bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Pleiotropic Effects of Statins in Endocrine Disorders
Authors: De La Cruz Javier A. , Mihos Christos G. , Horvath Sofia A. , Santana Orlando 
Published: April 2019

Background:The 3-Hydroxy-3-MethylGlutaryl-CoA reductase inhibitors, better known as statins, are used extensively in the treatment of dyslipidemia and cardiovascular risk reduction. They have also demonstrated a variety of non-lipid lowering, or pleiotropic effects. Pertaining to the endocrine system the benefits of statins can extend to patients with the polycystic ovarian syndrome and thyroid disease. However, there is also increasing evidence that statin use can lead to deleterious effects in different organs, including worsening glycemia and the development of diabetes mellitus.Objective:The aim of this review is to describe the most relevant and updated evidence regarding the pleiotropic effects of statins in endocrine disorders.Methods:We did a systematic review of scientific articles published in PubMed regarding the effects of statins on the different aspects of the endocrine system up until June 5th of 2018.Results:We identified preliminarily 61 publications, of which 4 were excluded due to having abstract format only, and 5 were excluded for not containing pertinent information to the study.Conclusion:Several aspects of the endocrine system have been shown to be influenced by the pleiotropic effects that statins exert, however, the benefits of statins on cardiovascular morbidity and mortality largely outweigh this deleterious effect, and statin therapy should continue to be recommended.
Heart Disease Prediction System using Ensemble of Machine Learning Algorithms
Authors: Rajendran Nandhini Abirami , Vincent Durai Raj 
Published: April 2019

Background:
Diagnosing diseases is an intricate job in medical field. Machine learning
when applied to health care is capable of early detection of disease which would aid to provide early
medical intervention. In heart disease prediction, machine learning techniques have played a
significant role. Analysis of disease has become vital in health care sectors. The massive data collected
by healthcare sectors are preprocessed and analyzed to discover the underlying information
in the data for effective decision making and to provide proper medical intervention. The success
of machine learning in medical industry is its capability in analyzing the huge amount of data gathered
by the health sector and its effectiveness in decision making. Since medical field involves too
many manual processes it has become necessary to automate these procedures. Remarkable advancements
in electronic medical records have made it possible. Diagnosing diseases is an intricate
job in medical field.

Objective:
The objective of this research is to design a robust machine learning algorithm to predict
heart disease. The prediction of heart disease is performed using Ensemble of machine learning algorithms.
This is to boost the accuracy achieved by individual machine learning algorithms.

Methods:
Heart Disease Prediction System is developed where the user can input the patient details
and the prediction for the particular patient is made using the model developed. The model will
predict the output to be either normal or risky. Linear Discriminant Analysis (LDA), Classification
and Regression Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and
Naïve Bayes classifier are used as base learners. These algorithms are combined using random forest
as the meta classifier.

Results:
The predictions of classifier are combined using random forest algorithm. The accuracy is
lifted from 85.53 % to 87.64 % which is an impressive improvement on accuracy.

Conclusion:
Various techniques were adopted to preprocess the data to suite the requirement of
analysis. Feature selections were made to optimize the performance of machine learning algorithms.
Ensemble prediction gave better accuracy when combined using Random forest algorithm
as combiner. Better feature selection techniques can be applied to further improve the accuracy.
Optical Characterization of Al-doped ZnO Films via Sol-gel Method Using Spectroscopic Ellipsometry
Authors: Aghkonbad Ehsan M. , Sedghi Hassan , Aghgonbad Maryam M. 
Published: April 2019

Background:
Al-doped ZnO thin films are considered as a promising alternative to ITO
in optoelectronic applications. In this work, Al-doped ZnO thin films were prepared using sol-gel
spin coating technique.

Experimental:
The optical properties of the films such as refractive index, extinction coefficient,
dielectric function and the absorption coefficient were examined using spectroscopic ellipsometry
method in the wavelength range of 300 to 900 nm. The effect of Al doping on ZnO thin films with
different Al concentrations was significant. Tauc relation was used to estimate the optical band
gap energy of the films.

Results:
The calculated values of band gap energy were obtained between 3.10 to 3.25 eV. Also
the fraction of voids was calculated using Aspnes theory.

Conclusion:
The free carrier concentration value was obtained in the order of 1019 cm-3.
Improving Sentiment Analysis using Hybrid Deep Learning Model
Authors: Pandey Avinash Chandra , Rajpoot Dharmveer Singh 
Published: April 2019

Background:
Sentiment analysis is a contextual mining of text which determines viewpoint of
users with respect to some sentimental topics commonly present at social networking websites. Twitter is
one of the social sites where people express their opinion about any topic in the form of tweets. These tweets
can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment
analysis methods use manually extracted features for opinion classification. The manual feature extraction
process is a complicated task since it requires predefined sentiment lexicons. On the other hand,
deep learning methods automatically extract relevant features from data hence; they provide better performance
and richer representation competency than the traditional methods.

Objective:
The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the
computational cost.

Method:
To achieve the objective, a hybrid deep learning model, based on convolution neural network and
bi-directional long-short term memory neural network has been introduced.

Results:
The proposed sentiment classification method achieves the highest accuracy for the most of the datasets.
Further, from the statistical analysis efficacy of the proposed method has been validated.

Conclusion:
Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover,
performance can also be enhanced by tuning the hyper parameters of deep leaning models.
IP Traceback using Flow Based Classification
Authors: Bhavani Yerram , Janaki Vinjamuri , Sridevi Rangu 
Published: April 2019

Background:Distributed Denial of Service (DDoS) attack is a major threat over the internet. The IP traceback mechanism defends against DDoS attacks by tracing the path traversed by attack packets. The existing traceback techniques proposed till now are found with few short comings. The victim required many number of packets to trace the attack path. The requirement of a large number of packets resulted in more number of combinations and more false positives.Methods:To generate a unique value for the IP address of the routers in the attack path Chinese Remainder theorem is applied. This helped in combining the exact parts of the IP address at the victim. We also applied K-Nearest Neighbor (KNN) algorithm to classify the packets depending on their traffic flow, this reduced the number of packets to reconstruct the attack path.Results:The proposed approach is compared with the existing approaches and the results demonstrated that the attack graph is effectively constructed with higher precision and lower combination overhead under large scale DDoS attacks. In this approach, packets from diverse flows are separated as per flow information by applying KNN algorithm. Hence, the reconstruction procedure could be applied on each group separately to construct the multiple attack paths. This results in reconstruction of the complete attack graph with fewer combinations and false positive rate.Conclusion:In case of DDoS attacks the reconstruction of the attack path plays a major role in revealing IP addresses of the participated routers without false positives and false negatives. Our algorithm FRS enhances the feasibility of information pertaining to even the farthest routers by incorporating a flag condition while marking the packets. The rate of false positives and false negatives are drastically reduced by the application of Chinese Remainder Theorem on the IP addresses of the router. At the victim, the application of KNN algorithm reduced the combination overhead and the computation cost enormously.
PERSONALITY, COGNITION AND LIFE EVENTS AS PREDICTORS OF EMOTIONAL DISTRESS IN ADOLESCENCE
Authors: Marić Mia , Sakač Marija 
Published: April 2019

The aim of this research was to examine the contribution of personality, cognitive style and life events to the prediction of the symptoms of emotional distress in adolescents. The sample consisted of 300 adolescents. In the analysis, the indicators of five personality dimensions were included - Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness, indicators of attribution dimensions- controllability, internality, stability and the global nature of causes, and importance of the situation, the number, type and occurrence period of life events, and the severity of symptoms of emotional distress in adolescents.Life events have proved to be the best predictors of the symptoms of distress in adolescents. The most significant contribution to the occurrence of distress provided the total number of events. The probability of the occurrence of distress increases with the occurrence of chronic stressors and family problems. Significant contributors to the prediction of distress are also the dimensions of personality - Agreeableness and Conscientiousness, and attribution dimensions - externality and the importance of negative situations.
INTEGRATING DIGITAL TECHNOLOGIES INTO TEACHING PROCESS
Authors: Novković Cvetković Biljana , Stanojević Dragana 
Published: April 2019

This paper wishes to highlight great significance of digital technologies that should be implemented to the highest extent possible in the teaching process. Digital literacy and digital competencies in today's highly developed technological world have been recognized as important aspects for the successful implementation of digital technologies in the teaching process. With this in mind, in this paper we tried to answer the questions as to for what purposes teachers use digital technologies the most and how interested the teachers are for the use of digital technologies. The conclusion is that teachers are highly interested in the application of digital technologies in teaching. Out of all the listed items, the teachers mostly used the Internet-based content and Correspondence by e-mail in their work. The research was based on a descriptive investigative method. Surveys and scaling techniques for measuring teachers' attitudes were used. The present research was carried out between March and April 2017.The basic cohort in this research comprised 276 primary school teachers in the cities of Vranje and Nis. The nature of a sample is that of a stratified simple random sample.
УТИЦАЈ ЉУДСКОГ КАПИТАЛА НА ПОСЛОВНЕ ПЕРФОРМАНСЕ ХОТЕЛСКИХ ПРЕДУЗЕЋА У СРБИЈИ
Authors: Slavković Marko , Ognjanović Jasmina Milorad 
Published: April 2019

In a knowledge-based economy, the success, growth and development of hotel companies relies in a large extent on knowledge, since employees are directly involved in the process of service production and consumption. The results of numerous studies demonstrate the impact of human capital on business performances, so management must invest and improve human capital in order to create differentiated products / services and ensure a stable competitive position. The main goal of the research is to analyze the interdependence of human capital components and business performance of the hotels in Serbia. The research results confirm the influence of human capital on the non-financial performance of hotel companies. In order to test the structural relationships between variables, reliability analysis, factor analysis and structural modeling equation are employed.
Cuestionario sobre conocimientos y prácticas de la prevención en odontólogos: diseño y validación
Authors: Aguilar Díaz Fátima Del Carmen , Arcos Velasco Magdalena Del Rocío , Ramírez Trujillo María De los Ángeles 
Published: April 2019

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><strong>Objetivo - </strong><span>diseñar y evaluar la validez y confiabilidad de un cuestionario para medir los conocimientos y prácticas de la prevención en odontólogos de práctica clínica en México.</span></p><p><strong>Método - </strong><span>se creó un banco de preguntas evaluado por un grupo de expertos a través de la prueba de Lawshe modificada para determinar la validez de contenido. Se realizaron 3 pruebas piloto para la evaluación y ajuste del cuestionario. Se aplicó el cuestionario a odontólogos (n=149), para evaluar la consistencia interna con el Coeficiente Alpha de Cronbach y la repetibilidad con el Coeficiente de Correlación de Intraclase (CCI); además se evalúo la validez de convergencia con el Coeficiente de Correlación de Spearman.</span></p><p><strong>Resultados - </strong>el CPPO se conformó por 37 ítems: conocimientos (23), prácticas (14) y dos preguntas globales. Validez de contenido: &gt;0.64. Los valores de consistencia interna: CPPO α= 0.606, Conocimientos y Prácticas α= 0.639, y 0.687 respectivamente. Los valores de CCI fueron para CPPO = 0.556, para Conocimientos CCI=0.815 y Prácticas CCI=0.929. Se observó correlación estadísticamente significativa entre los puntajes de las dimensiones de conocimientos y prácticas con las preguntas globales correspondientes.</p><p><strong>Limitaciones - </strong><span>la limitante principal en este estudio es el tamaño de muestra para la evaluación de la repetibilidad.</span></p><p><strong>Principales hallazgos - </strong><span>el cuestionario de Conocimientos y Prácticas sobre la Prevención en Odontólogos (CPPO) cuenta con validez y confiabilidad aceptables para ser aplicado en odontólogos mexicanos. </span></p></div></div></div>
Relación del contrato psicológico con la intención de compartir conocimiento tácito y transferencia intraorganizacional
Authors: Salazar Fierro Petra , Melgar Mayardo Josefa , Máynez Guaderrama Aurora Irma , Cavazos Arroyo Judith 
Published: April 2019

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><strong>Objetivo - </strong><span>analizar si el contrato psicológico relacional impacta a la transferencia de conocimiento tácito dentro de la organización, a través de la mediación de la intención para compartir este activo intangible.</span></p><p><strong>Método - </strong><span>se realizó una investigación cuantitativa, de alcance correlacional, de diseño no experimental transversal, y se utilizó una muestra no probabilística en la recolección de 346 cuestionarios de colaboradores mando medio en la industria de autopartes en Ciudad Juárez Chihuahua, México. El análisis de los datos se llevó a cabo mediante el modelado de ecuaciones estructurales utilizando Mínimos Cuadrados Parciales (PLS). </span></p><p><strong>Resultados - </strong><span>se confirma que el contrato psicológico relacional influye positivamente tanto con la intención de compartir conocimiento tácito, como con la transferencia de conocimiento tácito dentro de la organización y la influencia que recibe ésta de la intención de compartir dicho conocimiento.</span></p><p><strong>Limitaciones - </strong><span>los resultados obtenidos son evidencia única y exclusivamente de la industria de autopartes en esta ciudad, los cuales pueden ser diferentes a otras poblaciones, debido al contexto cultural y sector productivo. </span></p><p><strong>Principales hallazgos - </strong><span>existen fuertes implicaciones entre las variables estudiadas y este sector productivo, ya que las organizaciones reconocen la importancia y significado del conocimiento tácito de sus empleados, por lo que deben comprometerse a mantener buenas relaciones interpersonales e incentivos motivacionales para sus empleados con la finalidad de que este activo intangible sea compartido. </span></p></div></div></div></div></div></div><p> </p>
Búsqueda de primers y optimización de técnicas para la determinación de marcadores de ADN microsatélites en langostino argentino (Pleoticus muelleri)
Authors: Gesto Estefanía Soledad , Marcucci Valeria , De Carli Pedro 
Published: April 2019

El langostino argentino (Pleoticus muelleri) se distribuye a lo largo de las costas del Océano Atlántico Sudoccidental, entre los 20º y 50º de Latitud S. En Argentina constituye en la actualidad uno de los recursos de mayor importancia para la actividad pesquera. El elevado valor comercial de este crustáceo en los mercados internacionales lo ha situado como uno de los principales productos de exportación pesquera argentina, representando una participación del 61% del total de exportaciones del sector, con un ingreso anual superior a los 1.200 millones de dólares estadounidenses.
En la actualidad, se dispone para la especie de un estudio genético restringido a la zona sur de su distribución (entre los 42 y 47º de latitud S) y que analiza la diversidad de un segmento de ADN mitocondrial del gen COI, y otro que estudia la variabilidad genética del langostino P. muelleri en toda su área de distribución geográfica mediante el análisis de un fragmento de ADN mitocondrial correspondiente al gen región control (CR).
Este trabajo tiene como objetivo evaluar marcadores moleculares microsatélites como información de base para el estudio poblacional en el langostino argentino.
A partir de bibliografía se seleccionaron cuatro (4) primers que permitieron amplificar microsatélites en otras especies de la misma familia (Solenoceridae).
Solo se han obtenidos resultados de amplificación a partir de un par de iniciadores (ZG-71). La secuenciación de estos fragmentos no permitió detectar la conservación de los microsatélites esperados para el desarrollo del marcador específico.
La trayectoria profesional de los graduados de las carreras docentes de la UART-UNPA entre 1990 y 2015: Conocer para comprender y reflexionar sobre los 25 años de formación docente en la Cuenca Carbonífera
Authors: Assat Mónica Alejandra , Bazán Sonia Raquel , Ciancia Daniela Beatriz , Sandoval Griselda 
Published: April 2019

El presente artículo tiene como objetivo básico comunicar a los lectores los resultados del Proyecto de Investigación 29/C060 “La trayectoria profesional de los graduados de las carreras docentes de la UART-UNPA entre 1990 y 2015: Conocer para comprender y reflexionar sobre los 25 años de formación docente en la Cuenca Carbonífera”, radicado en el Instituto de Educación y Ciudadanía de la Unidad Académica Río Turbio, sede de la Universidad Nacional de la Patagonia Austral.
Comenzando por una mirada retrospectiva a través de la historia de la institución universitaria, luego se presentan las principales categorías de análisis que se han tenido en cuenta en la investigación. Posteriormente, se explica la metodología utilizada en el proceso de investigación, que consistió fundamentalmente en la construcción y aplicación de diversos dispositivos a fin de indagar en los diferentes trayectos laborales-profesionales de los graduados universitarios. A continuación, se exponen los resultados obtenidos a partir de dichos dispositivos constituidos por los talleres de docentes, la reflexión, la encuesta –presencial y mediada por TICs-, y la interpretación de un cortometraje, hasta arribar a las conclusiones a modo de palabras finales.
URINOTHORAX: AN UNUSUAL CAUSE OF MASSIVE PLEURAL EFFUSION
Authors: Bauza Quetglas Jose Luis , Fullana Maria Isabel , Asensio Javier , Díaz Manuel , Pinheiro A.M. , Asensio Javier , Casu Vasile Cristian , Del Rio Manuel 
Published: April 2019

A 74 year-old male with a history of high blood pressure and hyperuricemia was admitted to the hospital with asthenia, unquantified weight loss, dyspnea on moderate exertion for 2 weeks, and diffuse abdominal pain. Blood leukocyte count was 12400/uL, creatinine 0.98 mg/dL, CRP 19 mg/dL, and LDH 318 U/L. The chest X-ray showed a right pleural effusion and the pleural fluid was suggestive of an exudate. Thorax and abdominal CT scan revealed a polycystic right kidney with grade IV hydronephrosis and suggested the presence of a nephropleural fistula. The thoracocentesis was repeated and the pleural fluid / serum ratio of creatinine obtained was higher than 1 (1.35 mg/dL), which is a diagnostic criterion for urinothorax. Finally, a retrograde pyelography was carried out, and confirmed the passage of urinary tract fluid into the pleural cavity. A thoracic drainage tube and a nephrostomy through the superior calyx were placed, both draining purulent material. One month later, the control CT shows a right atrophic kidney and a reduction of the size of the fistulous path.
Urinothorax is an infrequent and underdiagnosed pathology, with few cases reported. It is usually presented as a transudative pleural effusion. Currently, no test is available to confirm the diagnosis, although the ratio of serum creatinine/pleural creatinine could suggest the presence of urinothorax. Radiographic imaging is useful to support the diagnosis. Management of a urinothorax requires a multidisciplinary approach with an emphasis on the correction of the underlying urinary tract pathology, and once corrected, this often leads to a rapid resolution of the pleural effusion.
Interacción de dos hongos toxicogénicos con una cepa de Streptomyces sobre el desarrollo de plantas de trigo
Authors: Fulgueira Cecilia , Borghi Alfredo , Gattuso Marta , Gattuso Susana , De Bracalenti Blanca 
Published: April 2019

Entre los microorganismos antagonistas de los suelos donde se cultivan cereales se hallan Streptomyces capaces de producir sustancias inhibidoras que pueden afectar la viabilidad y capacidad de diseminación de distintas especies de Aspergillus Y Fusarium y su capacidad para producir aflatoxinas y trichotecenos.El objetivo del presente estudio fue evaluar el efecto de la interacción entre hongos toxicogénicos (Aspergillus parasiticus y Fusarium tricinctum NRRL 3299) Y Streptomyces sp.antagónicos del crecimiento de tales hongos sobre el desarrollo de plantas de trigo.Se realizaron las siguientes actividades: a) inoculación de semillas de trigo, con el Streptomyces inhibidor, b) estudio del poder germinativo de las semillas de trigo, c)cultivo en cámara invernáculo de semillas inoculadas y sin inocular sembradas en tierras con y sin los hongos toxicogénicos, d) comprobación de la invasividad o ataque de los hongos mediante el rociado de espigas de plantas provenientes de suelos infectados con una suspensión de conidios de los hongos infectantes,e) determinación de las variaciones en las características botánicasde las plantas de trigo.Se pudo concluir que: 1) la presencia de hongos toxicogénicos no alteró el poder gemtinativo de las semillas de trigo, 2) el desarrollo vegetal, número y peso promedio de los granos de las plantas provenientes desuelos infectados con los hongos toxicogénicos fué significativamentemayor al de las plntas crecidas en tierra esteril, 3) el estudio anatómico de las plantas no puso en evidencia invasión o ataque por parte de los hongos.