Search results for: protein stability prediction

Authors: Ankush Verma, Rohit Bishist

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The present investigation was carried out at different locations of Shimla and Kinnaur district and nutrient analysis was done in the laboratory of Department of Silviculture and Agroforestry, Dr. Y.S. Parmar University of Horticulture and Forestry, Nauni, Distt. Solan, Himachal Pradesh during 2019-2020 with the objectives to study the seasonal variation in the nutritive value of different Quercus species and to study the farmers’ preference rating of fodder tress species. From each location leaf samples were collected at 3 months interval from each Quercus spp. The findings of the present study revealed that the nutritional traits of leaves of different Quercus species varied among different seasons throughout the year. The dry matter (61.12 to 64.99%), ether extract (4.07 to 4.42%), crude fibre (34.38 to 37.85%), neutral detergent fibre (57.70 to 61.54%), acid detergent fibre (44.64 to 48.51%), total ash (3.57 to 3.91%), acid insoluble ash (44.64 to 48.51%) and calcium (1.31 to 1.53%) increased with the maturity in the leaves of different Quercus species. While, crude protein (9.10 to 10.61%), nitrogen free extract (44.73 to 47.41%), organic matter (96.09 to 96.43%), and phosphorus (0.16 to 0.31%) decreased with the advancing maturity in the leaves of different Quercus species. Maximum mean values for dry matter (65.05%), ether extract (4.45%), crude fibre (40.82%), neutral detergent fibre (61.48%), acid detergent fibre (48.44%), and organic matter (96.67%) among different Quercus species were recorded in Quercus ilex, while, Maximum mean values for crude protein (10.54%), nitrogen free extract (50.53%), total ash (4.05%), acid insoluble ash (0.59%), calcium (1.61%) and phosphorus (0.40%) were recorded in Quercus leucotrichophora.

Keywords: nutritional evaluation, fodder species, crude protein, carbohydrates

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Authors: Don Buddika Oshadi Malaweera, Lora Harris, Bruce Rathgeber, Chibuike C. Udenigwe, Kirsti Rouvinen-Watt

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Fatty liver disease is among the three most severe health concerns for mink and believed to occur through the same mechanism as nursing sickness. In North America, nursing sickness affects about 45% of mink farms and in Canada, approximately 50,000 mink females is affected annually. Orotic acid (OA) plays a critical role in lipid metabolism and can increase hepatic lipids by enhancing Sterol regulatory element binding protein-1c expression and decreasing Carnitine palmitoyl transferase I activity. This study was conducted to identify particular pathways and regulatory control points involved in fatty liver development, and evaluate the effectiveness of arginine and bioactive peptides for prevention and treatment of fatty liver disease in mink. A total of 45 mink were used in 9 treatments. The experimental diets consisted of 1% OA, 2% L-arginine and 5% of whey protein hydrolysates. At the end of 10 days of experimental period, the mink were anaesthetized, sampled for blood and euthanized, samples were obtained for histological, biochemical and molecular assays. The blood samples will be analyzed for clinical chemistry and triacylglycerol. The liver samples will be analyzed for total lipid content and analyzed for 6 genes of interest involved in adipogenic transformation, ER stress, and liver inflammation.

Keywords: fatty liver, L-arginine, mink, orotic acid, whey protein hydrolysates

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Mahdi Nasiri Tabrizi, A. Dadkhah, M. Khirkhah

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In order to study the effect of planting on yield, yield components and quality traits in bread and durum wheat varieties, a field split-plot experiment based on complete randomized design with three replications was conducted in Agricultural and Natural Resources Research Center of Razavi Khorasan located in city of Mashhad during 2013-2014. Main factor were consisted of five sowing dates (first October, fifteenth December, first March, tenth March, twentieth March) and as sub-factors consisted of different bread wheat (Bahar, Pishgam, Pishtaz, Mihan, Falat and Karim) and two durum wheat (Dena and Dehdasht). According to results of analysis variance the effect of planting date was significant on all examined traits (grain yield, biological yield, harvest index, number of grain per spike, thousands kernel weight, number of spike per square meter, plant height, the number of days to heading, the number of days to maturity, during the grain filling period, percentage of wet gluten, percentage of dry gluten, gluten index, percentage of protein). By delay in planting, majority of traits significantly decreased, except quality traits (percentage of wet gluten, percentage of dry gluten and percentage of protein). Results of means comparison showed, among planting date the highest grain yield and biological yield were related to first planting date (Octobr) with mean of production of 5/6 and 1/17 tons per hectare respectively and the highest bread quality (gluten index) with mean of 85 and percentage of protein with mean of 13% to fifth planting date also the effect of genotype was significant on all traits. The highest grain yield among of studied wheat genotypes was related to Dehdasht cultivar with an average production of 4.4 tons per hectare. The highest protein percentage and bread quality (gluten index) were related to Dehdasht cultivar with 13.4% and Falat cultivar with number of 90 respectively. The interaction between cultivar and planting date was significant on all traits and different varieties had different trend for these traits. The highest grain yield was related to first planting date (October) and Falat cultivar with an average of production of 6/7 tons per hectare while in grain yield did not show a significant different with Pishtas and Mihan cultivars also the most of gluten index (bread quality index) and protein percentage was belonged to the third planting date and Karim cultivar with 7.98 and Dena cultivar with 7.14% respectively.

Keywords: yield component, yield, planting date, cultivar, quality traits, wheat

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

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Authors: A. Chellil, A. Nour, S. Lecheb, H.Mechakra, A. Bouderba, H. Kebir

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In this paper, the numerical study for the instability of a composite rotor is presented, under dynamic loading response in the harmonic analysis condition. The analysis of the stress which operates the rotor is done. Calculations of different energies and the virtual work of the aerodynamic loads from the rotor is developed. The use of the composite material for the rotor, offers a good Stability. Numerical calculations on the model develop of three dimensions prove that the damage effect has a negative effect on the stability of the rotor. The study of the composite rotor in transient system allowed to determine the vibratory responses due to various excitations.

Keywords: rotor, composite, damage, finite element, numerical

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Authors: Fitri Rahmi Fadhilah, Edhyana Sahiratmadja, Ani Melani Maskoen, Ratu Safitri, Supartini Syarif, Herman Susanto

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Cervical cancer is the second most common cancer in women after breast cancer. In Indonesia, the incidence of cervical cancer cases is estimated at 25-40 per 100,000 women per year. Human papillomavirus (HPV) infection is a major cause of cervical cancer, and HPV-16 is the most common genotype that infects the cervical tissue. The major late protein L1 may be associated with infectivity and pathogenicity and its variation can be used to classify HPV isolates. The aim of this study was to determine the phylogenetic tree of HPV 16 L1 gene from cervical cancer patient isolates in Bandung. After confirming HPV-16 by Linear Array Genotyping Test, L1 gene was amplified using specific primers and subject for sequencing. Phylogenetic analysis revealed that HPV 16 from Bandung was in the subgroup of Asia and East Asia, showing the close host-agent relationship among the Asian type.

Keywords: L1 HPV 16, cervical cancer, bandung, phylogenetic

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Authors: Yuliani Aisyah, Sri Haryani, Novi Safriani

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Nutmeg oil is one of the essential oils that have the ability as an antibacterial so it potentially uses to inhibit the growth of undesirable microbes in food. However, the essential oil that has low solubility in water, high volatile content, and strong aroma properties is difficult to apply in to foodstuffs. Therefore, the oil-in-water nanoemulsion system was used in this research. Gelatin, lecithin and tween 80 with 10%, 20%, 30% concentrations have been examined for the preparation of nutmeg oil nanoemulsions. The physicochemical properties and stability of nutmeg oil nanoemulsion were analyzed on viscosity, creaming index, emulsifying activity, droplet size, and polydispersity index. The results showed that the type and concentration stabilizer had a significant effect on viscosity, creaming index, droplet size and polydispersity index (P ≤ 0,01). The nanoemulsions stabilized with tween 80 had the best stability because the creaming index value was 0%, the emulsifying activity value was 100%, the droplet size was small (79 nm) and the polydispersity index was low (0.10) compared to the nanoemulsions stabilized with gelatin and lecithin. In brief, Tween 80 is strongly recommended to be used for stabilizing nutmeg oil nanoemulsions.

Keywords: nanoemulsion, nutmeg oil, stabilizer, stability

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Authors: E. Izsó, M. Bartalné-Berceli, Sz. Gergely, A. Salgó

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The aims of this investigation is to elaborate near-infrared methods for testing and recognition of chemical components and quality in “Pannon wheat” allied (i.e. true to variety or variety identified) milling fractions as well as to develop spectroscopic methods following the milling processes and evaluate the stability of the milling technology by different types of milling products and according to sampling times, respectively. This wheat categories produced under industrial conditions where samples were collected versus sampling time and maximum or minimum yields. The changes of the main chemical components (such as starch, protein, lipid) and physical properties of fractions (particle size) were analysed by dispersive spectrophotometers using visible (VIS) and near-infrared (NIR) regions of the electromagnetic radiation. Close correlation were obtained between the data of spectroscopic measurement techniques processed by various chemometric methods (e.g. principal component analysis (PCA), cluster analysis (CA) and operation condition of milling technology. Its obvious that NIR methods are able to detect the deviation of the yield parameters and differences of the sampling times by a wide variety of fractions, respectively. NIR technology can be used in the sensitive monitoring of milling technology.

Keywords: near infrared spectroscopy, wheat categories, milling process, monitoring

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Authors: Amit Kumar Verma

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The present study deals with fire prediction model in Terai Arc Landscape, one of the most dramatic ecosystems in Asia where large, wide-ranging species such as tiger, rhinos, and elephant will thrive while bringing economic benefits to the local people. Forest fires cause huge economic and ecological losses and release considerable quantities of carbon into the air and is an important factor inflating the global burden of carbon emissions. Forest fire is an important factor of behavioral cum ecological habit of tiger in wild. Post fire changes i.e. micro and macro habitat directly affect the tiger habitat or land. Vulnerability of fire depicts the changes in microhabitat (humus, soil profile, litter, vegetation, grassland ecosystem). Microorganism like spider, annelids, arthropods and other favorable microorganism directly affect by the forest fire and indirectly these entire microorganisms are responsible for the development of tiger (Panthera tigris) habitat. On the other hand, fire brings depletion in prey species and negative movement of tiger from wild to human- dominated areas, which may leads the conflict i.e. dangerous for both tiger & human beings. Early forest fire prediction through mapping the risk zones can help minimize the fire frequency and manage forest fires thereby minimizing losses. Satellite data plays a vital role in identifying and mapping forest fire and recording the frequency with which different vegetation types are affected. Thematic hazard maps have been generated by using IDW technique. A prediction model for fire occurrence is developed for TAL. The fire occurrence records were collected from state forest department from 2000 to 2014. Disciminant function models was used for developing a prediction model for forest fires in TAL, random points for non-occurrence of fire have been generated. Based on the attributes of points of occurrence and non-occurrence, the model developed predicts the fire occurrence. The map of predicted probabilities classified the study area into five classes very high (12.94%), high (23.63%), moderate (25.87%), low(27.46%) and no fire (10.1%) based upon the intensity of hazard. model is able to classify 78.73 percent of points correctly and hence can be used for the purpose with confidence. Overall, also the model works correctly with almost 69% of points. This study exemplifies the usefulness of prediction model of forest fire and offers a more effective way for management of forest fire. Overall, this study depicts the model for conservation of tiger’s natural habitat and forest conservation which is beneficial for the wild and human beings for future prospective.

Keywords: fire prediction model, forest fire hazard, GIS, landsat, MODIS, TAL

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Authors: Wasfi Al Salamat, Shaker Al-Kharouf

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This study aims to examine the determinants of financial stability for 13 commercial banks listed on the Amman stock exchange (ASE) over the period (2007-2016) after controlling for the independent variables: return on equity (ROE), return on assets (ROA), earnings per share (EPS), growth in gross domestic product (GDP), inflation rate and debt ratio to measure the financial stability by three main variables: capital adequacy, non-performing loans and the number of returned checks. The balanced panel data statistical approach has been used for data analysis. Results are estimated by using multiple regression models. The empirical results suggested that there is statistically significant negative effect of inflation rate and debt ratio on the capital adequacy while there is statistically significant positive effect of growth in gross domestic product on capital adequacy. In contrast, there is statistically significant negative effect of return on equity and growth in gross domestic product on the non-performing loans while there is statistically significant positive effect of inflation rate on non-performing loans. Finally, there is statistically significant negative effect of growth in gross domestic product on the number of returned checks while there is statistically significant positive effect of inflation rate on the number of returned checks.

Keywords: capital adequacy, financial stability, non-performing loans, number of returned checks, ASE

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Authors: Adil Loya, Kamran Maqsood, Muhammad Duraid

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Aerodynamic stability coefficients are necessary to be known before any unmanned aircraft flight is performed. This requires expertise on aerodynamics and stability control of the aircraft. To enable efficacious performance of aircraft requires that a well-defined flight path and aerodynamics should be defined beforehand. This paper presents a study on the aerodynamics of an unmanned aero vehicle (UAV) during flight conditions. Current research holds comparative studies of different parameters for flight aerodynamic, measured using two different open source analytical software programs. These software packages are DATCOM and XLRF5, which help in depicting the flight aerodynamic variables. Computational fluid dynamics (CFD) was also used to perform aerodynamic analysis for which Star CCM+ was used. Output trends of the study demonstrate high accuracies between the two software programs with that of CFD. It can be seen that the Coefficient of Lift (CL) obtained from DATCOM and XFLR is similar to CL of CFD simulation. In the similar manner, other potential aerodynamic stability parameters obtained from analytical software are in good agreement with CFD.

Keywords: XFLR5, DATCOM, computational fluid dynamic, unmanned aero vehicle

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Authors: Sajjad Rezaei, Mahdieh Molanouri Shamsi, Azadeh Jamali

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Background and purpose: Surfactant protein D (SP-D) is a lung-specific protein that is detectable in human plasma. Effect of exercise training on SP-D levels as well as its relation to metabolic indices is not known. The present study then aimed to investigate the effects of 10 weeks of aerobic training on serum levels of SP-D and insulin resistance in women with type 2 diabetes. Materials and methods: Twenty-two overweight women with type 2 diabetes mellitus were recruited through deliberate sampling and randomly assigned to intervention and control groups (11 in each group). The intervention group underwent a progressive aerobic training program for 10 weeks, 3 days per week, 30-55 min/day at 50-75% heart rate reserve (HRR). Control group continued with its everyday routine. Blood samples were obtained before and after training for biochemical analysis. Within-group and between-group differences were analyzed with paired and independent t-tests in spss software, respectively, and the relation between variables was analyzed with Pearson’s correlation coefficient (all at P = 0.05). Results: Significant differences were observed between groups in leptin, glucose, waist circumference and VO2 max after training. SP-D was decreased and VO2 max was increased significantly in intervention group. However, no significant correlation was observed between SP-D and other variables. Conclusion: Since there was no corresponding decrease in insulin resistance with decreased levels of SP-D, it seems unlikely for SP-D to mediate the association between obesity and insulin resistance in type 2 diabetics.

Keywords: exercise training, SP-D, insulin resistance, type 2 diabetes

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Authors: Abdullahi Jibrin, Aishetu Abdulkadir

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The development of allometric models is crucial to accurate forest biomass/carbon stock assessment. The aim of this study was to develop a set of biomass prediction models that will enable the determination of total tree aboveground biomass for savannah woodland area in Niger State, Nigeria. Based on the data collected through biometric measurements of 1816 trees and destructive sampling of 36 trees, five species specific and one site specific models were developed. The sample size was distributed equally between the five most dominant species in the study site (Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa, Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the equations were developed for five individual species. Secondly these five species were mixed and were used to develop an allometric equation of mixed species. Overall, there was a strong positive relationship between total tree biomass and the stem diameter. The coefficient of determination (R2 values) ranging from 0.93 to 0.99 P < 0.001 were realised for the models; with considerable low standard error of the estimates (SEE) which confirms that the total tree above ground biomass has a significant relationship with the dbh. The F-test value for the biomass prediction models were also significant at p < 0.001 which indicates that the biomass prediction models are valid. This study recommends that for improved biomass estimates in the study site, the site specific biomass models should preferably be used instead of using generic models.

Keywords: allometriy, biomass, carbon stock , model, regression equation, woodland, inventory

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Authors: Sarath Chandran Nair S, Srinivas Kodati, Vasudevan R, Asraff A. K

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Spacecrafts generally employ liquid propulsion for their attitude and orbital maneuvers or raising it from geo-transfer orbit to geosynchronous orbit. Liquid propulsion systems use either mono-propellant or bi-propellants for generating thrust. These propellants are generally stored in either spherical tanks or cylindrical tanks with spherical end domes. The propellant tanks are provided with a propellant acquisition system/propellant management device along with vanes and their conical mounting structure to ensure propellant availability in the outlet for thrust generation even under a low/zero-gravity environment. Slosh is the free surface oscillations in partially filled containers under external disturbances. In a spacecraft, these can be due to control forces and due to varying acceleration. Knowledge of slosh and its effect due to internals is essential for understanding its stability through control stability studies. It is mathematically represented by a pendulum-mass model. It requires parameters such as slosh frequency, damping, sloshes mass and its location, etc. This paper enumerates various numerical and experimental methods used for evaluating the slosh parameters required for representing slosh. Numerical methods like finite element methods based on linear velocity potential theory and computational fluid dynamics based on Reynolds Averaged Navier Stokes equations are used for the detailed evaluation of slosh behavior in one of the spacecraft propellant tanks used in an Indian space mission. Experimental studies carried out on a scaled-down model are also discussed. Slosh parameters evaluated by different methods matched very well and finalized their dispersion bands based on experimental studies. It is observed that the presence of internals such as propellant management devices, including conical support structure, alters slosh parameters. These internals also offers one order higher damping compared to viscous/ smooth wall damping. It is an advantage factor for the stability of slosh. These slosh parameters are given for establishing slosh margins through control stability studies and finalize the spacecraft control system design.

Keywords: control stability, propellant tanks, slosh, spacecraft, slosh spacecraft

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Authors: Lule Basha, Eralda Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

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Authors: Mennatallah M. Hussein, Olivier de Weck

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The advancement of space exploration demands a robust space launch services program capable of reliably propelling payloads into orbit. Despite rigorous testing and quality assurance, launch failures still occur, leading to significant financial losses and jeopardizing mission objectives. Traditional failure prediction methods often lack the sophistication to account for multi-mode failure scenarios, as well as the predictive capability in complex dynamic systems. Traditional approaches also rely on expert judgment, leading to variability in risk prioritization and mitigation strategies. Hence, there is a pressing need for robust approaches that enhance launch vehicle reliability from lift-off until it reaches its parking orbit through comprehensive simulation techniques. In this study, the developed model proposes a multi-mode launch vehicle simulation framework for predicting failure scenarios when incorporating new technologies, such as new propulsion systems or advanced staging separation mechanisms in the launch system. To this end, the model combined a 6-DOF system dynamics with comprehensive data analysis to simulate multiple failure modes impacting launch performance. The simulator utilizes high-fidelity physics-based simulations to capture the complex interactions between different subsystems and environmental conditions.

Keywords: launch vehicle, failure prediction, propulsion anomalies, rocket launch simulation, rocket dynamics

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Samantha A. Cintron, Janet Pierce, Mihaela E. Sardiu, Diane Mahoney, Jill Peltzer, Bhanu Gupta, Qiuhua Shen

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High output heart failure (HOHF) is characterized a high output state resulting from an underlying disease process and is commonly caused by obesity. As obesity levels increase, more individuals will be at risk for obesity-related HOHF. However, the underlying pathophysiologic mechanisms of obesity-related HOHF are not well understood and need further research. The aim of the study was to describe the differences in leukocyte transcriptomes of morbidly obese patients with HOHF and those with non-HOHF. In this cross-sectional study, the study team collected blood samples, demographics, and clinical data of six patients with morbid obesity and HOHF and six patients with morbid obesity and non-HOHF. The study team isolated the peripheral blood leukocyte RNA and applied stranded total RNA sequencing. Differential gene expression was calculated, and Ingenuity Pathway Analysis software was used to interpret the canonical pathways, functional changes, upstream regulators, and mechanistic and causal networks that were associated with the significantly different leukocyte transcriptomes. The study team identified 116 differentially expressed genes; 114 were upregulated, and 2 were downregulated in the HOHF group (Benjamini-Hochberg adjusted p-value ≤ 0.05 and log2(fold-change) of ±1). The differentially expressed genes were involved with cell proliferation, mitochondrial function, erythropoiesis, erythrocyte stability, and apoptosis. The top upregulated canonical pathways associated with differentially expressed genes were autophagy, adenosine monophosphate-activated protein kinase signaling, and senescence pathways. Upstream regulator GATA Binding Protein 1 (GATA1) and a network associated with nuclear factor kappa-light chain-enhancer of activated B cells (NF-kB) were also identified based on the different leukocyte transcriptomes of morbidly obese patients with HOHF and non-HOHF. To the author’s best knowledge, this is the first study that reported the differential gene expression in patients with obesity-related HOHF and demonstrated the unique pathophysiologic mechanisms underlying the disease. Further research is needed to determine the role of cellular function and maintenance, inflammation, and iron homeostasis in obesity-related HOHF.

Keywords: cardiac output, heart failure, obesity, transcriptomics

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: D. P. Evdokimov, A. N. Kirichenko, V. D. Blank, V. N. Denisov, B. A. Kulnitskiy

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In this study we investigated the stability of polyhedral carbon onions under influence of shear deformation and high pressures above 43 GPa by means of by transmission electron microscopy (TEM) and Raman spectroscopy (RS). It was found that at pressures up to 29 GPa and shear deformations of 40 degrees the onions are stable. At shear deformation applying at pressures above 30 GPa carbon onions collapsed with formation of amorphous carbon. At pressures above 43 GPa diamond-like carbon (DLC) was obtained.

Keywords: carbon onions, Raman spectroscopy, transmission electron spectroscopy

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Authors: Rami Georg Johann

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Against the background of the current geopolitical constellations, the author looks at various empire models, which are discussed and compared with each other with regard to their stability and functioning. The focus is on the fifth concept as a possible new world order in the 21st century. These will be discussed and compared to one another according to their stability and functioning. All empires to be designed will be conceptualised based on one, two, three, four, and five worlds. All worlds are made up of a different constellation of states and relating coalitions. All systems will be discussed in detail. The one-world-system, the“Western Empire,” will be presented as a possible solution to a new world order in the 21st century (fifth concept). The term “Western” in “Western Empire” describes the Western concept after World War II. This Western concept was the result of two horrible world wars in the 20th century.” With this in mind, the fifth concept forms a stable empire system, the “Western Empire,” by political measures tied to two issues. Thus, this world order provides a significantly higher long-term stability in contrast to all other empire models (comprising five, four, three, or two worlds). Confrontations and threats of war are reduced to a minimum. The two issues mentioned are “merger” and “competition.” These are the main differences in forming an empire compared to all empires and realms in the history of mankind. The fifth concept of this theory, the “Western Empire,” acts explicitly as a counter model. The Western Empire (fifth concept) is formed by the merger of world powers without war. Thus, a world order without competition is created. This merged entity secures long-term peace, stability, democratic values, freedom, human rights, equality, and justice in the new world order.

Keywords: empire formation, theory of international relations, Western Empire, world order

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Authors: Anjani Kumar Tiwari, Neelam Kumari, Anil Mishra

Abstract:

The 18-kDa translocator protein (TSPO) previously called as peripheral benzodiazepine receptor, is proven biomarker for variety of neuroinflammation. TSPO is tryptophane rich five transmembranal protein found on outer mitochondrial membrane of steroid synthesising and immunomodulatory cells. In case of neuronal damage or inflammation the expression level of TSPO get upregulated as an immunomodulatory response. By utilizing Benzoxazolone as a basic scaffold, series of TSPO ligands have been designed followed by their screening through in silico studies. Synthesis has been planned by employing convergent methodology in six high yielding steps. For the synthesized ligands the ‘in vitro’ assay was performed to determine the binding affinity in term of Ki. On ischemic rat brain, autoradiography studies were also carried to check the specificity and affinity of the designed radiolabelled ligand for TSPO.Screening was performed on the basis of GScore of CADD based schrodinger software. All the modified and better prospective compound were successfully carried out and characterized by spectroscopic techniques (FTIR, NMR and HRMS). In vitro binding assay showed best binding affinity Ki = 6.1+ 0.3 for TSPO over central benzodiazepine receptor (CBR) Ki > 200. ARG studies indicated higher uptake of two analogues on the lesion side compared with that on the non-lesion side of ischemic rat brains. Displacement experiments with unlabelled ligand had minimized the difference in uptake between the two sides which indicates the specificity of the ligand towards TSPO receptor.

Keywords: TSPO, PET, imaging, Acetamidobenzoxazolone

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Authors: Aulanni’am Aulanni’am

Abstract:

Incidence of Diabetes Mellitus (DM) progressively increasing it became a serious problem in Indonesia and it is a disease that government is priority to be addressed. The longer a person is suffering from diabetes the more likely to develop complications particularly diabetic patients who are not well maintained. Therefore, Incidence of Diabetes Mellitus needs to be done in the early diagnosis of pre-phase of the disease. In this pre-phase disease, already happening destruction of pancreatic beta cells and declining in beta cell function and the sign autoimmunity reactions associated with beta cell destruction. Type 1 DM is a multifactorial disease triggered by genetic and environmental factors, which leads to the destruction of pancreatic beta cells. Early marker of "beta cell autoreactivity" is the synthesis of autoantibodies against 65-kDa protein, which can be a molecule that can be detected early in the disease pathomechanism. The importance of early diagnosis of diabetic patients held in the phase of pre-disease is to determine the progression towards the onset of pancreatic beta cell destruction and take precautions. However, the price for this examination is very expensive ($ 150/ test), the anti-GAD65 abs examination cannot be carried out routinely in most or even in all laboratories in Indonesia. Therefore, production-based Rapid Test Recombinant Human Protein GAD65 with "Reverse Flow Immunchromatography Technique" in Indonesia is believed to reduce costs and improve the quality of care of patients with diabetes in Indonesia. Rapid Test Product innovation is very simple and suitable for screening and routine inspection of GAD65 autoantibodies. In the blood serum of patients with diabetes caused by autoimmunity, autoantibody-GAD65 is a major serologic marker to detect autoimmune reaction because their concentration level of stability.GAD65 autoantibodies can be found 10 years before clinical symptoms of diabetes. Early diagnosis is more focused to detect the presence autontibodi-GAD65 given specification and high sensitivity. Autoantibodies- GAD65 that circulates in the blood is a major indicator of the destruction of the islet cells of the pancreas. Results of research in collaboration with Biofarma has produced GAD65 autoantibodies based Rapid Test had conducted the soft launch of products and has been tested with the results of a sensitivity of 100 percent and a specificity between 90 and 96% compared with the gold standard (import product) which worked based on ELISA method.

Keywords: diabetes mellitus, GAD65 autoantibodies, rapid test, sensitivity, specificity

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Authors: Antoni Wibowo, Harry Pujianto, Dewi Retno Sari Saputro

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The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.

Keywords: NARX (Nonlinear Autoregressive Exogenous Model), prediction, stock market, time series

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Authors: Anita Singh, Richa Naula, Manoj Raghav

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Nutrient rich and high-yielding varieties of fenugreek can be developed by using genotypes which are naturally high in nutrients. Gene banks harbour scanty germplasm collection of Trigonella spp. and a very little background information about its genetic diversity. The extent of genetic diversity in a specific breeding population depends upon the genotype included in it. The present investigation aims at the estimation of macronutrient (phosphorus by spectrophotometer and potassium by flame photometer), micronutrients, namely, iron, zinc, manganese, and copper from seeds of fenugreek genotypes using atomic absorption spectrophotometer, protein by Rapid N Cube Analyser and Steroidal Saponins. Twenty-eight genotypes of fenugreek along with two standard checks, namely, Pant Ragini and Pusa Early Bunching were collected from different parts of India, and nutrient contents of each genotype were determined at G. B. P. U. A. & T. Laboratory, Pantnagar. Highest potassium content was observed in PFG-35 (1207 mg/100g). PFG-37 and PFG-20 were richest in phosphorus, iron and manganese content among all the genotypes. The lowest zinc content was found in PFG-26 (1.19 mg/100g), while the maximum zinc content was found in PFG- 28 (4.43 mg/100g). The highest content of copper was found in PFG-26 (1.97 mg/100g). PFG-39 has the highest protein content (29.60 %). Significant differences were observed in the steroidal saponin among the genotypes. Saponin content ranged from 0.38 g/100g to 1.31 g/100g. Steroidal Saponins content was found the maximum in PFG-36 (1.31 g/100g) followed by PFG-17 (1.28 g/100g). Therefore, the genotypes which are rich in nutrient and oil content can be used for plant biofortification, dietary supplements, and herbal products.

Keywords: genotypes, macronutrients, micronutrient, protein, seeds

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Authors: Benito Minjarez, Jesse Haramati, Yury Rodriguez-Yanez, Florencio Recendiz-Hurtado, Juan-Pedro Luna-Arias, Salvador Mena-Munguia

Abstract:

During the last decades, the world has experienced a rapid industrialization and an expanding economy favoring a demographic boom. As a consequence, countries around the world have focused on developing new strategies related to the production of different farm products in order to meet future demands. Consequently, different strategies have been developed seeking to improve the major food products for both humans and livestock. Corn, after wheat and rice, is the third most important crop globally and is the primary food source for both humans and livestock in many regions around the globe. In addition, maize (Zea mays) is an important source of protein accounting for up to 60% of the daily human protein supply. Generally, many of the cereal grains have proteins with relatively low nutritional value, when they are compared with proteins from meat. In the case of corn, much of the protein is found in the endosperm (75 to 85%) and is deficient in two essential amino acids, lysine, and tryptophan. This deficiency results in an imbalance of amino acids and low protein content; normal maize varieties have less than half of the recommended amino acids for human nutrition. In addition, studies have shown that this deficiency has been associated with symptoms of growth impairment, anemia, hypoproteinemia, and fatty liver. Due to the fact that most of the presently available maize varieties do not contain the quality and quantity of proteins necessary for a balanced diet, different countries have focused on the research of quality protein maize (QPM). Researchers have characterized QPM noting that these varieties may contain between 70 to 100% more residues of the amino acids essential for animal and human nutrition, lysine, and tryptophan, than common corn. Several countries in Africa, Latin America, as well as China, have incorporated QPM in their agricultural development plan. Large parts of these countries have chosen a specific QPM variety based on their local needs and climate. Reviews have described the breeding methods of maize and have revealed the lack of studies on genetic and proteomic diversity of proteins in QPM varieties, and their genetic relationships with normal maize varieties. Therefore, molecular marker identification using tools such as mass spectrometry may accelerate the selection of plants that carry the desired proteins with high lysine and tryptophan concentration. To date, QPM maize lines have played a very important role in alleviating the malnutrition, and better characterization of these lines would provide a valuable nutritional enhancement for use in the resource-poor regions of the world. Thus, the objectives of this study were to identify proteins in QPM maize in comparison with a common maize line as a control.

Keywords: corn, mass spectrometry, QPM, tryptophan

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Authors: D. Srinivasacharya, Nidhi Humnekar

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The goal of this research is to numerically investigate the convection of nanofluid flow in an inclined porous channel. Brownian motion and thermophoresis effects are accounted for by nanofluid. In addition, the flow in the porous region governs Brinkman’s equation. The perturbed state of the generalized eigenvalue problem is obtained using normal mode analysis, and Chebyshev spectral collocation was used to solve this problem. For various values of the governing parameters, the critical wavenumber and critical Rayleigh number are calculated, and preferred modes are identified.

Keywords: Brinkman model, inclined channel, nanofluid, linear stability, porous media

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Authors: Aboussalih Amira, Zarza Tahar, Fedaoui Kamel, Hammoudi Saleh

Abstract:

This work focuses on the simulation of the prediction of the behaviour of austenitic stainless steel (SS) 304L under complex loading in stress and imposed strain. The Chaboche model is a cable to describe the response of the material by the combination of two isotropic and nonlinear kinematic work hardening, the model is implemented in the ZébuLon computer code. First, we represent the evolution of the axial stress as a function of the plastic strain through hysteresis loops revealing a hardening behaviour caused by the increase in stress by stress in the direction of tension/compression. In a second step, the study of the ratcheting phenomenon takes a key place in this work by the appearance of the average stress. In addition to the solicitation of the material in the biaxial direction in traction / torsion.

Keywords: damage, 304L, Ratcheting, plastic strain

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