Correlation between radiation processes in silicon and long-time degradation of detectors for high energy physics experiments

In this contribution, the correlation between fundamental interaction processes induced by radiation in silicon and observable effects which limit the use of silicon detectors in high energy physics experiments is investigated in the frame of a phenomenological model which includes: generation of primary defects at irradiation starting from elementary interactions in silicon; kinetics of defects, effects at the p-n junction detector level. The effects due to irradiating particles (pions, protons, neutrons), to their flux, to the anisotropy of the threshold energy in silicon, to the impurity concentrations and resistivity of the starting material are investigated as time, fluence and temperature dependences of detector characteristics. The expected degradation of the electrical parameters of detectors in the complex hadron background fields at LHC & SLHC are predicted.Comment: prepared for the 10th International Symposium on Radiation Physics, 17-22 September, 2006, Coimbra, Portuga

The best CRISPR/Cas9 versus RNA interference approaches for Arabinogalactan proteins' study

Arabinogalactan Proteins (AGPs) are hydroxyproline-rich proteins containing a high proportion of carbohydrates, widely spread in the plant kingdom. AGPs have been suggested to play important roles in plant development processes, especially in sexual plant reproduction. Nevertheless, the functions of a large number of these molecules, remains to be discovered. In this review, we discuss two revolutionary genetic techniques that are able to decode the roles of these glycoproteins in an easy and efficient way. The RNA interference is a frequently technique used in plant biology that promotes genes silencing. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (CRISPR/Cas9), emerged a few years ago as a revolutionary genome-editing technique that has allowed null mutants to be obtained in a wide variety of organisms, including plants. The two techniques have some differences between them and depending on the research objective, these may work as advantage or disadvantage. In the present work, we propose the use of the two techniques to obtain AGP mutants easily and quickly, helping to unravel the role of AGPs, surely a great asset for the future

Potassium urinary excretion and dietary intake: a cross-sectional analysis in 8–10 year-old children

Background: Data from studies assessing the intake of potassium, and the concomitant sodium-to-potassium ratio are limited. The aim of this study was to evaluate potassium and sodium-to-potassium ratio intake in 8–10 year-old children. Methods: A cross-sectional survey was carried out from January to June 2014 and data from 163 children (81 boys) were included. Potassium intake was estimated by 24-h urine collection and coefficient of creatinine was used to validate completeness of urine collections. Urinary sodium and sodium-to-potassium ratio were also analysed. A 24-h dietary recall was used to provide information on dietary sources of potassium. Height and weight were measured according to international standards. Results: The mean urinary potassium excretion was 1701 ± 594 mg/day in boys, and 1682 ± 541 mg/day in girls (p = 0.835); 8.0 % of children met the WHO recommendations for potassium intake. The mean sodium excretion was 2935 ± 1075 mg/day in boys and 2381 ± 1045 mg/day in girls (p <0.001) and urinary sodium-to-potassium ratio was 3.2 ± 1.4 in boys, and 2.5 ± 1.1 in girls (p = 0.002). The mean fruit and vegetable intake was 353.1 ± 232.5 g/day in boys, and 290.8 ± 213.1 g/day in girls (p = 0.101). Conclusions: This study reported a low compliance of potassium intake recommendations in 8–10 year-old children. Health promotion interventions are needed in order to broaden public awareness of potassium inadequacy and to increase potassium intake

Urotensin II-Induced Increase in Myocardial Distensibility Is Modulated by Angiotensin II and Endothelin-1

Endogenous regulators, such as angiotensin-II (AngII), endothelin-1 (ET-1) and urotensin-II (U-II) are released from various cell types and their plasma levels are elevated in several cardiovascular diseases. The present study evaluated a potential crosstalk between these systems by investigating if the myocardial effects of U-II are modulated by AngII or ET-1. Effects of U-II (10(-8), 10(-7), 10(-6) M) were tested in rabbit papillary muscles in the absence and in the presence of losartan (selective AT, receptor antagonist), PD-145065 (nonselective ET-1 receptors antagonist), losartan plus PD-145065, AngII or ET-1. U-II promoted concentration-dependent negative inotropic and lusitropic effects that were abolished in all experimental conditions. Also, U-II increased resting muscle length up to 1.008 +/- 0.002 L/L(max). Correcting it to its initial value resulted in a 19.5 +/- 3.5 % decrease of resting tension, indicating increased muscle distensibility. This effect on muscle length was completely abolished in the presence of losartan and significantly attenuated by PD-145065 or losartan plus PD-145065. This effect was increased in the presence of AngII, resulting in a 27.5 +/- 3.9 % decrease of resting tension, but was unaffected by the presence of ET-1. This study demonstrated an interaction of the U-II system with the AngII and ET-1 systems in terms of regulation of systolic and diastolic function

Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs

Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy

A Systematic Analysis of Community Detection in Complex Networks

Numerous techniques have been proposed by researchers to uncover the hidden patterns of real-world complex networks. Finding a hidden community is one of the crucial tasks for community detection in complex networks. Despite the presence of multiple methods for community detection, identification of the best performing method over different complex networks is still an open research question. In this article, we analyzed eight state-of-the-art community detection algorithms on nine complex networks of varying sizes covering various domains including animal, biomedical, terrorist, social, and human contacts. The objective of this article is to identify the best performing algorithm for community detection in real-world complex networks of various sizes and from different domains. The obtained results over 100 iterations demonstrated that the multi-scale method has outperformed the other techniques in terms of accuracy. Multi-scale method achieved 0.458 average value of modularity metric whereas multiple screening resolution, unfolding fast, greedy, multi-resolution, local fitness optimization, sparse Geosocial community detection algorithm, and spectral clustering, respectively obtained the modularity values 0.455, 0.441, 0.436, 0.421, 0.368, 0.341, and 0.340.

Real-World Protein Particle Network Reconstruction Based on Advanced Hybrid Features

Biological network proteins are key operational particles that substantially and operationally cooperate to bring out cellular progressions. Protein links with some other biological network proteins to accomplish their purposes. Physical collaborations are commonly referred to by the relationships of domain-level. The interaction among proteins and biological network reconstruction can be predicted based on various methods such as social theory, similarity, and topological features. Operational particles of proteins collaboration can be indirect among proteins based on mutual fields, subsequently particles of proteins involved in an identical biological progression be likely to harbor similar fields. To reconstruct the real-world network of proteins particles, some methods need only the notations of proteins domain, and then, it can be utilized to multiple species. A novel method we have introduced will analyze and reconstruct the real-world network of protein particles. The proposed technique works based on protein closeness, algebraic connectivity, and mutual proteins. Our proposed method was practically tested over different data sets and reported the results. Experimental results clearly show that the proposed technique worked best as compared to other state-of-the-art algorithms

Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification

Author profiling is part of information retrieval in which different perspectives of the author are observed by considering various characteristics like native language, gender, and age. Different techniques are used to extract the required information using text analysis, like author identification on social media and for Short Text Message Service. Author profiling helps in security and blogs for identification purposes while capturing authors’ writing behaviors through messages, posts, comments, blogs, comments, and chat logs. Most of the work in this area has been done in English and other native languages. On the other hand, Roman Urdu is also getting attention for the author profiling task, but it needs to convert Roman-Urdu to English to extract important features like Named Entity Recognition (NER) and other linguistic features. The conversion may lose important information while having limitations in converting one language to another language. This research explores machine learning techniques that can be used for all languages to overcome the conversion limitation. The Vector Space Model (VSM) and Query Likelihood (Q.L.) are used to identify the author’s age and gender. Experimental results revealed that Q.L. produces better results in terms of accuracy

Sentence Embedding Approach using LSTM Auto-encoder for Discussion Threads Summarization

Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) model as a sentence embedding tool, which is poor at capturing the overall meaning of the sentence and is unable to grasp word dependency. To overcome these limitations, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the performance of DTS. The empirical result in the context of the proposed approach’s average precision, recall, and F-measure with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets demonstrates the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOW model in sentence embedding tasks and boost the performance of the automated DTS model