Information Management and Improvement of Citation Indices

Bibliometrics and citation analysis have become an important set of methods for library and information science, as well as an exceptional source of information and knowledge for many other areas. Their main sources are citation indices, which are bibliographic databases like Web of Science, Scopus, Google Scholar, etc. However, bibliographical databases lack perfection and standardization. There are several software tools that perform useful information management and bibliometric analysis importing data from them. A comparison has been carried out to identify which of them perform certain pre-processing tasks. Usually, they are not strong enough to detect all the duplications, mistakes, misspellings and variant names, leaving to the user the tedious and time-consuming task of correcting the data. Furthermore, some of them do not import datasets from different citation indices, but mainly from Web of Science (WoS). A new software tool, called STICCI.eu (Software Tool for Improving and Converting Citation Indices - enhancing uniformity), which is freely available online, has been created to solve these problems. STICCI.eu is able to do conversions between bibliographical citation formats (WoS, Scopus, CSV, BibTex, RIS), correct the usual mistakes appearing in those databases, detect duplications, misspellings, etc., identify and transform the full or abbreviated titles of the journals, homogenize toponymical names of countries and relevant cities or regions and list the processed data in terms of the most cited authors, journals, references, etc

Design of an Integrated Analytics Platform for Healthcare Assessment Centered on the Episode of Care

Assessing care quality and performance is essential to improve healthcare processes and population health management. However, due to bad system design and lack of access to required data, this assessment is often delayed or not done at all. The goal of our research is to investigate an advanced analytics platform that enables healthcare quality and performance assessment. We used a user-centered design approach to identify the system requirements and have the concept of episode of care as the building block of information for a key performance indicator analytics system. We implemented architecture and interface prototypes, and performed a usability test with hospital users with managerial roles. The results show that by using user-centered design we created an analytical platform that provides a holistic and integrated view of the clinical, financial and operational aspects of the institution. Our encouraging results warrant further studies to understand other aspects of usability

Pickering emulsions: Preparation processes, key parameters governing their properties and potential for pharmaceutical applications

International audienceAn increased interest in Pickering emulsions has emerged over the last 15 years, mainly related to their very attractive properties compared to regular emulsions, namely their excellent stability and their numerous possible applications. In this review, after detailing the interest of Pickering emulsions, their main preparation processes are presented and their advantages and disadvantages discussed. In the third part, the key parameters that govern Pickering emulsions type, droplet size and stability are analyzed. Finally, the interest and the potential of Pickering emulsions for pharmaceutical applications are exposed and discussed, taking all the administration routes into consideration and focusing on organic particles

Exploring Strategies to Integrate Disparate Bioinformatics Datasets

Distinct bioinformatics datasets make it challenging for bioinformatics specialists to locate the required datasets and unify their format for result extraction. The purpose of this single case study was to explore strategies to integrate distinct bioinformatics datasets. The technology acceptance model was used as the conceptual framework to understand the perceived usefulness and ease of use of integrating bioinformatics datasets. The population of this study included bioinformatics specialists of a research institution in Lebanon that has strategies to integrate distinct bioinformatics datasets. The data collection process included interviews with 6 bioinformatics specialists and reviewing 27 organizational documents relating to integrating bioinformatics datasets. Thematic analysis was used to identify codes and themes related to integrating distinct bioinformatics datasets. Key themes resulting from data analysis included a focus on integrating bioinformatics datasets, adding metadata with the submitted bioinformatics datasets, centralized bioinformatics database, resources, and bioinformatics tools. I showed throughout analyzing the findings of this study that specialists who promote standardizing techniques, adding metadata, and centralization may increase efficiency in integrating distinct bioinformatics datasets. Bioinformaticians, bioinformatics providers, the health care field, and society might benefit from this research. Improvement in bioinformatics affects poistevely the health-care field which has a positive social change. The results of this study might also lead to positive social change in research institutions, such as reduced workload, less frustration, reduction in costs, and increased efficiency while integrating distinct bioinformatics datasets

A Framework for Collaborative Curation of Neuroscientific Literature

Large models of complex neuronal circuits require specifying numerous parameters, with values that often need to be extracted from the literature, a tedious and error-prone process. To help establishing shareable curated corpora of annotations, we have developed a literature curation framework comprising an annotation format, a Python API (NeuroAnnotation Toolbox; NAT), and a user-friendly graphical interface (NeuroCurator). This framework allows the systematic annotation of relevant statements and model parameters. The context of the annotated content is made explicit in a standard way by associating it with ontological terms (e.g., species, cell types, brain regions). The exact position of the annotated content within a document is specified by the starting character of the annotated text, or the number of the figure, the equation, or the table, depending on the context. Alternatively, the provenance of parameters can also be specified by bounding boxes. Parameter types are linked to curated experimental values so that they can be systematically integrated into models. We demonstrate the use of this approach by releasing a corpus describing different modeling parameters associated with thalamo-cortical circuitry. The proposed framework supports a rigorous management of large sets of parameters, solving common difficulties in their traceability. Further, it allows easier classification of literature information and more efficient and systematic integration of such information into models and analyses

Information systems in clinical research : categorization and evaluation of information systems and development of a guide for choosing the appropriate information system

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.The development of information systems used in clinical research is constantly increasing, as their advantages are widely acknowledged. Although many researchers have introduced information systems which can be used during a clinical study’s process, a scarcity of information systems accommodating the complete process has been detected. Based on this finding, twenty-three (23) information systems and ontologies used in clinical research were retrieved, based on certain criteria. The information systems and ontologies were then categorized and evaluated based on categorization and evaluation tools. Finally, the result was the synthesis of the eligible-for-evaluation information systems and the development of a guide for choosing the appropriate information system during each step of a clinical trial; the data provided by each information system were identified. Unfortunately, some information systems and ontologies were excluded from the synthesis due to lack of information regarding the evaluation criteria. Therefore, future research should proceed with retrieving this information and developing a guide which will consider more information systems, especially for conducting observational studies

Dual Centrifugation - A Novel “in-vial” Liposome Processing Technique

Conventional liposome preparation methods bear many limitations, such as poor entrapment efficiencies for hydrophilic drugs, batch size limitations, and limited options for aseptic manufacturing. Liposome preparation by dual centrifugation (DC) is able to overcome most of these limitations. DC differs from normal centrifugation by an additional rotation of the samples during the centrifugation process. Thus, the direction of the centrifugal forces changes continuously in the sample vials. The consequential powerful sample movements inside the vials result in powerful homogenization of the sample. Since this “in-vial” homogenization is optimal for viscous samples, semisolid “vesicular phospholipid gels” (VPGs) are preferred intermediates in the liposome manufacturing by DC. The DC method easily enables aseptic preparation and is gentler as compared to other methods, such as high-pressure homogenization. The method allows very small samples to be prepared, and VPG batches down to 1–5 mg scale have been prepared successfully. VPGs have several applications; they are attractive as depot formulations, or as stable storage intermediates, and can be easily transferred into conventional liposomal formulations by simple dilution. Here, we aim to present the novel DC-liposome technique; the concept, advantages, and limitations; and provide an overview of the experiences of liposome preparation by DC so far

Enhanced clustering analysis pipeline for performance analysis of parallel applications

Clustering analysis is widely used to stratify data in the same cluster when they are similar according to the specific metrics. We can use the cluster analysis to group the CPU burst of a parallel application, and the regions on each process in-between communication calls or calls to the parallel runtime. The resulting clusters obtained are the different computational trends or phases that appear in the application. These clusters are useful to understand the behavior of the computation part of the application and focus the analyses on those that present performance issues. Although density-based clustering algorithms are a powerful and efficient tool to summarize this type of information, their traditional user-guided clustering methodology has many shortcomings and deficiencies in dealing with the complexity of data, the diversity of data structures, high-dimensionality of data, and the dramatic increase in the amount of data. Consequently, the majority of DBSCAN-like algorithms have weaknesses to handle high-dimensionality and/or Multi-density data, and they are sensitive to their hyper-parameter configuration. Furthermore, extracting insight from the obtained clusters is an intuitive and manual task. To mitigate these weaknesses, we have proposed a new unified approach to replace the user-guided clustering with an automated clustering analysis pipeline, called Enhanced Cluster Identification and Interpretation (ECII) pipeline. To build the pipeline, we propose novel techniques including Robust Independent Feature Selection, Feature Space Curvature Map, Organization Component Analysis, and hyper-parameters tuning to feature selection, density homogenization, cluster interpretation, and model selection which are the main components of our machine learning pipeline. This thesis contributes four new techniques to the Machine Learning field with a particular use case in Performance Analytics field. The first contribution is a novel unsupervised approach for feature selection on noisy data, called Robust Independent Feature Selection (RIFS). Specifically, we choose a feature subset that contains most of the underlying information, using the same criteria as the Independent component analysis. Simultaneously, the noise is separated as an independent component. The second contribution of the thesis is a parametric multilinear transformation method to homogenize cluster densities while preserving the topological structure of the dataset, called Feature Space Curvature Map (FSCM). We present a new Gravitational Self-organizing Map to model the feature space curvature by plugging the concepts of gravity and fabric of space into the Self-organizing Map algorithm to mathematically describe the density structure of the data. To homogenize the cluster density, we introduce a novel mapping mechanism to project the data from the non-Euclidean curved space to a new Euclidean flat space. The third contribution is a novel topological-based method to study potentially complex high-dimensional categorized data by quantifying their shapes and extracting fine-grain insights from them to interpret the clustering result. We introduce our Organization Component Analysis (OCA) method for the automatic arbitrary cluster-shape study without an assumption about the data distribution. Finally, to tune the DBSCAN hyper-parameters, we propose a new tuning mechanism by combining techniques from machine learning and optimization domains, and we embed it in the ECII pipeline. Using this cluster analysis pipeline with the CPU burst data of a parallel application, we provide the developer/analyst with a high-quality SPMD computation structure detection with the added value that reflects the fine grain of the computation regions.El análisis de conglomerados se usa ampliamente para estratificar datos en el mismo conglomerado cuando son similares según las métricas específicas. Nosotros puede usar el análisis de clúster para agrupar la ráfaga de CPU de una aplicación paralela y las regiones en cada proceso intermedio llamadas de comunicación o llamadas al tiempo de ejecución paralelo. Los clusters resultantes obtenidos son las diferentes tendencias computacionales o fases que aparecen en la solicitud. Estos clusters son útiles para entender el comportamiento de la parte de computación del aplicación y centrar los análisis en aquellos que presenten problemas de rendimiento. Aunque los algoritmos de agrupamiento basados en la densidad son una herramienta poderosa y eficiente para resumir este tipo de información, su La metodología tradicional de agrupación en clústeres guiada por el usuario tiene muchas deficiencias y deficiencias al tratar con la complejidad de los datos, la diversidad de estructuras de datos, la alta dimensionalidad de los datos y el aumento dramático en la cantidad de datos. En consecuencia, el La mayoría de los algoritmos similares a DBSCAN tienen debilidades para manejar datos de alta dimensionalidad y/o densidad múltiple, y son sensibles a su configuración de hiperparámetros. Además, extraer información de los clústeres obtenidos es una forma intuitiva y tarea manual Para mitigar estas debilidades, hemos propuesto un nuevo enfoque unificado para reemplazar el agrupamiento guiado por el usuario con un canalización de análisis de agrupamiento automatizado, llamada canalización de identificación e interpretación de clúster mejorada (ECII). para construir el tubería, proponemos técnicas novedosas que incluyen la selección robusta de características independientes, el mapa de curvatura del espacio de características, Análisis de componentes de la organización y ajuste de hiperparámetros para la selección de características, homogeneización de densidad, agrupación interpretación y selección de modelos, que son los componentes principales de nuestra canalización de aprendizaje automático. Esta tesis aporta cuatro nuevas técnicas al campo de Machine Learning con un caso de uso particular en el campo de Performance Analytics. La primera contribución es un enfoque novedoso no supervisado para la selección de características en datos ruidosos, llamado Robust Independent Feature. Selección (RIFS).Específicamente, elegimos un subconjunto de funciones que contiene la mayor parte de la información subyacente, utilizando el mismo criterios como el análisis de componentes independientes. Simultáneamente, el ruido se separa como un componente independiente. La segunda contribución de la tesis es un método de transformación multilineal paramétrica para homogeneizar densidades de clústeres mientras preservando la estructura topológica del conjunto de datos, llamado Mapa de Curvatura del Espacio de Características (FSCM). Presentamos un nuevo Gravitacional Mapa autoorganizado para modelar la curvatura del espacio característico conectando los conceptos de gravedad y estructura del espacio en el Algoritmo de mapa autoorganizado para describir matemáticamente la estructura de densidad de los datos. Para homogeneizar la densidad del racimo, introducimos un mecanismo de mapeo novedoso para proyectar los datos del espacio curvo no euclidiano a un nuevo plano euclidiano espacio. La tercera contribución es un nuevo método basado en topología para estudiar datos categorizados de alta dimensión potencialmente complejos mediante cuantificando sus formas y extrayendo información detallada de ellas para interpretar el resultado de la agrupación. presentamos nuestro Método de análisis de componentes de organización (OCA) para el estudio automático de forma arbitraria de conglomerados sin una suposición sobre el distribución de datos.Postprint (published version

DETECTION OF PERFLUORINATED COMPOUNDS IN THE ENVIRONMENT AND THEIR EFFECT ON CELLULAR ORGANISMS

Ph.DDOCTOR OF PHILOSOPH