Background: There are various secreted proteins affecting the prognosis of oral squamous cell carcinoma (OSCC) and one of them is Angiopoietin-2(Ang-2) which is thought to have an essential role in the development and progression of the tumor. Aim of the study: This study was conducted to determine the expression of (Ang-2) in (OSCC) to assess its correlations with clinicopathological parameters of the tumor. Material and Methods: 36 formalin- fixed, paraffin- embedded tissue blocks histologically diagnosed as OSCC were examined for Ang-2 immunohistochemical expression semi quantitively. Results: The expression of Ang-2 was significantly associated with histopathological grade (P value=0.023), while there is no significant association wi

Cryptosporidiosis is an intestinal protozoan parasitic disease that infects human and animals, caused by apicomplexan parasite belong to the genusof Cryptosporidium. The current study was done to record the infection rate of cryptosporidiosis in human and cattle, and genotype the clinical isolates of Cryptosporidium in Baghdad Province. A total of 265 stool sample were collected (150 from human and 115 from cattle) during the period from December 2016 to the May 2017. Cryptosporidial infection was detected using modified acid fast stain. DNA of the parasite was extracted from oocysts of positive fecal samples and nested PCR method was used for partial 60 kDa glycoprotein (gp60) gene amplification then sequence analysis for selected samples.

The achievements of the art that we know today are questioned in motives that differ from what art knew before, including dramatic artistic transformations, which he called modern art.
In view of the enormity of such a topic, its ramifications and its complexity, it was necessary to confine its subject to the origin of the motives of the transformations of its first pioneers, and then to stand on what resulted from that of the data of vision in composition and drawing exclusively, and through exploration in that, we got to know the vitality of change from the art of its time.
And by examining the ruling contemporary philosophical concepts and their new standards and their epistemological role in contemporary life, since they includ

Tourism plays an important role in Malaysia’s economic development as it can boost business opportunity in its surrounding economic. By apply data mining on tourism data for predicting the area of business opportunity is a good choice. Data mining is the process that takes data as input and produces outputs knowledge. Due to the population of travelling in Asia country has increased in these few years. Many entrepreneurs start their owns business but there are some problems such as wrongly invest in the business fields and bad services quality which affected their business income. The objective of this paper is to use data mining technology to meet the business needs and customer needs of tourism enterprises and find the most effective

A two time step stochastic multi-variables multi-sites hydrological data forecasting model was developed and verified using a case study. The philosophy of this model is to use the cross-variables correlations, cross-sites correlations and the two steps time lag correlations simultaneously, for estimating the parameters of the model which then are modified using the mutation process of the genetic algorithm optimization model. The objective function that to be minimized is the Akiake test value. The case study is of four variables and three sites. The variables are the monthly air temperature, humidity, precipitation, and evaporation; the sites are Sulaimania, Chwarta, and Penjwin, which are located north Iraq. The model performance was

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.

In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.

The longitudinal balanced data profile was compiled into subgroup

The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To