The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA molecule. A sequence DL model based on a bidirectional gated recurrent unit (GRU) is implemented. The model is applied to the Stanford COVID-19 mRNA vaccine dataset to predict the mRNA sequences deterioration by predicting five reactivity values for every base in the sequence, namely reactivity values, deterioration rates at high pH, at high temperature, at high pH with Magnesium, and at high temperature with Magnesium. The Stanford COVID-19 mRNA vaccine dataset is split into the training set, validation set, and test set. The bidirectional GRU model minimizes the mean column wise root mean squared error (MCRMSE) of deterioration rates at each base of the mRNA sequence molecule with a value of 0.32086 for the test set which outperformed the winning models with a margin of (0.02112). This study would help other researchers better understand how to forecast mRNA sequence molecule properties to develop a stable COVID-19 vaccine.
The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of
... Show MoreIs a high degree of economic freedom an important part in the development of the economies of developing countries in the last decade of the twentieth century and the beginning of the twentieth century and the atheist. This is because a test subject (deltoid analysis of the relationship between the degree of economic freedom and foreign trade of selected developing countries for the period
( 1990 -2005) to determine the degree of economic freedom in foreign trade promotion in Singapore and Turkey. The research recommends a number of recommendations, the most important is the responsibility of the Ministry of Planning in Iraq that is providing the necessary data for the Fraser Institute, the aim of increasing cooperation
In this paper, we will provide a proposed method to estimate missing values for the Explanatory variables for Non-Parametric Multiple Regression Model and compare it with the Imputation Arithmetic mean Method, The basis of the idea of this method was based on how to employ the causal relationship between the variables in finding an efficient estimate of the missing value, we rely on the use of the Kernel estimate by Nadaraya – Watson Estimator , and on Least Squared Cross Validation (LSCV) to estimate the Bandwidth, and we use the simulation study to compare between the two methods.
In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather t
... Show Moreبسبب محدودية الموارد الطبيعية، فأن سكان الخليج ومنذ القدم وجهوا جلّ نشاطاتهم توجهاً بحرياً: صيد الاسماك وصناعة اللؤلؤ العنصر الرئيس في حجم التشغيل، وتكوين الفائض الاقتصادي في المنطقة آنذاك.
لقد تزايدت أهمية هذا النشاط بخاصة بعد النصف الثاني من القرن التاسع عشر، لرواج تجارة اللؤلؤ عالمياً، وأنفتاح الخليج على الدول الاوروبية التي شهدت نهضة صناعية متسارعة، وحيث وصلت الخليج العديد من بضائع
... Show MoreThe main purpose of this investigation is to evaluate the concentrations of six essential metals (Na+, Mg2+, K+, Ca2+, Fe2+ and Zn2+) in saffron and a farm soil using the neutron activation analysis (NAA) as a nuclear spectrometry method. The stratified random sampling method was used here. The NAA results showed the well uptake of Mg2+, K+, Ca2+, Fe2+, and Zn2+ in saffron, which is lower than the toxicity range. Based on the contamination factor and geoaccumulation index, soil contamination levels were determined uncontaminated by Zn, moderately contaminated by Na+ and Fe2+, and strongly contamin
... Show MoreIn this research, the nonparametric technique has been presented to estimate the time-varying coefficients functions for the longitudinal balanced data that characterized by observations obtained through (n) from the independent subjects, each one of them is measured repeatedly by group of specific time points (m). Although the measurements are independent among the different subjects; they are mostly connected within each subject and the applied techniques is the Local Linear kernel LLPK technique. To avoid the problems of dimensionality, and thick computation, the two-steps method has been used to estimate the coefficients functions by using the two former technique. Since, the two-
... Show MoreSymmetric cryptography forms the backbone of secure data communication and storage by relying on the strength and randomness of cryptographic keys. This increases complexity, enhances cryptographic systems' overall robustness, and is immune to various attacks. The present work proposes a hybrid model based on the Latin square matrix (LSM) and subtractive random number generator (SRNG) algorithms for producing random keys. The hybrid model enhances the security of the cipher key against different attacks and increases the degree of diffusion. Different key lengths can also be generated based on the algorithm without compromising security. It comprises two phases. The first phase generates a seed value that depends on producing a rand
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