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Phycobiliproteins are gaining popularity as long-term, high-value natural products which can be alternatives to synthetic products. This study analyzed research trends of phycobiliproteins from 1909 to 2020 using a bibliometric approach based on the Scopus database. The current findings showed that phycobiliprotein is a burgeoning field in terms of publications outputs with “biochemistry, genetics, and molecular biology” as the most related and focused subject. The Journal of Applied Phycology was the most productive journal in publishing articles on phycobiliproteins. Although the United States of America (U.S.A.) contributed the most publications on phycobiliproteins, the Chinese Academy of Sciences (China) is the institution with the largest number of publications. The most productive author on phycobiliproteins was Glazer, Alexander N. (U.S.A.). The U.S.A. and Germany were at the forefront of international collaboration in this field. According to the keyword analysis, the most explored theme was the optimization of microalgae culture parameters and phycobiliproteins extraction methods. The bioactivity properties and extraction of phycobiliproteins were identified as future research priorities. Synechococcus and Arthrospira were the most cited genera. This study serves as an initial step in fortifying the phycobiliproteins market, which is expected to exponentially expand in the future. Moreover, further research and global collaboration are necessary to commercialize phycobiliproteins and increase the consumer acceptability of the pigments and their products.

Reducing energy consumption is one of the priority areas of development for all energy consumers. The choice of an efficient and reliable programming language will provide a qualitative analysis of all possible impacts, and consequently a more rational energy consumption by consumers and improve the quality of energy management. The main purpose of this work is to determine the means of R Studio to identify the most influential factors in energy consumption. Information technology allows you to process large databases, as well as use mathematical tools to identify factors that affect the level of energy consumption. R Studio is an open source integrated shell and has a user-friendly interface that simplifies working with R. A number of features, such as backlighting and auto-completion, easy script navigation and others, make R Studio attractive for analyzing statistical databases with many variables. This article describes in detail the contents of the main R Studio window and its main functionality. On the example of the enterprise database the mathematical tools of R Studio were considered: the matrix of pair correlation coefficients for factor signs is constructed, the correlation analysis of influence of factors on energy consumption is carried out.

[ES] El Departamento de Organización de Empresas (DOE) está compuesto por unos 160 profesores, de los cuales solo unos 66 pertenecen a la estructura de investigación del Departamento. Sobre esta muestra de profesores se quieren analizar las publicaciones de investigación tanto en revistas como en congresos para identificar el número de citas recibidas, las áreas de investigación más frecuentes, así como las colaboraciones entre los autores. El objetivo general de este trabajo consiste en un análisis bibliométrico con el fin de obtener el estado actual de las publicaciones del DOE. Los objetivos específicos sobre los datos exportados de la Web of Science y Scopus son los siguientes: 1) Analizar el tamaño por autor y número de citas de la producción científica del DOE; 2) Determinar las revistas y congresos que acogen las publicaciones del DOE; 3) Especificar las áreas de investigación más frecuentes del DOE; 4) Identificar las colaboraciones tanto de los grupos de investigación como del personal de investigación no agregado del DOE; 5) Analizar la evolución de la producción científica del DOE. La metodología seguida en este estudio es utilización de diversos programas para realizar visualizaciones. Una de ellas es Tableau, para crear los mapas donde se apreciaban las ubicaciones geográficas de las afiliaciones institucionales; y, por otro lado, VOSViewer y Gephi para realizar grafos con las palabras claves. Además de utilizar R Studio con códigos para localizar tanto publicaciones que estén en ambas listas de las bases de datos y realizar una comparación con los autores que se han encontrados en las bases de datos junto al registro oficial del DOE. Los docentes del DOE realizan muchas colaboraciones tanto entre sí como con autores tanto nacionales, sobre todo en Valencia, como internacionales. En ambas bases de datos, el artículo con más citas recibidas es coincide. Pero con respecto a los autores varían dependiendo de los documentos que indexa cada base de datos. Los autores del DOE escriben más artículos de revistas con respecto a otras tipologías documentales. Las áreas de conocimiento más usadas para los documentos de los autores del DOE son: “Business economics”, “Engineering” y “Computer Science”. Son muchos los profesores del DOE que han publicado en las bases de datos elegidas y en las listas individuales, entre los 33 y los 40 autores, en Scopus y WoS respectivamente. Por otro lado, conforme van pasando los años se produce un incremento de los documentos publicados por estos autores. Las citas de las publicaciones son un ejemplo del prestigio o reputación que van adquiriendo los autores y las propias publicaciones. Hay documentos que contienen más de 50 citas, pero también hay publicaciones en ambas bases de datos que tienen menos 5 citas., [EN] The Departamento de Organización de Empresas (DOE) is made up of about 160 professors, of which only about 66 belong to the research structure of the Department. About this sample of professors, we want to analyze research publications both in journals and in congresses to identify the number of citations received, the most frequent research areas, as well as collaborations between authors. The general objective of this work is a bibliometric analysis in order to obtain the current status of DOE publications. The specific objectives on data exported from the Web of Science and Scopus are the following: 1) Analyze the size by author and number of citations of the scientific production of the DOE; 2) Determine the journals and congresses that host the DOE publications; 3) Specify the most frequent research areas of the DOE; 4) Identify the collaborations of both the research groups and the non-aggregated research staff of the DOE; 5) Analyze the evolution of the scientific production of the DOE. The methodology followed in this study is the use of various programs to make visualizations. One of them is Tableau, to create the maps where the geographical locations of the institutional affiliations were appreciated; and, on the other hand, VOSViewer and Gephi to make graphs with the keywords. In addition to using R Studio with codes to locate both publications that are in both lists of the databases and make a comparison with the authors that have been found in the databases next to the official DOE registry. DOE teachers make many collaborations with each other as well as with national authors, especially in Valencia, as well as international ones. In both databases, the article with the most citations received is matches. But with respect to the authors they vary depending on the documents indexed by each database. The DOE authors write more magazine articles with respect to other documentary typologies. The areas of knowledge most used for the documents of the DOE authors are: "Business economics", "Engineering" and "Computer Science". There are many DOE professors who have published in the chosen databases and in the individual lists, between 33 and 40 authors, in Scopus and WoS respectively. On the other hand, as the years go by there is an increase in the documents published by these authors. The citations of the publications are an example of the prestige or reputation that the authors and the publications themselves acquire. There are documents that contain more than 50 citations, but there are also publications in both databases that have less than 5 citations.

Esse artigo tem o objetivo de realizar um levantamento bibliométrico sobre trabalhos publicados relacionados a depósitos antropogênicos ou depósitos tecnogênicos, para observação da evolução das formas de abordagem. Foram utilizados dados da Scopus, e a partir destes dados, realizou-se uma análise bibliométrica utilizando os softwares RStudio e Vosviwer. Como resultado, obtém-se que ocorreu um aumento no número de artigos publicados a partir do ano de 2006. A Rússia é o país que mais publica trabalhos nesse campo de pesquisa. Em contrapartida, os Estados Unidos apresentam mais trabalhos considerados relevantes, com maior número de citações por artigo. No total, 445 autores publicaram trabalhos relacionados a este tema, como autor principal ou coautor, sendo a revista com maior número de publicações a International Multidisciplinary Scientific GeoConference Surveying. As palavras-chave mais frequentes nos resumos dos artigos foram mapeadas e divididas em quatro grupos: Arqueologia; Geologia e Solos; Depósitos Urbanos; Mineração, representando áreas distintas desse campo de pesquisa.

A larger amount of data gives a better output but also working with it can become a challenge due to processing limitations. Nowadays companies are starting to realize the importance of using more data in order to support decision for their strategies. It was said and proved through study cases that “More data usually beats better algorithms”. With this statement companies started to realize that they can chose to invest more in processing larger sets of data rather than investing in expensive algorithms. During the last decade, large statistics evaluation has seen an exponential boom and will absolutely retain to witness outstanding tendencies due to the emergence of new interactive multimedia packages and extraordinarily incorporated systems driven via the speedy growth in statistics services and microelectronic gadgets. Up to now, maximum of the modern mobile structures are especially centered to voice communications with low transmission fees.

Big Data Analytics is a way of extracting value from these huge volumes of information, and it drives new market opportunities and maximizes customer retention. The rapid rise of the Internet and the digital economy has fuelled an exponential growth in demand for data storage and analytics, and IT department are facing tremendous challenge in protecting and analyzing these increased volumes of information. The reason organizations are collecting and storing more data than ever before is because their business depends on it. The type of information being created is no more traditional database-driven data referred to as structured data rather it is data that include documents, images, audio, video, and social media contents known as unstructured data or Big Data. This paper primarily focuses on discussing the various technologies that work together as a Big Data Analytics system that can help predict future volumes, gain insights, take proactive actions, and give way to better strategic decisionmaking.

// Jad El-Hoss 1, * , Duohui Jing 1, * , Kathryn Evans 1 , Cara Toscan 1 , Jinhan Xie 1 , Hyunjoo Lee 1 , Renea A. Taylor 2 , Mitchell G. Lawrence 3 , Gail P. Risbridger 3 , Karen L. MacKenzie 1 , Rosemary Sutton 1 , Richard B. Lock 1 1 Children’s Cancer Institute, Lowy Cancer Research Centre, Sydney, UNSW, Australia 2 Prostate Research Group, Department of Physiology, Biomedicine Discovery Institute, Monash Partners Comprehensive Cancer Consortium, Monash University, Clayton, VIC, Australia 3 Prostate Research Group, Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash Partners Comprehensive Cancer Consortium, Monash University, Clayton, VIC, Australia * These authors have contributed equally to this work Correspondence to: Richard B. Lock, email: rlock@ccia.unsw.edu.au Keywords: SNP genotyping, patient derived xenografts, authentication, OpenArray, R studio Received: December 06, 2015 Accepted: July 26, 2016 Published: August 09, 2016 ABSTRACT Patient derived xenografts (PDXs) have become a vital, frequently used, component of anti-cancer drug development. PDXs can be serially passaged in vivo for years, and shared across laboratories. As a consequence, the potential for mis-identification and cross-contamination is possible, yet authentication of PDXs appears limited. We present a PDX Authentication System (PAS), by combining a commercially available OpenArray assay of single nucleotide polymorphisms (SNPs) with in-house R studio programs, to validate PDXs established in individual mice from acute lymphoblastic leukemia biopsies. The PAS is sufficiently robust to identify contamination at levels as low as 3%, similar to the gold standard of short tandem repeat (STR) profiling. We have surveyed a panel of PDXs established from 73 individual leukemia patients, and found that the PAS provided sufficient discriminatory power to identify each xenograft. The identified SNP-discrepant PDXs demonstrated distinct gene expression profiles, indicating a risk of contamination for PDXs at high passage number. The PAS also allows for the authentication of tumor cells with complex karyotypes from solid tumors including prostate cancer and Ewing’s sarcoma. This study highlights the demands of authenticating PDXs for cancer research, and evaluates a reliable authentication platform that utilizes a commercially available and cost-effective system.

Зменшення рівня енергоспоживання є одним із пріоритетних напрямів розвитку для всіх енергоспоживачів. Вибіp ефективної та надійної мови програмування дозволить забезпечити якісний аналіз всіх можливих впливів, а відповідно і більш pаціональне енергоcпоживання споживачами та підвищити якіcть упpавління енергоcпоживанням. Основною метою даної роботи є визначення засобів R Studio саме для виявлення найбільш впливових на енергоспоживання факторів. Інформаційні технології дозволяють обробляти великі бази даних, а також застосовувати математичні засоби для виявлення факторів, які впливають на рівень енергоспоживання. R Studio є відкритим джерелом інтегрованої оболонки і має зручний інтерфейс, що спрощує роботу з R. Ряд особливостей, таких як підсвічування і автоматичне завершення коду, зручна навігація по скрипту і інші, роблять R Studio привабливою для аналізу статистичних баз даних з великою кількістю змінних. У даній статті докладно описаний вміст головного вікна R Studio та його основний функціонал. На прикладі бази даних підприємства було розглянуто математичні інструменти R Studio: побудовано матрицю парних коефіцієнтів кореляції для факторних ознак, проведено кореляційний аналіз впливу факторів на енергоспоживання. Reducing energy consumption is one of the priority areas of development for all energy consumers. The choice of an efficient and reliable programming language will provide a qualitative analysis of all possible impacts, and consequently a more rational energy consumption by consumers and improve the quality of energy management. The main purpose of this work is to determine the means of R Studio to identify the most influential factors in energy consumption. Information technology allows you to process large databases, as well as use mathematical tools to identify factors that affect the level of energy consumption. R Studio is an open source integrated shell and has a user-friendly interface that simplifies working with R. A number of features, such as backlighting and autocompletion, easy script navigation and others, make R Studio attractive for analyzing statistical databases with many variables. This article describes in detail the contents of the main R Studio window and its main functionality. On the example of the enterprise database the mathematical tools of R Studio were considered: the matrix of pair correlation coefficients for factor signs is constructed, the correlation analysis of influence of factors on energy consumption is carried out.

En el presente documento se realiza un análisis de la radiación solar en la ciudad de Cúcuta, Norte de Santander, Colombia, con el objetivo de brindar una descripción detallada de la energía del Sol o insolación presente en este territorio. Para realizar estos análisis se trabajó sobre una base de datos compuesta por muestras de radiación solar global por 24 horas del día durante un periodo de nueve años; por medio del software libre especializado en estadística denominado R, se ejecutaron diferentes tipos de cálculos. A partir de estos estudios se obtuvieron gráfi cas en donde se presentan en forma de resumen estadístico, los valores de radiación para cada mes del año y para un año completo, además de obtener un valor de 5.335 horas para las horas solares pico en la ciudad y encontrar una ecuación que prediga con base en los datos obtenidos, la radiación solar de este sitio. Asimismo, con el presente análisis se pretende cuantificar desde un punto de vista descriptivo, el recurso solar presente en la ciudad de Cúcuta. This work presents a statistical analysis of solar radiation in the city of Cúcuta, Norte de Santander, Colombia. The main purpose of this study is to provide a thorough description of the solar energy available in this territory. To perform the analysis, we processed data of hourly global solar radiation during a period of nine years; we used R software to execute diff erent types of statistical calculations. In this paper, we present graphs to summarize the values of global solar radiation for each month and the whole year. Furthermore, we obtained 5.335 sun peak hours for the city of Cucuta and proposed an equation to estimate global solar radiation from historical data for this site. This research aims to quantify, from a statistical point of view, the solar resource available in the city of Cucuta. Este documento analisa a radiação solar na cidade de Cúcuta, Norte de Santander, Colombia, com o objetivo de fornecer uma descrição detalhada da energia solar ou insolação presente neste território. Para realizar essas análises, fue realizado um trbalho em uma base de dados composta por amostras de radiação solar global por 24 horas do dia durante um período de nove anos; através de software livre especializado em estatística chamado R, foram executados diferentes tipos de cálculos. Através destes estudos, foram obtidos gráfi cos onde os valores de radiação para cada mês do ano e para um ano completo são apresentados como um resumo estatístico, além de obter um valor de 5.355 horas para as horas solares pico na cidade e encontrar uma equação que prevê, com base nos dados obtidos, a radiação solar deste site. Com a análise atual, procura-se quantifi car de um ponto de vista descritivo, o recurso solar presente na cidade de Cúcuta.

Fil: Espíndola, María del Carmen. Universidad Nacional de Misiones. Facultad de Humanidades y Ciencias Sociales. Departamento de Técnico en Investigación Socioeconómica; Argentina. Fil: González, Esteban Samuel. Universidad Nacional de Misiones. Facultad de Humanidades y Ciencias Sociales. Departamento de Técnico en Investigación Socioeconómica; Argentina. Fil: Rodríguez, Darío Hugo. Universidad Nacional de Misiones. Facultad de Humanidades y Ciencias Sociales. Departamento de Técnico en Investigación Socioeconómica; Argentina. En el marco del Proyecto de Extensión “Herramientas Informáticas para la Investigación Social”, conformamos un “grupo de estudio”1 con el fin de investigar, observar y divulgar las potencialidades del software libres. Partiendo de la necesidad del uso de software libre para tratar los datos en investigaciones socioeconómicas, nos propusimos mostrar a los investigadores de la comunidad de la Facultad de Humanidades y Ciencias Sociales las potencialidades del R Studio en el tratamiento y análisis de datos de estadísticas públicas. Los integrantes y quienes participan en los encuentros del grupo de estudio son los graduados y estudiantes avanzados de la carrera de Técnico en Investigación Socioeconómica (TISE) de manera de constituir una masa crítica de profesionales con habilidades en la utilización de ésta herramienta informática poco aprovechada aun en nuestro medio. En este artículo el grupo conformado parte desde la instalación del software hasta las primeras aproximaciones estadísticas sintetizadas en recursos gráficos. Para concluir con las diferentes modalidades de elaborar informes que el software analizado ofrece.

Objective: The aim of this project was to develop a nimble system to both monitor and report on the quality of Kentucky emergency department syndromic surveillance (SyS) data at system-wide and facility levels. Introduction: In 2016, the CDC funded 12 states, under the Enhanced State Opioid Overdose Surveillance (ESOOS) program, to utilize SyS to increase timeliness of state data on drug overdose events. In order to operationalize the objectives of the grant, there was a need to assess and monitor the quality of Kentucky’s SyS data, with limited resources. We leveraged the NSSP’s R Studio Server to automate quality assurance (QA) monitoring and reporting to meet these objectives. Methods: Using the R Server, we pulled data from the process messages table, aggregating messages to single patient encounters. In addition to compiling the code on a powerful remote server, the server can access the process table messages relatively quickly. We developed an R Markdown report to produce a report that includes a variety of system- and facility-level metrics that highlight key indicators of system performance and data flows. By using R, we were able to create an auto-generating QA report that runs weekly and e-mails for analyst review. Quality metrics included: % completeness of chief complaint and discharge diagnosis codes (overall and by facility)[Fig 1 & Fig 2]; visit trend by day of visit (with interactive spark lines)[Fig 2]; maximum date of message created, date message arrived at NSSP server, date of visit, and total messages[Fig 3]; message arrived trend (interactive sparklines)[Fig 3]; volume and type of error messages failing to process[Fig 4]; message volume by ADT type[Fig 5]; and volume of patient class by type by day[not shown]. Our SyS analyst reviews the report and delivers it to stakeholders with general comments about ongoing and newly emerging data quality concerns. Results: The report has proven to be beneficial in ongoing QA monitoring. The report is shared weekly with key stakeholders at the Kentucky Department for Public Health, Kentucky Health Information Exchange, NSSP, and regional ESSENCE users. Findings are reviewed at monthly SyS stakeholder meetings. The report has identified numerous errors, dead feeds, and other systems changes in near real-time; leading to corrective action and general data quality enhancement. Weekly monitoring of QA has improved data feed stability and communication of identified issue with key stakeholders. Conclusions: The R Studio Server provides a nimble platform to develop, refine, and automate a QA reporting system that can lead to improved SyS data quality. In Kentucky, in addition to improving overall data quality, these weekly reports and subsequent communication have help built relationships among key stakeholders and elevated the importance of syndromic surveillance data locally. Continual monitoring of data is critical to ensure quality and therefor the validity of the data.

Objective To develop a detailed data validation strategy for facilities sending emergency department data to the Massachusetts Syndromic Surveillance program and to evaluate the validation strategy by comparing data quality metrics before and after implementation of the strategy. Introduction As a participant in the National Syndromic Surveillance Program (NSSP), the Massachusetts Department of Public Health (MDPH) has worked closely with our statewide Health Information Exchange (HIE) and National Syndromic Surveillance Program (NSSP) technical staff to collect and transmit emergency department (ED) data from eligible hospitals (EHs) to the NSSP. Our goal is to ensure complete and accurate data using a multi-step process beginning with pre-production data and continuing after EHs are sending live data to production. Methods We used an iterative process to establish a framework for monitoring data quality during onboarding of EHs into our syndromic surveillance system and kept notes of the process. To evaluate the framework, we compared data received during the month of January 2016 to the most recent full month of data (June 2016) to describe the following primary data quality metrics and their change over time: total and daily average of message and visit volume; percent of visits with a chief complaint or diagnosis code received in the NSSP dataset; and percentage of visits with a chief complaint/diagnosis code received within a specified time of admission to the ED. Results The strategies for validation we found effective included examination of pre-production test HL7 messages and the execution of R scripts for validation of live data in the staging and production environments. Both the staging and production validations are performed at the individual message level as well as the aggregated visit level, and included measures of completeness for required fields (Chief Complaint, Diagnosis Codes, Discharge Dispositions), timeliness, examples of text fields (Chief Complaint and Triage Notes), and demographic information. We required EHs to pass validation in the staging environment before granting access to send data to the production environment. From January to June 2016, the number of EHs sending data to the production environment increased from 44 to 48, and the number of messages and visits captured in the production environment increased substantially (see Table 1). The percentage of visits with a chief complaint remained consistently high (>99%); however the percentage of visits with a chief complaint within three hours of admission decreased during the study period. Both the overall percentage of visits with a diagnosis code and the percentage of visits with a diagnosis code within 24 hours of admission increased. Conclusions From January to June 2016, Massachusetts syndromic surveillance data improved in the percentage of visits with diagnosis codes and the time from admission to first diagnosis code. This was achieved while the volume of data coming into the system increased. The timeliness of chief complaints decreased slightly during the study period, which may be due to the inclusion of several new facilities that are unable to send real-time data. Even with the improvements in the timeliness of the diagnosis code field, and the subsequent decrease in the timeliness of the chief complaint field, chief complaints remained a more timely option for syndromic surveillance. Pre-production and ongoing data quality assurance activities are crucial to ensure meaningful data are acquired for secondary analyses. We found that reviewing test HL7 messages and staging data, daily monitoring of production data for key factors such as message volume and percent of visits with a diagnosis code, and monthly full validation in the production environment were and will continue to be essential to ensure ongoing data integrity. Table 1: ED Data in the Production Environment

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Ljudje so že tisočletja imeli željo, da bi lahko napovedali prihodnost. Zelo pogosta je želja po tem, da bi predvideli, kaj se nam obeta v prihodnosti, kakšno bo naše življenje nekega dne. Prav takšno željo imajo tudi različna podjetja in raziskovalne ustanove, saj želijo vedeti ali bo nek izdelek ali storitev uspešen na trgu ter ali se bodo naložbe v prihodnosti izplačale. Na veliko veselje vseh je doba digitalizacije, s pomočjo podatkovnih baz in z močjo analitike, to omogočila z uporabo napovednih modelov. V magistrskem delu smo predstavili definicijo napovednih modelov in raziskali, kateri so tisti napovedni modeli, ki se najpogosteje uporabljajo na področju biomedicine. Najpogosteje uporabljene napovedne modele smo tudi podrobneje opisali in prikazali z grafičnim prikazom. Predstavili smo tudi uporabnost napovednih modelov na različnih področjih in njihovo praktično uporabo v programskem okolju R. Zastavili smo si dve raziskovalni vprašanji, na kateri smo dobili odgovore s pomočjo analize znanstvenih člankov iz baze PubMed. Kot prvo raziskovalo vprašanje smo ugotavljali, kateri so trije najpogostejši napovedni modeli, kot drugo raziskovalno vprašanje pa nas je zanimalo, kako se je v zadnjih dvajsetih letih spreminjalo število člankov, v katerih so uporabljeni napovedni modeli. Iz baze PubMed smo povzeli statistične podatke za zadnjih dvajset let in s pomočjo MS Excela prišli do zanimivih rezultatov. Ugotovili smo, da se kot trije najpogosteje uporabljeni napovedni modeli pojavljajo logistična regresija, linearna regresija in nevronske mreže. Po pričakovanjih pa smo tudi ugotovili, da je število člankov z uporabljenimi napovednimi metodami v zadnjih dvajsetih letih naraščalo, kar pomeni, da so jih raziskovalci vedno pogosteje vključevali v svoje raziskave. Iz tega lahko sklepamo, da se napovedni modeli v raziskovanju in praksi uporabljajo vse pogosteje, kar kaže na njihov pozitiven vpliv pri napovedovanju dogodkov in izidov. For thousands of years people had desire to be able to predict the future. A desire to see what is there for us in the future or how our life will change some day is very often among people. Various companies and research institutions have also the same desire or goal because they want to know whether a specific product or service will be successful on the market and whether the investments will be paid off in the future. The era of digitalization, use of databases with the power of analytics have this desire made possible by using predictive models. In master thesis we introduce the definition of predictive models and which are the most commonly used predictive models in the field of biomedicine. The most often used predictive models were described in details and visualized. The applicability of predictive models in various fields of science was also demonstrated as well as their practical application in the programming environment R. We have set two research questions and searched for answers through the analysis of scientific articles from the PubMed database. Our first question was to determine which are three most often used predictive models in biomedicine. And for the second question we were interested in how the number of articles using the predictive models has changed in the last twenty years. We summarized statistic data from the PubMed database for the last twenty years and by using MS Excel came up with some interesting results. We found out that three most often used predictive models are logistic regression, linear regression and neural networks. As expected, we also found out that the number of scientific articles using predictive models has increased during the last twenty years which suggests that researchers include them more often in their researches. From this results we can make a conclusion that the predictive models are used more often in research and practice environment, which shows their positive influence in predicting events and outcomes.

DOI: 10.12957/cadest.2016.25077 This article shows, using R software, how to seasonally adjust a time series using the X-13-ARIMA-SEATS program and the seasonal package developed by Christoph Sax. In addition to presenting step-by-step seasonal adjustment, the article also explores how to analyze the program output and how to forecast the original and seasonally adjusted time series. A case study was proposed using the Brazilian industrial production. It was verified that the effect of Carnival, Easter and working days improved the seasonal adjustment when treated by the model. Keywords: Seasonal Adjustment, X13-ARIMA-SEATS, R software, RStudio.