Equipping the saints : best practices in establishing a five-fold equipping ministry team

https://place.asburyseminary.edu/ecommonsatsdissertations/2642/thumbnail.jp

Wilson Loops as Precursors

There is substantial evidence that string theory on AdS_5 x S_5 is a holographic theory in which the number of degrees of freedom scales as the area of the boundary in Planck units. Precisely how the theory can describe bulk physics using only surface degrees of freedom is not well understood. A particularly paradoxical situation involves an event deep in the interior of the bulk space. The event must be recorded in the (Schroedinger Picture) state vector of the boundary theory long before a signal, such as a gravitational wave, can propagate from the event to the boundary. In a previous paper with Polchinski, we argued that the "precursor" operators which carry information stored in the wave during the time when it vanishes in a neighborhood of the boundary are necessarily non-local. In this paper we argue that the precursors cannot be products of local gauge invariant operators such as the energy momentum tensor. In fact gauge theories have a class of intrinsically non-local operators which cannot be built from local gauge invariant objects. These are the Wilson loops. We show that the precursors can be identified with Wilson loops whose spatial size is dictated by the UV-IR connection.Comment: 23 pages, no figure

Contacts with primary and secondary healthcare prior to suicide: case–control whole-population-based study using person-level linked routine data in Wales, UK, 2000–2017

Background Longitudinal studies of patterns of healthcare contacts in those who die by suicide to identify those at risk are scarce. Aims To examine type and timing of healthcare contacts in those who die by suicide. Method A population-based electronic case–control study of all who died by suicide in Wales, 2001–2017, linking individuals’ electronic healthcare records from general practices, emergency departments and hospitals. We used conditional logistic regression to calculate odds ratios, adjusted for deprivation. We performed a retrospective continuous longitudinal analysis comparing cases’ and controls’ contacts with health services. Results We matched 5130 cases with 25 650 controls (5 per case). A representative cohort of 1721 cases (8605 controls) were eligible for the fully linked analysis. In the week before their death, 31.4% of cases and 15.6% of controls contacted health services. The last point of contact was most commonly associated with mental health and most often occurred in general practices. In the month before their death, 16.6 and 13.0% of cases had an emergency department contact and a hospital admission respectively, compared with 5.5 and 4.2% of controls. At any week in the year before their death, cases were more likely to contact healthcare services than controls. Self-harm, mental health and substance misuse contacts were strongly linked with suicide risk, more so when they occurred in emergency departments or as emergency admissions. Conclusions Help-seeking occurs in those at risk of suicide and escalates in the weeks before their death. There is an opportunity to identify and intervene through these contacts

Reusable, set-based selection algorithm for matched control groups

ABSTRACT Aims The wealth of data available in linked administrative datasets offers great potential for research, but researchers face methodological and computational challenges in data preparation, due to the size and complexity of the data. The creation of matched control groups in the Secure Anonymised Information Linkage (SAIL) Databank illustrates this point: SAIL contains multiple health datasets describing millions of individuals in Wales. The volume of data creates the potential for more precise matching, but only if an appropriate algorithm can be applied. We aimed to create such an algorithm for reuse by many research projects. Methods We developed set-based code in SQL that efficiently selects matches from millions of potential combinations in a relational database environment. It is parameterized to allow different matching criteria to be employed as needed, including follow-up time around an index event. A combinatorial optimisation problem occurs when a potential control could match more than one subject, which we solved by ranking potential match pairs first by subject with the fewest potential matches, then by closeness of the match. Results One example of the algorithm’s use was the Suicide Information Database Cymru, an electronic case-control study on suicide in Wales between 2003 and 2011. Subjects who had a cause of death recorded as self-harm were each matched to twenty controls who were alive at the subject’s date of death and had the same gender and similar birth week. The rate of matching success was >99.9%, with all subjects but one matching the full twenty controls. >99.99% of the matched controls had a week of birth that was identical to the subject. The second example was a matched cohort study looking at hospital admissions and type 1 diabetes, using the Brecon register of childhood diabetes in Wales, with matching based on week of birth within two weeks, gender, county of residence, deprivation quintile, and residence in Wales at time of diagnosis. This study had a matching rate of 98.9%; 97.5% of subjects matched to five controls, and 69.8% of matches had the same week of birth. Conclusions This algorithm provides good matching performance while executing efficiently and scalably on large datasets. Its implementation as reusable code will facilitate more efficient, high-quality research in SAIL. Instead of spending many hours developing a custom solution, analysts can execute parameterized code in a few minutes. We hope it to be useful more widely beyond SAIL as well

Obtaining structured clinical data from unstructured data using natural language processing software

ABSTRACT Background Free text documents in healthcare settings contain a wealth of information not captured in electronic healthcare records (EHRs). Epilepsy clinic letters are an example of an unstructured data source containing a large amount of intricate disease information. Extracting meaningful and contextually correct clinical information from free text sources, to enhance EHRs, remains a significant challenge. SCANR (Swansea University Collaborative in the Analysis of NLP Research) was set up to use natural language processing (NLP) technology to extract structured data from unstructured sources. IBM Watson Content Analytics software (ICA) uses NLP technology. It enables users to define annotations based on dictionaries and language characteristics to create parsing rules that highlight relevant items. These include clinical details such as symptoms and diagnoses, medication and test results, as well as personal identifiers.   Approach To use ICA to build a pipeline to accurately extract detailed epilepsy information from clinic letters. Methods We used ICA to retrieve important epilepsy information from 41 pseudo-anonymized unstructured epilepsy clinic letters. The 41 letters consisted of 13 ‘new’ and 28 ‘follow-up’ letters (for 15 different patients) written by 12 different doctors in different styles. We designed dictionaries and annotators to enable ICA to extract epilepsy type (focal, generalized or unclassified), epilepsy cause, age of onset, investigation results (EEG, CT and MRI), medication, and clinic date. Epilepsy clinicians assessed the accuracy of the pipeline. Results The accuracy (sensitivity, specificity) of each concept was: epilepsy diagnosis 98% (97%, 100%), focal epilepsy 100%, generalized epilepsy 98% (93%, 100%), medication 95% (93%, 100%), age of onset 100% and clinic date 95% (95%, 100%). Precision and recall for each concept were respectively, 98% and 97% for epilepsy diagnosis, 100% each for focal epilepsy, 100% and 93% for generalized epilepsy, 100% each for age of onset, 100% and 93% for medication, 100% and 96% for EEG results, 100% and 83% for MRI scan results, and 100% and 95% for clinic date. Conclusions ICA is capable of extracting detailed, structured epilepsy information from unstructured clinic letters to a high degree of accuracy. This data can be used to populate relational databases and be linked to EHRs. Researchers can build in custom rules to identify concepts of interest from letters and produce structured information. We plan to extend our work to hundreds and then thousands of clinic letters, to provide phenotypically rich epilepsy data to link with other anonymised, routinely collected data

Transit Timing Observations from Kepler: III. Confirmation of 4 Multiple Planet Systems by a Fourier-Domain Study of Anti-correlated Transit Timing Variations

We present a method to confirm the planetary nature of objects in systems with multiple transiting exoplanet candidates. This method involves a Fourier-Domain analysis of the deviations in the transit times from a constant period that result from dynamical interactions within the system. The combination of observed anti-correlations in the transit times and mass constraints from dynamical stability allow us to claim the discovery of four planetary systems Kepler-25, Kepler-26, Kepler-27, and Kepler-28, containing eight planets and one additional planet candidate.Comment: Accepted to MNRA

Emission from the D1D5 CFT: Higher Twists

We study a certain class of nonextremal D1D5 geometries and their ergoregion emission. Using a detailed CFT computation and the formalism developed in arXiv:0906.2015 [hep-th], we compute the full spectrum and rate of emission from the geometries and find exact agreement with the gravity answer. Previously, only part of the spectrum had been reproduced using a CFT description. We close with a discussion of the context and significance of the calculation.Comment: 39 pages, 6 figures, late

Data Resource: population level family justice administrative data with opportunities for data linkage

Introduction Although there has been considerable progress in the use of administrative data for applied health research, the family justice field lags behind. Better use of administrative data is essential to enhance understanding of how the family justice system is working, as well as the characteristics of, and outcomes for, children and families. The Family Justice Data Partnership (FJDP) supports this aim through analyses of core family justice and linked datasets in the SAIL Databank (Secure Anonymised Information Linkage). Cafcass Cymru provide expert advice for children involved in family court proceedings in Wales, ensuring decisions are made in the best interests of the child. We provide an overview of Cafcass Cymru data. We also describe and illustrate linkage to administrative datasets within SAIL. Methods Cafcass Cymru data was transferred to SAIL using a standardised approach to provide de-identified data with Anonymised Linking Fields (ALF) for successfully matched records. Three cohorts were created: all individuals involved in family court applications; all individuals with an ALF allowing subsequent health data linkage; and all individuals with a Residential Anonymised Linking Field (RALF) enabling area-level deprivation analysis. Results Cafcass Cymru application data are available for child protection matters (public law, range 2011-2019, n=12,745), and child arrangement disputes (private law, range 2005-2019, n=52,023). An 80% data linkage match rate was achieved. 40% had hospital admissions within two years pre or post application; 54% had emergency department attendances and 61% had outpatient appointments. Individuals were more likely to reside in deprived areas regardless of law type. Conclusion Cafcass Cymru data can be accessed through the SAIL Databank. The FJDP will continue to enhance research opportunities for all to better understand the family justice system, and outcomes for those involved, such as health and wellbeing for children and family members

Bayesian Methods for Exoplanet Science

Exoplanet research is carried out at the limits of the capabilities of current telescopes and instruments. The studied signals are weak, and often embedded in complex systematics from instrumental, telluric, and astrophysical sources. Combining repeated observations of periodic events, simultaneous observations with multiple telescopes, different observation techniques, and existing information from theory and prior research can help to disentangle the systematics from the planetary signals, and offers synergistic advantages over analysing observations separately. Bayesian inference provides a self-consistent statistical framework that addresses both the necessity for complex systematics models, and the need to combine prior information and heterogeneous observations. This chapter offers a brief introduction to Bayesian inference in the context of exoplanet research, with focus on time series analysis, and finishes with an overview of a set of freely available programming libraries.Comment: Invited revie