The study area comprises Injana Formation (Late Miocene), exposed on the hills nearby of Tharthar Lake and about 120 km north of Baghdad city. This study depends on sedimentologic and facies analysis to recognize paleoenvironment and recognize the kinds of vertebrate bone fossils during Late Miocene. Sedimentologic and facies analysis showed many sedimentary facies: facies (Se) of scoured erosional surface, facies of (Sp) cross- bedded sandstones, facies (Fs) of fine sandstone facies, facies of (Fc) claystone, and facies of (C) calcareous clay. Facies analysis referred to the sub environments which are: point bar, over bank and floodplain in addition to fining upward cycles of deposition, which refers to meandering flu

Iraq hosts a diverse range of native species inhabiting its mountains, marshes, steppes, and deserts. Nevertheless, schoolchildren in Iraq know little about their native biodiversity. This is because biology lessons at schools usually concentrate on generic examples rather than local flora and fauna. This essay considers the importance of incorporating the local Iraqi biodiversity into the biology lesson plans. The learning process related to biodiversity should involve more than scientific information. Students need to be made to feel responsible for the environment and to become familiar with the natural history of their homeland. This work illustrates the benefits of using examples of native species and ecosystems in the learning process

Clostridioides difficile is an emerging pathogen of One Health significance. Its highly variable genome contains mobile genetic elements (MGEs) such as transposons and prophages that influence its biology. Systematic deletion of each genetic element is required to determine their precise role in C. difficile biology and contribution to the wider mobilome. Here, Tn5397 (21 kb) and ϕ027 (56 kb) were deleted from C. difficile 630 and R20291, respectively, using allele replacement facilitated by CRISPR-Cas9. The 630 Tn5397 deletant transferred PaLoc at the same frequency (1 × 10⁻⁷

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

In the present work, leaching process studiedusing organic acids (acetic acid and lactic acid) to extract phosphate from the Iraqi Akashat phosphate ore by separation of calcareous materials (mainly calcite). This approach characterized by energy conservation, environmental enhancement by recovery of calcite as calcium sulfate (gypsum), keeping the physical and chemical properties of apatite. Samples were analyzed using X-ray diffraction and FTIR spectrophotometer. From the obtained experimental data it was found that using the two organic acids yields closed purity values of the produced apatite at the optimum conditions, while at different acid concentrations, it was found that the efficiency of acetic acid is higher at the low acid co