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opencc-by-4.0Oct 2020View details →

Dataset of Correlation of clinical decision-making with probability of disease: A web-based study among general practitioners

In databases we find the following information: - the true probability of the disease in the clinical vignette - the probability of the disease estimated by the general practitioner (GP) in the clinical vignette - the clinical decision of the general practitioner - the baseline characteristics of the participants

opencc-zeroNov 2020View details →

Water-borne and plasma corticosterone are not correlated in spotted salamanders

Water-borne hormone measurement is a noninvasive method suitable for amphibians of all sizes that are otherwise difficult to sample. For this method, containment-water is assayed for hormones released by the animal. Originally developed in fish, the method has expanded to amphibians, but requires additional species-specific validations. We wanted to determine physiological relevance of water-borne corticosterone in spotted salamanders (Ambystoma maculatum) by comparing concentrations to those taken using established corticosterone sampling methods, such as plasma. Using a mixture of field and laboratory studies, we compared water-borne corticosterone levels to other traditional methods of sampling corticosterone for spotted salamander larvae, metamorphs, and adults. Despite multiple attempts, and detecting differences between age groups, we found no correlations between water-borne and plasma corticosterone levels in any age group. Water-borne sampling measures a rate of release; whereas plasma is the concentration circulating in the blood. The unique units of measurement may inherently prevent correlations between the two. These two methods may also require different interpretations of the data and the physiological meaning. We also note caveats with the method, including how to account for differences in body size and life history stages. Collectively, our results illustrate the importance of careful validation of water-borne hormone levels in each species in order to understand its physiological significance.

opencc-zeroNov 2020View details →

Data from: Combining correlative and mechanistic niche models with human activity data to elucidate the invasive potential of a sub-Antarctic insect

Aim Correlative Species Distribution Models (SDMs) are subject to substantial spatio-temporal limitations when historical occurrence records of data-poor species provide incomplete and outdated information for niche modelling. Complementary mechanistic modelling techniques can, therefore, offer a valuable contribution to underpin more physiologically-informed predictions of biological invasions, the risk of which is often exacerbated by climate change. In this study we integrate physiological and human pressure data to address the uncertainties and limitations of correlative SDMs and to better understand, predict, and manage biological invasions. Location Western archipelagos of the Southern Ocean and martime Antarctica Taxon Eretmoptera murphyi (Chironomidae), invertebrates. Methods Mahalanobis Distances were used for correlative SDM construction for a species with few records. A mechanistic SDM was built around different fitness components (larval survival and life stage progression) as a function of temperature. SDM predictions were combined with human activity levels in Antarctica to generate a site vulnerability index to the colonization of E. murphyi. Future scenarios of ecophysiological suitability were built around the warming trends in the region. Results Both SDMs converge to predict high environmental suitability in the species' native and introduced ranges. However, the mechanistic model indicates a slightly larger invasive potential based on larval performance at different temperatures. Human activity levels across the Antarctic Peninsula play a key role in discerning site vulnerabilities. Niche suitability in Antarctica grows considerably under long-term climate scenarios, leading to a substantially higher invasive threat to the Antarctic ecosystems. In turn changing conditions result on growing physiological mismatches with the environment in the native range on South Georgia. Main conclusions Long-term studies of invasion potential under climate benefit from integrating correlative predictions with physiological experiments, as the invasion potential varies depending on the area and the timescale examined. This study also highlights a conservation paradox whereby the accidental introduction of an insect represents a threat to the Antarctic ecoystems that contrasts with its endangered status at the native range.

opencc-by-4.0Dec 2020View details →

Study on the correlation of prevalence of ocular disease to different demographic variables, medical history and concomitant diseases.

Random blood sugar, cholesterol, and triglycerides were analyzed by taking the blood sample from each patient. The average of the parameters was recorded, and the standard deviation (SD) was calculated. The parameters have been presented in the study as mean± SD. Blood sample analysis revealed that the average random blood glucose level (mg/dl) was 113.3±26.9 (Mean ±SD). The average cholesterol level was 189.35± 49.7 mg/dl, and the average triglycerides level was 124.25±50.0 mg/dl. Average systolic and diastolic blood pressure was found to be 126.0 ± 15.5 mmHg and 81.43 ± 8.0 mmHg, respectively.  The patients were examined by the dermatologist. The dermatologists observed the signs, and the patients were asked about the presence of the symptoms to evaluate the severity of psoriasis and psoriatic arthropathy. The patients were asked about the duration for which they had been suffering from psoriasis. On average, a patient was found to be suffering from psoriasis for 11.4 ±9.9 years (the duration ranged from a minimum of 1 year to a maximum of 45 years). The doctor screened the patients for the number of sites involved and evaluated the severity of psoriasis via psoriasis area and severity index (PASI) score. Average Psoriasis Area and Severity Index (PASI) for the patients, as recorded by the dermatologists, was found to be 5.94 ± 6.8.

opencc-zeroDec 2020View details →

Building façade-level correlates of bird-window collisions in a small urban area

Urbanization increasingly exposes birds to multiple sources of direct anthropogenic mortality. Collisions with buildings, and windows in particular, are a top bird mortality source, annually causing 365-988 million fatalities in the United States. Correlates of window collision rates have been studied at the scale of entire buildings and in relation to the surrounding landscape, and most studies have only assessed correlates for all birds combined without considering season- and species-specific risk factors. In Stillwater, Oklahoma, USA, we conducted bird collision surveys at 16 buildings to assess building structural-, vegetation-, and land cover-related collision correlates. Unlike past studies, we focused at the scale of individual building façades, and in addition to considering correlates for total collisions, we assessed correlates for different seasons and separately for eight collision-prone species. Several façade-related features, including proportional glass coverage, façade length, and façade height, were positively associated with total collisions and collisions for most separate seasons and species. Total collisions were also greater at alcove-shaped façades than flat, curved, and portico-shaped façades. We found that collision correlates varied among seasons (e.g., surrounding lawn cover important in summer and fall, but not spring) and among species (e.g., surrounding impervious cover positively and negatively related to collisions of Painted Bunting and American Robin, respectively). Given the importance of glass proportion, collision reduction efforts should continue to focus on minimizing and/or treating glass surfaces on new and existing buildings. Our species and season-specific assessments indicate that management of some collision risk factors may not be equally effective for all seasons and species. Future research, policy, and management that integrates information about collision risk for all bird species and seasons, and at multiple scales from building façades to the surrounding landscape, will be most effective at reducing total mortality from bird-window collisions.

opencc-zeroDec 2020View details →

Negative correlations between native macrophyte diversity and water hyacinth abundance are stronger in its introduced than in its native range

Aim: We tested the hypothesis that the diversity and abundance of aquatic macrophytes are negatively related with Eichhornia crassipes abundance in its introduced range, but not in its native range. Location: Upper Parana River Floodplain, Brazil and Southeast China Methods: We sampled aquatic macrophytes patches in Brazil (native range) and China (introduced range) along a biomass gradient of E. crassipes. For each patch, we obtained values of species richness and aquatic macrophytes percentage cover, as response variables in regression models. We also used species accumulation curves to quantify the total plot diversity in dominated and non-dominated plots for both countries. Finally, we compared the influence of E. crassipes dominance on community composition and beta diversity with Permanova and Permdisp, respectively. Results: The regression analyses revealed a negative correlation between macrophyte richness and cover and E. crassipes biomass only in the introduced range. The cumulative number of species decreased at a higher extent in plots dominated by E. crassipes in China, compared to Brazil. Also, species composition changed and beta diversity decreased in the dominated plots in China, but not in Brazil. Main conclusions: The reduction of all diversity attributes related to E. crassipes probably results from its engineer species role, which decreases littoral region habitat heterogeneity and affects rare species in the introduced range. Differences between countries may be associated with impacts of water hyacinth on native macrophytes since this plant grows very fast and is highly competitive. Although less probable, biotic resistance at the establishment phase of water hyacinth in sites with higher number of native species is also a possibility. Regardless of the main mechanism explaining our patterns, it is suggested that invasion by water hyacinth is a cause for concern for its higher impacts in the introduced ranges than the native ranges.

opencc-by-4.0Dec 2020View details →

Computing a correlation length scale from MFLL-OCO2 CO2 differences, and accounting for correlated errors when assimilating OCO-2 data

This dataset contains code and data used in  'A new exponentially-decaying error correlation model for assimilating OCO-2 column-average CO2 data, using a length scale computed from airborne lidar measurements'  by David F. Baker, Emily Bell, Kenneth J. Davis, Joel F. Campbell, Bing Lin, and Jeremy Dobler, submitted to Geoscientific Model Development. In particular, the MATLAB script used to compute the autocorrelation spectrum of  Multi-functional Fiber Laser LiDAR (MFLL) and Orbiting Carbon Observatory (OCO-2) column CO2 differences (in Section 2.2 of the paper) is given here as file comp_MFLL_OCO2_autocorrl_spectrum.m along with the needed MFLL and OCO-2 CO2 data for each of the six flights analyzed (as described in Section 2.1 of the paper) in files: 20160727_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20160805_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20170215_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20170308_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20171022_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20171027_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20160727_oco_averaged_B9_GMAO_ACTadj.h5 20160805_oco_averaged_B9_GMAO_ACTadj.h5 20170215_oco_averaged_B9_GMAO_ACTadj.h5 20170308_oco_averaged_B9_GMAO_ACTadj.h5 20171022_oco_averaged_B9_GMAO_ACTadj.h5 20171027_oco_averaged_B9_GMAO_ACTadj.h5 The MFLL data given here was downloaded in late 2018 in the form of L1b files (calibrated radiances), as described in Bell et al (2020). In the second part of the paper, different error correlation models are presented and applied to the averaging of OCO-2 column CO2 data.  The original bias-corrected OCO-2 data, in the form of daily OCO-2 version 10 "Lite" files, have been from obtained from NASA's GES DISC data repository, here: https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_10r/summary?keywords=OCO2_L2_Lite_FP The bias-corrected column CO2 retrievals, their uncertainties, and other parameters needed for this analysis were extracted from these "Lite" files and saved to daily files, which have been packaged up in the following compressed tarball: OCO2_XCO2_2014_2020.tar.gz These daily files are read in and averaged across 2-second and 10-second spans (as described in Section 3.5 of the paper), using the different error correlation models outlined in the paper.  The code that implements these averages is given in the following two FORTRAN programs: Make_OCO2_2sec_averages.f90 Make_OCO2_10sec_averages.f90 which need the following list of days having good OCO-2 data: OCO2_dates.txt Program "Make_OCO2_2sec_averages.f90" averages the OCO-2 data across a 2-second (~13.5 km long) span along the groundtrack, collapsing the relatively thin data swath into a one-dimensional data record, upon which the one-dimensional averaging models describe in Sections 3.1 and 3.2 of the paper may be applied.  Program "Make_OCO2_10sec_averages.f90" implements these averaging models, which average the 2-second averages across longer, 10-second (~67.5 km) spans.  Please see the manuscript for more information on the data and methods provided here.

FIGURES 49–50. Maps. 49 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 49–50. Maps. 49, Distribution of endemic Schausiana spp. and Pallas sp. in Central America with respect to the Motagua-Polochic fracture zone, S. chalciope sp. nov.—dark blue, S. maishei sp. nov.—pale blue, Pallas reynaudi—yellow circles. 50, Approximate location of the Guerrero terrane, the Greater Antilles and other island arcs (colored areas) within the Pacific basin ~140 Ma. Hypothetical ancestral range of Schausiana as blue dashed line. Map modified from Clennett et al. (2020) with the inferred addition of the Greater Antilles arcs, including portions that have since accreted to Colombia.

FIGURE 48 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURE 48. Distribution of Schausiana spp. within southern North and Central America Eastern boundary of Guerrero terrane shown as blue dashed line.

FIGURES 37–47. 37–41 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 37–47. 37–41. Schausiana spp. ♀ genitalia. 37, S. trojesa (CGCM 33.184): 37a, posterior view; 37b, lateral view. 38, S. chalciope, sp. nov., PT (CGCM 41.258): posterior view. 39, S. pharus (CGCM 5.797): posterior view. Ductus and corpus bursae: 40, S. trojesa (CGCM 33.184); 41, S. pharus (CGCM 5.797). 42–47. Dorsal forewing cubital patch (yellow dotted line). 42, Pallas reynaudi C. Mielke & Grehan, 2015. 43, Schausiana trojesa. 44, S. maishei, sp. nov. 45, S. phalerus. 46, S. chalciope, sp. nov. 47. S. pharus.

FIGURES 34–36 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 34–36. Schausiana spp. ♂ genitalia. 34, S. trojesa (CGCM 39.417): 34a, ventral view; 34b, ventral view with base of valva exposed (arrow indicates the hook at the base of the valva); 34c, dorsal view (internal; arrow indicating the mesal ridge of fultura inferior); 34d, lateral view. 35, S. maishei, sp. nov., ♂ PT (CGCM 23.236): 35a, ventral view (arrow indicates the hook at the base of the valva); 35b, ventral view with pseudotegumen moved upwards (arrow indicating the mesal ridge of fultura inferior); 35c, dorsal view (internal); 35d, lateral view. 36, S. pharus (CGCM 7.110): 36a, ventral view (arrow indicates the hook at the base of the valva); 36b, dorsal view (internal; arrow indicating the mesal ridge of fultura inferior); 36c, lateral view. Scale bar = 1 mm.

FIGURES 22–27 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 22–27. Schausiana spp. labial palpi, legs and wing venation. 22–23, labial palpi of S. trojesa: 22, ♀ (Morelos, Cuernavaca (UNAM)); 23, ♂ (Morelos, Tepoztlán (UNAM)). Unscaled. 24–25, legs: 24, S. maishei, sp. nov. (CGCM 23.236); 25, S. pharus (CGCM 7.110). Scale bar = 5 mm. 26–27, wing venation of S. trojesa: 26, ♂ (arrows indicating 1A incomplete and 2A+3A); 27, ♀ (arrow indicating CuP mesally incomplete).

FIGURES 14–16 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 14–16. Schausiana chalciope, sp. nov. 14, ♂ PT (CGCM 6.216): 14a, dorsal view; 14b, ventral view (CGCM). 15,

FIGURES 28–33 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 28–33. Schausiana spp. abdomen (upper side, tergites; lower side, sternites). 28–29, S. trojesa: 28, ♂ (CGCM 39417); 29, ♀ (CGCM 33.184). 30, S. maishei, sp. nov., ♂ PT (CGCM 23.236). 31, S. chalciope, sp. nov., ♀ PT (CGCM 41.258). 32–33, S. pharus: 32, ♂ (CGCM 7.110); 33, ♀ (CGCM 5.797). Scale bar = 5 mm.

FIGURES 12–13. Schausiana phalerus. 12 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 12–13. Schausiana phalerus. 12, ♂ HT: 12a, dorsal view; 12b, ventral view (NHMUK). 13, ♀ (CGCM 24.122): 13a, dorsal view; 13b, ventral view (CGCM). Scale bar = 1 cm.

FIGURES 1–5 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 1–5. Schausiana spp. FW areas and specialized scales. Areas: 1, S. trojesa ♀. Specialized scales: 2, S. trojesa ♀; 3, S. maishei, sp. nov. ♀; 4, S. chalciope, sp. nov. ♀; 5. S. pharus ♀. Arrows indicate the position of such scales.

FIGURES 9–11 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 9–11. Schausiana maishei, sp. nov. 9, ♂ PT (CGCM 32.071): 9a, dorsal view; 9b, ventral view (CGCM). 10, ♀ PT (CGCM 36.646): 10a, dorsal view; 10b, ventral view (CGCM). 11, ♀ HT: dorsal view (UVG). Scale bar = 1 cm.

FIGURES 17–21. Schausiana pharus. 17 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 17–21. Schausiana pharus. 17, ♂ LT: dorsal view (NHMUK). 18, ♂ (CGCM 26.702): 18a, dorsal view; 18b, ventral view (CGCM). 19, ♀ (CGCM 25.341): dorsal view (CGCM). 20, ♀ (CGCM 36.194): dorsal view (CGCM). 21, ♀ (CGCM 33.341): dorsal view (CGCM). Scale bar = 1 cm.

FIGURES 6–8. Schausiana trojesa. 6 in Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

FIGURES 6–8. Schausiana trojesa. 6, ♂ LT: dorsal view (USNM). 7, ♂ (CGCM 39.417): 7a, dorsal view; 7b, ventral view (CGCM). 8, ♀ (CGCM 33.184): dorsal view (CGCM). Scale bar = 1 cm.