Search results for: protein fold recognition

Authors: Prativa Das, Lay Poh Tan

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Three-dimensional microenvironment is a need to study the event cascades of liver fibrosis in vitro. Electrospun nanofibers modified with essential extracellular matrix proteins can closely mimic the random fibrous structure of native liver extracellular matrix (ECM). In this study, we fabricate a series of 3D electrospun scaffolds by wet electrospinning process modified with different ratios of collagen-I to fibronectin to achieve optimized distribution of these two ECM proteins on the fiber surface. A ratio of 3:1 of collagen-I to fibronectin was found to be optimum for surface modification of electrospun poly(lactic-co-glycolic acid) (PLGA) fibers by chemisorption process. In 3:1 collagen-I to fibronectin modified scaffolds the total protein content increased by ~2 fold compared to collagen-I modified and ~1.5 fold compared to 1:1/9:1 collagen-I to fibronectin modified scaffolds. We have cultured LX-2 cells on this scaffold over 14 days and found that LX-2 cells acquired more quiescent phenotype throughout the culture period and shown significantly lower expression of alpha smooth muscle actin and collagen-I. Thus, this system can be used as a model to study liver fibrosis by using different fibrogenic mediators in vitro.

Keywords: electrospinning, collagen-I and fibronectin, surface modification of fiber, LX-2 cells, liver fibrosis

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Authors: Saleh H. Salmen, Sulaiman Ali Alharbi, Hany M. Yehia, Mohammad A. Khiyami, Milton Wainwright, Naiyf S. Alharbi, Arunachalam Chinnathambi

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An antifungal protein synthesized by Bacillus cereus has been partially purified by the use of ammonium sulfate precipitation and Sephadex-G-200 column chromatography. The protein was produced from Bacillus cereus grown in potato Dextrose Broth Medium (PDB) at 30 ºC for 3 days at 100 rpm. The protein showed antagonistic effect against some fungi and yeasts. Crude extract from medium and semi-purified protein were tested in vitro against both fungi and yeasts using the disc diffusion method in order to detect the inhibitory effect of the protein. Zones of inhibition of the following diameter were found (mm) were Alternaria alternate (28), Rhodotorula glutinis (20), Fusarium sp. (16), Rhizopus sp. (15), Penicillium digitatum (13), Mucor sp. (13) and Aspergillus niger (10). The isolated protein was found to have a molecular weight of ~35kDa by sodium deodecyl sulfate-poly acrylamide gel electrophoresis. The data showed that the protein of Bacillus cereus has antifungal activity, a fact which points to the possibility of using it as a bio-control agent against some fungi, findings which emphasize the potential role of B. cereus as an important bio-control agent.

Keywords: bacillus cereus, ~35kDa protein, molds, yeasts

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Authors: Ashutosh Mishra, Nikhil Goyal

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A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations.

Keywords: facial recognition, computational intelligence, convolutional neural network, depth map

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Authors: Salma A. El-Marasy, Reham M. Abd-Elsalam, Omar A. Ahmed-Farid

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Dementia is characterized by impairments in memory and other cognitive abilities. This study aims to elucidate the possible ameliorative effect of silymarin on scopolamine-induced dementia using the object recognition test (ORT). The study was extended to demonstrate the role of cholinergic activity, oxidative stress, neuroinflammation, brain neurotransmitters and histopathological changes in the anti-amnestic effect of silymarin in demented rats. Wistar rats were pretreated with silymarin (200, 400, 800 mg/kg) or donepezil (10 mg/kg) orally for 14 consecutive days. Dementia was induced after the last drug administration by a single intraperitoneal dose of scopolamine (16 mg/kg). Then behavioral, biochemical, histopathological, and immunohistochemical analyses were then performed. Rats pretreated with silymarin counteracted scopolamine-induced non-spatial working memory impairment in the ORT and decreased acetylcholinesterase (AChE) activity, reduced malondialdehyde (MDA), elevated reduced glutathione (GSH), restored gamma-aminobutyric acid (GABA) and dopamine (DA) contents in the cortical and hippocampal brain homogenates. Silymarin dose-dependently reversed scopolamine-induced histopathological changes. Immunohistochemical analysis showed that silymarin dose-dependently mitigated protein expression of a glial fibrillary acidic protein (GFAP) and nuclear factor kappa-B (NF-κB) in the brain cortex and hippocampus. All these effects of silymarin were similar to that of the standard anti-amnestic drug, donepezil. This study reveals that the ameliorative effect of silymarin on scopolamine-induced dementia in rats using the ORT maybe in part mediated by, enhancement of cholinergic activity, anti-oxidant and anti-inflammatory activities as well as mitigation in brain neurotransmitters and histopathological changes.

Keywords: dementia, donepezil, object recognition test, rats, silymarin, scopolamine

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Authors: Ceyda Okudu, Secil Eroglu, Khandakar A. S. M. Saadat, Sibel O. Balci

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Ring Finger Protein 2 (RNF2) is a member of polycomb repressive complex 1 (PRC1), which is one of the epigenetic regulators in the genome. When RNF2 combines with other PRC1 members, it mediates the mono-ubiquitination of Histon2A (H2A). In breast cancer, RNF2 is commonly overexpressed, and also it promotes metastasis and invasion in other aggressive tumors like melanoma, prostate, and hepatocarcinoma. The role of RNF2 in the metastasis and invasion of breast cancer has not yet been elucidated. Our aim is to observe the role of RNF2 in metastasis and invasion in this study by miRNA mediated RNF2 gene silencing in breast cancer cell lines. We selected miRNAs, targeting to RNF2 by searching online databases. miR-17-5p, miR20a-5p, and miR-106b-5p were transfected to breast cancer cell lines (MCF-7, MDA-MB-231, SK-BR-3, and ZR-75-1), and also we used normal breast epithelial cell line (hTERT-HME1) to compare RNF2 gene expression level. After 48-72 hours post-transfection, mRNAs were isolated from the cells, and gene expressions were measured by RT-qPCR after from cDNA syntheses. We observed that RNF2 was highly expressed in SK-BR-3 and MDA-MB-231 cell lines opposite to MCF-7 and ZR-75-1 cell lines. RNF2 was downregulated 5, 5 and 7 fold by miR17-5p, miR20a-5p and miR106b-5p respectively in MCF-7. However, in SK-BR-3 and ZR-75-1 cell lines, miRNAs did not affect significantly RNF2 gene expression level. miR20a-5p decreased RNF2 3 fold and miR17-5p and miR106b-5p did not affect MDA-MB-231. After gene expression analysis, we performed metastasis and invasion assay in MCF-7 cells. For metastasis, we used both wound healing assay and Transwell Cell Migration Assay, and we used Transwell Cell Invasion Assay for invasion. The data of this assay showed that miR17-5p and miR20a-5p decreased both invasion and metastasis level, but miR106b-5p has no effect. We would like to conclude that RNF2 can be targeted by miR17-5p, miR20a-5p and miR106b-5p in MCF-7 cells and also RNF2, which is one of the upregulated genes in aggressive tumor, can be decreased by using these miRNAs. In future, we would like to confirm these results at the protein level and also whether these miRNAs are direct target of RNF2 or not.

Keywords: breast cancer, epigenetic, microRNAs, RNF2

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Authors: M. Kowalski, M. Brodowski, K. Dziabowska, E. Czaczyk, W. Bialobrzeska, N. Malinowska, S. Zoledowska, R. Bogdanowicz, D. Nidzworski

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Boron-doped-carbon nanowall (BCNW) electrodes are recently in much interest among scientists. BCNWs are good candidates for biosensor purposes as they possess interesting electrochemical characteristics like a wide potential range and the low difference between redox peaks. Moreover, from technical parameters, they are mechanically resistant and very tough. The production process of the microwave plasma-enhanced chemical vapor deposition (MPECVD) allows boron to build into the structure of the diamond being formed. The effect is the formation of flat, long structures with sharp ends. The potential of these electrodes was checked in the biosensing field. The procedure of simple carbon electrodes modification by antibodies was adopted to BCNW for specific antigen recognition. Surface protein D deriving from H. influenzae pathogenic bacteria was chosen as a target analyte. The electrode was first modified with the aminobenzoic acid diazonium salt by electrografting (electrochemical reduction), next anti-protein D antibodies were linked via 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) chemistry, and free sites were blocked by BSA. Cyclic voltammetry measurements confirmed the proper electrode modification. Electrochemical impedance spectroscopy records indicated protein detection. The sensor was proven to detect protein D in femtograms. This work was supported by the National Centre for Research and Development (NCBR) TECHMATSTRATEG 1/347324/12/NCBR/ 2017.

Keywords: anti-protein D antibodies, boron-doped carbon nanowall, impedance spectroscopy, Haemophilus influenzae.

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Authors: Mohd Noah A. Rahman, Afzaal H. Seyal, Norhafilah Bara

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Developing a software application using an interface with computers and peripheral devices using gestures of human body such as hand movements keeps growing in interest. A review on this hand gesture detection and recognition based on computer vision technique remains a very challenging task. This is to provide more natural, innovative and sophisticated way of non-verbal communication, such as sign language, in human computer interaction. Nevertheless, this paper explores hand detection and hand gesture recognition applying a vision based approach. The hand detection and recognition used skin color spaces such as HSV and YCrCb are applied. However, there are limitations that are needed to be considered. Almost all of skin color space models are sensitive to quickly changing or mixed lighting circumstances. There are certain restrictions in order for the hand recognition to give better results such as the distance of user’s hand to the webcam and the posture and size of the hand.

Keywords: hand detection, hand gesture, hand recognition, sign language

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Authors: Rominkumar Busa, Shahira K. C., Lijiya A.

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Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.

Keywords: small text extraction, OCR, scene text recognition, CRNN

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Authors: Ahmed Abdulghani Taha, Mohammad Abdulghani Taha

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This study aims at being acquainted with the using the body fat percentage (%BF) with body Mass Index (BMI) as input parameters in fuzzy logic decision support system to predict properly the lifted weight for students at weightlifting class lift according to his abilities instead of traditional manner. The sample included 53 male students (age = 21.38 ± 0.71 yrs, height (Hgt) = 173.17 ± 5.28 cm, body weight (BW) = 70.34 ± 7.87.6 kg, Body mass index (BMI) 23.42 ± 2.06 kg.m-2, fat mass (FM) = 9.96 ± 3.15 kg and fat percentage (% BF) = 13.98 ± 3.51 %.) experienced the weightlifting class as a credit and has variance at BW, Hgt and BMI and FM. BMI and % BF were taken as input parameters in FUZZY logic whereas the output parameter was the lifted weight (LW). There were statistical differences between LW values before and after using fuzzy logic (Diff 3.55± 2.21, P > 0.001). The percentages of the LW categories proposed by fuzzy logic were 3.77% of students to lift 1.0 fold of their bodies; 50.94% of students to lift 0.95 fold of their bodies; 33.96% of students to lift 0.9 fold of their bodies; 3.77% of students to lift 0.85 fold of their bodies and 7.55% of students to lift 0.8 fold of their bodies. The study concluded that the characteristic changes in body composition experienced by students when undergoing weightlifting could be utilized side by side with the Fuzzy logic decision support system to determine the proper workloads consistent with the abilities of students.

Keywords: fuzzy logic, body mass index, body fat percentage, weightlifting

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Authors: Shikha Masand, Sudesh Kumar Yadav

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Certain protein kinases have been shown to be crucial for plant cell signaling pathways associated with plant immune responses. Here we identified a horsegram [Macrotyloma uniflorum (Lam.) Verdc.] malectin-like leucine rich receptor-like protein kinase (RLK) gene MuRLK. The functional MuRLK protein preferentially binds to mannose and N-acetyl glucosamine residues. MuRLK exists in the cytoplasm and also localizes to the plasma membrane of plant cells via its N-terminus. Over-expression of MuRLK in Arabidopsis enhances the basal resistance to infection with Pseudomonas syringae pv. tomato, Alternaria brassicicola and Hyaloperonospora arabidopsidis, are associated with elevated ROS bursts, MAPK activation, thus ultimately leading to hypersensitive cell death. Moreover, salicylic acid-dependent and jasmonic acid-dependent defense responses are also enhanced in the MuRLK-overexpressed plants that lead to HR-induced cell death. Together, these results suggest that MuRLK plays a key role in the regulation of plant cell death, early and late defense responses after the recognition of microbial pathogens.

Keywords: horsegram, Pseudomonas syringae pv. tomato, MuRLK, ROS burst, cell death, plant defense

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Authors: Nais Pinta Adetya, H. Hadiyanto

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Microalgae has a potential to be utilized as food and natural colorant. The microalgae components consists of three main parts, these are lipid, protein, and carbohydrate. Crucial step in producing lipid and protein from microalgae is extraction. Microalgae has high water level (70-90%), it causes drying process of biomass needs much more energy and also has potential to distract lipid and protein from microalgae. Extraction of lipid from wet biomass is able to take place efficiently with cell disruption of microalgae by osmotic shock method. In this study, osmotic shock method was going to be integrated with ultrasound to maximalize the extraction yield of lipid and protein from wet biomass Spirulina sp. with osmotic shock method assisted ultrasound. This study consisted of two steps, these were osmotic shock process toward wet biomass and ultrasound extraction assisted. NaCl solution was used as osmotic agent, with the variation of concentrations were 10%, 20%, and 30%. Extraction was conducted in 40°C for 20 minutes with frequency of ultrasound wave was 40kHz. The optimal yield of protein (2.7%) and (lipid 38%) were achieved at 20% osmotic agent concentration.

Keywords: extraction, lipid, osmotic shock, protein, ultrasound

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Authors: Triyanto, Rima Vien Permata Hartanto

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Indonesia is a legal state. The consequence of this status is the recognition and protection of the existence of indigenous peoples. This paper aims to describe the dynamics of legal recognition and protection for indigenous peoples within the framework of Indonesian law. This paper is library research based on literature. The result states that although the constitution has normatively recognized the existence of indigenous peoples and their traditional rights, in reality, not all rights were recognized and protected. The protection and recognition for indigenous people need to be strengthened.

Keywords: indigenous peoples, customary law, state law, state of law

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Authors: Alden Boby, Dane Brown, James Connan

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Character recognition is a well-researched topic across disciplines. Regardless, creating a solution that can cater to multiple situations is still challenging. Vehicle licence plates lack an international standard, meaning that different countries and regions have their own licence plate format. A problem that arises from this is that the typefaces and designs from different regions make it difficult to create a solution that can cater to a wide range of licence plates. The main issue concerning detection is the character recognition stage. This paper aims to create an object detection-based character recognition model trained on a custom dataset that consists of typefaces of licence plates from various regions. Given that characters have featured consistently maintained across an array of fonts, YOLO can be trained to recognise characters based on these features, which may provide better performance than OCR methods such as Tesseract OCR.

Keywords: computer vision, character recognition, licence plate recognition, object detection

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets.

Keywords: discriminative LMA features, features reduction, human motion recognition, random forest

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Authors: Jer-Yuh Liu, Je-Chiuan Ye, Jin-Ming Hwang

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Protein kinase C alpha (PKCα) is a key signaling molecule in human cancer development. As a therapeutic strategy, targeting PKCα is difficult because the molecule is ubiquitously expressed in non-malignant cells. PKCα is regulated by the cooperative interaction of the transcription factors myeloid zinc finger 1 (MZF-1) and Ets-like protein-1 (Elk-1) in human cancer cells. By conducting tissue array analysis, herein, we determined the protein expression of MZF-1/Elk-1/PKCα in various cancers. The data show that the expression of MZF-1/Elk-1 is correlated with that of PKCα in hepatocellular carcinoma (HCC), but not in bladder and lung cancers. In addition, the PKCα down-regulation by shRNA Elk-1 was only observed in the HCC SK-Hep-1 cells. Blocking the interaction between MZF-1 and Elk-1 through the transfection of their binding domain MZF-160–72 decreased PKCα expression. This step ultimately depressed the epithelial-mesenchymal transition potential of the HCC cells. These findings could be used to develop an alternative therapeutic strategy for patients with the PKCα-derived HCC.

Keywords: protein kinase C alpha, myeloid zinc finger 1, ets-like protein-1, hepatocellular carcinoma

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Authors: Andrew Lock, Alastair Allen

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Fragile watermarking has been proposed as a means of adding additional security or functionality to biometric systems, particularly for authentication and tamper detection. In this paper we describe an experimental study on the effect of watermarking iris images with a particular class of fragile algorithm, reversible algorithms, and the ability to correctly perform iris recognition. We investigate two scenarios, matching watermarked images to unmodified images, and matching watermarked images to watermarked images. We show that different watermarking schemes give very different results for a given capacity, highlighting the importance of investigation. At high embedding rates most algorithms cause significant reduction in recognition performance. However, in many cases, for low embedding rates, recognition accuracy is improved by the watermarking process.

Keywords: biometrics, iris recognition, reversible watermarking, vision engineering

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

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Authors: Hatairuk Tungkasen, Somrudee Phetchrid, Suwapat Jaidee, Supinya Yoomak, Chantana Kankamol, Mayuree Pumipaiboon, Mayuva Areekijseree

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Ovaries of reproductive female pigs were obtained from local slaughterhouses in Nakorn Pathom Province, Thailand. Follicular fluid of medium follicle (5-6 diameters) and large follicles (7-8 mm and 10 mm in diameter) were aspirated and collected by sterile technique and analyzed protein pattern. The follicular fluid protein bands were found by SDS-PAGE which has no protein band in difference compared to standard protein band. So we chose protein band molecular weight 50, 62-65, 75-80, 90, 120-160, and >220 kDa were analyzed by LC/MS/MS. The result was found immunoglobulin gamma chain, keratin, transferrin, heat shock protein, and plasminogen precursor, ceruloplasmin, and hemopexin, and protease, respectively. All proteins play important roles in promotion and regulation on growth and development of reproductive cells. The result of this study found many proteins which were useful and important for in vitro oocyte maturation and embryonic development of cell technology in animals. The further study will be use porcine follicular fluid protein of medium and large follicles as feeder cells in in vitro condition to promote oocyte and embryo maturation.

Keywords: follicular fluid protein, LC/MS/MS, porcine oocyte, SDS-PAGE

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Authors: Sarah Gaßmeyer, Nadine Hülsemann, Raphael Stoll, Kenji Miyamoto, Robert Kourist

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Enzymatic racemization allows the smooth interconversion of stereocenters under very mild reaction conditions. Racemases find frequent applications in deracemization and dynamic kinetic resolutions. Arylmalonate decarboxylase (AMDase) from Bordetella Bronchiseptica has high structural similarity to amino acid racemases. These cofactor-free racemases are able to break chemically strong CH-bonds under mild conditions. The racemase-like catalytic machinery of mutant G74C conveys it a unique activity in the racemisation of pharmacologically relevant derivates of 2-phenylpropionic acid (profenes), which makes AMDase G74C an interesting object for the mechanistic investigation of cofactor-independent racemases. Structure-guided protein engineering achieved a variant of this unique racemase with 40-fold increased activity in the racemisation of several arylaliphatic carboxylic acids. By saturation–transfer–difference NMR spectroscopy (STD-NMR), substrate binding during catalysis was investigated. All atoms of the substrate showed interactions with the enzyme. STD-NMR measurements revealed distinct nuclear Overhauser effects in experiments with and without molecular conversion. The spectroscopic analysis led to the identification of several amino acid residues whose variation increased the activity of G74C. While single-amino acid exchanges increased the activity moderately, structure-guided saturation mutagenesis yielded a quadruple mutant with a 40 times higher reaction rate. This study presents STD-NMR as versatile tool for the analysis of enzyme-substrate interactions in catalytically competent systems and for the guidance of protein engineering.

Keywords: racemase, rational protein design, STD-NMR, structure guided saturation mutagenesis

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Authors: Hanan Azzam1, Nashwa Abousamra1, Amany Mansour1, Yaser Abd El-dayem2, , Solafa Elsharawy1

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Background: Inherited thrombophilias are a heterogenous group of conditions which have been implicated in a variety of pregnancy complications. More recently, deficiency of protein Z (PZ) has been liked to pregnancy complications, including preterm delivery. Aim: We designed this study to evaluate the association of inherited thrombophilias including [Protein C (PC), Protein S (PS), Anti thrombin III (ATIII) deficiency and activated protein C (APC) resistance] and protein Z deficiency with a variety of pregnancy complications. Patients and Methods: 60 women with different pregnancy complications, including 20 patients with preeclampsia, 20 patients with intrauterine growth resistance (IUGR), and 20 patients with intrauterine fetal death (IUFD), in addition to 30 healthy pregnant women were recruited for the present study. PC and free PS antigen, ATIII activity, modified functional APC-resistance, and PZ levels were determined. Results: There was no significant association between inherited thrombophilias and complicated pregnancies as regards PC deficiency (p=1.0), AT III and PS deficiency (p=0.312), and APC-resistance (P=0.083). PZ was significantly associated with complicated pregnancies (p=0.012). Patients with protein Z levels below 1.5 µg/ml were considered deficient. Accordingly, we demonstrated protein Z deficiency in 30% of complicated pregnancies (RR 6.0, 95% CI 1.29-27.90;p=0.022), 20% of preeclampsia (RR 3.5, 95% CI 0.57 – 21.28; P = 0.174), 40% of IUGR (RR 9.3 95% CI 1.72-50.61; P = 0.010) and 30% of IUFD (RR 6, 95% CI 1.07 – 33.64; P = 0.042). Conclusions: These findings indicate the absence of association of inherited thrombophilias, including PC, PS, AT III deficiency, and APC resistance with pregnancy complications. However, PZ deficiency is associated with increased risk of pregnancy complications, especially intrauterine growth restriction and intrauterine fetal death.

Keywords: protein C, protein S, thrombophelia, pregnancy, protein Z

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

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Authors: Ali Ganoun, Wesam Algablawi, Wasim BenAnaif

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Video based traffic surveillance based on License Plate Recognition (LPR) system is an essential part for any intelligent traffic management system. The LPR system utilizes computer vision and pattern recognition technologies to obtain traffic and road information by detecting and recognizing vehicles based on their license plates. Generally, the video based LPR system is a challenging area of research due to the variety of environmental conditions. The LPR systems used in a wide range of commercial applications such as collision warning systems, finding stolen cars, controlling access to car parks and automatic congestion charge systems. This paper presents an automatic LPR system of Libyan license plate. The performance of the proposed system is evaluated with three video sequences.

Keywords: license plate recognition, localization, segmentation, recognition

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Authors: Delowar Hossain, Genci Capi

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This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping.

Keywords: deep learning, genetic algorithm, object recognition, robot grasping

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Authors: Fatima Akhmedova, Simon Liao

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One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work, we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, non-redundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.

Keywords: face recognition, Hahn moments, recognition-by-parts, time-lapse

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Diana Karina Baigts Allende, Mariana Gonzalez Diaz, Luis Antonio Chel Guerrero, Mukthar Sandoval Peraza

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Poverty and food insecurity are still incident problems in the developing countries, where population´s diet is based on cereals which are lack in protein content. Nevertheless, during last years the use of native plants has been studied as an alternative source of protein in order to improve the nutritional intake. Chaya crop also called Spinach tree, is a prehispanic plant native from Central America and South of Mexico (Mayan culture), which has been especially valued due to its high nutritional content particularly protein and some medicinal properties. The aim of this work was to study the effect of protein isolation processing from Chaya leaf harvest in Yucatan, Mexico on its structure quality in order: i) to valorize the Chaya crop and ii) to produce low-cost and high-quality protein. Chaya leaf was extruded, clarified and recovered using: a) acid precipitation by decreasing the pH value until reach the isoelectric point (3.5) and b) thermal coagulation, by heating the protein solution at 80 °C during 30 min. Solubilized protein was re-dissolved in water and spray dried. The presence of Fraction I protein, known as RuBisCO (Rubilose-1,5-biphosfate carboxylase/oxygenase) was confirmed by gel electrophoresis (SDS-PAGE) where molecular weight bands of 55 KDa and 12 KDa were observed. The infrared spectrum showed changes in protein structure due to the isolation method. The use of high temperatures (thermal coagulation) highly decreased protein solubility in comparison to isoelectric precipitated protein, the nutritional properties according to amino acid profile was also disturbed, showing minor amounts of overall essential amino acids from 435.9 to 367.8 mg/g. Chaya protein isolate obtained by acid precipitation showed higher protein quality according to essential amino acid score compared to FAO recommendations, which could represent an important sustainable source of protein for human consumption.

Keywords: chaya leaf, nutritional properties, protein isolate, protein structure

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Authors: Xingyu Pan, Laure Tougne

Abstract:

For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: coin, detection, character recognition, topology

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

Abstract:

Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Nijole Savickiene, Antonella Di Sotto, Gabriela Mazzanti, Rasa Starselskyte, Silvia Di Giacomo, Annabella Vitalone

Abstract:

Plant lectins are non-enzymic and non-immune origin proteins that specifically recognize and bind to various sugar structures and possess the activity to agglutinate cells and/or precipitate polysaccharides and glycoconjugates. The emerging evidences showed that plant lectins contribute not only to tumour cell recognition but also to cell adhesion and localization, to signal transduction, to mitogenic cytotoxicity and apoptosis. Among chitin-binding lectins, the Urtica dioica agglutinin (UDA), which is a complex of different isoforms, has been poorly studied for its biological activity. In this context and according to the increasing interest for lectins as novel antitumor drugs, present paper aimed at evaluating the potential antimutagenic activity of a lectin-like glycoprotein-enriched fraction from aerial part of Urtica dioica L. Aim: to evaluate the potential chemopreventive properties of a protein - lectin fraction from the aerial part of Urtica dioica. Materials and methods: Protein – lectin fraction has been tested for the antimutagenic activity in bacteria (50–800 mg/plate; Ames test by the preincubation method) and for the cytotoxicity on human hepatoma HepG2 cells (0.06–2 mg/mL; 24 and 48 h incubation). Results: Protein – lectin fraction from stinging nettle was not cytotoxic on HepG2 cells up to 2 mg/mL; conversely, it exhibited a strong antimutagenic activity against the mutagen 2-aminoanthracene (2AA) in all strains tested (maximum inhibition of 56.78 and 61% in TA98, TA100, and WP2uvrA strains, respectively, at 800 mg/plate). Discussion and conclusions: Protein – lectin fraction from Urtica dioica L. possesses antimutagenic and radical scavenging properties. Being 2AA a pro-carcinogenic agent, we hypothesize that the antimutagenicity of it can be due to the inhibition of CYP450-isoenzymes, involved in the mutagen bioactivation.

Keywords: lectins, antimutagenicity, chemoprevention, Urtica dioica

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Authors: Ayoola, Simeon Oluwatoyin, Omogoriola Hannah Omoloye

Abstract:

The effects of 17α-Methyltestosterone Hormone on blood composition of the Sex Reversed Sarotherodon melanotheron were investigated. S. melanotheron fry were reared in six (6) plastic tanks for three (3) months, of which three (3) tanks served as treatment tanks while the other three (3) served as the control. The fry were fed with 17α-methyl testosterone enzyme, which functions as a sex reversal hormone. The fry were administered this hormone for 30 days, to ensure complete sex reversal. All the S. melanotheron fry were reared to table size for duration of three (3) months, after which, blood samples were taken from both the control and treatment fishes. The blood parameters showed no significant differences with the same values of White Blood Cell count (WBC) and Total plasma protein for the control and experimental fishes. A total protein value for sex reversed specimens was 3.99g/dL, while urea and creatinine values were 0.2g/dL. Alkaline Phosphatase, Aspartate transaminase and Alanine transaminase for the treatment specimen were 183nm/mg protein/min, 98nm/mg protein/min and 105nm/mg protein/min respectively. A total protein value for control specimens was 2.81g/dL, while urea and creatinine values were 0.2g/dL. Alkaline Phosphatase, Aspartate transaminase and Alanine transaminase for the control species were 174nm/mg protein/min, 93nm/mg protein/min and 106nm/mg protein/min respectively. The safety of MT on S. melanotheron is therefore proved since there is no adverse effect on the fish.

Keywords: 17α-Methyltestosterone, haematology, sex reversal, sarotherodon melanotheron

Procedia PDF Downloads 466