dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, the decay of similarity with distance pattern is predicted by several theories to be a negative exponential function  (1). The slope of this function on logarithmic scale should be scale-invariant  (2). However, a plot of the local slope of empirical data (3) might show a peak at large distance scales (4) indicative of a second-order effect that can only be explained by additional mechanisms not incorporated into the original biodiversity theories. Here we use this approach to provide general insights into the degree to which non-ecological systems can be explained by ecological theory. We compared first-and second-order effects across a broad set of ecological and non-ecological systems. We compiled five large datasets that each describe the abundance, location, and identity of objects in multiple categories (species) throughout the continental United States, encompassing two ecological systems (North American birds and trees), two economic systems (US Census county business patterns, and a commercial database of franchise locations for several hundred major corporations) and one geological system (USGS mineral resources database) (Table  2 ).
Macroecological pattern First-order effect Second-order effect  Left column, site locations for each dataset (colored as described in Table  2 ). Site brightness is proportional to richness. Right column, relative abundance distribution for log-transformed abundance data at full scale (a first-order effect). All datasets are shown with the same axes. doi:10. 1371/journal. pone. 0112850. g001 species j at site i. Each community matrix was augmented with the latitude and longitude of each site. Each dataset was clipped to sites contained within the continental United States. All datasets were transformed from their raw form to community matrices using R for data manipulation and MATLAB for GIS analyses. These datasets are available in Information S1. Dataset 1: Corporate locations. We purchased a commercial dataset containing the street addresses and latitude/longitudes of all locations of several hundred major corporations (AggData, Inc.
). The data represent a census obtained between 2008 and 2010. We used shapefiles for the United States Census zip code tabulation areas (ZCTAs) to assign these point occurrences into assemblages. These approximately 20,000 areas cover the entire United States. While ZCTAs have unique complex boundaries and variable areas, they each cover roughly equivalent population levels and are a good comparable assemblage unit for this study. We then determined the latitude and longitude of each assemblage as the centroid of each ZCTA. Dataset 2: Industrial codes. We downloaded the United States Census County Business Patterns dataset, which counts the numbers of businesses of different size classes in each of the North American Industrial Classification System's (NAICS) nested categories, within each of the counties of every state of the United States.
These data were valid for the 2007 census year. The data include some intentional inaccuracies (low-abundance data swapped between sites or abundances randomized) to comply with privacy laws, but these effects are small in magnitude and should not affect our analyses. We restricted our analysis to only the most specific (six-digit) level of NAICS classification in order to closely match between biological and business species. To further improve this correspondence, we also assumed that businesses that fell into different size classes (1-10, 10-100, 100-1000, 1000+ employees) within a given NAICS category represented different species. We also obtained shapefiles for county boundaries and determined the latitude and longitude of each assembly as the centroid of each county. Dataset 3: Birds.
We obtained data from the North American Breeding Bird Survey, which counts the abundance of the bird species observed along hundreds of multi-kilometer transect routes by multiple volunteer birders. We used data from the 2007 counts. We treated each route as an assemblage and determined its latitude and longitude as the midpoint of the route. Dataset 4: Trees. We obtained data from the United States Forest Service's Forest Inventory of America, which counts the abundances of several hundred species of trees at hundreds of thousands of plots across the United States. At each plot, we used data from its most recent census, which ranged from 1985-2008. Plot data were pre-corrected for variable plot size and only included live trees. Because of privacy laws, these data contain intentional inaccuracies (plots on private land have their coordinates fuzzed and their abundances swapped) that are small in magnitude and do not affect our analyses.
We treated each plot as an assemblage and used the plot center for the assemblage latitude and longitude. Dataset 5: Minerals. We downloaded data from the United States Geological Survey's Mineral Resource Data System, which describes the locations of metallic and nonmetallic minerals throughout the world. We pooled the abundances of commodities, ores, and gangue at each site, because we were interested in geological processes and did not wish to stratify the data by economic value. Because each site contained a very low number of minerals (typically representing the useful output of a single mine) we chose to generate an equal-area grid (100061000) covering the bounding box of the continental United States, and pooled mineral abundances for all sites falling within each grid cell. We then defined the assemblage latitude and longitude as the center of the grid cell.
Data analysis Species-abundance distribution. We sampled the abundance distribution at 100 spatial scales that logarithmically spanned a range from 0. 1u to 40u. At each scale, we chose 500 random sites. We defined a small circle on the surface of the earth whose radius was determined by the current spatial scale and whose center was the location of the current site. We then intersected this circle with a polygon defining the boundary of the region of interest (here, the continental United States). We calculated the surface area of this new polygon (in km 2 ) using spherical geometry and the known radius of the earth to determine the effective area of the site. We then pooled abundances for all species at all sites enclosed within this polygon, applied a log transformation, and calculated the mean, coefficient of variation, skewness, and kurtosis of this distribution.
We calculated the pairwise distance distribution as the vector of distances (accounting for the curvature of the earth, as defined for the similarity-distance function) between every pair of sites at which this species occurred. We determined the null pairwise distance distribution by counting the number of sites at which this species occurred, randomly assigning that many occurrences of this species to randomly chosen sites, and repeating the pairwise distance calculation. This method accounts for sites that are nonuniformly or non-randomly positioned, corrects distances for the curvature of the earth, and generates conclusions that are consistent with more established methods for detecting clumping (e. g. pair correlation/o-ring function  [6] ). However, this method is computationally much faster for large datasets, because distance and intersection calculations can be pre-computed a single time.
First-order effects. Statistics were based on the metrics calculated using the methods described in the previous section. For the species-abundance distribution, we used abundance data at the largest spatial scale. We fit several candidate distributions to the data (Pareto, power-bend  [35] , Poisson log-normal, log-series, Weibull) and identified the distribution with the lowest AIC. For the species-area relationship, we used the log-transformed area and richness values described in the previous section, then reported the slope of the model (i. e. the power law exponent). For the similarity-distance relationship, we constructed a linear model using the sampled similarities and log-transformed distances for 10 4 km 2 assemblages and reported the slope of the model. For these two analyses, all models were highly significant (p,0.
Results We first quantified first-order effects in each ecological, economic, and geological dataset neglecting the effect of spatial scale. Our analyses show that, when data are aggregating at a continental spatial scale, each dataset is characterized by the expected first order effects (see Methods for details). All species abundance distributions were best fit by a log-normal distribution (DAIC to the next-best distribution. 30), except for the tree dataset for which a log-normal or Weibull distribution were both appropriate (DAIC = 4). All species-area relationships had log-log slopes ranging from 0. 28 to 0. 50. All distance decay relationships had log-linear slopes ranging from -0. 17 to -0. 72. Lastly, most datasets showed intraspecific clumping (7-52% of species significantly clumped).
The only exception was for trees, but this dataset included many widely cultivated species. In contrast, we found that second-order effects can distinguish between these datasets, suggesting the operation of different processes structuring each system across a range of scales. We found that for scales of up to ,10 5 km 2 the coefficient of variation for the species-abundance distribution (Figure  2 ) for skewness, and kurtosis was larger for the economic datasets than for the ecological or geological datasets. These differences indicate the existence of a process specific to these economic systems that generates higher fractions of rare species at local scales. For example, it could be the case that rapidly growing economies have both higher birth and death rates reflected in large numbers of new businesses.
However, all datasets had lower positive skewness at large scales, consistent with dispersal limiting the spread of rare species across space. Across all systems, species-area relationships displayed a range of slopes and curvature indicating scale-dependent processes of richness accumulation (Figure  3 ). Consistent with dispersal limitation and a transition to novel species pools, we found a small increase in slope at intermediate scales for the biological data, A more striking pattern is the decrease in slope at large scales of the economic data, consistent with convergence to similar species pools on both coasts. The similarity-distance function showed a range of decay rates and minimum similarities when comparing datasets (Figure  4 ). Ecological datasets decayed faster than economic or geological datasets, and an ecological dataset (trees) had the lowest minimum similarity at large distances.
Minimum bird similarity was higher than minimum tree similarity, presumably because of the high dispersal potential and large range size of many birds. Economic datasets showed an increase in similarity at very large distances, consistent with high similarity of species pools on both coasts. This change in similarity was weaker for the geological dataset. Changes in the similarity-distance function with assemblage area were also consistent with dispersal limitation being more important in biological systems. We found that in communities encompassing larger areas, ecological systems maintained their decay rates and reached comparable minimum similarities, but that economic systems exhibited very limited decay and high minimum similarity. The intraspecific clumping function showed more clumping at longer distances in ecological data sets compared to economic or geological datasets (Figure  5 ).
The width of this leftmost part of the clumping function may provide insight into the average dispersal distance for species  [6]. At intermediate distances, we found very low levels of clumping in all datasets, indicating that species distributions are spatially random at mid-continent scales. However, we also found increased clumping at whole-continent distances, especially in the economic datasets. This is broadly consistent with low dispersal limitation of businesses, and the high similarity of species pools on both coasts in economic systems.
Discussion We have shown that spatial scaling can successfully separate four universal macroecological patterns. The consistency of firstorder effects across ecological, economic, and geological systems  [23, 24]  indicates that they provide little power to distinguish ecological mechanisms. We then identified second-order effects that were able to separate these five datasets -some of which are consistent with known ecological processes (e. g. dispersal limitation), and others not. Thus, future biodiversity theories should make simultaneous predictions for these scale-dependent secondorder effects, in order to provide stronger tests and more separable predictions of theory. Our results indicate that theory especially should be able to make system-dependent predictions for the   2. doi:10.
Our results leave unresolved a potentially important zero-th order effect describing each system's state variables: the number of species and individuals found in each dataset. Although the value of these numbers set the scale of all first-order effects they may also ultimately constrain levels of variation in second-order effects. However, in all major theories of biodiversity, the number of individuals and the number of species are treated as free parameters  [1]. Addressing the origin of the zeroth-order effect may provide as much insight as addressing the origin of secondorder effects  [36]. Our results question the importance of the species concept is to macroecological theory. All biodiversity theories and macroecological patterns are expressed in terms of species and individuals. For ecological systems these are natural and potentially preferred scales for understanding a system.
However, there are many possible ways to partition objects in to categories for nonecological systems, and it is unclear if any particular aggregation method should be preferred. For example, individuals businesses can be aggregated into NAICS codes, but the taxonomy and resolution of these codes is necessarily a human choice. Thus, macroecological theory may be applied best to biological species. However, many biodiversity theories are derived from very limited or no biological processes, suggesting that they should apply equally well to any partitioning of objects in to categories (e. g. taxon-invariance in the species-area relationship  [37, 38] ). Therefore deviations from predictions, such as our second-order effects, should still reflect additional mechanisms. We showed that our approach could be used for distinguishing different datasets or detecting situations where theory could be modified to better accommodate empirical data.
Funding: BB was supported by a  National Science Foundation  pre-doctoral fellowship. LS was supported by the  National Center for Ecological Analysis and Synthesis. BJE was supported by a  National Science Foundation Advancing Theory in Biology  grant. BJM was funded by a  National Science Foundation Experimental Program  to  Stimulate Competitive Research  grant  EPS-0904155  at the  University of Maine. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The topic guide was created using the available literature on the migration motives of South African health workers. Interviews took approximately 60 min. All of the interviews were audio recorded and later transcribed verbatim by either the interviewer or another member of the research team. All transcripts were anonymous. Once interviews began, it became apparent that the data was very rich and yielded a great depth of information that had not been obtained before. Participants were recruited until the point whereby all emerging concepts had been explored and no new themes emerged, that is, data saturation had been reached. The research team decided that data saturation was reached after 16 interviews. Interviews were conducted between January and August 2012.
Study design This randomized, open-label, multicenter, phase II study (Clinical Trials. gov: NCT01013740, GSK protocol number: LAP112620) included women with HER2-positive MBC who had received no more than one chemotherapeutic regimen in the metastatic setting. Patients were enrolled between November 2009 and February 2012 from 40 sites in 10 countries. The study was performed in accordance with the Declaration of Helsinki and approved by local ethics committees. Sample size was based on feasibility. A 2:1 randomization scheme was used targeting 70 subjects in the lapatinib plus vinorelbine group and 35 subjects in the lapatinib plus capecitabine group. Patients were randomized to receive either lapatinib 1,250 mg orally once daily (QD) continuously plus vinorelbine 20 mg/m 2-/day intravenously on days 1 and 8 every 3 weeks, or lapatinib 1,250 mg orally QD continuously plus capecitabine 2,000 mg/m 2 /day orally in 2 doses (12 h apart on days 1-14 every 3 weeks).
Study endpoints Tumor response data were assessed by the investigator according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines  [17]. The primary endpoint was PFS, defined as the time from randomization to the time of first documented disease progression at any site, or death due to any cause. Secondary endpoints included: OS, defined as the time from randomization until death due to any cause; ORR, defined as the percentage of patients experiencing confirmed complete response (CR) and partial response (PR); CBR, defined as the percentage of patients achieving either a confirmed CR or PR, or having stable disease (SD) for at least 24 weeks (patients with unknown or missing response were treated as non-responders and included in the denominator when calculating the CBR percentage); for the subset of patients who showed a confirmed response (CR or PR), duration of response (DoR) is defined as time from first documented evidence of CR or PR until the first documented sign of disease progression or death due to any cause; and time to response (TTR) is the time from randomization until the first documented evidence of CR or PR (whichever is recorded first).
The time to second progression following crossover was included as an exploratory outcome measure. Data regarding the number of patients with CNS metastases were collected post hoc as an exploratory outcome measure. Toxicities were also measured by recording the incidence and grading of adverse events (AEs) and serious AEs (SAEs; the study protocol extended the definition of an SAE to include any grade 4 laboratory abnormality in order to expedite reporting of grade 4 neutropenia).
Statistical analysis All efficacy analyses were performed using SAS version 9. 1 and conducted on the intent-to-treat population, which comprised all patients who were randomized to study treatment, regardless of whether they actually received study medication. Clinical safety and tolerability were assessed in the safety population, which comprised all patients who took at least one dose of study medication. PFS, OS, DoR, and TTR were summarized using Kaplan-Meier survival curves, from which the median and 95 % confidence intervals (CIs) were calculated. Greenwood's formula was used to calculate the standard error of the estimates from the Kaplan-Meier curve. The treatment hazard ratio (HR) and 95 % CI were based on the stratified log-rank test (the Pike estimator  [18]  ), stratifying for prior receipt of therapy for MBC (yes or no), and site of metastatic disease (visceral/soft tissue or bone only) was calculated for PFS and OS.
Acknowledgments The authors thank the patients who participated in this study and the following study site staff, for study management oversight:  Dimitar Kalev ,  Constanta Timcheva ,  Maya Velikova  (Bulgaria);  Alejandro Acevedo ,  Christian Caglevic ,  Alejandro Majlis  (Chile);  Etienne Brain ,  Robert Herve ,  Eric Legouffe  (France);  Lars Hanker ,  Jens Huober ,  Agnieszka Korfel ,  Arnd Nusch ,  Christoph Salat ,  Wolfgang Wiest (Germany) ;  Vassilis Georgoulias ,  Georgios Koumakis ,  Christos Papadimitriou  (Greece);  Michele Caruso ,  Stefano Cascinu ,  Lucia Del Mastro ,  Annamaria Molino ,  Alberto Zaniboni (Italy) ;  Carlos Alberto Hernandez-Hernandez, Francisco Alejo Medina Soto (Mexico) ;  Boguslawa Karaszewska ,  Elzbieta Nowara ,  Joanna Pikiel ,  Piotr Potemski ,  Pawel Rozanowski ,  Tomasz Sarosiek ,  Elzbieta Staroslawska  (Poland);  Vladimir Kovcin ,  Zora Neskovic-Konstantinovic (Serbia) ; and  Kepa Amillano Parraga ,  Paula Gonzalez Villarroel ,  Jose Juan Illarramendi , JoseValero  Alvarez Gallego ,  Pilar Zamora Aunon (Spain).
Data management and statistical analyses Data were recorded on a case reporting form (CRF), double-entered into an electronic database and analyzed using MedCalc software (version 9. 1. 0. 1; Mariakerke, Belgium). According to the different cut-offs used to define G6PDd and using the quantitative G6PD assay as the gold standard, classical diagnostic test performance measures were determined: Sensitivity (Se), Specificity (Sp), Deficient/Positive Predictive Value (PPV) and Normal/Negative Predictive Value (NPV)  [28]. Test performances of PoCs were also compared between samples collected from capillary/venous blood, and from capillary/white cell depleted venous blood. The mean, median, standard deviation and ranges were determined for all G6PD enzyme activity values by gender and type of blood sample (capillary, venous, white cell depleted venous).
In this group, no false normal was recorded. This is a considerable improvement on the previous generation test, which demonstrated lower sensitivity and unacceptably high risk of diagnosing false 'normal' status to several severe class II variants  [12]. The reason for testing for patients for G6PDd is to identify those who may be at risk of PQ induced haemolysis. This risk is greatest in those with lower G6PD enzyme activities who will receive PQ for radical cure  [3, [30] [31] [32]. The current recommended dose for vivax ('Chesson' strain) is 0. 5 mg/kg (30 mg in an adult) daily for 14 days in G6PD normal individuals. Data from G6PD deficient Cambodian airmen showed that 15 mg of daily primaquine resulted in mean fall in Hb of 3 g/dl on Day 7. Their G6PD activities were generally between 4-11% of normal; thus, most had severe class II G6PDd  [4].
Acknowledgments Disclaimer: The opinions expressed herein are those of the author(s) and do not necessarily reflect the views of the Centers for Disease Control and Prevention or the  U. S. Agency for International Development. We thank all individuals from villages in Pailin province for participating in the study and health workers and the staff of the  Ministry of Health of Cambodia  for their collaboration. We are also grateful to  Young Choi ,  Hyeonsuk Kim  and  Tae Hee Koo  from  Access Bio  for providing the CareStart G6PD RDT.
4 M NaCl, pH 7. 86) completed with buffer A (40 mM NaH 2 PO 4 pH 7. 86). Proteins and phosphorylated proteins were detected at 210 nm and 280 nm. The ratio of phosphorylated and to non-phosphorylated proteins was determined by the integration of peaks using the program System Gold (version 1990). The determination of phosphorylated residues was assessed using MALDI-TOF mass spectrometry on tryptic fragments of both C-HsCentrins 1 and 2. All proteins (wild type and variants) were first tested for their stability in buffer (50 mM Tris-HCl pH 7. 5, 10 mM MgCl 2 , 100 mM NaCl, 0. 02% NaN 3 ) at 37 °C for 40 h, in the presence or absence of 2 mM CaCl 2.