Search results for: extractor
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 40

Search results for: extractor

10 Convolutional Neural Networks versus Radiomic Analysis for Classification of Breast Mammogram

Authors: Mehwish Asghar

Abstract:

Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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9 Removal of Nickel and Vanadium from Crude Oil by Using Solvent Extraction and Electrochemical Process

Authors: Aliya Kurbanova, Nurlan Akhmetov, Abilmansur Yeshmuratov, Yerzhigit Sugurbekov, Ramiz Zulkharnay, Gulzat Demeuova, Murat Baisariyev, Gulnar Sugurbekova

Abstract:

Last decades crude oils have tended to become more challenge to process due to increasing amounts of sour and heavy crude oils. Some crude oils contain high vanadium and nickel content, for example Pavlodar LLP crude oil, which contains more than 23.09 g/t nickel and 58.59 g/t vanadium. In this study, we used two types of metal removing methods such as solvent extraction and electrochemical. The present research is conducted for comparative analysis of the deasphalting with organic solvents (cyclohexane, carbon tetrachloride, chloroform) and electrochemical method. Applying the cyclic voltametric analysis (CVA) and Inductively coupled plasma mass spectrometry (ICP MS), these mentioned types of metal extraction methods were compared in this paper. Maximum efficiency of deasphalting, with cyclohexane as the solvent, in Soxhlet extractor was 66.4% for nickel and 51.2% for vanadium content from crude oil. Percentage of Ni extraction reached maximum of approximately 55% by using the electrochemical method in electrolysis cell, which was developed for this research and consists of three sections: oil and protonating agent (EtOH) solution between two conducting membranes which divides it from two capsules of 10% sulfuric acid and two graphite electrodes which cover all three parts in electrical circuit. Ions of metals pass through membranes and remain in acid solutions. The best result was obtained in 60 minutes with ethanol to oil ratio 25% to 75% respectively, current fits into the range from 0.3A to 0.4A, voltage changed from 12.8V to 17.3V.

Keywords: demetallization, deasphalting, electrochemical removal, heavy metals, petroleum engineering, solvent extraction

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8 Compost Bioremediation of Oil Refinery Sludge by Using Different Manures in a Laboratory Condition

Authors: O. Ubani, H. I. Atagana, M. S. Thantsha

Abstract:

This study was conducted to measure the reduction in polycyclic aromatic hydrocarbons (PAHs) content in oil sludge by co-composting the sludge with pig, cow, horse and poultry manures under laboratory conditions. Four kilograms of soil spiked with 800 g of oil sludge was co-composted differently with each manure in a ratio of 2:1 (w/w) spiked soil:manure and wood-chips in a ratio of 2:1 (w/v) spiked soil:wood-chips. Control was set up similar as the one above but without manure. Mixtures were incubated for 10 months at room temperature. Compost piles were turned weekly and moisture level was maintained at between 50% and 70%. Moisture level, pH, temperature, CO2 evolution and oxygen consumption were measured monthly and the ash content at the end of experimentation. Bacteria capable of utilizing PAHs were isolated, purified and characterized by molecular techniques using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), amplification of the 16S rDNA gene using the specific primers (16S-P1 PCR and 16S-P2 PCR) and the amplicons were sequenced. Extent of reduction of PAHs was measured using automated soxhlet extractor with dichloromethane as the extraction solvent coupled with gas chromatography/mass spectrometry (GC/MS). Temperature did not exceed 27.5O°C in all compost heaps, pH ranged from 5.5 to 7.8 and CO2 evolution was highest in poultry manure at 18.78 µg/dwt/day. Microbial growth and activities were enhanced. Bacteria identified were Bacillus, Arthrobacter and Staphylococcus species. Results from PAH measurements showed reduction between 77 and 99%. The results from the control experiments may be because it was invaded by fungi. Co-composting of spiked soils with animal manures enhanced the reduction in PAHs. Interestingly, all bacteria isolated and identified in this study were present in all treatments, including the control.

Keywords: bioremediation, co-composting, oil refinery sludge, PAHs, bacteria spp, animal manures, molecular techniques

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7 Biodiesel Production from Yellow Oleander Seed Oil

Authors: S. Rashmi, Devashish Das, N. Spoorthi, H. V. Manasa

Abstract:

Energy is essential and plays an important role for overall development of a nation. The global economy literally runs on energy. The use of fossil fuels as energy is now widely accepted as unsustainable due to depleting resources and also due to the accumulation of greenhouse gases in the environment, renewable and carbon neutral biodiesel are necessary for environment and economic sustainability. Unfortunately biodiesel produced from oil crop, waste cooking oil and animal fats are not able to replace fossil fuel. Fossil fuels remain the dominant source of primary energy, accounting for 84% of the overall increase in demand. Today biodiesel has come to mean a very specific chemical modification of natural oils. Objectives: To produce biodiesel from yellow oleander seed oil, to test the yield of biodiesel using different types of catalyst (KOH & NaOH). Methodology: Oil is extracted from dried yellow oleander seeds using Soxhlet extractor and oil expeller (bulk). The FFA content of the oil is checked and depending on the FFA value either two steps or single step process is followed to produce biodiesel. Two step processes includes esterfication and transesterification, single step includes only transesterification. The properties of biodiesel are checked. Engine test is done for biodiesel produced. Result: It is concluded that biodiesel quality parameters such as yield(85% & 90%), flash point(1710C & 1760C),fire point(1950C & 1980C), viscosity(4.9991 and 5.21 mm2/s) for the biodiesel from seed oil of Thevetiaperuviana produced by using KOH & NaOH respectively. Thus the seed oil of Thevetiaperuviana is a viable feedstock for good quality fuel.The outcomes of our project are a substitute for conventional fuel, to reduce petro diesel requirement,improved performance in terms of emissions. Future prospects: Optimization of biodiesel production using response surface method.

Keywords: yellow oleander seeds, biodiesel, quality parameters, renewable sources

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6 Extraction, Recovery and Bioactivities of Chlorogenic Acid from Unripe Green Coffee Cherry Waste of Coffee Processing Industry

Authors: Akkasit Jongjareonrak, Supansa Namchaiya

Abstract:

Unripe green coffee cherry (UGCC) accounting about 5 % of total raw material weight receiving to the coffee bean production process and is, in general, sorting out and dump as waste. The UGCC is known to rich in phenolic compounds such as caffeoylquinic acids, feruloylquinic acids, chlorogenic acid (CGA), etc. CGA is one of the potent bioactive compounds using in the nutraceutical and functional food industry. Therefore, this study aimed at optimization the extraction condition of CGA from UGCC using Accelerated Solvent Extractor (ASE). The ethanol/water mixture at various ethanol concentrations (50, 60 and 70 % (v/v)) was used as an extraction solvent at elevated pressure (10.34 MPa) and temperatures (90, 120 and 150 °C). The recovery yield of UGCC crude extract, total phenolic content, CGA content and some bioactivities of UGCC extract were investigated. Using of ASE at lower temperature with higher ethanol concentration provided higher CGA content in the UGCC crude extract. The maximum CGA content was observed at the ethanol concentration of 70% ethanol and 90 °C. The further purification of UGCC crude extract gave a higher purity of CGA with a purified CGA yield of 4.28 % (w/w, of dried UGCC sample) containing 72.52 % CGA equivalent. The antioxidant activity and antimicrobial activity of purified CGA extract were determined. The purified CGA exhibited the 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity at 0.88 mg Trolox equivalent/mg purified CGA sample. The antibacterial activity against Escherichia coli was observed with the minimum inhibitory concentration (MIC) at 3.12 mg/ml and minimum bactericidal concentration (MBC) at 12.5 mg/ml. These results suggested that using of high concentration of ethanol and low temperature under elevated pressure of ASE condition could accelerate the extraction of CGA from UGCC. The purified CGA extract could be a promising alternative source of bioactive compound using for nutraceutical and functional food industry.

Keywords: bioactive, chlorogenic acid, coffee, extraction

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5 Processing and Economic Analysis of Rain Tree (Samanea saman) Pods for Village Level Hydrous Bioethanol Production

Authors: Dharell B. Siano, Wendy C. Mateo, Victorino T. Taylan, Francisco D. Cuaresma

Abstract:

Biofuel is one of the renewable energy sources adapted by the Philippine government in order to lessen the dependency on foreign fuel and to reduce carbon dioxide emissions. Rain tree pods were seen to be a promising source of bioethanol since it contains significant amount of fermentable sugars. The study was conducted to establish the complete procedure in processing rain tree pods for village level hydrous bioethanol production. Production processes were done for village level hydrous bioethanol production from collection, drying, storage, shredding, dilution, extraction, fermentation, and distillation. The feedstock was sundried, and moisture content was determined at a range of 20% to 26% prior to storage. Dilution ratio was 1:1.25 (1 kg of pods = 1.25 L of water) and after extraction process yielded a sugar concentration of 22 0Bx to 24 0Bx. The dilution period was three hours. After three hours of diluting the samples, the juice was extracted using extractor with a capacity of 64.10 L/hour. 150 L of rain tree pods juice was extracted and subjected to fermentation process using a village level anaerobic bioreactor. Fermentation with yeast (Saccharomyces cerevisiae) can fasten up the process, thus producing more ethanol at a shorter period of time; however, without yeast fermentation, it also produces ethanol at lower volume with slower fermentation process. Distillation of 150 L of fermented broth was done for six hours at 85 °C to 95 °C temperature (feedstock) and 74 °C to 95 °C temperature of the column head (vapor state of ethanol). The highest volume of ethanol recovered was established at with yeast fermentation at five-day duration with a value of 14.89 L and lowest actual ethanol content was found at without yeast fermentation at three-day duration having a value of 11.63 L. In general, the results suggested that rain tree pods had a very good potential as feedstock for bioethanol production. Fermentation of rain tree pods juice can be done with yeast and without yeast.

Keywords: fermentation, hydrous bioethanol, fermentation, rain tree pods, village level

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4 In vitro Antifungal Activity of Methanolic Extracts of Eight Various Cultivar of Persian Punica granatum L. against Candida Species

Authors: Shahindokht Bassiri-Jahromi, Mohammad Reza Pourshafie, Farzad Katiraee, Mannan Hajimahmoodi, Ehsan Mostafavi, Malihe Talebi

Abstract:

Objective: Resistance of Candida species to antifungal agents has potentially serious implications for management of infections. Candida species are now fourth common organisms isolated from hospitalized patients. It is important to increase effective therapy. In the past decade, numerous reports of treatment failures were reported. Prevention and control of these infections will require new antimicrobial agents. Plant-derived antifungal have always been a source of novel therapeutics. The aim of this study was to investigate the antifungal effect of methanolic extract of pomegranate peel and pulp against Candida species. Material and Methods: Eight cultivars of Punica granatum L. were collected from Saveh Agricultural Investigation Center in Iran. Both pomegranate pulp and peel were dried and powdered separately. The dried powders were extracted by using a soxhlet extractor. The antifungal effect of methanolic extract of pomegranate peel and pulp were determined in vitro by minimum inhibitory concentration (MIC) against five standard species of (ATCC 10231), C. parapsilosis (ATCC 22019), C. tropicalis (ATCC 750), C. glabrata (PTCC 5297), and C. kroseii (PTCC 5295). Results: Maximum inhibitions of antifungal effect were attributed to peel extract pomegranate cultivar and Candida species. The most potential antifungal inhibition among 8 different cultivars observed by sour malas, sour white peel, and sour summer extracts respectively, against five Candida strains. The antifungal activity of pulp extracts against Candida species was approximately negative. Conclusion: The use of Punica granatum peel extract has been shown to possess antifungal activities. The phytochemistry and pharmacological actions of Punica granatum peel components suggest a wide range of clinical applications for the treatment and prevention of candidiasis.

Keywords: antifungal activity, Candida species, Punica granatum L., pharmacognosy

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3 Eco-Friendly Softener Extracted from Ricinus communis (Castor) Seeds for Organic Cotton Fabric

Authors: Fisaha Asmelash

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The processing of textiles to achieve a desired handle is a crucial aspect of finishing technology. Softeners can enhance the properties of textiles, such as softness, smoothness, elasticity, hydrophilicity, antistatic properties, and soil release properties, depending on the chemical nature used. However, human skin is sensitive to rough textiles, making softeners increasingly important. Although synthetic softeners are available, they are often expensive and can cause allergic reactions on human skin. This paper aims to extract a natural softener from Ricinus communis and produce an eco-friendly and user-friendly alternative due to its 100% herbal and organic nature. Crushed Ricinus communis seeds were soaked in a mechanical oil extractor for one hour with a 100g cotton fabric sample. The defatted cake or residue obtained after the extraction of oil from the seeds, also known as Ricinus communis meal, was obtained by filtering the raffinate and then dried at 1030c for four hours before being stored under laboratory conditions for the softening process. The softener was applied directly to 100% cotton fabric using the padding process, and the fabric was tested for stiffness, crease recovery, and drape ability. The effect of different concentrations of finishing agents on fabric stiffness, crease recovery, and drape ability was also analyzed. The results showed that the change in fabric softness depends on the concentration of the finish used. As the concentration of the finish was increased, there was a decrease in bending length and drape coefficient. Fabrics with a high concentration of softener showed a maximum decrease in drape coefficient and stiffness, comparable to commercial softeners such as silicon. The highest decrease in drape coefficient was found to be comparable with commercial softeners, silicon. Maximum increases in crease recovery were seen in fabrics treated with Ricinus communis softener at a concentration of 30gpl. From the results, the extracted softener proved to be effective in the treatment of 100% cotton fabric

Keywords: ricinus communis, crease recovery, drapability, softeners, stiffness

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2 Phytochemical Composition, Antimicrobial Potential and Antioxidant Activity of Peganum harmala L. Extracts

Authors: Narayana Bhat, Majda Khalil, Hamad Al-Mansour, Anitha Manuvel, Vimla Yeddu

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The aim of this study was to assess the antimicrobial and antioxidant potential and phytochemical composition of Peganum harmala L. For this purpose, powdered shoot, root, and seed samples were extracted in an accelerated solvent extractor (ASE) with methanol, ethanol, acetone, and dichloromethane. The residues were reconstituted in the above solvents and 10% dimethyl sulphoxide (DMSO). The antimicrobial activity of these extracts was tested against two bacterial (Escherichia coli E49 and Staphylococcus aureus CCUG 43507) and two fungi Candida albicans ATCC 24433, Candida glabrata ATCC 15545) strains using the well-diffusion method. The minimum inhibitory concentration (MIC) and growth pattern of these test strains were determined using microbroth dilution method, and the phospholipase assay was performed to detect tissue damage in the host cells. Results revealed that ethanolic, methanolic, and dichloromethane extracts of seeds exhibited significant antimicrobial activities against all tested strains, whereas the acetone extract of seeds was effective against E. coli only. Similarly, ethanolic and methanolic extracts of roots were effective against two bacterial strains only. One sixth of percent (0.6%) yield of methanol extract of seeds was found to be the MIC for Escherichia coli E49, Staphylococcus aureus CCUG 43507, and Candida glabrata ATCC 15545. Overall, seed extracts had greater antimicrobial activities compared to roots and shoot extracts. The original plant extract and MIC dilutions prevented phospholipase secretion in Staphylococcus aureus CCUG 43507 and Candida albicans ATCC 24433. The 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging assay revealed radical scavenging activities ranging from 71.80 ± 4.36% to 87.75 ± 1.70%. The main compound present in the root extract was 1-methyl-7-methoxy-beta-carboline (RT: 44.171), followed by norlapachol (3.62%), benzopyrazine (2.20%), palmitic acid (2.12%) and vasicinone (1.96%). In contrast, phenol,4-ethenyl-2-methoxy was in abundance in the methonolic extract of the shoot, whereas 1-methyl-7-methoxy-beta-carboline (79.59%), linoleic acid (9.05%), delta-tocopherol (5.02%), 9,12-octadecadienoic acid, methyl ester (2.65%), benzene, 1,1-1,2 ethanediyl bis 3,4dimethyl (1.15%), anthraquinone (0.58%), hexadecanoic acid, methyl ester (0.54%), palmitic acid (0.35%) and methyl stearate (0.18%) were present in the methanol extract of seeds. Major findings of this study, along with their relevance to developing effective, safe drugs, will be discussed in this presentation.

Keywords: medicinal plants, secondary metabolites, phytochemical screening, bioprospecting, radical scavenging

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1 Deep Learning in Chest Computed Tomography to Differentiate COVID-19 from Influenza

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

Abstract:

Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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