapsulated simultaneously. The efficacy of the nanoparticle’s apoptotic effects were analyzed in-vitro by incubation with prostate cancer (LNCaP) and healthy (CHO) cells. MTT assays and fluorescence microscopy were used to assess the cellular uptake and viability of the cells after 24 hours at 37 °C and 5% CO2 atmosphere. Results of the assays and fluorescence imaging confirmed that the nanoparticles were successful in both selectively targeting and inducing apoptosis in 80% of the LNCaP cells within 24 hours without affecting the viability of the CHO cells. These results show the potential of using biodegradable polymers as a vehicle for receptor-specific drug delivery and a potential alternative for traditional systemic chemotherapy. Detailed experimental results will be discussed in the e-poster.

Keywords: chemotherapeutic drug, click chemistry, nanoparticle, prostat cancer

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Authors: Sima Najafzadehkhoei, George Vega Yon

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This paper presents an approach for calibrating Agent-Based Models (ABMs) efficiently, utilizing Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. These machine learning techniques are applied to Susceptible-Infected-Recovered (SIR) models, which are a core framework in the study of epidemiology. Our method replicates parameter values from observed trajectory curves, enhancing the accuracy of predictions when compared to traditional calibration techniques. Through the use of simulated data, we train the models to predict epidemiological parameters more accurately. Two primary approaches were explored: one where the number of susceptible, infected, and recovered individuals is fully known, and another using only the number of infected individuals. Our method shows promise for application in other ABMs where calibration is computationally intensive and expensive.

Keywords: ABM, calibration, CNN, LSTM, epidemiology

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Authors: A. Shoiynbek, K. Kozhakhmet, P. Menezes, D. Kuanyshbay, D. Bayazitov

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Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Yi-Feng Kao, Huey-Jine Chai, Chin-I Chang, Yi-Chen Chen, June-Ru Chen

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Microglia is a macrophage that resides in brain, and overactive microglia may result in brain neuron damage or inflammation. In this study, the phospholipids was extracted from squid skin and manufactured into a liposome (SQ liposome) to mimic apoptotic body. We then evaluated anti-inflammatory effects of SQ liposome on mouse microglial cell line (BV-2) by lipopolysaccharide (LPS) induction. First, the major phospholipid constituents in the squid skin extract were including 46.2% of phosphatidylcholine, 18.4% of phosphatidylethanolamine, 7.7% of phosphatidylserine, 3.5% of phosphatidylinositol, 4.9% of Lysophosphatidylcholine and 19.3% of other phospholipids by HPLC-UV analysis. The contents of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in the squid skin extract were 11.8 and 28.7%, respectively. The microscopic images showed that microglia cells can engulf apoptotic cells or SQ-liposome. In cell based studies, there was no cytotoxicity to BV-2 as the concentration of SQ-liposome was less than 2.5 mg/mL. The LPS induced pro-inflammatory cytokines, including tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), were significant suppressed (P < 0.05) by pretreated 0.03~2.5mg/ml SQ liposome. Oppositely, the anti-inflammatory cytokines transforming growth factor-beta (TGF-β) and interleukin-10 (IL-10) secretion were enhanced (P < 0.05). The results suggested that SQ-liposome possess anti-inflammatory properties on BV-2 and may be a good strategy for against neuro-inflammatory disease.

Keywords: apoptotic mimicry, neuroinflammation, microglia, squid processing by-products

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Authors: Javed Iqbal

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The growing need for energy by the human society and depletion of conventional energy sources demands a renewable, safe, infinite, low-cost and omnipresent energy source. One of the most suitable ways to solve the foreseeable world’s energy crisis is to use the power of the sun. Photovoltaic devices are especially of wide interest as they can convert solar energy to electricity. Recently the best performing solar cells are silicon-based cells. However, silicon cells are expensive, rigid in structure and have a large timeline for the payback of cost and electricity. Organic photovoltaic cells are cheap, flexible and can be manufactured in a continuous process. Therefore, organic photovoltaic cells are an extremely favorable replacement. Organic photovoltaic cells utilize sunlight as energy and convert it into electricity through the use of conductive polymers/ small molecules to separate electrons and electron holes. A major challenge for these new organic photovoltaic cells is the efficiency, which is low compared with the traditional silicon solar cells. To overcome this challenge, usually two straightforward strategies have been considered: (1) reducing the band-gap of molecular donors to broaden the absorption range, which results in higher short circuit current density (JSC) of devices, and (2) lowering the highest occupied molecular orbital (HOMO) energy of molecular donors so as to increase the open-circuit voltage (VOC) of applications devices.8 Keeping in mind the cost of chemicals it is hard to try many materials on test basis. The best way is to find the suitable material in the bulk. For this purpose, we use computational approach to design molecules based on our organic chemistry knowledge and determine their physical and electronic properties. In this study, we did DFT calculations with different options to get high open circuit voltage and after getting suitable data from calculation we finally did synthesis of a novel D–π–A–π–D type low band-gap small molecular donor material (ZOPTAN-TPA). The Aarylene vinylene based bis(arylhalide) unit containing a cyanostilbene unit acts as a low-band- gap electron-accepting block, and is coupled with triphenylamine as electron-donating blocks groups. The motivation for choosing triphenylamine (TPA) as capped donor was attributed to its important role in stabilizing the separated hole from an exciton and thus improving the hole-transporting properties of the hole carrier.3 A π-bridge (thiophene) is inserted between the donor and acceptor unit to reduce the steric hindrance between the donor and acceptor units and to improve the planarity of the molecule. The ZOPTAN-TPA molecule features a low HOMO level of 5.2 eV and an optical energy gap of 2.1 eV. Champion OSCs based on a solution-processed and non-annealed active-material blend of [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) and ZOPTAN-TPA in a mass ratio of 2:1 exhibits a power conversion efficiency of 1.9 % and a high open-circuit voltage of over 1.0 V.

Keywords: high open circuit voltage, donor, triphenylamine, organic solar cells

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Authors: B. González-Yebra, B. Flores-Nieto, P. Aguilar-Salinas, M. Preciado Puga, A. L. González Yebra

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The monitoring of populations occupationally exposed to organic solvents has been an important issue for several shoe factories for years since the International Agency for Research on Cancer (IARC) has advised on the potential carcinogenic risk of chemicals related to occupations. In order to detect if exposition to organic solvents used in some Mexican shoe factories contributes to oral carcinogenesis, we performed monitoring in three factories. Occupational exposure was determined by using monitors 3M. Organic solvents were assessed by gas chromatography. Then, we recruited 30 shoe workers (30.2 ± 8.4 years) and 10 unexposed subjects (43.3 ± 11.2 years) for the micronuclei (MN) test and immunodetection of some cancer biomarkers (ki-67, p16, caspase-3) in scraped oral epithelial cells. Monitored solvents detected were acetone, benzene, hexane, methyl ethyl ketone, and toluene in acceptable levels according to Official Mexican Norm. We found by MN test higher incidence of nuclear abnormalities (karyorrhexis, pycnosis, karyolysis, condensed chromatin, and macronuclei) in the exposed group than the non-exposed group. On the other hand, we found, a negative expression for Ki-67 and p16 in exfoliated epithelial cells from exposed and non-exposed to organic solvents subjects. Only caspase-3 shown positive patter of expression in 9/30 (30%) exposed subjects, and we detected high karyolysis incidence in caspase-3 subjects (p = 0.021). The absence of expression of proliferation markers p16 and ki-67 and presence of apoptosis marker caspase-3 are indicating the absence of malignancy in oral epithelial cells and low risk for oral cancer. It is a fact that the MN test is a very effective method to detect nuclear abnormalities in exfoliated buccal cells from subjects that have been exposed to organic solvents in the shoe industry. However, in order to improve this tool and predict cancer risk is it is mandatory to implement complementary tests as other biomarkers that can help to detect malignancy in individuals occupationally exposed.

Keywords: biomarkers, oral cancer, organic solvents, shoe industries

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Authors: Awol Seid Ebrie

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HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease.

Keywords: nonproportional odds model, proportional odds model, random coefficients model, random intercepts model

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Authors: Z. Khalifa, K. M. Hassan, M. Abdel Azzem

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Potential strategies for deriving useful forms of renewable high density energy from abundant energy stored in carbohydrates is direct conversion of glucose (GLU) to electrical power. A three novel versatile modified electrodes, synthesized by electrochemical polymerization of organic monomers on glassy carbon electrodes (GC), have been developed for biofuel cells results in stable and long-term power production. Electrocatalytic oxidation of glucose in alkaline solution on conducting polymers electrodes modified by incorporation of Ni nanoparticles (NiNPs) onto poly(1,5-aminonaphthalene) (1,5-PDAN), poly(1,8-diaminonaphthalene) (1,8-PDAN) and poly(1-amino-2-methyl-9,10-anthraquinone) (PAMAQ) was investigated. The electrocatalytic oxidation of glucose at NiNPs-modified 1,5-PDAN/GC, 1,8-PDAN/GC and PAMAQ/GC electrodes has been studied using voltammetry technique. The PDAN electrodes show a slight activity in the potential of interest. The prepared NiNPs/PAMAQ/GC catalyst showed a very interesting catalytic activity that was nicely comparable to the NiNPs/1,5-PDAN/GC, NiNPs/1,8-PDAN/GC modified electrodes. In advance, both shows a significant more catalytic activity compared to the reported data for electrodes for glucose electrocatalytic oxidation.

Keywords: biofuel cells, glucose oxidation, electrocatalysis, nanoparticles and modified electrodes

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Authors: Dimakatso B. Gumede, Nicolette N. Houreld

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Diabetic foot ulcers (DFUs) are a complication of diabetes mellitus (DM), a metabolic disease caused by insulin resistance or insufficiency, resulting in hyperglycaemia and low-grade chronic inflammation. Current therapies for treating DFUs include wound debridement, glycaemic control, and wound dressing. However, these therapies are moderately effective as there is a recurrence of these ulcers and an increased risk of lower limb amputations. Photobiomodulation (PBM), which is the application of non-invasive low-level light for wound healing at the spectrum of 660-1000 nm, has shown great promise in accelerating the healing of chronic wounds. However, its underlying mechanisms are not clearly defined. Studies have indicated that PBM induces wound healing via the activation of signaling pathways that are involved in tissue repair, such as the transforming growth factor-β (TGF-β). However, other signaling pathways, such as the WNT/β-catenin pathway, which is also critical for wound repair, have not been investigated. This study aimed to elucidate if PBM at 660 nm and a fluence of 5 J/cm² activates the WNT/β-catenin signaling pathway for wound healing in a diabetic cellular model. Human dermal fibroblasts (WS1) were continuously cultured high-glucose (26.5 mM D-glucose) environment to create a diabetic cellular model. A central scratch was created in the diabetic model to ‘wound’ the cells. The diabetic wounded (DW) cells were thereafter irradiated at 660 nm and a fluence of 5 J/cm². Cell migration, gene expression and protein assays were conducted at 24- and 48-h post-PBM. The results showed that PBM at 660 nm and a fluence of 5 J/cm² significantly increased cell migration in diabetic wounded cells at 24-h post-PBM. The expression of CTNNB1, ACTA2, COL1A1 and COL3A1 genes was also increased in DW cells post-PBM. Furthermore, there was increased cytoplasmic accumulation and nuclear localization of β-catenin at 24 h post-PBM. The findings in this study demonstrate that PBM activates the WNT/β-catenin signaling pathway by inducing the accumulation of β-catenin in diabetic wounded cells, leading to increased cell migration and expression of wound repair markers. These results thus indicate that PBM has the potential to improve wound healing in diabetic ulcers via activation of the WNT/β-catenin signaling pathway.

Keywords: wound healing, diabetic ulcers, photobiomodulation, WNT/β-catenin, signalling pathway

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Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, convolutional neural network

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Authors: Marziyeh Khavari

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In recent decades, climate change, global warming, and the growing population worldwide face some challenges, such as increasing food consumption and shortage of resources. Assessing how climate change could disturb crops, especially hazelnut production, seems crucial for sustainable agriculture production. For hazelnut cultivation in the mid-warm condition, such as in Iran, here we present an investigation of climate parameters and how much they are effective on fertility and nut production of hazelnut trees. Therefore, the climate change of the northern zones in Iran has investigated (1960-2017) and was reached an uptrend in temperature. Furthermore, the descriptive analysis performed on six cultivars during seven years shows how this small-scale survey could demonstrate the effects of climate change on hazelnut production and stability. Results showed that some climate parameters are more significant on nut production, such as solar radiation, soil temperature, relative humidity, and precipitation. Moreover, some cultivars have produced more stable production, for instance, Negret and Segorbe, while the Mervill de Boliver recorded the most variation during the study. Another aspect that needs to be met is training and predicting an actual model to simulate nut production through a neural network and linear regression simulation. The study developed and estimated the ANN model's generalization capability with different criteria such as RMSE, SSE, and accuracy factors for dependent and independent variables (environmental and yield traits). The models were trained and tested while the accuracy of the model is proper to predict hazelnut production under fluctuations in weather parameters.

Keywords: climate change, neural network, hazelnut, global warming

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Authors: Madhu Gupta, G. P. Agrawa, Suresh P. Vyas

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Certain tumor cells overexpress a membrane-spanning molecule aminopeptidase N (CD13) isoform, which is the receptor for peptides containing the NGR motif. NGR-modified Docetaxel (DTX)-loaded PEG-b-PLGA polymeric nanoparticles (cNGR-DNB-NPs) were developed and evaluated for their in vitro potential in HT-1080 cell line. The cNGR-DNB-NPs containing particles were about 148 nm in diameter with spherical shape and high encapsulation efficiency. Cellular uptake was confirmed both qualitatively and quantitatively by Confocal Laser Scanning Microscopy (CLSM) and flow cytometry. Both quantitatively and qualitatively results confirmed the NGR conjugated nanoparticles revealed the higher uptake of nanoparticles by CD13-overexpressed tumor cells. Free NGR inhibited the cellular uptake of cNGR-DNB-NPs, revealing the mechanism of receptor mediated endocytosis. In vitro cytotoxicity studies demonstrated that cNGR-DNB-NPs, formulation was more cytotoxic than unconjugated one, which were consistent well with the observation of cellular uptake. Hence, the selective delivery of cNGR-DNB-NPs formulation in CD13-overexpressing tumors represents a potential approach for the design of nanocarrier-based dual targeted delivery systems for targeting the tumor cells and vasculature.

Keywords: solid Tumor, docetaxel, targeting, NGR ligand

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

Abstract:

The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Mansa Radhakrishnan, Anwar Ali, Muhammad Rizwan Mughal

Abstract:

Many universities around the world are working on modular and low budget architecture of small spacecraft to reduce the development cost of the overall system. This paper focuses on the design of a modular solar power harvesting system for a hexagonal-shaped small satellite. The designed solar power harvesting systems are composed of solar panels and power converter subsystems. The solar panel is composed of solar cells mounted on the external face of the printed circuit board (PCB), while the electronic components of power conversion are mounted on the interior side of the same PCB. The solar panel with dimensions 16.5cm × 99cm is composed of 36 solar cells (each solar cell is 4cm × 7cm) divided into four parallel banks where each bank consists of 9 solar cells. The output voltage of a single solar cell is 2.14V, and the combined output voltage of 9 series connected solar cells is around 19.3V. The output voltage of the solar panel is boosted to the satellite power distribution bus voltage level (28V) by a boost converter working on a constant voltage maximum power point tracking (MPPT) technique. The solar panel module is an eight-layer PCB having embedded coil in 4 internal layers. This coil is used to control the attitude of the spacecraft, which consumes power to generate a magnetic field and rotate the spacecraft. As power converter and distribution subsystem components are mounted on the PCB internal layer, therefore it is mandatory to do thermal analysis in order to ensure that the overall module temperature is within thermal safety limits. The main focus of the overall design is on compactness, miniaturization, and efficiency enhancement.

Keywords: small satellites, power subsystem, efficiency, MPPT

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Authors: Camellia Al-Ibrahim, Jenny Yiend, Sukhwinder S. Shergill

Abstract:

Background: Attention bias to threat play a role in the development, maintenance, and exacerbation of delusional beliefs in schizophrenia in which patients emphasize the threatening characteristics of stimuli and prioritise them for processing. Cognitive control deficits arise when task-irrelevant emotional information elicits attentional bias and obstruct optimal performance. This study is investigating neural correlates of interference effect of linguistic threat and whether these effects are independent of delusional severity. Methods: Using an event-related functional magnetic resonance imaging (fMRI), neural correlates of interference effect of linguistic threat during the emotional Stroop task were investigated and compared patients with schizophrenia with high (N=17) and low (N=16) paranoid symptoms and healthy controls (N=20). Participants were instructed to identify the font colour of each word presented on the screen as quickly and accurately as possible. Stimuli types vary between threat-relevant, positive and neutral words. Results: Group differences in whole brain effects indicate decreased amygdala activity in patients with high paranoid symptoms compared with low paranoid patients and healthy controls. Regions of interest analysis (ROI) validated our results within the amygdala and investigated changes within the striatum showing a pattern of reduced activation within the clinical group compared to healthy controls. Delusional severity was associated with significant decreased neural activity in the striatum within the clinical group. Conclusion: Our findings suggest that the emotional interference mediated by the amygdala and striatum may reduce responsiveness to threat-related stimuli in schizophrenia and that attenuation of fMRI Blood-oxygen-level dependent (BOLD) signal within these areas might be influenced by the severity of delusional symptoms.

Keywords: attention bias, fMRI, Schizophrenia, Stroop

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Authors: Rama Shanker, Anil Kumar Sachan

Abstract:

Basic ingredients of concrete are cement, fine aggregate, coarse aggregate and water. To produce a concrete of certain specific properties, optimum proportion of these ingredients are mixed. The important factors which govern the mix design are grade of concrete, type of cement and size, shape and grading of aggregates. Concrete mix design method is based on experimentally evolved empirical relationship between the factors in the choice of mix design. Basic draw backs of this method are that it does not produce desired strength, calculations are cumbersome and a number of tables are to be referred for arriving at trial mix proportion moreover, the variation in attainment of desired strength is uncertain below the target strength and may even fail. To solve this problem, a lot of cubes of standard grades were prepared and attained 28 days strength determined for different combination of cement, fine aggregate, coarse aggregate and water. An artificial neural network (ANN) was prepared using these data. The input of ANN were grade of concrete, type of cement, size, shape and grading of aggregates and output were proportions of various ingredients. With the help of these inputs and outputs, ANN was trained using feed forward back proportion model. Finally trained ANN was validated, it was seen that it gave the result with/ error of maximum 4 to 5%. Hence, specific type of concrete can be prepared from given material properties and proportions of these materials can be quickly evaluated using the proposed ANN.

Keywords: aggregate proportions, artificial neural network, concrete grade, concrete mix design

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Authors: Amir Seyfoori, Ehsan Samiei, Mohsen Akbari

Abstract:

The use of microtechnology for detection and high yield isolation of circulating tumor cells (CTCs) has shown enormous promise as an indication of clinical metastasis prognosis and cancer treatment monitoring. The Immunomagnetic assay has been also coupled to microtechnology to improve the selectivity and efficiency of the current methods of cancer biomarker isolation. In this way, generation and configuration of the local high gradient magnetic field play essential roles in such assay. Additionally, considering the intrinsic heterogeneity of cancer cells, real-time analysis of isolated cells is necessary to characterize their responses to therapy. Totally, on-chip isolation and monitoring of the specific tumor cells is considered as a pressing need in the way of modified cancer therapy. To address these challenges, we have developed a bi-layer magnetic-based microfluidic chip for enhanced CTC detection and capturing. Micromagnet arrays at the bottom layer of the chip were fabricated using a new method of magnetic nanoparticle paste deposition so that they were arranged at the center of the chain microchannel with the lowest fluid velocity zone. Breast cancer cells labelled with EPCAM-conjugated smart microgels were immobilized on the tip of the micromagnets with greater localized magnetic field and stronger cell-micromagnet interaction. Considering different magnetic nano-powder usage (MnFe2O4 & gamma-Fe2O3) and micromagnet shapes (ellipsoidal & arrow), the capture efficiency of the systems was adjusted while the higher CTC capture efficiency was acquired for MnFe2O4 arrow micromagnet as around 95.5%. As a proof of concept of on-chip tumor cell monitoring, magnetic smart microgels made of thermo-responsive poly N-isopropylacrylamide-co-acrylic acid (PNIPAM-AA) composition were used for both purposes of targeted cell capturing as well as cell monitoring using antibody conjugation and fluorescent dye loading at the same time. In this regard, magnetic microgels were successfully used as cell tracker after isolation process so that by raising the temperature up to 37⁰ C, they released the contained dye and stained the targeted cell just after capturing. This microfluidic device was able to provide a platform for detection, isolation and efficient real-time analysis of specific CTCs in the liquid biopsy of breast cancer patients.

Keywords: circulating tumor cells, microfluidic, immunomagnetic, cell isolation

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Authors: Salma Loukman, Reda Benmrid, Najat Bouchmaa, Hicham Hboub, Rachid El Fatimy, Rachid Benhida

Abstract:

In this study, it use integrated transcriptomic datasets from the GEO repository with the purpose of investigating immune dysregulation in COVID-19. Thus, in this context, we decided to be focused on NK cells and CD14+ monocytes gene expression, considering datasets GSE165461 and GSE198256, respectively. Other datasets with PBMCs, lung, olfactory, and sensory epithelium and lymph were used to provide robust validation for our results. This approach gave an integrated view of the immune responses in COVID-19, pointing out a set of potential biomarkers and therapeutic targets with special regard to standards of physiological conditions. IFI27, MKI67, CENPF, MBP, HBA2, TMEM158, THBD, HBA1, LHFPL2, SLA, and AC104564.3 were identified as key genes from our analysis that have critical biological processes related to inflammation, immune regulation, oxidative stress, and metabolic processes. Consequently, such processes are important in understanding the heterogeneous clinical manifestations of COVID-19—from acute to long-term effects now known as 'long COVID'. Subsequent validation with additional datasets consolidated these genes as robust biomarkers with an important role in the diagnosis of COVID-19 and the prediction of its severity. Moreover, their enrichment in key pathophysiological pathways presented them as potential targets for therapeutic intervention.The results provide insight into the molecular dynamics of COVID-19 caused by cells such as NK cells and other monocytes. Thus, this study constitutes a solid basis for targeted diagnostic and therapeutic development and makes relevant contributions to ongoing research efforts toward better management and mitigation of the pandemic.

Keywords: SARS-COV-2, RNA-seq, biomarkers, severity, long COVID-19, bio analysis

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Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

Abstract:

First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model

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Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

Abstract:

This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 113