dataesr/scientific-paragraphs-categorization huggingface dataset:
Location prior to admission In the ICNARC model, for admissions to the critical care unit from an imaging department and those from the recovery area (not for postoperative use but when used as a temporary critical care area), the previous location is used to assign a weight. For admissions collected to Version 0 of the SICSAG dataset (phased out from June 2008 to May 2009), only a single location immediately prior to the critical care unit was recorded and therefore the weightings for location prior to admission for these admissions was assigned based on the most common previous location in both SICSAG Version 203 data (introduced from June 2008) and Case Mix Programme data. Admissions from an imaging department were assumed to have previously been in an emergency department and admissions from the recovery area were assumed to have previously been on a general ward.
Systolic blood pressure In the ICNARC Physiology Score, weighting of the systolic blood pressure (SBP) is based on the lowest value during the first 24 hours following admission to the critical care unit. For SICSAG data (all Versions), only the highest SBP with paired diastolic blood pressure (DBP) and the lowest DBP with paired SBP were recorded. The lowest SBP was therefore imputed using a regression model fitted to 574,864 admissions to 181 critical care units in the Case Mix Programme between 1995 and 2008 with all these parameters recorded. The resulting equation was: Estimated lowest SBP ¼ Lowest DBP þ 0:862 Â Paired SBP-Lowest DBP ð Þ.
Arterial pH In the ICNARC Physiology Score, weighting of arterial pH is based on the lowest pH during the first 24 hours following admission to the critical care unit. For SIC-SAG data (all Versions), only the pH from the arterial blood gas with the lowest partial pressure of oxygen (PaO 2 ) was recorded. The lowest pH was therefore imputed using a regression model fitted to 1,011,217 admissions to 224 critical care units in the Case Mix Programme between 1995 and 2013 with both pH measurements recorded. The resulting equation was: Estimated lowest pH ¼ 0:991 Â pH associated with lowest PaO 2.
Neurological status In the ICNARC Physiology score, weighting of neurological status is based on either the lowest Glasgow Coma Score during the first 24 hours following admission to the critical care unit (for admissions not sedated during that entire period) or a separate weighting for patients that were sedated or paralysed and sedated during the first 24 hours. For admissions collected to Version 203 of the SICSAG dataset (introduced from June 2008), sedation was not recorded. Admissions were therefore assumed to be sedated if they had no lowest Glasgow Coma Score recorded during the first 24 hours following admission to the critical care unit (this was true for 99% of such admissions in SICSAG Version 0 data).
These diagnoses were mapped to appropriate codes within the ICNARC Coding Method by a consultant intensivist with extensive experience of coding data for the Case Mix Programme. Of the 423 SICS diagnoses in use, 295 (70%) were mapped to a specific condition in the ICNARC Coding Method, 44 (10%) were mapped to the process tier of the hierarchy, 37 (9%) to the site tier, 28 (7%) to the system tier, and 19 (4%) were unable to be mapped (see Additional file 1).
The APACHE II model The Acute Physiology And Chronic Health Evaluation (APACHE) II model was selected as a comparator for this study as it was the model in use in Scotland at that time. The SICSAG database does not include all the requisite fields to enable a head-to-head comparison against other, more recent, risk prediction models. The APACHE II model was originally developed using data from 19 critical care units in 13 US hospitals  [10] , and has subsequently been validated and recalibrated using UK data  [6, 11]. Risk predictions are calculated for each admission based on the following predictors: the APACHE II Scorean integer score between 0 and 71 comprising an Acute Physiology Score (0-60 points) based on derangement in 12 physiological parameters during the first 24 hours following admission to the critical care unit, age points (0-6) for age categories of ≤44, 45-54, 55-64, 65-74 or ≥75 years, and chronic health points (0-5) for very severe conditions in the past medical history; admission to the critical care unit following emergency surgery; and diagnostic categories based on the primary reason for admission to the critical care unit.
Statistical methods The ICNARC model was validated using measures of calibration, discrimination and overall fit, as described below. The validation was conducted in the full three-year SICSAG database extract and for each year separately. Discrimination was assessed by the c index  [12] , which is equivalent to the area under the receiver operating characteristic (ROC) curve  [13]. Calibration was assessed graphically and tested using the Hosmer-Lemeshow test for perfect calibration in ten equal sized groups by predicted probability of survival  [14]. As the Hosmer-Lemeshow test does not provide a measure of the magnitude of miscalibration and is very sensitive to sample size  [15] , calibration was also assessed using Cox's calibration regression, which assesses the degree of linear miscalibration by fitting a logistic regression of observed survival on the predicted log odds of survival from the risk model  [16].
Results Data were extracted from the SICSAG database for 29,626 admissions to 24 adult, general critical care units between 1 January 2007 and 31 December 2009. The following admissions were excluded: 3,599 admissions (12. 1%) flagged in the database as 'Exclude from severity of illness scoring' (see Table  1  for breakdown of reasons for exclusion); 1,324 (4. 5%) readmissions of the same patient within the same acute hospital stay; 173 (0. 6%) admissions missing the outcome of acute hospital mortality; 869 (2. 9%) admissions missing location prior to admission (n = 16) or primary reason for admission to the critical care unit (n = 864)no admissions were missing age; and 392 (1. 3%) admissions for whom the primary reason for admission was unable to be mapped. This resulted in a cohort of 23,269 (78.
5%) admissions for analysis. Of the admissions flagged as 'Exclude from severity of illness scoring' , acute hospital mortality was reported for 3,529 admissions (98. 1%) and, of these, 731 (20. 7%) died before discharge from acute hospital (see Table  1  for breakdown). It was not possible to include these patients in the analysis, even using statistical imputation methods to account for missing data, as insufficient predictor data were recorded. Due to the large number of admissions flagged as 'Exclude from severity of illness scoring' , a post hoc analysis was undertaken to investigate the potential impact of such exclusions using Case Mix Programme data (see below). Table  2  summarises the case mix and outcomes for the included admissions, overall and for each year. The mean age was 57 years, 56% of admissions were male, and two thirds of admissions were non-surgical.
In contrast, the APACHE II model both underpredicted risk (intercept < 0) and underpredicted variability (slope < 1). Performance of the ICNARC model remained consistent across the three years studied. In simulations using Case Mix Programme data to reproduce the potential impact of the exclusion of patients flagged as 'Exclude from severity of illness scoring' , randomly excluding an equivalent proportion of the same types of patients resulted in the following percentage changes in measures of model performance: c index from -0. 3% to +0. 02%; Brier's score from -0. 8% to +3. 8%; and ratio of observed to expected deaths from -1. 1% to +0. 6%.
Discussion The ICNARC model demonstrated excellent performance when validated in an external sample of data collected   from adult, general critical care units in Scotland. The model performance exceeded that of the APACHE II model, being used for benchmarking outcomes in Scotland at the time of this study, on all measures and was consistent over time. The discrimination of the ICNARC model (c index 0. 848) was slightly lower than that reported previously from the original development and validation samples (0. 872 and 0. 870, respectively)  [7]  and previous external validation using data from the same source but from different critical care units (0. 868)  [8]. The finding that all measures of model performance were consistent over time was surprising, as previous studies have suggested that while discrimination of risk models is maintained, calibration deteriorates over time, necessitating regular recalibration of the models  [6, 22].
The main strength of this study is the large, representative dataset. As these data come from a very similar healthcare system to the rest of the UK, where the model was developed, but were collected, managed and validated independently, they represent the ideal setting in which to validate the ICNARC model. Independent, external validation of the ICNARC model within the rest of the UK is impossible as the Case Mix Programme has 96% coverage meaning that there are not sufficient critical care units outside of the Case Mix Programme for this to be done. The study does have some limitations, most notably the number of admissions that it was necessary to exclude. One fifth of exclusions were of multiple admissions of the same patient, which are essential to exclude as outcomes for these admissions are not independent, and follow-up was excellent, with only 0.
6% of admissions excluded due to missing outcomes. However, the largest category of exclusions was those flagged as 'Exclude from severity of illness scoring' (12. 1% of all admissions). The main reason for these exclusions seems to have been to reduce the data collection burden for admissions that would not have been included in benchmarking using the APACHE II model and those considered to have a very low risk of death. However, 761 admissions (2. 6% of all admissions) were excluded without any clear reason being specified. The excluded admissions did not have sufficient data recorded to be able to reinstate them into the analysis, however simulating similar exclusions in Case Mix Programme data demonstrated that the impact of these exclusions was likely to be small. It was necessary to apply some assumptions and mapping of data in order to be able to apply the ICNARC model to the SICSAG dataset.
The simplest approach to assigning weights for lowest systolic blood pressure and lowest arterial pH would have been to use the most similar available value of these parameters (the systolic blood pressure associated with the lowest diastolic blood pressure and the pH from the arterial blood gas with the lowest PaO 2 ), however, this would have resulted in measurements that were slightly less extreme than the true values and therefore potentially underestimated risk of death. We therefore used data from the Case Mix Programme to develop appropriate regression imputation equations. Following a dataset revision, explicit recording of sedation during the first 24 hours in the critical care unit was removed from the SICSAG dataset. It was therefore necessary to make the assumption that patients with no Glasgow Coma Score recorded were sedated.
Using the earlier portion of the dataset, where explicit recording of sedation was available, this assumption was demonstrated to be reasonable, with 99% of missing Glasgow Coma Score values being due to sedation. Any impact on risk predictions will therefore have been minimal. It was also necessary to map reasons for admission to critical care, which had been recorded using a different coding system. Although only 70% of the diagnostic categories could be mapped to a specific condition in the ICNARC Coding Method, the hierarchical nature of the ICNARC Coding Method enabled most of the remaining diagnostic categories to be mapped to a higher level in the hierarchy, and only 4% of diagnostic categories were unable to be mapped resulting in the exclusion of 1. 3% of admissions. It is possible that the slightly less specific diagnostic coding, combined with the need to map these onto a different coding system, may have contributed to the slightly lower discrimination of the ICNARC model than seen in Case Mix Programme data.
Measures of model performance Measures of model performance for the ICNARC model (2009 recalibration) compared with the APACHE II model for the full three-year SICSAG database extract and for each year from 2007 to 2009. APACHE, Acute Physiology And Chronic Health Evaluation; CI, confidence interval; ICNARC, Intensive Care National Audit & Research Centre; SICSAG, Scottish Intensive Care Society Audit Group.
Acknowledgements This project was supported by the  National Institute for Health Research Health Services and Delivery Research (NIHR HS&DR) programme  (project number  09/2000/65 ). Visit the HS&DR website for more information. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the  HS&DR Programme ,  NIHR ,  NHS  or the  Department of Health. The study sponsor had no involvement in the study design, in the collection, analysis and interpretation of data, in the writing of the manuscript, or in the decision to submit the manuscript for publication. The authors wish to thank all the staff at critical care units participating in the SICSAG audit (http://www. sicsag. scot. nhs. uk/About/Participants. html),  Dr Alasdair Short  for his assistance with mapping reason for admission data, and the  Risk Modelling Expert Group  ( D Altman ,  N Black ,  J Carpenter ,  G Collins ,  M Dalziel ,  M Grocott ,  S Harris ,  J Nichol ,  A Padkin ).
In the 7 TeV analysis  [1] , the pointing measurement was used to extract the result, with the time measurement used only qualitatively as a cross-check. The 7 TeV analysis set exclusion limits within the context of GMSB SPS8 models and similar results were obtained in a CMS analysis  [30]  of their full 7 TeV dataset, but investigating a final state with at least one photon, at least three jets, and E miss T. The current analysis utilizes both the pointing and time measurements. As described in Sec. VII, the current analysis divides the sample into six exclusive categories, according to the value of |∆z γ |, and then simultaneously fits the t γ distributions of each of the categories to determine the possible contribution from signal. The use of both variables greatly improves the sensitivity.
The results are obtained from Monte Carlo (MC) simulations (see Sec. III) by fitting to a Gaussian function the difference between the values of z origin obtained from the calorimeter measurement and the MC generator-level information. The pointing resolution degrades with increasing |z origin |, but remains much smaller than |z origin | in the region where the signal is expected. The calorimeter pointing performance was verified in data by using the finite spread of the LHC collision region along the z-axis. The pointing resolution achieved for a sample of electrons from Z → ee events is also shown in Fig. 1 , where the distance, z PV , between the PV and the nominal center of the detector serves the role of z origin. In this case, the pointing resolution is obtained by fitting to a Gaussian the difference between z PV , obtained from reconstructed tracks, and the calorimeter measurement of the origin along the beamline of the electron.
During 2012, the various LAr channels were timed-in online with a precision of order 1 ns. A large sample of W → eν events in the 8 TeV dataset was used to determine calibration corrections that need to be applied to optimize the time resolution for EM clusters. The calibration includes corrections of various offsets in the time of individual channels, corrections for the energy dependence of the time measurement, crosstalk corrections, and flight-path corrections depending on the PV position. To cover the full dynamic range of physics signals of interest, the ATLAS LAr calorimeter readout boards  [32]  employ three overlapping linear gain scales, dubbed High, Medium and Low, where the relative gain is reduced by a factor of about ten for each successive scale. For a given event, any individual LAr readout channel is digitized using the gain scale that provides optimal energy resolution, given the energy deposited in that calorimeter cell.
III. DATA AND MONTE CARLO SIMULATION SAMPLES This analysis uses the full dataset of pp collision events at a center-of-mass energy of √ s = 8 TeV, recorded with the ATLAS detector in 2012. The data sample, after applying quality criteria that require all ATLAS subdetector systems to be functioning normally, corresponds to a total integrated luminosity of 20. 3 fb -1. While all background studies, apart from some crosschecks, are performed with data, MC simulations are used to study the response to GMSB signal models, as a function of the free parameters Λ and τ. The other GMSB parameters are fixed to the following SPS8 model values: the messenger mass M mess = 2Λ, the number of SU(5) messengers N 5 = 1, the ratio of the vacuum expectation values of the two Higgs doublets tan β = 15, and the Higgs-sector mixing parameter µ > 0  [29].
B. Backgrounds The background is expected to be completely dominated by pp collision events, with possible backgrounds due to cosmic rays, beam-halo events, or other noncollision processes being negligible. The source of the loose photons in background events contributing to the selected sample is expected to be either a prompt photon, an electron misidentified as a photon, or a jet misidenti-fied as a photon. In each case, the object providing the loose photon signature originates from the PV. The pointing and time distributions expected for the background sources are determined using control samples in data. In addition to avoiding a reliance on the precise MC simulation of the pointing and timing performance for the backgrounds, and particularly of the tails of their ∆z γ and t γ distributions, using data samples naturally accounts for the influence of pile-up, the possibility of selecting the wrong PV, and any instrumental or other effects that might influence the background measurements.
Source of uncertainty Value The expected signal distributions are determined using the GMSB SPS8 MC signal events. Therefore, limitations in the MC simulation could lead to differences between data and MC events in the predicted signal behavior. Any such discrepancies in the shapes of the signal distributions must be handled by corresponding systematic uncertainties on the signal shapes. Since signal templates for both |∆z γ | and t γ are used in the statistical analysis, systematic uncertainties on the signal shapes of both must be taken into account in the fitting procedure. The dominant systematic uncertainty on the shape of the signal t γ distribution arises from the impact of the time reconstruction algorithm on the measurement of delayed signals. As discussed in Sec. II C, the use of fixed OFC values causes a bias in the energy and time reconstructed for signals that are sufficiently early or late compared to the nominal time.
IX. RESULTS AND INTERPRETATION Before examining the |∆z γ | and t γ distributions of the data in the signal region, the two control regions, CR1 with 20 < E miss T < 50 GeV and CR2 with 50 < E miss T < 75 GeV, are used to validate the analysis technique and background modeling. Since the control regions should be dominated by background, their data distributions are expected to be well described by the background-only fit. Table  V  summarizes the number of selected events in CR1 and CR2, as well as those in the signal region (SR), showing that the control region datasets are much larger than that of the signal region. It is of interest whether the background modeling, including the assigned systematic uncertainties, is adequate to describe the control region data within the statistical uncertainties of the data in the signal region.
For comparison, the results from the ATLAS analysis of the 7 TeV dataset  [1]  are also shown in Fig. 7 , indicating the significantly larger reach of the 8 TeV analysis. The observed and expected 95% CL limits in the two-dimensional GMSB signal space of χ0 1 lifetime versus Λ, the effective scale of SUSY breaking, and also versus the corresponding χ0 1 and χ± 1 masses in the SPS8 model. For comparison, the results from the 7 TeV analysis are also shown. The regions to the left of the limit curves are excluded at 95% CL. The horizontal dashed line indicates the lowest lifetime value, namely τ = 250 ps, for which the analysis is applied.
Results Expression of selected HDAC isoforms is modulated during progression of the disease in mice. In order to get a complete picture of known HDACs in the course of ALS, we have performed an extended analysis of the expression of all HDACs by real-time PCR (RT-PCR) on mRNA extracted from the spinal cord of two well-characterized models for mutant SOD1-linked ALS. Both G93A-SOD1 and G86R-SOD1 transgenic mice essentially recapitulate the human form of the disease, although with a difference in age of onset and survival (see Materials and Methods). In this paper, we report data on all the 11 canonical class I-II-IV isoforms and two class III Sirtuins (SIRT1 and SIRT2). We have not been able to monitor SIRT4-7, possibly because of their low level of expression. Data on SIRT3 are the subject of a separate paper (C Valle et al.
Myogenic differentiation of C2C12 cells at B80% cell confluence was obtained by substituting the medium with fresh medium supplemented with 2% horse serum (EuroClone, Milan, Italy) to induce expression of mutant SOD1 under the myosin heavy chain promoter. 39, 40 onstruction of recombinant adenoviruses expressing Wt-SOD1 as well as G93A-SOD1 was carried out in a previous work. 37 Infection of SH-SY5Y cells was carried out for 1 h in OPTIMEM (Invitrogen); after removal of the virus, cells were subjected to the differentiation protocol and grown for the indicated period of times before being used for further experimental manipulations. Human cDNA coding for SIRT1 (accession number AF083106) was cloned by reverse transcription-PCR from human SH-SY5Y neuroblastoma cell cDNA using the forward primer 5 0 -AAAAAGCTTATGGCGGACGAGGCGGCC-3 0 and the reverse primer 5 0 -TTTCTCGAGCTATGATTTGTTTGATGGATAG-3 0.
The resulting PCR fragment was inserted into HindIII/XhoI restriction sites of pcDNA3 (Invitrogen). Plasmid construction was verified by automated sequencing. Transfection for either transient or stable expression of SIRT1 was obtained with Lipofectamine Plus reagent (Invitrogen) according to the manufacturer's instructions. For stable expression, after selection with 400 mg/ml G418 (Gibco BRL, Grand Island, NY, USA), B25 clones were isolated independently and analyzed in western blot with anti-SIRT1 antibodies. Three clones were chosen for equivalent expression of SIRT1 proteins and used for further analysis. All the clones analyzed gave consistent results and data from one clone are shown. Conditions for treatments with Sirtuins modulators are given in the Figure legends. All drugs were added to the culture medium starting from infection/ differentiation protocol and were maintained in all medium changes.
RT-PCR was performed on G93A-SOD1, G86R-SOD1 and the corresponding nontransgenic control littermate mice. The plates were sealed with a sheet of adhesive plastic and the reaction was carried out in the thermal cycler LightCycler 480 (Roche). Data analysis was made using LightCycler 480 SW1. 5 software (Roche) and the subsequent analysis to make a comparison of different plates was made using LightCycler 480 Multiple Plate Analysis software (Roche). Tissue homogenates and cell lysis. Total tissue homogenates were obtained by mechanical dissociation using a Teflon manual homogenizer for 30 s in ice in RIPA buffer (50 mM Tris-HCl, pH 8. 0, 1% Triton X-100, 0. 25% Na-deoxycholate, 0. 1% SDS, 250 mM NaCl, 5 mM EDTA and 5 mM MgCl 2 ) containing a 1 : 1000 dilution of protease inhibitor cocktail (Sigma-Aldrich).