Viruses have not previously been reported to act as chemotactic/chemoattractive agents. Rather, viruses as extracellular entities are generally viewed as non-metabolically active spore-like agents that await further infection events upon collision with appropriate host cells. That a virus might actively contribute to its fate via chemotaxis and change the behavior of an organism independent of infection is unprecedented.

Lignin has emerged as a promising asphalt binder modifier due to its sustainable and renewable nature, with the potential to improve flexible pavement performance. This study investigates the use of Soda Lignin Powder (SLP), derived from Pinus wood sawdust via alkaline treatment, as an asphalt modifier to enhance mixture durability. SLP was characterized using Fourier Transformation Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), and Scanning Electron Microscopy with Energy Dispersive X-ray Analysis (SEM/EDX), revealing significant changes in its chemical structure post-extraction. These analyses showed the presence of phenolic units, including hydroxyphenyl propane, syringyl, and guaiacyl units. The morphology of SLP was identified

The sol-gel route using an agar gel with calcium nitrate and phosphate solution as starting materials for producing hydroxyapatite (HAP). The product formed were needle like, zigzag and straight fibres. The fibrous products on sintering transformed into stoichiometric HAP with a biological Ca/P ratio of 1.67. The influences of pH, temperature, nature of base and phosphate solution on the growth of fibrous HAP were studied. The pH of the solution was found to greatly influence the growth rate and morphology of the resultant product. The optimum gel temperature was found to be 60oC and sintering temperature of 900oC for 1 hour. The crystalline, thermal, functional and morphological characteristics of the fibrous HAP were investigated.

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone

Acute Respiratory Distress Syndrome (ARDS) causes up to 40% mortality in humans and is difficult to treat. ARDS is also one of the major triggers of mortality associated with coronavirus-induced disease (COVID-19). We used a mouse model of ARDS induced by Staphylococcal enterotoxin B (SEB), which triggers 100% mortality, to investigate the mechanisms through which Δ9-tetrahydrocannabinol (THC) attenuates ARDS. SEB was used to trigger ARDS in C3H mice. These mice were treated with THC and analyzed for survival, ARDS, cytokine storm, and metabolome. Additionally, cells isolated from the lungs were used to perform single-cell RNA sequencing and transcriptome analysis. A database analysis of human COVID-19 patients was also performed t

In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB