Search results for: feature detection

Authors: Tomoya Oshiro, Hiroko Ono

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Japan's aging population is increasing due to the decrease in birthrate. It causes various problems like the decrease in the gross domestic product of the country. The reason is why the local government of Japan has been on the way to a sustainable city recently. Then it is essential to get control of an urban structure to make the compact city successful. There are many kinds of paper about the compact city; however, the paper about a compact city of the island-type city is less. The purpose of this study is to clarify difference of urban structure between an island-type and a general city type. The method which has conducted in this research has two steps. First of all, by using evaluation indexes in the handbook, we evaluated the urban structures among each same -population-class cities from 50,000 to 100,000 people. Next, to clear the difference about the urban structure and feature between island-type and general-type cities compare the radar chart which is composed with each evaluation indexes of urban structure. Moreover, in order to clarify the relationship between evaluation indexes and the place of residence by using GIS software to show up population density on the map. As a result of this research, the management of local government and the local economy in evaluation indexes are indicated to be negative point in comparison of island-type cities with general cities. However, evaluation indexes of safety/security and low-carbon/energy are proved to be positive point. The research to find the difference features of the island-type of urban structure proves that the management of local government or the local economy is negative point in these island-type cities. In addition, the public transportation coverage in Miyako Island, Sado Island, and Amakusa Island show low value compare with other islands and average value. Relationship between evaluation indexes of an urban structure and the place of residence prove that the place of residence is related to public transportation coverage. If the place of residence is spread out, the public transportation coverage will be decreased. The results of this research reveal that the finances in island-type cities are negative point compare to general cities. This problem is caused by declining population. In addition, the place of residence is related to the public transportation coverage. Even though, it needs a much money to increase the public transportation coverage. It is possibly to cause other problems furthermore the aspect of finance is influenced by that as well. The conclusion in this research suggests that it is important for creating the compact city in island-type cities that we first need to address solving the problems about the management of local government and the local economy.

Keywords: sustainable city, comparative analysis, geographic information system, urban structure

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Authors: Janos Juhasz, Sandor Pongor, Balazs Ligeti

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Next-generation sequencing, especially whole genome shotgun sequencing, is becoming a common approach to gain insight into the microbiomes in a culture-independent way, even in clinical practice. It does not only give us information about the species composition of an environmental sample but opens the possibility to detect antimicrobial resistance and novel, or currently unknown, pathogens. Accurately and reliably detecting the microbial strains is a challenging task. Here we present a sensitive approach for detecting pathogens in metagenomics samples with special regard to detecting novel variants of known pathogens. We have developed a pipeline that uses fast, short read aligner programs (i.e., Bowtie2/BWA) and comprehensive nucleotide databases. Taxonomic binning is based on the lowest common ancestor (LCA) principle; each read is assigned to a taxon, covering the most significantly hit taxa. This approach helps in balancing between sensitivity and running time. The program was tested both on experimental and synthetic data. The results implicate that our method performs as good as the state-of-the-art BLAST-based ones, furthermore, in some cases, it even proves to be better, while running two orders magnitude faster. It is sensitive and capable of identifying taxa being present only in small abundance. Moreover, it needs two orders of magnitude less reads to complete the identification than MetaPhLan2 does. We analyzed an experimental anthrax dataset (B. anthracis strain BA104). The majority of the reads (96.50%) was classified as Bacillus anthracis, a small portion, 1.2%, was classified as other species from the Bacillus genus. We demonstrate that the evaluation of high-throughput sequencing data is feasible in a reasonable time with good classification accuracy.

Keywords: metagenomics, taxonomy binning, pathogens, microbiome, B. anthracis

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Authors: Bahareh Golchin, Nooshin Riahi

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One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: Peng Hou

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Natural products produced from marine bacteria serve as an immense reservoir for anti-infective drugs and therapeutic agents. Nowadays, heterologous expression of gene clusters of interests has been widely adopted as an effective strategy for natural product discovery. Briefly, the heterologous expression flowchart would be: biosynthetic gene cluster identification, pathway construction and expression, and product detection. However, gene cluster capture using traditional Transformation-associated recombination (TAR) protocol is low-efficient (0.5% positive colony rate). To make things worse, most of these putative new natural products are only predicted by bioinformatics analysis such as antiSMASH, and their corresponding natural products biosynthetic pathways are either not expressed or expressed at very low levels under laboratory conditions. Those setbacks have inspired us to focus on seeking new technologies to efficiently edit and refractor of biosynthetic gene clusters. Recently, two cutting-edge techniques have attracted our attention - the CRISPR-Cas9 and Gibson Assembly. By now, we have tried to pretreat Brevibacillus laterosporus strain genomic DNA with CRISPR-Cas9 nucleases that specifically generated breaks near the gene cluster of interest. This trial resulted in an increase in the efficiency of gene cluster capture (9%). Moreover, using Gibson Assembly by adding/deleting certain operon and tailoring enzymes regardless of end compatibility, the silent construct (~80kb) has been successfully refactored into an active one, yielded a series of analogs expected. With the appearances of the novel molecular tools, we are confident to believe that development of a high throughput mature pipeline for DNA assembly, transformation, product isolation and identification would no longer be a daydream for marine natural product discovery.

Keywords: biosynthesis, CRISPR-Cas9, DNA assembly, refactor, TAR cloning

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Authors: Lina T. Al Kury, Oleg I. Voitychuk, Ramiz M. Ali, Sehamuddin Galadari, Keun-Hang Susan Yang, Frank Christopher Howarth, Yaroslav M. Shuba, Murat Oz

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A role for anandamide (N-arachidonoyl ethanolamide; AEA), a major endocannabinoid, in the cardiovascular system in various pathological conditions has been reported in earlier studies. In the present work, we have hypothesized that the antiarrhythmic effects reported for AEA are due to its negative inotropic effect and altered action potential (AP) characteristics. Therefore, we tested the effects of AEA on contractility and electrophysiological properties of rat ventricular myocytes. Video edge detection was used to measure myocyte shortening. Intracellular Ca2+ was measured in cells loaded with the fluorescent indicator fura-2 AM. Whole-cell patch-clamp technique was employed to investigate the effect of AEA on the characteristics of APs. AEA (1 μM) caused a significant decrease in the amplitudes of electrically-evoked myocyte shortening and Ca2+ transients and significantly decreased the duration of AP. The effect of AEA on myocyte shortening and AP characteristics was not altered in the presence of pertussis toxin (PTX, 2 µg/ml for 4 h), AM251 and SR141716 (cannabinoid type 1 receptor antagonists) or AM630 and SR 144528 (cannabinoid type 2 receptor antagonists). Furthermore, AEA inhibited voltage-activated inward Na+ (INa) and Ca2+ (IL,Ca) currents; major ionic currents shaping the APs in ventricular myocytes, in a voltage and PTX-independent manner. Collectively, the results suggest that AEA depresses ventricular myocyte contractility, by decreasing the action potential duration (APD), and inhibits the function of voltage-dependent Na+ and L-type Ca2+ channels in a manner independent of cannabinoid receptors. This mechanism may be importantly involved in the antiarrhythmic effects of anandamide.

Keywords: action potential, anandamide, cannabinoid receptor, endocannabinoid, ventricular myocytes

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Authors: Z. Abdollahi Biron, P. Pisu

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Connected vehicles are one of the promising technologies for future Intelligent Transportation Systems (ITS). A connected vehicle system is essentially a set of vehicles communicating through a network to exchange their information with each other and the infrastructure. Although this interconnection of the vehicles can be potentially beneficial in creating an efficient, sustainable, and green transportation system, a set of safety and reliability challenges come out with this technology. The first challenge arises from the information loss due to unreliable communication network which affects the control/management system of the individual vehicles and the overall system. Such scenario may lead to degraded or even unsafe operation which could be potentially catastrophic. Secondly, faulty sensors and actuators can affect the individual vehicle’s safe operation and in turn will create a potentially unsafe node in the vehicular network. Further, sending that faulty sensor information to other vehicles and failure in actuators may significantly affect the safe operation of the overall vehicular network. Therefore, it is of utmost importance to take these issues into consideration while designing the control/management algorithms of the individual vehicles as a part of connected vehicle system. In this paper, we consider a connected vehicle system under Co-operative Adaptive Cruise Control (CACC) and propose a fault diagnosis scheme that deals with these aforementioned challenges. Specifically, the conventional CACC algorithm is modified by adding a Kalman filter-based estimation algorithm to suppress the effect of lost information under unreliable network. Further, a sliding mode observer-based algorithm is used to improve the sensor reliability under faults. The effectiveness of the overall diagnostic scheme is verified via simulation studies.

Keywords: fault diagnostics, communication network, connected vehicles, packet drop out, platoon

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Authors: Michael Dare Asemoloye, Segun Gbolagade Jonathan, Rafiq Ahmad, Odunayo Joseph Olawuyi, D. O. Adejoye

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Fungi have potentials for degrading hydrocarbons through the secretion of different enzymes. Crude oil tolerance and degradation by Trichoderma harzianum was investigated in this study with its ability to produce peroxidase enzymes (LiP and MnP). Many fungal strains were isolated from rhizosphere of grasses growing on a crude oil spilled site, and the most frequent strain based on percentage incidence was further characterized using morphological and molecular characteristics. Molecular characterization was done through the amplification of Ribosomal-RNA regions of 18s (1609-1627) and 28s (287-266) using ITS1 and ITS4 combinations and it was identified using NCBI BLAST tool. The selected fungus was also subjected to an in-vitro tolerance test at crude oil concentrations of 5, 10, 15, 20 and 25% while 0% served as control. In addition, lignin peroxidase genes (lig1-6) and manganese peroxidase gene (mnp) were detected and expressed in this strain using RT-PCR technique, its peroxidase producing activities was also studied in aliquots (U/ml). This strain had highest incidence of 80%, it was registered in NCBI as Trichoderma harzianum asemoJ KY488466. The strain KY488466 responded to crude oil concentrations as it increase, the dose inhibition response percentage (DIRP) increased from 41.67 to 95.41 at 5 to 25 % crude oil concentrations. All the peroxidase genes are present in KY488466, and expressed with amplified 900-1000 bp through RT-PCR technique. In this strain, lig2, lig4 and mnp genes were over-expressed, lig 6 was moderately expressed, while none of the genes was under-expressed. The strain also produced 90±0.87 U/ml lignin peroxidase and 120±1.23 U/mil manganese peroxidase enzymes in aliquots. These results imply that KY488466 can tolerate and survive high crude oil concentration and could be exploited for bioremediation of oil-spilled soils, the produced peroxidase enzymes could also be exploited for other biotechnological experiments.

Keywords: crude oil, enzymes, expression, peroxidase genes, tolerance, Trichoderma harzianum

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Authors: Pappu Kumar, Ajai Singh, Anshuman Singh

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There are different water user sectors such as rural, urban, mining, subsistence and commercial irrigated agriculture, commercial forestry, industry, power generation which are present in the catchment in Subarnarekha River Basin and Ranchi city. There is an inequity issue in the access to water. The development of the rural area, construction of new power generation plants, along with the population growth, the requirement of unmet water demand and the consideration of environmental flows, the revitalization of small-scale irrigation schemes is going to increase the water demands in almost all the water-stressed catchment. The WEAP Model was developed by the Stockholm Environment Institute (SEI) to enable evaluation of planning and management issues associated with water resources development. The WEAP model can be used for both urban and rural areas and can address a wide range of issues including sectoral demand analyses, water conservation, water rights and allocation priorities, river flow simulation, reservoir operation, ecosystem requirements and project cost-benefit analyses. This model is a tool for integrated water resource management and planning like, forecasting water demand, supply, inflows, outflows, water use, reuse, water quality, priority areas and Hydropower generation, In the present study, efforts have been made to access the utility of the WEAP model for water supply and demand analysis for Ranchi city. A detailed works have been carried out and it was tried to ascertain that the WEAP model used for generating different scenario of water requirement, which could help for the future planning of water. The water supplied to Ranchi city was mostly contributed by our study river, Hatiya reservoir and ground water. Data was collected from various agencies like PHE Ranchi, census data of 2011, Doranda reservoir and meteorology department etc. This collected and generated data was given as input to the WEAP model. The model generated the trends for discharge of our study river up to next 2050 and same time also generated scenarios calculating our demand and supplies for feature. The results generated from the model outputs predicting the water require 12 million litter. The results will help in drafting policies for future regarding water supplies and demands under changing climatic scenarios.

Keywords: WEAP model, water demand analysis, Ranchi, scenarios

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Authors: A. Schuchter, M. Promegger

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The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning

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Authors: Ignacio W. Loor

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In the context of informal settlements in Quito, this paper provides evidence that slopes and deep ravines typical of Andean cities, around which marginalized urban communities sit, constitute a platform for green infrastructure that supports mobility for pedestrians in an incremental fashion. This is informally shaped green infrastructure that provides connectivity to other mobility infrastructures such as roads and public transport, which permits relegated dwellers reach their daily destinations and reclaim their rights to the city. This is relevant in that walking has been increasingly neglected as a viable mean of transport in Latin American cities, in favor of rather motorized means, for which the mobility benefits of green infrastructure have remained invisible to policymakers, contributing to the progressive isolation of informal settlements. This research leverages greatly on an ecological rejuvenation programme led by the municipality of Quito and the Andean Corporation for Development (CAN) intended for rehabilitating the ecological functionalities of ravines. Accordingly, four ravines in different stages of rejuvenation were chosen, in order to through ethnographic methods, capture the practices they support to dwellers of informal settlements across different stages, particularly in terms of issues of mobility. Then, by presenting fragments of interviews, description of observed phenomena, photographs and narratives published in institutional reports and media, the production process of mobility infrastructure over unoccupied slopes and ravines, and the roles that this infrastructure plays in the mobility of dwellers and their quotidian practices are explained. For informal settlements, which normally feature scant urban infrastructure, mobility embodies an unfavourable driver for the possibilities of dwellers to actively participate in the social, economic and political dimensions of the city, for which their rights to the city are widely neglected. Nevertheless, informal green infrastructure for mobility provides some alleviation. This infrastructure is incremental, since its features and usability gradually evolves as users put into it knowledge, labour, devices, and connectivity to other infrastructures in different dimensions which increment its dependability. This is evidenced in the diffusion of knowledge of trails and routes of footpaths among users, the implementation of linking stairs and bridges, the improved access by producing public spaces adjacent to the ravines, the illuminating of surrounding roads, and ultimately, the restoring of ecological functions of ravines. However, the perpetuity of this type of infrastructure is also fragile and vulnerable to the course of urbanisation, densification, and expansion of gated privatised spaces.

Keywords: green infrastructure, informal settlements, urban mobility, walkability

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Authors: Naser Kazemi Eilaki, Ilona Heldal, Carolyn Ahmer, Bjarne Christian Hagen

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Elderly people and people with disabilities are recognized as at-risk groups when it comes to egress and travel from hazard zone to a safe place. One's disability can negatively influence her or his escape time, and this becomes even more important when people from this target group live alone. While earlier studies have frequently addressed quantitative measurements regarding at-risk groups' physical characteristics (e.g., their speed of travel), this paper considers the influence of at-risk groups’ characteristics on their decision and determining better escape routes. Most of evacuation models are based on mapping people's movement and their behaviour to summation times for common activity types on a timeline. Usually, timeline models estimate required safe egress time (RSET) as a sum of four timespans: detection, alarm, premovement, and movement time, and compare this with the available safe egress time (ASET) to determine what is influencing the margin of safety.This paper presents a cross-sectional study for identifying the most critical items on RSET and people's decision-making and with possibilities to include safety knowledge regarding people with physical or cognitive functional impairments. The result will contribute to increased knowledge on considering at-risk groups and disabilities for designing and developing safe escape routes. The expected results can be an asset to predict the probabilistic behavioural pattern of at-risk groups and necessary components for defining a framework for understanding how stakeholders can consider various disabilities when determining the margin of safety for a safe escape route.

Keywords: fire safety, evacuation, decision-making, at-risk groups

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Authors: Manjula Weerasekera, Chris Sissons, Lisa Wong, Sally Anderson, Ann Holmes, Richard Cannon

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The aim of this study was to characterize bacterial population and yeasts in saliva by Polymerase chain reaction followed by denaturing gradient gel electrophoresis (PCR-DGGE) and measure yeast levels by culture. PCR-DGGE was performed to identify oral bacteria and yeasts in 24 saliva samples. DNA was extracted and used to generate DNA amplicons of the V2–V3 hypervariable region of the bacterial 16S rDNA gene using PCR. Further universal primers targeting the large subunit rDNA gene (25S-28S) of fungi were used to amplify yeasts present in human saliva. Resulting PCR products were subjected to denaturing gradient gel electrophoresis using Universal mutation detection system. DGGE bands were extracted and sequenced using Sanger method. A potential relationship was evaluated between groups of bacteria identified by cluster analysis of DGGE fingerprints with the yeast levels and with their diversity. Significant interpersonal variation of salivary microbiome was observed. Cluster and principal component analysis of the bacterial DGGE patterns yielded three significant major clusters, and outliers. Seventeen of the 24 (71%) saliva samples were yeast positive going up to 10³ cfu/mL. Predominately, C. albicans, and six other species of yeast were detected. The presence, amount and species of yeast showed no clear relationship to the bacterial clusters. Microbial community in saliva showed a significant variation between individuals. A lack of association between yeasts and the bacterial fingerprints in saliva suggests the significant ecological person-specific independence in highly complex oral biofilm systems under normal oral conditions.

Keywords: bacteria, denaturing gradient gel electrophoresis, oral biofilm, yeasts

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Authors: Huinan Zhang, Wenjie Jiang

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Accurate estimation of tropical cyclone intensity is of great importance for disaster prevention and mitigation. Existing techniques are largely based on satellite imagery data, and research and utilization of the inner thermal core structure characteristics of tropical cyclones still pose challenges. This paper presents a two-stage tropical cyclone intensity estimation network based on the fusion of coarse and fine-grained features from microwave brightness temperature data. The data used in this network are obtained from the thermal core structure of tropical cyclones through the Advanced Technology Microwave Sounder (ATMS) inversion. Firstly, the thermal core information in the pressure direction is comprehensively expressed through the maximal intensity projection (MIP) method, constructing coarse-grained thermal core images that represent the tropical cyclone. These images provide a coarse-grained feature range wind speed estimation result in the first stage. Then, based on this result, fine-grained features are extracted by combining thermal core information from multiple view profiles with a distributed network and fused with coarse-grained features from the first stage to obtain the final two-stage network wind speed estimation. Furthermore, to better capture the long-tail distribution characteristics of tropical cyclones, focal loss is used in the coarse-grained loss function of the first stage, and ordinal regression loss is adopted in the second stage to replace traditional single-value regression. The selection of tropical cyclones spans from 2012 to 2021, distributed in the North Atlantic (NA) regions. The training set includes 2012 to 2017, the validation set includes 2018 to 2019, and the test set includes 2020 to 2021. Based on the Saffir-Simpson Hurricane Wind Scale (SSHS), this paper categorizes tropical cyclone levels into three major categories: pre-hurricane, minor hurricane, and major hurricane, with a classification accuracy rate of 86.18% and an intensity estimation error of 4.01m/s for NA based on this accuracy. The results indicate that thermal core data can effectively represent the level and intensity of tropical cyclones, warranting further exploration of tropical cyclone attributes under this data.

Keywords: Artificial intelligence, deep learning, data mining, remote sensing

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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Authors: Majid Farsadrouh Rashti, Parisa Jahani, Amir Shafiee, Mehrdad Mofidi

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The dihydroxybenzene isomers, hydroquinone (HQ), catechol (CC) and resorcinol (RS) have been widely recognized as important environmental pollutants due to their toxicity and low degradability in the ecological environment. Speciation of HQ, CC and RS is very important for environmental analysis because they co-exist of these isomers in environmental samples and are too difficult to degrade as an environmental contaminant with high toxicity. There are many analytical methods have been reported for detecting these isomers, such as spectrophotometry, fluorescence, High-performance liquid chromatography (HPLC) and electrochemical methods. These methods have attractive advantages such as simple and fast response, low maintenance costs, wide linear analysis range, high efficiency, excellent selectivity and high sensitivity. A novel modified glassy carbon electrode (GCE) with Pd@ Cu/CNTs core−shell nanowires for the simultaneous determination of hydroquinone (HQ), catechol (CC) and resorcinol (RS) is described. A detailed investigation by field emission scanning electron microscopy and electrochemistry was performed in order to elucidate the preparation process and properties of the GCE/ Pd/CuNWs-CNTs. The electrochemical response characteristic of the modified GPE/LFOR toward HQ, CC and RS were investigated by cyclic voltammetry, differential pulse voltammetry (DPV) and Chronoamperometry. Under optimum conditions, the calibrations curves were linear up to 228 µM for each with detection limits of 0.4, 0.6 and 0.8 µM for HQ, CC and RS, respectively. The diffusion coefficient for the oxidation of HQ, CC and RS at the modified electrode was calculated as 6.5×10⁻⁵, 1.6 ×10⁻⁵ and 8.5 ×10⁻⁵ cm² s⁻¹, respectively. DPV was used for the simultaneous determination of HQ, CC and RS at the modified electrode and the relative standard deviations were 2.1%, 1.9% and 1.7% for HQ, CC and RS, respectively. Moreover, GCE/Pd/CuNWs-CNTs was successfully used for determination of HQ, CC and RS in real samples.

Keywords: dihydroxybenzene isomers, galvanized copper nanowires, electrochemical sensor, Palladium, speciation

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Authors: Chetna Tyagi. G. B. V. S. Lakshmi, Ambuj Tripathi, D. K. Avasthi

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Graphene oxide provides a good host matrix for preparing nanocomposites due to the different functional groups attached to its edges and planes. Being biocompatible, it is used in therapeutic applications. As enzyme-based biosensor requires complicated enzyme purification procedure, high fabrication cost and special storage conditions, we need enzyme-less biosensors for use even in a harsh environment like high temperature, varying pH, etc. In this work, we have prepared both enzyme-based and enzyme-less graphene oxide-based biosensors for glucose detection using glucose-oxidase as enzyme and gold nanoparticles, respectively. These samples were characterized using X-ray diffraction, UV-visible spectroscopy, scanning electron microscopy, and transmission electron microscopy to confirm the successful synthesis of the working electrodes. Electrochemical measurements were performed for both the working electrodes using a 3-electrode electrochemical cell. Cyclic voltammetry curves showed the homogeneous transfer of electron on the electrodes in the scan range between -0.2V to 0.6V. The sensing measurements were performed using differential pulse voltammetry for the glucose concentration varying from 0.01 mM to 20 mM, and sensing was improved towards glucose in the presence of gold nanoparticles. Gold nanoparticles in graphene oxide nanocomposite played an important role in sensing glucose in the absence of enzyme, glucose oxidase, as evident from these measurements. The selectivity was tested by measuring the current response of the working electrode towards glucose in the presence of the other common interfering agents like cholesterol, ascorbic acid, citric acid, and urea. The enzyme-less working electrode also showed storage stability for up to 15 weeks, making it a suitable glucose biosensor.

Keywords: electrochemical, enzyme-less, glucose, gold nanoparticles, graphene oxide, nanocomposite

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Authors: Masoomeh Estaji, Ahdie Tahamtani

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Considering the necessity of conducting research and publishing academic papers during Master’s and Ph.D. programs, graduate students are in dire need of improving their writing skills through either writing courses or self-study planning. One key feature that could aid academic papers to look more sophisticated is the application of grammatical metaphors (GMs). These types of metaphors represent the ‘non-congruent’ and ‘implicit’ ways of decoding meaning through which one grammatical category is replaced by another, more implied counterpart, which can alter the readers’ understanding of the text as well. Although a number of studies have been conducted on the application of GMs across various disciplines, almost none has been devoted to the field of environmental management, and the scope of the previous studies has been relatively limited compared to the present work. In the current study, attempts were made to analyze different types of GMs used in academic papers published in top-tiered journals in the field of environmental management, and make a list of the most frequently used GMs based on their functions in this particular discipline to make the teaching of academic writing courses more explicit and the composition of academic texts more well-structured. To fulfill these purposes, a corpus-based analysis based on the two theoretical models of Martin et al. (1997) and Liardet (2014) was run. Through two stages of manual analysis and concordancers, ten recent academic articles entailing 132490 words published in two prestigious journals were precisely scrutinized. The results yielded that through the whole IMRaD sections of the articles, among all types of ideational GMs, material processes were the most frequent types. The second and the third ranks would apply to the relational and mental categories, respectively. Regarding the use of interpersonal GMs, objective expanding metaphors were the highest in number. In contrast, subjective interpersonal metaphors, either expanding or contracting, were the least significant. This would suggest that scholars in the field of Environmental Management tended to shift the focus on the main procedures and explain technical phenomenon in detail, rather than to compare and contrast other statements and subjective beliefs. Moreover, since no instances of verbal ideational metaphors were detected, it could be deduced that the act of ‘saying or articulating’ something might be against the standards of the academic genre. One other assumption would be that the application of ideational GMs is context-embedded and that the more technical they are, the least frequent they become. For further studies, it is suggested that the employment of GMs to be studied in a wider scope and other disciplines, and the third type of GMs known as ‘textual’ metaphors to be included as well.

Keywords: English for specific purposes, grammatical metaphor, academic texts, corpus-based analysis

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Authors: Nusrat Zabeen Islam

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Partnership between local Non-Government organizations (NGO) and International development organizations has become an important feature in the development sector of Bangladesh. It is an important challenge for International development organizations to work with local NGOs with proper HR practice. Local NGOs have a lack of quality working environment and this affects the employee’s work experiences and overall performance at individual, partnership with International development organizations and organizational level. Many local development organizations due to the size of the organization and scope do not have a human resource (HR) unit. Inadequate Human Resource Policies, skills, leadership and lack of effective strategy is now a common scenario in Non-Government organization sector of Bangladesh. So corruption, nepotism, and fraud, risk of Political Contribution in office /work space, Sexual/ gender based abuse, insecurity take place in work place of development sector. The Complaint Management Mechanism (CMM) in human resource management could be one way to improve human resource competence in these organizations. The responsibility of Complaint Management Unit (CMU) of an International development organization is to make workplace maltreating, discriminating communities free. The information of impact of CMM was collected through case study of an International organization and some of its partner national organizations in Bangladesh who are engaged in different projects/programs. In this mechanism International development organizations collect complaints from beneficiaries/ staffs by complaint management unit and investigate by segregating the type and mood of the complaint and find out solution to improve the situation within a very short period. A complaint management committee is formed jointly with HR and management personnel. Concerned focal point collect complaints and share with CM unit. By conducting investigation, review of findings, reply back to CM unit and implementation of resolution through this mechanism, a successful bridge of communication and feedback can be established within beneficiaries, staffs and upper management. The overall result of Complaint management mechanism application indicates that by applying CMM accountability and transparency of workplace and workforce in development organization can be increased significantly. Evaluations based on outcomes, and measuring indicators such as productivity, satisfaction, retention, gender equity, proper judgment will guide organizations in building a healthy workforce, and will also clearly articulate the return on investment and justify any need for further funding.

Keywords: human resource management in NGOs, challenges in human resource, workplace environment, complaint management mechanism

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Authors: Nawras Kurzom, Avi Mendelsohn

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The effects of background music on learning and memory are inconsistent, partly due to the intrinsic complexity and variety of music and partly to individual differences in music perception and preference. A prominent musical feature that is known to elicit strong emotional responses is musical tension. Musical tension can be brought about by building anticipation of rhythm, harmony, melody, and dynamics. Delaying the resolution of dominant-to-tonic chord progressions, as well as using dissonant harmonics, can elicit feelings of tension, which can, in turn, affect memory formation of concomitant information. The aim of the presented studies was to explore how forming declarative memory is influenced by musical tension, brought about within continuous music as well as in the form of isolated chords with varying degrees of dissonance/consonance. The effects of musical tension on long-term memory of declarative information were studied in two ways: 1) by evoking tension within continuous music pieces by delaying the release of harmonic progressions from dominant to tonic chords, and 2) by using isolated single complex chords with various degrees of dissonance/roughness. Musical tension was validated through subjective reports of tension, as well as physiological measurements of skin conductance response (SCR) and pupil dilation responses to the chords. In addition, music information retrieval (MIR) was used to quantify musical properties associated with tension and its release. Each experiment included an encoding phase, wherein individuals studied stimuli (words or images) with different musical conditions. Memory for the studied stimuli was tested 24 hours later via recognition tasks. In three separate experiments, we found positive relationships between tension perception and physiological measurements of SCR and pupil dilation. As for memory performance, we found that background music, in general, led to superior memory performance as compared to silence. We detected a trade-off effect between tension perception and memory, such that individuals who perceived musical tension as such displayed reduced memory performance for images encoded during musical tension, whereas tense music benefited memory for those who were less sensitive to the perception of musical tension. Musical tension exerts complex interactions with perception, emotional responses, and cognitive performance on individuals with and without musical training. Delineating the conditions and mechanisms that underlie the interactions between musical tension and memory can benefit our understanding of musical perception at large and the diverse effects that music has on ongoing processing of declarative information.

Keywords: musical tension, declarative memory, learning and memory, musical perception

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Authors: Biaou Jean-Baptiste Kouchoro, Anissa Meziane, Philippe Micheau, Mathieu Renier, Nicolas Quaegebeur

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Numerous experimental and numerical studies have shown the interest of the local defects resonance (LDR) for the Non-Destructive Testing of metallic and composite plates. Indeed, guided ultrasonic waves such as Lamb waves, which are increasingly used for the inspection of these flat structures, enable the generation of local resonance phenomena by their interaction with a damaged area, allowing the detection of defects. When subjected to a large amplitude motion, a nonlinear behavior can predominate in the damaged area. This work presents a 2D Finite Element Model of the local resonance of a 12 mm long and 5 mm deep Flat Bottom Hole (FBH) in a 6 mm thick aluminum plate under the excitation induced by an incident A0 Lamb mode. The analysis of the transient response of the FBH enables the precise determination of its resonance frequencies and the associate modal deformations. Then, a linear parametric study varying the geometrical properties of the FBH highlights the sensitivity of the resonance frequency with respect to the plate thickness. It is demonstrated that the resonance effect disappears when the ratio of thicknesses between the FBH and the plate is below 0.1. Finally, the nonlinear behavior of the FBH is considered and studied introducing geometrical (taken into account the nonlinear component of the strain tensor) nonlinearities that occur at large vibration amplitudes. Experimental analysis allows observation of the resonance effects and nonlinear response of the FBH. The differences between these experimental results and the numerical results will be commented on. The results of this study are promising and allow to consider more realistic defects such as delamination in composite materials.

Keywords: guided waves, non-destructive testing, dynamic field testing, non-linear ultrasound/vibration

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Authors: V. A. Dubovsky, V. V. Savchenko, A. A. Baryskevich

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The current trend in the automotive industry towards automatic vehicles is creating new challenges related to human factors. This occurs due to the fact that the driver is increasingly relieved of the need to be constantly involved in driving the vehicle, which can negatively impact his/her situation awareness when manual control is required, and decrease driving skills and abilities. These new problems need to be studied in order to provide road safety during the transition towards self-driving vehicles. For this purpose, it is important to develop an appropriate conceptual model of the interaction between the driver and the automated vehicle, which could serve as a theoretical basis for the development of mathematical and simulation models to explore different aspects of driver behaviour in different road situations. Well-known driver behaviour models describe the impact of different stages of the driver's cognitive process on driving performance but do not describe how the driver controls and adjusts his actions. A more complete description of the driver's cognitive process, including the evaluation of the results of his/her actions, will make it possible to more accurately model various aspects of the human factor in different road situations. This paper presents a conceptual model of the 'driver – highly automated vehicle' system based on the P.K. Anokhin's theory of functional systems, which is a theoretical framework for describing internal processes in purposeful living systems based on such notions as goal, desired and actual results of the purposeful activity. A central feature of the proposed model is a dynamic coupling mechanism between the decision-making of a driver to perform a particular action and changes of road conditions due to driver’s actions. This mechanism is based on the stage by stage evaluation of the deviations of the actual values of the driver’s action results parameters from the expected values. The overall functional structure of the highly automated vehicle in the proposed model includes a driver/vehicle/environment state analyzer to coordinate the interaction between driver and vehicle. The proposed conceptual model can be used as a framework to investigate different aspects of human factors in transitions between automated and manual driving for future improvements in driving safety, and for understanding how driver-vehicle interface must be designed for comfort and safety. A major finding of this study is the demonstration that the theory of functional systems is promising and has the potential to describe the interaction of the driver with the vehicle and the environment.

Keywords: automated vehicle, driver behavior, human factors, human-machine system

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: Radul Shishkov, Orlin Davchev

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The digital era has revolutionized architectural practices, with building information modeling (BIM) emerging as a pivotal tool for architects, engineers, and construction professionals. However, the transition from traditional methods to BIM-centric approaches poses significant challenges, particularly in the context of existing structures. This research introduces a technical approach to bridge this gap through the development of algorithms that facilitate the automated transformation of 3D point cloud data into detailed BIM models. The core of this research lies in the application of algorithmic modeling and computational design methods to interpret and reconstruct point cloud data -a collection of data points in space, typically produced by 3D scanners- into comprehensive BIM models. This process involves complex stages of data cleaning, feature extraction, and geometric reconstruction, which are traditionally time-consuming and prone to human error. By automating these stages, our approach significantly enhances the efficiency and accuracy of creating BIM models for existing buildings. The proposed algorithms are designed to identify key architectural elements within point clouds, such as walls, windows, doors, and other structural components, and to translate these elements into their corresponding BIM representations. This includes the integration of parametric modeling techniques to ensure that the generated BIM models are not only geometrically accurate but also embedded with essential architectural and structural information. Our methodology has been tested on several real-world case studies, demonstrating its capability to handle diverse architectural styles and complexities. The results showcase a substantial reduction in time and resources required for BIM model generation while maintaining high levels of accuracy and detail. This research contributes significantly to the field of architectural technology by providing a scalable and efficient solution for the integration of existing structures into the BIM framework. It paves the way for more seamless and integrated workflows in renovation and heritage conservation projects, where the accuracy of existing conditions plays a critical role. The implications of this study extend beyond architectural practices, offering potential benefits in urban planning, facility management, and historic preservation.

Keywords: BIM, 3D point cloud, algorithmic modeling, computational design, architectural reconstruction

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Authors: Yi-Lin Chen, Sheng-Jou Hung, Tsunglin Liu

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Adaptive immune system recognizes a wide range of antigens via expressing a large number of structurally distinct T cell and B cell receptor genes. The distinct receptor genes arise from complex rearrangements called V(D)J recombination, and constitute the immune repertoire. A common method of profiling immune repertoire is via amplifying recombined receptor genes using multiple primers and high-throughput sequencing. This multiplex-PCR approach is efficient; however, the resulting repertoire can be distorted because of primer bias. To eliminate primer bias, 5’ RACE is an alternative amplification approach. However, the application of RACE approach is limited by its low efficiency (i.e., the majority of data are non-regular receptor sequences, e.g., containing intronic segments) and lack of the convenient tool for analysis. We propose a computational tool that can correctly identify non-regular receptor sequences in RACE data via aligning receptor sequences against the whole gene instead of only the exon regions as done in all other tools. Using our tool, the remaining regular data allow for an accurate profiling of immune repertoire. In addition, a RACE approach is improved to yield a higher fraction of regular T-cell receptor sequences. Finally, we quantify the degree of primer bias of a multiplex-PCR approach via comparing it to the RACE approach. The results reveal significant differences in frequency of VJ combination by the two approaches. Together, we provide a new experimental and computation pipeline for an unbiased profiling of immune repertoire. As immune repertoire profiling has many applications, e.g., tracing bacterial and viral infection, detection of T cell lymphoma and minimal residual disease, monitoring cancer immunotherapy, etc., our work should benefit scientists who are interested in the applications.

Keywords: immune repertoire, T-cell receptor, 5' RACE, high-throughput sequencing, sequence alignment

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Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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Authors: Javad Mohiti-Ardekani, Shabodin Asadii, Ali Moradi

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Introduction: Curcumin, the yellow pigment in turmeric, has been shown as an anti-diabetic agent for centuries but only in recent few years, its mechanism of action has been under investigation. Some studies showed that curcumin might exert its anti-diabetic effect via increasing glucose transporter isotype-4 (GLUT4) gene and glycoprotein contents in cells. To investigate this possibility, we investigate the effects of extract and commercial curcumin with and without insulin on GLUT4 translocation from intracellular compartments of nuclear or endoplasmic reticulum membranes (N/ER) into the cytoplasmic membrane (CM). Methods and Material: C2C12 myoblastic cell line were seeded in DMEM plus 20 % FBS and differentiated to myotubes using 2 % horse serum. After myotubes formation, 40 µmolar Extract and Commercial curcumin, with or without insulin as intervention, and as control 1 % DMSO were added for 3 h. Cells were washed and homogenized followed by ultracentrifuge fractionation, protein separation by SDS-PAGE and GLUT4 detection using semi-quantitative Western blotting. Data analysis was done by two independent samples t-test for comparison of mean ± SD of GLUT4 percent in categories. GLUT4 contents were higher in CM groups curcumin and curcumin with insulin in comparison to 1 % DMSO-treated myotubes control group. Results: As our results have shown extract and commercial curcumin induces GLUT4 translocation from intra-cell into cell surface. The results have also shown synergic effect of curcumin on translocation of GLUT4 from intra-cell into cell surface in the presence of 100 nm insulin. Discussion: We conclude that curcumin may be a choice of type-2 diabetes mellitus treatment because its extract and commercial enhances GLUT4 contents in CM where it facilitates glucose entrance into the cell. However, it is necessary to trace the signaling pathways which are activated by curcumin.

Keywords: Curcumin, insulin, Diabetes type-2, GLUT4

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Authors: Ziwei Wang, Andrea Lucotti, Luigi Brambilla, Matteo Tommasini, Chiara Bertarelli

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Surface-enhanced Raman Spectroscopy (SERS) is a highly sensitive detection that provides abundant information on low concentration analytes from various researching areas. Based on localized surface plasmon resonance, metal nanostructures including gold, silver and copper have been investigated as SERS substrate during recent decades. There has been increasing more attention of exploring good performance, homogenous, repeatable SERS substrates. Here, we show that electrospinning, which is an inexpensive technique to fabricate large-scale, self-standing and repeatable membranes, can be effectively used for producing SERS substrates. Nanoparticles and nanorods are added to the feed electrospinning solution to collect functionalized polymer fibrous mats. We report stable electrospun membranes as SERS substrate using gold nanorods (AuNRs) and poly(vinyl alcohol). Particularly, a post-processing crosslinking step using glutaraldehyde under acetone environment was carried out to the electrospun membrane. It allows for using the membrane in any liquid environment, including water, which is of interest both for sensing of contaminant in wastewater, as well as for biosensing. This crosslinked AuNRs/PVA membrane has demonstrated excellent performance as SERS substrate for low concentration 10-6 M Rhodamine 6G (Rh6G) aqueous solution. This post-processing for fabricating SERS substrate is the first time reported and proved through Raman imaging of excellent stability and outstanding performance. Finally, SERS tests have been applied to several analytes, and the application of AuNRs/PVA membrane is broadened by removing the detected analyte by rinsing. Therefore, this crosslinked AuNRs/PVA membrane is re-usable.

Keywords: SERS spectroscopy, electrospinning, crosslinking, composite materials

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Authors: Sara Arafeh, Kovriguine Evguine

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Cytochromes P450 are enzymes that require a supply of electrons to catalyze the synthesis of steroid hormones, fatty acids, and prostaglandin hormone. Cytochrome P450 Oxidoreductase (CYPOR), a membrane bound enzyme, provides these electrons in its open conformation. CYPOR has two cytosolic domains (FAD domain and FMN domain) and an N-terminal in the membrane. In its open conformation, electrons flow from NADPH, FAD, and finally to FMN where cytochrome P450 picks up these electrons. In the closed conformation, cytochrome P450 does not bind to the FMN domain to take the electrons. It was found that when the cytosolic domains are isolated, CYPOR could not bind to cytochrome P450. This suggested that the membrane environment is important for CYPOR function. This project takes the initiative to better understand the dynamics of CYPOR in its full length. Here, we determine the distance between specific sites in the FAD and FMN binding domains in CYPOR by Forster Resonance Energy Transfer (FRET) and Ultrafast TA spectroscopy with and without NADPH. The approach to determine these distances will rely on labeling these sites with red and infrared fluorophores. Mimic membrane attachment is done by inserting CYPOR in lipid nanodiscs. By determining the distances between the donor-acceptor sites in these domains, we can observe the open/closed conformations upon reducing CYPOR in the presence and absence of cytochrome P450. Such study is important to better understand CYPOR mechanism of action in various endosomal membranes including hepatic CYPOR which is vital in plasma cholesterol homeostasis. By investigating the conformational cycles of CYPOR, we can synthesize drugs that would be more efficient in affecting the steroid hormonal levels and metabolism of toxins catalyzed by Cytochrome P450.

Keywords: conformational cycle of CYPOR, cytochrome P450, cytochrome P450 oxidoreductase, FAD domain, FMN domain, FRET, Ultrafast TA Spectroscopy

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Authors: Tugba Sevinc Gamsiz, Emine Koroglu, Ozcan Keskin

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Aim: Nonalcoholic fatty liver disease (NAFLD) is considered the most common chronic liver disease in the general population. The higher mortality and morbidity among NAFLD patients and lack of symptoms makes early detection and management important. In our study, we aimed to evaluate the relationship between noninvasive imaging and biochemical markers in diabetic and nondiabetic patients diagnosed with NAFLD. Materials and Methods: The study was conducted from (September 2017) to (December 2017) on adults admitted to Internal Medicine and Gastroenterology outpatient clinics with hepatic steatosis reported on ultrasound or transient elastography within the last six months that exclude patients with other liver diseases or alcohol abuse. The data were collected and analyzed retrospectively. Number cruncher statistical system (NCSS) 2007 program was used for statistical analysis. Results: 116 patients were included in this study. Diabetic patients compared to nondiabetics had significantly higher Controlled Attenuation Parameter (CAP), Liver Stiffness Measurement (LSM) and fibrosis values. Also, hypertension, hepatomegaly, high BMI, hypertriglyceridemia, hyperglycemia, high A1c, and hyperuricemia were found to be risk factors for NAFLD progression to fibrosis. Advanced fibrosis (F3, F4) was present in 18,6 % of all our patients; 35,8 % of diabetic and 5,7 % of nondiabetic patients diagnosed with hepatic steatosis. Conclusion: Transient elastography is now used in daily clinical practice as an accurate noninvasive tool during follow-up of patients with fatty liver. Early diagnosis of the stage of liver fibrosis improves the monitoring and management of patients, especially in those with metabolic syndrome criteria.

Keywords: diabetes, elastography, fatty liver, fibrosis, metabolic syndrome

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