dataesr/scientific-paragraphs-categorization huggingface dataset:
Converting scanner-generated pixel values to standardized gray-scale values makes calibrated data independent of variation in scanner properties. Such variation may be caused by production-linked scanner-to-scanner variability or deterioration of properties over time (aging of lamps, scratches etc. ) or differences between scanner types or brands. Here, scanners were maintained in a 30°, high humidity environment in a thermostat controlled room and kept covered by boxes, preventing inflow of light. See File S1, sections 1 and 8, for a complete description of the physical arrangement. Scan-o-matic software, image acquisition, and analysis Scan-o-matic is written in Python 2. 7 and can be installed from https:// github. com/local-minimum/scanomatic/wiki. Matplotlib is used for graph production.
2016). Here, we showed that Scan-o-matic nicely captures the known salt biology of S. cerevisiae, largely uncovering the same cellular functions as earlier found to be important by microcultivation  (Warringer et al. 2003). However, we also firmly established the context-dependence of the salinity response with several deletion mutants displaying rather distinct effects between agar-or liquid-growth cultivation. This provides stark arguments for taking the growth regime into account when comparing phenotypes obtained using different platforms and when annotating genes in phenotype databases. Surprisingly, more and stronger phenotypic interactions were identified on agar compared to liquid microcultivation. This raises the question of what is the most natural lifestyle for S. cerevisiae: being dispersed in liquid media or grown as microcolonies on solid substrates? S.
The validity of the data can be improved either by increasing precision, i. e. , reducing random noise, or by reducing bias. Reductions in random noise would be achievable by changing from 8to 16-bit image depth. However, 16-bit image depth increases file size considerably, and given the vast amount of hard-disc space taken by the files currently generated from one single experiment ( 5-10 Gb) this would pose a serious challenge to the logistics of data storage and analysis. Analyses of 16-bit image depth data are also poorly supported in the Python packages upon which Scan-o-matic is based. This is not a trivial challenge to overcome. Second, precision could be improved by increasing image resolution. This would ensure that each colony is represented by a larger number of pixels, reducing both the stochastic noise originating in errors in assigning pixels to colonies and the noise deriving from true colony edges going through, rather than following the boundaries of, pixels.
ACKNOWLEDGMENTS We thank  Olle Nerman  and  Mats Kvarnström  for much appreciated statistical and analytical support, and  Charlie Boone  for access to strains. Financial support from the  Swedish Research Council  ( 325-2014-6547  and  621-2014-4605 ) and from the  Carl Trygger Foundation  ( CTS 12:521 ) to J. W. , from the  Swedish Research Council Forskningsrådet  för miljö, areella näringar och samhällsbyggande (FORMAS) to A. B. , from the  Slovenian Research Agency  ( P1-207  and  L2-1112 ) to U. P. and P. K. , from  Research Council of Norway  ( 222364/F20 ) to S. W. O. , and from  Labex SIGNALIFE  ( ANR-11-LABX-0028-01 ) to J. H. , is acknowledged.
Methods We estimated incidence and prevalence by age, sex, cause, year, and geography with a wide range of updated and standardised analytical procedures. Improvements from GBD 2013 included the addition of new data sources, updates to literature reviews for 85 causes, and the identifi cation and inclusion of additional studies published up to November, 2015, to expand the database used for estimation of non-fatal outcomes to 60 900 unique data sources. Prevalence and incidence by cause and sequelae were determined with DisMod-MR 2. 1, an improved version of the DisMod-MR Bayesian meta-regression tool fi rst developed for GBD 2010 and GBD 2013. For some causes, we used alternative modelling strategies where the complexity of the disease was not suited to DisMod-MR 2. 1 or where incidence and prevalence needed to be determined from other data.
Geographies in GBD 2015 The geographies included in GBD 2015 have been arranged into a set of hierarchical categories composed of seven super-regions and a further nested set of 21 regions containing 195 countries and territories. Eight additional subnational assessments were done for Brazil, China, India, Japan, Kenya, Saudi Arabia, South Africa, Sweden, and the USA (methods appendix pp 611 -24). For this study we present data at the national and territory level. See Online for appendix.
Data sources For this iteration of the study, we updated data searches through systematic data and literature reviews for 85 causes published up to Oct 31, 2015. For other causes, input from GBD collaborators resulted in the identifi cation and inclusion of a small number of additional studies published after January, 2013. Data were systematically screened from household surveys archived in the Global Health Data Exchange, sources suggested to us by in-country experts, and surveys identifi ed in major multinational survey data catalogues and Ministry of Health and Central Statistical Offi ce websites. Case notifi cations reported to WHO were updated up to and including 2015. Citations for all data sources used for nonfatal estimation in GBD 2015 are provided in searchable form through a new web tool.
A description of the search terms used for cause-specifi c systematic reviews, inclusion and exclusion criteria, and the preferred and alternative case defi nitions and study methods are detailed by cause in the methods appendix (pp 26-601). Hospital inpatient data were extracted from 284 country-year and 976 subnational-year combinations from 27 countries in North America, Latin America, Europe, and New Zealand. Outpatient encounter data were available from the USA, Norway, Sweden, and Canada for 48 country-years. For GBD 2015, we also accessed aggregate data derived from claims information in a database of US private and public insurance schemes for the years 2000, 2010, and 2012. From the linked claims data, we generated several correction factors to account for bias in health service encounter data from elsewhere, which were largely available to us aggregated by ICD code and by primary diagnosis only.
First, for chronic disorders, we estimated the ratio between prevalence from primary diagnoses and prevalence from all diagnoses associated with a claim. Second, we used the claims data to generate the average number of outpatient visits per disorder. Similarly, we generated per person discharge rates from hospital inpatient data in the USA and New Zealand, the only sources with unique patient identifi ers available for GBD 2015. In GBD 2013, we calculated a geographical and temporal data representativeness index (DRI) of non-fatal data sources for each cause or impairment. The DRI represents the fraction of countries for which any incidence, prevalence, remission, or mortality risk data were available for a cause. This metric quantifi es data availability, not data quality. The overall DRI and period-specifi c DRI measures for each cause and impairment are presented in the methods appendix (pp 662-68).
Non-fatal disease models In addition to the corrections applied to claims and hospital data, a number of other adjustments were applied including age-sex splitting, bias correction, adjustments for under-reporting of notifi cation data, and computing expected values of excess mortality. In GBD 2013, we estimated expected values of excess mortality from prevalence or incidence and cause-specifi c mortality rate data for a few causes only, including tuberculosis and chronic obstructive pulmonary disease. In order to achieve greater consistency between our cause of death and non-fatal data, we adopted this strategy systematically for GBD 2015. We matched every prevalence data point (or incidence datapoint for short duration disorders) with the cause-specifi c mortality rate value corresponding to the age range, sex, year, and location of the datapoint.
The ratio of cause-specifi c mortality rate to prevalence is conceptually equivalent to an excess mortality rate. To estimate non-fatal health outcomes in previous iterations of GBD, most diseases and impairments were modelled in DisMod-MR, a Bayesian meta-regression tool originally developed for GBD 2010 (step 3a in fi gure 1). 18 isMod-MR was designed to address statistical challenges in estimation of non-fatal health outcomes, and for For Global Health Data Exchange see http://ghdx. healthdata. org For data in GBD 2015 see http:// ghdx. healthdata. org/globalburden-disease-study-2015 For data visualisations at vizhub see http://vizhub. healthdata. org/gbd-compare synthesis of often sparse and heterogeneous epidemiological data. For GBD 2015, the computational engine of DisMod-MR 2. 1 remained unchanged, but we substantially rewrote the code that organises the fl ow of data and settings at each level of the analytical cascade.
Step 3b of fi gure 1 describes the development of custom models with greater detail shown in the methods appendix fi gure 1B (p 604, and for associated write-ups pp 26-601) for HIV/AIDS, tuberculosis, malaria, cancer, neonatal disorders, infectious diseases for which we derived incidence from seroprevalence data, and infectious diseases for which we derived incidence from cause of death rates and pooled estimates of the case fatality proportion. In GBD 2013, we estimated the country-age-sex-year prevalence of nine impairments (step 4 of fi gure 1). Impairments in GBD are disorders or specifi c domains of functional health loss that are spread across many GBD causes as sequelae and for which there are better data to estimate the occurrence of the overall impairment than for each sequela based on the underlying cause.
Severity distributions In step 5, sequelae were further defined in terms of severity for 194 causes at Level 4 of the hierarchy (fi gure 1A). We generally followed the same approach for estimating the distribution of severity as in GBD 2013. For Ebola virus disease, we created a health state for the infectious disease episode with duration derived from average hospital admission times, and a health state for ongoing postinfection malaise and joint problems based on four follow-up studies  [19] [20] [21] [22]  from which we derived an average duration. The health states for the subtypes of leukaemia and non-melanoma skin cancer were the same as the general cancer health states. For motorneuron disease we accessed the Pooled Resource Open-Access ALS Clinical Trials (PROACT) database containing detailed information on symptoms and impairments for more than 8500 patients who took part in the trials.
Comorbidity In step 7, we estimated the co-occurrence of diff erent diseases by simulating 40 000 individuals in each geography-age-sex-year combination as exposed to the independent probability of having any of the sequelae included in GBD 2015 based on disease prevalence. We tested the contribution of dependent and independent comorbidity in the US Medical Expenditure Panel Surveys (MEPS) data, and found that independent comorbidity was the dominant factor even though there are well known examples of dependent comorbidity. Age was the main predictor of comorbidity such that age-specifi c microsimulations accommodated most of the required comorbidity correction. Taking dependent comorbidity into account changed the overall YLDs estimated in the MEPS data by only 2•5% (and ranging from 0•6% to 3•4% depending on age) in comparison to assuming independent comorbidity (methods appendix pp 18-20).
YLD computation We report 95% uncertainty intervals (95% UI) for each quantity in this analysis using 1000 samples from the posterior distribution of prevalence and 1000 samples of the disability weight to generate 1000 samples of the YLD distribution. The 95% UI is reported as the 25th and 975th values of the distribution. We report signifi cant changes in disease estimates between countries or over time if the change was noted in more than 950 of the 1000 samples computed for each result. For GBD 2015, we computed age-standardised prevalence YLD rates from the updated world population age standard developed for GBD 2013. 25 ess common diseases and their sequelae were included in 35 residual categories (methods appendix pp 695-97). For 22 of these residual categories, estimates were made from epidemiological data for incidence or prevalence.
Diabetes To obtain standard estimates for health loss due to diabetes for GBD 2015 using both fasting plasma glucose (FPG) means and standard deviations as well as diabetes prevalence, we re-extracted all available data from 1990 to 2015. Across studies, we found 20 diff erent case defi nitions for diabetes. We also included, wherever possible, studies reporting on mean FPG but not diabetes prevalence, using a regression between mean FPG and diabetes prevalence from studies reporting on both. We have also made the assessment of diabetes prevalence more consistent with cause of death data for diabetes. Our improved eff orts at measuring the prevalence of diabetes confi rms the increase in global age-standardised incidence, prevalence, and YLDs. Of note, the increase in YLDs at the global scale is greater than the increase in YLLs, which refl ects improved access to treatments that lower case fatality.
Disability weights For GBD 2015, we have not incorporated any new population-based data on disability weights; we have used the same disability weights as for GBD 2013. The present set of disability weights were based on population surveys in nine countries and an open internet survey. So far, these studies have not found systematic variation in disability weights across populations or within populations as a function of education, income per capita, or other variables. In future, we hope that more population surveys on disability weights will continue to be done to enrich the database for disability weight estimation. Given that we have not been able to collect disability weights in every location included in the GBD, it is possible that disability weights might vary systematically across communities.
GATHER compliance Providing all documentation for data sources, establishing access to the datasets used in modelling, and posting the code has been a labour-intensive activity; GBD 2015 is compliant with the new GATHER guidelines as a result. 17 osting code, we believe, will stimulate other researchers to explore the methods used in GBD estimation of nonfatal outcomes and hopefully lead to suggestions for improved estimation. With a steadily growing set of coinvestigators and a widening community interested in global health estimation, the enhanced transparency of GBD will improve the effi cacy of the overall eff ort.
Using claims data For the USA we used claims data for selected disorders. These data were particularly useful in the estimation of state-by-state prevalence for some disorders. Claims data, however, have important potential biases. Because of exclusion of some disadvantaged groups from health insurance, disease rates might be diff erent for individuals that are covered compared with those from the general population. 69, 70 As the claims dataset included information from Medicaid and Medicare, the coverage schemes for low-income citizens and older adults (older than 65 years) in the USA, the bias toward underestimation might not be so great. Indeed, we found that for many diseases, such as asthma, diabetes, or rheumatoid arthritis, the claims data were consistent with high-quality survey estimates.
Crosswalks Because of the diversity of data sources available, we estimated the statistical association between measurements taken using diff erent case defi nitions, diff erent assays, diff erent survey items, or diff erent ascertainment methods to a reference category. We used these statistical associations to crosswalk each type of data to the reference category. Crosswalks are a crucial dimension in the GBD analysis; in most cases we estimate these crosswalks from within DisMod-MR 2. 1. If crosswalks were believed to vary substantially by age, sex, or location, these crosswalks were estimated outside of DisMod-MR 2. 1 and adjusted data were used as inputs. Where data are sparse, the estimated crosswalks can shift substantially when new studies are identifi ed that inform the crosswalk.
Limitations The GBD 2015 study has some key limitations. First, although we have sought to capture and include in our estimations many sources of uncertainty, we have not captured all sources. We have not been able to routinely capture uncertainty due to diff erent model specifi cations in our results. We do not have the ability to refl ect the uncertainty of data sources that exist but of which we are not aware. Subnational collaborations in China and Mexico revealed many data sources not captured in previous iterations of the GBD study. Inclusion of these data sources can lead to shifts in estimates that are outside the previously estimated 95% UIs. Second, within DisMod-MR 2. 1 we estimate the average association between diff erent types of studies for reporting on a specifi c outcome such as the diff erence between 12-month recall and point prevalence in a survey.
Apparatus The visual stimuli were presented with 85Hz (exp. 1 and 2) or 100 Hz (exp. 3 and 4) CRT monitors, 15,6"large, placed at approximately 85cm from the participant in a dark room. Chin fixation was applied in order to keep the distance between the eyes and the screen constant and to guarantee that stimuli arrived at the same retinal location for both the task and stimulation. Each face stimulus subtended 7. 3 x 4. 6 degrees of visual angle (vertically and horizontally respectively). Stimuli were presented bilaterally and were equidistant from the central fixation cross of approximately 2. 1 degrees. Responses were recorded with a standard keyboard. Psychtoolbox  [53, 54]  a free toolbox of Matlab (The MathWorks Inc. , Natick, MA, 2000) was used to deliver stimuli for the task and stimulation.
F. P. was funded by  KU Leuven University  ( ZKC5610 ,  F+/12/029 ,  3H120620 ) and  FWO (Fonds Wetenschappelijk Onderzoek) postdoc fellowship  ( 12Q4615N ). H. O. B. was supported by  ERC -2011-Stg-284101  and  IUAP P7/11. B. B. , J. S. and H. O. B. were supported by  Interdisciplinary Research Fund  ( IDO/10/003 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Air mass classification has become an important area in synoptic climatology, simplifying the complexity of the atmosphere by dividing the atmosphere into discrete similar thermodynamic patterns. However, the constant growth of atmospheric databases in both size and complexity implies the need to develop new adaptive classifications. Here, we propose a robust unsupervised and supervised classification methodology of a large thermodynamic dataset, on a global scale and over several years, into discrete air mass groups homogeneous in both temperature and humidity that also provides underlying probability laws. Temperature and humidity at different pressure levels are aggregated into a set of cumulative distribution function (CDF) values instead of classical ones. The method is based on a Gaussian mixture model and uses the expectation-maximization (EM) algorithm to estimate the parameters of the mixture.
Spatially gridded thermodynamic profiles come from ECMWF reanalyses spanning the period 2000-2009. Different aspects are investigated, such as the sensitivity of the classification process to both temporal and spatial samplings of the training dataset. Comparisons of the classifications made either by the EM algorithm or by the widely used k-means algorithm show that the former can be viewed as a generalization of the latter. Moreover, the EM algorithm delivers, for each observation, the probabilities of belonging to each class, as well as the associated uncertainty. Finally, a decision tree is proposed as a tool for interpreting the different classes, highlighting the relative importance of temperature and humidity in the classification process.
This paper focuses on the classification of a large dataset of "atmospheric columns" homogeneous in both temperature and humidity on a global spatial grid and at different times. Such entities are closely related to the notion of air mass, and as such, will be mentioned by this term from now on. They are of great importance, in particular in inverse problems where climate variables as well as the threedimensional structure of the atmosphere can be estimated from satellite data via inverse radiative transfer models (e. g. Chédin and Scott, 1985; Scott et al. , 1999). Such models need to be initialized by a priori information as thermodynamic profiles and surface variables. For that purpose, real profiles and variables are usually considered, in particular those coming from radiosonde reports, as the thermodynamic Initial Guess Retrieval (TIGR) dataset  (Chédin et al.
, 1985; Chevallier et al. , 2000). With the technological advance of satellite instruments, such databases need to be enhanced in both sampling and air mass classification. Furthermore, adding prior information directly to the data leads to Bayesian statistics, in which the knowledge of the distribution of each statistic variable is essential, hence a probability point of view adopted here. Over the past year, use of symbolic data has gained popularity  (Bock and Diday, 2000; Diday, 2001; Floriana and Diday. , 2003; Billard and Diday, 2012) , since, as principal component analysis (PCA), they turn large databases into summarized data with manageable size while keeping useful information through a process commonly called "data aggregation" or "data compression". This process is often necessary as a step of pre-processing before any classification procedure.
Symbolic data change the way in which the description of the data is viewed, since they refer to data which do not only contain values as in classical data, but also have a structure and include internal variations. This is the case with distributions, considered by  Schweizer (1984)  as the "the numbers of the future", such as probability density functions (PDFs) or cumulative distribution functions (CDFs). In the present work, CDFs are used in combination with a probabilistic classification method based on a Gaussian mixture model that has been used for example in  Vrac et al. (2007) ,  Rust et al. (2010)  or  Carreau and Vrac (2011). Such a model relies on the assumption that observations from a given dataset come from several sub-populations and that the overall population can therefore be modelled as a Gaussian finite mixture model, or in other words a mixture of weighted PDFs, each one corresponding to a given sub-population  (Fraley and Raftery, 2002).
The algorithm chosen for this purpose, initialized by a partition based on seven zonal clusters, was a dynamical clustering method (clustering approach). As a first step, the results were validated only for a limited dataset of 1 winter day and shortrange projections not exceeding 1 month. Setting aside copula, this paper aims at consolidating the results of  Vrac (2002)  and  Vrac et al. (2005 Vrac et al. ( , 2011) )  by proposing a robust air mass classification method based on a Gaussian mixture model and the EM algorithm (estimation approach). This method is able to deal with a much larger dataset covering a decade and providing larger-range projections without having to use arbitrary a priori information as an initialization strategy in the absence of a prior reference atmospheric column type classification.