First draft genome assembly of the Argane tree (Argania spinosa)

Background: The Argane tree (Argania spinosa L. Skeels) is an endemic tree of southwestern Morocco that plays an important socioeconomic and ecologic role for a dense human population in an arid zone. Several studies confirmed the importance of this species as a food and feed source and as a resource for both pharmaceutical and cosmetic compounds. Unfortunately, the argane tree ecosystem is facing significant threats from environmental changes (global warming, over-population) and over-exploitation. Limited research has been conducted, however, on argane tree genetics and genomics, which hinders its conservation and genetic improvement. Methods: Here, we present a draft genome assembly of A. spinosa. A reliable reference genome of A. spinosa was created using a hybrid de novo assembly approach combining short and long sequencing reads. Results: In total, 144 Gb Illumina HiSeq reads and 7.2 Gb PacBio reads were produced and assembled. The final draft genome comprises 75 327 scaffolds totaling 671 Mb with an N50 of 49 916 kb. The draft assembly is close to the genome size estimated by k-mers distribution and covers 89% of complete and 4.3 % of partial Arabidopsis orthologous groups in BUSCO. Conclusion: The A. spinosa genome will be useful for assessing biodiversity leading to efficient conservation of this endangered endemic tree. Furthermore, the genome may enable genome-assisted cultivar breeding, and provide a better understanding of important metabolic pathways and their underlying genes for both cosmetic and pharmacological purposes

First draft genome assembly of the Argane tree (Argania spinosa) [version 2; peer review: 2 approved]

BACKGROUND : The Argane tree (Argania spinosa L. Skeels) is an endemic tree of mid-western Morocco that plays an important socioeconomic and ecologic role for a dense human population in an arid zone. Several studies confirmed the importance of this species as a food and feed source and as a resource for both pharmaceutical and cosmetic compounds. Unfortunately, the argane tree ecosystem is facing significant threats from environmental changes (global warming, over-population) and over-exploitation. Limited research has been conducted, however, on argane tree genetics and genomics, which hinders its conservation and genetic improvement. METHODS : Here, we present a draft genome assembly of A. spinosa. A reliable reference genome of A. spinosa was created using a hybrid de novo assembly approach combining short and long sequencing reads. RESULTS : In total, 144 Gb Illumina HiSeq reads and 7.6 Gb PacBio reads were produced and assembled. The final draft genome comprises 75 327 scaffolds totaling 671 Mb with an N50 of 49 916 kb. The draft assembly is close to the genome size estimated by k-mers distribution and covers 89% of complete and 4.3 % of partial Arabidopsis orthologous groups in BUSCO. CONCLUSION : The A. spinosa genome will be useful for assessing biodiversity leading to efficient conservation of this endangered endemic tree. Furthermore, the genome may enable genome-assisted cultivar breeding, and provide a better understanding of important metabolic pathways and their underlying genes for both cosmetic and pharmacological.DATA AVAILABILITY: All of the A. spinosa datasets can be retrieved under BioProject accession number PRJNA294096: http://identifiers.org/ bioproject:PRJNA294096. The raw reads are available at NCBI Sequence Reads Archive under accession number SRP077839: http://identifiers.org/insdc.sra:SRP077839. The complete genome sequence assembly project has been deposited at GenBank under accession number QLOD00000000: http://identifiers. org/ncbigi/GI:1408199612. Data can also be retrieved via the International Argane Genome Consortium (IAGC) website: http://www.arganome.org.https://f1000research.compm2021BiochemistryGeneticsMicrobiology and Plant Patholog

Predicting the Stock Market Using News Sentiment Analysis

ABSTRACT MAJID MEMARI, for the Masters of Science degree in Computer Science, presented on November 3rd, 2017 at Southern Illinois University, Carbondale, IL. Title: PREDICTING THE STOCK MARKET USING NEWS SENTIMENT ANALYSIS Major Professor: Dr. Norman Carver Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. GDELT is the largest, most comprehensive, and highest resolution open database ever created. It is a platform that monitors the world\u27s news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day that stretches all the way back to January 1st, 1979, and updates daily [1]. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock\u27s future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable [2]. On the other hand, other studies show that it is predictable. The stock market prediction has been a long-time attractive topic and is extensively studied by researchers in different fields with numerous studies of the correlation between stock market fluctuations and different data sources derived from the historical data of world major stock indices or external information from social media and news [6]. The main objective of this research is to investigate the accuracy of predicting the unseen prices of the Dow Jones Industrial Average using information derived from GDELT database. Dow Jones Industrial Average (DJIA) is a stock market index, and one of several indices created by Wall Street Journal editor and Dow Jones & Company co-founder Charles Dow. This research is based on data sets of events from GDELT database and daily prices of the DJI from Yahoo Finance, all from March 2015 to October 2017. First, multiple different classification machine learning models are applied to the generated datasets and then also applied to multiple different Ensemble methods. In statistics and machine learning, Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Afterwards, performances are evaluated for each model using the optimized parameters. Finally, experimental results show that using Ensemble methods has a significant (positive) impact on improving the prediction accuracy. Keywords: Big Data, GDELT, Stock Market, Prediction, Dow Jones Index, Machine Learning, Ensemble Method

A huiristic method for information scaling in manufacturing organizations

Protecting information assets is very vital to the core survival of an organization. By increasing in cyber-attacks and viruses worldwide, it has become essential for organizations to adopt innovative and rigorous procedures to keep these vital assets out of the reach of exploiters. Although worldwide complying with an international information security standard such as ISO 27001 has been raised, with over 7000 registered certificates, few Iranian companies are under ISO 27001 certified. Also organization needs to perform a risk assessment in order to determine the organization's asset exposure to risk and determine the best way to manage this. The determination of risk within the methodology is based upon the standard formula, which the risk is calculated from the multiplication of the asset value, threats and vulnerability. The ISO 27001 requires is that 'An appropriate risk assessment shall be undertaken'. One of the main factors for risk assessment is identifying and scoring of Information asset in this process. Due to different values of asset in organizations, the main purpose of this study is to identify and investigate a weighted method to assign different values of assets in order to minimize vulnerability in manufacturing systems. This study also aims at improving asset value scoring by using heuristic methods. A real world case study was selected for implementation of this approach based on ISO27001 in Ira

Survival and outcomes following cardiopulmonary resuscitation; a descriptive study in Iran

Objective: Cardiopulmonary resuscitation (CPR) has been known in its present form since 1960. Different studies have reported variable outcomes among different countries. Therefore, the purpose of this study was to assess the rate of CPR success and the survival rate in managing cardiac arrest among patients in an educational medical center. Methods: This cross-sectional study was performed at Imam Hosein hospital, Tehran, Iran. All patients, admitted to the emergency department with cardiac arrest between March 2007 and January 2008 were included. We used a formerly designed registration form and hospital documentation to retrieve the data of included patients. The main outcomes were the rate of CPR success and the survival rate of these patients. Results: Totally 855 patients were included, from which 510 (59.64%) were males. The mean age of included patients was 63 ± 17.6. The CPR process was successful among 364 (42.58%) patients. A total number of 101 (11.82%) patients were discharged from the hospital. Different factors as the cause of cardiac arrest and past medical problems affected the probability of CPR success and the survival of patients with cardiac arrest. Conclusion: Survival rate at hospital discharge was less than one-third of patients and nearly half of the patients received successful CPR. More intensive care unit (ICU) facilities and educational interventions for the emergency staff and the community can enhance the survival of cardiac arrest patients in our health system

First draft genome assembly of the Argane tree (Argania spinosa)

BACKGROUND : The Argane tree (Argania spinosa L. Skeels) is an endemic tree of southwestern Morocco that plays an important socioeconomic and ecologic role for a dense human population in an arid zone. Several studies confirmed the importance of this species as a food and feed source and as a resource for both pharmaceutical and cosmetic compounds. Unfortunately, the argane tree ecosystem is facing significant threats from environmental changes (global warming, over-population) and over-exploitation. Limited research has been conducted, however, on argane tree genetics and genomics, which hinders its conservation and genetic improvement. METHODS : Here, we present a draft genome assembly of A. spinosa. A reliable reference genome of A. spinosa was created using a hybrid de novo assembly approach combining short and long sequencing reads. RESULTS : In total, 144 Gb Illumina HiSeq reads and 7.2 Gb PacBio reads were produced and assembled. The final draft genome comprises 75 327 scaffolds totaling 671 Mb with an N50 of 49 916 kb. The draft assembly is close to the genome size estimated by k-mers distribution and covers 89% of complete and 4.3 % of partial Arabidopsis orthologous groups in BUSCO. CONCLUSION : The A. spinosa genome will be useful for assessing biodiversity leading to efficient conservation of this endangered endemic tree. Furthermore, the genome may enable genome-assisted cultivar breeding, and provide a better understanding of important metabolic pathways and their underlying genes for both cosmetic and pharmacological purposes.This work was supported by the Iridian Genome Foundation (MD, USA). H.G. is supported by a Grant from the NIH (MD, USA) for H3ABioNet/H3Africa (grant numbers U41HG006941 and U24 HG006941). O.B. and B.C. are Fulbright JSD (USA) grant recipients. This work also benefited from support of Midterm Research Program of INRA-Morocco through the use of its bioinformatics platform.https://f1000research.comam2019Genetic