Search results for: protein structure classification

Authors: Daina Jonkus, Solvita Petrovska, Dace Smiltina, Lasma Cielava

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The beta-lactoglobulin and kappa-casein are milk proteins which are important for milk composition. Cows with beta-lactoglobulin and kappa-casein gene BB genotypes have highest milk crude protein and fat content. The aim of the study was to determinate the frequencies of milk protein gene polymorphisms in local Latvian Brown (LB) cows breed and analyze the influence of beta-lactoglobulin and kappa-casein genotypes to milk productivity traits. 102 cows’ genotypes of milk protein genes were detected using Polymerase Chain Reaction and Restriction Fragment Length Polymorphism (PCR-RFLP) and electrophoresis on 3% agarose gel. For beta-lactoglobulin were observed 2 types of alleles A and B and for kappa-casein 3 types: A, B and E. Highest frequency in beta-lactoglobulin gene was observed for B allele – 0.926. Molecular analysis of beta-lactoglobulin gene shows 86.3% of individuals are homozygous by B allele and animals are with genotypes BB and 12.7% of individuals are heterozygous with genotypes AB. The highest milk yield 4711.7 kg was for 1st lactation cows with AB genotypes, whereas the highest milk protein content (3.35%) and fat content (4.46 %) was for BB genotypes. Analysis of the kappa-casein locus showed a prevalence of the A allele – 0.750. The genetic variant of B was characterized by a low frequency – 0.240. Moreover, the frequency of E occurred in the LB cows’ population with very low frequency – 0.010. 54.9 % of cows are homozygous with genotypes AA, and only 4.9 % are homozygous with genotypes BB. 32.8 % of individuals are heterozygous with genotypes AB, and 2.0 % are with AE. The highest milk productivity was for 1st lactation cows with AB genotypes: milk yield 4620.3 kg, milk protein content 3.39% and fat content 4.53 %. According to the results, in local Latvian brown there are only 2.9% of cows are with BB-BB genotypes, which is related to milk coagulation ability and affected cheese production yield. Acknowledgment: the investigation is supported by VPP 2014-2017 AgroBioRes Project No. 3 LIVESTOCK.

Keywords: beta-lactoglobulin, cows, genotype frequencies, kappa-casein

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Authors: Mohamed M. Alsull

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In the present study, two microalgae species “Nannochloropsis sp. and Tetraselmis sp.” isolated from Penang National Park coastal waters, Malaysia; were cultivated under combined various laboratory conditions “salinity, light, nitrogen limitation and starvation”. Growth rate, dry weight, chlorophyll a content, total lipid and protein contents, were estimated at mid exponential growth phase. Both Nannochloropsis sp. and Tetraselmis sp. showed remarkable decrease in growth rate, chlorophyll a content and protein content companied with increase in lipid content under nitrogen limitation and starvation conditions. Maintaining Nannochloropsis sp. under salinity 15‰ caused only significant decrease in total protein content; while Tetraselmis sp. grown at the same salinity caused decrease in the growth rate, chlorophyll a, dry weight and total protein content only when nitrogen was available.

Keywords: biochemical composition, light, microalgae, nitrogen limitation, salinity

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Authors: Sena Su, Burak Ozbek, Yesim M. Sahin, Sevil Yucel, Dilek Kazan, Faik N. Oktar, Nazmi Ekren, Oguzhan Gunduz

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In recent years, protein, lipid or polysaccharide-based polymers have been used in order to develop biodegradable materials and their chemical nature determines the physical properties of the resulting films. Among these polymers, proteins from different sources have been extensively employed because of their relative abundance, film forming ability, and nutritional qualities. In this study, the biodegradable composite nanofiber films based on fish sarcoplasmic protein (FSP) were prepared via electrospinning technique. Biodegradable polycaprolactone (PCL) was blended with the FSP to obtain hybrid FSP/PCL nanofiber mats with desirable physical properties. Mixture solutions of FSP and PCL were produced at different concentrations and their density, viscosity, electrical conductivity and surface tension were measured. Mechanical properties of electrospun nanofibers were evaluated. Morphology of composite nanofibers was observed using scanning electron microscopy (SEM). Moreover, Fourier transform infrared spectrometer (FTIR) studies were used for analysis chemical composition of composite nanofibers. This study revealed that the FSP based nanofibers have the potential to be used for different applications such as biodegradable packaging, drug delivery, and wound dressing, etc.

Keywords: edible film, electrospinning, fish sarcoplasmic protein, nanofiber

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Authors: Hawbash Jalil

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In this study, the effects of whey protein based films on various properties of kashar cheese were examined. In the study, edible film solutions based on whey protein isolate, whey protein isolate + transglutaminase enzyme and whey protein isolate + chitosan were produced and Kashar cheese samples were coated with these films by dipping method and stored at +4 ºC for 60 days. Chemical, microbiological and textural analyzes were carried out on samples at 0, 30 and 60 days of storage. As a result of the study, the highest dry matter and total nitrogen values were obtained from uncoated control samples This is an indication that the coatings limit water vapor permeability. The highest acidity and pH values obtained from the samples as storage results were 3.33% and 5.86%, respectively, in the control group samples. Both acidity and pH rise in these groups, is a consequence of the buffering of pH changes of hydrolsis products which are as a result of proteolysis occurring in the sample. Nitrogen changes and lipolysis values, which are indicative of the degree of hydrolysis of proteins and triglycerides in kashar cheese, were generally higher in the control group This result is due to limiting the micro organism reproduction by limiting the gas passage of the coatings. Hardness and chewiness values of the textural properties of the samples were significantly reduced in uncoated control samples compared to the coated samples due to maturation. The chitosan film coatings used in the study limited the development of mold yeast until the 30th day but after that did not yield successful results in this respect.

Keywords: chitosan, edible film, transglutaminase, whey protein

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Authors: David Pham, Yongfeng Zhang

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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: R. Periyasamy, Deepak Joshi, Sneh Anand

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Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution.

Keywords: arch index, computational tool, static foot pressure intensity image, foot pressure distribution, linear discriminant analysis

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Mohammad Javad Esmaeili

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Background: Coagulopathy is one of the complications with end stage renal disease with high prevalence in the world. Thromboelastogram is adynamic test for evaluation of coagulopathy and we have compared our patient's coagulation profiles with the results of TEG. Material and methods: In this study 50 patients with ESRD who were on regular hemodialysis for at least 6 months was selected with simple sampling and their coagulation profile was done with blood sampling and also TEG was done for every patient. Data were analyzed with SPSS and P<0.05 consider significant. Results: Protein s, Protein c and Antithrombin III deficiency was detected in 32%, 16% and 20% of patients and activated protein c resistance was abnormal in 2% of patients. In TEG, R time in 49% and K in 22/5% of patients was lower than normal and a-angle in 26% and maximum amplitude in 36% of patients was upper than normal (Hypercoagulable state). PS with R and ATIII with K have correlation. Conclusion: R time and K in TEG can be a suitable screening test in patients with suspicious to PS and ATIII deficiency.

Keywords: thromboelastography, chronic kidney disease, Coagulating disorder, hemodialysis

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Authors: Tokihiko Akita, Seiichi Mita

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A millimeter-wave radar is the most robust against adverse environments, making it an essential environment recognition sensor for automated driving. However, the reflection signal is sparse and unstable, so it is difficult to obtain the high recognition accuracy. Deep learning provides high accuracy even for them in recognition, but requires large scale datasets with ground truth. Specially, it takes a lot of cost to annotate for a millimeter-wave radar. For the solution, utilizing a simulator that can generate an annotated huge dataset is effective. Simulation of the radar is more difficult to match with real world data than camera image, and recognition by deep learning with higher-order features using the simulator causes further deviation. We have challenged to improve the accuracy of traffic participant classification by fusing simulator and real-world data with domain adaptation technique. Experimental results with the domain adaptation network created by us show that classification accuracy can be improved even with a few real-world data.

Keywords: millimeter-wave radar, object classification, deep learning, simulation, domain adaptation

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Authors: Siddharth Deshpande, Nihar Masurkar, Vallerinteavide Mavelli Girish, Malan Desai, Chester Drum

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Folding and stabilization of recombinant proteins remain a consistent challenge for industrial and therapeutic applications. Proteins derived from thermophilic bacteria often have superior expression and stability qualities. To develop a generalizable approach to protein folding and stabilization, we tested the hypothesis that wrapping a thermostable exoshell around a protein substrate would aid folding and impart thermostable qualities to the internalized substrate. To test the effect of internalizing a protein within a thermostable exoshell (tES), we tested in vitro folding and stability using green fluorescent protein (GFPuv), horseradish peroxidase (HRP) and renilla luciferase (rLuc). The 8nm interior volume of a thermostable ferritin assembly was engineered to accommodate foreign proteins and either present a positive, neutral or negative interior charge environment. We further engineered the tES complex to reversibly assemble and disassemble with pH titration. Template proteins were expressed as inclusion bodies and an in vitro folding protocol was developed that forced proteins to fold inside a single tES. Functional yield was improved 100-fold, 100-fold and 150-fold with use of tES for GFPuv, HRP and rLuc respectively and was highly dependent on the internal charge environment of the tES. After folding, functional proteins could be released from the tES folding cavity using size exclusion chromatography at pH 5.8. Internalized proteins were tested for improved stability against thermal, organic, urea and guanidine denaturation. Our results demonstrated that thermostable exoshells can efficiently refold and stabilize inactive aggregates into functional proteins.

Keywords: thermostable shell, in vitro folding, stability, functional yield

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Authors: Usamah Al-Ali

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We explore the effect of expanding space on the exoticity of the matter supporting a traversable Lorentzian wormhole of zero radial tide whose line element is given by ds2 = dt^2 − a^2(t)[ dr^2/(1 − kr2 −b(r)/r)+ r2dΩ^2 in the context of General Relativity. This task is achieved by deriving the Einstein field equations for anisotropic matter field corresponding to the considered cosmological wormhole metric and performing a classification of their solutions on the basis of a variable equations of state (EoS) of the form p = ω(r)ρ. Explicit forms of the shape function b(r) and the scale factor a(t) arising in the classification are utilized to construct the corresponding energy-momentum tensor where the energy conditions for each case is investigated. While the violation of energy conditions is inevitable in case of static wormholes, the classification we performed leads to interesting solutions in which this violation is either reduced or eliminated.

Keywords: general relativity, Einstein field equations, energy conditions, cosmological wormhole

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Authors: Asad Naeem, Mohamed Nour Eldin, Jinkoo Kim

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In this study, the seismic performance and life cycle cost (LCC) are evaluated of the structure retrofitted with the damped cable system (DCS). The DCS is a seismic retrofit system composed of a high-strength steel cable and pressurized viscous dampers. The analysis model of the system is first derived using various link elements in SAP2000, and fragility curves of the structure retrofitted with the DCS and viscous dampers are obtained using incremental dynamic analyses. The analysis results show that the residual displacements of the structure equipped with the DCS are smaller than those of the structure with retrofitted with only conventional viscous dampers, due to the enhanced stiffness/strength and self-centering capability of the damped cable system. The fragility analysis shows that the structure retrofitted with the DCS has the least probability of reaching the specific limit states compared to the bare structure and the structure with viscous damper. It is also observed that the initial cost of the DCS method required for the seismic retrofit is smaller than that of the structure with viscous dampers and that the LCC of the structure equipped with the DCS is smaller than that of the structure with viscous dampers.

Keywords: damped cable system, fragility curve, life cycle cost, seismic retrofit, self-centering

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Authors: Lei Wang, Yingzhou Ni, Amr M. Bakry, Hao Cheng, Li Liang

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Food proteins have been widely used as carrier materials because of their multiple functional properties. In this study, alfa-tocopherol was encapsulated in the oil phase of an oil-in-water emulsion stabilized with whey protein isolate (WPI). The influence of WPI concentration and resveratrol or ascorbic acid on the decomposition of alfa-tocopherol in the emulsion during storage is discussed. Decomposition decreased as WPI concentrations increased. Decomposition was delayed at ascorbic acid/WPI molar ratios lower than 5 but was promoted at higher ratios. Resveratrol partitioned into the oil-water interface by binding to WPI and its cis-isomer is believed to have contributed most of the protective effect of this polyphenol. These results suggest the possibility of using the emulsifying and ligand-binging properties of WPI to produce carriers for simultaneous encapsulation of alfa-tocopherol and resveratrol in a single emulsion system.

Keywords: stability, alfa-tocopherol, resveratrol, whey protein isolate

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Authors: Iftikhar Tayubi, Tabrej Khan, Rayan Alsulmi, Abdulrahman Labban

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Spider produces a special kind of substance. This special kind of substance is called a toxin. The toxin is composed of many types of protein, which differs from species to species. Spider toxin consists of several proteins and non-proteins that include various categories of toxins like myotoxin, neurotoxin, cardiotoxin, dendrotoxin, haemorrhagins, and fibrinolytic enzyme. Protein Sequence information with references of toxins was derived from literature and public databases. From the previous findings, the Spider toxin would be the best choice to treat different types of tumors and cancer. There are many therapeutic regimes, which causes more side effects than treatment hence a different approach must be adopted for the treatment of cancer. The combinations of drugs are being encouraged, and dramatic outcomes are reported. Spider toxin is one of the natural cytotoxic compounds. Hence, it is being used to treat different types of tumors; especially its positive effect on breast cancer is being reported during the last few decades. The efficacy of this database is that it can provide a user-friendly interface for users to retrieve the information about Spiders, toxin and toxin protein of different Spiders species. SPIDTOXD provides a single source information about spider toxins, which will be useful for pharmacologists, neuroscientists, toxicologists, medicinal chemists. The well-ordered and accessible web interface allows users to explore the detail information of Spider and toxin proteins. It includes common name, scientific name, entry id, entry name, protein name and length of the protein sequence. The utility of this database is that it can provide a user-friendly interface for users to retrieve the information about Spider, toxin and toxin protein of different Spider species. The database interfaces will satisfy the demands of the scientific community by providing in-depth knowledge about Spider and its toxin. We have adopted the methodology by using A MySQL and PHP and for designing, we used the Smart Draw. The users can thus navigate from one section to another, depending on the field of interest of the user. This database contains a wealth of information on species, toxins, and clinical data, etc. This database will be useful for the scientific community, basic researchers and those interested in potential pharmaceutical Industry.

Keywords: siptoxd, php, mysql, toxin

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Authors: Vadim Vagin, Marina Fomina, Oleg Morosin

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This paper contains the description of argumentation approach for the problem of inductive concept formation. It is proposed to use argumentation, based on defeasible reasoning with justification degrees, to improve the quality of classification models, obtained by generalization algorithms. The experiment’s results on both clear and noisy data are also presented.

Keywords: argumentation, justification degrees, inductive concept formation, noise, generalization

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Authors: S. Junaid S. Qazi, Denice C. Bay, Raymond Chew, Raymond J. Turner

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EmrE, a member of the small multidrug resistance protein family in bacteria is considered to be the archetypical member of its family. It confers host resistance to a wide variety of quaternary cation compounds (QCCs) driven by proton motive force. Generally, purification yield is a challenge in all membrane proteins because of the difficulties in their expression, isolation and solubilization. EmrE is extremely hydrophobic which make the purification yield challenging. We have purified EmrE protein using two different approaches: organic solvent membrane extraction and hexahistidine (his6) tagged Ni-affinity chromatographic methods. We have characterized changes present between ligand affinity of untagged and his6-tagged EmrE proteins in similar membrane mimetic environments using biophysical experimental techniques. Purified proteins were solubilized in a buffer containing n-dodecyl-β-D-maltopyranoside (DDM) and the conformations in the proteins were explored in the presence of four QCCs, methyl viologen (MV), ethidium bromide (EB), cetylpyridinium chloride (CTP) and tetraphenyl phosphonium (TPP). SDS-Tricine PAGE and dynamic light scattering (DLS) analysis revealed that the addition of QCCs did not induce higher multimeric forms of either proteins at all QCC:EmrE molar ratios examined under the solubilization conditions applied. QCC binding curves obtained from the Trp fluorescence quenching spectra, gave the values of dissociation constant (Kd) and maximum specific one-site binding (Bmax). Lower Bmax values to QCCs for his6-tagged EmrE shows that the binding sites remained unoccupied. This lower saturation suggests that the his6-tagged versions provide a conformation that prevents saturated binding. Our data demonstrate that tagging an integral membrane protein can significantly influence the protein.

Keywords: small multidrug resistance (SMR) protein, EmrE, integral membrane protein folding, quaternary ammonium compounds (QAC), quaternary cation compounds (QCC), nickel affinity chromatography, hexahistidine (His6) tag

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Authors: Dalia. G. Aseel, E. E. Hafez, S. M. Hammad

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Viral RNAs of both potato leaf roll virus (PLRV) and potato virus Y (PVY) were extracted from infected potato leaves collected from different Egyptian regions. Differential Display Polymerase Chain Reaction (DD-PCR) using (Endogluconase, β-1,3-glucanases, Chitinase, Peroxidase and Polyphenol oxidase) primers (forward strand) for was performed. The obtained data revealed different banding patterns depending on the viral type and the region of infection. Regarding PLRV, a 58 up regulated and 19 down regulated genes were detected, while, 31 up regulated and 14 down regulated genes were observed in case of PVY. Based on the nucleotide sequencing, variable phylogenetic relationships were reported for the three sequenced genes coding for: Induced stolen tip protein, Disease resistance RPP-like protein and non-specific lipid-transfer protein. In a complementary approach, using the quantitative Real-time PCR, the expressions of PRs genes understudy were estimated in the infected leaves by PLRV and PVY of three potato cultivars (Spunta, Diamont and Cara). The infection with both viruses inhibited the expressions of the five PRs genes. On the contrary, infected leaves by PLRV or PVY elevated the expression of some defense genes. This interaction also may be enhanced and/or inhibited the expression of some genes responsible for the plant defense mechanisms.

Keywords: PLRV, PVY, PR genes, DD-PCR, qRT-PCR, sequencing

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Authors: Beema Akbar, Varun P. Gopi, V. Suresh Babu

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Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability.

Keywords: colon cancer, histopathological image, structural and statistical pattern recognition, multilayer perception

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Authors: Dionisio Andujar, Juan lópez-Correa, Hugo Moreno, Angela Ri

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The increased number of weeds in tomato crops highly lower yields. Weed identification with the aim of machine learning is important to carry out site-specific control. The last advances in computer vision are a powerful tool to face the problem. The analysis of RGB (Red, Green, Blue) images through Artificial Neural Networks had been rapidly developed in the past few years, providing new methods for weed classification. The development of the algorithms for crop and weed species classification looks for a real-time classification system using Object Detection algorithms based on Convolutional Neural Networks. The site study was located in commercial corn fields. The classification system has been tested. The procedure can detect and classify weed seedlings in tomato fields. The input to the Neural Network was a set of 10,000 RGB images with a natural infestation of Cyperus rotundus l., Echinochloa crus galli L., Setaria italica L., Portulaca oeracea L., and Solanum nigrum L. The validation process was done with a random selection of RGB images containing the aforementioned species. The mean average precision (mAP) was established as the metric for object detection. The results showed agreements higher than 95 %. The system will provide the input for an online spraying system. Thus, this work plays an important role in Site Specific Weed Management by reducing herbicide use in a single step.

Keywords: deep learning, object detection, cnn, tomato, weeds

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: Yung-Chung Chuang

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The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate.

Keywords: digital aerial survey, canopy characters classification, archaeological sites, multivariate statistics

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Authors: Narin Salehiyan, Ramin Ghasemi Shayan

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In order to investigate coronavirus RNA replication, transcription, recombination, protein processing and transport, virion assembly, the identification of coronavirus-specific cell receptors, and polymerase processing, the manipulation of coronavirus clones and complementary DNAs (cDNAs) of defective-interfering (DI) RNAs is the subject of this chapter. The idea of the Covid genome is nonsegmented, single-abandoned, and positive-sense RNA. When compared to other RNA viruses, its size is significantly greater, ranging from 27 to 32 kb. The quality encoding the enormous surface glycoprotein depends on 4.4 kb, encoding a forcing trimeric, profoundly glycosylated protein. This takes off exactly 20 nm over the virion envelope, giving the infection the appearance-with a little creative mind of a crown or coronet. Covid research has added to the comprehension of numerous parts of atomic science as a general rule, like the component of RNA union, translational control, and protein transport and handling. It stays a fortune equipped for creating startling experiences.

Keywords: covid-19, corona, virus, genome, genetic

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Awf A. Al-Khan, Michael J. Day, Judith Nimmo, Mourad Tayebi, Stewart D. Ryan, Samantha J. Richardson, Janine A. Danks

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Osteosarcoma (OS) is the most common type of malignant primary bone tumour in dogs. In addition to their critical roles in bone formation and remodeling, parathyroid hormone-related protein (PTHrP) and its receptor (PTHR1) are involved in progression and metastasis of many types of tumours in humans. The aims of this study were to determine the localisation and expression levels of PTHrP and PTHR1 in canine OS tissues using immunohistochemistry and to investigate if this expression is correlated with survival time. Formalin-fixed, paraffin-embedded tissue samples from 44 dogs with known survival time that had been diagnosed with primary osteosarcoma were analysed for localisation of PTHrP and PTHR1. Findings showed that both PTHrP and PTHR1 were present in all OS samples. The dogs with high level of PTHR1 protein (16%) had decreased survival time (P<0.05) compared to dogs with less PTHR1 protein. PTHrP levels did not correlate with survival time (P>0.05). The results of this study indicate that the PTHR1 is expressed differently in canine OS tissues and this may be correlated with poor prognosis. This may mean that PTHR1 may be useful as a prognostic indicator in canine OS and could represent a good therapeutic target in OS.

Keywords: dog, expression, osteosarcoma, parathyroid hormone receptor 1 (PTHR1), parathyroid hormone-related protein (PTHrP), survival

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Authors: Ibrahim Iddrisu, Joseph Ayariga, Junhuan Xu, Ayomide Adebanjo, Boakai K. Robertson, Michelle Samuel-Foo, Olufemi Ajayi

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The rise of antimicrobial resistance is a global public health crisis that threatens the effective control and prevention of infections. Due to the emergence of pan drug-resistant bacteria, most antibiotics have lost their efficacy. Bacteriophages or their components are known to target bacterial cell walls, cell membranes, and lipopolysaccharides (LPS) and hydrolyze them. Bacteriophages, being the natural predators of pathogenic bacteria, are inevitably categorized as ‘human friends’, thus fulfilling the adage that ‘the enemy of my enemy is my friend’. Leveraging on their lethal capabilities against pathogenic bacteria, researchers are searching for more ways to overcome the current antibiotic resistance challenge. In this study, we expressed and purified epsilon 34 phage tail spike protein (E34 TSP) from the E34 TSP gene, then assessed the ability of this bacteriophage protein in the killing of two CBD-resistant strains of Salmonella spp. We also assessed the ability of the tail spike protein to cause bacteria membrane disruption and dehydrogenase depletion. We observed that the combined treatment of CBD-resistant strains of Salmonella with CBD and E34 TSP showed poor killing ability, whereas the mono treatment with E34 TSP showed considerably higher killing efficiency. This study demonstrates that the inhibition of the bacteria by E34 TSP was due in part to membrane disruption and dehydrogenase inactivation by the protein. The results of this work provide an interesting background to highlight the crucial role phage proteins such as E34 TSP could play in pathogenic bacterial control.

Keywords: cannabidiol, resistance, Salmonella, antimicrobials, phages

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Authors: Payel Ghosh, Rama Chandra Pradhan, Sabyasachi Mishra

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Jamun (Syzygium cuminii L.) is one of the important indigenous minor fruit with high medicinal value. The jamun cultivation is unorganized and there is huge loss of this fruit every year. The perishable nature of the fruit makes its postharvest management further difficult. Due to the strong cell wall structure of pectin-protein bonds and hard seeds, extraction of juice becomes difficult. Enzymatic treatment has been commercially used for improvement of juice quality with high yield. The objective of the study was to optimize the best treatment method for juice extraction. Enzymes (Pectinase and Tannase) from different stains had been used and for each enzyme, best result obtained by using response surface methodology. Optimization had been done on the basis of physicochemical property, nutritional property, sensory quality and cost estimation. According to quality aspect, cost analysis and sensory evaluation, the optimizing enzymatic treatment was obtained by Pectinase from Aspergillus aculeatus strain. The optimum condition for the treatment was 44 ^oC with 80 minute with a concentration of 0.05% (w/w). At these conditions, 75% of yield with turbidity of 32.21NTU, clarity of 74.39%T, polyphenol content of 115.31 mg GAE/g, protein content of 102.43 mg/g have been obtained with a significant difference in overall acceptability.

Keywords: enzymatic treatment, Jamun, optimization, physicochemical property, sensory analysis

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Authors: Chia-Hau Liu, Tai-Yue Wang

Abstract:

Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.

Keywords: multivariate control chart, statistical process control, one-class classification method, non-normal data

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Authors: Chuanpeng Zhou

Abstract:

The effect of dietary carbohydrate (CHO) level on serum physiological response, hepatic antioxidant abilities and heat shock protein 70 (HSP70) expression of Wuchang bream (Megalobrama amblycephala) was studied. Two isonitrogenous (28.56% crude protein) and isolipidic (5.28% crude lipid) diets were formulated to contain 30% or 53% wheat starch. Diets were fed for 90 days to fish in triplicate tanks (28 fish per tank). At the end of feeding trial, significantly higher serum triglyceride level, insulin level, cortisol level, malondialdehyde (MDA) content were observed in fish fed the 53% CHO diet, while significantly lower serum total protein content, alkaline phosphatase (AKP) activity, superoxide dismutase (SOD) activity and total antioxidative capacity (T-AOC) were found in fish fed the 53% CHO diet compared with those fed the 30% diet. The relative level of hepatic heat shock protein 70 mRNA was significantly higher in the 53% CHO group than that in the 30% CHO at 6, 12, and 48 h after feeding. The results of this study indicated that ingestion of 53% dietary CHO impacted the nonspecific immune ability and caused metabolic stress of Megalobrama amblycephala.

Keywords: Megalobrama amblycephala, carbohydrate, fasted and postprandial response, immunity, Hsp70

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Authors: Océane Grosset, Charles Pézerat, Jean-Hugh Thomas, Frédéric Ablitzer

Abstract:

This paper presents a method to take into account the fluid-structure coupling into an inverse method, the Force Analysis Technique (FAT). The FAT method, also called RIFF method (Filtered Windowed Inverse Resolution), allows to identify the force distribution from local vibration field. In order to only identify the external force applied on a structure, it is necessary to quantify the fluid-structure coupling, especially in naval application, where the fluid is heavy. This method can be decomposed in two parts, the first one consists in identifying the fluid-structure coupling and the second one to introduced it in the FAT method to reconstruct the external force. Results of simulations on a plate coupled with a cavity filled with water are presented.

Keywords: aeroacoustics, fluid-structure coupling, inverse methods, naval, turbulent flow

Procedia PDF Downloads 519

Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

Abstract:

In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

Procedia PDF Downloads 126