The Love Creek Nature Center, one of the three nature centers located within the boundaries of Berrien County, is owned and operated by the county for public enjoyment and instruction of nature. The 44.5 ha study area, located seven km east of Berrien Springs, and two km southwest of Berrien Center, on Huckleberry Road, in T6S, R17W, sections 16, 17 (Lat. 41° 56' N; Long. 86° 18' W) is made up of deciduous woods and abandoned fields at various stages of succession. It is bounded on the east by the Berrien County Dog Pound and Huckleberry Road, to the north by cultivated Berrien County land and the Berrien General Hospital, to the west by the recently closed Berrien - Oronoko Township Landfill Dump; and to the south b

Background: The SARS-CoV-2 virus causes COVID-19, a respiratory syndrome. It causes inflammation and damages several organs in the body. miRNAs play a role in regulating the infection resulting from SARS-CoV-2. MicroRNA-155, a kind of microRNA linked to viral defences, can affect the immune responses during COVID-19. Objectives: Examination of the involvement of microRNA-155 in the development and severity of COVID-19, as well as finding the correlation between microRNA-155 and viral load (copies/mL) in severe cases of the disease. Materials and Method: A case-control research study was performed between October 2022 and June 2023. It included a cohort of 120 hospitalised individuals with severe cases of COVID-19, together with 115 individu

Background: Background : Patients with non-rheumatic atrial fibrillation have high risk of thromboembolism especially ischemic stroke usually arising from left atrial appendage .Transoesophageal echocardiography provides useful information for risk stratification in these patients as it detects thrombus in the left atrial or left atrial appendage. Objective : This study was conducted at Al-Kadhimiya Teaching Hospital to assess the prevalence of left atrial chamber thrombi in patients with chronic non-rheumatic atrial fibrillation using transoesophageal echocardiography and its clinical significance as well as to verify the superiority of transoesophageal over transthoracic echocardiography in the detection of these abnormalities. Type of

The aim of the research is to identify the problems of teaching computer material concerning fourth grade students’ point of view in the computer department in the College of Education for Women by identifying the statistically significant differences between the answers of female students to problems in the field of school administration and in the field of teaching computer subject included the sample of research On (39) of (50) students and (78%) From the fourth grade students in the department of computer who had achieved the theoretical part in the first semester and the assembly application in the second semester in the schools of Baghdad city for the aca

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are