Magnetic resonance spectroscopy. Data analysis for clinical applications

This text is taken from the postgraduate thesis, which one of the authors (A.B.) developed for the degree of Medical Physicist in the School on Medical Physics of the University of Florence. The text explores the feasibility of quantitative Magnetic Resonance Spectroscopy as a tool for daily clinical routine use. The results and analysis comes from two types of hyper spectral images: the first set are hyper spectral images coming from a standard phantom (reference images); and hyper spectral images obtained from a group of patients who have undergone MRI examinations at the Santa Maria Nuova Hospital. This interdisciplinary work stems from the IFAC-CNR know how in terms of data analysis and nanomedicine, and the clinical expertise of Radiologists and Medical Physicists. The results reported here, which were the subject of the thesis, are original, unpublished, and represent independent work.

The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections

For a long time, electronic data analysis has been associated with quantitative methods. However, Computer Assisted Qualitative Data Analysis Software (CAQDAS) are increasingly being developed. Although the CAQDAS has been there for decades, very few qualitative health researchers report using it. This may be due to the difficulties that one has to go through to master the software and the misconceptions that are associated with using CAQDAS. While the issue of mastering CAQDAS has received ample attention, little has been done to address the misconceptions associated with CAQDAS. In this paper, the author reflects on his experience of interacting with one of the popular CAQDAS (NVivo) in order to provide evidence-based implications of using the software. The key message is that unlike statistical software, the main function of CAQDAS is not to analyse data but rather to aid the analysis process, which the researcher must always remain in control of. In other words, researchers must equally know that no software can analyse qualitative data. CAQDAS are basically data management packages, which support the researcher during analysis.

Sparse time-frequency data analysis : a multi-scale approach

In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. Moreover, we propose a new STFR algorithm to study intrawave signals with strong frequency modulation and analyze the convergence of this new algorithm for periodic signals. Such signals have previously remained a bottleneck for all signal processing methods. Furthermore, we propose a modified version of STFR that facilitates the extraction of intrawaves that have overlaping frequency content. We show that the STFR methods can be applied to the realm of dynamical systems and cardiovascular signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis of some cardiovascular diseases. We further explain some preliminary work on the nature of Intrinsic Mode Functions (IMFs) and how they can have different representations in different phase coordinates. This analysis shows that the uncertainty principle is fundamental to all oscillating signals.

Detecting phase transitions in community structures using big data analysis of the engineering education research landscape: a European perspective

Comunicação apresentada na 44th SEFI Conference, 12-­15 September 2016, Tampere, Finland

Heart failure following cancer therapy in patients with hematological malignancies aged 18 years and under : a linked health data analysis (1996-2009)

Introduction: The causal link between chemotherapy treatments and subsequent cardiotoxicity is well established, particularly for children with hematological malignancies. Little information exists on the characteristics and outcomes for patients with heart failure (HF) after chemotherapy. This study aimed to describe the characteristics, survival and mortality of patients who received chemotherapy for hematological cancer (leukemias, lymphomas and related disorders) before 18 years old and subsequently developed HF compared to those who did not.

Methods: Linked health data (1996-2009) from the Queensland Cancer Registry, Death Registry and Hospital Administration records for HF and chemotherapy admissions were reviewed. From all breast and hematological cancers patients (n=73,158), 15,987 received chemotherapy, including 819 patients aged ≤18 years at time of cancer diagnosis. Patients were categorized as those with an index HF admission (occurred after cancer diagnosis) and those without an index HF admission (non HF).

Results: Of the 819 patients, 3.7% (n=30) had an index HF admission. Median age of HF patients at time of cancer diagnosis was 5 years (IQR 3-12) compared to 7 years (IQR 3-14) in the non HF group (p=0.503). Median follow up from cancer diagnosis was 2.5 years in the HF group compared to 5.42 years in the non HF group (p<0.01). Of those who developed HF, 70% (n=21) had the index admission within 12 months of their cancer diagnosis. Of those with HF, 53.3% (n=16) died (all cause) compared to 14.6% (n=115) with no HF. On adjustment for age, sex and chemotherapy admissions, HF patients had an almost 5 fold increased mortality risk compared to non HF patients (HR 4.91 [95% CI, 2.88-8.36]) (Figure 1).

Conclusions: This study demonstrated that in children with hematological cancers the onset of HF occurred soon after chemotherapy and mortality risk is almost 5 times that of children who do not develop HF. Innovative strategies are still needed for the prevention and management of cardiotoxicity in this population.

User centric design, data analysis and performance of snowboard bindings

This study focuses on soft boot snowboard bindings by looking at how users interact with their binding and proposes a possible solution to overcome such issues. Snowboarding is a multibillion-dollar sport that has only reached mainstream in the last 30 years its levels of progression in technology have evolved in that time. However, snowboard bindings for the most part still consist of the same basic architecture in the last 20 years. This study was aimed at taking a user centric point of view and using additive manufacturing technologies to be able to generate a new snowboard binding that is completely adaptable to the user. The initial part of the study was a survey of 280 snowboarders focussing on preferences, style and habits. This survey was generated from over 15 nations with the vast majority of boarders on the snow for five to fifty days a year. Significant emphasis was placed on the relationship between boarder binding set-up and occurrence of pain and/or injury. From the detailed survey it was found that boarder's experienced pain in the front foot/toe area as a result from the toe strap being too tight. However boarders wanted tighter bindings to increase responsiveness. Survey data was compared to ankle and foot biomechanics to build a relationship to assess the problem of pain versus responsiveness. The design stage of the study was to develop a binding that overcame the over-tightening of the binding but still maintain equivalent or better responsiveness compared to traditional bindings. The resulting design integrated the snowboard boot much more into the design, by using the sole as a "semi-rigid" platform and locking it in laterally between the heel cup and the new toe strap arrangement. The new design developed using additive manufacturing techniques was tested via qualitative and quantitative measures in the snow and in the lab. It was found that using the new arrangement in a system resulted in no loss of performance or responsiveness to the user. Due to the design and manufacturing approach users have the ability to customise the design to their specific needs.

POPLAR GENE EXPRESSION DATA ANALYSIS PIPELINES

Analyzing large-scale gene expression data is a labor-intensive and time-consuming process. To make data analysis easier, we developed a set of pipelines for rapid processing and analysis poplar gene expression data for knowledge discovery. Of all pipelines developed, differentially expressed genes (DEGs) pipeline is the one designed to identify biologically important genes that are differentially expressed in one of multiple time points for conditions. Pathway analysis pipeline was designed to identify the differentially expression metabolic pathways. Protein domain enrichment pipeline can identify the enriched protein domains present in the DEGs. Finally, Gene Ontology (GO) enrichment analysis pipeline was developed to identify the enriched GO terms in the DEGs. Our pipeline tools can analyze both microarray gene data and high-throughput gene data. These two types of data are obtained by two different technologies. A microarray technology is to measure gene expression levels via microarray chips, a collection of microscopic DNA spots attached to a solid (glass) surface, whereas high throughput sequencing, also called as the next-generation sequencing, is a new technology to measure gene expression levels by directly sequencing mRNAs, and obtaining each mRNA’s copy numbers in cells or tissues. We also developed a web portal (http://sys.bio.mtu.edu/) to make all pipelines available to public to facilitate users to analyze their gene expression data. In addition to the analyses mentioned above, it can also perform GO hierarchy analysis, i.e. construct GO trees using a list of GO terms as an input.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Cultura corporal: pesquisa com adolescentes do Centro Cultural Cartola (CCC) Rio de Janeiro

A definição mais plausível para esta pesquisa é aquela onde a adolescência se estabelece de acordo com a história de vida e o contexto sociocultural no qual o sujeito está inserido, que envolve, na transição para a fase adulta, transformações na mente, na personalidade, no comportamento e principalmente no corpo. O grupo das adolescentes pesquisadas encontra-se numa fase da vida de descobertas, mudanças (psicológicas identitárias e acima de tudo corporais) assim como todo e qualquer sujeito que encontra-se nessa fase da vida. Com a entrada na adolescência, ocorre a formação de grupos no qual as adolescentes vão se identificar. Cada grupo tem a sua marca, o seu estilo, e o corpo parece ser o grande tradutor dessa nova fase, os efeitos de comparação começam a surgir, o medo de não ser aceita pelo grupo faz com que a adolescente recorra a métodos que acredita ser eficazes na busca de um eu ideal e consequentemente um corpo ideal. Esta pesquisa discute as idealizações corporais das jovens encontradas durante a realização da pesquisa de campo, e as ligações que existem entre essa idealização e as constituições desse ideal sob a concepção do movimento corporal. A análise de dados se baseou em interpretações psicológicas acerca dos desenhos realizados pelas adolescentes. Visto que a Comunidade da Mangueira é um espaço multicultural (estilos de músicas, danças, esportes, entre outros), mas que em comum tem o corpo como o principal tradutor dessas vivências. A busca pelo corpo ideal está longe de ser integralmente satisfeito, sendo a cultura a grande responsável pela elaboração emocional, mental e principalmente corporal das adolescentes do grupo de Jazz do CCC. A aplicação da metodologia do Grupo Operativo teve como um dos objetivos desconstruir e reestruturar o imaginário das adolescentes o conceito de corpo ideal, que atualmente é visto de forma rígida e estereotipada pela sociedade contemporânea. Tal ferramenta foi muito útil no processo de desconstrução do conceito de corpo ideal por parte das meninas. As dinâmicas utilizadas tiveram como objetivos despertar novos olhares sobre o corpo, assim como criar situações onde as jovens pudessem buscar o melhor de si, mas sem fugir da própria realidade corporal.

Short- and long-term movement patterns in the freshwater whipray (Himantura dalyensis) determined by the signal processing of passive acoustic telemetry data.

Patterns of movement in aquatic animals reflect ecologically important behaviours. Cyclical changes in the abiotic environment influence these movements, but when multiple processes occur simultaneously, identifying which is responsible for the observed movement can be complex. Here we used acoustic telemetry and signal processing to define the abiotic processes responsible for movement patterns in freshwater whiprays (Himantura dalyensis). Acoustic transmitters were implanted into the whiprays and their movements detected over 12 months by an array of passive acoustic receivers, deployed throughout 64 km of the Wenlock River, Qld, Australia. The time of an individual's arrival and departure from each receiver detection field was used to estimate whipray location continuously throughout the study. This created a linear-movement-waveform for each whipray and signal processing revealed periodic components within the waveform. Correlation of movement periodograms with those from abiotic processes categorically illustrated that the diel cycle dominated the pattern of whipray movement during the wet season, whereas tidal and lunar cycles dominated during the dry season. The study methodology represents a valuable tool for objectively defining the relationship between abiotic processes and the movement patterns of free-ranging aquatic animals and is particularly expedient when periods of no detection exist within the animal location data.

Extracting more out of relocation data: building movement models as mixtures of random walks

We present a framework for fitting multiple random walks to animal movement paths consisting of ordered sets of step lengths and turning angles. Each step and turn is assigned to one of a number of random walks, each characteristic of a different behavioral state. Behavioral state assignments may be inferred purely from movement data or may include the habitat type in which the animals are located. Switching between different behavioral states may be modeled explicitly using a state transition matrix estimated directly from data, or switching probabilities may take into account the proximity of animals to landscape features. Model fitting is undertaken within a Bayesian framework using the WinBUGS software. These methods allow for identification of different movement states using several properties of observed paths and lead naturally to the formulation of movement models. Analysis of relocation data from elk released in east-central Ontario, Canada, suggests a biphasic movement behavior: elk are either in an "encamped" state in which step lengths are small and turning angles are high, or in an "exploratory" state, in which daily step lengths are several kilometers and turning angles are small. Animals encamp in open habitat (agricultural fields and opened forest), but the exploratory state is not associated with any particular habitat type.

Analysis of acoustic emission data for structural health monitoring applications

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. It is one of the several diagnostic techniques currently used for structural health monitoring (SHM) of civil infrastructure such as bridges. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. But several challenges still exist. Due to high sampling rate required for data capture, large amount of data is generated during AE testing. This is further complicated by the presence of a number of spurious sources that can produce AE signals which can then mask desired signals. Hence, an effective data analysis strategy is needed to achieve source discrimination. This also becomes important for long term monitoring applications in order to avoid massive date overload. Analysis of frequency contents of recorded AE signals together with the use of pattern recognition algorithms are some of the advanced and promising data analysis approaches for source discrimination. This paper explores the use of various signal processing tools for analysis of experimental data, with an overall aim of finding an improved method for source identification and discrimination, with particular focus on monitoring of steel bridges.

Development of a data mining-based analysis framework for multi-attribute construction project information

Data mining techniques extract repeated and useful patterns from a large data set that in turn are utilized to predict the outcome of future events. The main purpose of the research presented in this paper is to investigate data mining strategies and develop an efficient framework for multi-attribute project information analysis to predict the performance of construction projects. The research team first reviewed existing data mining algorithms, applied them to systematically analyze a large project data set collected by the survey, and finally proposed a data-mining-based decision support framework for project performance prediction. To evaluate the potential of the framework, a case study was conducted using data collected from 139 capital projects and analyzed the relationship between use of information technology and project cost performance. The study results showed that the proposed framework has potential to promote fast, easy to use, interpretable, and accurate project data analysis.

Single-subject analysis reveals variation in knee mechanics during step landing

Introduction: Evidence concerning the alteration of knee function during landing suffers from a lack of consensus. This uncertainty can be attributed to methodological flaws, particularly in relation to the statistical analysis of variable human movement data. Aim: The aim of this study was to compare single-subject and group analysis in quantifying alterations in the magnitude and within-participant variability of knee mechanics during a step landing task. Methods: A group of healthy men (N = 12) stepped-down from a knee-high platform for 60 consecutive trials, each trial separated by a 1-minute rest. The magnitude and within-participant variability of sagittal knee stiffness and coordination of the landing leg during the immediate postimpact period were evaluated. Coordination of the knee was quantified in the sagittal plane by calculating the mean absolute relative phase of sagittal shank and thigh motion (MARP1) and between knee rotation and knee flexion (MARP2). Changes across trials were compared between both group and single-subject statistical analyses. Results: The group analysis detected significant reductions in MARP1 magnitude. However, the single-subject analyses detected changes in all dependent variables, which included increases in variability with task repetition. Between-individual variation was also present in the timing, size and direction of alterations to task repetition. Conclusion: The results have important implications for the interpretation of existing information regarding the adaptation of knee mechanics to interventions such as fatigue, footwear or landing height. It is proposed that a familiarisation session be incorporated in future experiments on a single-subject basis prior to an intervention.