A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.

The Prevalence of Depression among patients and its detection by Primary Health Care Workers at Matawale Health Centre (Zomba)

Background Little information is available on the prevalence of depression in Malawi in primary health care settings and yet there is increased number of cases of depression presenting at tertiary level in severe form. Aim The aim of the study was to determine the prevalence of depression among patients and its detection by health care workers at a primary health care clinic in Zomba. Methods A cross-sectional survey was done among patients attending outpatient department at Matawale Health Centre, in Zomba from 1st July 2009 through to 31st July 2009. A total of 350 adults were randomly selected using systematic sampling. The “Self Reporting Questionnaire”, a questionnaire measuring social demographic factors and the Structured Clinical Interview for DSM-IV Axis I disorders Non-Patient Version (SCID-NP) were administered verbally to the participants. Findings The prevalence of depression among the patients attending the outpatients department was found to be 30.3% while detection rate of depression by clinician was 0%. Conclusion The results revealed the magnitude of depression which is prevalent in the primary health care clinic that goes undiagnosed and unmanaged. It is therefore recommended that primary health care providers do thorough assessments to address common mental disorders especially depression and they should be educated to recognise and manage depression appropriately at primary care level.

Social buffering of fear in zebrafish

Tese de Doutoramento em Biologia Comportamental apresentada ao ISPA - Instituto Universitário

Synthetic Circuits for Feedback and Detection in Bacteria

Synthetic biology, by co-opting molecular machinery from existing organisms, can be used as a tool for building new genetic systems from scratch, for understanding natural networks through perturbation, or for hybrid circuits that piggy-back on existing cellular infrastructure. Although the toolbox for genetic circuits has greatly expanded in recent years, it is still difficult to separate the circuit function from its specific molecular implementation. In this thesis, we discuss the function-driven design of two synthetic circuit modules, and use mathematical models to understand the fundamental limits of circuit topology versus operating regimes as determined by the specific molecular implementation. First, we describe a protein concentration tracker circuit that sets the concentration of an output protein relative to the concentration of a reference protein. The functionality of this circuit relies on a single negative feedback loop that is implemented via small programmable protein scaffold domains. We build a mass-action model to understand the relevant timescales of the tracking behavior and how the input/output ratios and circuit gain might be tuned with circuit components. Second, we design an event detector circuit with permanent genetic memory that can record order and timing between two chemical events. This circuit was implemented using bacteriophage integrases that recombine specific segments of DNA in response to chemical inputs. We simulate expected population-level outcomes using a stochastic Markov-chain model, and investigate how inferences on past events can be made from differences between single-cell and population-level responses. Additionally, we present some preliminary investigations on spatial patterning using the event detector circuit as well as the design of stationary phase promoters for growth-phase dependent activation. These results advance our understanding of synthetic gene circuits, and contribute towards the use of circuit modules as building blocks for larger and more complex synthetic networks.

Asfixia perinatal y proteína S100B

La asfixia perinatal es la principal causa de muerte en la primera semana de vida la nivel mundial, los niños que sufren esta complicación y sobreviven pueden presentar trastornos neurológicos de diferente nivel de compromiso que inciden en su desarrollo personal y social. Las cifras de muerte por este problema de salud han disminuido de manera importante, sin embargo en el reporte de la Organización Mundial de Salud (OPS) del 2010, la asfixia perinatal es causa del 29% de muertes infantiles en los países de América Latina y el Caribe 2. Es necesario conocer además la extensión del daño neurológico que sufren estos niños, con este fin se desarrolló un estudio piloto en el Hospital Universitario Mayor Mederi de Bogotá, en el cual se determinó la concentración de un marcador metabólico de daño cerebral, la proteína S100B en suero de 60 recién nacidos sanos, con el objetivo de analizar la asociación del mismo con el peso al nacer, la edad gestacional y el diagnóstico. Los resultados no mostraron diferencias significativas entre este marcador y las variables analizadas que puede asociarse al pequeño número de pacientes, sin embargo han sentado las bases para el desarrollo de un estudio que incluya varios hospitales de Bogotá y sobre todo la determinación del mismo en recién nacidos con diagnóstico de hipoxia en el período perinatal, lo cual aportará información del grado de la alteración que puedan tener a nivel cerebral y contribuya al mejor manejo evolutivo con la aplicación de medidas de intervención en estadios tempranos de la vida.

Relación entre los factores de riesgo psicosocial con síntomas músculo-esqueléticos en trabajadores vinculados a una empresa social del estado, Bogotá, 2014

Introducción Los lugares de trabajo contribuyen al bienestar del individuo y en algunos casos pueden constituirse en factores que llevan a alteraciones en la condición de salud. Los trabajadores pueden estar predispuestos a algún tipo de desórdenes musculo-esqueléticos que se generan durante la jornada laboral creando molestia y algunas veces estar asociados a factores de riesgo psicosocial. Objetivo Establecer la relación entre los factores de riesgo psicosocial con síntomas músculo-esqueléticos en trabajadores vinculados a una empresa social del estado Bogotá, 2014. Métodos Se realizó un estudio de corte transversal en una muestra de 203 trabajadores. Como instrumentos se utilizó la Batería de riesgo psicosocial y cuestionario Nórdico. Se realizó análisis estadístico empleando medidas de tendencia central y de dispersión y se midieron asociaciones con el fin de conocer las variables que se relacionan con el evento. Se manejó el programa estadístico SPSS 20 para Windows. Resultados El 78,8% de los trabajadores correspondieron al sexo femenino, con una edad media de 38 ±10,28 años. El promedio de años de antigüedad dentro de la empresa fue de 3,9 ±,6553, se encontró que el 90.4% están expuestos a factores psicosocial extra laborales con clasificación de riesgo despreciable y el 91,6% a factores intralaboral con clasificación de riesgo muy alto. Se encontró prevalencia de sintomatología musculo esquelética a nivel de cuello con un 70%, dorso lumbar con el 56,2%, mano o muñeca el 54,7% y hombro con el 51,7%. Se encontró diferencia significativa entre el dominio de demandas del trabajo con síntomas presentes en hombro y mano/muñeca (p<0,05), seguido de las dimensiones de control sobre el trabajo con síntomas en hombro (p<0,05). Conclusiones La población estudiada presento una elevada prevalencia de síntomas musculo esqueléticos y un alto riesgo psicosocial intralaboral probablemente debido a características del trabajo y de su organización que influyen en la salud y bienestar del individuo.

Educational Intervention Project "R.E.D. Bull(ying)"

Introduction: Adolescence is a stage of life cycle marked by various physical, psychological and social changes. During this stage, young people are faced with the feeling of threat of identity, which may trigger aggressive behaviours. Bullying is a form of school violence with high prevalence, that shouldn't be a "normal" occurrence or a event that young people should experience during the transition between childhood and adolescent. In order to reduce the prevalence of bullying in the school community, we elaborated the Educational Intervention Project "R.E.D. BULL(ying)", with the specific objectives: Evaluate the knowledge level about bullyng, before and after the Project, and increase the level of literacy about the subject in the school community (students and teachers). Methodology: Our target population consisted in a total of 203 students from 5th to 9th grade and 13 teachers of school. It's a cross-sectional study of research - action, with the application of a diagnostic questionnaire, before and after, we conducted the educational sessions. Results: After the educational sessions, 93,1% of students identified what to do in a bullying situation, and 62,6% of students responded that in an assault situation, called an adult; 95,1% said they knew what was bullying, 56,8% associated the concept to physical aggression and 92,6 % mentioned to know the types of bullying, and physical bullying (71,9%) and verbal bullying (69,5%) were the most mentioned types. Meanwhile, the teachers: 76,9% considered that the school environment was pleasant, 84,6% characterized the relationship between the students as "adequate" and 77% said they didn't experience any bullying situation. Conclusions: We found an overall improvement to the level of bullying related knowledge after the educational intervention. So, we verified that the integrated intervention in the school health teams, allows greater attention to the detection, signalling and routing situations of violence.

EDUCATIONAL INTERVENTION PROJECT "R.E.D. BULL(YING)"

Introduction: Adolescence is a stage of life cycle marked by various physical, psychological and social changes. During this stage, young people are faced with the feeling of threat of identity, which may trigger aggressive behaviours. Bullying is a form of school violence with high prevalence, that shouldn't be a "normal" occurrence or a event that young people should experience during the transition between childhood and adolescent. In order to reduce the prevalence of bullying in the school community, we elaborated the Educational Intervention Project "R.E.D. BULL(ying)", with the specific objectives: Evaluate the knowledge level about bullyng, before and after the Project, and increase the level of literacy about the subject in the school community (students and teachers). Methodology: Our target population consisted in a total of 203 students from 5th to 9th grade and 13 teachers of school. It's a cross-sectional study of research - action, with the application of a diagnostic questionnaire, before and after, we conducted the educational sessions. Results: After the educational sessions, 93,1% of students identified what to do in a bullying situation, and 62,6% of students responded that in an assault situation, called an adult; 95,1% said they knew what was bullying, 56,8% associated the concept to physical aggression and 92,6 % mentioned to know the types of bullying, and physical bullying (71,9%) and verbal bullying (69,5%) were the most mentioned types. Meanwhile, the teachers: 76,9% considered that the school environment was pleasant, 84,6% characterized the relationship between the students as "adequate" and 77% said they didn't experience any bullying situation. Conclusions: We found an overall improvement to the level of bullying related knowledge after the educational intervention. So, we verified that the integrated intervention in the school health teams, allows greater attention to the detection, signalling and routing situations of violence.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Proximity detection protocols for IoT devices

In recent years, we have witnessed the growth of the Internet of Things paradigm, with its increased pervasiveness in our everyday lives. The possible applications are diverse: from a smartwatch able to measure heartbeat and communicate it to the cloud, to the device that triggers an event when we approach an exhibit in a museum. Present in many of these applications is the Proximity Detection task: for instance the heartbeat could be measured only when the wearer is near to a well defined location for medical purposes or the touristic attraction must be triggered only if someone is very close to it. Indeed, the ability of an IoT device to sense the presence of other devices nearby and calculate the distance to them can be considered the cornerstone of various applications, motivating research on this fundamental topic. The energy constraints of the IoT devices are often in contrast with the needs of continuous operations to sense the environment and to achieve high accurate distance measurements from the neighbors, thus making the design of Proximity Detection protocols a challenging task.

[the Teaching Of Social Sciences In Health: Between Practice And Theory].

The models of teaching social sciences and clinical practice are insufficient for the needs of practical-reflective teaching of social sciences applied to health. The scope of this article is to reflect on the challenges and perspectives of social science education for health professionals. In the 1950s the important movement bringing together social sciences and the field of health began, however weak credentials still prevail. This is due to the low professional status of social scientists in health and the ill-defined position of the social sciences professionals in the health field. It is also due to the scant importance attributed by students to the social sciences, the small number of professionals and the colonization of the social sciences by the biomedical culture in the health field. Thus, the professionals of social sciences applied to health are also faced with the need to build an identity, even after six decades of their presence in the field of health. This is because their ambivalent status has established them as a partial, incomplete and virtual presence, requiring a complex survival strategy in the nebulous area between social sciences and health.

[almost An Autobiography: A Study Of Social Scientists In Health Based On The Lattes Curriculum].

Among the various ways of adopting the biographical approach, we used the curriculum vitaes (CVs) of Brazilian researchers who work as social scientists in health as our research material. These CVs are part of the Lattes Platform of CNPq - the National Council for Scientific and Technological Development, which includes Research and Institutional Directories. We analyzed 238 CVs for this study. The CVs contain, among other things, the following information: professional qualifications, activities and projects, academic production, participation in panels for the evaluation of theses and dissertations, research centers and laboratories and a summarized autobiography. In this work there is a brief review of the importance of autobiography for the social sciences, emphasizing the CV as a form of autobiographical practice. We highlight some results, such as it being a group consisting predominantly of women, graduates in social sciences, anthropology, sociology or political science, with postgraduate degrees. The highest concentration of social scientists is located in Brazil's southern and southeastern regions. In some institutions the main activities of social scientists are as teachers and researchers with great thematic diversity in research.

The Lea Grating Test In Assessing Detection Grating Acuity In Normal Infants Less Than 4 Months Of Age.

To assess binocular detection grating acuity using the LEA GRATINGS test to establish age-related norms in healthy infants during their first 3 months of life. In this prospective, longitudinal study of healthy infants with clear red reflex at birth, responses to gratings were measured at 1, 2, and 3 months of age using LEA gratings at a distance of 28 cm. The results were recorded as detection grating acuity values, which were arranged in frequency tables and converted to a one-octave scale for statistical analysis. For the repeated measurements, analysis of variance (ANOVA) was used to compare the detection grating acuity results between ages. A total of 133 infants were included. The binocular responses to gratings showed development toward higher mean values and spatial frequencies, ranging from 0.55 ± 0.70 cycles per degree (cpd), or 1.74 ± 0.21 logMAR, in month 1 to 3.11 ± 0.54 cpd, or 0.98 ± 0.16 logMAR, in month 3. Repeated ANOVA indicated differences among grating acuity values in the three age groups. The LEA GRATINGS test allowed assessment of detection grating acuity and its development in a cohort of healthy infants during their first 3 months of life.

Determination Of Tetrakis(hydroxymethyl)phosphonium Sulfate In Commercial Formulations And Cooling Water By Capillary Electrophoresis With Contactless Conductivity Detection.

A novel capillary electrophoresis method using capacitively coupled contactless conductivity detection is proposed for the determination of the biocide tetrakis(hydroxymethyl)phosphonium sulfate. The feasibility of the electrophoretic separation of this biocide was attributed to the formation of an anionic complex between the biocide and borate ions in the background electrolyte. Evidence of this complex formation was provided by (11) B NMR spectroscopy. A linear relationship (R(2) = 0.9990) between the peak area of the complex and the biocide concentration (50-900 μmol/L) was found. The limit of detection and limit of quantification were 15.0 and 50.1 μmol/L, respectively. The proposed method was applied to the determination of tetrakis(hydroxymethyl)phosphonium sulfate in commercial formulations, and the results were in good agreement with those obtained by the standard iodometric titration method. The method was also evaluated for the analysis of tap water and cooling water samples treated with the biocide. The results of the recovery tests at three concentration levels (300, 400, and 600 μmol/L) varied from 75 to 99%, with a relative standard deviation no higher than 9%.

Finite-element Analysis Of 3 Situations Of Trauma In The Human Edentulous Mandible.

Maxillofacial trauma resulting from falls in elderly patients is a major social and health care concern. Most of these traumatic events involve mandibular fractures. The aim of this study was to analyze stress distributions from traumatic loads applied on the symphyseal, parasymphyseal, and mandibular body regions in the elderly edentulous mandible using finite-element analysis (FEA). Computerized tomographic analysis of an edentulous macerated human mandible of a patient approximately 65 years old was performed. The bone structure was converted into a 3-dimensional stereolithographic model, which was used to construct the computer-aided design (CAD) geometry for FEA. The mechanical properties of cortical and cancellous bone were characterized as isotropic and elastic structures, respectively, in the CAD model. The condyles were constrained to prevent free movement in the x-, y-, and z-axes during simulation. This enabled the simulation to include the presence of masticatory muscles during trauma. Three different simulations were performed. Loads of 700 N were applied perpendicular to the surface of the cortical bone in the symphyseal, parasymphyseal, and mandibular body regions. The simulation results were evaluated according to equivalent von Mises stress distributions. Traumatic load at the symphyseal region generated low stress levels in the mental region and high stress levels in the mandibular neck. Traumatic load at the parasymphyseal region concentrated the resulting stress close to the mental foramen. Traumatic load in the mandibular body generated extensive stress in the mandibular body, angle, and ramus. FEA enabled precise mapping of the stress distribution in a human elderly edentulous mandible (neck and mandibular angle) in response to 3 different traumatic load conditions. This knowledge can help guide emergency responders as they evaluate patients after a traumatic event.