dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors are indebted to the many colleagues who provided material to illustrate the wealth of applications and results that have been achieved using synchrotron light over the recent years. Gary Admans (ESRF) is gratefully acknowledge for critical reading of the manuscript. Part of this work was supported by the  Cross Disciplinary Program Glyco@Alps, within the framework "Investissements d'avenir" program  ( ANR-15-IDEX-02 ). The three-dimensional representations of the proteins were drawn using the  PyMol Molecular Graphics Systems , Version 1. 8 Schrödinger, LLC.
Technology Technology with applications to environmental exposure and health outcome assessment is evolving rapidly. Technology will generate new opportunities, particularly in regards to population datasets, e-and m (obile)-health, personal and remote sensor technology, and OMICs data. Below we highlight technologies that lie on the horizon and how they could be applied to environmental epidemiology. Expanding data availability will allow prediction of diverse population exposures and create new opportunities for exploring novel exposures that have been previously difficult to quantify. Importantly, geo-referenced data are becoming more widely available in low-and middle-income countries, reducing barriers for conducting environmental epidemiology in these countries. Such data include those collected through remote sensing, sensor networks, smartphones, as well as the "internet of things" (i.
A new exposure pathwayvisual exposure -will be easily investigated using miniaturized cameras or virtual reality to understand how people internalize and interact with their environment. For example, such technology will allow advances beyond simple proximity to green space to determine whether individuals are visually exposed to green space and which activities they engage in using that green space. Social media data will increasingly play a role in assessing behaviors, exposures and outcomes. Such approaches have already been used to identify symptoms, behavioral risk factors, and population mobility patterns  (Lee et al. , 2016; Paul and Dredze, 2011). The rise of e/m-health and new personal sensor technologies is opening tremendous opportunity for environmental epidemiology. Recruiting participants, collecting survey data, assessing outcomes, and incorporating medical health records using mobile platforms (e.
Study design, models of research We will increasingly move towards studies with very large sample sizes with individual exposure information, even including the entire population (N = all) as is currently possible with census cohorts. One may think that statistics are no longer needed if the entire population is observed, but this is probably the case only when computing simple summary statistics (e. g. a mean). Environmental epidemiology often deals with complex causal questions, which require the observation of a set of confounders. Empirical estimations of those effects would require calculating averages over all possible combinations of confounders. With just 10 confounders, this becomes unfeasible with huge datasets, because of empty cells, so statistical models will continue to be needed (van der Laan and Starmans, 2014).
The full potential of the vast exposure data that will be collected in the future is best realized when those data are open (in a sufficiently anonymized form to protect privacy). Open exposure data will allow for use, reuse, and redistribution of data with proper attribution. Open data platforms that provide standards of data quality could be used as a global repository for crowd sourced exposure data. A useful example in this regard is Open Street Map, a collaborative project to create a free, editable, global geodatabase. Although currently limited to data from official, stationary government monitors, openaq. org is another example of a collaborative project to aggregate, standardize, and share realtime air quality data from around the world. Platforms that standardize and harmonize environmental data globally will play an increasingly important role in maximizing value from environmental exposure data for societal gain.
Reduce participant burden Data collection methods should become increasingly passive to minimize the burden to participants. Already several countries allow linkage of administrative and registry data for research purposes, allowing for epidemiology on entire populations with minimal participant burden. Response rates in many population studies are falling compared to past decades; this has been cited as a possible factor leading to the cancellation of a major birth cohort study in the UK due to low recruitment  (Pearson, 2015). By embedding data collection in routine medical care, longitudinal OMICs data could be collected with little additional burden. Potentially thousands of analytes could be measured on each blood or urine sample collected as part of clinical care; access to these samples for research would be an important way to enable lifecourse epidemiology.
We present the scaling relation between Sunyaev-Zeldovich (SZ) signal and stellar mass for almost 260,000 locally brightest galaxies (LBGs) selected from the Sloan Digital Sky Survey (SDSS). These are predominantly the central galaxies of their dark matter halos. We calibrate the stellar-to-halo mass conversion using realistic mock catalogues based on the Millennium Simulation. Applying a multi-frequency matched filter to the Planck data for each LBG, and averaging the results in bins of stellar mass, we measure the mean SZ signal down to M * ∼ 2 × 10 11 M , with a clear indication of signal at even lower stellar mass. We derive the scaling relation between SZ signal and halo mass by assigning halo properties from our mock catalogues to the real LBGs and simulating the Planck observation process.
These were selected from the Sloan Digital Sky Survey (SDSS) using criteria designed to maximize the fraction of objects that are the central galaxies of their dark matter halos. We stack the Planck data in order to estimate the mean SZ signal for LBGs in a series of stellar mass bins. We then use mock galaxy catalogues based on the Millennium Simulation and tuned to fit the observed abundance and clustering of SDSS galaxies to establish the relation between stellar and halo mass. Planck is a unique SZ instrument for this purpose because of its large frequency coverage and the fact that it observes the entire SDSS survey area, allowing study of large samples of galaxy systems with extensive multi-wavelength data. We unambiguously (>3σ) detect the SZ signal down to stellar masses of 2 × 10 11 M , corresponding to an effective halo mass M 500 of 2 × 10 13 M (see Sect.
Planck data set Planck  (Tauber et al. 2010; Planck Collaboration 2011a)  is the third generation space mission to measure the anisotropy of the cosmic microwave background (CMB). It observes the sky in nine frequency bands covering 30-857 GHz with high sensitivity and angular resolution from 31 to 5. The Low Frequency Instrument (LFI;  Mandolesi et al. 2010; Bersanelli et al. 2010; Mennella et al. 2011 ) covers the 30, 44, and 70 GHz bands with amplifiers cooled to 20 K. The High Frequency Instrument (HFI;  Lamarre et al. 2010 ; Planck HFI Core Team 2011a) covers the  100, 143, 217, 353, 545, and 857  GHz bands with bolometers cooled to 0. 1 K. Polarisation is measured in all but the highest two bands  (Leahy et al. 2010; Rosset et al. 2010). A combination of radiative cooling and three mechanical coolers produces the temperatures needed for the detectors and optics (Planck Collaboration 2011b).
A locally brightest galaxy catalogue To select a sample of central galaxies, we first define a parent population with r < 17. 7 (r-band, extinction-corrected, Petrosian magnitude) from the spectroscopic galaxy catalogue of the New York University Value Added Galaxy Catalogue 2. This was built by  Blanton et al. (2005)  based on the seventh data release of the Sloan Digital Sky Survey (SDSS/DR7  Abazajian et al. 2009 ). This parent catalogue contains 602 251 galaxies. We then define "locally brightest galaxies" to be the set of all galaxies with z > 0. 03 that are brighter in r than all other sample galaxies projected within 1. 0 Mpc and with redshift differing by less than 1000 km s -1. After this cut 347 486 locally brightest galaxies remain. The SDSS spectroscopic sample is incomplete because it proved impossible to place a fibre on every object satisfying the photometric selection criteria, and because some spectra failed to give acceptable redshifts.
0 Mpc, unless the photometric redshift distribution of the "companion" is inconsistent with the spectroscopic redshift of the candidate. (Our definition of "inconsistent" is that the total probability for the companion to have a redshift equal to or less than that of the candidate is less than 0. 1; in practice this eliminates "companions" that are too red to be at a redshift as low as that of the candidate. ) This procedure leaves us with a cleaned sample of 259 579 locally brightest galaxies. The NYU-VAGC provides a variety of data for each galaxy. In addition to the positions, magnitudes, and redshifts used to create our sample, we will make use of rest-frame colours and stellar masses. The latter are based on stellar population fits to the five-band SDSS photometry and on the measured redshifts, assuming a  Chabrier (2003)  stellar initial mass function  (Blanton & Roweis 2007).
Stability of the signal in different sky surveys We have also checked that the SZ signal is stable against splitting the Planck data into complementary subsets. For instance, the signal obtained from the maps of the first 6 months of observing time is fully consistent with that obtained from maps of the second 6 months and the last 3. 5 months (of course the latter has larger error bars due to its smaller sky coverage). The simulations assign to each observed LBG the halo mass and positional offset of a randomly chosen simulated LBG of the same stellar mass (compare Fig. 3 ). Our best fit Y 500 -M 500 scaling relation is then used, together with the universal pressure profile, to inject a simulated signal into the Planck maps (see text). An "observed" signal is obtained by applying the MMF exactly as for the real data.
We give each halo a SZ signal distributed according to the "universal pressure profile" and normalized using a specific model Y 500 -M 500 scaling relation. Each synthesised object is then observed with the three-band MMF centred on the galaxy's position, and the measurements are binned and weighted in the same way as the real data to obtain Y s. This procedure enables us to translate a model Y 500 -M 500 relation to our observational plane, Y 500 -M * , and thus to fit for the underlying scaling relation with halo mass M 500. We model this relation as Ỹ500 = Y M M 500 3 × 10 14 M α M , (1) fixing the mass exponent to its self-similar value, α M = 5/3, and fitting for the normalization Y M. Restricting the fit to log 10 (M * /M ) > ∼ 11. 5, for direct comparison to X-ray samples in the discussion below, we find Y M = (0.
6. Our power-law fit adequately describes the data points over more than two orders of magnitude in halo mass down to this remarkably low value with no hint of a significant deviation. The inset in the right panel of Fig. 9  shows the ratio of our measured mean SZ signal to that predicted by the self-similar scaling relation deduced from X-ray observations of clusters (the dashed blue line,  Arnaud et al. 2010). Direct measurements obtained by binning the MCXC clusters (the green points) agree with this relation. This was the principal result of Planck Collaboration (2011e). The SZ measurements for our LBGs fall below the relation, however. The horizontal dot-dashed line gives the ratio our LBG fit to the X-ray model (this is the offset between the two blue lines in the main figure). Recall that the fit to the LBG catalogue was restricted to masses overlapping the X-ray sample, log 10 M 500 /M > 13.
This is the lowest mass scale to which an SZ scaling relation has so far been measured. The fact that the signal is close to the self-similar prediction implies that Planck-detected hot gas represents roughly the mean cosmic fraction of the mass even in such low-mass systems. Consistency with their low observed X-ray luminosities then requires the gas to be less concentrated than in more massive systems. Integration of the halo mass function down to M 500 = 4×10 12 M shows that Planck has now seen about a quarter of all cosmic baryons in the form of hot gas, about four times as many as are inferred from X-ray data in clusters with M 500 > 10 14 M. At the high mass end, the scaling relation we derive from our LBG data shows reasonable agreement with X-ray cluster results. The 20% lower normalisation that we find (significant at the 2.
The main presenting clinical features in patients from the ICGG registry were splenomegaly (55% with spleen volume 5-15 multiples of normal [MN]), hepatomegaly (53% with liver volume 1. 25-2. 5 MN), moderate thrombocytopenia (50-70% with platelet count ≤60-120 × 10 9 /L) and anaemia (20-30% patients), consistent with findings from both the physician and patient surveys. Bone pain was experienced by 38% patients and bone crisis by 12% patients in the ICGG registry at the time of diagnosis  [20] , compared with 13% of patients in the patient survey recalling bone symptoms at time of presentation. Although the consistency of these survey findings with previously published registry data are reassuring, data from surveys are inherently limited because of the voluntary nature of participation, leading to possible bias in the selection of participants as well as the potential for missing, incomplete or inaccurate responses.
Acknowledgements Initial findings from the patient and physician surveys were presented at the  Society for the Study of Inborn Errors of Metabolism (SSIEM) Annual Symposium , 1-4 September 2015, Lyon, France (Pastores et al. and Mehta et al. ,  P532  and  P531 ). The patient and physician surveys were sponsored by  Shire. Shire  employees  Quinn Dinh ,  Paul Elson  and  Stephanie MacDonald  supported with survey logistics. All patients and physicians are sincerely thanked for their participation in the survey. The authors thank  Dr. Ludger Poll  for his contribution to the physician survey. Under the direction of the authors,  Lindsay Napier, PhD , employee of Excel Scientific Solutions, provided editorial assistance for this article. Editorial assistance in formatting, proofreading, copy editing and fact-checking also was provided by  Excel Scientific Solutions.
The iso, hypo or hyper intensity, similarity of shape, size and location complicates the identification of brain tumors. Therefore, an adequate Computer Aided Diagnosis (CAD) system is designed for classification of brain tumor for assisting inexperience radiologists in diagnosis process. A multifarious database of real post contrast T1-weighted MR images from 10 patients has been taken. This database consists of primary brain tumors namely Meningioma (MENI-class 1), Astrocytoma (AST-class 2), and Normal brain regions (NORM-class 3). The region of interest(s) (ROIs) of size 20 x 20 is extracted by the radiologists from each image in the database. A total of 371 texture and intensity features are extracted from these ROI(s). An Artificial Neural Network (ANN) is used to classify these three classes as it shows better classification results on multivariate non-linear, complicated, rule based domains, and decision making domains.
These images are obtained by introducing gadolinium material which enhances contrast. Typically, 0. 15-0. 20 mMol/kg gadolinium is introduced in patients for contrast enhancement. This results a considerable contrast difference between fluid and solid anatomical structures within the body. Post-contrast T1-weighted MR images are taken as a database for better and large feature bank extraction based on intensity and texture discrimination. In conventional echo repetition time (TR) (<750ms) and echo time (TE) (<40ms) and in gradient echo sequences it can be achieved by flipping angles more than 50 • with TE value (<15ms). The reason for attaining these images is conventional spin echo and gradient spin echo sequences which enhances both visual interpretation as well as feature discrimination capability between different classes.
The database consists of MENI (class-1), AST (class-2) and NORM (class-3) as in Fig. 1. The clinical decisions concerning diagnosis of brain tumors is a primary part of the treatment process. Mostly clinical decisions are based on visual interpretation for classifying brain tumors. This visualized classification is less accurate for classification of brain tumors thereby radiologist(s) opt for a better accurate solution which can be obtained through computer aided diagnosis (CAD) systems. A boost in accuracy can be achieved for classification with the help of CAD system as it can retrieve various intensity and textural features. The CAD system can be applied to (i) deliver extra reliable differentiation, especially with similar tumors (ii) expedite the diagnosis. The main objective of this research is to articulate LAWs texture energy feature analysis  [16]  to describe the textural and spatial structural variations of tumor cells.
Background Theory Many authors have proposed various algorithms for classification of brain tumors by developing various computer aided diagnostic (CAD) system based on different features and classification algorithm  [1] [2] [3] [4]. Generally, these systems use image processing techniques such as feature extraction, selection, and classification. The primary motive behind all these developed CAD systems is to achieve maximum accuracy. These systems used different types of brain tumor database such as MR spectroscopy, and echo planar maps related to cerebral blood volume (rCBV) along with MR imaging. The echo planar maps, MR images and MR spectroscopy are used for low grade and high grade brain tumor classification  [5]. Along with these techniques, many a times MR image and perfusion data was also used for the same purpose  [6, 7].
These are some feature which depends on the neighbor pixels as Gray Level Run-Length Matrix (GLRLM), Gray Level Co-Occurrence Matrix (GLCM). Haralick, Shanmugum, R M K, and Dinstein I  [15]  introduced GLCM approach in 1973. The GLCM feature matrix is most commonly used in feature extraction by many researchers. 100 Features are also extracted by Zarchari et al. [17]  where in these features are extracted from GLCM, intensity, Gabor, statistical, and shape based techniques. Many studies have been already performed on multiclass brain tumor classification. Sachdeva et al. [18]  took a dataset of 856 SROIs from 428 postcontrast T1 MR-images. A Principal Component Analysis (PCA) along with Artificial Neural Network (ANN) had been used which gave an overall accuracy of 85. 23% and an individual class accuracy for Astrocytoma is 86.
A dataset of 75 images of Glioma, Meningiomas, and Metastatic had been collected. An individual class accuracy of 96. 67%, 95. 24%, and 87. 50% had been achieved for Gliomas, Meningiomas, and Metastates respectively. Al-Shaikhli et al. [10]  performed a different study on a database which has 50 normal brain images, 50 Glioma brain tumor images, 50 Glioblastoma brain tumor images, and 50 images of Metastatic brain tumor. This database was experimented with dictionary learning and sparse coding classifier which has K-SVD algorithm. An overall accuracy of 93. 75% was achieved with this method. It is being observed that there are only few researchers who have done the classification of Meningioma and Astrocytoma together with Normal brain regions. Mostly researchers classified Glioma, Meningioma, and Metastatic brain tumors.
These brain tumors are easily classified as they have distinctive features. Besides this, a very few features were extracted and as a result low accuracy were obtained. No attempts have been made to include LAW s textural energy measures (LTEM) in feature extraction part to classify brain tumors. A very few studies have been done on classification of Meningioma and Astrocytoma brain tumors with lower classification accuracies due to their similar textural and intensity patterns. In this papers, these above limitations are surmounted with the help of GUI developed for ROI segregation. 371 texture and intensity features are obtained from the ROI(s) extracted from the database of post-contrast T1 weighted MR-images of 10 patients. Initially, ANN classifier is used to test these ROI(s) with limited number of features excluding LTEM features.
Segregating Regions of Interest (ROIs) from Database A secondary database of ROI(s) is obtained by developing a GUI using MATLAB 2015a which guides the user to mark as well as segregate the region of interest from the main image. These ROI(s) are a sub part of main image in medical imaging. A ROI has the fundamental diagnosis information which can be used for computations and diagnosis process. Depending on the methodology used, optimal size of the ROI varies. The ROI size also depends on the application for which it will be used. Either too large ROI like 40×40 or too small ROI like 10×10 does contain too much or too less information for computation respectively  [14]. An average size of brain tumor is 30×30. The same type of tumors show similar information except for some typical cases as stated below.
Database The database used is of post-contrast T1 weighted MR images. These images are provided by Surgical Planning Laboratory, Departments of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA  [32, 33]. This database contains brain MR-images of Astrocytoma, Meningioma, and Glioma. All images are obtained using the same MRI equipment (Siemens Verio, Erlangen Germany, and 3 Tesla MR Scanner). From this database, 105 images of MENI (Class-1) & AST (Class-2) are taken. Gliomas are rejected due to the high salt and pepper noise on the images. The Normal regions (NORM-Class 3) are marked from these 105 images to have variant data of white matter and gray matter. T1 images are especially used to distinguish gray and white matter where gray matter is dark gray (iso to hypo), white matter is light gray (hyper).
The dataset used for training is not repeated in testing i. e. random selection method has not been applied. Experiment 4: In the fourth and last experiment also, available dataset is used. The dataset used for training is not repeated in testing i. e. random selection method has not been applied. A selection of 30% ROIs has been made for the training set from each class. The validation set has 10% ROIs from each class. As the validation process is completed, ANN sets all its parameters optimally fixed. Now, ANN is ready for independent evaluation of the test set which consists 60% ROIs. For this experiment, PCA is applied on 371 features as described in experiment 2. The evaluation of this experiment is done using confusion matrix.
Results and Discussion Experiment 1: Initially, in this experiment, three tumor classes are classified using ANN approach without LTEM features. A feature bank consisting FOS, GLCM, GLRLM, and GWT features (a total of 263 features) is taken as an input to the multi-layer ANN classifier. Three different classes viz. MENI (Class-1), AST (Class-2), and NORM (Class-3) are classified. An overall classification accuracy of 78. 10% is being observed as given in Table 4. Individual class accuracy for MENI (class-1) is 77. 14%, 74. 30% for AST (class-2), and 82. 86% for NORM (class-3). Training, validation and testing are the three stage used for multi-layer ANN classifier. Depending upon the stages of multi-layer ANN classifier, three sets of database has been built namely: training set, validation set, and testing set.