dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, MB data from sources 1 and 2 are also subject to uncertainties. In particular, for Aletsch Glacier (from source 2), the largest glacier of the Alps (83 km 2 ), the lowest R 2 w (0. 31) for winter was found. A possible explanation for the poor performance of our approach for Aletsch is that its ablation area reaches to relatively low elevation. It is thus snow-free for a considerable part of the period used for winter balance estimate (October to April). Winter MB data for Aletsch Glacier thus include both accumulation and melting, whereas the NDSI approach for winter is optimized to represent snow accumulation, as for the other glaciers.
Moreover, the method could also allow for glaciers with recently initialized MB series to extend them backwards in timer, for example by using the Advanced Very High Resolution Radiometer satellite data available since 1978. Interrupted MB time series could be reconstructed as well. The ultimate and long-term goal of this work is to apply the method at large scales (i. e. thousands of glaciers in a mountain range for which no or very few direct MB data are available). Therefore, the next step is to assess how accurately the approach can estimate the MB of an unmeasured glacier. For that purpose, it is necessary to build and apply a generic transposable relation between Z and MB with fixed parameters. The sensitivity of the coefficients of the linear regressions towards explicative factors (in particular topographic factors such as glacier size, aspect, etc.
e. a -1 ). Cross-validation of the 55 individual linear regressions allows assessment of the temporal stability and the robustness of all the derived relationships in winter and of ∼60% in summer. Estimations of seasonal MB over 2009-2014 is also performed for these 55 glaciers, and a mean global error is calculated for some estimates, based on a more limited dataset of observed MBs. We are able to estimate winter MB with an acceptable mean global error (405 mm w. e. a -1 ) and without bias. In summer, the mean error of MB estimation over 2009-2014 is higher (561 mm w. e. a -1 ) but less observed MB measurements are available for validating the regressions during this season. Moreover, the subset of glaciers used for evaluation in summer tends to perform more poorly during calibration. Still, these results are promising and estimates of the seasonal MB could be performed as soon as the SPOT/VGT data (or data from similar satellites) are available and processed.
A real-time seasonal MB 'monitoring' is thus conceivable, a few weeks after the end of each season for a large sample (here 55) of Alpine glaciers. Our method performs better in winter than in summer. This is mainly explained by the fact that interannual dynamics of altitudinal snow cover distribution is more difficult to capture in summer as there is less snow. We also highlight the fact that interpolation of cloudy pixels has more impact in summer than in winter. However, an in-depth analysis needs to be carried out. The greater accuracy in winter emphasizes the value of this method over the classical snowline approach that does not provide any estimate in winter when accumulation area ratio is 100%. The SPOT / VGT mission ended in May 2014 but the PROBA-V satellite, launched in May 2013, ensures continuity and has been providing data since October 2013, with images at 1 km, 300 m and 100 m resolution.
ACKNOWLEDGEMENTS We acknowledge the  WGMS  for the mass-balance data. We also thank all the observers who contributed to collect the seasonal mass balances and shared them with the community through the WGMS database. We are grateful to  VITO and Belspo  for the SPOT/VGT satellite images distribution, and the financial and scientific support to carry out this study. We acknowledge support from the  CNES/TOSCA programme  (in particular  Juliette Lambin ) as well as funding of a Ph. D. fellowship by  VITO/CLS  (and especially  Eric Gontier ,  VITO  and  Estelle Obligis , CLS). We thank the Scientific Editor,  David Rippin  and two anonymous reviewers for their comments and suggestions which significantly improved the manuscript. This article is also a tribute to  Gilbert Saint  and his vision during the early 90s, of what an operational satellite mission should be.
Data and Cycles The Russell 3000 Index measures the performance of the largest 3,000 U. S. companies representing approximately 98% of the investable U. S. equity market. Our sample contains 3904 daily observations from the Russell 3000 (February 2000 to September 2015) taken from Factset. Factset is a financial database and designates a software editor. The company provides financial information and analytical software for investment professionals. Factset also offers access to data for analysts, portfolio managers and investment banks. The objective of this paper is to study the recent evolution of seasonal anomalies in the American stock market. To this end, we will define the economic cycles between 2000 and 2015 in the U. S. The OECD system of Composite Leading Indicators (CLIs) is designed to provide early signals of turning points in business cycles -fluctuation of the economic activity around its long term potential level.
Questionnaire survey Personal data about age, gender and faculty were collected. The ISI is a 7-item self-report questionnaire assessing the nature, severity, and impact of insomnia. The evaluated domains are: severity of sleep onset, sleep maintenance, early morning awakening problems, sleep dissatisfaction, interference of sleep difficulties with daytime functioning, perception of sleep difficulties by others, and distress caused by the sleep difficulties. A 5-point Likert scale was used to rate each item (0 to 4 where 0 indicates no problem and 4 corresponds to a very severe problem), yielding a total score ranging from 0 to 28. The total score was interpreted as follows: absence of insomnia (0-7); sub-clinical (mild) insomnia  (8) (9) (10) (11) (12) (13) (14) ; moderate insomnia  (15) (16) (17) (18) (19) (20) (21) ; and severe insomnia  (22) (23) (24) (25) (26) (27) (28).
Data analysis The statistical analysis was carried out using SPSS software for Windows (version 19, Chicago, IL, USA). The significance level was set at 5%. Sample characteristics were summarized using the mean and the standard deviation (SD) for continuous variables and percentage for categorical variables. Insomnia prevalence rate was calculated using descriptive data, along with its corresponding 95% confidence interval (CI). For statistical comparison, analysis of variance or Student's t-tests were used for continuous variables, Chi-square test (χ 2 ) or Fisher Exact test were used for categorical variables and Spearman correlation coefficient test for correlations.
Its use was restricted to difficult-to-treat disease, including refractory GCA and patients with serious GC-induced side effects. Pretreatment clinical, laboratory, and pathological data were recorded prospectively by a senior internist at the time of first admittance to our department. The questionnaire was completed in 96% of the cases. Data from patients recruited since 1990 were stored in real time in a computer file and regularly updated by one of the authors (EL). Individual questionnaires from 87 patients recruited before 1990 were initially stored in a personal library and then transferred to the computer file.
Data on DDS treatment were retrieved from the computerized file and individual clinical charts, as needed, and from the FNSIM prospective study database. All patients who received DDS were included, irrespective of treatment duration. These patients were categorized into 2 groups according to the timing of DDS initiation: "first-line" patients (DDS-1) and patients with "difficult-to-treat disease" (DDS-2) for which DDS was considered in addition to prednisone after at least 2 flares or relapses.
Statistical analyses Continuous variables were expressed as means ± SD and categorical variables as frequencies and percentages. The normality of the distribution of quantitative variables was checked using the Shapiro-Wilk method. Comparisons of continuous variables were performed using the Mann-Whitney unmatched-pair test. Proportions were analyzed using the chisquared or Fisher exact test. The significance threshold for all statistical analyses was 0. 05. Calculations were performed using the statistical package SAS, release 9. 1. 3 (SAS Institute, Cary, NC). Calculation of the mean monthly decrease in the prednisone dose during DDS treatment was obtained by dividing, for each patient, the delta prednisone dose (number of mg/d before DDS introduction and at the time of DDS discontinuation) by the duration (months) of DDS treatment.
insulin dose requirements, and an acceptable safety profile. Randomized controlled trials have followed, with inconclusive findings. While two small but well-designed crossover studies  [5, 6]  resulted in gains in glycaemic control with pump therapy versus multiple daily injections (MDI) with basal and prandial insulin, three parallel-group studies found equal improvements in HbA1c both in subjects assigned to pump therapy and those continuing MDI  [7] [8] [9]. The OpT2mise trial (ClinicalTrials. gov number: NCT01182493) was designed to evaluate the efficacy and safety of pump therapy in type 2 diabetes by comparing pump therapy and MDI therapy in patients with diabetes who were already receiving basal and prandial therapy with insulin analogues, and who remained uncontrolled despite a period of insulin dose optimization  [10].
Statistical Methods The statistical analyses have been described in detail elsewhere  [11]. Efficacy analyses of the primary endpoint were performed on an intention-to-treat basis, including all randomized patients. Missing data were imputed using the multiple imputation method  [14]. Given the descriptive nature of the CP of the protocol, available data from this period were analysed without imputation of missing values. Analyses of secondary endpoints accounted for the fact that at the month 12 study visit, the duration of pump therapy differed between the two groups. In order to assess the effects of long-term (12 months) pump therapy in the pump-pump arm, the mean change from baseline (at the beginning of the RP) to 12 months was tested using a t-test. A t-test was also used to assess the short-term pump therapy effect in the MDI-pump arm (6 months from end of RP to the end of CP).
Acknowledgements We thank the  OpT2mise  study team at  Medtronic  for their support in this study, and the monitors, investigators, study coordinators and patients for having conducted this trial. We also thank  John Shin  and  Severine Liabat , employees of Medtronic, for their assistance throughout the conduct of the study. Editorial assistance in the preparation of this paper, funded by  Medtronic , was provided by  Dr Michael Shaw  ( MScript Ltd, Hove, UK ).
48-0. 90], p = 0. 008) (Table  2 ). DFS was not significantly affected according to MSH2 level (Table  2  and Figure  3 , right panel). Finally, MSH2 influence on prognosis did not differ according *information missing for 3 patients to treatment arm (paclitaxel versus gemcitabine), since no significant interaction was observed between MSH2 expression status and treatment arm for DFS prediction (data not shown). For external validation of our data, we used kpm. plot. com online software, computing the MSH2 mRNA prognostic analyses in 681 Stage I-to-III patients, with gene-expression data and OS information downloaded from the GEO (Affymetrix microarrays only), EGA, and TCGA databases (2015 database release)  [27]. OS analysis was dichotomized according to the median value (Figure  4 ) and revealed high MSH2 mRNA content to also be significantly associated with poorer survival in this series of patients (HR = 1.
In line with the clinical trial findings (i. e. , high MSH2 expression correlating with poor OS), we found that the cell lines expressing the highest MSH2 mRNA basal content were the H1299 and A549 cell lines (Figure  5 ), namely those with the highest tumorigenic potential when xenografted in nude mice  [28] [29] [30]. Cells were incubated in the presence of cisplatin/gemcitabine or cisplatin/ paclitaxel, as detailed in Materials and Methods. Treatment efficacy was evaluated by measuring DNA fragmentation and immunolabeling cytochrome C as readouts for cell apoptosis. In the HBEC-3 non-tumorigenic cell line, both cisplatin/paclitaxel and cisplatin/gemcitabine treatments significantly increased DNA fragmentation 2. 5-to 2. 8fold (Figure  6A , t-test, treated cells vs. control cells, p <0. 05), as in the HBEC-3-RasV12, A549, H1650, and H1299 cells (data not shown).
BRCA1 low H-score predicted worse OS in our Cox model adjusted for variables significantly affecting OS (adjusted HR = 1. 58 95% CI [1. 07-2. 34], p = 0. 02) while leading to worse DFS (HR = 1. 44 95% CI [1. 01-2. 04], p = 0. 04), though not in a Cox model adjusted for histology and stage (Table  2  and Figure  8 , A and B left panel, respectively). No significant interaction between BRCA1 expression status and treatment arm was observed for DFS or OS prediction (data not shown). In our experiments, the 8F7 clone did not enable reliable western blot (WB) analyses in lung cancer cell line extracts, despite claims that it functions for WB application. We then checked for any chemotherapy effects on cells in BRCA1 mRNA species, as measured by semi-quantitative qRT-PCR. We found only a slight increase in BRCA1 mRNA in non-tumorigenic cells following treatment by both doublets in HBEC-3 cells, potentiated by p38 inhibition, and only after paclitaxelbased doublet exposure in HBEC-3-RasV12 cells (Figure  9 ).
All these findings were generated by means of mRNA analyses differing from ours using IHC analysis of protein expression. Other striking differences between our study and the literature are the proportion of squamous-cell carcinomas (SCC). SCC accounted for 36. 5% of patients in the Bio-IFCT0002 trial versus 47% in the Taron study and 74% in the Rosell study  [13, 38]. Moreover, Rosell reported a significantly higher content of BRCA1 mRNA in SCCs than in adenocarcinomas. Both of these studies also included Stage III patients (17. 5% and 81%, respectively), rendering any comparison with the Bio-IFCT 0002 dataset challenging. The largest dataset of BRCA1 and MSH2 IHC (769 samples) was derived from the IALT adjuvant study  [39]. which used tissue microarrays (TMAs) and digital-based automated scoring.
Western blot The procedure followed foreseen the protein content extraction from full-thickness samples of ascending colon. After spectrophotometrically protein quantification, samples and appropriate molecular-weight markers were resolved by standard electrophoresis and blotted onto polyvinylidene fluoride microporous membrane. After the incubation with primary and peroxidase-conjugated secondary antibodies, the immunoreaction products were revealed by chemiluminescence reagent. To normalize the values of the antibody, the runs were also immunostained with anti-b-actin, assumed as housekeeping protein. The details about western blot procedures were explained in Data S1. Information on primary antibody sources and used concentrations for fluorescence, light microscope examination and WB analysis is shown in Table  S2.
E. M. The quantitative analysis of the number of IR neurons or inflammatory cells was done by two observers, blind to each other, along the entire section (three sections/animal; five animals/group) and expressed as number AE S. E. M. The quantitative analysis of western blot-IR bands was performed by computer-assisted densitometry by using the QuantityOne analysis software (Bio-Rad, Hercules, CA, USA) with each band corresponding to a single specimen. The results for the protein of interest were normalized to the corresponding b-actin band. Statistical analysis was performed by one-way ANOVA followed by Newman-Keuls post-test to compare the three groups. Differences were considered significant when P < 0. 05.
Admissions' screening Two investigators, a pharmacist (PAJ) and an ICU physician (CP), screened independently all ICU admissions during the morning staff meeting from Monday to Friday. After reviewing each medical chart, they independently sorted all included admissions into two groups: the ADE group (ICU admissions due to ADE) and the control group (ICU admissions for a matter other than ADE). In case of disagreement, BG (Professor of Intensive Care) or CF (Professor of Clinical Pharmacy) classified the admission as related or not to an ADE. For both assessors, the causal relationship between drugs and clinical features was assessed according to chronological, semiological and bibliographical data. Chronological data included the chronology of the events (drug administration or interruption and clinical signs), assessment of drug exposure at the beginning of the first clinical signs, taking into account drug pharmacokinetics.
Additionally, clinical consequences (recovering or not) after drug dechallenge/rechallenge were assessed when possible. Semiological data were based on determining the possible etiologies of the observed clinical signs and on specific laboratory test results. Bibliographical data search was mainly based on the Summary Product Characteristics (SmPC), and in the case of lack of information in the SmPC, additional sources of data were used (Micromedex ® and/or search in Embase database or MEDLINE database via PubMed). ADEs were classified as preventable or unpreventable according to the Schumock and Thornton modified criteria (see the subsection Data sources/measurement-preventability in the "Methods" section)  [20]. Preventable ADEs were subcategorized into 3 classes according to the cause of the event: drug overuse, underuse and misuse.
Data sources/measurement For all included ICU admissions The following data, available in medical records, were collected for every included ICU admission: age, gender, comorbidities, patients' origin (home or hospital), main reason for admission (according to the 10th International Classification of Diseases)  [21]  and LOS or vital status at ICU and hospital discharge. The Simplified Acute Physiology Score II  [22]  and the Sequential Organ Failure Assessment  [23]  were calculated within a period of 24 h after ICU admission. The McCabe score was used as an integrative index of the severity of underlying medical condition  [24]. The need for and the number of organ supports were recorded (invasive or noninvasive mechanical ventilation, vasopressors, massive transfusion or renal replacement therapy).
Data collection The following data were collected from patient files: history of antibiotic, acid-suppressive, or laxative consumption; Cd infection; active chronic bowel inflammatory disease; immunosuppression (including active malignancy or transplantation); chronic renal failure; mobility; cognitive impairment; and dependency. Regarding the present diarrheic episode, the date of the first clinical sign, stool specimen collection, and contact precaution implementation were collected.
e. too high sinks needed to offset CO 2 gradients due to too high emissions  23. During 2006-2009, China emitted RCC at a rate of 0. 102 ± 0. 007 PgC yr -1 , including 0. 072 ± 0. 005 PgC yr -1 of CO, 0. 019 ± 0. 001 PgC yr -1 of NMVOCs and 0. 011 ± 0. 001 PgC yr -1 of CH 4 on average, roughly 14% of these emissions were converted to CO 2 in the boundary layer over China, 12% were deposited to the land surface, and the remaining 74% were transported to the global atmosphere  24. Therefore, the "fossil fuel RCC transferred to global atmosphere" term in Eq. 1 is 0. 076 ± 0. 0050 PgC yr -1 , and the "fossil fuel RCC deposited to land" term in Eq. 1 is 0. 012 ± 0. 0010 PgC yr -1. The Global Fire Emission Database (GFED) biomass burning emission dataset, explicitly separates CO 2 , CO, CH 4 and NMVOC emissions.
The net imports of forest and crop products from outside China, which are decomposed in China and become additional sources of carbon to the atmosphere are included in the top-down sink estimates, but should be subtracted from it to be compared with the bottom-up ecosystem carbon stock change estimate, which does not count wood and crop products stocks. Moreover, carbon accumulated in forest products is a net accumulation of carbon that should be included in the bottom-up estimate  26. Based on the Food and Agriculture Organization of the United Nations (FAO) statistical databases  27  , we estimate that 0. 012 PgC of wood and 0. 019 PgC of food were imported into China every year during 2000s. The net imported food is assumed to be fully consumed and oxidized to CO 2 in the same year, while only a portion of the net imported wood products is fully oxidized.
The carbon burial in lakes and reservoirs are estimated using the data reported by Gui et al. 47  and Dong et al. 48 , which both covered lakes in the middle and lower reaches of the Yangtze River Basin. The mean rate of these two studies is about two times the global mean rate. For the reservoirs, due to lack of observations, we assume that the carbon burial rate in Chinese reservoirs is also about two times of the global mean rate. Trade of food and wood. The import and export data of food and wood products from the FAO statistical databases  27  are used. The food products include cereals, roots, sugar, soybeans and pulses, oil crops, vegetables, fruits, coffee and teas. The wood products include sawn wood, wood-based panels, paper and paperboard, recovered paper, other industry roundwood, and wood fuel and charcoal.
Every year, the food products and the wood products of wood fuel and charcoal are assumed to be totally consumed and oxidized to CO 2 , while the other wood products are partially oxidized and partially go into uses or long-term storage, the CO 2 release by these products are calculated using the method of Winjum et al. 28. Carbon accumulated in wood products. This carbon is calculated using the local production data reported in the FAO statistical databases and the method of Winjum et al. 28.
Acknowledgements We kindly acknowledge all atmospheric data providers to the ObsPack version 1. 0. 2,including the  NOAA Cooperative Air Sampling network  and those that contribute their data to WDCGG. This work was supported by the  National Key Basic Research Development Program of China  (Grant No:  2010CB950704 ,  2010CB950601  and  2010CB833503 ),  National Natural Science Foundation of China  (Grant No:  41201194 ), and the  Priority Academic Development Program of Jiangsu Higher Education Institutions. W. Peters was supported by an  NWO.
Acknowledgments The authors thank  Karima Zouache ,  Pei-Shi Yen ,  Fadila Amraoui  for technical help. We thank  Pei-Shi Yen  for having provided mosquitoes from Orlando and  L Philip Lounibos  (and his laboratory) for mosquitoes collected in  Vero Beach ,  Pascal Gaborit  and  Jean Issaly  for helping in mosquito collections in Cayenne, and  Céline Charles in Martinique. We are grateful to  Joel Gustave  and  Isabelle Dusfour  for their support, and  Olivia O'Connor  for virus isolation. We warmly thank  Richard Paul  for correcting the manuscript. We also thank the two  PI of the Labex IBEID program ,  Pascale Cossart  and  Philippe Sansonetti  for supporting our studies on vector-borne-diseases.
8 +0. 7 -0. 6 × 10 -9 and B(B 0 → μ + μ -) = 3. 9 +1. 6  -1. 4 × 10 -10 [13]. This paper reports the result of a search for B 0 s → μ + μ - and B 0 → μ + μ -decays performed using pp collision data corresponding to an integrated luminosity of 25 fb -1 , collected at 7 and 8 TeV in the full LHC Run 1 data-taking period e-mail: atlas. publications@cern. ch using the ATLAS detector. This analysis supersedes the previous result  [14]  based on 2011 data and exploits improved analysis techniques in addition to the larger dataset.