dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical Implementation Mesh Generation. The creation of computational grids able to reproduce the patient-specific brain geometry without exceeding in the computational costs is a challenging task. The first step to generate a patient-specific computational mesh is the medical image segmentation, which is the process of identifying and labeling regions of interest within an image. To generate the anatomical mesh of the brain and tumor we use the post-contrast T1-MR sequence (Fig 2  (A) ). Using an expectation maximization approach  [66]  implemented in the open source software package 3D Slicer  [67] , the anatomical structures are automatically segmented and the four areas of interest (i. e. gray matter, white matter, cerebrospinal (CSF) fluid and background) are identified and labeled.
The segmented image obtained is depicted in Fig  2(B ). Once the brain segmentation is done, we manually segment the GBM region, since voxels occupied by the tumor have an intensity comparable to ones occupied by grey matter an automatic process cannot be implemented. After the creation of the brain labeled map and the identification of the tumor region, the computational mesh is obtained first by extracting the external brain surface using the marching cube algorithm, then operating a Taubin surface smoothing and a uniform mesh refinement with the scripts implemented in The Vascular Modeling Toolkit (www. vmtk. org) [68]. Thirdly, we build the tetrahedral mesh using the TetGen library  [69]. The mesh is then refined near the area of interest (e. g. in the region in which the tumor grows) in order to control the numerical error without exceeding in computational costs.
After the mesh refinement, we assign the information contained in the labeled map to the computational grid in order to obtain a labeled mesh. This procedure is implemented in Python using the Visualization Toolkit (www. vtk. org) library. Finite Element discretization. Once the brain mesh is created, it is possible to proceed to the spatio-temporal discretization of the system Eqs (  5 ) and  (6). In particular, we perform a spatial discretization with linear tetrahedron P 1 elements and a time discretization with the Crank-Nicholson algorithm  [70]. Once the equations are discretized, they can be easily implemented using the open source software FEniCS  [71] , using Python as programming language. The only required mathematical trick is to rephrase the fourth-order Eq (6) as the following second-order equations @ @t À r Á ðKðÞrSÞ À nð n n s À dÞð1 À Þ þ r Á ðk n TrnÞ ¼ 0 ð10Þ S ¼ f ðÞ À 2 D: ð11Þ One of the main advantage of using FEniCS as computational resource is that it offers builtin classes and an automatic approach to nonlinear variational problems.
The physiological oxygen concentration n s has been evaluated to be in the range 0. 07-0. 28 mM in  [81]. Unfortunately, data on the chemotactic coefficient k n of glioma cells in response to oxygen concentration are not present in literature and we had to refer to the typical chemotactic coefficient found for bacterial cells in response to glucose. Finally,  [13]  reported a Young's Modulus E for both grey matter and white matter of about 694 Pa.
Then, we discretized the resulting system of Eqs (  5 ) and (  6 ) using the finite elements method and we used the open-source software FEniCS  [71]  to develop the numerical codes. Considering simplified conditions, we performed the sensitivity analysis of the model with respect to the biological parameters appearing in the governing equations to test their influence on the anisotropic growth of the tumor. We have found that both the chemotactic coefficient and the ratio between the nutrient supply and the consumption rate have a huge influence on the anisotropic growth of the tumor. The latter, indeed, determines not only the availability of nutrients in the environment but also cellular proliferation and migration, leading to an anisotropic cancer expansion in the case of low S n /δ n ratios.
1 ; : : : ; n / D d g. 1 ; : : : ; n / A. M. was supported by the Sloan Foundation and the NSF through grant DMS 1303389. D. O. was supported by the Sloan Foundation and the NSF through grants DMS 1001486 and DMS 1150675. R. P. was supported by the Swiss National Science Foundation and the European Research Council through grants SNF-200021-143274 and ERC-2012-AdG-320368-MCSK. A. P. was supported by the Clay Foundation. D. Z. was supported by the Agence Nationale de la Recherche through grant ANR-09-JCJC-0104-01. Brought to you by | MIT Libraries Authenticated Download Date | 5/29/18 7:01 PM.
, 2013). Following validation, this imaging approach may enable the differential diagnosis between the two disorders, thereby rapidly initiating appropriate treatment for people with either disorder. In addition to the neuroimaging strategies, a number of other clinical markers are being assessed for their diagnostic utility. With the current focus on the contribution of cognitive deficits to psychiatric disorders  (Millan et al. , 2012; Andreou and Bozikas, 2013; Trivedi and Greer, 2014) , it is not surprising that neurocognitive data has been used to investigate whether specific deficits correlate with defined illness stages  (Pukrop et al. , 2006). To date, no individual test has achieved the level of sensitivity and specificity required for a diagnostic marker. However, using patterns of neurocognitive performance across premorbid verbal IQ, processing speed, working memory, verbal and visual memory, and executive function has been reported to distinguish healthy controls from people with an at-risk mental state-with a sensitivity of 0.
Acknowledgements This work was supported by the  Australian Research Council  ( FT100100689  to  Dr Scarr ),  BMBF QuantPro  grant and the  Max Planck Society  ( Dr Turck ), and the  National Health and Medical Research Council  (# APP1002240  to  Dr Dean ). The authors would like to thank all participants of the Think Tank 2013 for their stimulating discussions and insightful questions, particularly  Professor Angelos Halaris  for his cogent summation. The 2013  Think Tank  was sponsored by the  International College of Neuropsychopharmacology (CINP). Astra-Zeneca ,  Lilly ,  Lundbeck ,  Pfizer ,  Schwabe , and  Servier. Dr Dean  has received travel support from  GSK  and honoraria from  Pfizer ,  Eli Lilly , and  MSD.
A relatively recent detailed study on the topic showed that these GDP PPPs do not reflect the cost of R&D very accurately as compared with estimated R&D PPPs  [8]. However, the study covers only the manufacturing industry in six developed countries and two years  (1987 and 1997). For world-wide comparisons one must still make a "least unsatisfactory" choice between current exchange rates and GDP PPPs. The WHO health accounts database offers both options for total health expenditures. Chakma et al. [4]  chose current exchange rates for their main tables; we followed the recommendation of the Frascati manual and used GDP PPPs (as Chakma et al. did in a pie-chart in the online annex to their paper  [4] ). Using current exchange rates instead of PPPs underestimates the contribution of countries such as China and India, because, for the reasons given above, exchange rates in these countries overstate the cost of domestic activities and thus probably of R&D.
These results support the authors' thesis, though the decline in the United States is not so marked. Besides different approaches to analysis, the use of different data sources can also lead to diverging estimates of countries' health R&D expenditures. To see whether the use of an alternative dataset changes the results of the analysis, we have also calculated the trends in health R&D expenditures in the countries in Asia-Oceania using our data  [5]  instead of Chakma et al. 's  [4] , and applying both their method and the best practice methods of deflation and currency conversion (Table  1 ). This analysis confirms the growth in health R&D expenditures in Asia-Oceania, though in most countries at a slightly lower level. This difference likely stems from our data being derived from standard surveys of the performers of R&D (supplementary appendix to  [5] ), whose results tend to change less rapidly than data taken from the reports of funding bodies used in a number of cases by Chakma et al.
Study design This retrospective chart review was conducted from April to June 2012 by physicians or healthcare providers (HCPs) in the areas of gynecology and medical or clinical oncology who treat ABC. The participating medical professionals were recruited from across France, Germany, The Netherlands, Belgium, and Sweden to complete a questionnaire based on their patient charts. Selection of the medical professionals was based on years of clinical practice postresidency or postfellowship (≥5 but ≤35 years), time spent treating patients (≥60 %), and the number of patients with breast cancer for whom they were responsible for systemic treatment decisions in the year preceding the study (≥50 but ≤1000 patients). Medical professionals were contacted via email to assess their interest in participation (based on a database of breast cancer oncologists and record of previous participation in such research), and a follow-up phone call was made to discuss details of the research when requested by the potential participant.
All participating physicians electronically signed a consent form before entering data. Data collected in the questionnaire were from anonymous patient charts, and the study was compliant with both European and individual country regulations. Ethics approval was deemed not applicable for this study because it was done under market research regulations through a physician panel (fully double blinded physician list) and only collected fully anonymous patient chart information without any patient identifiers or ability to follow-up with physicians. Online patient record forms did not collect any data that would (or could reasonably) lead to the patient being identified (no name, address, postal code, date of birth, etc. ). No patient or physician identifier is recorded in the database, and only aggregated data were shared with the sponsor.
Data collection Data collected in the questionnaire included patient demographics and disease state and characteristics at the initiation of each treatment line, together with information on any/all metastases, and all comorbidities (please see Additional file 2: Figure  S1  for a copy of the full questionnaire). Maintenance therapy was considered a separate treatment line. Treatment details were requested at each line, including agent dose, duration, and administration route. Data were also collected on patient performance status and side effects of chemotherapy and any complementary treatments to alleviate those effects. Reasons for switching to the next line of treatment were also collected. The information collected on resource utilization at each treatment line included number of physician visits (office and outpatient), hospitalizations (by diagnosis-related group codes, where available) and duration of stay, any additional treatments or HCP visits, disease monitoring information (type, frequency, location, and medical professional responsible), and working status.
The frequency of the data is daily and like  Gallegati (2012)  we consider that contagion corresponds to the wavelet details of level 1 (1 day), level 2 (2 days) and level 3 (4 days). Computations have been performed using the waveslim package developed by Whitcher for the R statistical package of R Core Team (2012) and the wavelet toolbox of MATLAB (2010).
This question has no definitive answer in the literature due to a number of estimation difficulties that we discuss below. In this paper we contribute what we believe to be a valuable new estimate using several novel methods. First, we have data on the global revenues of all pharmaceutical products over an eleven year period, as collected by pharmaceutical data provider IMS. These detailed data allow us to calculate an excellent measure of innovation, namely the number of new molecular entities released on the global market during our time period. Much of the existing literature measures intermediate outcomes in the innovation process such as the number of new clinical trials or available regimens. Other literature focuses only on data from the United States. While the US is the largest and most important national market for pharmaceutical products (with approximately 40% of demand), revenue in other nations is large and growing and serves as an important stimulus to innovation.
In this paper our dataset includes sales from fourteen countries over twelve years. For these reasons, we think it particularly useful to study factors of the first type, which are rarely under the influence of either government or industry participants. We exploit these exogenous changes in potential demand to estimate elasticities of innovation, which we think are a clean measures of the causal impact of market size on innovation. The empirical section of the paper examines the response of global new drug launches to global expected market size measured in revenues and instrumented with demographics. Our identification strategy is as follows. If the cardiovascular market is expected to grow due to an aging population, firms will want to serve that demand. Firms have rational expectations concerning demand, the actions of rivals, and the timing of innovation and regulatory approval.
Data description We measure expected market size as the lifetime revenue accruing to the average product that is launched during a particular time window. Our measure of innovation is a count of new chemical entities (NCE) by therapeutic category launched anywhere in the 14 countries from which we have revenue data. While lumpy because of their small numbers, we consider these products to be uncontroversially innovative. Drug approval agencies such as the FDA in the US will approve new varieties and forms of existing medications (e. g. extended release, injectable versus oral) and generic drugs; these do not fall into our definition of innovative. We limit our measure to products that have actually been marketed -and therefore survived all the steps in the innovative process from research through to marketing in order to take advantage of the information contained in those stages.
However, to the extent that unmeasured discounts exist, prices and revenues may be overestimated here. We also use demographic data (population and mortality data) from the World Health Organization (WHO) 9. We extracted population data for the studied countries from available years since 1970. For all available country-years, we have population by sex (M/F) and by age distribution (0, 1-4, 5-9,. , 70-74, 75+), except for Mexico in 1977, where only total population by sex is available. Missing population information is reconstructed through a regression in logs on a country specific effect and a time trend. This amounts to assuming that population evolves between available years of data at a country specific annual growth rate that changes over time at the same rate across countries. Mortality data across years and countries are available per disease category as classified using the ICD-10 classification.
The data report sales by value and unit volume for all pharmaceutical and biologic products. All values are deflated into 2007 US$ using the US consumer price index. The database reflects sales for all compounds in all 14 countries through retail pharmacies, hospitals and HMOs, and includes characteristics of drugs like their four-digit Anatomical Therapeutic Classification (ATC4) (607 different classes are reported), the main active ingredient of the drug (6216 different active ingredients are reported), the name of the firm producing the drug, whether it has been licensed, the patent start date, and the format of the drug (471 different formats are reported). Products in the same ATC4 by definition have the same indication and mechanism of action. Some other available and interesting drug characteristics are its brand type -licensed brand, original brand, other brand, or unbranded -and (when applicable) its patent expiry date -ranging from 1966 to 2030 in the available data, and which may differ from one country to another.
We consider only ethical drugs and not OTC drugs. Quantities are given in standard units, one standard unit corresponding to the smallest common dose of a product form, as defined by IMS Health. Our dataset comes from IMS (Intercontinental Marketing Services) Health and includes all product sales in 14 countries (Australia, Brazil, Canada, China, France, Germany, Italy, Japan, Mexico, Korea, Spain, Turkey, United Kingdom, USA) between 1997 and 2007 (except for China, where 1997 and 1998 are missing). Overall, the initial dataset has 6,163,465 observations (one per drug, country, year) but only 2,972,419 have strictly positive revenues and quantities (many observations correspond to years where the drug was not yet launched, or had disappeared in a given year and country, or to missing data on revenues or quantities).
In this case, the absolute share of source kin total inequality is S k = cov(Y k , Y ) 2 2 , the sum of a 4 These calculations can be performed using the Stata package ineqfac  (Jenkins (1999b) ). 5 In particular,  Shorrocks (1982)  shows that these shares are applicable to any inequality measure that is symmetric, continuous and equal to zero if and only if all incomes are equal -in practice, this applies to almost all commonly used measures, but not decile ratios like the 90/10 ratio.
7 The decomposition will also calculate the share given by the residual term. Using the shares calculated in equation (4), we can then express the contribution of the cth characteristic in the change in any suitable inequality measure, I , between time tand t as s c I -s c I (5) As with the income source decomposition, these shares are invariant to the inequality measure used. However,  Yun (2006)  shows that if we use the variance of logs of incomes as an inequality measure, we can decompose these changes further. 8 He constructs an auxiliary distribution of income, where y * i = c c X ci + e i , i. e. the distribution of income if the coefficients changed but not the individual characteristics or residuals. He then shows that I -I = c=N c=1 (s c I -s * c I * ) + c=N c=1 (s * c I * -s c I ) ( 6 ) 6 These calculations can be performed using the Stata package ineqrbd, written by  Fiorio and Jenkins (2007).
III. Empirical implementation Our analysis is performed on annual cross-sectional household surveys covering Great Britain from (calendar) 1978 to (financial) 2008-09. We detail these, and how we construct our final data set for analysis, in Appendix A, but we would highlight six points here. First, in common with almost all research on income inequality in the UK, our measure of income is a measure of 'weekly net disposable equivalent household income', in which incomes, usually measured over a short period of time, are measured net of taxes and benefits, summed across all individuals living in the same household, and then equivalised to take into account the size and composition of households (we use the modified OECD equivalence scale). Second, we then trim the top and bottom 1% of households in each year; in doing so, we remove all of the very high and very low income households whose reported incomes are adjusted by government statisticians before the micro data are made available.
Third, although we have a measure of pretax earnings at the individual level, we have a measure of the tax paid on employment income only at the household level; we assume that the each adult's share of household net earnings is given by their share of pretax earnings. Fourth, we classify income from state benefits according to the type of household that receives the income. Fifth, no allowance is made for the implicit income that accrues to owner-occupiers or those with below-market-rent social housing. Sixth, our measures of inequality are taken among individuals, though income is assumed to be shared equally across the household; this is equivalent to analysing income inequality at the household level but weighting each household by the number of inhabitants. We have not used longitudinal data as the main source of household panel data in the UK (the British Household Panel Study) began in 1991, thereby preventing any assessment of what was different in the pre-and post-1991 periods (see, though  Jenkins and van Kerm (2011) , who analyse the incomes changes from 1991 to 2005).
Region also explains a small part of the rise in inequality. Table  4  shows a positive price effect for region during the 1980s -reflecting that income became more unequally distributed across regions -but this only explains about 4% of the rise in income inequality    6 ). Data on education begins in 1978 and therefore is counted as zero in years prior to then. Income inequality is measured as 1,000 times the variance of logs. Source: Authors' calculations using Family Expenditure Survey and Family Resources Survey, various years, as described in Appendix D. Bulletin during the period. The subgroup decomposition confirms that this is explained by a relative decline in the income of the North of England, Wales and Scotland, especially when compared to London and the South East (this is consistent with  Jenkins (1995) , who finds some evidence of a small but growing North-South divide over the period between 1971 and 1986).
We have also noted that our results regarding income from investments may be driven by nominal interest rates, and this exposes the need to consider changes in income inequality alongside studies of inequality in consumption and wealth. UK government officials created these HBAI data sets from the FES for 1979 and from the mid 1980s to 1994, and from the FRS for all years from 1994-95, and researchers at the Institute for Fiscal Studies created their own, equivalent version from the FES from 1961 to 1993. We take our data for 1978-91 from  Goodman and Webb (1995) ; we take our data from 1994-95 from DWP (2014a); and our data from 1992 and 1993 were produced by IFS researchers using the same rules as generated the series from 1968 to 1991. There is, therefore, the potential for a discontinuity in our time series between 1993 and 1994-95.
We also find no noticeable discontinuities between the FES and FRS for any of the variables used in our analysis. The advantage of using these HBAI data sets is that they have created to be consistent and comparable, as far as is possible given the inevitable small changes in the concept of income and the content of the survey instruments. The disadvantage is that is very hard to use different concepts of income. In particular, we use throughout the HBAI measure of 'weekly net disposable equivalent household income', in which incomes are measured net of taxes and benefits, summed across all individuals living in the same household and then equivalised to take into account the size and composition of households (we use the modified OECD equivalence scale). Two small very-near-cash benefits-in-kind are included: free meals for school children whose parents receive certain means-tested benefits, and free TV licences for those aged 75 or over.