Female Imitatio Christi in early modernity – the search for strong virgins like St Wilgefortis

At first glance you think that this is Christ crucified. At a second glance, you recognize a woman hanging on a cross. This is not an invention of the 20th century but reaches back to history where we we can find women cross-dressed or even bearded as men. St Wilgefortis or St Uncumber was a bearded and crucified woman who was venerated widely in northern Europe during the fifteeneth and sixteenth centuries. Wilgefortis is a corruption of the term „virgo fortis“ („strong virgin“). She and other female saints were considered as „imitations of Christ“. The paper deals with the reasons why this saint became so popular and how even today ideas about such strong virgins which mirror androgynous symbolism live on in popular culture.

Modifier genes and susceptibility to colorectal cancer in individuals with Lynch syndrome

Lynch syndrome, is caused by inherited germ-line mutations in the DNA mismatch repair genes resulting in cancers at an early age, predominantly colorectal (CRC) and endometrial cancers. Though the median age at onset for CRC is about 45 years, disease penetrance varies suggesting that cancer susceptibility may be modified by environmental or other low-penetrance genes. Genetic variation due to polymorphisms in genes encoding metabolic enzymes can influence carcinogenesis by alterations in the expression and activity level of the enzymes. Variation in MTHFR, an important folate metabolizing enzyme can affect DNA methylation and DNA synthesis and variation in xenobiotic-metabolizing enzymes can affect the metabolism and clearance of carcinogens, thus modifying cancer risk. ^ This study examined a retrospective cohort of 257 individuals with Lynch syndrome, for polymorphisms in genes encoding xenobiotic-metabolizing enzymes-- CYP1A1 (I462V and MspI), EPHX1 (H139R and Y113H), GSTP1 (I105V and A114V), GSTM1 and GSTT1 (deletions) and folate metabolizing enzyme--MTHFR (C677T and A1298C). In addition, a series of 786 cases of sporadic CRC were genotyped for CYP1A1 I462V and EPHX1 Y113H to assess gene-gene interaction and gene-environment interaction with smoking in a case-only analysis. ^ Prominent findings of this study were that the presence of an MTHFR C677T variant allele was associated with a 4 year later age at onset for CRC on average and a reduced age-associated risk for developing CRC (Hazard ratio: 0.55; 95% confidence interval: 0.36–0.85) compared to the absence of any variant allele in individuals with Lynch syndrome. Similarly, Lynch syndrome individuals heterozygous for CYP1A1 I462V A>G polymorphism developed CRC an average of 4 years earlier and were at a 78% increased age-associated risk (Hazard ratio for AG relative to AA: 1.78; 95% confidence interval: 1.16-2.74) than those with the homozygous wild-type genotype. Therefore these two polymorphisms may be additional susceptibility factors for CRC in Lynch syndrome. In the case-only analysis, evidence of gene-gene interaction was seen between CYP1A1 I462V and EPHX1 Y113H and between EPHX1 Y113H and smoking suggesting that genetic and environmental factors may interact to increase sporadic CRC risk. Implications of these findings are the ability to identify subsets of high-risk individuals for targeted prevention and intervention. ^

Management and outcomes of anorectal infection in the cancer patient

Background. The objective of this retrospective cohort study is to examine the presentation and outcomes for a contemporary series of cancer patients with anorectal infection. In addition, we seek to identify factors which are associated with surgical intervention. ^ Methods. The study cohort was identified from ICD-9 codes for diagnosis of infection of the anal and rectal region and patients who underwent a surgical oncology consultation between 1/2000 and 12/2006. Clinical presentation, treatment rendered, and outcomes were retrospectively recorded. ^ Results. Of the 100 patients evaluated by the surgical oncology service for anorectal infection, 42 were treated non-operatively and 58 underwent surgical intervention. Factors associated with surgical intervention based on logistic multivariable analysis included the diagnosis of an abscess (odds ratio [OR] 10.5; 95% confidence interval [CI] 2.9-38.5) and the documentation of erythema on physical examination (OR 3.1; 95% CI 1.1-8.4). Thrombocytopenia (platelets < 50,000) was associated with non-operative management (OR 0.3; 95% CI 0.1-0.7). Incision and drainage was the most common surgical procedure (79%) while a wide debridement for a necrotizing soft tissue infection was required in 2 patients. Infection-specific 90-day mortality was only 1% (N=1). However, the median overall survival for the entire cohort was only 14.4 months (95% CI 7.9-19.5). ^ Conclusions. Non-operative management is a reasonable treatment option for anorectal infection in patients with cancer. Identification of an abscess, erythema on physical exam, and thrombocytopenia were associated with management strategy. Although rare, necrotizing soft tissue infections are associated with significant mortality. ^

Use of tabletop exercises for disaster preparedness training

Background. In public health preparedness, disaster preparedness refers to the strategic planning of responses to all types of disasters. Preparation and training for disaster response can be conducted using different teaching modalities, ranging from discussion-based programs such as seminars, drills and tabletop exercises to more complex operation-based programs such as functional exercises and full-scale exercises. Each method of instruction has its advantages and disadvantages. Tabletop exercises are facilitated discussions designed to evaluate programs, policies, and procedures; they are usually conducted in a classroom, often with tabletop props (e.g. models, maps or diagrams). ^ Objective. The overall goal of this project was to determine whether tabletop exercises are effective teaching modalities for disaster preparedness, with an emphasis on intentional chemical exposure. ^ Method. The target audience for the exercise was the Medical Reserve Brigade of the Texas State Guard, a group of volunteer healthcare providers and first responders who prepare for response to local disasters. A new tabletop exercise was designed to provide information on the complex, interrelated organizations within the national disaster preparedness program that this group would interact with in the event of a local disaster. This educational intervention consisted of a four hour multipart program that included a pretest of knowledge, lecture series, an interactive group discussion using a mock disaster scenario, a posttest of knowledge, and a course evaluation. ^ Results. Approximately 40 volunteers attended the intervention session; roughly half (n=21) had previously participated in a full scale drill. There was an 11% improvement in fund of knowledge between the pre- and post-test scores (p=0.002). Overall, the tabletop exercise was well received by those with and without prior training, with no significant differences found between these two groups in terms of relevance and appropriateness of content. However, the separate components of the tabletop exercise were variably effective, as gauged by written text comments on the questionnaire. ^ Conclusions. Tabletop exercises can be a useful training modality in disaster preparedness, as evidenced by improvement in knowledge and qualitative feedback on its value. Future offerings could incorporate recordings of participant responses during the drill, so that better feedback can be provided to them. Additional research should be conducted, using the same or similar design, in different populations that are stakeholders in disaster preparedness, so that the generalizability of these findings can be determined.^

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^

Introduction to E-Science

An introduction to computationally-enabled science, challenges, and opportunities.

Overview of Data Management Planning Tools

These Data Management Plans are more comprehensive and complex than in the past. Libraries around the nation are trying to put together tools to help researchers write plans that conform to the new requirements. This session will look at some of these tools.

April 25, 2012

Geneva Henry, Executive Director of the Center for Digital Scholarship, Rice University. Data rights and ownership of digital research data can impact how you use data, how others use data you've collected, and how rights are determined in collaborative research. Copyright rules governing data vary from one country to the next, making data ownership in international collaborations particularly murky. Licensing the use of data sets from the start is one way to address these issues early and provide a means for easily sharing datasets that can be cited and properly attributed. This talk with introduce issues associated with digital research data governance and how to protect your rights with data you work with.

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.

Miscanthus Establishment and Survival

Rising costs of petroleum fuels and increased awareness of the adverse effects of greenhouse gases have spurred interest in renewable fuels and other ‘green’ products. Recent legislation has set goals of approximately 20 billion gallons of renewable fuel produced from non-corn starch sources by the year 2022. These driving forces have increased interest in dedicated bioenergy crops. Among perennial grasses, which have received an exceptional amount of attention as dedicated energy crops, one stands out: Miscanthus (Miscanthus x giganteus).

Miscanthus Establishment and Survival

Rising costs of petroleum fuels and increased awareness of the adverse effects of greenhouse gases have spurred interest in renewable fuels and other ‘green’ products. Recent legislation has set goals of approximately 20 billion gallons of renewable fuel produced from non-corn starch sources by the year 2022. These driving forces have increased interest in dedicated bioenergy crops. Among perennial grasses, which have received an exceptional amount of attention as dedicated energy crops, one stands out: Miscanthus (Miscanthus x giganteus).