Search results for: protein prediction

Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Ahmed S. Eissa

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Whey represents about 85-95% of the milk volume and about 55% of milk nutrients. Whey proteins are of special importance in formulated foods due to their rich nutritional and functional benefits. Whey proteins form large polymers upon heating to a temperature greater than the denaturation temperature. Hydrophobic interactions play an important role in building whey protein polymers. In this study, dissociation of hydrophobic interactions of whey protein polymers was done by adding Sodium Dodecyl Sulphonate (SDS). At low SDS concentrations, protein polymers were dissociated to smaller chains, as revealed by dilution solution viscometry (DSV). Interestingly, at higher SDS concentrations, polymer molecules got larger in size. Intrinsic viscosity was increased to many folds when raising the SDS concentration from 0.5% to 2%. Complex molecular arrangement leads to the formation of larger macromolecules, due to micelle formation. The study opens a venue for manipulating and enhancing whey protein functional properties by manipulating the hydrophobic interactions.

Keywords: whey proteins, hydrophobic interactions, SDS

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Authors: Amel Benhadji, Mourad Taleb Ahmed, Rachida Maachi

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The challenge for the new millennium is to develop an industrial system that has minimal socio-ecological impacts, without compromising quality of life. Leather industry is one of these industries demanding environmentally friendly products. In this study, we investigated the possibility of applying innovative low cost biological treatment using Pseudomonas aeruginosa. This strain tested the efficiency of the batch biological treatment in the recovery of protein and hexavalent chromium from chromium tanning bath. We have compared suspended and fixed bacteria culture. The results showed the removal of the total protein of treatment and a decrease of hexavalent chromium concentration is during the treatment. The better efficiency of the biological treatment is obtained when using fixed culture of P. aeruginosa.

Keywords: tanning wastewater, biological treatment, protein removal, hexavalent chromium

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Authors: F. Yelles-Allam, A. A. Nouani

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In Algeria, whey is discarded without any treatment and this causes not only pollution problem, but also a loss in nutritive components of milk. In this paper, characterization of EDAM whey, which is resulted from pasteurised mixture of cow’s milk and skim milk, and recovery of whey protein by ultrafiltration / diafiltration, was studied. The physical-chemical analysis of whey has emphasized on its pollutant and nutritive characteristics. In fact, its DBO5 and DCO are 49.33, and 127.71 gr of O2/l of whey respectively. It contains: fat (1,90±0,1 gr/l), lactose (47.32±1,57 gr/l), proteins (8.04±0,2 gr/l) and ashes (5,20±0,15 gr/l), calcium (0,48±0,04 gr/l), Na (1.104gr/l), K (1.014 gr/l), Mg (0.118 gr/l) and P (0.482 gr/l). Ultrafiltration was carried out in a polyetersulfone membrane with a cut-off of 10K. Its hydraulic intrinsic resistance and permeability are respectively: 2.041.1012 m-1 and 176,32 l/h.m2 at PTM of 1 bar. The retentate obtained at FC6, contains 16,33g/l of proteins and 70,25 g/l of dry matter. The retention rate of protein is 97, 7% and the decrease in DBO5 and DCO are at 18.875 g /l and 42.818 g/l respectively. Diafiltration performed on protein concentrates allowed the complete removal of lactose and minerals. The ultrafiltration of the whey before the disposal is an alternative for Algéria dairy industry.

Keywords: diafiltration, DBO, DCO, protein, ultrafiltration, whey

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: Andi Aliah Hidayani, Asmi Citra Malina A. R. Tassakka, Andi Parenrengi

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Vanname Shrimp is one of the high yielding varieties that are more resistant to virus attacks. However, now this shrimp more death due to virus attack such as white spot disease caused by white spot syndrome virus (WSSV). Various efforts have done to prevent the disease, like immunostimulatory, probiotics, and vaccine. White spot syndrome virus (WSSV) envelope protein VP19 gene is important because of its involvement in the system infection of shrimp. This study aimed to isolate and characterize an envelope protein VP19 – encoding gene of WSSV using WSSV infected Vanname Shrimp sample from some areas in South Sulawesi (Pangkep, Barru and Pinrang). The genomic of DNA were isolated from shrimp muscle using DTAB-CTAB method. Isolation of gene encoding envelope protein VP19 WSSV ws successfully performed with the results of the length of DNA fragment was 387 bp. The results of homology analysis using BLASTn homology suggested that these isolates genes from Barru, Pangkep and Pinrang have closest relationship with isolates from Mexican.

Keywords: vanname, shrimp, WSSV, viral protein 19

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Authors: Aylin W Sahin, Alice Jaeger, Laura Nyhan, Gregory Belt, Steffen Münch, Elke K. Arendt

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Our current food systems cause an increase in malnutrition resulting in more people being overweight or obese in the Western World. Additionally, our natural resources are under enormous pressure and the greenhouse gas emission increases yearly with a significant contribution to climate change. Hence, transforming our food systems is of highest priority. Plant-based food products have a lower environmental impact compared to their animal-based counterpart, representing a more sustainable protein source. However, most plant-based protein ingredients, such as soy and pea, are lacking indispensable amino acids and extremely limited in their functionality and, thus, in their food application potential. They are known to have a low solubility in water and change their properties during processing. The low solubility displays the biggest challenge in the development of milk alternatives leading to inferior protein content and protein quality in dairy alternatives on the market. Moreover, plant-based protein ingredients often possess an off-flavour, which makes them less attractive to consumers. EverPro, a plant-protein isolate originated from Brewer’s Spent Grain, the most abundant by-product in the brewing industry, represents the missing piece in the plant protein portfolio. With a protein content of >85%, it is of high nutritional value, including all indispensable amino acids which allows closing the protein quality gap of plant proteins. Moreover, it possesses high techno-functional properties. It is fully soluble in water (101.7 ± 2.9%), has a high fat absorption capacity (182.4 ± 1.9%), and a foaming capacity which is superior to soy protein or pea protein. This makes EverPro suitable for a vast range of food applications. Furthermore, it does not cause changes in viscosity during heating and cooling of dispersions, such as beverages. Besides its outstanding nutritional and functional characteristics, the production of EverPro has a much lower environmental impact compared to dairy or other plant protein ingredients. Life cycle assessment analysis showed that EverPro has the lowest impact on global warming compared to soy protein isolate, pea protein isolate, whey protein isolate, and egg white powder. It also contributes significantly less to freshwater eutrophication, marine eutrophication and land use compared the protein sources mentioned above. EverPro is the prime example of sustainable ingredients, and the type of plant protein the food industry was waiting for: nutritious, multi-functional, and environmentally friendly.

Keywords: plant-based protein, upcycled, brewers' spent grain, low environmental impact, highly functional ingredient

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Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Alka Yadav, Mayank Jain, Rajan Saxena, Niraj Kumari, Narendra Krishnani, Ashok Kumar

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Purpose: To study the mismatch repair (MMR) protein expression and its clinicopathological correlation in colorectal cancer patients in North India. Methods: A prospective study was conducted on histologically proven 52 (38 males and 14 females) patients with adenocarcinoma of colorectum. MMR protein loss was determined by using immunohistochemistry for MLH1, MSH2, PMS2 and MSH6. Results: 52 patients (38 males and 14 females) underwent resection for colorectal cancer with the median age of 52 years (16-81 years). 35% of the patients (n=18) were younger than 50 years of the age. 3 patients had associated history of malignancy in the family. 29 (56%) patients had right colon cancer, 9 (17%) left colon cancer and 14 (27%) rectal cancer. 2 patients each had synchronous and metachronous cancer. Histology revealed well-differentiated tumour in 16, moderately differentiated in 10 and poorly differentiated tumour in 26 patients. MMR protein loss was seen in 15 (29%) patients. Seven (46%) of these patients were less than 50 years of age. Combined loss of MSH2 and MSH6 was seen most commonly and it was found in 6 patients. 12 (80%) patients with MMR protein loss had tumour located proximal to the splenic flexure compared to 3 (20%) located distal to the splenic flexure. There was no difference in MMR protein loss based on patients' age, gender, degree of tumour differentiation, stage of the disease and tumour histological characteristics. Conclusions: This study revealed that there was less than 30% MMR protein loss in colorectal cancer patients. The loss was most commonly seen in right sided colon cancer than left. A larger study is further required to validate these findings.

Keywords: colorectal cancer, mismatch repair protein, immunohitochemistry, clinicopathological correlation

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Authors: Sweta Pandey, Samridhi Dhyani, Susmita Chaudhuri

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Klebsiella pneumoniae bacteria is a global threat to human health due to an increase in multi-drug resistance among strains. The hypervirulent strains of Klebsiella pneumoniae is a major trouble due to their association with life-threatening infections in a healthy population. One of the major virulence factors of hyper virulent strains of Klebsiella pneumoniae is the T6SS (Type six secretary system) which is majorly involved in microbial antagonism and causes interaction with the host eukaryotic cells during infections. T6SS mediates some of the crucial factors for establishing infection by the bacteria, such as cell adherence, invasion, and subsequent in vivo colonisation. The antibacterial activity and the cell invasion property of the T6SS system is a major requirement for the establishment of K. pneumoniae infections within the gut. The T6SS can be an appropriate target for developing therapeutics. The T6SS consists of an inner tube comprising hexamers of Hcp (Haemolysin -regulated protein) protein, and at the top of this tube sits VgrG (Valine glycine repeat protein G); the tip of the machinery consists of PAAR domain containing proteins which act as a delivery system for bacterial effectors. For this study, immune response to recombinant VgrG protein was generated to establish this protein as a potential immunogen for the development of therapeutic leads. The immunogenicity of the selected protein was determined by predicting the B cell epitopes by the BCEP analysis tool. The gene sequence for multiple domains of VgrG protein (phage_base_V, T6SS_Vgr, DUF2345) was selected and cloned in pMAL vector in E. coli. The construct was subcloned and expressed as a fusion protein of 203 residue protein with mannose binding protein tag (MBP) to enhance solubility and purification of this protein. The purified recombinant VgrG fusion protein was used for mice immunisation. The antiserum showed reactivity with the recombinant VgrG in ELISA and western blot. The immunised mice were challenged with K. pneumoniae bacteria and showed bacterial clearance in immunised mice. The recombinant VgrG protein can further be used for studying downstream signalling of VgrG protein in mice during infection and for therapeutic MAb development to eradicate K. pneumoniae infections.

Keywords: immune response, Klebsiella pneumoniae, multi-drug resistance, recombinant protein expression, T6SS, VgrG

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Authors: Vernie C. Convicto, Henry P. Aringa, Wilson I. Barredo

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This study presents a conformational model of the helical structures of globular protein particularly ferritin in the framework of white noise path integral formulation by using Associated Legendre functions, Bessel and convolution of Bessel and trigonometric functions as modulating functions. The model incorporates chirality features of proteins and their helix-turn-helix sequence structural motif.

Keywords: globular protein, modulating function, white noise, winding probability

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Authors: B. Coşgun, O. Ozturk

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Canola is a specific edible type of rapeseed, developed in the 1970s, which contains about 40 percent oil. This research was carried out to determine the yield and some quality characteristics of some winter canola cultivars during the 2010-2011 vegetation period in Central Anatolia of Turkey. In this research; Oase, Dante, Californium, Excalibur, Elvis, ES Hydromel, Licord, Orkan, Vectra, Nelson, Champlain and NK Petrol winter canola varieties were used as material. The field experiment was set up in a “Randomized Complete Block Design” with three replications on 21 September 2010. In this research; seed yield, oil content, protein content, oil yield and protein yield were examined. As a result of this research; seed yield, oil content, oil yield and protein yield (except protein content) were significant differences between the cultivars. The highest seed yield (6348 kg ha-1) was obtained from the NK Petrol, while the lowest seed yield (3949 kg ha-1) was determined from the Champlain cultivar was obtained. The highest oil content (46.73%) was observed from Oase and the lowest value was obtained from Vectra (41.87%) cultivar. The highest oil yield (2950 kg ha-1) was determined from NK Petrol while the least value (1681 kg ha-1) was determined from Champlain cultivar. The highest protein yield (1539.3 kg ha-1) was obtained from NK Petrol and the lowest protein yield (976.5 kg ha-1) was obtained from Champlain cultivar. The main purpose of the cultivation of oil crops, to increase the yield of oil per unit area. According the result of this research, NK Petrol cultivar which ranks first with regard to both seed yield and oil yield between cultivars as the most suitable winter canola cultivar of local conditions.

Keywords: rapeseed, cultivar, seed yield, crude oil ratio, crude protein ratio, crude oil yield, crude protein yield

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Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Tarek Aboueldahab, Amin Mohamed Nassar

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Wind energy is a fluctuating energy source unlike conventional power plants, thus, it is necessary to accurately predict short term wind speed to integrate wind energy in the electricity supply structure. To do so, we present a hybrid artificial intelligence model of short term wind speed prediction based on passive aggregation of the particle swarm optimization and neural networks. As a result, improvement of the prediction accuracy is obviously obtained compared to the standard artificial intelligence method.

Keywords: artificial intelligence, neural networks, particle swarm optimization, passive aggregation, wind speed prediction

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Authors: Ali Taheri Mirghaed, Sara Ahani, Ashkan Zargar, Seyyed Morteza Hoseini

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Diseases are the major challenges in intensive aquaculture that cause significant annual losses. Antibiotic-therapy is a common way to control bacterial disease in fish, and oxytetracycline (OTC) is the only oral antibiotic in aquaculture approved FDA. OTC has been found to have negative effects on fish, such as oxidative stress and immune-suppression, thus, it is necessary to mitigate such effects. Medicinal herbs have various benefits on fish, including antioxidant, immunostimulant, and anti-microbial effects. Therefore, we hypothesized if dietary ginger meal (GM) interacts with dietary OTC by monitoring plasma protein fractions in rainbow trout. The study was conducted as a 2 × 2 factorial design, including diets containing 0 and 1% GM and 0 and 1.66 % OTC (corresponding to 100 mg/kg fish biomass per day). After ten days treating the fish (60 g individual weight) with these feeds, blood samples were taken from al treatments (n =3). Plasma was separated by centrifugation, and protein fractions were determined by electrophoresis. The results showed that OTC and GM had interaction effects on total protein (P<0.001), albumin (P<0.001), alpha-1 fraction (P=0.010), alpha-2 fraction (P=0.001), beta-2 fraction (P=0.014), and gamma fraction (P<0.001). Beta-1 fraction was significantly (P=0.030) affected by dietary GM. GM decreased plasma total protein, albumin, and beta-2 but increased beta-1 fraction. OTC significantly decreased total protein (P<0.001), albumin (P=0.001), alpha-2 fraction (P<0.001), beta-2 fraction (P=0.004), and gamma fraction (P<0.001) but had no significant effects on alpha-1 and beta-1 fractions. Dietary GM inhibited/suppressed the effects of dietary OTC on the plasma total protein and protein fractions. In conclusion, adding 1% GM to diet can mitigate the negative effects of dietary OTC on plasma proteins. Thus, GM may boost health of rainbow trout during the period of medication with OTC.

Keywords: ginger, plasma protein electrophoresis, dietary additive, rainbow trout

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Authors: Khaled Khaladi, Rafika Bibi, Hind Mokrane, Boubekeur Nadjemi

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Sorghum (Sorghum bicolor (L.) Moench) is an important cereal crop grown in the semi-arid tropics of Africa and Asia due to its drought tolerance. Sorghum grain has protein content varying from 6 to 18%, with an average of 11%, Sorghum proteins can be broadly classified into prolamin and non-prolamin proteins. Kafirins, the major storage proteins, are classified as prolamins, and as such, they contain high levels of proline and glutamine and are soluble in non-polar solvents such as aqueous alcohols. Kafirins account for 77 to 82% of the protein in the endosperm, whereas non-prolamin proteins (namely, albumins, globulins, and glutelins) make up about 30% of the proteins. To optimize the extraction of sorghum proteins, several variables were examined: detergent type and concentration, reducing agent type and concentration, and buffer pH and concentration. Samples were quantified and characterized by RP-HPLC.

Keywords: sorghum, protein extraction, detergent, food science

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Authors: Fadilah Aleanizy

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Colicins are a family of bacterial toxins that kill Escherichia coli and other closely related species. The mode of action of colicins involves binding to an outer membrane receptor and translocation across the cell envelope, leading to cytotoxicity through specific targets. The mechanism of colicin cytotoxicity includes a non-specific endonuclease activity or depolarization of the cytoplasmic membrane by pore-forming activity. For Group A colicins, translocation requires an interaction between the N-terminal domain of the colicin and a series of membrane- bound and periplasmic proteins known as the Tol system (TolB, TolR, TolA, TolQ, and Pal and the active domain must be translocated through the outer membranes. Protein-protein interactions are intrinsic to virtually every cellular process. The transient protein-protein interactions of the colicin include the interaction with much more complicated assemblies during colicin translocation across the cellular membrane to its target. The potassium release assay detects variation in the K+ content of bacterial cells (K+in). This assays is used to measure the effect of pore-forming colicins such as ColA on an indicator organism by measuring the changes of the K+ concentration in the external medium (K+out ) that are caused by cell killing with a K+ selective electrode. One of the goals of this work is to employ a quantifiable in-vivo method to spot which Tol protein are more implicated in the interaction with colicin A as it is translocated to its target.

Keywords: K+ efflux, Colicin A, Tol-proteins, E. coli

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Pengwei Qiao, Sucai Yang, Wenxia Wei

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Soil pollution has become an important issue in China. Accurate spatial distribution prediction of pollutants with interpolation methods is the basis for soil remediation in the site. However, a relatively strong variability of pollutants would decrease the prediction accuracy. Theoretically, partition interpolation can result in accurate prediction results. In order to verify the applicability of partition interpolation for a site, benzo (b) fluoranthene (BbF) in four soil layers was adopted as the research object in this paper. IDW (inverse distance weighting)-, RBF (radial basis function)-and OK (ordinary kriging)-based partition interpolation accuracies were evaluated, and their influential factors were analyzed; then, the uncertainty and applicability of partition interpolation were determined. Three conclusions were drawn. (1) The prediction error of partitioned interpolation decreased by 70% compared to unpartitioned interpolation. (2) Partition interpolation reduced the impact of high CV (coefficient of variation) and high concentration value on the prediction accuracy. (3) The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation, and it was suitable for the identification of highly polluted areas at a contaminated site. These results provide a useful method to obtain relatively accurate spatial distribution information of pollutants and to identify highly polluted areas, which is important for soil pollution remediation in the site.

Keywords: accuracy, applicability, partition interpolation, site, soil pollution, uncertainty

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Authors: Elaheh Jooybar, Mohammad J. Abdekhodaie, Marcel Karperien, Pieter J. Dijkstra

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In this study, hyaluronic acid (HA) microgels were developed for the goal of protein delivery. First, a hyaluronic acid-tyramine conjugate (HA-TA) was synthesized with a degree of substitution of 13 TA moieties per 100 disaccharide units. Then, HA-TA microdroplets were produced using a water in oil emulsion method and crosslinked in the presence of horseradish peroxidase (HRP) and hydrogen peroxide (H₂O₂). Loading capacity and the release kinetics of lysozyme and BSA, as model proteins, were investigated. It was shown that lysozyme, a cationic protein, can be incorporated efficiently in the HA microgels, while the loading efficiency for BSA, as a negatively charged protein, is low. The release profile of lysozyme showed a sustained release over a period of one month. The results demonstrated that the HA-TA microgels are a good carrier for spatial delivery of cationic proteins for biomedical applications.

Keywords: microgel, inverse emulsion, protein delivery, hyaluronic acid, crosslinking

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Authors: Nichole Haag, Kimberly Velk, Tyler McCune, Chun Wu

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Methicillin/multiple-resistant Staphylococcus aureus (MRSA) are infectious bacteria that are resistant to common antibiotics. A previous in silico study in our group has identified a hypothetical protein SAV1226 as one of the potential drug targets. In this study, we reported the bioinformatics characterization, as well as cloning, expression, purification and kinetic assays of hypothetical protein SAV1226 from methicillin/vancomycin-resistant Staphylococcus aureus Mu50 strain. MALDI-TOF/MS analysis revealed a low degree of structural similarity with known proteins. Kinetic assays demonstrated that hypothetical protein SAV1226 is neither a domain of an ATP dependent dihydroxyacetone kinase nor of a phosphotransferase system (PTS) dihydroxyacetone kinase, suggesting that the function of hypothetical protein SAV1226 might be misannotated on public databases such as UniProt and InterProScan 5.

Keywords: Methicillin-resistant Staphylococcus aureus, dihydroxyacetone kinase, essential genes, drug target, phosphoryl group donor

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Authors: Zarmina Gillani, Nuzhat Huma, Aysha Sameen, Mulazim Hussain Bukhari

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Whey is an excellent food ingredient owing to its high nutritive value and its functional properties. However, composition of whey varies depending on composition of milk, processing conditions, processing method, and its whey protein content. The aim of this study was to prepare a whey powder from raw whey and to determine the influence of different processing temperatures (160 and 180 °C) on the physicochemical, functional properties during storage of 180 days and on whey protein denaturation. Results have shown that temperature significantly (P < 0.05) affects the pH, acidity, non-protein nitrogen (NPN), protein total soluble solids, fat and lactose contents. Significantly (p < 0.05) higher foaming capacity (FC), foam stability (FS), whey protein nitrogen index (WPNI), and a lower turbidity and solubility index (SI) were observed in whey powder processed at 160 °C compared to whey powder processed at 180 °C. During storage of 180 days, slow but progressive changes were noticed on the physicochemical and functional properties of whey powder. Reverse phase-HPLC analysis revealed a significant (P < 0.05) effect of temperature on whey protein contents. Denaturation of β-Lactoglobulin is followed by α-lacalbumin, casein glycomacropeptide (CMP/GMP), and bovine serum albumin (BSA).

Keywords: whey powder, temperature, denaturation, reverse phase, HPLC

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Authors: Atlal El-Assaad

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Viruses change with time through mutations and result in new variants that may persist or disappear. A Mutation refers to an actual change in the virus genetic sequence, and a variant is a viral genome that may contain one or more mutations. Critical mutations may cause the virus to be more transmissible, with high disease severity, and more vulnerable to diagnostics, therapeutics, and vaccines. Thus, variants carrying such mutations may increase the risk to human health and are considered variants of concern (VOC). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) - the contagious in humans, positive-sense single-stranded RNA virus that caused coronavirus disease 2019 (COVID-19) - has been studied thoroughly, and several variants were revealed across the world with their corresponding mutations. SARS-CoV-2 has four structural proteins, known as the S (spike), E (envelope), M (membrane), and N (nucleocapsid) proteins, but prior study and vaccines development focused on genetic mutations in the S protein due to its vital role in allowing the virus to attach and fuse with the membrane of a host cell. Specifically, subunit S1 catalyzes attachment, whereas subunit S2 mediates fusion. In this perspective, we studied all charged amino acid mutations of the SARS-CoV-2 viral spike protein S1 when bound to Antibody CC12.1 in a crystal structure and assessed the effect of different mutations. We generated all missense mutants of SARS-CoV-2 protein amino acids (AAs) within the SARS-CoV-2:CC12.1 complex model. To generate the family of mutants in each complex, we mutated every charged amino acid with all other charged amino acids (Lysine (K), Arginine (R), Glutamic Acid (E), and Aspartic Acid (D)) and studied the new binding of the complex after each mutation. We applied Poisson-Boltzmann electrostatic calculations feeding into free energy calculations to determine the effect of each mutation on binding. After analyzing our data, we identified charged amino acids keys for binding. Furthermore, we validated those findings against published experimental genetic data. Our results are the first to propose in silico potential life-threatening mutations of SARS-CoV-2 beyond the present mutations found in the five common variants found worldwide.

Keywords: SARS-CoV-2, variant, ionic amino acid, protein-protein interactions, missense mutation, AESOP

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Authors: Indra Gonzalez Ojeda, Tatiana Quinones, Manuel Rosario, Igor Lednev, Juan Lopez Garriga

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Amyloid fibrils are stable aggregates of misfolded protein associated with many neurodegenerative disorders. It has been shown that hydrogen sulfide (H2S), inhibits the fibrillation of lysozyme through the formation of trisulfide (S-S-S) bonds. However, the overall mechanism remains elusive. Here, the concentration dependence of H2S effect was investigated using Atomic force microscopy (AFM), non-resonance Raman spectroscopy, Deep-UV Raman spectroscopy and circular dichroism (CD). It was found that small spherical aggregates with trisulfide bonds and a unique secondary structure were formed instead of amyloid fibrils when adding concentrations of 25 mM and 50 mM of H2S. This could indicate that H2S might serve as a protecting agent for the protein. However, further characterization of these aggregates and their trisulfide bonds is needed to fully unravel the function H2S has on protein fibrillation.

Keywords: amyloid fibrils, hydrogen sulfide, protein folding, raman spectroscopy

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Authors: Bin Liu

Abstract:

As one of the most important tasks in protein sequence analysis, protein remote homology detection has been studied for decades. Currently, the profile-based methods show state-of-the-art performance. Position-Specific Frequency Matrix (PSFM) is widely used profile. However, there exists noise information in the profiles introduced by the amino acids with low frequencies. In this study, we propose a method to remove the noise information in the PSFM by removing the amino acids with low frequencies called Top frequency profile (TFP). Three new matrix transformation methods, including Autocross covariance (ACC) transformation, Tri-gram, and K-separated bigram (KSB), are performed on these profiles to convert them into fixed length feature vectors. Combined with Support Vector Machines (SVMs), the predictors are constructed. Evaluated on two benchmark datasets, and experimental results show that these proposed methods outperform other state-of-the-art predictors.

Keywords: protein remote homology detection, protein fold recognition, top frequency profile, support vector machines

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Authors: Engin Eyceyurt, Josko Zec

Abstract:

The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

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Authors: Athar Hussain Memon

Abstract:

Objectives: We tried to determine the frequency of raised C-reactive protein (CRP) in patients with type 2 diabetes mellitus. Patients and Methods: This cross-sectional descriptive study of six months study was conducted at Liaquat University Hospital Hyderabad from March 2013 to August 2013. All diabetic patients of ≥35 years age of either gender for >01 year duration visited at OPD were evaluated for C-reactive protein and their glycemic status by hemoglobin A1c. The data was analyzed in SPSS and the frequency and percentage were calculated. Results: During six month study period, total 100 diabetic patients were evaluated for C-reactive protein. The majority of patients were from urban areas 75/100 (75%). The mean ±SD for age of patients with diabetes mellitus was 51.63±7.82. The mean age ±SD of patient with raised CRP was 53±7.21. The mean ±SD for HbA1c in patients with raised CRP is 9.55±1.73. The mean random blood sugar level in patients with raised CRP was 247.42 ± 6.62. The majority of subjects were of 50-69 years of age group with female predominance (p=0.01) while the CRP was raised in 70 (70%) patients in relation to age (p=0.02) and gender (p=0.01), respectively. Both HbA1c and CRP were raised in 64.9% (p=0.04) in patients with type 2 diabetes mellitus. The mean ±SD of CRP was 5.8±1.21 while for male and female individuals with raised CRP was 3.52±1.22 and 5.7±1.63, respectively. Conclusions: The raised CRP was observed in patients with type 2 diabetes mellitus.

Keywords: diabetes mellitus, C-reactive protein, hemoglobin A1c, diabetes and metabolism

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Authors: Jae Young Song, In-Ohk Ouh, Seyeon Park, Byeong Sul Kang, Soo Dong Cho, In-Soo Cho

Abstract:

Canine parvovirus (CPV) is a major pathogen of diarrhea disease in dogs. CPV type 2 has three of antigenic variants such as 2a, 2b, and 2c. CPV constructs a small non-enveloped, icosahedral capsid that contains single-stranded DNA. It has capsids that two largely overlapping virion proteins (VP), VP1 (82 kDa), and VP2 (65 kDa). Baculoviruses are insect pathogens that regulate insect populations in nature and are being successfully used to control insect pests. The proteins produced in the baculovirus-expression system are used for instance for functional studies, vaccine preparations, or diagnostics. The vaccines produced by baculovirus-expression system showed elicitation of antibodies. The recombinant baculovirus infected SF9 cells showed broken shape. The recombinant VP2 proteins from cell pellet or supernatant were confirmed by western blotting. The result showed that the recombinant VP2 protein bands were appeared at 65 kDa molecular weight in both cell pellet and supernatant of infected SF9 cell. These results indicated that the recombinant baculovirus infected SF9 cell express the recombinant VP2 protein successfully. In addition, the expressed recombinant VP2 protein is secreted from cell to supernatant. The baculovirus expression system can be used to produce the VP2 protein of CPV 2c. In addition, the secretion property of the expression of VP2 protein may decrease the cost of production, because it can be skipped the cell breaking step. The produced VP2 protein could be used for vaccine and the agent of diagnostic tests. This study provides the foundation of the production of CPV 2c vaccine and the diagnostic agent.

Keywords: baculovirus, canine parvovirus 2c, dog, Korea

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Authors: Pingshan Chen, Zhiliang Dong

Abstract:

In order to estimate the post-construction settlement more objectively, the power-polynomial model is proposed, which can reflect the trend of settlement development based on the observed settlement data. It was demonstrated by an actual case history of an embankment, and during the prediction. Compared with the other three prediction models, the power-polynomial model can estimate the post-construction settlement more accurately with more simple calculation.

Keywords: prediction, model, post-construction settlement, soft ground

Procedia PDF Downloads 398