Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co

Both traditional and novel techniques were employed in this work for magnetic shielding evaluation to shed new light on the magnetic and aromaticity properties of benzene and 12 [n]paracyclophanes with n = 3–14. Density functional theory (DFT) with the B3LYP functional and all-electron Jorge-ATZP and x2c-TZVPPall-s basis sets was utilized for geometry optimization and magnetic shielding calculations, respectively. Additionally, the 6-311+G(d,p) basis set was incorporated for the purpose of comparing the magnetic shielding results. In addition to traditional evaluations such as NICS/NICSzz-Scan, and 2D-3D σiso(r)/σzz(r) maps, two new techniques were implemented: bendable grids (BGs) and cylindrical grids (CGs) of ghost atoms (Bqs). BGs a

Breast cancer is one of the comments malignant tumors worldwide especially in Iraq; it is a leading cause of death in Iraqi women. Determination of estrogen and progesterone receptors status is helpful in selecting the patients most likely to receive benefit from endocrine therapy, and provide prognostic information on recurrence and survival since their expression is related to the degree of the tumor differentiation. From November 2012 to March 2013, 150 breast cancer patients at Al-Amal Hospital in Baghdad were attended to start treatment of disease for the first time. All patients included in this study did not receive chemotherapy. Patients were asked to bring their paraffin embedded tissue blocks to participate in estrogen, progest

Carbohydrate antigen 19-9 (CA 19-9) levels were measured in sera and tissues of 40 patients with breast cancer (01), 8 patients with prostate cancer (G2)and 12 patients with thyroid cancer (G3), by the enzyme­ linked immunosorbent assay (ELISA) technique.

The patients were admitted to Medical City Hospitals (Baghdad Teaching Hospital and Nursing Home Hospital). The sera were taken just before surgery, where the specimens were taken immediately after surgery and kept in saline solution at -20°C until the time of homogenizing process.

The results of CA 19-9 levels in sera were (16.309±7.143; 31.281±0.766;

11.5±0.707 U/ml respectively compared with serum CA 19-9 level of control group G4 which was 7.74

    Currently, there is a growing interest in medicinal plants extracts as some plants have shown antitumor potential. The goal of this study was to test the anticancer activity of methanol extract of Conocarpous erectus leaves in breast cancer cells.  Cytotoxicity was tested in vitro on breast cancer cell lines, MCF₇ [Estrogen receptor + (ER+)] and MDA-MB₂₃₁ [Estrogen receptor - (ER-)], in addition to normal fibroblast cells (REF). MTT assay was utilized to measure the growth inhibitory effects after 48 hours exposure to extracts.  Viability results indicated  that MDA-MB231 were sensitive (GI50 = 56.1µg/ml).However, no sensitivity was seen in both MCF7 and REF cells (GI50>100 µg/ml). I

Background: Breast cancer is the culmination of a multi-step process that occurs over a period of several years or decades and as a cause of death, is a salient "free radical" disease. Aim: The present study aims on investigating the possible protective role of antioxidant drugs (vitamins E and C) to cardiac cells against the oxidative stress induced damage during doxorubicin chemotherapy in patients with breast cancer.
Patients and methods: Thirty two patients with different stages of breast carcinoma attending to Baghdad Teaching Hospital and ten healthy control subjects with age range between (29-61) years, mean (43.6±1.37) were included in this study. The patients were randomized into 3 groups, they

Breast cancer is one of the most important malignant diseases all over the world. The incidence of breast cancer is increasing around the world and it is still the leading cause of cancer mortality An Approximately 1.3 million new cases were diagnosed worldwide last year. With areas rising increasing, risk factors for breast cancer including obesity, early menarche, alcohol and smoking, environmental contamination and reduced or late birth rates become more prevalent. In Iraq, breast cancer ranks first among types of cancers diagnosed in women. This study was conducted on one hundred twenty women with breast cancer that was evaluated and investigated for the possible role of the risk factors on the development of breast cancer in females. T

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a