Search results for: protein structure classification

Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

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Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline that ensures that data mirrors real-world settings by incorporating Gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification, and explainability in a single pipeline called DeClEx.

Keywords: machine learning, healthcare, classification, explainability

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Vijay Kumar Kutala, Addepalli Pavani, M. Amresh Rao, Naushad Sm

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In this study, we evaluated the impact of CYP2C9*2 and CYP2C9*3 variants on binding and hydroxylation of warfarin. In silico data revealed that warfarin forms two hydrogen bonds with protein backbone i.e. I205 and S209, one hydrogen bond with protein side chain i.e. T301 and stacking interaction with F100 in CYP2C9*1. In CYP2C9*2 and CYP2C9*3 variants, two hydrogen bonds with protein backbone are disrupted. In double variant, all the hydrogen bonds are disrupted. The distances between C7 of S-warfarin and Fe-O in CYP2C9*1, CYP2C9*2, CYP2C9*3 and CYP2C9*2/*3 were 5.81A°, 7.02A°, 7.43° and 10.07°, respectively. The glide scores (Kcal/mol) were -7.698, -7.380, -6.821 and -6.986, respectively. Increase in warfarin/7-hydroxy warfarin ratio was observed with increase in variant alleles. To conclude, CYP2C9*2 and CYP2C9*3 variants result in disruption of hydrogen bonding interactions with warfarin and longer distance between C7 and Fe-O thus impairing warfarin 7-hydroxylation due to lower binding affinity of warfarin.

Keywords: warfarin, CYP2C9 polymorphism, personalized medicine, in Silico

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Authors: Rugang Tian

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In this study, we generated whole-genome sequence (WGS) data from110 native cattle. Together with whole-genome sequences from world-wide cattle populations, we estimated the genetic diversity and population genetic structure of different cattle populations. Our findings revealed clustering of cattle groups in line with their geographic locations. We identified noticeable genetic diversity between indigenous cattle breeds and commercial populations. Among all studied cattle groups, lower genetic diversity measures were found in commercial populations, however, high genetic diversity were detected in some local cattle, particularly in Rashoki and Mongolian breeds. Our search for potential genomic regions under selection in native cattle revealed several candidate genes related with immune response and cold shock protein on multiple chromosomes such as TRPM8, NMUR1, PRKAA2, SMTNL2 and OXR1 that are involved in energy metabolism and metabolic homeostasis.

Keywords: cattle, whole-genome, population structure, adaptation

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Authors: Shiva Emami, Jenny Persson, Bengt Johansson Lindbom

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Group A streptococcus (GAS) is a prevalent human pathogen, causing a wide range of infections and diseases. One of the most well-known virulence factors in GAS is M protein, a surface protein that facilitates bacterial invasion. In this study, we used a recombinant GAS strain (GAS-2W) expressing M protein containing a hyper immunogenic peptide (2W). Mice were immunized three times with heat-killed-GAS subcutaneously at three weeks intervals. Three weeks post last immunization, mice were challenged intraperitoneally with a lethal dose of live GAS. In order to investigate the impact of IFN-ƴ and antibodies in protection against GAS infection, we used a mouse model knock-out for IFN-ƴ (IFN-ƴ KO). We observed immunization with GAS-2W strain can increase protection against GAS infection in mice compared with the original GAS strain. Higher levels of antibodies against M1 protein were measured in GAS-2W-immunized mice. There was also a significant increase in IgG2c response in mice immunized with GAS2W. By using IFN-ƴ KO mice, we showed that not a high level of total IgG, but IgG2c was correlated with protection through the i.p challenge. It also emphasizes the importance of IFN-ƴ cytokine to combat GAS by isotype switching to IgG2c (which is opsonic for phagocytosis). Our data indicate the crucial role of IFN-ƴ in the protective immune response that, together with IgG2c, can induce protection against GAS.

Keywords: Group A streptococcus, IgG2c, IFN-γ, protection

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Authors: Farnaz Firuzian, Farhad Choobdar, Ali Mazouri

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As Very Low Birth Weight (VLBW) neonates cannot tolerate enteral feeding, parenteral nutrition (PN) must be administered shortly after birth. To find an optimal combination of nutrition, in this study, we compare administering high glucose versus high protein intake as a component of total parenteral nutrition (TPN) to test their effect on birth weight (BW) regain in VLBW. This study employs a multi-arm randomized trial: 145 newborns with BW < 1500 g were randomized to control (C) or experimental groups: high glucose (G) or high protein (P). All samples in each group received the same TPN regimens except glucose and protein intake: Glocuse was provided by dextrose water (DW) serum: 7-15 g/kg/d (10% DW) in groups C and P versus 8.75-18.75 g/kg/d (12.5% DW) in group G. Protein provided by amino acids 3 g/kg/d for groups C and G versus 4 g/kg/d for group P. Outcomes (weight, height, and head circumference) was monitored on a daily basis until the BW was regained. Data has been gathered recently and is being processed. We hypothesize that neonates with higher amino acid intake will result in sooner BW regain than other groups. The result will be presented at the conference. The findings of this study not only can help optimize nutrition, cost reduction, and shorter NICU admission of VLBW neonates at the hospital level but eventually contribute to reduced healthcare-associated infections (HAIs) and an improved health economy.

Keywords: very low birth weight neonates, weight gain, parenteral nutrition, glucose, amino acids

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Refilwe Moeletsi

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Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region.

Keywords: remote sensing, GIS, change detection, granite quarries

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Authors: Lukas Stiburek, Jana Cesnekova, Josef Houstek, Jiri Zeman

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Cellular function depends on mitochondrial function and integrity that is therefore maintained by several classes of proteins possessing chaperone and/or proteolytic activities. In this work, we focused on characterization of LACE1 (lactation elevated 1) function in mitochondrial protein homeostasis maintenance. LACE1 is the human homologue of yeast mitochondrial Afg1 ATPase, a member of SEC18-NSF, PAS1, CDC48-VCP, TBP family. Yeast Afg1 was shown to be involved in mitochondrial complex IV biogenesis, and based on its similarity with CDC48 (p97/VCP) it was suggested to facilitate extraction of polytopic membrane proteins. Here we show that LACE1, which is a mitochondrial integral membrane protein, exists as part of three complexes of approx. 140, 400 and 500 kDa and is essential for maintenance of fused mitochondrial reticulum and lamellar cristae morphology. Using affinity purification of LACE1-FLAG expressed in LACE1 knockdown background we show that the protein physically interacts with mitochondrial inner membrane protease YME1L. We further show that human LACE1 exhibits significant pro-apoptotic activity and that the protein is required for normal function of the mitochondrial respiratory chain. Thus, our work establishes LACE1 as a novel factor with the crucial role in mitochondrial homeostasis maintenance.

Keywords: LACE1, mitochondria, apoptosis, protease

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Authors: Oqba Basal, Andras Szabo

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Using mineral fertilization is increasing worldwide, as it is claimed to be majorly responsible for achieving high yields; however, the negative impacts of mineral fertilization on soil and environment are becoming more obvious, with alternative methods being more necessary and applicable, especially with the current climatic changes which have imposed serious abiotic stresses, such as drought. An experiment was made during 2017 growing season in Debrecen, Hungary to investigate the effects of inoculation and N fertilization on the seed yield and protein concentration of the soybean (Glycine max (L.) Merr.) cultivar (Panonia Kincse) under three different irrigation regimes: severe drought stress (SD), moderate drought stress (MD) and control with no drought stress (ND). Three N fertilizer rates were applied: no N fertilizer (0 N), 35 kg ha⁻¹ of N fertilizer (35 N) and 105 kg ha⁻¹ of N fertilizer (105 N). Half of the seeds in each treatment was inoculated with Bradyrhizobium japonicum inoculant, and the other half was not inoculated. The results showed significant differences in the seed yield associated with inoculation, irrigation and the interaction between them, whereas there were no significant differences in the seed yield associated with fertilization alone or in interaction with inoculation or irrigation or both. When seeds were inoculated, yield was increased when (35 N) was applied compared to (0 N) but not significantly; however, the high rate of N fertilizer (105 N) reduced the yield to a level even less than (0 N). When seeds were not inoculated, the highest rate of N increased the yield the most compared to the other two N fertilizer rates whenever the drought was present (moderate or severe). Under severe drought stress, inoculation was positively and significantly correlated with yield; however, adding N fertilizer increased the yield of uninoculated plants compared to the inoculated ones, regardless of the rate of N fertilizer. Protein concentration in the seeds was significantly affected by irrigation and by fertilization, but not by inoculation. Protein concentration increased as the N fertilization rate increased, regardless of the inoculation or irrigation treatments; moreover, increasing the N rate reduced the correlation coefficient of protein concentration with the irrigation. It was concluded that adding N fertilizer is not always recommended, especially when seeds are inoculated before being sown; however, it is very important under severe drought stress to sustain yield. Enhanced protein concentrations could be achieved by applying N fertilization, whether the seeds were pre-inoculated or not.

Keywords: drought stress, N fertilization, protein concentration, soybean

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: classification, data mining, evaluation measures, groundwater

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Authors: Andrea Vasquez, Pedro Maldonado-Alvarado

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In general, 'gluten-free' foods like breadmaking products provide functional or nutraceutical benefits for the consumer's health and increased their demand on the market. In Ecuador, there is an overproduction of superfoods, and the food by-products are undervalued. For the first time, to the author's best knowledge, gluten-free bread mixtures from quinoa and banana flour, cassava starch, lupine flour (LF), or whey protein (WP) with hydroxypropylmethylcellulose (HPMC) and transglutaminase (TG) were evaluated on their functional and sensory properties. Free amino groups and thiols, rheology, and electrophoresis SDS PAGE were performed to analyze the crosslinking of TG at different concentrations with HC or PL proteins. Dough characterization, pasting properties were evaluated, respectively, by a MIXOLAB and a rheometer with a pasting cell. The texture, porosity, and loaf volume were characterized using a texturometer, ImageJ software, and breadmaking ability, respectively. Finally, a breadmaking aptitude and sensorial bread acceptability were performed. A significant decrease in the content of free amino groups (0.16 to 0.11 and 0.46 to 0.36 mM/mg of protein) and free thiol groups (0.37 to 0.21 and 1.79 to 1.32 mM/mg protein) was observed when 1.0% and 0.5% TG were added to LF and WP, respectively. In apparent viscosity analysis, the action of TG on HC proteins changes their viscosity, while the viscosity of LF is not modified by TG. Results of electrophoresis in PL showed bands of higher molecular weight of different fragments of proteins with 1% TG. Formulation with 59.8, 39.9, 160.8, 6.0, 1.0, and 1.5% of, respectively, QF, BF, CS, LF or WP, TG, and HPMC had the best properties in dough parameters, pasting parameters (lower pasting temperature and higher peak viscosity), best crumb structure, lower crumb hardness and higher loaf volume (2.24 and 2.28 mL/g). All the loaves of bread were acceptable in baking aptitude and general acceptability.

Keywords: breadmaking, gluten-free, superfoods, by-products, Ecuador

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Authors: Gong Kangming, Zhao Caiqi

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High-rise special-shaped structure, such as main frame structure of the statues, is one of the structure forms in irregular structure widely used. Due to the complex shape of the statue structure, with a large aspect ratio, its wind load value and the overall mechanical properties are very different from the high-rise buildings with the general rules. The paper taking a certain 48 meters high main frame structure of the statue located in Shuangyashan City, Heilongjiang Province, static and dynamic properties are analyzed by the finite element software. Through static and dynamic analysis, it got a number of useful conclusions that have a certain reference value for the analysis and design of the future similar structure.

Keywords: a Buddhism goddess Guanyin body, wind load, dynamic analysis, bolster, node design

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Authors: Rabia Shabbir, Sheikh Saeed Ahmad, Amna Butt

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Rapid land use changes have taken place in Islamabad, the capital city of Pakistan, over the past decades due to accelerated urbanization and industrialization. In this study, land use changes in the metropolitan area of Islamabad was observed by the combined use of GIS and satellite remote sensing for a time period of 15 years. High-resolution Google Earth images were downloaded from 2000-2015, and object-based classification method was used for accurate classification using eCognition software. The information regarding urban settlements, industrial area, barren land, agricultural area, vegetation, water, and transportation infrastructure was extracted. The results showed that the city experienced a spatial expansion, rapid urban growth, land use change and expanding transportation infrastructure. The study concluded the integration of GIS and remote sensing as an effective approach for analyzing the spatial pattern of urban growth and land use change.

Keywords: land use change, urban growth, Islamabad, object-based classification, Google Earth, remote sensing, GIS

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Authors: Ziyue Zeng

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Bacteriophages are viruses which infect bacteria specifically. Over the past decades, phage λ has been extensively studied, especially its interaction with the Escherichia coli LamB (EcLamB) protein receptor. Nonetheless, despite the enormous numbers and near-ubiquity of environmental phages, aside from phage λ, there is a paucity of information on other phages which target EcLamB as a receptor. In this study, to answer the question of whether there are other EcLamB-targeting phages in the natural environment, a simple and convenient method was developed and used for isolating environmental phages which target a particular surface structure of a particular bacterium; in this case, the EcLamB outer membrane protein. From the enrichments with the engineered bacterial hosts, a collection of EcLamB-targeting phages (ΦZZ phages) were easily isolated. Intriguingly, unlike phage λ, an obligate EcLamB-dependent phage in the Siphoviridae family, the newly isolated ΦZZ phages alternatively recognised EcLamB or E. coli OmpC (EcOmpC) as a receptor when infecting E. coli. Furthermore, ΦZZ phages were suggested to represent new species in the Tequatrovirus genus in the Myoviridae family, based on phage morphology and genomic sequences. Most phages are thought to have a narrow host range due to their exquisite specificity in receptor recognition. With the ability to optionally recognise two receptors, ΦZZ phages were considered relatively promiscuous. Via the heterologous expression of EcLamB on the bacterial cell surface, the host range of ΦZZ phages was further extended to three different enterobacterial genera. Besides, an interesting selection of evolved phage mutants with a broader host range was isolated, and the key mutations involved in their evolution to adapt to new hosts were investigated by genomic analysis. Finally, and importantly, two ΦZZ phages were found to be putative generalised transducers, which could be exploited as tools for DNA manipulations.

Keywords: environmental microbiology, phage, microbe-host interactions, microbial ecology

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Authors: D. I. George Amalarethinam, A. Emima

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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Javid Iqbal, Zhu Shifan

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The development in the construction of unconventional ships and the implementation of lightweight materials have shown a large impulse towards finite element (FE) method, making it a general tool for ship design. This paper briefly presents the modeling and analysis techniques of ship structures using FE method for complex boundary conditions which are difficult to analyze by existing Ship Classification Societies rules. During operation, all ships experience complex loading conditions. These loads are general categories into thermal loads, linear static, dynamic and non-linear loads. General strength of the ship structure is analyzed using static FE analysis. FE method is also suitable to consider the local loads generated by ballast tanks and cargo in addition to hydrostatic and hydrodynamic loads. Vibration analysis of a ship structure and its components can be performed using FE method which helps in obtaining the dynamic stability of the ship. FE method has developed better techniques for calculation of natural frequencies and different mode shapes of ship structure to avoid resonance both globally and locally. There is a lot of development towards the ideal design in ship industry over the past few years for solving complex engineering problems by employing the data stored in the FE model. This paper provides an overview of ship modeling methodology for FE analysis and its general application. Historical background, the basic concept of FE, advantages, and disadvantages of FE analysis are also reported along with examples related to hull strength and structural components.

Keywords: dynamic analysis, finite element methods, ship structure, vibration analysis

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Authors: Sarita Puri, Tapan K. Chaudhuri

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Chaperonin GroEL is a tetradecameric Escherichia coli protein having identical subunits of 57 kDa. The elucidation of thermodynamic parameters related to stability for the native GroEL is not feasible as it undergoes irreversible unfolding because of its large size (800kDa) and multimeric nature. Nevertheless, it is important to determine the thermodynamic stability parameters for the highly stable GroEL protein as it helps in folding and holding of many substrate proteins during many cellular stresses. Properly folded monomers work as building-block for the formation of native tetradecameric GroEL. Spontaneous refolding behavior of monomeric GroEL makes it suitable for protein-denaturant interactions and thermodynamic stability based studies. The urea mediated unfolding is a three state process which means there is the formation of one intermediate state along with native and unfolded states. The heat mediated denaturation is a two-state process. The unfolding process is reversible as observed by the spontaneous refolding of denatured protein in both urea and head mediated refolding processes. Analysis of folding/unfolding data provides a measure of various thermodynamic stability parameters for the monomeric GroEL. The proposed mechanism of unfolding of monomeric GroEL is a three state process which involves formation of one stable intermediate having folded apical domain and unfolded equatorial, intermediate domains. Research in progress is to demonstrate the importance of specific residues in stability and oligomerization of GroEL protein. Several mutant versions of GroEL are under investigation to resolve the above mentioned issue.

Keywords: equilibrium unfolding, monomeric GroEl, spontaneous refolding, thermodynamic stability

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Authors: Elif Gencturk, Ekin Yurdakul, Ahmet Y. Celik, Senol Mutlu, Kutlu O. Ulgen

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Yeast cells are generally used as a model system of eukaryotes due to their complex genetic structure, rapid growth ability in optimum conditions, easy replication and well-defined genetic system properties. Thus, yeast cells increased the knowledge of the principal pathways in humans. During fermentation, carbohydrates (hexoses and pentoses) degrade into some toxic by-products such as 5-hydroxymethylfurfural (5-HMF or HMF) and furfural. HMF influences the ethanol yield, and ethanol productivity; it interferes with microbial growth and is considered as a potent inhibitor of bioethanol production. In this study, yeast single cell behavior under HMF application was monitored by using a continuous flow single phase microfluidic platform. Microfluidic device in operation is fabricated by hot embossing and thermo-compression techniques from cyclo-olefin polymer (COP). COP is biocompatible, transparent and rigid material and it is suitable for observing fluorescence of cells considering its low auto-fluorescence characteristic. The response of yeast cells was recorded through Red Fluorescent Protein (RFP) tagged Nop56 gene product, which is an essential evolutionary-conserved nucleolar protein, and also a member of the box C/D snoRNP complexes. With the application of HMF, yeast cell proliferation continued but HMF slowed down the cell growth, and after HMF treatment the cell proliferation stopped. By the addition of fresh nutrient medium, the yeast cells recovered after 6 hours of HMF exposure. Thus, HMF application suppresses normal functioning of cell cycle but it does not cause cells to die. The monitoring of Nop56 expression phases of the individual cells shed light on the protein and ribosome synthesis cycles along with their link to growth. Further computational study revealed that the mechanisms underlying the inhibitory or inductive effects of HMF on growth are enriched in functional categories of protein degradation, protein processing, DNA repair and multidrug resistance. The present microfluidic device can successfully be used for studying the effects of inhibitory agents on growth by single cell tracking, thus capturing cell to cell variations. By metabolic engineering techniques, engineered strains can be developed, and the metabolic network of the microorganism can thus be manipulated such that chemical overproduction of target metabolite is achieved along with the maximum growth/biomass yield.  

Keywords: COP, HMF, ribosome biogenesis, thermoplastic microbioreactor, yeast

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

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Authors: Lavanya Annadi, Fahimeh Jafari

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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

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Authors: Saba Belgheisi

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Food packaging, is an important discipline in the area of food technology, concerns preservation and protection of foods. The objective of this research was to determine of the effect of whey protein based edible coating on the moisture loss and sensory attributes of fresh mutton after 0, 1, 3 and 5 days at 5° C. The moisture content, moisture loss and sensory attributes (juiciness, color and odor) of the coated and uncoated samples were analyzed. The results showed that, moisture content, moisture loss, juiciness and color of the coated and uncoated samples have significant differences (p < 0.05) at the intervals of 0 to 1 and 1 to 3 days of storage. But no significant difference was observed at interval time 3 to 5 days of storage (p > 0.05). Also, there was no significant differences in the odor values of the coated and uncoated samples (p > 0.05). Therefore, the coated samples had consistently more moisture, juiciness and colored values than uncoated samples after 3 days at 5° C. So, whey protein edible coating could enhance product presentation and eliminate the need for placing absorbent pads at the bottom of the trays.

Keywords: coating, whey protein, mutton, moisture, sensory

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

Abstract:

For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

Abstract:

Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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Authors: Marlon P. Perez

Abstract:

The study intended to determine the level of occupational stress that was experienced by faculty members of private and public business schools in Batangas City with the end in view of proposing organizational development interventions in managing occupational stressors. Stressors such as factors intrinsic to the job, role in the organization, relationships at work, career development and organizational structure and climate were used as determinants of occupational stress level. Descriptive method of research was used as its research design. There were only 64 full-time faculty members coming from private and public business schools in Batangas City – University of Batangas, Lyceum of the Philippines University-Batangas, Golden Gate Colleges, Batangas State University and Colegio ng Lungsod ng Batangas. Survey questionnaire was used as data gathering instrument. It was found out that all occupational stressors were assessed stressful when grouped according to its classification of tertiary schools while response of subject respondents differs on their assessment of occupational stressors. Age variable has become significantly related to respondents’ assessments on factors intrinsic to the job and career development; however, it was not significantly related to role in the organization, relationships at work and organizational structure and climate. On the other hand, gender, marital status, highest educational attainment, employment status, length of service, area of specialization and classification of tertiary school were revealed to be not significantly related to all occupational stressors. Various organizational development interventions have been proposed to manage the occupational stressors that are experienced by business faculty members in the institution.

Keywords: occupational stress, business school, organizational development, intervention, stressors, faculty members, assessment, manage

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Authors: Sodamade A., Bolaji K. A.

Abstract:

Problems associated with protein malnutrition are still prevalent in third-world countries, leading to the constant search for plants that could serve as nutrients and medicinal purposes. Huckleberry is one of the plants that has been proven useful locally in the treatment of numerous ailments and diseases. A fresh sample of the plant (Vaccinium pavifolium) was collected from a vegetable garden situated near the Erelu dam of the Emmanuel Alayande College of Education Campus, Oyo. The sample was authenticated at the Forestry Research Institute of Nigeria (FRIN) Ibadan. The leaves of the plant were plucked and processed for leaf protein concentrates before proximate composition, mineral analysis phytochemical and antimicrobial properties were determined using a standard method of analysis. The results of proximate constituents showed; moisture content; 9.89±0.051g/100g, Ash; 3.23±0.12g/100g, crude fat; 3.96±0.11g/100g and 61.27±0.56g/100g of Nitrogen free extractive. The mineral analysis of the sample showed; Mg; 0.081±0.00mg/100g, Ca; 42.30±0.05mg/100g, Na; 27.57±0.09mg/100g, K; 6.81±0.01mg/100g, P; 8.90±0.03mg/100g, Fe; 0.51±0.00mg/100g, Zn; 0.021±0.00mg/100g, Cd; 0.04±0.04mg/100g, Pb; 0.002±0.00mg/100g, Cr; 0.041±0.00mg/100g Cadmium and Mercury were not detected in the sample. The result of phytochemical analysis of leaf protein concentrates of the Huckleberry showed the presence of Alkaloid, Saponin, Flavonoid, Tanin, Coumarin, Steroids, Terpenoids, Cardiac glycosides, Glycosides, Quinones, Anthocyanin, phytosterols, and phenols. Ethanolic extracts of the Vaccinium parvifolium L. leaf protein concentrates showed that it contains bioactive compounds that are capable of combating the following microorganisms; Staphylococcus aureus, Streptococcus pyogenes, Streptococcus faecalis, Pseudomonas aeruginosa, Klebisialae pneumonia and Proteus mirabilis. The results of the analysis of Vaccinium parvifolium L. leaf protein concentrates showed that the sample contains valuable nutrient and mineral constituents, and phytochemical compounds that could make the sample useful for medicinal activities.

Keywords: leaf protein concentrates, vaccinium parvifolium, nutritional characteristics, mineral composition, antimicrobial activity

Procedia PDF Downloads 78

Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

Abstract:

Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

Procedia PDF Downloads 235