The Effect of Dehydroepiandrosterone on Ovarian Reserve in Ovarian Damage Caused by Methotrexate

To determine the possible detrimental effects of multiple methotrexate doses has on the ovarian reserve and to determine the beneficial effects of dehydroepiandrosterone supplementation. The rats (n:24) divided into three groups; Group 1: control group, Group 2: dehydroepiandrosterone and methotrexate group (6mg/kg dehydroepiandrosterone dissolved in 0.1 ml sesame seed oil subcutaneously for ten days and 1mg/kg intramuscular methotrexate at the 1st, 3rd, 5th and 7th days) and Group 3: methotrexate group (1mg/kg intramuscular methotrexate at the 1st, 3rd, 5th and 7th day). The groups compared in regards to their histopathological ovarian damage scores and AMH values. It established that multiple methotrexate applications had a considerable effect on reducing vascular congestion in the ovarian tissue. Both in groups 2 and 3 AMH values found to be significantly lower. When this decline in the ovarian reserve examined comparatively; while both the group 2 and 3 reported having a considerable and continuous reduction in the AMH levels correlative to the control group; the primordial, primary and total follicle counts shown to stay statistically the same in the group 2 (

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks

Two methods have been used to model residential end-use energy consumption at the national or regional level: the engineering method and the conditional demand-analysis method. It was recently shown that the neural network (NN) method is capable of accurately modeling the behaviours of the appliances, lighting, and space-cooling energy consumption in the residential sector. As a continuation of the work on the use of the NN method for modeling residential end-use energy-consumption, two NN based energy-consumption models were developed to estimate the space and domestic hot-water heating energy consumptions in the Canadian residential sector. This paper presents the NN methodology used in developing the models, the accuracy of the predictions, and some sample results.Residential energy-consumption modeling Space-heating energy Domestic hot-water heating energy Neural-network modeling

Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks

Two methods are currently used to model residential energy consumption at the national or regional level: the engineering method and the conditional demand analysis method. Another potentially feasible method to model residential energy consumption is the neural network (NN) method. Using the NN method, it is possible to determine causal relationships amongst a large number of parameters, such as occur in the energy consumption patterns in the residential sector. A review of the published literature indicates that the NN method has not been used or tested for housing-sector energy consumption modeling. A NN based energy consumption model is being developed for the Canadian residential sector. This paper presents the NN methodology used in developing the appliances, lighting, and space-cooling component of the model, the accuracy of its predictions, and some sample results.Residential energy consumption modeling Appliance, lighting, and space-cooling energy Neural networks modeling