dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis of Performance Analyses were conducted using SAS 9. 4 (SAS Institute, Inc. , Cary, NC, USA). The device's rDR and vtDR output sensitivity, specificity, negative and positive predictive value, and their 95% CIs were calculated, based on exact binomial distribution, at set points, 0. 37 for rDR and 0. 329 for vtDR, chosen as high sensitivity set points, in order to compare with the published IDP,  1  which was also set at high sensitivity. We test the differences in sensitivity and specificity of the IDx-DR X2. 1 device and IDP using Fisher's exact test. Sensitivity for detecting vtDR and ME was also calculated for the device's rDR output. The AUC for rDR and vtDR indices were determined by logistic regression (SAS PROC Logistic) from the adjudicated reference standard. 28, 29 We compared rDR AUC with the theoretical maximum AUC of 0.
615), but its specificity, at 87. 0%, was significantly better than that of IDP (P value < 0. 0001). The 6/874 false negatives are shown in Figure  2. For the device's vtDR output, sensitivity to detect vtDR was 100. 0% (95% CI: 96. 1%-100. 0%) and specificity was 90. 8% (95% CI: 88. 5%-92. 7%), resulting in a negative predictive value of 100. 0% (95% CI 99. 5%-100. 0%), and positive predictive value of 56. 4% (95% CI 48. 4%-64. 1%). The AUC for the device's vtDR index to detect vtDR was 0. 989 (95% CI: 0. 984-0. 994; Fig. 1 ). We previously reported that the theoretical maximum AUC measurable on this specific dataset and reference standard has a 95% CI of 0. 939 to 0. 972, which thus overlaps with the 95% CI of 0. 968 to 0. 992 of the measured AUC for the device's rDR index. 1, 18 hirty-four subjects (4%) had at least one image that was deemed insufficient by the quality algorithm run outside the device.
DISCUSSION The results show that a device to detect rDR, where standard Gaussian derivatives and similarly designed, lowest level   features are replaced by lesion specific trained CNNs, performs significantly better, over a range of performance balances. The device with CNN based detectors, IDx-DR X2. 1, achieved a high performance of 96. 8% sensitivity and 87. 0% specificity to detect rDR. All false negatives had moderate NPDR according to ICDR (i. e. , the lowest level of referable DR), thus the sensitivity to detect vtDR and ME were both 100% (Fig. 2 ), showing all of the images showing moderate NPDR that were missed by the device's rDR output. The device's AUC 95% CI ranged from 0. 968 to 0. 992, which overlaps with the 95% CI of 0. 939 to 0. 972 for achievable AUC given the Messidor-2 dataset and the associated reference standard with three readers.
The prevalence of vtDR in the first year of running a screening program can be as high as 30% to 40%, as this is primarily prevalence (number of existing cases that had developed over the years without screening), not incidence (number of new cases that develop in a year). After the vtDR cases have been 'caught' in the first year, in the following years the number of cases of vtDR will reflect only the much lower incidence, depending on the presence of risk factors in the population. It is of utmost importance that DR detection algorithms operate well within these ranges of prevalence, including inner city populations at high risk that have never had screening. Advantages of this study are that it was performed on the publicly available Messidor-2 dataset,  1, 23  and a published adjudicated standard of three experts, that is now available in the public domain at http://www.
medicine. uiowa. edu/eye/ abramoff. For the device, some of the IDP's classic feature based lesion detectors were replaced by CNN based lesion detectors, leading to significantly better performance in a hybrid architecture. The device was never trained on any of the Messidor-2 images. Other groups have used full image-based CNNs (i. e. , the CNN is trained with only complete retinal images), lacking explicit lesion detection,  24  and in the Introduction we explained the unanticipated, but undetected, associations that may result in. The study has potential limitations. The purpose was to determine whether the use of deep learning techniques for DR detection results in performance improvements compared with a high performing classic, nondeep learning algorithm on a standardized dataset, that has been previously published.
1 he purpose was not to determine real world performance of the device or other DR detection algorithms. Real world performance can only be adequately determined from a prospective study on people with diabetes and standardized reading. Though Messidor-2 contains 93 cases of vtDR according to the present reference standard, of which only eight have neovascularizations on the disc or elsewhere (i. e. , PDR),  34  and two of eight have isolated PDR without DME. Thus, this study is underpowered to determine the detection performance of isolated PDR without ME-though none were missed by the device. The Messidor-2 dataset used for this comparison study consists of high quality retinal images,  9  which are not necessarily a good representation of data from screening programs, generally, and certainly not reflective of the quality of images that are seen in the non-eye care settings where screening algorithms have the potential to deliver their biggest impact.
24 The ability to detect an ungradeable image is an important component when assessing the capabilities of a device for automated detection of diabetic retinopathy in the real world. Because of the relatively high quality of the images in Messidor-2, only a small number (4%) would have had an insufficient image quality output if the protocol had been complete. Thus, while Messidor-2 is a dataset that is useful in measuring performance of an algorithm on high quality exams, or comparing it with other algorithms, as in the present study, it is not sufficient to establish an algorithm's performance in broader clinical use. In addition, Messidor-2 images contain a single image per eye, limiting the area of retina covered. Many screening programs,  9  and algorithms such as the device, are designed with two images per eye, one fovea centered and one disc centered, leading to a larger area of retina examined.
Using two or more images per eye, algorithms as well as human experts may find additional cases of DR not visible on the single image, 8 leading to different measured performance. Similarly, in the real world, reference standards often differ, depending on the characteristics of the clinicians reading the images and how many are involved in reading and how consensus is reached. For example, the ME reference standard was graded from the retinal images, which lack stereo, and no optical coherence tomography (OCT) was available. This implies that isolated retinal thickening cannot be appreciated,  7  though human expert detection of ME from exudates only, in single images, may be almost as sensitive as clinical stereo biomicroscopic analysis of retinal thickening. 35 Thus, DR and ME prevalence and severity may be underestimated in this dataset, and a different reference standard could lead to differences in a device's measured algorithmic performance.
2, 41 n summary, the IDx-DR X2. 1 device has achieved significantly better performance to detect referable DR, quantifiable by a 30% increase in specificity at the high reported sensitivity of 0. 97 on a public dataset, using deep learning, through the use of CNN-based lesion detectors in a hybrid architecture. Deep-learning enhanced systems for automated detection of DR, thus have the potential to improve the efficiency and accessibility of DR screening, and thereby to prevent visual loss and blindness from this devastating disease.
Acknowledgments The authors thank  J. C. Klein, MD ,  Pascale Massin, MD ,  Beatrice Cochener, MD, PhD , and  Philippe Gain, MD  for organizing the Messidor study and creating the Messidor-2 resource, as well as  Dennis P. Han, MD ,  Jonathan D. Walker, MD  and  David F. Williams, MD  for creating the reference standard for Messidor-2. Supported by grants from  IDx LLC (Iowa City, IA, USA) ,  Research to Prevent Blindness (New York, NY, USA ). MDA is recipient of the Robert C. Watzke Endowment. This material is the result of work supported with resources and the use of facilities at the  Iowa City Veterans Affairs Medical Center. Disclosure: M. D. Abràmoff, IDx LLC (C, F, I, R, S), Research to Prevent Blindness (F) , P;  Y. Lou , None;  A. Erginay , None;  W. Clarida ,  IDx LLC (E, I) ;  R.
Acknowledgements This work wass upported by the  United Kingdom Engineering and Physical SciencesR esearch Council (EPSRC)  (grant  EP/ K502996/1 ). Mass spectrometric analysis was performed on instrumentation bought through the  Core Capability for Chemistry Research -S trategic Investment in Mass Spectrometry EPSRC grant  ( EP/K03927X/1 ). The facilities and staff of the Mass Spectrometry service and the NMR service at the  Universityo f Bristol ,a sw ell as computing resources from the  Centre for Computational Chemistry ,a re kindly acknowledged. In particular,w ew ould like to thank  Dr Craig Butts  for discussing the NMR data. We also thank the  EPSRC NationalS ervice for Electron Paramagnetic Resonance Spectroscopy at the University of Manchester ,f undedu nder  EPSRC  grant  NS/A000014/1.
Trends in height for the adult populations of selected countries in Asia. The solid line represents the posterior mean and the shaded area the 95% credible interval of the estimates. The points show the actual data from each country, together with its 95% confidence interval due to sampling. The solid line and shaded area show estimated height at 18 years of age, while the data points show height at the actual age of measurement. The divergence between estimates and data for earlier birth cohorts is because participants from these birth cohorts were generally older when their heights were measured. DOI: 10. 7554/eLife. 13410. 008 mid-1990s  (Stevens et al. , 2012). Our results indicate that such childhood adversity may have carried forward to adulthood and be affecting health in the region.
We achieved this by using a large number of population-based data sources from all regions of the world. We put particular emphasis on data quality and used only population-based data that had measured height, which avoids bias in self-reported height. Data were analysed according to a common protocol before being pooled, and characteristics and quality of data sources were verified through repeated checks by Collaborating Group members. Finally, we pooled data using a statistical model that could characterize non-linear trends and that used all available data while giving more weight to national data than to subnational and community surveys.
Serbia • • • • • • • • • • • • • • • • • • • • • • • • • • • Women Birth cohort Figure 7. Trends in height for the adult populations of selected countries in Europe. The solid line represents the posterior mean and the shaded area the 95% credible interval of the estimates. The points show the actual data from each country, together with its 95% confidence interval due to sampling. The solid line and shaded area show estimated height at 18 years of age, while the data points show height at the actual age of measurement. The divergence between estimates and data for earlier birth cohorts is because participants from these birth cohorts were generally older when their heights were measured. DOI: 10. 7554/eLife. 13410. 009 Although we have gathered an unprecedentedly comprehensive database of human height and growth, and have applied a statistical model that maximally utilizes the information in these sources, data in some countries were rather limited or were from community or sub-national studies.
Iran • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Women Birth cohort Figure 8. Trends in height for the adult populations of selected countries in the Middle East, North Africa, and sub-Saharan Africa. The solid line represents the posterior mean and the shaded area the 95% credible interval of the estimates. The points show the actual data from each country, together with its 95% confidence interval due to sampling. The solid line and shaded area show estimated height at 18 years of age, while the data points show height at the actual age of measurement. The divergence between estimates and data for earlier birth cohorts is because participants from these birth cohorts were generally older when their heights were measured.
Data sources We used data sources that were representative of a national, subnational, or community population and had measured height. We did not use self-reported height because it is subject to systematic bias that varies by geography, time, age, sex, and socioeconomic characteristics like education and ethnicity  (Engstrom et al. , 2003; Connor Gorber et al. , 2007; Wetmore and Mokdad, 2012; Schenker et al. , 2010; Ezzati et al. , 2006; Clarke et al. , 2014; Hayes et al. , 2011). measured data on height, weight, waist circumference, or hip circumference were available;. study participants were five years of age and older;. data were collected using a probabilistic sampling method with a defined sampling frame;. data were representative of the general population at the national, subnational, or community level;.
data were from the countries and territories listed in Supplementary file 1. We excluded data sources on population subgroups whose anthropometric status may differ systematically from the general population, including:. studies that had included or excluded people based on their health status or cardiovascular risk;. ethnic minorities;. specific educational, occupational, or socioeconomic subgroups of the population; and. those recruited through health facilities, with the exception noted below. We used school-based data in countries where secondary school enrolment was 70% or higher, and used data whose sampling frame was health insurance schemes in countries where at least 80% of the population were insured. We used data collected through general practice and primary care clinics in high-income countries with universal insurance, because contact with the primary care systems tends to be at least as good as response rates for population-based surveys.
No studies were excluded based on the level of height. We used multiple routes for identifying and accessing data. We accessed publicly available population-based multi-country and national measurement surveys (e. g. , Demographic and Health Surveys, and surveys identified via the Inter-University Consortium for Political and Social Research and European Health Interview & Health Examination Surveys Database) as well as the World Health Organization (WHO) STEPwise approach to Surveillance (STEPS) surveys. We requested identification and access to population-based data sources from ministries of health and other national health agencies, via WHO and its regional offices. Requests were also sent via the World Heart Federation to its national partners. We made a similar request to the NCD Risk Factor Collaboration (NCD-RisC; www.
ncdrisc. org), a worldwide network of health researchers and practitioners working on NCD risk factors. To identify major sources not accessed through the above routes, we searched and reviewed published studies. Specifically, we searched Medline (via PubMed) for articles published between 1st January 1950 and 12th March 2013 using the search terms 'body size  '[mh:noexp]   Articles were screened according to the inclusion and exclusion criteria described above. The number of articles identified and retained is summarised in Supplementary file 2. As described above, we contacted the corresponding authors of all eligible studies and invited them to join NCD-RisC. We did similar searches for other cardio-metabolic risk factors including blood pressure, serum cholesterol, and blood glucose. All eligible studies were invited to join NCD-RisC and were requested to analyse data on all cardio-metabolic risk factors.
Anonymised individual record data from sources included in NCD-RisC were re-analysed by the Pooled Analysis and Writing Group or by data holders according to a common protocol. All re-analysed data sources included mean height in standard age groups (18 years, 19 years, 20-29 years, followed by 10 year age groups and 80+ years), as well as sample sizes and standard errors. All  analyses incorporated appropriate sample weights and complex survey design when applicable. To ensure summaries were prepared according to the study protocol, the Pooled Analysis and Writing Group provided computer code to NCD-RisC members who requested assistance. We also recorded information about the study population, period of measurement and sampling approach. This information was used to establish that each data source was population-based, and to assess whether it covered the whole country, multiple subnational regions, or one or a small number of communities, and whether it was rural, urban, or combined.
All submitted data were checked by at least two independent members of the Pooled Analysis and Writing Group to ensure that their sample selection met the inclusion criteria and that height was measured and not self-reported. Questions and clarifications about sample design and measurement method were discussed with the Collaborating Group members and resolved before data were incorporated in the database. We also extracted data from additional national health surveys, one subnational STEPS survey, and six MONICA sites from published reports. We identified duplicate data sources by comparing studies from the same country and year. Additionally, NCD-RisC members received the list of all data sources in the database and were asked to ensure that the included data from their country met the inclusion criteria and that there were no duplicates.
Data sources used in our analysis are listed in Supplementary file 3. In this paper, we used data on height in adulthood (18 years of age and older) from the NCD-RisC database for participants born between 1896 and 1996. We used 1472 population-based data sources with measurements on over 18. 6 million adults born between 1896 and 1996 whose height had been measured. We did not use data from the 1860-1895 cohorts because data on these early cohorts were available for only six countries (American Samoa, India, Japan, Norway, Switzerland and USA). We had data for 179 of the 200 countries for which estimates were made; these 179 countries covered 97% of the world's population. All countries had some data on people born after 1946 (second half of analysis period); 134 had data on people born between 1921 and 1945; and 72 had data on people born in 1920 or earlier.
We used birth cohort as the time scale of analysis. We calculated the birth cohort for each observation by subtracting the mid-age of its age group from the year in which data were collected. We modelled trends in height by birth cohort as a combination of linear and non-linear trends, both with a hierarchical structure; the non-linear trend was specified using a second-order random walk  (Rue and Held, 2005). The model also included a term that allowed each birth cohort's height to change as it aged, e. g. , because there is gradual loss of height during ageing and because as a cohort ages those who survive may be taller. The model described by Finucane et al  (Finucane et al. , 2014)  had used a cubic spline for age associations of risk factor levels. In practice, the estimated change in population mean height over age was linear with a small slope of over 0.
We monitored mixing and convergence using trace plots and Brooks-Gelman-Rubin diagnostics  (Brooks and Gelman, 1998). We obtained 5000 post burn-in samples from the posterior distribution of model parameters, used to obtain the posterior distribution of mean height. The reported credible intervals represent the 2. 5th-97. 5th percentiles of the posterior distribution. We report mean height at age 18 years for each birth cohort; heights at other ages are available from the authors. All analyses were done separately by sex because height and its trends over time may differ between men and women. We tested how well our statistical model predicts missing values by removing data from 10% of countries with data (i. e. , created the appearance of countries with no data where we actually had data).
However, it is still lacking a general dynamical system framework able to relate the emerging connectivity pattern of social networks to the combined action of social actors activity and their heterogeneity in distributing resources in social capital allocation. Here we analyze seven time-resolved datasets describing three different types of social interactions: scientific collaborations, Twitter mentions, and mobile phone calls. For all network datasets we define two functions statistically encoding the instantaneous activity of nodes and the allocation of social capital, respectively. The latter function is regulated by two parameters -system dependent-that define a simple reinforcement mechanism. In particular we observe in all datasets that the larger the number of social ties already activated by each node and the smaller is the probability of creating a new tie.
We provide a thorough statistical characterization of the activity and reinforcement dynamics at play in each network and identify the basic parameters defining the dynamic of ties evolution. Prompted by this statistical analysis, we propose a dynamic network model that includes the heterogenous activity of nodes and the the tie formation mechanisms. This model allows the definition of a formal Master Equation (ME) describing the evolution of the network connectivity structure that can be solved in the asymptotic regime (large network size and long time evolution). The solution of the ME provides the asymptotic form the degree distribution and the scaling relations relating degree, activity and and the functions characterizing the social capital allocation. The analytical solutions are capturing very well the empirical behavior measured in the analyzed datasets, connecting explicitly the evolution of social networks to the parameter regulating the emergence of heterogenous social ties.
Results We analyze seven datasets containing time-stamped information about three different type of social interactions: scientific collaborations, Twitter mentions, and mobile phone calls. While we refer the reader to the Material and Methods section for the details of each data set, we represent all datasets as timevarying networks. Each node describes an individual. Each time-resolved link describes a social act. The nature of connections is different according to the specific dataset. Links might represent a collaboration resulting in a publication in a scientific journal, a Twitter mention, or a mobile phone call. We considered five scientific collaborations networks obtained from five different journals (P RA, P RB, P RD, P RE, and P RL) of the American Physical Society (APS), one Twitter mentions network (T M N ), and one mobile phone network (M P N ).
In order to characterize the time-varying properties of such networks we first measure the activity a i. Formally, a i is defined as the fraction of interactions in which node i is engaged per unit of time with respect to all the interactions per unit time occurring in the network. This quantity describes the propensity of nodes i to be involved in social interactions. Empirical measurements in a wide set of social networks show broad distributions of activity  [23, 28, 29, 16, 33]. As shown in Figure  1 [A-D],  we confirm these observations in our datasets. In particular we find that in the APS and MPN datasets the activity is well fitted by a truncated power law, while in the TMN we find a Log-Normal distribution (see Material and Methods and Supplementary Online Materials for details).