Search results for: protein stability prediction

Authors: Jineetkumar Gawad, Chandrakant Bonde

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Tuberculosis (TB) is a major worldwide concern whose control has been exacerbated by HIV, the rise of multidrug-resistance (MDR-TB) and extensively drug resistance (XDR-TB) strains of Mycobacterium tuberculosis. The interest for newer and faster acting antitubercular drugs are more remarkable than any time. To search potent compounds is need and challenge for researchers. Here, we tried to design lead for inhibition of Decaprenyl phosphoryl-β-D-ribose 2′-epimerase (DprE1) enzyme. Arabinose is an essential constituent of mycobacterial cell wall. DprE1 is a flavoenzyme that converts decaprenylphosphoryl-D-ribose into decaprenylphosphoryl-2-keto-ribose, which is intermediate in biosynthetic pathway of arabinose. Latter, DprE2 converts keto-ribose into decaprenylphosphoryl-D-arabinose. We had a selection of 23 compounds from azaindole series for computational study, and they were drawn using marvisketch. Ligands were prepared using Maestro molecular modeling interface, Schrodinger, v10.5. Common pharmacophore hypotheses were developed by applying dataset thresholds to yield active and inactive set of compounds. There were 326 hypotheses were developed. On the basis of survival score, ADRRR (Survival Score: 5.453) was selected. Selected pharmacophore hypotheses were subjected to virtual screening results into 1000 hits. Hits were prepared and docked with protein 4KW5 (oxydoreductase inhibitor) was downloaded in .pdb format from RCSB Protein Data Bank. Protein was prepared using protein preparation wizard. Protein was preprocessed, the workspace was analyzed using force field OPLS 2005. Glide grid was generated by picking single atom in molecule. Prepared ligands were docked with prepared protein 4KW5 using Glide docking. After docking, on the basis of glide score top-five compounds were selected, (5223, 5812, 0661, 0662, and 2945) and the glide docking score (-8.928, -8.534, -8.412, -8.411, -8.351) respectively. There were interactions of ligand and protein, specifically HIS 132, LYS 418, TRY 230, ASN 385. Pi-pi stacking was observed in few compounds with basic Imidazo [4,5-b] pyridine ring. We had basic azaindole ring in parent compounds, but after glide docking, we received compounds with Imidazo [4,5-b] pyridine as a basic ring. That might be the new lead in the process of drug discovery.

Keywords: DprE1 inhibitors, in silico drug designing, imidazo [4, 5-b] pyridine, lead, tuberculosis

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Authors: M. R. Jokanović, V. M. Tomović, M. T. Jović, S. B. Škaljac, B. V. Šojić, P. M. Ikonić, T. A. Tasić

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Proximate (moisture, protein, total fat, total ash) and mineral (K, P, Na, Mg, Ca, Zn, Fe, Cu and Mn) composition of chicken giblets (heart, liver and gizzard) were investigated. Phosphorous content, as well as proximate composition, were determined according to recommended ISO methods. The content of all elements, except phosphorus, of the giblets tissues were determined using inductively coupled plasma-optical emission spectrometry (ICP-OES), after dry ashing mineralization. Regarding proximate composition heart was the highest in total fat content, and the lowest in protein content. Liver was the highest in protein and total ash content, while gizzard was the highest in moisture and the lowest in total fat content. Regarding mineral composition liver was the highest for K, P, Ca, Mg, Fe, Zn, Cu, and Mn, while heart was the highest for Na content. The contents of almost all investigated minerals in analysed giblets tissues of chickens from Vojvodina were similar to values reported in the literature, i.e. in national food composition databases of other countries.

Keywords: chicken giblets, proximate composition, mineral composition, inductively coupled plasma-optical emission spectrometry (ICP-OES)

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Authors: C. E. Madubueze, S. C. Madubueze, S. Ajama

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Cholera is a disease that is predominately common in developing countries due to poor sanitation and overcrowding population. In this paper, a deterministic model for the dynamics of cholera is developed and control measures such as health educational message, therapeutic treatment, and vaccination are incorporated in the model. The effective reproduction number is computed in terms of the model parameters. The existence and stability of the equilibrium states, disease free and endemic equilibrium states are established and showed to be locally and globally asymptotically stable when R0 < 1 and R0 > 1 respectively. The existence of backward bifurcation of the model is investigated. Furthermore, numerical simulation of the model developed is carried out to show the impact of the control measures and the result indicates that combined control measures will help to reduce the spread of cholera in the population

Keywords: backward bifurcation, cholera, equilibrium, dynamics, stability

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Authors: Hossein Rassi, Marjan Moradi Fard, Samaneh Niko

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Human papillomavirus type 16 and 18 are closely associated with the development of human cervical carcinoma, which is one of the most common causes of cancer death in women worldwide. At present, HPV type 18 accounts for about 34 % of all HPV infections in Iran and the most promising vaccine against HPV infection is based on the L1 major capsid protein. The L1 protein of HPV18 has the capacity to self-assemble into capsomers or virus-like particles (VLPs) that are non-infectious, highly immunogenic and allowing their use in vaccine production. The methylotrophic yeast Pichia pastoris is an efficient and inexpensive expression system used to produce high levels of heterologous proteins. In this study we expressed HPV18 L1 VLPs in P. pastoris. The gene encoding the major capsid protein L1 of the high-risk HPV type 18 was isolated from Iranian patient by PCR and inserted into pTG19-T vector to obtain the recombinant expression vector pTG19-HPV18-L1. Then, the pTG19-HPV18-L1 was transformed into E. coli strain DH5α and the recombinant protein HPV18 L1 was expressed under IPTG induction in soluble form. The HPV18 L1 gene was excised from recombinant plasmid with XhoI and EcoRI enzymes and ligated into the yeast expression vector pPICZα linearized with the same enzymes, and transformed into P. pastoris. Induction and expression of HPV18 L1 protein was demonstrated by BMGY/BMMY and RT PCR. The parameters for induced cultivation for strain in P. pastoris KM71 with HPV16L1 were investigated in shaking flask cultures. After induced cultivation BMMY (pH 7.0) medium supplemented with methanol to a final concentration of 1.0% every 24 h at 37 degrees C for 96 h, the recombinant produced 78.6 mg/L of L1 protein. This work offers the possibility for the production of prophylactic vaccine for cervical carcinoma by P. pastoris for HPV-18 L1 gene. The VLP-based HPV vaccines can prevent persistent HPV18 infections and cervical cancer in Iran. The HPV-18 L1 gene was expressed successfully in E.coli, which provides necessary basis for preparing HPV-18 L1 vaccine in human. Also, HPV type 6 L1 proteins expressed in Pichia pastoris will facilitate the HPV vaccine development and structure-function study.

Keywords: Pichia pastoris, L1 virus-like particles, human papillomavirus type 18, biotechnology

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Jesus Hernandez, Zead Elzoeiry, Md. S. Islam, Abel E. Navarro

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The adsorption of bovine serum albumin (BSA) and human hemoglobin (Hb) on naturally-occurring adsorbents was studied to evaluate the potential recovery of proteins from meat industry residues. Spent peppermint tea (PM), powdered purple corn cob (PC), natural clay (NC) and chemically-modified clay (MC) were investigated to elucidate the effects of pH, adsorbent dose, initial protein concentration, presence of salts and heavy metals. Equilibrium data were fitted according to isotherm models, reporting a maximum adsorption capacity at pH 8 of 318 and 344 mg BSA/g of PM and NC, respectively. Moreover, Hb displayed maximum adsorption capacity at pH 5 of 125 and 143 mg/g of PM and PC, respectively. Hofmeister salt effect was only observed for PM/Hb system. Salts tend to decrease protein adsorption, and the presence of Cu(II) ions had negligible impacts on the adsorption onto NC and PC. Desorption experiments confirmed that more than 85% of both proteins can be recovered with diluted acids and bases. SEM, EDX, and TGA analyses demonstrated that the adsorbents have favorable morphological and mechanical properties. The long-term goal of this study aims to recover soluble proteins from industrial wastewaters to produce animal food or any protein-based product.

Keywords: adsorption, albumin, clay, hemoglobin, spent peppermint leaf

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Authors: Nurudeen Oluwasola Lasisi, Abdulkareem Afolabi Ibrahim

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Newcastle disease is an infection of domestic poultry and other bird species with virulent Newcastle disease virus (NDV). In this paper, we study the dynamics of modeling the Newcastle disease virus (NDV) using a novel quarantine-adjusted incidence. We do a comparison of Vaccination, linear incident rate, and novel quarantine adjusted incident rate in the models. The dynamics of the models yield disease free and endemic equilibrium states. The effective reproduction numbers of the models are computed in order to measure the relative impact for the individual bird or combined intervention for effective disease control. We showed the local and global stability of endemic equilibrium states of the models, and we found that stability of endemic equilibrium states of models are globally asymptotically stable if the effective reproduction numbers of the models equations are greater than a unit.

Keywords: effective reproduction number, endemic state, mathematical model, Newcastle disease virus, novel quarantine-adjusted incidence, stability analysis

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Authors: Sanchir Erdenebayar, Namuuntsetseg Oyunbaatar

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Objectives: Peritoneal dialysis treatment is one of the important forms of kidney replacement therapy, and it has recently developed instantly in Mongolia for the past five years. Currently, more than 120 patients undergo peritoneal dialysis nationwide. These patients lack nutritional education, which predisposes them to protein deficiency and further impairs their quality of life. However, there is no study which is conducted among those about their dietary in Mongolia. Therefore, integrated nutrition information and educating them about dietary patterns to follow are urgently needed for PD patients. Methods: A cross-sectional study was carried out on 45 patients aged between 18 and 60 years who were undergoing CAPD at the biggest Medvic dialysis center in Ulaanbaatar. The knowledge of nutrition and food intake is assessed by interview based on a validated questionnaire prepared from KDIGO guidelines, semi-FFQ and a 24-hour dietary recall method. In addition, a biochemical blood test that includes total protein, albumin, calcium, phosphorus, potassium, and hemoglobin is used for an assessment of the patient’s current nutritional status. Results: Knowledge of nutritional status for CAPD was great, with 21.4% of patients and 78.65% having poor nutrition knowledge. The rate of mild to moderate malnutrition was 48.8% among research participants. Serum albumin was 38.4 ± 4.7 g/L, and total protein was 67.3±7.5g/l. Patients met 62.5± 26.5% of their daily intake nutritional requirement for calories and 72±40% of their nutritional requirement for protein. All patients’ energy intake was significantly /1328±304kcal/ lower than the energy requirement (2124±378kcal). Only 14.2% met the recommended dietary protein intake recommended to them of greater than 1.2 g/kg. Conclusions: As was established before, nutritional education has a vital positive impact on the health and nutritional status of peritoneal dialysis patients. The results of this study show that nutritional education programs are not enough adequate in peritoneal dialysis patients. There is a crucial priority to establish nutritional educational programs and guidelines for PD patients in Mongolia.

Keywords: renal diet, peritoneal dialysis, nutrition education, CKD diet

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Authors: Abubakar Sadiq Mensah

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The Knowledge of the population growth of a nation is paramount to national planning. The population of a place is studied and a model developed over a period of time, Matrices is used to form model for population growth. The eigenvalue ƛ of the matrix A and its corresponding eigenvector X is such that AX = ƛX is calculated. The stable age distribution of the population is obtained using the eigenvalue and the characteristic polynomial. Hence, estimation could be made using eigenvalues and eigenvectors.

Keywords: eigenvalues, eigenvectors, population, growth/stability

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Authors: Nuri Başpınar, Abdullah Başoğlu, Özgür Özdemir, Çağlayan Özel, FundaTerzi, Özgür Yaman

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Current research is targeting new molecular mechanisms that underlie non-alcoholic fatty liver disease (NAFLD) and associated metabolic disorders like nonalcoholic steatohepatitis (NASH). Forty New Zealand White rabbits have been used and fed a high protein (HP) and energy diet based on grains and containing 11.76 MJ/kg. Boron added to 3 experimental groups’ drinking waters (30 mg boron/L) as boron compounds. Biochemical analysis including boron levels, and nuclear magnetic resonance (NMR) based metabolomics evaluation, and mRNA expression of peroxisome proliferator-activated receptor (PPAR) family were performed. LDL-cholesterol concentrations alone were decreased in all the experimental groups. Boron levels in serum and feces were increased. Content of acetate was in about 2x higher for anhydrous borax group, at least 3x higher for boric acid group. PPARα mRNA expression was significantly decreased in boric acid group. Anhydrous borax attenuated mRNA levels of PPARα, which was further suppressed by boric acid. Boron supplementation decreased the degenerative alterations in hepatocytes. Except borax group other boron groups did not have a pronounced change in tubular epithels of kidney. In conclusion, high protein and energy diet leads hepatocytes’ degenerative changes which can be prevented by boron supplementation. Boric acid seems to precede in this effectiveness.

Keywords: high protein and energy diet, boron, metabolomics, transcriptomic

Procedia PDF Downloads 627

Authors: Maneesh Kumar, Roshan Kamal Topno, Manas Ranjan Dikhit, Vahab Ali, Ganesh Chandra Sahoo, Bhawana, Major Madhukar, Rishikesh Kumar, Krishna Pandey, Pradeep Das

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Chandipura vesiculovirus is an emerging (-) ssRNA viral entity belonging to the genus Vesiculovirus of the family Rhabdoviridae, associated with fatal encephalitis in tropical regions. The multi-functionally active viral RNA-dependent RNA polymerase (vRdRp) that has been incorporated with conserved amino acid residues in the pathogens, assigned to synthesize distinct viral polypeptides. The lack of proofreading ability of the vRdRp produces many mutated variants. Here, we have performed the evolutionary analysis of 20 viral protein sequences of vRdRp of different strains of Chandipura vesiculovirus along with other viral species from genus Vesiculovirus inferred in MEGA6.06, employing the Neighbour-Joining method. The p-distance algorithmic method has been used to calculate the optimum tree which showed the sum of branch length of about 1.436. The percentage of replicate trees in which the associated taxa are clustered together in the bootstrap test (1000 replicates), is shown next to the branches. No mutation was observed in the Indian strains of Chandipura vesiculovirus. In vRdRp, 1230(His) and 1231(Arg) are actively participated in catalysis and, are found conserved in different strains of Chandipura vesiculovirus. Both amino acid residues were also conserved in the other viral species from genus Vesiculovirus. Many isolates exhibited maximum number of mutations in catalytic regions in strains of Chandipura vesiculovirus at position 26(Ser→Ala), 47 (Ser→Ala), 90(Ser→Tyr), 172(Gly→Ile, Val), 172(Ser→Tyr), 387(Asn→Ser), 1301(Thr→Ala), 1330(Ala→Glu), 2015(Phe→Ser) and 2065(Thr→Val) which make them variants under different tropical conditions from where they evolved. The result clarifies the actual concept of RNA evolution using vRdRp to develop as an evolutionary marker. Although, a limited number of vRdRp protein sequence similarities for Chandipura vesiculovirus and other species. This might endow with possibilities to identify the virulence level during viral multiplication in a host.

Keywords: Chandipura, (-) ssRNA, viral RNA-dependent RNA polymerase, neighbour-joining method, p-distance algorithmic, evolutionary marker

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: O. D. A. N. Perera, H. G. Wanigasinghe

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Tender jackfruit is an underutilized biomass, which still has a good consumer demand. Valorization of this ingredient into meat analog would obtain greater consumer acceptance due to concerns about health, the environment, and living sustainably of mankind have increased significantly in this decade, opening the market for meat substitutes. The objective of this research was to create a plant-based meat substitute with a structure similar to meat products. In this study, three different combinations of tender jackfruit were used to create vegan burger patties, which were then examined for their textural, physico-chemical, and sensory qualities. The developed burger patties have been compared with store-bought chicken patties. The developed vegan burger patties P1, P2, and P3 had a comparable flavor preference to the control and demonstrated considerable general acceptability (p >.05). P3 has a high quantity of protein (17.10 ± 0.02%) and fiber (6.40 ± 0.06%). At the same time, the vegan burger patty resulted in less fat, high fiber, and high protein which meets the vegan consumer requirements.

Keywords: underutilized, high fibre, soya protein isolate, cooking yield

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Authors: K. Jarmkom, P. Eakwaropas, W. Khobjai, S. Techaeoi

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Ancient Thai perfumed powder was used as a fragrance for clothing, food, and the body. Plant-based natural Thai perfume products are known as Pang-Rum. The objective of this study was to evaluate the stability of essential oils after six months of incubation. The chemical compositions were determined by gas chromatography-mass spectrometry (GC-MS), in terms of the qualitative composition of the isolated essential oil. The isolation of the essential oil of natural products by incubate sample for 5 min at 40 ºC is described. The volatile components were identified by percentage of total peak areas comparing their retention times of GC chromatograph with NIST mass spectral library. The results show no significant difference in the seven chromatograms of perfumed powder (Pang-Rum) both with binder and without binder. Further identification was done by GC-MS. Some components of Pang-Rum with/without binder were changed by temperature and time.

Keywords: GC-MS analysis, essential oils, stability, Pang-Rum

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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: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

Procedia PDF Downloads 271

Authors: Maysam Mard-Soltani, Mohamad Javad Rasaee, Saeed Khalili, Abdol Karim Sheikhi, Mehdi Hedayati

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Production of specific antibody responses against hTSH is a cumbersome process due to the high identity between the hTSH and the other members of the glycoprotein hormone family (FSH, LH and HCG) and the high identity between the human hTSH and host animals for antibody production. Therefore, two polyclonal antibodies were purified against two recombinant proteins. Four possible ELISA tests were designed based on these antibodies. These ELISA tests were checked against hTSH and other glycoprotein hormones, and their sensitivity and specificity were assessed. Bioinformatics tools were used to analyze the immunological properties. After the immunogen region selection from hTSH protein, c terminal of B hTSH was selected and applied. Two recombinant genes, with these cut pieces (first: two repeats of C terminal of B hTSH, second: tetanous toxin+B hTSH C terminal), were designed and sub-cloned into the pET32a expression vector. Standard methods were used for protein expression, purification, and verification. Thereafter, immunizations of the white New Zealand rabbits were performed and the serums of them were used for antibody titration, purification and characterization. Then, four ELISA tests based on two antibodies were employed to assess the hTSH and other glycoprotein hormones. The results of these assessments were compared with standard amounts. The obtained results indicated that the desired antigens were successfully designed, sub-cloned, expressed, confirmed and used for in vivo immunization. The raised antibodies were capable of specific and sensitive hTSH detection, while the cross reactivity with the other members of the glycoprotein hormone family was minimum. Among the four designed tests, the test in which the antibody against first protein was used as capture antibody, and the antibody against second protein was used as detector antibody did not show any hook effect up to 50 miu/l. Both proteins have the ability to induce highly sensitive and specific antibody responses against the hTSH. One of the antibody combinations of these antibodies has the highest sensitivity and specificity in hTSH detection.

Keywords: hTSH, bioinformatics, protein expression, cross reactivity

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Authors: Leila Safazadeh, Brad Berron

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Self-assembled monolayers have tremendous impact in interfacial science, due to the unique opportunity they offer to tailor surface properties. Low-density self-assembled monolayers are an emerging class of monolayers where the environment-interfacing portion of the adsorbate has a greater level of conformational freedom when compared to traditional monolayer chemistries. This greater range of motion and increased spacing between surface-bound molecules offers new opportunities in tailoring adsorption phenomena in sensing systems. In particular, we expect low-density surfaces to offer a unique opportunity to intercalate surface bound ligands into the secondary structure of protiens and other macromolecules. Additionally, as many conventional sensing surfaces are built upon gold surfaces (SPR or QCM), these surfaces must be compatible with gold substrates. Here, we present the first stable method of generating low-density self assembled monolayer surfaces on gold for the analysis of their interactions with protein targets. Our approach is based on the 2:1 addition of thiol-yne chemistry to develop new classes of y-shaped adsorbates on gold, where the environment-interfacing group is spaced laterally from neighboring chemical groups. This technique involves an initial deposition of a crystalline monolayer of 1,10 decanedithiol on the gold substrate, followed by grafting of a low-packed monolayer on through a photoinitiated thiol-yne reaction in presence of light. Orthogonality of the thiol-yne chemistry (commonly referred to as a click chemistry) allows for preparation of low-density monolayers with variety of functional groups. To date, carboxyl, amine, alcohol, and alkyl terminated monolayers have been prepared using this core technology. Results from surface characterization techniques such as FTIR, contact angle goniometry and electrochemical impedance spectroscopy confirm the proposed low chain-chain interactions of the environment interfacing groups. Reductive desorption measurements suggest a higher stability for the click-LDMs compared to traditional SAMs, along with the equivalent packing density at the substrate interface, which confirms the proposed stability of the monolayer-gold interface. In addition, contact angle measurements change in the presence of an applied potential, supporting our description of a surface structure which allows the alkyl chains to freely orient themselves in response to different environments. We are studying the differences in protein adsorption phenomena between well packed and our loosely packed surfaces, and we expect this data will be ready to present at the GRC meeting. This work aims to contribute biotechnology science in the following manner: Molecularly imprinted polymers are a promising recognition mode with several advantages over natural antibodies in the recognition of small molecules. However, because of their bulk polymer structure, they are poorly suited for the rapid diffusion desired for recognition of proteins and other macromolecules. Molecularly imprinted monolayers are an emerging class of materials where the surface is imprinted, and there is not a bulk material to impede mass transfer. Further, the short distance between the binding site and the signal transduction material improves many modes of detection. My dissertation project is to develop a new chemistry for protein-imprinted self-assembled monolayers on gold, for incorporation into SPR sensors. Our unique contribution is the spatial imprinting of not only physical cues (seen in current imprinted monolayer techniques), but to also incorporate complementary chemical cues. This is accomplished through a photo-click grafting of preassembled ligands around a protein template. This conference is important for my development as a graduate student to broaden my appreciation of the sensor development beyond surface chemistry.

Keywords: low-density self-assembled monolayers, thiol-yne click reaction, molecular imprinting

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Farramae Francisco, Rhoda Mae Simora, Sharon Nunal

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Deproteination and demineralization efficiencies of shrimp waste using two Lactobacillus species treated with different carbohydrate sources for chitin production, its chemical conversion to chitosan and the quality of chitin and chitosan produced were determined. Using 5% glucose and 5% cassava starch as carbohydrate sources, pH slightly increased from the initial pH of 6.0 to 6.8 and 7.2, respectively after 24 h and maintained their pH at 6.7 to 7.3 throughout the treatment period. Demineralization (%) in 5 % glucose and 5 % cassava was highest during the first day of treatment which was 82% and 83%, respectively. Deproteination (%) was highest in 5% cassava starch on the 3rd day of treatment at 84.4%. The obtained chitin from 5% cassava and 5% glucose had a residual ash and protein below 1% and solubility of 59% and 44.3%, respectively. Chitosan produced from 5% cassava and 5% glucose had protein content below 0.05%; residual ash was 1.1% and 0.8%, respectively. Chitosan solubility and degree of deacetylation were 56% and 33% in 5% glucose and 48% and 29% in 5% cassava, respectively. The advantage this alternative technology offers over that of chemical extraction is large reduction in chemicals needed thus less effluent production and generation of a protein-rich liquor, although the demineralization process should be improved to achieve greater degree of deacetylation.

Keywords: alternative carbon source, bioprocessing, lactic acid bacteria, waste utilization

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Authors: Bahadir Batar, Elif Serdal, Berna Erdal, Hasan Ogul

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Micro-ribonucleic acids (miRNAs) are small short non-coding ribonucleic acid molecules about 22 nucleotides long. miRNAs play a key role in response to chemotherapeutic agents. WW domain-containing oxidoreductase (WWOX) gene encodes a tumor suppressor protein. Loss or reduction of Wwox protein is observed in many breast cancer cases. WWOX protein deficiency is increased in triple-negative breast cancer (TNBC). TNBC is a heterogeneous, highly aggressive, and difficult to treat tumor type. WWOX loss contributes to resistance to cisplatin therapy in patients with TNBC. Here, the aim of the study was to investigate the potential role of miRNAs in cisplatin therapy resistance of WWOX-deficient TNBC cells. This was a cell culture study. miRNA expression profiling was analyzed by LightCycler 480 system. miRNA Set Enrichment Analysis tool was used to integrate experimental data with literature-based biological knowledge to infer a new hypothesis. Increased miR-182 and decreased miR-214 were significantly correlated with cisplatin resistance in WWOX-deficient TNBC cells. miR-182 and miR-214 may involve in cisplatin resistance of WWOX-deficient TNBC cells by deregulating the DNA repair, apoptosis, or protein kinase B signaling pathways. These data highlight the mechanism by which WWOX regulates cisplatin resistance of TNBC and the potential use of WWOX as a predictor biomarker for cisplatin resistance.

Keywords: cisplatin, microRNA, triple-negative breast cancer, WWOX

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Authors: Jin Boo Jeong

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In this study, we elucidated anti-cancer activity and potential molecular mechanism of DKC against human colorectal cancer cells. DKC-E70 suppressed the proliferation of human colorectal cancer cell lines such as HCT116, SW480, LoVo and HT-29. Although DKC-E70 decreased cyclin D1 expression in protein and mRNA level, decreased level of cyclin D1 protein by DKC-E70 occurred at the earlier time than that of cyclin D1 mRNA, which indicates that DKC-E70-mediated downregulation of cyclin D1 protein may be a consequence of the induction of degradation and transcriptional inhibition of cyclin D1. In cyclin D1 degradation, we found that cyclin D1 downregulation by DKC-E70 was attenuated in presence of MG132. In addition, DKC-E70 phosphorylated threonine-286 (T286) of cyclin D1 and T286A abolished cyclin D1 downregulation by DKC-E70. We also observed that DKC-E70-mediated T286 phosphorylation and subsequent cyclin D1 degradation was blocked in presence of the inhibitors of ERK1/2, p38 or GSK3β. In cyclin D1 transcriptional inhibition, DKC-E70 inhibited the expression of β-catenin and TCF4, and β–catenin/TCF-dependent luciferase activity. Our results suggest that DKC-E70 may downregulate cyclin D1 as one of the potential anti-cancer targets through cyclin D1 degradation by T286 phosphorylation dependent on ERK1/2, p38 or GSK3β, and cyclin D1 transcriptional inhibition through Wnt signaling. From these findings, DKC-E70 has potential to be a candidate for the development of chemoprevention or therapeutic agents for human colorectal cancer. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A3B03931713).

Keywords: anticancer, calyx of persimmon, cyclin D1, Diospyros kaki Thunb., human colorectal cancer

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Authors: Cristian Păuna

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After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Jong-Min Park, Hyung-Ki Park, Seok Hong Min, Tae Kwon Ha

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Titanium is widely used due to its high specific strength, good biocompatibility, and excellent corrosion resistance. In order to produce titanium powders, it is necessary to melt titanium, and generally it is conducted by an induction heating method using Al₂O₃ ceramic crucible. However, since titanium reacts chemically with Al₂O₃, it is difficult to melt titanium by the induction heating method using Al₂O₃ crucible. To avoid this problem, we studied the chemical stability of the various crucibles such as Al₂O₃, MgO, ZrO₂, and Y₂O₃ crucibles to molten titanium. After titanium lumps (Grade 2, O(oxygen)<0.25wt%) were placed in each crucible, they were heated to 1800℃ with a heating rate of 5 ℃/min, held at 1800℃ for 30 min, and finally cooled to room temperature with a cooling rate of 5 ℃/min. All heat treatments were carried out in high purity Ar atmosphere. To evaluate the chemical stability, thermodynamic data such as Ellingham diagram were utilized, and also Vickers hardness test, microstructure analysis, and EPMA quantitative analysis were performed. As a result, Al₂O₃, MgO and ZrO₂ crucibles chemically reacted with molten titanium, but Y₂O₃ crucible rarely reacted with it.

Keywords: titanium, induction melting, crucible, chemical stability

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Authors: S. Zidani, A. Benakmoum, A. Mansouri, A. Ammouche

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In this research paper, we studied the oxidative stability of table margarine and soya/sunflower oils rich in lycopene with tomato peel powder (TPP). For this 1%, 2%, and 3% (w/w) of TPP was added to oil used in margarine manufacture. Chromatic characteristics of margarine and soya/sunflower oil have been studied using 'Tristimulus Colorimetry' method. The main point of the research was to determine the antioxidant activity and the oxidative resistance of soya/sunflower and margarine with TPP (peroxide index, TBA index, and rancimat test). The sensory and textural properties, overall acceptability of margarine and oil were good, indicating that TPP could be added to oil to produce a margarine enriched in lycopene with excellent stability oxidative.

Keywords: tomato peel powder, lycopene, table margarine, soya/sunflower oils, antioxidant activity, stability oxidative

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Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

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This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

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Authors: Imad Eddine Charif, Wafaa Benchouk, Sidi Mohamed Mekelleche

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The regioselectivity of a series of 16 1,3-dipolar cycloaddition reactions of nitrile oxides with 2(5H)-furanones has been analysed by means of global and local electrophilic and nucleophilic reactivity indices using density functional theory at the B3LYP level together with the 6-31G(d) basis set. The local electrophilicity and nucleophilicity indices, based on Fukui and Parr functions, have been calculated for the terminal sites, namely the C1 and O3 atoms of the 1,3-dipole and the C4 and C5 atoms of the dipolarophile. These local indices were calculated using both Mulliken and natural charges and spin densities. The results obtained show that the C5 atom of the 2(5H)-furanones is the most electrophilic site whereas the O3 atom of the nitrile oxides is the most nucleophilic centre. It turns out that the experimental regioselectivity is correctly reproduced, indicating that both Fukui- and Parr-based indices are efficient tools for the prediction of the regiochemistry of the studied reactions and could be used for the prediction of newly designed reactions of the same kind.

Keywords: 1, 3-dipolar cycloaddition, density functional theory, nitrile oxides, regioselectivity, reactivity indices

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Authors: Ekta, G. K. Darbha

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Nanotechnology offers the potential of simultaneously increasing efficiency as compared to their bulk material as well as reducing harmful environmental impacts of pesticides in field of agriculture. The term nanopesticide covers different pesticides that are cumulative of several surfactants, polymers, metal ions, etc. of nanometer size ranges from 1-1000 nm and exhibit abnormal behavior (high efficacy and high specific surface area) of nanomaterials. Commercial formulations of pesticides used by farmers nowadays cannot be used effectively due to a number of problems associated with them. For example, more than 90% of applied formulations are either lost in the environment or unable to reach the target area required for effective pest control. Around 20−30% of pesticides are lost through emissions. A number of factors (application methods, physicochemical properties of the formulations, and environmental conditions) can influence the extent of loss during application. It is known that among various formulations, polymer-based formulations show the greatest potential due to their greater efficacy, slow release and protection against premature degradation of active ingredient as compared to other commercial formulations. However, the nanoformulations can have a significant effect on the fate of active ingredient as well as may release some new ingredients by reacting with existing soil contaminants. Environmental fate of these newly generated species is still not explored very well which is essential to field scale experiments and hence a lot to be explored in the field of environmental fate, nanotoxicology, transport properties and stability of such formulations. In our preliminary work, we have synthesized polymer based nanoformulation of commercially used weedicide atrazine. Atrazine belongs to triazine class of herbicide, which is used in the effective control of seed germinated dicot weeds and grasses. It functions by binding to the plastoquinone-binding protein in PS-II. Plant death results from starvation and oxidative damage caused by breakdown in electron transport system. The stability of the suspension of nanoformulation containing herbicide has been evaluated by considering different parameters like polydispersity index, particle diameter, zeta-potential under different environmental relevance condition such as pH range 4-10, temperature range from 25°C to 65°C and stability of encapsulation also have been studied for different amount of added polymer. Morphological characterization has been done by using SEM.

Keywords: atrazine, nanoformulation, nanopesticide, nanotoxicology

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Authors: Hasan Keshavarzian, Tayebeh Nesari

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Bolted rail joint is one of the most vulnerable areas in railway track. A comprehensive approach was developed for studying the reliability of fatigue crack initiation of railroad bolt hole under random axle loads and random material properties. The operation condition was also considered as stochastic variables. In order to obtain the comprehensive probability model of fatigue crack initiation life prediction in railroad bolt hole, we used FEM, response surface method (RSM), and reliability analysis. Combined energy-density based and critical plane based fatigue concept is used for the fatigue crack prediction. The dynamic loads were calculated according to the axle load, speed, and track properties. The results show that axle load is most sensitive parameter compared to Poisson’s ratio in fatigue crack initiation life. Also, the reliability index decreases slowly due to high cycle fatigue regime in this area.

Keywords: rail-wheel tribology, rolling contact mechanic, finite element modeling, reliability analysis

Procedia PDF Downloads 381