An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing

Satellite remote sensing of ocean colour is the only method currently available for synoptically measuring wide-area properties of ocean ecosystems, such as phytoplankton chlorophyll biomass. Recently, a variety of bio-optical and ecological methods have been established that use satellite data to identify and differentiate between either phytoplankton functional types (PFTs) or phytoplankton size classes (PSCs). In this study, several of these techniques were evaluated against in situ observations to determine their ability to detect dominant phytoplankton size classes (micro-, nano- and picoplankton). The techniques are applied to a 10-year ocean-colour data series from the SeaWiFS satellite sensor and compared with in situ data (6504 samples) from a variety of locations in the global ocean. Results show that spectral-response, ecological and abundance-based approaches can all perform with similar accuracy. Detection of microplankton and picoplankton were generally better than detection of nanoplankton. Abundance-based approaches were shown to provide better spatial retrieval of PSCs. Individual model performance varied according to PSC, input satellite data sources and in situ validation data types. Uncertainty in the comparison procedure and data sources was considered. Improved availability of in situ observations would aid ongoing research in this field. (C) 2010 Elsevier B.V. All rights reserved.

Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations

The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R-2 = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called "EucVI") which gave similar LAI retrieval results but in a simpler way. The R-2 of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R-2 = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way.

A Phase Space Box-counting based Method for Arrhythmia Prediction from Electrocardiogram Time Series

Arrhythmia is one kind of cardiovascular diseases that give rise to the number of deaths and potentially yields immedicable danger. Arrhythmia is a life threatening condition originating from disorganized propagation of electrical signals in heart resulting in desynchronization among different chambers of the heart. Fundamentally, the synchronization process means that the phase relationship of electrical activities between the chambers remains coherent, maintaining a constant phase difference over time. If desynchronization occurs due to arrhythmia, the coherent phase relationship breaks down resulting in chaotic rhythm affecting the regular pumping mechanism of heart. This phenomenon was explored by using the phase space reconstruction technique which is a standard analysis technique of time series data generated from nonlinear dynamical system. In this project a novel index is presented for predicting the onset of ventricular arrhythmias. Analysis of continuously captured long-term ECG data recordings was conducted up to the onset of arrhythmia by the phase space reconstruction method, obtaining 2-dimensional images, analysed by the box counting method. The method was tested using the ECG data set of three different kinds including normal (NR), Ventricular Tachycardia (VT), Ventricular Fibrillation (VF), extracted from the Physionet ECG database. Statistical measures like mean (μ), standard deviation (σ) and coefficient of variation (σ/μ) for the box-counting in phase space diagrams are derived for a sliding window of 10 beats of ECG signal. From the results of these statistical analyses, a threshold was derived as an upper bound of Coefficient of Variation (CV) for box-counting of ECG phase portraits which is capable of reliably predicting the impeding arrhythmia long before its actual occurrence. As future work of research, it was planned to validate this prediction tool over a wider population of patients affected by different kind of arrhythmia, like atrial fibrillation, bundle and brunch block, and set different thresholds for them, in order to confirm its clinical applicability.

The Perception of American Adolescent Culture Through the Dubbing and Fansubbing of a Selection of US Teen Series from 1990 to 2013

The present dissertation aims at analyzing the construction of American adolescent culture through teen-targeted television series and the shift in perception that occurs as a consequence of the translation process. In light of the recent changes in television production and consumption modes, largely caused by new technologies, this project explores the evolution of Italian audiences, focusing on fansubbing (freely distributed amateur subtitles made by fans for fan consumption) and social viewing (the re-aggregation of television consumption based on social networks and dedicated platforms, rather than on physical presence). These phenomena are symptoms of a sort of ‘viewership 2.0’ and of a new type of active viewing, which calls for a revision of traditional AVT strategies. Using a framework that combines television studies, new media studies, and fandom studies with an approach to AVT based on Descriptive Translation Studies (Toury 1995), this dissertation analyzes the non-Anglophone audience’s growing need to participation in the global dialogue and appropriation process based on US scheduling and informed by the new paradigm of convergence culture, transmedia storytelling, and affective economics (Jenkins 2006 and 2007), as well as the constraints intrinsic to multimodal translation and the different types of linguistic and cultural adaptation performed through dubbing (which tends to be more domesticating; Venuti 1995) and fansubbing (typically more foreignizing). The study analyzes a selection of episodes from six of the most popular teen television series between 1990 and 2013, which has been divided into three ages based on the different modes of television consumption: top-down, pre-Internet consumption (Beverly Hills, 90210, 1990 – 2000), emergence of audience participation (Buffy the Vampire Slayer, 1997 – 2003; Dawson’s Creek, 1998 – 2003), age of convergence and Viewership 2.0 (Gossip Girl, 2007 – 2012; Glee, 2009 – present; The Big Bang Theory, 2007 - present).

Comparison of biological stability and metabolism of CCK2 receptor targeting peptides, a collaborative project under COST BM0607

Stability of radiolabelled cholecystokinin 2 (CCK2) receptor targeting peptides has been a major limitation in the use of such radiopharmaceuticals especially for targeted radionuclide therapy applications, e.g. for treatment of medullary thyroid carcinoma (MTC). The purpose of this study was to compare the in vitro stability of a series of peptides binding to the CCK2 receptor [selected as part of the COST Action on Targeted Radionuclide Therapy (BM0607)] and to identify major cleavage sites.

Playground Series

Playground is intended to open a window into a world, however familiar it may first appear, that resonates with one of our universal compassions and the icons of contemporary life: the act and ritual of taking photographs. This project aims to magnify the extraordinary in the ordinary, revealing facial expressions, gestures or body language of the subjects behind their own visual recording device. It is about the drama of people in their private moments, when their face or body language reveals the most hidden parts of their inner world in public places. The interplay between private and public, individual and social are the main inhabitants of this photographic project.

ARIS-Campaign: Intercomparison of three ground based 22 GHz radiometers for middle atmospheric water vapor at the Zugspitze in winter 2009

This paper presents the Alpine Radiometer Intercomparison at the Schneefernerhaus (ARIS), which took place in winter 2009 at the high altitude station at the Zugspitze, Germany (47.42° N, 10.98° E, 2650 m). This campaign was the first direct intercomparison between three new ground based 22 GHz water vapor radiometers for middle atmospheric profiling with the following instruments participating: MIRA 5 (Karlsruhe Institute of Technology), cWASPAM3 (Max Planck Institute for Solar System Research, Katlenburg-Lindau) and MIAWARA-C (Institute of Applied Physics, University of Bern). Even though the three radiometers all measure middle atmospheric water vapor using the same rotational transition line and similar fundamental set-ups, there are major differences between the front ends, the back ends, the calibration concepts and the profile retrieval. The spectrum comparison shows that all three radiometers measure spectra without severe baseline artifacts and that the measurements are in good general agreement. The measurement noise shows good agreement to the values theoretically expected from the radiometer noise formula. At the same time the comparison of the noise levels shows that there is room for instrumental and calibration improvement, emphasizing the importance of low elevation angles for the observation, a low receiver noise temperature and an efficient calibration scheme. The comparisons of the retrieved profiles show that the agreement between the profiles of MIAWARA-C and cWASPAM3 with the ones of MLS is better than 0.3 ppmv (6%) at all altitudes. MIRA 5 has a dry bias of approximately 0.5 ppm (8%) below 0.1 hPa with respect to all other instruments. The profiles of cWASPAM3 and MIAWARA-C could not be directly compared because the vertical region of overlap was too small. The comparison of the time series at different altitude levels show a similar evolution of the H2O volume mixing ratio (VMR) for the ground based instruments as well as the space borne sensor MLS.

Double Take Project: Using Applied Theatre For Campus Climate Change

Despite research gathered in the Campus Climate Report, I believe that it underrepresented the student experience of the social scene. The document primarily served as an identification tool for four major problems on campus: binge drinking, sexual assault, diversity, and disengagement in the classroom. Double Take Project also identifies similar issues however, this project uses theatrical techniques to gather the anecdotal reality of the student perspective. Double Take Project expands beyond the Campus Climate Report to inspire dialogue in a variety of student-to-student interactions and, more importantly, the project seeks action and solution plans. The social scene dominates our culture and its many issues result in concern for the safety, self-identity, and development of Bucknell students into thriving adults. Double Take Project is rooted in the belief that theatre is a palpable tool for social change. Over the course of many events, Double Take Project has utilized facets of theatre to provide opportunities to voice discontent, widen perception of normalcy on campus, and inspire confidence to act on personal beliefs. The Double Take Project uses many Applied Theatre methods to impact the social scene. For example, I conducted 36 student interviews and transformed the stories into a one-woman show, Rage Behind Curtains, which I performed at multiple venues across campus. I also used interviews to create a radio show airing one story per day. I conducted ten workshops with student groups, Fraternities and Sororities, and in the classroom utilizing Augusto Boal’s Theatre of the Oppressed (TO) techniques. I also created a “social scene confessional” where I stood outside the Elaine Langone Center with a sign that read, “Tell me a story about the social scene” from a wide variety of Bucknell students. Finally, I have assembled a Forum Theatre Company based on Augusto Boal’s method of the spect-actor, utilizing participants as both actors and spectators in the theatre piece. All of the names indicated in this paper have been altered to protect the identity of the participants. While planning events and conducting various theatrical experiences, I learned that there are a series of internal and external issues contributing to our social environment. Internally, students are conflicted with personal beliefs while battling outward social pressure. Whether they are on the outskirts or center of the social scene determines their response to this conflict. For example, I have discovered that students on the borders of the social culture respond with criticism because they feel excluded, whereas the student’s centrally involved critique the culture in private and while their persona appears to not want change. Externally, there are many structural issues that contribute to the current social climate such as without Fraternity meal plans, Cafeteria space is not sufficient to feed all of the students, exclusive party culture, and gendered housing. Through meetings with Deans and staff, I have learned there are also problems between administration and students, resulting in resentment and blame. Although addressing structural issues would instigate immediate change, in my opinion, internal student conflicts are the primary cause for the current negative social atmosphere. I believe that pressure to conform is rooted in lack of personal identity. Because students simply do not know themselves, they form strong social groups that become the definition of themselves. Without confident self-awareness, large and powerful groups coerce students to accept social norms resulting in the individual’s outward distaste for change, yet internal discomfort.

Camp For All Connection: a community health information outreach project

Purpose: The purpose of the Camp For All Connection project is to facilitate access to electronic health information resources at the Camp For All facility. Setting/Participants/Resources: Camp For All is a barrier-free camp working in partnership with organizations to enrich the lives of children and adults with chronic illnesses and disabilities and their families by providing camping and retreat experiences. The camp facility is located on 206 acres in Burton, Texas. The project partners are Texas Woman's University, Houston Academy of Medicine-Texas Medical Center Library, and Camp For All. Brief Description: The Camp For All Connection project placed Internet-connected workstations at the camp's health center in the main lodge and provided training in the use of electronic health information resources. A train-the-trainer approach was used to provide training to Camp For All staff. Results/Outcome: Project workstations are being used by health care providers and camp staff for communication purposes and to make better informed health care decisions for Camp For All campers. Evaluation Method: A post-training evaluation was administered at the end of the train-the-trainer session. In addition, a series of site visits and interviews was conducted with camp staff members involved in the project. The site visits and interviews allowed for ongoing dialog between project staff and project participants.

Intercomparison of stratospheric ozone profiles for the assessment of the upgraded GROMOS radiometer at Bern

Since November 1994, the GROund-based Millimeter-wave Ozone Spectrometer (GROMOS) measures stratospheric and lower mesospheric ozone in Bern, Switzerland (47.95° N, 7.44° E). GROMOS is part of the Network for the Detection of Atmospheric Composition Change (NDACC). In July 2009, a Fast-Fourier-Transform spectrometer (FFTS) has been added as backend to GROMOS. The new FFTS and the original filter bench (FB) measured parallel for over two years. In October 2011, the FB has been turned off and the FFTS is now used to continue the ozone time series. For a consolidated ozone time series in the frame of NDACC, the quality of the stratospheric ozone profiles obtained with the FFTS has to be assessed. The FFTS results from July 2009 to December 2011 are compared to ozone profiles retrieved by the FB. FFTS and FB of the GROMOS microwave radiometer agree within 5% above 20 hPa. A later harmonization of both time series will be realized by taking the FFTS as benchmark for the FB. Ozone profiles from the FFTS are also compared to coinciding lidar measurements from the Observatoire Haute Provence (OHP), France. For the time period studied a maximum mean difference (lidar – GROMOS FFTS) of +3.8% at 3.1 hPa and a minimum mean difference of +1.4% at 8 hPa is found. Further, intercomparisons with ozone profiles from other independent instruments are performed: satellite measurements include MIPAS onboard ENVISAT, SABER onboard TIMED, MLS onboard EOS Aura and ACE-FTS onboard SCISAT-1. Additionally, ozonesondes launched from Payerne, Switzerland, are used in the lower stratosphere. Mean relative differences of GROMOS FFTS and these independent instruments are less than 10% between 50 and 0.1 hPa.

The Moderating Role of Electoral Pressure on Congressional Ideological Polarization: A Cross-Sectional Time-Series Analysis

Reelection and self-interest are recurring themes in the study of our congressional leaders. To date, many studies have already been done on the trends between elections, party affiliation, and voting behavior in Congress. However, because a plethora of data has been collected on both elections and congressional voting, the ability to draw a connection between the two provides a very reasonable prospect. This project analyzes whether voting shifts in congressional elections have an effect on congressional voting. Will a congressman become ideologically more polarized when his electoral margins increase? Essentially, this paper assumes that all congressmen are ideologically polarized, and it is elections which serve to reel congressmen back toward the ideological middle. The election and ideological data for this study, which spans from the 56th to the 107th Congress, finds statistically significant relationships between these two variables. In fact, congressman pay attention to election returns when voting in Congress. When broken down by party, Democrats are more exhibitive of this phenomenon, which suggest that Democrats may be more likely to intrinsically follow the popular model of representation. Meanwhile, it can be hypothesized that insignificant results for Republicans indicate that Republicans may follow a trustee model of representation.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Physical oceanography from the TRACTOR project

Primary Objectives - Describe and quantify the present strength and variability of the circulation and oceanic processes of the Nordic Seas regions using primarily observations of the long term spread of a tracer purposefully released into the Greenland Sea Gyre in 1996. - Improve our understanding of ocean processes critical to the thermaholine circulation in the Nordic Seas regions so as to be able to predict how this region may respond to climate change. - Assess the role of mixing and ageing of water masses on the carbon transport and the role of the thermohaline circulation in carbon storage using water transports and mixing coefficients derived from the tracer distribution. Specific Objectives Perform annual hydrographic, chemical and SF6 tracer surveys into the Nordic regions in order to: - Measure lateral and diapycnal mixing rates in the Greenland Sea Gyre and in the surrounding regions. - Document the depth and rates of convective mixing in the Greenland Sea using the SF6 and the water masses characteristics. - Measure the transit time and transport of water from the Greenland Sea to surrounding seas and outflows. Document processes of water mass transformation and entrainment occurring to water emanating from the central Greenland Sea. - Measure diapycnal mixing rates in the bottom and margins of the Greenland Sea basin using the SF6 signal observed there. Quantify the potential role of bottom boundary-layer mixing in the ventilation of the Greenland Sea Deep Water in absence of deep convection. Monitor the variability of the entrainment of water from the Greenland Sea using time series auto-sampler moorings at strategic positions i.e., sill of the Denmark Strait, Labrador Sea, Jan Mayen fracture zone and Fram Strait. Relate the observed variability of the tracer signal in the outflows to convection events in the Greenland Sea and local wind stress events. Obtain a better description of deepwater overflow and entrainment processes in the Denmark Strait and Faeroe Bank Channel overflows and use these to improve modelling of deepwater overflows. Monitor the tracer invasion into the North Atlantic using opportunistic SF6 measurements from other cruises: we anticipate that a number of oceanographic cruises will take place in the north-east Atlantic and the Labrador Sea. It should be possible to get samples from some cruises for SF6 measurements. Use process models to describe the spread of the tracer to achieve better parameterisation for three-dimensional models. One reason that these are so resistant to prediction is that our best ocean models are as yet some distance from being good enough, to predict climate and climate change.