Search results for: algorithm genetic

Authors: Rowane May A. Fesalbon, Greyland C. Agno, Jodel L. Cuasay, Dindo A. Malonzo, Ma. Rosario Concepcion O. Ang

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The Department of Energy of the Republic of the Philippines estimates that the country’s energy reserves for 2015 are dwindling– observed in the rotating power outages in several localities. To aid in the energy crisis, a national hydropower resource assessment scheme is developed. Hydropower is a resource that is derived from flowing water and difference in elevation. It is a renewable energy resource that is deemed abundant in the Philippines – being an archipelagic country that is rich in bodies of water and water resources. The objectives of this study is to develop a methodology for a national hydropower resource assessment using hydrologic modeling and geospatial techniques in order to generate resource maps for future reference and use of the government and other stakeholders. The methodology developed for this purpose is focused on two models – the implementation of the Soil and Water Assessment Tool (SWAT) for the river discharge and the use of geospatial techniques to analyze the topography and obtain the head, and generate the theoretical hydropower potential sites. The methodology is highly coupled with Geographic Information Systems to maximize the use of geodatabases and the spatial significance of the determined sites. The hydrologic model used in this workflow is SWAT integrated in the GIS software ArcGIS. The head is determined by a developed algorithm that utilizes a Synthetic Aperture Radar (SAR)-derived digital elevation model (DEM) which has a resolution of 10-meters. The initial results of the developed workflow indicate hydropower potential in the river reaches ranging from pico (less than 5 kW) to mini (1-3 MW) theoretical potential.

Keywords: ArcSWAT, renewable energy, hydrologic model, hydropower, GIS

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Authors: Danny Barash

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Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is that many are found in prokaryotes but only a small percentage of known riboswitches have been found in certain eukaryotic organisms. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods that include some slight structural considerations. These pattern-matching methods were the first ones to be applied for the purpose of riboswitch detection and they can also be programmed very efficiently using a data structure called affix arrays, making them suitable for genome-wide searches of riboswitch patterns. However, they are limited by their ability to detect harder to find riboswitches that deviate from the known patterns. Several methods have been developed since then to tackle this problem. The most commonly used by practitioners is Infernal that relies on Hidden Markov Models (HMMs) and Covariance Models (CMs). Profile Hidden Markov Models were also carried out in the pHMM Riboswitch Scanner web application, independently from Infernal. Other computational approaches that have been developed include RMDetect by the use of 3D structural modules and RNAbor that utilizes Boltzmann probability of structural neighbors. We have tried to incorporate more sophisticated secondary structure considerations based on RNA folding prediction using several strategies. The first idea was to utilize window-based methods in conjunction with folding predictions by energy minimization. The moving window approach is heavily geared towards secondary structure consideration relative to sequence that is treated as a constraint. However, the method cannot be used genome-wide due to its high cost because each folding prediction by energy minimization in the moving window is computationally expensive, enabling to scan only at the vicinity of genes of interest. The second idea was to remedy the inefficiency of the previous approach by constructing a pipeline that consists of inverse RNA folding considering RNA secondary structure, followed by a BLAST search that is sequence-based and highly efficient. This approach, which relies on inverse RNA folding in general and our own in-house fragment-based inverse RNA folding program called RNAfbinv in particular, shows capability to find attractive candidates that are missed by Infernal and other standard methods being used for riboswitch detection. We demonstrate attractive candidates found by both the moving-window approach and the inverse RNA folding approach performed together with BLAST. We conclude that structure-based methods like the two strategies outlined above hold considerable promise in detecting riboswitches and other conserved RNAs of functional importance in a variety of organisms.

Keywords: riboswitches, RNA folding prediction, RNA structure, structure-based methods

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Authors: Wassim Jerbi, Abderrahmen Guermazi, Hafedh Trabelsi

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The optimum use of coverage in wireless sensor networks (WSNs) is very important. LEACH protocol called Low Energy Adaptive Clustering Hierarchy, presents a hierarchical clustering algorithm for wireless sensor networks. LEACH is a protocol that allows the formation of distributed cluster. In each cluster, LEACH randomly selects some sensor nodes called cluster heads (CHs). The selection of CHs is made with a probabilistic calculation. It is supposed that each non-CH node joins a cluster and becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the network. Thus, several sensor nodes cannot reach any CH. to solve this problem. We created an O-LEACH Orphan nodes protocol, its role is to reduce the sensor nodes which do not belong the cluster. The cluster member called Gateway receives messages from neighboring orphan nodes. The gateway informs CH having the neighboring nodes that not belong to any group. However, Gateway called (CH') attaches the orphaned nodes to the cluster and then collected the data. O-Leach enables the formation of a new method of cluster, leads to a long life and minimal energy consumption. Orphan nodes possess enough energy and seeks to be covered by the network. The principal novel contribution of the proposed work is O-LEACH protocol which provides coverage of the whole network with a minimum number of orphaned nodes and has a very high connectivity rates.As a result, the WSN application receives data from the entire network including orphan nodes. The proper functioning of the Application requires, therefore, management of intelligent resources present within each the network sensor. The simulation results show that O-LEACH performs better than LEACH in terms of coverage, connectivity rate, energy and scalability.

Keywords: WSNs; routing; LEACH; O-LEACH; Orphan nodes; sub-cluster; gateway; CH’

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Authors: A. A. Sadia, A. Emdad, E. Hossain

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The increasing importance of mushrooms as a source of nutrition, health benefits, and even potential cancer treatment has raised awareness of the impact of climate-sensitive variables on their cultivation. Factors like temperature, relative humidity, air quality, and substrate composition play pivotal roles in shaping mushroom growth, especially in Bangladesh. Oyster mushrooms, a commonly cultivated variety in this region, are particularly vulnerable to climate fluctuations. This research explores the climatic dynamics affecting oyster mushroom cultivation and, presents an approach to address these challenges and provides tangible solutions to fortify the agro-economy, ensure food security, and promote the sustainability of this crucial food source. Using climate and production data, this study evaluates the performance of three clustering algorithms -KMeans, OPTICS, and BIRCH- based on various quality metrics. While each algorithm demonstrates specific strengths, the findings provide insights into their effectiveness for this specific dataset. The results yield essential information, pinpointing the optimal temperature range of 13°C-22°C, the unfavorable temperature threshold of 28°C and above, and the ideal relative humidity range of 75-85% with the suitable production regions in three different seasons: Kharif-1, 2, and Robi. Additionally, a user-friendly web application is developed to support mushroom farmers in making well-informed decisions about their cultivation practices. This platform offers valuable insights into the most advantageous periods for oyster mushroom farming, with the overarching goal of enhancing the efficiency and profitability of mushroom farming.

Keywords: climate variability, mushroom cultivation, clustering techniques, food security, sustainability, web-application

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Authors: Deepak Kumar, Jahangeer, Brijesh Kumar Yadav, Shashi Mathur

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In-situ remediation is a technique which can remediate either surface or groundwater at the site of contamination. In the present study, simulation optimization approach has been used to develop management strategy for remediating LNAPL (Light Non-Aqueous Phase Liquid) contaminated aquifers. Benzene, toluene, ethyl benzene and xylene are the main component of LNAPL contaminant. Collectively, these contaminants are known as BTEX. In in-situ bioremediation process, a set of injection and extraction wells are installed. Injection wells supply oxygen and other nutrient which convert BTEX into carbon dioxide and water with the help of indigenous soil bacteria. On the other hand, extraction wells check the movement of plume along downstream. In this study, optimal design of the system has been done using PSO (Particle Swarm Optimization) algorithm. A comprehensive management strategy for pumping of injection and extraction wells has been done to attain a maximum allowable concentration of 5 ppm and 4.5 ppm. The management strategy comprises determination of pumping rates, the total pumping volume and the total running cost incurred for each potential injection and extraction well. The results indicate a high pumping rate for injection wells during the initial management period since it facilitates the availability of oxygen and other nutrients necessary for biodegradation, however it is low during the third year on account of sufficient oxygen availability. This is because the contaminant is assumed to have biodegraded by the end of the third year when the concentration drops to a permissible level.

Keywords: groundwater, in-situ bioremediation, light non-aqueous phase liquid, BTEX, particle swarm optimization

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Authors: Itzel Amaro-Estrada, Eduardo Vergara-Rivera, Virginia Juarez-Flores, Mayra Cobaxin-Cardenas, Rosa Estela Quiroz, Jesus F. Preciado, Sergio Rodriguez-Camarillo

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Bovine anaplasmosis is an infectious, tick-borne disease caused mainly by Anaplasma marginale; typical signs include anemia, fever, abortion, weight loss, decreased milk production, jaundice, and potentially death. Sick bovine can recover when antibiotics are administered; however, it usually remains as carrier for life, being a risk of infection for susceptible cattle. Anaplasma marginale is an obligate intracellular Gram-negative bacterium with genetic composition highly diverse among geographical isolates. There are currently no vaccines fully effective against bovine anaplasmosis; therefore, the economic losses due to disease are present. Vaccine formulation became a hard task for several pathogens as Anaplasma marginale, but peptide-based vaccines are an interesting proposal way to induce specific responses. Phage-displayed peptide libraries have been proved one of the most powerful technologies for identifying specific ligands. Screening of these peptides libraries is also a tool for studying interactions between proteins or peptides. Thus, it has allowed the identification of ligands recognized by polyclonal antiserums, and it has been successful for the identification of relevant epitopes in chronic diseases and toxicological conditions. Protective immune response to bovine anaplasmosis includes high levels of immunoglobulins subclass G2 (IgG2) but not subclass IgG1. Therefore, IgG2 from the serum of protected bovine can be useful to identify ligands, which can be part of an immunogen for cattle. In this work, phage display random peptide library Ph.D. ™ -12 was incubating with IgG2 or blood sera of immunized bovines against A. marginale as targets. After three rounds of biopanning, several candidates were selected for additional analysis. Subsequently, their reactivity with sera immunized against A. marginale, as well as with positive and negative sera to A. marginale was evaluated by immunoassays. A collection of recognized peptides tested by ELISA was generated. More than three hundred phage-peptides were separately evaluated against molecules which were used during panning. At least ten different peptides sequences were determined from their nucleotide composition. In this approach, three phage-peptides were selected by their binding and affinity properties. In the case of the development of vaccines or diagnostic reagents, it is important to evaluate the immunogenic and antigenic properties of the peptides. Immunogenic in vitro and in vivo behavior of peptides will be assayed as synthetic and as phage-peptide for to determinate their vaccine potential. Acknowledgment: This work was supported by grant SEP-CONACYT 252577 given to I. Amaro-Estrada.

Keywords: bovine anaplasmosis, peptides, phage display, veterinary vaccines

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Authors: F. M. Pisano, M. Ciminello

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Aerospace, mechanical, and civil engineering infrastructures can take advantages from damage detection and identification strategies in terms of maintenance cost reduction and operational life improvements, as well for safety scopes. The challenge is to detect so called “barely visible impact damage” (BVID), due to low/medium energy impacts, that can progressively compromise the structure integrity. The occurrence of any local change in material properties, that can degrade the structure performance, is to be monitored using so called Structural Health Monitoring (SHM) systems, in charge of comparing the structure states before and after damage occurs. SHM seeks for any "anomalous" response collected by means of sensor networks and then analyzed using appropriate algorithms. Independently of the specific analysis approach adopted for structural damage detection and localization, textual reports, tables and graphs describing possible outlier coordinates and damage severity are usually provided as artifacts to be elaborated for information extraction about the current health conditions of the structure under investigation. Visual Analytics can support the processing of monitored measurements offering data navigation and exploration tools leveraging the native human capabilities of understanding images faster than texts and tables. Herein, a SHM system enrichment by integration of a Visual Analytics component is investigated. Analytical dashboards have been created by combining worksheets, so that a useful Visual Analytics tool is provided to structural analysts for exploring the structure health conditions examined by a Principal Component Analysis based algorithm.

Keywords: interactive dashboards, optical fibers, structural health monitoring, visual analytics

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Authors: Yana Posmitnaya, Galina Rudnitskaya, Tatyana Lukashenko, Anton Bukatin, Anatoly Evstrapov

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An important area of biological and medical research is the study of genetic mutations and polymorphisms that can alter gene function and cause inherited diseases and other diseases. The following methods to analyse DNA fragments are used: capillary electrophoresis and electrophoresis on microfluidic chip (MFC), mass spectrometry with electrophoresis on MFC, hybridization assay on microarray. Electrophoresis on MFC allows to analyse small volumes of samples with high speed and throughput. A soft lithography in polydimethylsiloxane (PDMS) was chosen for operative fabrication of MFCs. A master-form from silicon and photoresist SU-8 2025 (MicroChem Corp.) was created for the formation of micro-sized structures in PDMS. A universal topology which combines T-injector and simple cross was selected for the electrophoretic separation of the sample. Glass K8 and PDMS Sylgard® 184 (Dow Corning Corp.) were used for fabrication of MFCs. Electroosmotic flow (EOF) plays an important role in the electrophoretic separation of the sample. Therefore, the estimate of the quantity of EOF and the ways of its regulation are of interest for the development of the new methods of the electrophoretic separation of biomolecules. The following methods of surface modification were chosen to change EOF: high-frequency (13.56 MHz) plasma treatment in oxygen and argon at low pressure (1 mbar); 1% aqueous solution of polyvinyl alcohol; 3% aqueous solution of Kolliphor® P 188 (Sigma-Aldrich Corp.). The electroosmotic mobility was evaluated by the method of Huang X. et al., wherein the borate buffer was used. The influence of physical and chemical methods of treatment on the wetting properties of the PDMS surface was controlled by the sessile drop method. The most effective way of surface modification of MFCs, from the standpoint of obtaining the smallest value of the contact angle and the smallest value of the EOF, was the processing with aqueous solution of Kolliphor® P 188. This method of modification has been selected for the treatment of channels of MFCs, which are used for the separation of mixture of oligonucleotides fluorescently labeled with the length of chain with 10, 20, 30, 40 and 50 nucleotides. Electrophoresis was performed on the device MFAS-01 (IAI RAS, Russia) at the separation voltage of 1500 V. 6% solution of polydimethylacrylamide with the addition of 7M carbamide was used as the separation medium. The separation time of components of the mixture was determined from electropherograms. The time for untreated MFC was ~275 s, and for the ones treated with solution of Kolliphor® P 188 – ~ 220 s. Research of physical-chemical methods of surface modification of MFCs allowed to choose the most effective way for reducing EOF – the modification with aqueous solution of Kolliphor® P 188. In this case, the separation time of the mixture of oligonucleotides decreased about 20%. The further optimization of method of modification of channels of MFCs will allow decreasing the separation time of sample and increasing the throughput of analysis.

Keywords: electrophoresis, microfluidic chip, modification, nucleic acid, polydimethylsiloxane, soft lithography

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Authors: J.P. Henriques, E. R. Neto, G. Almeida, G. Ribeiro, J.V. Coutinho, A.B. Lugli

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Industry 4.0, or the Fourth Industrial Revolution, is transforming the relationship between people and machines. In this scenario, some technologies such as Cloud Computing, Internet of Things, Augmented Reality, Artificial Intelligence, Additive Manufacturing, among others, are making industries and devices increasingly intelligent. One of the most powerful technologies of this new revolution is the Digital Twin, which allows the virtualization of a real system or process. In this context, the present paper addresses the linear and nonlinear dynamic study of a didactic level plant using Digital Twin. In the first part of the work, the level plant is identified at a fixed point of operation, BY using the existing method of least squares means. The linearized model is embedded in a Digital Twin using Automation Studio® from Famous Technologies. Finally, in order to validate the usage of the Digital Twin in the linearized study of the plant, the dynamic response of the real system is compared to the Digital Twin. Furthermore, in order to develop the nonlinear model on a Digital Twin, the didactic level plant is identified by using the method proposed by Hammerstein. Different steps are applied to the plant, and from the Hammerstein algorithm, the nonlinear model is obtained for all operating ranges of the plant. As for the linear approach, the nonlinear model is embedded in the Digital Twin, and the dynamic response is compared to the real system in different points of operation. Finally, yet importantly, from the practical results obtained, one can conclude that the usage of Digital Twin to study the dynamic systems is extremely useful in the industrial environment, taking into account that it is possible to develop and tune controllers BY using the virtual model of the real systems.

Keywords: industry 4.0, digital twin, system identification, linear and nonlinear models

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Authors: Alireza Jalalvand, Maryam Saleh, Somayeh Behjat Khatouni, Zahra Bahri Najafi, Foroozan Fatahinia, Narges Ismailzadeh, Behrokh Farahmand

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Object: In the last two decades, the recent outbreak of Coronavirus (SARS-CoV-2) imposed a global pandemic in the world. Despite the increasing prevalence of the disease, there are no effective drugs to treat it. A suitable and rapid way to afford an effective drug and treat the global pandemic is a computational drug study. This study used molecular docking methods to examine the potential inhibition of over 50 antiviral drugs against three fundamental proteins of SARS-CoV-2. METHODS: Through a literature review, three important proteins (a key protease, RNA-dependent RNA polymerase (RdRp), and spike) were selected as drug targets. Three-dimensional (3D) structures of protease, spike, and RdRP proteins were obtained from the Protein Data Bank. Protein had minimal energy. Over 50 antiviral drugs were considered candidates for protein inhibition and their 3D structures were obtained from drug banks. The Autodock 4.2 software was used to define the molecular docking settings and run the algorithm. RESULTS: Five drugs, including indinavir, lopinavir, saquinavir, nelfinavir, and remdesivir, exhibited the highest inhibitory potency against all three proteins based on the binding energies and drug binding positions deduced from docking and hydrogen-bonding analysis. Conclusions: According to the results, among the drugs mentioned, saquinavir and lopinavir showed the highest inhibitory potency against all three proteins compared to other drugs. It may enter laboratory phase studies as a dual-drug treatment to inhibit SARS-CoV-2.

Keywords: covid-19, drug repositioning, molecular docking, lopinavir, saquinavir

Procedia PDF Downloads 86

Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

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Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: autonomous vehicles, deformable part model, dpm, pedestrian detection, real time

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Authors: W. Hamaidia, T. Zebbiche

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This paper presents the design of axisymmetric supersonic nozzles, in order to accelerate a supersonic flow to the desired Mach number and that having a small weight, in the same time gives a high thrust. The concerned nozzle gives a parallel and uniform flow at the exit section. The nozzle is divided into subsonic and supersonic regions. The supersonic portion is independent to the upstream conditions of the sonic line. The subsonic portion is used to give a sonic flow at the throat. In this case, nozzle gives a uniform and parallel flow at the exit section. It’s named by minimum length Nozzle. The study is done at high temperature, lower than the dissociation threshold of the molecules, in order to improve the aerodynamic performances. Our aim consists of improving the performances both by the increase of exit Mach number and the thrust coefficient and by reduction of the nozzle's mass. The variation of the specific heats with the temperature is considered. The design is made by the Method of Characteristics. The finite differences method with predictor-corrector algorithm is used to make the numerical resolution of the obtained nonlinear algebraic equations. The application is for air. All the obtained results depend on three parameters which are exit Mach number, the stagnation temperature, the chosen mesh in characteristics. A numerical simulation of nozzle through Computational Fluid Dynamics-FASTRAN was done to determine and to confirm the necessary design parameters.

Keywords: flux supersonic flow, axisymmetric minimum length nozzle, high temperature, method of characteristics, calorically imperfect gas, finite difference method, trust coefficient, mass of the nozzle, specific heat at constant pressure, air, error

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Authors: J. S. Saini, P. P. K. Sandhu

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The multi-hop nature of Wireless Mesh Networks and the hasty progression of throughput demands results in multi- channels and multi-radios structures in mesh networks, but the main problem of co-channels interference reduces the total throughput, specifically in multi-hop networks. Quality of Service mentions a vast collection of networking technologies and techniques that guarantee the ability of a network to make available desired services with predictable results. Quality of Service (QoS) can be directed at a network interface, towards a specific server or router's performance, or in specific applications. Due to interference among various transmissions, the QoS routing in multi-hop wireless networks is formidable task. In case of multi-channel wireless network, since two transmissions using the same channel may interfere with each other. This paper has considered the Destination Sequenced Distance Vector (DSDV) routing protocol to locate the secure and optimised path. The proposed technique also utilizes the Lempel–Ziv–Welch (LZW) based lossless data compression and intra cluster data aggregation to enhance the communication between the source and the destination. The use of clustering has the ability to aggregate the multiple packets and locates a single route using the clusters to improve the intra cluster data aggregation. The use of the LZW based lossless data compression has ability to reduce the data packet size and hence it will consume less energy, thus increasing the network QoS. The MATLAB tool has been used to evaluate the effectiveness of the projected technique. The comparative analysis has shown that the proposed technique outperforms over the existing techniques.

Keywords: WMNS, QOS, flooding, collision avoidance, LZW, congestion control

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Authors: Javier Carroquino, Nieves García-Casarejos, Pilar Gargallo, F. Javier García-Ramos

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The energy used in agriculture is a source of global emissions of greenhouse gases. The two main types of this energy are electricity for pumping and diesel for agricultural machinery. In order to reduce these emissions, the European project LIFE REWIND addresses the supply of this demand from renewable sources. First of all, comprehensive data on energy demand and available renewable resources have been obtained in several case studies. Secondly, a set of simulations and optimizations have been performed, in search of the best configuration and sizing, both from an economic and emission reduction point of view. For this purpose, it was used software based on genetic algorithms. Thirdly, a prototype has been designed and installed, that it is being used for the validation in a real case. Finally, throughout a year of operation, various technical and economic parameters are being measured for further analysis. The prototype is not connected to the utility grid, avoiding the cost and environmental impact of a grid extension. The system includes three kinds of photovoltaic fields. One is located on a fixed structure on the terrain. Another one is floating on an irrigation raft. The last one is mounted on a two axis solar tracker. Each has its own solar inverter. The total amount of nominal power is 44 kW. A lead acid battery with 120 kWh of capacity carries out the energy storage. Three isolated inverters support a three phase, 400 V 50 Hz micro-grid, the same characteristics of the utility grid. An advanced control subsystem has been constructed, using free hardware and software. The electricity produced feeds a set of seven pumps used for purification, elevation and pressurization of water in a drip irrigation system located in a vineyard. Since the irrigation season does not include the whole year, as well as a small oversize of the generator, there is an amount of surplus energy. With this surplus, a hydrolyser produces on site hydrogen by electrolysis of water. An off-road vehicle with fuel cell feeds on that hydrogen and carries people in the vineyard. The only emission of the process is high purity water. On the one hand, the results show the technical and economic feasibility of stand-alone renewable energy systems to feed seasonal pumping. In this way, the economic costs, the environmental impacts and the landscape impacts of grid extensions are avoided. The use of diesel gensets and their associated emissions are also avoided. On the other hand, it is shown that it is possible to replace diesel in agricultural machinery, substituting it for electricity or hydrogen of 100% renewable origin and produced on the farm itself, without any external energy input. In addition, it is expected to obtain positive effects on the rural economy and employment, which will be quantified through interviews.

Keywords: drip irrigation, greenhouse gases, hydrogen, renewable energy, vineyard

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Jonghoon Chun, Insung Kim, Jonghyun Lim, Gun Ro

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We believe that it is possible to provide a solution to reduce patient safety accidents by displaying correct medical records and prescription information through interactive patient identification. Our system is based on the use of smart bands worn by patients and these bands communicate with the hybrid gateways which understand both BLE and Wifi communication protocols. Through the convergence of low-power Bluetooth (BLE) and hybrid gateway technology, which is one of short-range wireless communication technologies, we implement ‘Intelligent Patient Identification and Location Tracking System’ to prevent medical malfunction frequently occurring in medical institutions. Based on big data and IOT technology using MongoDB, smart band (BLE, NFC function) and hybrid gateway, we develop a system to enable two-way communication between medical staff and hospitalized patients as well as to store locational information of the patients in minutes. Based on the precise information provided using big data systems, such as location tracking and movement of in-hospital patients wearing smart bands, our findings include the fact that a patient-specific location tracking algorithm can more efficiently operate HIS (Hospital Information System) and other related systems. Through the system, we can always correctly identify patients using identification tags. In addition, the system automatically determines whether the patient is a scheduled for medical service by the system in use at the medical institution, and displays the appropriateness of the medical treatment and the medical information (medical record and prescription information) on the screen and voice. This work was supported in part by the Korea Technology and Information Promotion Agency for SMEs (TIPA) grant funded by the Korean Small and Medium Business Administration (No. S2410390).

Keywords: BLE, hybrid gateway, patient identification, IoT, safety management, smart band

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Authors: Richard Draper

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A supercomputer can be constructed from identical building blocks which are small parallel processors connected by a network referred to as the local network. The routers have unused ports which are used to interconnect the building blocks. These connections are referred to as the global network. The address space has a global and a local component (g, l). The conventional way to connect the building blocks is to connect (g, l) to (g’,l). If there are K blocks, this requires K global ports in each router. If a block is of size M, the result is a machine with KM routers having diameter two. To increase the size of the machine to 2K blocks, each router connects to only half of the other blocks. The result is a larger machine but also one with greater diameter. This is a crude description of how the network of the CRAY XC® is designed. In this paper, a family of interconnection networks using routers with K global and M local ports is defined. Coordinates are (c,d, p) and the global connections are (c,d,p)↔(c’,p,d) which swaps p and d. The network is denoted D3(K,M) and is called a Swapped Dragonfly. D3(K,M) has KM2 routers and has diameter three, regardless of the size of K. To produce a network of size KM2 conventionally, diameter would be an increasing function of K. The family of Swapped Dragonflies has other desirable properties: 1) D3(K,M) scales linearly in K and quadratically in M. 2) If L < K, D3(K,M) contains many copies of D3(L,M). 3) If L < M, D3(K,M) contains many copies of D3(K,L). 4) D3(K,M) can perform an all-to-all exchange in KM2+KM time which is only slightly more than the time to do a one-to-all. This paper makes several contributions. It is the first time that a swap has been used to define a linearly scalable family of networks. Structural properties of this new family of networks are thoroughly examined. A synchronizing packet header is introduced. It specifies the path to be followed and it makes it possible to define highly parallel communication algorithm on the network. Among these is an all-to-all exchange in time KM2+KM. To demonstrate the effectiveness of the swap properties of the network of the CRAY XC® and D3(K,16) are compared.

Keywords: all-to-all exchange, CRAY XC®, Dragonfly, interconnection network, packet switching, swapped network, topology

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Authors: Yakup Ulusu, Faruk Ozel, Numan Eczacioglu, Abdurrahman Ozen, Sabriye Acikgoz

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GFP discovered in the mid-1970s, has been used as a marker after replicated genetic study by scientists. In biotechnology, cell, molecular biology, the GFP gene is frequently used as a reporter of expression. In modified forms, it has been used to make biosensors. Many animals have been created that express GFP as an evidence that a gene can be expressed throughout a given organism. Proteins labeled with GFP identified locations are determined. And so, cell connections can be monitored, gene expression can be reported, protein-protein interactions can be observed and signals that create events can be detected. Additionally, monitoring GFP is noninvasive; it can be detected by under UV-light because of simply generating fluorescence. Moreover, GFP is a relatively small and inert molecule, that does not seem to treat any biological processes of interest. The synthesis of GFP has some steps like, to construct the plasmid system, transformation in E. coli, production and purification of protein. GFP carrying plasmid vector pBAD–GFPuv was digested using two different restriction endonuclease enzymes (NheI and Eco RI) and DNA fragment of GFP was gel purified before cloning. The GFP-encoding DNA fragment was ligated into pET28a plasmid using NheI and Eco RI restriction sites. The final plasmid was named pETGFP and DNA sequencing of this plasmid indicated that the hexa histidine-tagged GFP was correctly inserted. Histidine-tagged GFP was expressed in an Escherichia coli BL21 DE3 (pLysE) strain. The strain was transformed with pETGFP plasmid and grown on LuiraBertoni (LB) plates with kanamycin and chloramphenicol selection. E. coli cells were grown up to an optical density (OD 600) of 0.8 and induced by the addition of a final concentration of 1mM isopropyl-thiogalactopyranoside (IPTG) and then grown for additional 4 h. The amino-terminal hexa-histidine-tag facilitated purification of the GFP by using a His Bind affinity chromatography resin (Novagen). Purity of GFP protein was analyzed by a 12 % sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). The concentration of protein was determined by UV absorption at 280 nm (Varian Cary 50 Scan UV/VIS spectrophotometer). Synthesis of GFP-Polymer composite nanofibers was produced by using GFP solution (10mg/mL) and polymer precursor Polyvinylpyrrolidone, (PVP, Mw=1300000) as starting materials and template, respectively. For the fabrication of nanofibers with the different fiber diameter; a sol–gel solution comprising of 0.40, 0.60 and 0.80 g PVP (depending upon the desired fiber diameter) and 100 mg GFP in 10 mL water: ethanol (3:2) mixtures were prepared and then the solution was covered on collecting plate via electro spinning at 10 kV with a feed-rate of 0.25 mL h-1 using Spellman electro spinning system. Results show that GFP-based nano-fiber can be used plenty of biomedical applications such as bio-imaging, bio-mechanic, bio-material and tissue engineering.

Keywords: biomaterial, GFP, nano-fibers, protein expression

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Authors: Juni C. Kim, Anna R. Robuck, Douglas I. Walker

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The human exposome, which includes chemical exposures over the lifetime and their effects, is now recognized as an important measure for understanding human health; however, the complexity of the data makes the identification of environmental chemicals challenging. The goal of our project was to establish a computational workflow for the improved identification of environmental pollutants containing chlorine or bromine. Using the “pattern. search” function available in the R package NonTarget, we wrote a multifunctional script that searches mass spectral clusters from untargeted gas-chromatography high-resolution mass spectrometry (GC-HRMS) for the presence of spectra consistent with chlorine and bromine-containing organic compounds. The “pattern. search” function was incorporated into a different function that allows the evaluation of clusters containing multiple analyte fragments, has multi-core support, and provides a simplified output identifying listing compounds containing chlorine and/or bromine. The new function was able to process 46,000 spectral clusters in under 8 seconds and identified over 150 potential halogenated spectra. We next applied our function to a deidentified dataset from patients diagnosed with primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and healthy controls. Twenty-two spectra corresponded to potential halogenated compounds in the PSC and PBC dataset, including six significantly different in PBC patients, while four differed in PSC patients. We have developed an improved algorithm for detecting halogenated compounds in GC-HRMS data, providing a strategy for prioritizing exposures in the study of human disease.

Keywords: exposome, metabolome, computational metabolomics, high-resolution mass spectrometry, exposure, pollutants

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Ethan James

Abstract:

Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Erifyli Tsagkari, Stephanie Connelly, Zhaowei Liu, Andrew McBride, William Sloan

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Biofilms play an essential role in treating water in biofiltration systems. The biofilm morphology and function are inextricably linked to the hydrodynamics of flow through a filter, and yet engineers rarely explicitly engineer this interaction. We develop a system that links computer simulation and 3-D printing to optimize and rapidly prototype filter media to optimize biofilm function with the hypothesis that biofilm function is intimately linked to the flow passing through the filter. A computational model that numerically solves the incompressible time-dependent Navier Stokes equations coupled to a model for biofilm growth and function is developed. The model is imbedded in an optimization algorithm that allows the model domain to adapt until criteria on biofilm functioning are met. This is applied to optimize the shape of filter media in a simple flow channel to promote biofilm formation. The computer code links directly to a 3-D printer, and this allows us to prototype the design rapidly. Its validity is tested in flow visualization experiments and by microscopy. As proof of concept, the code was constrained to explore a small range of potential filter media, where the medium acts as an obstacle in the flow that sheds a von Karman vortex street that was found to enhance the deposition of bacteria on surfaces downstream. The flow visualization and microscopy in the 3-D printed realization of the flow channel validated the predictions of the model and hence its potential as a design tool. Overall, it is shown that the combination of our computational model and the 3-D printing can be effectively used as a design tool to prototype filter media to optimize biofilm formation.

Keywords: biofilm, biofilter, computational model, von karman vortices, 3-D printing.

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Authors: Lamees Nasser, Yago Diez, Robert Martí, Joan Martí, Ibrahim Sadek

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Automated 3D breast ultrasound (ABUS) screening is a novel modality in medical imaging because of its common characteristics shared with other ultrasound modalities in addition to the three orthogonal planes (i.e., axial, sagittal, and coronal) that are useful in analysis of tumors. In the literature, few automatic approaches exist for typical tasks such as segmentation or registration. In this work, we deal with two problems concerning ABUS images: nipple and rib detection. Nipple and ribs are the most visible and salient features in ABUS images. Determining the nipple position plays a key role in some applications for example evaluation of registration results or lesion follow-up. We present a nipple detection algorithm based on color and shape of the nipple, besides an automatic approach to detect the ribs. In point of fact, rib detection is considered as one of the main stages in chest wall segmentation. This approach consists of four steps. First, images are normalized in order to minimize the intensity variability for a given set of regions within the same image or a set of images. Second, the normalized images are smoothed by using anisotropic diffusion filter. Next, the ribs are detected in each slice by analyzing the eigenvalues of the 3D Hessian matrix. Finally, a breast mask and a probability map of regions detected as ribs are used to remove false positives (FP). Qualitative and quantitative evaluation obtained from a total of 22 cases is performed. For all cases, the average and standard deviation of the root mean square error (RMSE) between manually annotated points placed on the rib surface and detected points on rib borders are 15.1188 mm and 14.7184 mm respectively.

Keywords: Automated 3D Breast Ultrasound, Eigenvalues of Hessian matrix, Nipple detection, Rib detection

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Authors: Flavio Cannavo

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Continuous evaluation of the status of potentially hazardous volcanos plays a key role for civil protection purposes. The importance of monitoring volcanic activity, especially for energetic paroxysms that usually come with tephra emissions, is crucial not only for exposures to the local population but also for airline traffic. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the high nonlinearity of the complex and coupled volcanic dynamics leads to a large variety of different volcanic behaviors. Moreover, continuously measured parameters (e.g. seismic, deformation, infrasonic and geochemical signals) are often not able to fully explain the ongoing phenomenon, thus making the fast volcano state assessment a very puzzling task for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, here we introduce a system based on an ensemble of data-driven classifiers to infer automatically the ongoing volcano status from all the available different kind of measurements. The system consists of a heterogeneous set of independent classifiers, each one built with its own data and algorithm. Each classifier gives an output about the volcanic status. The ensemble technique allows weighting the single classifier output to combine all the classifications into a single status that maximizes the performance. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision-making purposes.

Keywords: Bayesian networks, expert system, mount Etna, volcano monitoring

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Authors: Safia Hasan Varachhia, Neela Badrie, Marsha Singh

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Genetic modification of food is an innovative technology that offers a host of benefits and advantages to consumers. Consumer attitudes towards GM food and GM technologies can be identified a major determinant in conditioning market force and encouraging policy makers and regulators to recognize the significance of consumer influence on the market. This study aimed to investigate and evaluate the extent of consumer awareness, knowledge, perception and acceptance of GM foods and its associated health risks and benefit in Trinidad and Tobago, West Indies. The specific objectives of this study were to (determine consumer awareness to GM foods, ascertain their perspectives on health and safety risks and ethical issues associated with GM foods and determine whether labeling of GM foods and ingredients will influence consumers’ willingness to purchase GM foods. A survey comprising of a questionnaire consisting of 40 questions, both open-ended and close-ended was administered to 240 shoppers in small, medium and large-scale supermarkets throughout Trinidad between April-May, 2015 using convenience sampling. This survey investigated consumer awareness, knowledge, perception and acceptance of GM foods and its associated health risks/benefits. The data was analyzed using SPSS 19.0 and Minitab 16.0. One-way ANOVA investigated the effects categories of supermarkets and knowledge scores on shoppers’ awareness, knowledge, perception and acceptance of GM foods. Linear Regression tested whether demographic variables (category of supermarket, age of consumer, level of were useful predictors of consumer’s knowledge of GM foods). More than half of respondents (64.3%) were aware of GM foods and GM technologies, 28.3% of consumers indicated the presence of GM foods in local supermarkets and 47.1% claimed to be knowledgeable of GM foods. Furthermore, significant associations (P < 0.05) were observed between demographic variables (age, income, and education), and consumer knowledge of GM foods. Also, significant differences (P < 0.05) were observed between demographic variables (education, gender, and income) and consumer knowledge of GM foods. In addition, age, education, gender and income (P < 0.05) were useful predictors of consumer knowledge of GM foods. There was a contradiction as whilst 35% of consumers considered GM foods safe for consumption, 70% of consumers were wary of the unknown health risks of GM foods. About two-thirds of respondents (67.5%) considered the creation of GM foods morally wrong and unethical. Regarding GM food labeling preferences, 88% of consumers preferred mandatory labeling of GM foods and 67% of consumers specified that any food product containing a trace of GM food ingredients required mandatory GM labeling. Also, despite the declaration of GM food ingredients on food labels and the reassurance of its safety for consumption by food safety and regulatory institutions, the majority of consumers (76.1%) still preferred conventionally produced foods over GM foods. The study revealed the need to inform shoppers of the presence of GM foods and technologies, present the scientific evidence as to the benefits and risks and the need for a policy on labeling so that informed choices could be taken.

Keywords: genetically modified foods, income, labeling consumer awareness, ingredients, morality and ethics, policy

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Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

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Authors: Sibgha Noreen, Muhammad Salim Akhter, Seema Mahmood

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Salt stress is an abiotic stress that causes a heavy toll on growth and development and also reduces the productivity of arable and horticultural crops. Globally, a quarter of total arable land has fallen prey to this menace, and more is being encroached because of the usage of brackish water for irrigation purposes. Though barley is categorized as salt-tolerant crop, but cultivars show a wide genetic variability in response to it. In addressing salt stress, silicon nutrition would be a facile tool for enhancing salt tolerant to sustain crop production. A greenhouse study was conducted to evaluate the response of barley (Hordeum vulgare L.) cultivars to silicon nutrition under salt stress. The treatments included [(a) four barley cultivars (Jou-87, B-14002, B-14011, B-10008); (b) two salt levels (0, 200 mM, NaCl); and (c) two silicon levels (0, 200ppm, K2SiO3. nH2O), arranged in a factorial experiment in a completely randomized design with 16 treatments and repeated 4 times. Plants were harvested at 15 days after exposure to different experimental salinity and silicon foliar conditions. Results revealed that various physiological and biochemical attributes differed significantly (p<0.05) in response to different treatments and their interactive effects. Cultivar “B-10008” excelled in biological yield, chlorophyll constituents, antioxidant enzymes, and grain yield compared to other cultivars. The biological yield of shoot and root organs was reduced by 27.3 and 26.5 percent under salt stress, while it was increased by 14.5 and 18.5 percent by exogenous application of silicon over untreated check, respectively. The imposition of salt stress at 200 mM caused a reduction in total chlorophyll content, chl ‘a’ , ‘b’ and ratio a/b by 10.6,16.8,17.1 and 7.1, while spray of 200 ppm silicon improved the quantum of the constituents by 10.4,12.1,10.2,10.3 over untreated check, respectively. The quantum of free amino acids and protein content was enhanced in response to salt stress and the spray of silicon nutrients. The amounts of superoxide dismutase, catalases, peroxidases, hydrogen peroxide, and malondialdehyde contents rose to 18.1, 25.7, 28.1, 29.5, and 17.6 percent over non-saline conditions under salt stress. However, the values of these antioxidants were reduced in proportion to salt stress by 200 ppm silicon applied as rooting medium on barley crops. The salt stress caused a reduction in the number of tillers, number of grains per spike, and 100-grain weight to the amount of 29.4, 8.6, and 15.8 percent; however, these parameters were improved by 7.1, 10.3, and 9.6 percent by foliar spray of silicon over untreated crop, respectively. It is concluded that the barley cultivar “B-10008” showed greater tolerance and adaptability to saline conditions. The yield of barley crops could be potentiated by a foliar spray of 200 ppm silicon at the vegetative growth stage under salt stress.

Keywords: salt stress, silicon nutrition, chlorophyll constituents, antioxidant enzymes, barley crop

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Authors: Manav Sabharwal, Ruda Rai Md, Candace Parker, James Ridley

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Wheat is one of the most commonly consumed grains worldwide, which contains gluten. Nowadays, gluten intake is considered to be a trigger for GRDs, including Celiac disease (CD), a common genetic disease affecting 1% of the US population, non-celiac gluten sensitivity (NCGS) and wheat allergy. NCGS is being recognized as an acquired gluten-sensitive enteropathy that is prevalent across age, ethnic and geographic groups. The cause of this entity is not fully understood, and recent studies suggest that it is more common in participants with irritable bowel syndrome (IBS), with iron deficiency anemia, symptoms of fatigue, and has considerable overlap in symptoms with IBS and Crohn’s disease. However, these studies were lacking in availability of complete serologies, imaging tests and/or pan-endoscopy. We performed a prospective study of 745 adult patients who presented to an outpatient clinic for evaluation of chronic upper gastro-intestinal symptoms and subsequently underwent an upper endoscopic (EGD) examination as standard of care. Evaluation comprised of comprehensive celiac antibody panel, inflammatory bowel disease (IBD) serologic markers, duodenal biopsies and Small Bowel Video Capsule Endoscopy (VCE), when available. At least 6 biopsy specimens were obtained from the duodenum and proximal jejunum during EGD, and CD3+ Intraepithelial lymphocytes (IELs) and villous architecture were evaluated by a single experienced pathologist, and VCE was performed by a single experienced gastroenterologist. Of the 745 patients undergoing EGD, 12% (93/745) patients showed elevated CD3+ IELs in the duodenal biopsies. 52% (387/745) completed a comprehensive CD panel and 7.2% (28/387) were positive for at least 1 CD antibody (Tissue transglutaminase (tTG), being the most common antibody in 65% (18/28)). Of these patients, 18% (5/28) showed increased duodenal CD3+ IELs, but 0% showed villous blunting or distortion to meet criteria for CD. Surprisingly, 43% (12/28) were positive for at 1 IBD serology (ASCA, ANCA or expanded IBD panel (LabCorp)). Of these 28 patients, 29% (8/28) underwent a SB VCE, of which 100 % (8/8) showed significant jejuno-ileal mucosal lesions diagnostic for IBD. Findings of abnormal CD antibodies (7.2%, 28/387) and increased CD3+ IELs on duodenal biopsy (12%, 93/745) were observed frequently in patients with UGI symptoms undergoing EGD in an outpatient clinic. None met criteria for CD, and a high proportion (43%, 12/28) showed evidence of overlap with IBD. This suggests a potential causal link of acquired GRDs to underlying inflammation and gut mucosal barrier disruption. Further studies to investigate a role for abnormal antigen presentation of dietary gluten to gut associated lymphoid tissue as a cause are justified. This may explain a high prevalence of GRDs in the population and correlation with IBS, IBD and other gut inflammatory disorders.

Keywords: celiac, gluten sensitive enteropathy, lymphocitic enteritis, IBS, IBD

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Authors: Bhat Geetalaxmi Jairam, D. V. Ashoka

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Wireless Sensor Networks are used in many applications to collect sensed data from different sources. Sensed data has to be delivered through sensors wireless interface using multi-hop communication towards the sink. The data collection in wireless sensor networks consumes energy. Energy consumption is the major constraints in WSN .Reducing the energy consumption while increasing the amount of generated data is a great challenge. In this paper, we have implemented two data gathering protocols with multiple mobile sinks/elements to collect data from sensor nodes. First, is Energy-Efficient Data Gathering with Tour Length-Constrained Mobile Elements in Wireless Sensor Networks (EEDG), in which mobile sinks uses vehicle routing protocol to collect data. Second is An Intelligent Agent-based Routing Structure for Mobile Sinks in WSNs (IAR), in which mobile sinks uses prim’s algorithm to collect data. Authors have implemented concepts which are common to both protocols like deployment of mobile sinks, generating visiting schedule, collecting data from the cluster member. Authors have compared the performance of both protocols by taking statistics based on performance parameters like Delay, Packet Drop, Packet Delivery Ratio, Energy Available, Control Overhead. Authors have concluded this paper by proving EEDG is more efficient than IAR protocol but with few limitations which include unaddressed issues likes Redundancy removal, Idle listening, Mobile Sink’s pause/wait state at the node. In future work, we plan to concentrate more on these limitations to avail a new energy efficient protocol which will help in improving the life time of the WSN.

Keywords: aggregation, consumption, data gathering, efficiency

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Authors: Chettyparambil Lalchand Thejalakshmi, Sonal Sabu Edattukaran

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Hair loss is one of the main issues that contemporary society is dealing with. It can be attributable to a wide range of factors, listing from one's genetic composition and the anxiety we experience on a daily basis. Telogen effluvium [TE] is a condition that causes temporary hair loss after a stressor that might shock the body and cause the hair follicles to temporarily rest, leading to hair loss. Most frequently, women are the ones who bring up these difficulties. Extreme illness or trauma, an emotional or important life event, rapid weight loss and crash dieting, a severe scalp skin problem, a new medication, or ceasing hormone therapy are examples of potential causes. Men frequently do not notice hair thinning with time, but women with long hair may be easily identified when shedding, which can occasionally result in bias because women tend to be more concerned with aesthetics and beauty standards of the society, and approach frequently with the concerns .The woman, who formerly possessed a full head of hair, is worried about the hair loss from her scalp . There are several cases of hair loss reported every day, and Telogen effluvium is said to be the most prevalent one of them all without any hereditary risk factors. While the patient has loss in hair volume, baldness is not the result of this problem . The exponentially growing Dermatology and Aesthetic medical division has discovered that this problem is the most common and also the easiest to cure since it is feasible for these people to regrow their hair, unlike those who have scarring alopecia, in which the follicle itself is damaged and non-viable. Telogen effluvium comes in two different forms: acute and chronic. Acute TE occurs in all the age groups with a hair loss of less than three months, while chronic TE is more common in those between the ages of 30 and 60 with a hair loss of more than six months . Both kinds are prevalent throughout all age groups, regardless of the predominance. It takes between three and six months for the lost hair to come back, although this condition is readily reversed by eliminating stresses. After shedding their hair, patients frequently describe having noticeable fringes on their forehead. The current medical treatments for this condition include topical corticosteroids, systemic corticosteroids, minoxidil and finasteride, CNDPA (caffeine, niacinamide, panthenol, dimethicone, and an acrylate polymer) .Individual terminal hair growth was increased by 10% as a result of the innovative intervention CNDPA. Botulinum Toxin A, Scalp Micro Needling, Platelet Rich Plasma Therapy [PRP], and sessions with Multivitamin Mesotherapy Injections are some recently enhanced techniques with partially or completely reversible hair loss. Also, it has been shown that supplements like Nutrafol and Biotin are producing effective outcomes. There is virtually little evidence to support the claim that applying sulfur-rich ingredients to the scalp, such as onion juice, can help TE patients' hair regenerate.

Keywords: dermatology, telogen effluvium, hair loss, modern hair loass treatments

Procedia PDF Downloads 88