| 1. |
- Barnes, Susana, et al.
(författare)
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An East Timorese Domain Luca from Central and Peripheral Perspectives
- 2017
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Ingår i: Bijdragen Tot de Taal-, Land- en Volkenkunde. - : Brill Academic Publishers. - 0006-2294 .- 2213-4379. ; 173:2-3, s. 325-355
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Tidskriftsartikel (refereegranskat)abstract
- The East Timorese kingdom Luca is described as the hegemon of the eastern parts of Timor in some nineteenth-century works. This is gainsaid by other data, which point to the existence of a multitude of petty kingdoms. This article scrutinizes Luca's claim to power from a number of angles, utilizing European records and contemporary anthropological fieldwork. First, we analyse the claims of the centre as reflected in colonial and indigenous narratives. Second, we investigate narratives from the 'periphery', that is, the minor adjacent domains of Vessoro and Babulo. Third, we offer a comprehensive discussion of Luca's role from a wider geographical perspective. In this way we produce a 'general account' that situates the symbolic and historical significance of Luca within the Timorese understanding of time, ritual, and power.
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| 2. |
- Potapov, Anton M., et al.
(författare)
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Global fine-resolution data on springtail abundance and community structure
- 2024
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Ingår i: Scientific Data. - : Nature Publishing Group. - 2052-4463. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Springtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.
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| 3. |
- Williams, David D., et al.
(författare)
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An "All-Data-on-Hand" Deep Learning Model to Predict Hospitalization for Diabetic Ketoacidosis in Youth With Type 1 Diabetes: Development and Validation Study
- 2023
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Ingår i: JMIR Diabetes. - 2371-4379. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Background: Although prior research has identified multiple risk factors for diabetic ketoacidosis (DKA), clinicians continue to lack clinic-ready models to predict dangerous and costly episodes of DKA. We asked whether we could apply deep learning, specifically the use of a long short-term memory (LSTM) model, to accurately predict the 180-day risk of DKA-related hospitalization for youth with type 1 diabetes (T1D). Objective: We aimed to describe the development of an LSTM model to predict the 180-day risk of DKA-related hospitalization for youth with T1D. Methods: We used 17 consecutive calendar quarters of clinical data (January 10, 2016, to March 18, 2020) for 1745 youths aged 8 to 18 years with T1D from a pediatric diabetes clinic network in the Midwestern United States. The input data included demographics, discrete clinical observations (laboratory results, vital signs, anthropometric measures, diagnosis, and procedure codes), medications, visit counts by type of encounter, number of historic DKA episodes, number of days since last DKA admission, patient-reported outcomes (answers to clinic intake questions), and data features derived from diabetes- and nondiabetes-related clinical notes via natural language processing. We trained the model using input data from quarters 1 to 7 (n=1377), validated it using input from quarters 3 to 9 in a partial out-of-sample (OOS-P; n=1505) cohort, and further validated it in a full out-of-sample (OOS-F; n=354) cohort with input from quarters 10 to 15. Results: DKA admissions occurred at a rate of 5% per 180-days in both out-of-sample cohorts. In the OOS-P and OOS-F cohorts, the median age was 13.7 (IQR 11.3-15.8) years and 13.1 (IQR 10.7-15.5) years; median glycated hemoglobin levels at enrollment were 8.6% (IQR 7.6%-9.8%) and 8.1% (IQR 6.9%-9.5%); recall was 33% (26/80) and 50% (9/18) for the top-ranked 5% of youth with T1D; and 14.15% (213/1505) and 12.7% (45/354) had prior DKA admissions (after the T1D diagnosis), respectively. For lists rank ordered by the probability of hospitalization, precision increased from 33% to 56% to 100% for positions 1 to 80, 1 to 25, and 1 to 10 in the OOS-P cohort and from 50% to 60% to 80% for positions 1 to 18, 1 to 10, and 1 to 5 in the OOS-F cohort, respectively. Conclusions: The proposed LSTM model for predicting 180-day DKA-related hospitalization was valid in this sample. Future research should evaluate model validity in multiple populations and settings to account for health inequities that may be present in different segments of the population (eg, racially or socioeconomically diverse cohorts). Rank ordering youth by probability of DKA-related hospitalization will allow clinics to identify the most at-risk youth. The clinical implication of this is that clinics may then create and evaluate novel preventive interventions based on available resources.
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