cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1580065.v1
title: Parameter Estimation Robust to Low-Frequency Contamination

description: We provide methods to robustly estimate the parameters of stationary ergodic short-memory time series models in the potential presence of additive low-frequency contamination. The types of contamination covered include level shifts (changes in mean) and monotone or smooth time trends, both of which have been shown to bias parameter estimates toward regions of persistence in a variety of contexts. The estimators presented here minimize trimmed frequency domain quasi-maximum likelihood (FDQML) objective functions without requiring specification of the low-frequency contaminating component. When proper sample size-dependent trimmings are used, the FDQML estimators are consistent and asymptotically normal, asymptotically eliminating the presence of any spurious persistence. These asymptotic results also hold in the absence of additive low-frequency contamination, enabling the practitioner to robustly estimate model parameters without prior knowledge of whether contamination is present. Popular time series models that fit into the framework of this article include autoregressive moving average (ARMA), stochastic volatility, generalized autoregressive conditional heteroscedasticity (GARCH), and autoregressive conditional heteroscedasticity (ARCH) models. We explore the finite sample properties of the trimmed FDQML estimators of the parameters of some of these models, providing practical guidance on trimming choice. Empirical estimation results suggest that a large portion of the apparent persistence in certain volatility time series may indeed be spurious. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1580068.v1
title: NEON Spatio-Temporal Teaching Dataset

description: This is a beta teaching dataset that contains spatio-temporal data for the Harvard Forest and SJER field sites.
DOI: 10.6084/m9.figshare.1580063
title: A Class of Non-Gaussian State Space Models With Exact Likelihood Inference

description: The likelihood function of a general nonlinear, non-Gaussian state space model is a high-dimensional integral with no closed-form solution. In this article, I show how to calculate the likelihood function exactly for a large class of non-Gaussian state space models that include stochastic intensity, stochastic volatility, and stochastic duration models among others. The state variables in this class follow a nonnegative stochastic process that is popular in econometrics for modeling volatility and intensities. In addition to calculating the likelihood, I also show how to perform filtering and smoothing to estimate the latent variables in the model. The procedures in this article can be used for either Bayesian or frequentist estimation of the model’s unknown parameters as well as the latent state variables. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1580068.v2
title: NEON Spatio-Temporal Teaching Dataset

description: This is a beta teaching dataset that contains spatio-temporal data for the Harvard Forest and SJER field sites.
DOI: 10.6084/m9.figshare.1580058
title: Extent of Occurrence and Area of Occupancy of Mitracarpus pusillus

description: Kml file for conservation status assessment of <i>Mitracarpus pusillus</i>.
DOI: 10.6084/m9.figshare.1580059
title: Extent of Occurrence and Area of Occupancy of Philcoxia minensis

description: Kml file for conservation status assessment of <i>Philcoxia minensis</i>.<br>
DOI: 10.6084/m9.figshare.1580063.v2
title: A Class of Non-Gaussian State Space Models With Exact Likelihood Inference

description: The likelihood function of a general nonlinear, non-Gaussian state space model is a high-dimensional integral with no closed-form solution. In this article, I show how to calculate the likelihood function exactly for a large class of non-Gaussian state space models that include stochastic intensity, stochastic volatility, and stochastic duration models among others. The state variables in this class follow a nonnegative stochastic process that is popular in econometrics for modeling volatility and intensities. In addition to calculating the likelihood, I also show how to perform filtering and smoothing to estimate the latent variables in the model. The procedures in this article can be used for either Bayesian or frequentist estimation of the model’s unknown parameters as well as the latent state variables. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1580059.v2
title: Extent of Occurrence and Area of Occupancy of Philcoxia minensis

description: Kml file for conservation status assessment of <i>Philcoxia minensis</i>.<br>
DOI: 10.6084/m9.figshare.1580068.v3
title: NEON Spatio-Temporal Teaching Dataset

description: This is a beta teaching dataset that contains spatio-temporal data for the Harvard Forest and SJER field sites. CONTENTS: AtmosData.zip: Precipitation, temperature and other variables collected from a flux tower at the NEON Harvard Forest. NOTE: these data are not collected from the NEON tower. boundaryFiles: A set of shapefiles for the Harvard Forest Field Site daylength: daily daylength for the HArvard Forest Field site. Landsat_NDVI: NDVI data product provided by USGS cropped to the Harvard Forest AOI used in the teaching lessons for 2011. NEON_RemoteSensing: LIdar data including a canopy height model, digital elevation model and digital surface model for the harvard forest field site.
DOI: 10.6084/m9.figshare.1580059.v1
title: philcoxia_minensis_eoo_aoo.kml

description: Kml file for conservation status assessment of Philcoxia minensis.<br>
DOI: 10.6084/m9.figshare.1580063.v1
title: A Class of Non-Gaussian State Space Models with Exact Likelihood Inference

description: The likelihood function of a general non-linear, non-Gaussian state space model is a high-dimensional integral with no closed-form solution. In this paper, I show how to calculate the likelihood function exactly for a large class of non-Gaussian state space models that includes stochastic intensity, stochastic volatility, and stochastic duration models among others. The state variables in this class follow a non-negative stochastic process that is popular in econometrics for modeling volatility and intensities. In addition to calculating the likelihood, I also show how to perform filtering and smoothing to estimate the latent variables in the model. The procedures in this paper can be used for either Bayesian or frequentist estimation of the model’s unknown parameters as well as the latent state variables.
DOI: 10.6084/m9.figshare.1580058.v2
title: Extent of Occurrence and Area of Occupancy of Mitracarpus pusillus

description: Kml file for conservation status assessment of <i>Mitracarpus pusillus</i>.
DOI: 10.6084/m9.figshare.1580068.v4
title: NEON Spatio-Temporal Teaching Dataset

description: This is a beta teaching dataset that contains spatio-temporal data for the Harvard Forest and SJER field sites. CONTENTS: AtmosData.zip: Precipitation, temperature and other variables collected from a flux tower at the NEON Harvard Forest. NOTE: these data are not collected from the NEON tower. boundaryFiles: A set of shapefiles for the Harvard Forest Field Site daylength: daily daylength for the HArvard Forest Field site. Landsat_NDVI: NDVI data product provided by USGS cropped to the Harvard Forest AOI used in the teaching lessons for 2011. NEON_RemoteSensing: LIdar data including a canopy height model, digital elevation model and digital surface model for the harvard forest field site.
DOI: 10.6084/m9.figshare.1580058.v1
title: mitracarpus_pusillus_eoo_aoo.kml

description: Kml file for conservation status assessment of Mitracarpus pusillus.
DOI: 10.6084/m9.figshare.1580068.v5
title: NEON Spatio-Temporal Teaching Dataset

description: This is a beta teaching dataset that contains spatio-temporal data for the Harvard Forest and SJER field sites. CONTENTS: AtmosData.zip: Precipitation, temperature and other variables collected from a flux tower at the NEON Harvard Forest. NOTE: these data are not collected from the NEON tower. boundaryFiles: A set of shapefiles for the Harvard Forest Field Site daylength: daily daylength for the HArvard Forest Field site. Landsat_NDVI: NDVI data product provided by USGS cropped to the Harvard Forest AOI used in the teaching lessons for 2011. NEON_RemoteSensing: LIdar data including a canopy height model, digital elevation model and digital surface model for the harvard forest field site.
DOI: 10.6084/m9.figshare.1580068.v7
title: NEON Spatio-Temporal Teaching Dataset

description: This is a beta teaching dataset that contains spatio-temporal data for the Harvard Forest and SJER field sites. CONTENTS: AtmosData.zip: Precipitation, temperature and other variables collected from a flux tower at the NEON Harvard Forest. NOTE: these data are not collected from the NEON tower. boundaryFiles: A set of shapefiles for the Harvard Forest Field Site daylength: daily daylength for the HArvard Forest Field site. Landsat_NDVI: NDVI data product provided by USGS cropped to the Harvard Forest AOI used in the teaching lessons for 2011. NEON_RemoteSensing: LIdar data including a canopy height model, digital elevation model and digital surface model for the harvard forest field site.
DOI: 10.6084/m9.figshare.1580068.v6
title: NEON Spatio-Temporal Teaching Dataset

description: This is a beta teaching dataset that contains spatio-temporal data for the Harvard Forest and SJER field sites. CONTENTS: AtmosData.zip: Precipitation, temperature and other variables collected from a flux tower at the NEON Harvard Forest. NOTE: these data are not collected from the NEON tower. boundaryFiles: A set of shapefiles for the Harvard Forest Field Site daylength: daily daylength for the HArvard Forest Field site. Landsat_NDVI: NDVI data product provided by USGS cropped to the Harvard Forest AOI used in the teaching lessons for 2011. NEON_RemoteSensing: LIdar data including a canopy height model, digital elevation model and digital surface model for the harvard forest field site.
DOI: 10.6084/m9.figshare.1580036
title: External validation of a normal tissue complication probability model for radiation-induced hypothyroidism in an independent cohort

description: ABSTRACT<b>Background.</b> A normal tissue complication probability (NTCP) model for radiation-induced hypothyroidism (RIHT) was previously derived in patients with squamous cell carcinoma of the head and neck (HNSCC) discerning thyroid volume (V<sub>thyroid</sub>), mean thyroid dose (D<sub>mean</sub>), and latency as predictive factors. The purpose of this study was to test the performance of this model in an independent cohort of patients receiving primary radiotherapy (RT) for HNSCC.<b>Material and methods.</b> A validation cohort of 198 patients with HNSCC was included after plasma thyrotropin (TSH) assessment. RIHT was defined as TSH &gt; 4.0 mU/l from blood samples obtained during follow-up. A new mixture NTCP model was developed from the validation cohort after multivariable analysis. Due to only one follow-up TSH assessment in the validation cohort, the time factor derived from the original cohort was fixed in a mixture model and applied for the NTCP validation. Association between model predictions of the initial model and observed clinical outcome in the validation cohort was investigated by applying the previous model (V<sub>thyroid</sub>, D<sub>mean</sub> and time) on the new cohort and comparing it to the clinical outcome.<b>Results.</b> Both D<sub>mean</sub> and V<sub>thyroid</sub> were confirmed as significant risk factors for RIHT in the validation cohort, odds ratio (OR) 1.19 (1.1–1.37) and OR 0.75 (0.57–0.9), respectively. A small difference in overall probability of RIHT was observed between the cohorts, further analysis indicated this to be related to less frequent blood tests in the validation cohort relative to the original cohort. However, Pearson's correlation coefficients between model and clinical outcome were high: r = 0.97 estimated by the original model versus the original cohort, and r = 0.97 estimated by the original model versus the new cohort.<b>Conclusion.</b> D<sub>mean</sub> and V<sub>thyroid</sub> were significant predictors of RIHT in both cohorts. The original NTCP model demonstrated external validity owing to high Pearson's correlation coefficients between estimated and observed incidence rates of RIHT in the original as well as in the validation cohort. This model may facilitate clinically relevant estimations of RIHT after RT to the neck.
DOI: 10.6084/m9.figshare.1579947
title: Tables & Supporting information file

description: Tables
DOI: 10.6084/m9.figshare.1580032
title: Sampling guidelines and analytical optimization for direct greenhouse gas emissions from tropical rice and upland cropping systems

description: We describe a modified manual closed-chamber approach with detachable lid and vertically stackable chambers for sampling followed by simultaneous analysis of nitrous oxide (N<sub>2</sub>O) and methane (CH<sub>4</sub>) for measuring greenhouse gas flux from rice and upland cropping systems in peninsular India. A meta-analysis of leading internationally/regionally recommended approaches to monitor agricultural GHG emissions is presented to put our sampling choices (e.g., chamber design, sampling intensity, sample storage and analytical corrections) into perspective. Given our set-up, the sample retention capacity of polypropylene syringes and crimped glass vials with grey butyl-rubber septa was ∼6 hours and 10 days, respectively; and temperature correction of N<sub>2</sub>O and CH<sub>4</sub> concentrations was essential but plant volume correction did not affect the flux rates substantially. Optimization of gas flow rates, pre-column sample retention period, oxygen venting and temperature/current were found to reduce run time from &gt;14 to 7 min per sample and enhance sensitivity by 30–40% while improving analytical precision from 15–30% to &lt; 2% relative standard deviation (RSD). We suggest an alternative to the linear interpolation approach of integrating the area under the N<sub>2</sub>O peak because linear interpolation can overestimate the cumulative seasonal N<sub>2</sub>O emissions by 50–100%, especially after fertilization and/or rain events.
DOI: 10.6084/m9.figshare.1580032.v2
title: Sampling guidelines and analytical optimization for direct greenhouse gas emissions from tropical rice and upland cropping systems

description: We describe a modified manual closed-chamber approach with detachable lid and vertically stackable chambers for sampling followed by simultaneous analysis of nitrous oxide (N<sub>2</sub>O) and methane (CH<sub>4</sub>) for measuring greenhouse gas flux from rice and upland cropping systems in peninsular India. A meta-analysis of leading internationally/regionally recommended approaches to monitor agricultural GHG emissions is presented to put our sampling choices (e.g., chamber design, sampling intensity, sample storage and analytical corrections) into perspective. Given our set-up, the sample retention capacity of polypropylene syringes and crimped glass vials with grey butyl-rubber septa was ∼6 hours and 10 days, respectively; and temperature correction of N<sub>2</sub>O and CH<sub>4</sub> concentrations was essential but plant volume correction did not affect the flux rates substantially. Optimization of gas flow rates, pre-column sample retention period, oxygen venting and temperature/current were found to reduce run time from &gt;14 to 7 min per sample and enhance sensitivity by 30–40% while improving analytical precision from 15–30% to &lt; 2% relative standard deviation (RSD). We suggest an alternative to the linear interpolation approach of integrating the area under the N<sub>2</sub>O peak because linear interpolation can overestimate the cumulative seasonal N<sub>2</sub>O emissions by 50–100%, especially after fertilization and/or rain events.
DOI: 10.6084/m9.figshare.1579947.v1
title: Tables

description: Tables
DOI: 10.6084/m9.figshare.1580032.v1
title: Sampling guidelines and analytical optimization for direct greenhouse gas emissions from tropical rice and upland cropping systems

description: ABSTRACTWe describe a modified manual closed-chamber approach with detachable lid and vertically stackable chambers for sampling followed by simultaneous analysis of nitrous oxide (N<sub>2</sub>O) and methane (CH<sub>4</sub>) for measuring greenhouse gas flux from rice and upland cropping systems in peninsular India. A meta-analysis of leading internationally/regionally recommended approaches to monitor agricultural GHG emissions is presented to put our sampling choices (e.g., chamber design, sampling intensity, sample storage and analytical corrections) into perspective. Given our set-up, the sample retention capacity of polypropylene syringes and crimped glass vials with grey butyl-rubber septa was ∼6 hours and 10 days, respectively; and temperature correction of N<sub>2</sub>O and CH<sub>4</sub> concentrations was essential but plant volume correction did not affect the flux rates substantially. Optimization of gas flow rates, pre-column sample retention period, oxygen venting and temperature/current were found to reduce run time from &gt;14 to 7 min per sample and enhance sensitivity by 30–40% while improving analytical precision from 15–30% to &lt; 2% relative standard deviation (RSD). We suggest an alternative to the linear interpolation approach of integrating the area under the N<sub>2</sub>O peak because linear interpolation can overestimate the cumulative seasonal N<sub>2</sub>O emissions by 50–100%, especially after fertilization and/or rain events.
DOI: 10.6084/m9.figshare.1580036.v1
title: External validation of a normal tissue complication probability model for radiation-induced hypothyroidism in an independent cohort

description: ABSTRACT<b>Background.</b> A normal tissue complication probability (NTCP) model for radiation-induced hypothyroidism (RIHT) was previously derived in patients with squamous cell carcinoma of the head and neck (HNSCC) discerning thyroid volume (V<sub>thyroid</sub>), mean thyroid dose (D<sub>mean</sub>), and latency as predictive factors. The purpose of this study was to test the performance of this model in an independent cohort of patients receiving primary radiotherapy (RT) for HNSCC.<b>Material and methods.</b> A validation cohort of 198 patients with HNSCC was included after plasma thyrotropin (TSH) assessment. RIHT was defined as TSH &gt; 4.0 mU/l from blood samples obtained during follow-up. A new mixture NTCP model was developed from the validation cohort after multivariable analysis. Due to only one follow-up TSH assessment in the validation cohort, the time factor derived from the original cohort was fixed in a mixture model and applied for the NTCP validation. Association between model predictions of the initial model and observed clinical outcome in the validation cohort was investigated by applying the previous model (V<sub>thyroid</sub>, D<sub>mean</sub> and time) on the new cohort and comparing it to the clinical outcome.<b>Results.</b> Both D<sub>mean</sub> and V<sub>thyroid</sub> were confirmed as significant risk factors for RIHT in the validation cohort, odds ratio (OR) 1.19 (1.1–1.37) and OR 0.75 (0.57–0.9), respectively. A small difference in overall probability of RIHT was observed between the cohorts, further analysis indicated this to be related to less frequent blood tests in the validation cohort relative to the original cohort. However, Pearson's correlation coefficients between model and clinical outcome were high: r = 0.97 estimated by the original model versus the original cohort, and r = 0.97 estimated by the original model versus the new cohort.<b>Conclusion.</b> D<sub>mean</sub> and V<sub>thyroid</sub> were significant predictors of RIHT in both cohorts. The original NTCP model demonstrated external validity owing to high Pearson's correlation coefficients between estimated and observed incidence rates of RIHT in the original as well as in the validation cohort. This model may facilitate clinically relevant estimations of RIHT after RT to the neck.
DOI: 10.6084/m9.figshare.1579947.v2
title: Tables

description: Tables
DOI: 10.6084/m9.figshare.1579947.v4
title: Tables & Supporting information file

description: Tables
DOI: 10.6084/m9.figshare.1579947.v3
title: Tables

description: Tables
DOI: 10.6084/m9.figshare.1579813
title: 2,4-D Dislodgeable Residues from Bermudagrass

description: Data from experiments investigating effects of post application irrigation and sample timing (within a day) on dislodgeable 2,4-D residues from bermudagrass turf.
DOI: 10.6084/m9.figshare.1579616
title: Repeated local emergence of carbapenem resistant Acinetobacter baumannii in a single hospital ward

description: Supplementary data and figures.
DOI: 10.6084/m9.figshare.1579813.v1
title: 2,4-D Dislodgeable Residues from Bermudagrass

description: Data from experiments investigating effects of post application irrigation and sample timing (within a day) on dislodgeable 2,4-D residues from bermudagrass turf.
DOI: 10.6084/m9.figshare.1579607
title: Genome-wide analysis of microsatellite markers based on the sequenced database of Chinese spring wheat (Triticum aestivum L.)

description: Microsatellites or simple sequence repeats (SSRs) are distributed across both prokaryotic and eukaryotic genomes and have been widely used for genetic studies and molecular marker assisted breeding of crops. Although a draft sequence for hexaploid bread wheat is available, to date, there is no published systemic analysis of SSRs in wheat. In the present study, we identified 364,347 SSRs from among 10,603,760 sequences of the Chinese spring wheat (CSW) genome, which were present at a density of 36.68 SSR/Mb. In total, we detected 488 types of motifs ranging from di- to hexanucleotides, among which dinucleotide repeats dominated, accounting for approximately 42.52% of the genome. The density of tri- to hexanucleotide repeats was 24.97%, 4.62%, 3.25% and 24.65%, respectively. AG/CT, AAG/CTT, AGAT/ATCT, AAAAG/CTTTT and AAAATT/AATTTT were the most frequent repeats among di- to hexanucleotide repeats. Among the 21 chromosomes of CSW, the density of repeats was highest on chromosome 2D and lowest on chromosome 3A. The proportions of di-, tri-, tetra-, penta- and hexanucleotide repeats on each chromosome, and even on the whole genome, were almost identical. In addition, 295,267 SSR markers were successfully developed from the 21 chromosomes of CSW, which cover the entire genome at a density of 29.73 per Mb. All of the SSR markers were validated by reverse electronic-Polymerase Chain Reaction (re-PCR); 70,564 (23.9%) were found to be monomorphic and 224,703 (76.1%) were found to be polymorphic. A total of 45 monomorphic markers were selected randomly for validation purposes; 24 (53.3%) amplified one locus, 8 (17.8%) amplified multiple identical loci, and 13 (28.9%) did not amplify any fragments from the genomic DNA of CSW. Then a dendrogram was generated based on the 24 monomorphic SSR markers among 20 wheat cultivars and three species of its diploid ancestors showing that monomorphic SSR markers represented a promising source to increase the number of genetic markers available for the wheat genome. The results of this study will be useful for investigating the genetic diversity and evolution of wheat and related species. At the same time, the results will facilitate comparative genomic studies and marker-assisted breeding (MAS) in plants.
DOI: 10.6084/m9.figshare.1579616.v1
title: Repeated local emergence of carbapenem resistant Acinetobacter baumannii in a single hospital ward

description: Supplementary data and figures.