The biosensitivity of certain organs in rats exposed to low doses of γ-radiation

AbstractTrace elements as Fe, Cu, Zn and Ca are essential for life. They approximately involved in all living processes, they play an important role in the hormones and enzymes activities. The present study demonstrate the biosensitivity of certain organs (spleen, intestine, heart and brain) in rats exposed to low doses of γ-radiation by determine the effectiveness on essential metal levels. Rats were exposed to 0.06, 0.126 and 0.227 Gy as a total doses at a low dose rate 2.5 mGy/h by two models of exposure, continuous and fractionated (along one and two weeks). Results indicated that the metal levels affected by the time exposure and organ sensitivity. Continuous exposure manifested increase in brain Fe, spleen Cu and Ca and decrease Ca in intestine and brain at all doses. After one week, intestine Cu and heart Zn decreased. After two weeks, decrease in Fe levels was observed in intestine, heart and brain at all doses. Also heart Zn and brain Ca decreased, Ca heart increased where Cu exhibit elevation in spleen and lowering in intestine at all doses. The statistic analysis presented significant effect between groups according the time factor and/or dose levels on Fe and Ca in all organs. Also significant effect present in Zn levels due to the time factor and/or dose levels in all organs except intestine. In conclusion, the rat organs have been responded to the low doses of γ-radiation at low dose rate by significant changes in essential metals concentrations

Exploration the extrudability of aluminum matrix composite (LM6/TIC) through modeling, simulation and experimental process

Aluminum matrix composites (LM6/TiC) is a mix of excellent properties of aluminum casting alloy (LM6), and particles of (TiC) which make it the first choice in many applications like airplane and marine industries. During this research the extrudability and mechanical specifications of this composite (LM6/TiC) are investigated before and after extrusion theoretically and experimentally. In this research; ABAQUS/CAE software has been successfully employed for Modeling and simulation the extrusion process before experiments in order to predict any error before fabrication. The experimental works includes design and fabrication the extrusion mold. The extruded parts are test by (SEM) to show the microstructure properties. Simulation results indicate the positions of stresses concentration (Mises stresses), and also the velocity of dislocation elements during extrusion. Experimental results show that, many mechanical properties are improved and enhanced after extrusion like stiffness and wear resistance. The microstructure test show that, the addition of (5%) wt. of (TiC) particulate with (T6) heat treatment (treating the solution in (525°C) and then ageing for (8) h at 180 °C) to the master alloy (LM6) will improve the strength about more than (15%) comparing with original matrix (LM6). Comparison between theoretical and practical results before and after extrusion indicates significant improvements after adding (TiC) particulates. This improvement is due to the high interference and bonding forces between the master alloy and composite particulates, which result in a fine grain size after the process. Keywords: Aluminum, Extrusion, Composite, TiC, LM6

Urinary and serum neutrophil gelatinase-associated lipocalin as a biomarker in Egyptian systemic lupus erythematosus patients: Relation to lupus nephritis and disease activity

AbstractBackgroundNeutrophil gelatinase-associated lipocalin (NGAL) is an excellent structural biomarker for the early diagnosis of acute kidney injury, prognosis, dialysis requirement and mortality in several common clinical scenarios.Aim of the workThe aim of this work is to detect the levels of both urinary and serum NGAL in SLE patients with and without lupus nephritis (LN) and to correlate their levels with renal biopsy class and disease activity.Patients and methodsThe study included 35 SLE patients; 22 with LN and 13 without as well as 30 matched controls. The SLE Disease Activity Index (SLEDAI) was assessed and the renal biopsy class determined. Urinary and serum levels of NGAL were assessed by ELISA.ResultsThe 35 patients had a median age of 30years and disease duration of 4years. They were 31 females and 4 males. The SLE patients had an elevated urinary NGAL (UNGAL) (median 19ng/ml, IQR 8–87) as compared to controls (median 2ng/ml, IQR 1–18.3) (p<0.006). Levels of UNGAL were higher in patients with LN than those without (p<0.023). In patients with LN, serum levels of NGAL were not significantly different from controls (p=0.6). The UNGAL level significantly correlated with the renal score of SLEDAI (r=0.54, p=0.001) but serum NGAL level did not (r=0.25, p=0.15). UNGAL significantly correlated with grade III and IV of renal biopsy (r=0.67, p=0.009). The sensitivity of UNGAL levels for the diagnosis of LN was 85.7%, with a specificity of 80%.ConclusionUrinary NGAL is a sensitive marker of proliferative nephritis in SLE and disease activity

Fetal lung volume and pulmonary artery resistance index for prediction of neonatal respiratory distress syndrome

Background: As gestational age grows, the risk of newborn respiratory distress syndrome (RDS) diminishes because the lungs are the last foetal organs to properly mature. While neonatal RDS does not just occur following preterm births, it is often thought of as a disorder of premature babies. This study sought to determine how prenatal lung capacity and foetal Pulmonary artery resistance index (PARI) affected the probability that newborn RDS would occur. Methods: This prospective observational study was carried out on 200 pregnant women aged 20-35 years, with gestational age between 36-40 weeks and singleton pregnancy. According to neonatal outcome the patients were classified into two groups: group A: 26 cases with noenatal RDS and group B: 174 cases without neonatal RDS. All patients were subjected to 2D ultrasonography and 3D ultrasonography. Results: Fetal lung volume (FLV) is a significant predictor of neonatal RDS (AUC: 0.820, p &lt;0.001), at a cut off value of ≤35, with 88.5% sensitivity and 68.4% specificity. PARI is not a significant predictor of neonatal RDS. 1 and 5 min Apgar score were significantly lower in neonates who developed RDS and those who didn’t (p&lt;0.001). Conclusions: 3D FLV and estimated fetal weight measurement using ultrasonography may be a reliable non-invasive indicator of the incidence of newborn RDS in preterm pregnancies when the risk of RDS progression is present. FLV is a significant predictor for neonatal RDS at a cutoff for ≤35 cm3 with sensitivity 88.5% and specificity 68.4%

POTENTIAL ROLE OF MILK THISTLE SEED AND ITS OIL EXTRACTS AGAINST HEART AND BRAIN INJURIES INDUCED BY γ-RADIATION EXPOSURE

Objective: This study aimed to investigate the protective effect of Silybum marianum (S. marianum) seeds extract its oil fraction against damage effect of γ-radiation in female albino rats.Methods: Ultrasonic-assisted extraction was used for the extraction of S. marianum seeds. Lipid patterns of S. marianum seeds oil were elucidated using gas chromatography-mass spectrometry (GC-MS). S. marianum seeds extract was analyzed using high-performance liquid chromatography (HPLC). Malondialdehyde (MDA), reduced glutathione (GSH) and metallothionein (MT) were estimated in heart and brain tissues of the examined rats. Lactate dehydrogenase (LDH) and creatine kinase-MB (CKMB) were measured in the serum of the examined rats, and the brain biomarkers; dopamine and serotonin were also measured.Results: The oil was found to be rich in linoleic acid (58.20%) and arachidic acid (23.38%). S. marianum seeds extract revealed the presence of taxifolin and six main active constituents of silymarin, including silydianin, silychristin, silybin A, silybin B, isosilybin A and isosilybin B. Treatment of γ-radiation damage effect using S. marianum seeds extract and its oil fraction led to a significant reduction of MDA levels in heart (139.6 and 165.5 nmol/g, respectively) and brain (158.5 and 135.2 nmol/g, respectively) tissues, however, significant increase of GSH levels in heart (316.4 and 293 mg/g, respectively) and brain (210.4 and 227 mg/g, respectively) tissues was observed, also a significant increase of dopamine levels (85.27 and 65.74 ng/g, respectively) and MT levels of heart tissues (108.5 and 70.52 mg/g, respectively) was observed.Conclusion: S. marianum seeds extract and its oil fraction showed a protective effect against γ-radiation-induced damage in heart and brain.Â

Code for time-series forecasting by using a recurrent neural network model

Version 2 changes: - Added a description and an instruction for random_seed_changer function and removed unnecessary draft code. General description: - This dataset comprises a Jupyter notebook that includes a Python code for sequence-to-sequence time-series forecasting by training and evaluating recurrent neural network models. - The code was developed to enable rapid and wide-scale development, production and evaluation of time-series models and predictions. - The RNN's architecture has a convolutional layer for handling inputs, within a composite autoencoder’s neural network. Instructions for usage: - The Python code is located in a Jupyter notebook that can be opened online or locally, by using a Jupyter Notebook compatible platform as: https://jupyter.org (accessed 11 July 2020). https://colab.research.google.com (accessed 11 July 2020). - In order to use the code, a data source should exist in a "csv" file extension and it should be named as 'data_input.csv' or alternatively, an online link to the data source could be entered when executing the code. The data source should have first 4 columns for metadata. The unique name or identifier for each row will be located in the 2nd column, otherwise, a change has to be made in the code in the gen_data function (line 282) and line 286 in case of the need to change metadata columns size, into less or more. The rest of the columns indicate the accumulated number or value in each column. Important parameters: - target_pred: specifies which row in the data to predict. - crop_point: specifies which data point to crop the time-series data at, ex. training data = before crop_point, evaluation data = after crop_point. - time_steps: specifies which time-steps to use, ex. 15 or 20, meaning: 15 for X and 15 for Y in the sequence-to-sequence model. - RNN parameters: ex. batch size, epochs, layer sizes, RNN architecture (GRU or LSTM). - ext: specifies the end date of predictions

Evaluation of dependability of MAC and routing protocols in personal networks

In this project we have investigated how to enable dependable routing inside personal network (PN )cluster.Dependable routing among different clusters is out of the scope of this thesis.In practice, personal network cluster is considered as mobile ad-hoc network (MANET) with heterogeneous radioaccess technologies and nodes with different capabilities(processingpower,batterylevel,etc.) Although a single proposal for dependable routing inside the personal networks cluster could span over all OSI layers, but we considered only the network and mediumaccess control (MAC)layrs.TelecommunicationsElectrical Engineering, Mathematics and Computer Scienc

Code for time-series forecasting by using a recurrent neural network model

General description: - This dataset comprises a Jupyter notebook that includes a Python code for sequence-to-sequence time-series forecasting by training and evaluating recurrent neural network models. - The code was developed to enable rapid and wide-scale development, production and evaluation of time-series models and predictions. - The RNN's architecture has a convolutional layer for handling inputs, within a composite autoencoder’s neural network. Instructions for usage: - The Python code is located in a Jupyter notebook that can be opened online or locally, by using a Jupyter Notebook compatible platform as: https://jupyter.org (accessed 11 July 2020). https://colab.research.google.com (accessed 11 July 2020). - In order to use the code, a data source should exist in a "csv" file extension and it should be named as 'data_input.csv' or alternatively, an online link to the data source could be entered when executing the code. The data source should have first 4 columns for metadata. The unique name or identifier for each row will be located in the 2nd column, otherwise, a change has to be made in the code in the gen_data function (line 282) and line 286 in case of the need to change metadata columns size, into less or more. The rest of the columns indicate the accumulated number or value in each column. Important parameters: - target_pred: specifies which row in the data to predict. - crop_point: specifies which data point to crop the time-series data at, ex. training data = before crop_point, evaluation data = after crop_point. - time_steps: specifies which time-steps to use, ex. 15 or 20, meaning: 15 for X and 15 for Y in the sequence-to-sequence model. - RNN parameters: ex. batch size, epochs, layer sizes, RNN architecture (GRU or LSTM). - ext: specifies the end date of predictions

Generated Prediction Data of COVID-19&apos;s Daily Infections in Brazil

Dataset general description:• This dataset reports 4200 recurrent neural network models, their settings, and their relevant generated files (including prediction csv files, graphs, and metadata files, as applicable), for predicting COVID-19&apos;s daily infections in Brazil by training on limited raw data (30 and 40 time-steps). The used code is developed by the author and located in the following online data repository link:http://dx.doi.org/10.17632/yp4d95pk7n.3Dataset content: • Models, Graphs, and csv predictions files: 1. Deterministic mode (DM): includes 1197 generated models&apos; files (30 time-steps), and their generated 2835 graphs and 2835 predictions files. Similarly, this mode includes 1976 generated models&apos; files (40 time-steps), and their generated 7301 graphs and 7301 predictions files. 2. Non-deterministic mode (NDM): includes 20 generated models&apos; files (30 time-steps), and their generated 53 graphs and 53 predictions files. 3. Technical validation mode (TVM): includes 1001 generated models&apos; files (30 time-steps), and their generated 3619 graphs and 3619 predictions files for 349 models (out of a 358 sample but 9 models didn&apos;t achieve the accuracy threshold), which are a sample of 1001 models. Also, all data of the control group - India (1 model). 4. 1 graph and 1 prediction files for each of DM and NDM, reporting evaluation till 2020-07-11. 5. The evaluation of performance for 10, 20, 30, 40, and 50 time-steps alternatives (5 models).• Settings and metadata for the above 3 categories: 1. Used settings during the training session in json files. 2. Metadata: training / prediction setup and accuracy in csv files.Raw data source used to train the models:• The used raw data [1] for training the models is from: COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University) : https://github.com/CSSEGISandData/COVID-19 (accessed 2020-07-20)• The following raw data links were used (both accessed 2020-07-08): 1. till 2020-06-29:https://github.com/CSSEGISandData/COVID-19/raw/78d91b2dbc2a26eb2b2101fa499c6798aa22fca8/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv 2. till 2020-06-13:https://github.com/CSSEGISandData/COVID-19/raw/02ea750a263f6d8b8945fdd3253b35d3fd9b1bee/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv References: 1- Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Code for time-series forecasting by using a recurrent neural network model

General description: - This dataset comprises a Jupyter notebook that includes a Python code for sequence-to-sequence time-series forecasting by training and evaluating recurrent neural network models. - The code was developed to enable rapid and wide-scale development, production and evaluation of time-series models and predictions. - The RNN's architecture has a convolutional layer for handling inputs, within a composite autoencoder’s neural network. Instructions for usage: - The Python code is located in a Jupyter notebook that can be opened online or locally, by using a Jupyter Notebook compatible platform as: https://jupyter.org (accessed 11 July 2020). https://colab.research.google.com (accessed 11 July 2020). - In order to use the code, a data source should exist in a "csv" file extension and it should be named as 'data_input.csv' or alternatively, an online link to the data source could be entered when executing the code. The data source should have first 4 columns for metadata. The unique name or identifier for each row will be located in the 2nd column, otherwise, a change has to be made in the code in the gen_data function (line 282) and line 286 in case of the need to change metadata columns size, into less or more. The rest of the columns indicate the accumulated number or value in each column. Important parameters: - target_pred: specifies which row in the data to predict. - crop_point: specifies which data point to crop the time-series data at, ex. training data = before crop_point, evaluation data = after crop_point. - time_steps: specifies which time-steps to use, ex. 15 or 20, meaning: 15 for X and 15 for Y in the sequence-to-sequence model. - RNN parameters: ex. batch size, epochs, layer sizes, RNN architecture (GRU or LSTM). - ext: specifies the end date of predictions