Search results for: drug property prediction

Authors: Jan-Peter Mund, Christian Kind

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In view of the ongoing climate change and global warming, fires along railways in Germany are occurring more frequently, with sometimes massive consequences for railway operations and affected railroad infrastructure. In the absence of systematic studies within the infrastructure network of German Rail, little is known about the causes of such embankment fires. Since a further increase in these hazards is to be expected in the near future, there is a need for a sound knowledge of triggers and drivers for embankment fires as well as methodical knowledge of prediction tools. Two predictable future trends speak for the increasing relevance of the topic: through the intensification of the use of rail for passenger and freight transport (e.g..: doubling of annual passenger numbers by 2030, compared to 2019), there will be more rail traffic and also more maintenance and construction work on the railways. This research project approach uses satellite data to identify historical embankment fires along rail network infrastructure. The team links data from these fires with infrastructure and weather data and trains a machine-learning model with the aim of predicting fire hazards on sections of the track. Companies reflect on the results and use them on a pilot basis in precautionary measures.

Keywords: embankment fires, railway maintenance, machine learning, remote sensing, VIIRS data

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Use of speed prediction models sometimes appears as a feasible alternative to laborious field measurement particularly, in case when field data cannot fulfill designer’s requirements. However, developing speed models is a challenging task specifically in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way without any segregation. Here the traffic composition plays a significant role in determining the vehicular speed. The present research was carried out to examine the effects of traffic volume and its composition on vehicular speed under mixed traffic conditions. Classified traffic volume and speed data were collected from different geometrically identical six lane divided arterials in New Delhi. Based on these field data, speed prediction models were developed for individual vehicle category adopting Kriging approximation technique, an alternative for commonly used regression. These models are validated with the data set kept aside earlier for validation purpose. The predicted speeds showed a great deal of agreement with the observed values and also the model outperforms all other existing speed models. Finally, the proposed models were utilized to evaluate the effect of traffic volume and its composition on speed.

Keywords: speed, Kriging, arterial, traffic volume

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Authors: Lina T. Codilla

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This study primarily designed a laboratory activity in ecology to demonstrate the allelopathic property of the Philippine Chromolaena odorata L. (hagonoy) leaf extracts to Lycopersicum esculentum (M), commonly known as tomatoes. Ethanol extracts of C. odorata leaves were tested on seed germination and seedling growth of L. esculentum in 7-day and 14-day observation periods. Analysis of variance and Tukey’s HSD post hoc test was utilized to determine differences among treatments while Pre–test – Post–test experimental design was utilized in the determination of the effectiveness of the designed laboratory activity. Results showed that the 0.5% concentration level of ethanol leaf extracts significantly inhibited germination and seedling growth of L. esculentum in both observation periods. These results were used as the basis in the development of instructional material in ecology. The laboratory activity underwent face validation by five (5) experts in various fields of specialization, namely, Biological Sciences, Chemistry and Science Education. The readability of the designed laboratory activity was determined using a Cloze Test. Pilot testing was conducted and showed that the laboratory activity developed is found to be a very effective tool in supplementing learning about allelopathy in ecology class. Thus, it is recommended for use among ecology classes but modification will be made in a small – scale basis to minimize time consumption.

Keywords: allelopathy, chromolaena odorata l. (hagonoy), designed-laboratory activity, organic herbicide students’ performance

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Authors: K. R. Thabethe, G. A. Adefolaju, M. J. Hosie

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Cervical cancer is the third most commonly diagnosed cancer globally and it is one of three AIDS defining malignancies. Highly active antiretroviral therapy (HAART) is a combination of three or more antiretroviral drugs and has been shown to play a significant role in reducing the incidence of some AIDS defining malignancies, although its effect on cervical cancer is still unclear. The aim of this study was to investigate the relationship between cervical cancer and HAART. This was achieved by studying the expression of two signalling molecules expressed in cervical cancer; MUC1 and P65. Following the 24 hour treatment of a cervical cancer cell line, HCS-2, with drugs which are commonly used as part of HAART at their clinical plasma concentrations, real-time qPCR and immunofluorescence were used in order to study gene and protein expression. A one way ANOVA followed by a Tukey Kramer Post Hoc test was conducted using JMP 11 software on both sets of data. The drug classified as a protease inhibitor (PI) (i.e. LPV/r) reduced MUC1 and P65 gene and protein expression more than the other drug tested. PIs are known to play a significant role in cell death, therefore the cells were thought to be more susceptible to cell death following treatment with PIs. In conclusion, the drugs used, especially the PI showed some anticancer effects by facilitating cell death through decreased gene and protein expression of MUC1 and P65 and present promising agents for cancer treatment.

Keywords: cervical cancer, haart, MUC1, P65

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Authors: Vichayada Laohapiboolkul

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Introduction: As the legal use of cannabis is being widely accepted throughout the world, medical cannabis has been explored in order to become an alternative cure for patients. Tetrahydrocannabinol (THC) and Cannabidiol (CBD) are natural cannabinoids found in the Cannabis plant which is proved to have positive treatment for various diseases and symptoms such as chronic pain, neuropathic pain, spasticity resulting from multiple sclerosis, reduce cancer-associated pain, autism spectrum disorders (ASD), dementia, cannabis and opioid dependence, psychoses/schizophrenia, general social anxiety, posttraumatic stress disorder, anorexia nervosa, attention-deficit hyperactivity disorder, and Tourette's disorder. Regardless of all the medical benefits, THC, if not metabolized, can lead to mild up to severe adverse drug reactions (ADR). The enzyme CYP2C19 was found to be one of the metabolizers of THC. However, the suballele CYP2C19*2 manifests as a poor metabolizer which could lead to higher levels of THC than usual, possibly leading to various ADRs. Objective: The aim of this study was to investigate the distribution of CYP2C19, specifically CYP2C19*2, genes in Thai patients treated with medical cannabis along with adverse drug reactions. Materials and Methods: Clinical data and EDTA whole blood for DNA extraction and genotyping were collected from patients for this study. CYP2C19*2 (681G>A, rs4244285) genotyping was conducted using the Real-time PCR (ABI, Foster City, CA, USA). Results: There were 42 medical cannabis-induced ADRs cases and 18 medical cannabis tolerance controls who were included in this study. A total of 60 patients were observed where 38 (63.3%) patients were female and 22 (36.7%) were male, with a range of age approximately 19 - 87 years. The most apparent ADRs for medical cannabis treatment were dry mouth/dry throat (76.7%), followed by tachycardia (70%), nausea (30%) and a few arrhythmias (10%). In the total of 27 cases, we found a frequency of 18 CYP2C19*1/*1 alleles (normal metabolizers, 66.7%), 8 CYP2C19*1/*2 alleles (intermediate metabolizers, 29.6%) and 1 CYP2C19*2/*2 alleles (poor metabolizers, 3.7%). Meanwhile, 63.6% of CYP2C19*1/*1, 36.3% and 0% of CYP2C19*1/*2 and *2/*2 in the tolerance controls group, respectively. Conclusions: This is the first study to confirm the distribution of CYP2C19*2 allele and the prevalence of poor metabolizer genes in Thai patients who received medical cannabis for treatment. Thus, CYP2C19 allele might serve as a pharmacogenetics marker for screening before initiating treatment.

Keywords: medical cannabis, adverse drug reactions, CYP2C19, tetrahydrocannabinol, poor metabolizer

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Authors: Lojain Suliman, Xinrong Liu, Xiaohan Zhou

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Since most tunnels are built in crowded metropolitan settings, the excavation process must take place in highly condensed locations, including high-density cities. In this way, the tunnels are typically located close together, which leads to more interaction between the parallel existing tunnels, and this, in turn, leads to more settlement. This research presents an examination of the impact of a large-scale tunnel excavation on two forms of settlement: surface settlement and settlement surrounding the tunnel. Additionally, research has been done on the properties of interactions between two and three parallel tunnels. The settlement has been evaluated using three primary techniques: theoretical modeling, numerical simulation, and data monitoring. Additionally, a parametric investigation on how distance affects the settlement characteristic for parallel tunnels with short distances has been completed. Additionally, it has been observed that the sequence of excavation has an impact on the behavior of settlements. Nevertheless, a comparison of the model test and numerical simulation yields significant agreement in terms of settlement trend and value. Additionally, when compared to the FEM study, the suggested analytical solution exhibits reduced sensitivity in the settlement prediction. For example, the settlement of the small tunnel diameter does not appear clearly on the settlement curve, while it is notable in the FEM analysis. It is advised, however, that additional studies be conducted in the future employing analytical solutions for settlement prediction for parallel tunnels.

Keywords: settlement, FEM, analytical solution, parallel tunnels

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Authors: F. O. Adeosun, I. O. Orubuloye, O. O. Babalola

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Although caesarean section has greatly improved obstetric care throughout the world, in developing countries there is a great aversion to caesarean section. This study was carried out to examine the rate at which pregnant women with pre-eclampsia choose caesarean section over natural birth. A cross-sectional study was conducted among 500 pre-eclampsia antenatal clients seen at the States University Teaching Hospitals in the last one year. The sample selection was purposive. Information on their educational background, beliefs and attitudes were collected. Data analysis was presented using simple percentages. Out of 500 women studied, 38% favored caesarean section while 62% were against it. About 89% of them understood what caesarean section is, 57.3% of those who understood what caesarean section is will still not choose it as an option. Over 85% of the women believed caesarean section is done for medical reasons. If caesarean section is given as an option for childbirth, 38% would go for it, 29% would try religious intervention, 5.5% would not choose it because of fear, while 27.5% would reject it because they believe it is culturally wrong. Majority of respondents (85%) who favored caesarean delivery are aware of the risk attached to choosing virginal birth but go an extra mile in sourcing funds for a caesarean session while over 64% cannot afford the cost of caesarean delivery. It is therefore pertinent to encourage research in prediction methods and prevention of occurrence, since this would assist patients to plan on how to finance treatment.

Keywords: caesarean section, choice, cost, pre eclampsia, prediction methods

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: Nura Brimo, Dilek Cokeliler Serdaroglu, Tansel Uyar, Busra Uysal, Elif Bahar Cakici, Miris Dikmen, Zerrin Canturk

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Background: A combination of antibiotics, including metronidazole (MET), ciprofloxacin (CIP), and minocycline (MINO), has been demonstrated to disinfect bacteria in necrotic teeth before regenerative processes. It has been presented clinically that antibiotic pastes may drive to possible stem cell death and difficulties in removing from the canal system, which can limit the regenerative procedure. This study was designed to (1) synthesize nanofibrous webs containing various concentrations of different medicaments (triple, double, and calcium hydroxide,Ca(OH)2), and (2) coat thiselectrospun fibrous gutta-percha (GP) cones. Methods: Poly(vinylpyrrolidone) (PVP)-based electrospun fibrous webs were processed with low medicaments concentrations. Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray Spectroscopy (EDX), and X-Ray Photoelectron Spectroscopy (XPS) were carried out to investigate fiber morphology, antibiotic incorporation, and characterized GP-coated fibrous webs, respectively. The chemical and physical properties of dentine were carried out via Fourier Transform Infrared Spectroscopy (FTIR) and Nano-SEM, respectively. The antimicrobial properties of the different fibrous webs were assessed against various bacteria by direct nanofiber/bacteria contact. Cytocompatibility was measured by applying the MTT method. Results: The mean fiber diameter of the experiment groups of medicament-containing fibers ranged in the nm scale and was significantly smaller than PVP fibers. EDX analysis confirmed the presence of medicaments in the nanofibers. XPS analysis presented a complete coating of the fibers with GPs; FTIR and Nano-SEM showed no chemical and physical configuration of intracanal medicaments on the dentine surface. Meanwhile, nanofibrous webs led to a significant reduction in the percentage of viable bacteria compared with the negative control and PVP. Conclusion: Our findings suggest that TA-NFs, DA-NFs, and Cₐ(OH)₂)-NFs coated GP cones have significant potential in eliminating intracanal bacteria, cell-friendly behavior, and clinical usage features.

Keywords: drug delivery, drug carrier, electrospinning, nano/microfibers, regenerative endodontic, morphology

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Authors: Lin Qing

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[Objective/Significance] The cultivation of high-value patents is the focus and difficulty of patent work, which is of great significance to the construction of a powerful country with intellectual property rights. This work should not only pay attention to the existing patent applications, but also start from the pre-application to explore the high-value technical solutions as the core of high-value patents. [Methods/processes] Comply with the principle of scientific and technological knowledge diffusion, this study studies the top academic conference papers and their cited patent applications, taking the power semiconductor field as an example, using facts date show the feasibility and rationality of mining technology solutions from high quality research results to foster high value patents, stating the actual benefits of these achievements to the industry, giving patent protection suggestions for Chinese applicants comparative with field situation. [Results/Conclusion] The research shows that the quality of citation applications of ISPSD papers is significantly higher than the field average level, and the ability of Chinese applicants to use patent protection related achievements needs to be improved. This study provides a practical and highly targeted reference idea for patent administrators and researchers, and also makes a positive exploration for the practice of the spirit of breaking the five rules.

Keywords: high-value patents cultivation, technical solutions, knowledge diffusion, top academic conference papers, intellectual property information analysis

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

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Authors: S. Roy, R. Rekha, K. Sriram, G. Subhadra, R. Johana

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Cardiovascular disease remains a leading cause of death in most industrialized countries. Many chemical drugs are available in the market which targets different receptor proteins related to cardiovascular diseases. Of late the traditional herbal drugs are safer when compared to chemical drugs because of its side effects. However, many herbal remedies used in treating cardiovascular diseases have not undergone scientific assessment to prove its pharmacological activities. There are many natural compounds, nature derived and Natural product mimic compounds are available which are in the market as approved drug. In the most of the cases drug activity at the molecular level are not known. Here we have categorized those compounds with our experimental compounds in different classes based on the structural similarity and physicochemical properties, using a tool, Chemmine and has attempted to understand the mechanism of the action of a experimental compound, which are clustered with Simvastatin, Lovastatin, Mevastatin and Pravastatin. Target protein molecule for Simvastatin, Lovastatin, Mevastatin and Pravastatin is HMG-CoA reductase, so we concluded that the experimental compound may be able to bind to the same target. Molecular docking and atomic interaction studies with simvastatin and our experimental compound were compared. A pharmacophore modeling was done based on the experimental compound and HMG-CoA reductase inhibitor.

Keywords: molecular docking, physicochemical properties, pharmacophore modeling structural similarity, pravastatin

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Authors: Cristian Păuna

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In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

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Authors: Vibhuti Amarnath Madhu Agrawal

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Artificial Intelligence (AI) and Intellectual Property are two emerging concepts that are growing at a fast pace and have the potential of having a huge impact on the economy in the coming times. In simple words, AI is nothing but work done by a machine without any human intervention. It is a coded software embedded in a machine, which over a period of time, develops its own intelligence and begins to take its own decisions and judgments by studying various patterns of how people think, react to situations and perform tasks, among others. Intellectual Property, especially Copyright Law, on the other hand, protects the rights of individuals and Companies in content creation that primarily deals with application of intellect, originality and expression of the same in some tangible form. According to some of the reports shared by the media lately, ChatGPT, an AI powered Chatbot, has been involved in the creation of a wide variety of original content, including but not limited to essays, emails, plays and poetry. Besides, there have been instances wherein AI technology has given creative inputs for background, lights and costumes, among others, for films. Copyright Law offers protection to all of these different kinds of content and much more. Considering the two key parameters of Copyright – application of intellect and originality, the question, therefore, arises that will awarding Copyright protection to a person who has not directly invested his / her intellect in the creation of that content go against the basic spirit of Copyright laws? This study aims to analyze the current scenario and provide answers to the following questions: a. If the content generated by AI technology satisfies the basic criteria of originality and expression in a tangible form, why should such content be denied protection in the name of its creator, i.e., the specific AI tool / technology? B. Considering the increasing role and development of AI technology in our lives, should it be given the status of a ‘Legal Person’ in law? C. If yes, what should be the modalities of awarding protection to works of such Legal Person and management of the same? Considering the current trends and the pace at which AI is advancing, it is not very far when AI will start functioning autonomously in the creation of new works. Current data and opinions on this issue globally reflect that they are divided and lack uniformity. In order to fill in the existing gaps, data obtained from Copyright offices from the top economies of the world have been analyzed. The role and functioning of various Copyright Societies in these countries has been studied in detail. This paper provides a roadmap that can be adopted to satisfy various objectives, constraints and dynamic conditions related AI technology and its protection under Copyright Law.

Keywords: artificial intelligence technology, copyright law, copyright societies, intellectual property

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Authors: Sujosh Nandi, Proshanta Guha

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Synthetic plastics are preferred for food packaging due to high strength, stretch-ability, good water vapor and gas barrier properties, transparency and low cost. However, environmental pollution generated by these synthetic plastics is a major concern of modern human civilization. Therefore, use of biodegradable polymers as a substitute for synthetic non-biodegradable polymers are encouraged to be used even after considering drawbacks related to mechanical and barrier properties of the films. Starch is considered one of the potential raw material for the biodegradable polymer, encounters poor water barrier property and mechanical properties due to its hydrophilic nature. That apart, recrystallization of starch molecules occurs during aging which decreases flexibility and increases elastic modulus of the film. The recrystallization process can be minimized by blending of other hydrocolloids having similar structural compatibility, into the starch matrix. Therefore, incorporation of guar gum having a similar structural backbone, into the starch matrix can introduce a potential film into the realm of biodegradable polymer. However, hydrophilic nature of both starch and guar gum, water barrier property of the film is low. One of the prospective solution to enhance this could be modification of the potato starch/guar gum (PSGG) composite film using cross-linker. Over the years, several cross-linking agents such as phosphorus oxychloride, sodium trimetaphosphate, etc. have been used to improve water vapor permeability (WVP) of the films. However, these chemical cross-linking agents are toxic, expensive and take longer time to degrade. Therefore, naturally available carboxylic acid (tartaric acid, malonic acid, succinic acid, etc.) had been used as a cross-linker and found that water barrier property enhanced substantially. As per our knowledge, no works have been reported with tartaric acid and succinic acid as a cross-linking agent blended with the PSGG films. Therefore, the objective of the present study was to examine the changes in water vapor barrier property and mechanical properties of the PSGG films after cross-linked with tartaric acid (TA) and succinic acid (SA). The cross-linkers were blended with PSGG film-forming solution at four different concentrations (4, 8, 12 & 16%) and cast on teflon plate at 37°C for 20 h. From the fourier-transform infrared spectroscopy (FTIR) study of the developed films, a band at 1720cm-1 was observed which is attributed to the formation of ester group in the developed films. On the other hand, it was observed that tensile strength (TS) of the cross-linked film decreased compared to non-cross linked films, whereas strain at break increased by several folds. Moreover, the results depicted that tensile strength diminished with increasing the concentration of TA or SA and lowest TS (1.62 MPa) was observed for 16% SA. That apart, maximum strain at break was also observed for TA at 16% and the reason behind this could be a lesser degree of crystallinity of the TA cross-linked films compared to SA. However, water vapor permeability of succinic acid cross-linked film was reduced significantly, but it was enhanced significantly by addition of tartaric acid.

Keywords: cross linking agent, guar gum, organic acids, potato starch

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Authors: Abdelmalk Brahma

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Drying is a phenomenon that accompanies the hardening of hydraulic materials. It can, if it is not prevented, lead to significant spontaneous dimensional variations, which the cracking is one of events. In this context, cracking promotes the transport of aggressive agents in the material, which can affect the durability of concrete structures. Drying shrinkage develops over a long period almost 30 years although most occurred during the first three years. Drying shrinkage stabilizes when the material is water balance with the external environment. The drying shrinkage of cementitious materials is due to the formation of capillary tensions in the pores of the material, which has the consequences of bringing the solid walls of each other. Knowledge of the shrinkage characteristics of concrete is a necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable shrinkage movement in reinforced or prestressed concrete and the appropriate steps can be taken in design to accommodate this movement. This study is concerned the modelling of drying shrinkage of the hydraulic materials and the prediction of the rate of spontaneous deformations of hydraulic materials during hardening. The model developed takes in consideration the main factors affecting drying shrinkage. There was agreement between drying shrinkage predicted by the developed model and experimental results. In last we show that developed model describe the evolution of the drying shrinkage of high performances concretes correctly.

Keywords: drying, hydraulic concretes, shrinkage, modeling, prediction

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Authors: Tarek Duzan

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Fractures are a fundamental property of most reservoirs. Despite their abundance, they remain difficult to detect and quantify. The most effective characterization of fractured reservoirs is accomplished by integrating geological, geophysical, and engineering data. Detection of fractures and defines their relative contribution is crucial in the early stages of exploration and later in the production of any field. Because fractures could completely change our thoughts, efforts, and planning to produce a specific field properly. From the structural point of view, all reservoirs are fractured to some point of extent. North Gialo field is thought to be a naturally fractured reservoir to some extent. Historically, natural fractured reservoirs are more complicated in terms of their exploration and production efforts, and most geologists tend to deny the presence of fractures as an effective variable. Our aim in this paper is to determine the degree of fracturing, and consequently, our evaluation and planning can be done properly and efficiently from day one. The challenging part in this field is that there is no enough data and straightforward well testing that can let us completely comfortable with the idea of fracturing; however, we cannot ignore the fractures completely. Logging images, available well testing, and limited core studies are our tools in this stage to evaluate, model, and predict possible fracture effects in this reservoir. The aims of this study are both fundamental and practical—to improve the prediction and diagnosis of natural-fracture attributes in N. Gialo hydrocarbon reservoirs and accurately simulate their influence on production. Moreover, the production of this field comes from 2-phase plan; a self depletion of oil and then gas injection period for pressure maintenance and increasing ultimate recovery factor. Therefore, well understanding of fracturing network is essential before proceeding with the targeted plan. New analytical methods will lead to more realistic characterization of fractured and faulted reservoir rocks. These methods will produce data that can enhance well test and seismic interpretations, and that can readily be used in reservoir simulators.

Keywords: natural fracture, sandstone reservoir, geological, geophysical, and engineering data

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Authors: Ishapathik Das

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The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: Weiam Daear, Patrick Lai, Elmar Prenner

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The human body offers many avenues that could be used for drug delivery. The pulmonary route, which is delivered through the lungs, presents many advantages that have sparked interested in the field. These advantages include; 1) direct access to the lungs and the large surface area it provides, and 2) close proximity to the blood circulation. The air-blood barrier of the alveoli is about 500 nm thick. The air-blood barrier consist of a monolayer of lipids and few proteins called the lung surfactant and cells. This monolayer consists of ~90% lipids and ~10% proteins that are produced by the alveolar epithelial cells. The two major lipid classes constitutes of various saturation and chain length of phosphatidylcholine (PC) and phosphatidylglycerol (PG) representing 80% of total lipid component. The major role of the lung surfactant monolayer is to reduce surface tension experienced during breathing cycles in order to prevent lung collapse. In terms of the pulmonary drug delivery route, drugs pass through various parts of the respiratory system before reaching the alveoli. It is at this location that the lung surfactant functions as the air-blood barrier for drugs. As the field of nanomedicine advances, the use of nanoparticles (NPs) as drug delivery vehicles is becoming very important. This is due to the advantages NPs provide with their large surface area and potential specific targeting. Therefore, studying the interaction of NPs with lung surfactant and whether they affect its stability becomes very essential. The aim of this research is to develop a biomimetic model of the human lung surfactant followed by a biophysical analysis of the interaction of polymeric NPs. This biomimetic model will function as a fast initial mode of testing for whether NPs affect the stability of the human lung surfactant. The model developed thus far is an 8-component lipid system that contains major PC and PG lipids. Recently, a custom made 16:0/16:1 PC and PG lipids were added to the model system. In the human lung surfactant, these lipids constitute 16% of the total lipid component. According to the author’s knowledge, there is not much monolayer data on the biophysical analysis of the 16:0/16:1 lipids, therefore more analysis will be discussed here. Biophysical techniques such as the Langmuir Trough is used for stability measurements which monitors changes to a monolayer's surface pressure upon NP interaction. Furthermore, Brewster Angle Microscopy (BAM) employed to visualize changes to the lateral domain organization. Results show preferential interactions of NPs with different lipid groups that is also dependent on the monolayer fluidity. Furthermore, results show that the film stability upon compression is unaffected, but there are significant changes in the lateral domain organization of the lung surfactant upon NP addition. This research is significant in the field of pulmonary drug delivery. It is shown that NPs within a certain size range are safe for the pulmonary route, but little is known about the mode of interaction of those polymeric NPs. Moreover, this work will provide additional information about the nanotoxicology of NPs tested.

Keywords: Brewster angle microscopy, lipids, lung surfactant, nanoparticles

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Authors: Tang Wei, Yang Xiaofeng, Gui Yewei, Du Yanxia

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Complex lifting entry was selected for precise landing performance during the Mars Science Laboratory entry. This study aims to develop the three-dimensional numerical method for precise computation and the surface panel method for rapid engineering prediction. Detailed flow field analysis for Mars exploration mission was performed by carrying on a series of fully three-dimensional Navier-Stokes computations. The static aerodynamic performance was then discussed, including the surface pressure, lift and drag coefficient, lift-to-drag ratio with the numerical and engineering method. Computation results shown that the shock layer is thin because of lower effective specific heat ratio, and that calculated results from both methods agree well with each other, and is consistent with the reference data. Aerodynamic performance analysis shows that CG location determines trim characteristics and pitch stability, and certain radially and axially shift of the CG location can alter the capsule lifting entry performance, which is of vital significance for the aerodynamic configuration des0ign and inner instrument layout of the Mars entry capsule.

Keywords: Mars entry capsule, static aerodynamics, computational fluid dynamics, hypersonic

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Authors: Nisa Mohan

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Clinical coding is a feasible method for estimating the national prevalence of adverse drug event (ADE) admissions. However, under-coding of ADE admissions is a limitation of this method. Whilst the under-coding will impact the accurate estimation of the actual burden of ADEs, the feasibility of the coded data in estimating the adverse drug event admissions goes much further compared to the other methods. Therefore, it is necessary to know the reasons for the under-coding in order to improve the clinical coding of ADE admissions. The ability to identify the reasons for the under-coding of ADE admissions rests on understanding the decision-making process of coding ADE admissions. Hence, the current study aimed to explore the decision-making process of clinical coders when coding cases of ADE admissions. Clinical coders from different levels of coding job such as trainee, intermediate and advanced level coders were purposefully selected for the interviews. Thirteen clinical coders were recruited from two Auckland region District Health Board hospitals for the interview study. Semi-structured, one-on-one, face-to-face interviews using open-ended questions were conducted with the selected clinical coders. Interviews were about 20 to 30 minutes long and were audio-recorded with the approval of the participants. The interview data were analysed using a general inductive approach. The interviews with the clinical coders revealed that the coders have targets to meet, and they sometimes hesitate to adhere to the coding standards. Coders deviate from the standard coding processes to make a decision. Coders avoid contacting the doctors for clarifying small doubts such as ADEs and the name of the medications because of the delay in getting a reply from the doctors. They prefer to do some research themselves or take help from their seniors and colleagues for making a decision because they can avoid a long wait to get a reply from the doctors. Coders think of ADE as a small thing. Lack of time for searching for information to confirm an ADE admission, inadequate communication with clinicians, along with coders’ belief that an ADE is a small thing may contribute to the under-coding of the ADE admissions. These findings suggest that further work is needed on interventions to improve the clinical coding of ADE admissions. Providing education to coders about the importance of ADEs, educating clinicians about the importance of clear and confirmed medical records entries, availing pharmacists’ services to improve the detection and clear documentation of ADE admissions, and including a mandatory field in the discharge summary about external causes of diseases may be useful for improving the clinical coding of ADE admissions. The findings of the research will help the policymakers to make informed decisions about the improvements. This study urges the coding policymakers, auditors, and trainers to engage with the unconscious cognitive biases and short-cuts of the clinical coders. This country-specific research conducted in New Zealand may also benefit other countries by providing insight into the clinical coding of ADE admissions and will offer guidance about where to focus changes and improvement initiatives.

Keywords: adverse drug events, clinical coders, decision making, hospital admissions

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Authors: Shishay Kidanu, Abdullah Alhaj

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Sinkholes in the karst terrain of Greene County, Missouri, pose significant geohazards, imposing challenges on construction and infrastructure development, with potential threats to lives and property. To address these issues, understanding the influencing factors and modeling sinkhole susceptibility is crucial for effective mitigation through strategic changes in land use planning and practices. This study utilizes geographic information system (GIS) software to collect and process diverse data, including topographic, geologic, hydrogeologic, and anthropogenic information. Nine key sinkhole influencing factors, ranging from slope characteristics to proximity to geological structures, were carefully analyzed. The Frequency Ratio method establishes relationships between attribute classes of these factors and sinkhole events, deriving class weights to indicate their relative importance. Weighted integration of these factors is accomplished using the Analytic Hierarchy Process (AHP) and the Weighted Linear Combination (WLC) method in a GIS environment, resulting in a comprehensive sinkhole susceptibility index (SSI) model for the study area. Employing Jenk's natural break classifier method, the SSI values are categorized into five distinct sinkhole susceptibility zones: very low, low, moderate, high, and very high. Validation of the model, conducted through the Area Under Curve (AUC) and Sinkhole Density Index (SDI) methods, demonstrates a robust correlation with sinkhole inventory data. The prediction rate curve yields an AUC value of 74%, indicating a 74% validation accuracy. The SDI result further supports the success of the sinkhole susceptibility model. This model offers reliable predictions for the future distribution of sinkholes, providing valuable insights for planners and engineers in the formulation of development plans and land-use strategies. Its application extends to enhancing preparedness and minimizing the impact of sinkhole-related geohazards on both infrastructure and the community.

Keywords: sinkhole, GIS, analytical hierarchy process, frequency ratio, susceptibility, Missouri

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Authors: Numair A. Siddiqui, Usman Muhammad, Manoj J. Mathew, Ramkumar M., Benjamin Sautter, Muhammad A. K. El-Ghali, David Menier, Shiqi Zhang

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The sedimentological parameterization of shallow-marine siliciclastic reservoirs in terms of reservoir quality and heterogeneity from outcrop study can help improve the subsurface reservoir prediction. An architectural analysis has documented variations in sandstone geometry and rock properties within shallow-marine sandstone exposed in the Miocene Sandakan Formation of Sabah, Borneo. This study demonstrates reservoir sandstone quality assessment for subsurface rock evaluation, from well-exposed successions of the Sandakan Formation, Borneo, with which applicable analogues can be identified. The analyses were based on traditional conventional field investigation of outcrops, grain-size and petrographic studies of hand specimens of different sandstone facies and gamma-ray and permeability measurements. On the bases of these evaluations, the studied sandstone was grouped into three qualitative reservoir rock classes; high (Ø=18.10 – 43.60%; k=1265.20 – 5986.25 mD), moderate (Ø=17.60 – 37%; k=21.36 – 568 mD) and low quality (Ø=3.4 – 15.7%; k=3.21 – 201.30 mD) for visualization and prediction of subsurface reservoir quality. These results provided analogy for shallow marine sandstone reservoir complexity that can be utilized in the evaluation of reservoir quality of regional and subsurface analogues.

Keywords: architecture and sedimentology, subsurface rock evaluation, reservoir quality, borneo

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Authors: Hanjun Zhao, Ke Zhang, Bojian Zheng

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Background: So far, there is no universal antiviral agent which can inhibit multiple viral infections. More and more drug-resistant viral strains emerge after the antiviral drug application for treatment. Defensins are the front line of host innate immunity and have broad spectrum antibacterial and antiviral effects. However, there is limited data to show if these defensins have good antiviral activity in vivo and what the antiviral mechanism is. Subjects: To investigate a peptide with widespread antivirus activity in vitro and in vivo and illustrate the antiviral mechanism. Methods: Antiviral peptide library designed from mouse beta defensins was synthesized by the company. Recombinant beta defensin was obtained from E. coli. Antiviral activity in vitro was assayed by plaque assay, qPCR. Antiviral activity in vivo was detected by animal challenge with 2009 pandemic H1N1 influenza A virus. The antiviral mechanism was assayed by western blot, ELISA, and qPCR. Conclusions: We identify a new peptide which has widespread effects against multiple viruses (H1N1, H5N1, H7N9, MERS-CoV) in vitro and has efficient antivirus activity in vivo. This peptide inhibits viral entry into target cells and subsequently blocks viral replication. The in vivo study of the antiviral peptide against other viral infections and the investigation of its more detail antiviral mechanism are ongoing.

Keywords: antiviral peptide, defensin, Influenza A virus, mechanism

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Authors: Simona Dostalova, Pavel Kopel, Marketa Vaculovicova, Vojtech Adam, Rene Kizek

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Protein apoferritin seems to be a very promising structure for use as a nanocarrier. It is prepared from intracellular ferritin protein naturally found in most organisms. The role of ferritin proteins is to store and transport ferrous ions. Apoferritin is a hollow protein cage without ferrous ions that can be prepared from ferritin by reduction with thioglycolic acid or dithionite. The structure of apoferritin is composed of 24 protein subunits, creating a sphere with 12 nm in diameter. The inner cavity has a diameter of 8 nm. The drug encapsulation process is based on the response of apoferritin structure to the pH changes of surrounding solution. In low pH, apoferritin is disassembled into individual subunits and its structure is “opened”. It can then be mixed with any desired cytotoxic drug and after adjustment of pH back to neutral the subunits are reconnected again and the drug is encapsulated within the apoferritin particles. Excess drug molecules can be removed by dialysis. The receptors for apoferritin, SCARA5 and TfR1 can be found in the membrane of both healthy and cancer cells. To enhance the specific targeting of apoferritin nanocarrier, it is possible to modify its surface with targeting moieties, such as antibodies. To ensure sterically correct complex, we used a a peptide linker based on a protein G with N-terminus affinity towards Fc region of antibodies. To connect the peptide to the surface of apoferritin, the C-terminus of peptide was made of cysteine with affinity to gold. The surface of apoferritin with encapsulated doxorubicin (ApoDox) was coated either with gold nanoparticles (ApoDox-Nano) or gold (III) chloride hydrate reduced with sodium borohydride (ApoDox-HAu). The applied amount of gold in form of gold (III) chloride hydrate was 10 times higher than in the case of gold nanoparticles. However, after removal of the excess unbound ions by electrophoretic separation, the concentration of gold on the surface of apoferritin was only 6 times higher for ApoDox-HAu in comparison with ApoDox-Nano. Moreover, the reduction with sodium borohydride caused a loss of doxorubicin fluorescent properties (excitation maximum at 480 nm with emission maximum at 600 nm) and thus its biological activity. Fluorescent properties of ApoDox-Nano were similar to the unmodified ApoDox, therefore it was more suited for the intended use. To evaluate the specificity of apoferritin modified with antibodies, we used ELISA-like method with the surface of microtitration plate wells coated by the antigen (goat anti-human IgG antibodies). To these wells, we applied ApoDox without targeting antibodies and ApoDox-Nano modified with targeting antibodies (human IgG antibodies). The amount of unmodified ApoDox on antigen after incubation and subsequent rinsing with water was 5 times lower than in the case of ApoDox-Nano modified with targeting antibodies. The modification of non-gold ApoDox with antibodies caused no change in its targeting properties. It can therefore be concluded that the demonstrated procedure allows us to create nanocarrier with enhanced targeting properties, suitable for nanomedicine.

Keywords: apoferritin, doxorubicin, nanocarrier, targeting antibodies

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Authors: Kazunari Ozaki, Mitsuru Kageyama, Kenki Miyazawa, Yoshio Nakamura

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Background: Kampo (Japanese Traditional medicine) is a medicine traditionally practiced in Japan, based on ancient Chinese medicine. Most Kampo doctors have used decoction of crude drug pieces for treatment. 93% of the Kampo drugs sold in Japan are Kampo products nowadays. Of all Kampo products, 81% of them are Kampo extract formulations for prescription, which is prepared in powdered or granulated form from medicinal crude drug extracts mixed with appropriate excipient. Physicians with medical license for Western medicine prescribe these Kampo extract formulations for prescription in Japan. Objectives: Our study aims at presenting a brief history of Kampo extract formulations for prescription in Japan. Methods: Systematic searches for relevant studies were conducted using not only printed journals but also electronic journals from the bibliographic databases, such as PubMed/Medline, Ichushi-Web, and university/institutional websites, as well as search engines, such as Google and Google Scholar. Results: The first commercialization of Kampo extract formulations for general use (or OTC (over-the-counter) Kampo extract formulation) was achieved after 1957. The number of drugs has been subsequentially increased, reaching 148 Kampo extract formulation for prescription currently. Conclusion: We provide a history of Kampo extract formulations for prescription in Japan. The originality of this research is that it analyzes the background history of Kampo in parallel with relevant transitions in the government and insurance systems.

Keywords: health insurance system, history, Kampo, Kampo extract formulation for prescription, OTC Kampo extract formulation, pattern corresponding prescription (Ho-sho-so-tai) system

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Authors: Arsalan Ghaderi

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Introduction: Inactivity is one of the most important risk factors for cardiovascular disease. According to the study prevalence of inactivity in Iran, about 67.5% and in the staff, and especially nurses, are similar. The inflammatory index (HS-CRP) is highly predictive of the progression of these diseases. Physical activity education is very important in preventing these diseases. One of the modern educational methods is web-based theory-based education. Methods: This is a semi-experimental interventional study which was conducted in Isfahan and Kurdistan universities of medical sciences in two stages. A cross-sectional study was done to determine the status of physical activity and its predictive factors. Then, intervention was performed, and six months later the data were retrieved. The data was collected using a demographic questionnaire, an integrative model of behavior prediction constructs, a standard physical activity questionnaire and (HS-CRP) test. Data were analyzed by SPSS software. Results: Physical activity was low in 66.6% of nurses, 25.4% were moderate and 8% severe. According to Pearson correlation matrix, the highest correlation was found between behavioral intention and skill structures (0.553**), subjective norms (0.222**) and self-efficacy (0.198**). The relationship between age and physical activity in the first study was reverse and significant. After intervention, there was a significant change in attitudes, self-efficacy, skill and behavioral intention in the intervention group. This change was significant in attitudes, self-efficacy and environmental conditions of the control group. HS-CRP index decreased significantly after intervention in both groups, but there was not a significant relationship between inflammatory index and physical activity score. The change in physical activity level was significant only in the control group. Conclusion: Despite the effect of educational intervention on attitude, self-efficacy, skill, and behavioral intention, the results showed that if factors such as environmental factors are not corrected, training and changing structures cannot lead to physical activity behavior. On the other hand, no correlation between physical activity and HS-CRP showed that this index can be influenced by other factors, and this should be considered in any intervention to reduce the HS-CRP index.

Keywords: HS-CRP, integrative model of behavior prediction, physical activity, nurses, web-based education

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Authors: Amrit Ladhani

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The cyclic universe theory is a model of cosmic evolution according to which the universe undergoes endless cycles of expansion and cooling, each beginning with a “big bang” and ending in a “big crunch”. In this paper, we propose a unique property of Space-time. This particular and marvelous nature of space shows us that space can stretch, expand, and shrink. This property of space is caused by the size of the universe change over time: growing or shrinking. The observed accelerated expansion, which relates to the stretching of Shrunk space for the new theory, is derived. This theory is based on three underlying notions: First, the Big Bang is not the beginning of Space-time, but rather, at the very beginning fraction of a second, there was an infinite force of infinite Shrunk space in the cosmic singularity that force gave rise to the big bang and caused the rapidly growing of space, and all other forms of energy are transformed into new matter and radiation and a new period of expansion and cooling begins. Second, there was a previous phase leading up to it, with multiple cycles of contraction and expansion that repeat indefinitely. Third, the two principal long-range forces are the gravitational force and the repulsive force generated by shrink space. They are the two most fundamental quantities in the universe that govern cosmic evolution. They may provide the clockwork mechanism that operates our eternal cyclic universe. The universe will not continue to expand forever; no need, however, for dark energy and dark matter. This new model of Space-time and its unique properties enables us to describe a sequence of events from the Big Bang to the Big Crunch.

Keywords: dark matter, dark energy, cosmology, big bang and big crunch

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