dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on a data set of 1,000 private Turkish firms, their results support a negative impact of exchange rate volatility on productivity growth which is downplayed by a better access to external finance. We depart from these previous works by using a much wider data set of firms, by looking at whether firms move their exports away from partners characterized by higher exchange rate volatility, and more importantly, by investigating the presence of a non-linear effect of exchange rate volatility on performance depending on the level of financial constraints, in the Chinese context. The latter is apprehended through two complementary dimensions. First, we infer firm-level financial vulnerability from the financial dependence of their activities. This approach was pioneered by  Rajan and Zingales (1998)  and has proved to be a robust methodology to detect credit constraints and assess their evolution  (Kroszner et al.
This export-deterring effect is magnified for financially vulnerable firms: for those most dependent on external finance, a 10% increase in RER volatility decreases the value exported by 14%, and the probability of entering by 3%. As expected, financial development seems to dampen this negative impact, especially on the intensive margin of export. These results are robust to various definitions of trade margins, measures of RER volatility and financial dependence, subsamples, and to the inclusion of additional controls. We therefore provide micro support to the macro literature which points at financial development as a key determinant in identifying the impact of RER volatility on real outcomes. In the next section, we survey the different theoretical mechanisms underlying our approach, before discussing our general methodology and presenting our database in section 3.
Exchange Rate Volatility Exchange rate volatility is computed as the yearly standard deviation of monthly log differences in the real exchange rate. We compute the real exchange rate as the ratio of the nominal exchange rate of the yuan with respect to the partner's currency divided by the partner's price level. Monthly data on nominal exchange rates and prices are taken from the IFS. As a robustness check, we consider two alternative measures of volatility, the two-year standard deviation of monthly log differences in the real exchange rate and the yearly standard deviation of monthly log differences from the HP detrended real exchange rate  (Hodrick and Prescott, 1997).
(2011) , the use of US data is not only motivated by the lack of data for most other countries, including China, but it has several advantages. Rajan and Zingales (1998)   In addition to these firm-sector indicators of financial vulnerability, we also use the level of financial development at the regional level. We thus adapt the methodology first used in Rajan and Zingales (1998), which consists in filtering the impact of financial liberalization by the financial vulnerability, in order to isolate its direct finance-related causal effect. We measure local financial development as the share of total credit over GDP in the province. 6 Finally, descriptive statistics of key variables are given in Tables  1  and Tables 2 below. Table.
Conclusion This paper relies on a firm-level database covering exporters from China to study how export performance is affected by real exchange rate volatility. Our results confirm a trade-deterring effect of RER volatility, but suggest that its magnitude depends mainly on the extent of financial constraints. While firms tend to export less and to reduce their entry into destinations with higher exchange rate volatility, this negative effect is even stronger for financially vulnerable firms. Also, financial development appears to dampen this negative impact, especially on the intensive margin of export. These results suggest that the development of credit markets would help firms to overcome the additional export (both variable and sunk) costs related to RER volatility. This could support the expansion of exports by firms, particularly to those destinations characterized by RER-related uncertainty.
Participants Twenty-seven marijuana users (MJU: 16 males, 11 females) were recruited into the study through local advertisements on the University of Minnesota-Twin Cities campus. Marijuana users' ages ranged from 18-to-21 years, with a mean and standard deviation of 19. 5 ± 0. 6 years (Table  2 ). Exclusion criteria are described below. Twenty-six healthy young adult non-users (10 males, 16 females), who were participants in a large, longitudinal study of normal brain development, served as a control sample. Control participants' ages ranged from 13-to-24 years, with a mean and standard deviation of 19. 3 ± 3. 1 years. The recruitment strategy for the control sample has been described elsewhere  (Muetzel et al. , 2008; Olson et al. , 2009; Porter et al. , 2011). Briefly, participants younger than 18 years of age were recruited through a database of research volunteers throughout the Metro community, through post-cards mailed to University of Minnesota civil service employees, and through local advertisements.
MR data acquisition Magnetic resonance data were acquired using a 3 Tesla (T) whole-body TIM TRIO system (Siemens, Erlangen, Germany) housed at the University of Minnesota's Center for Magnetic Resonance Research. Radiofrequency transmission was performed with a whole body coil, and signal was received with a 12-channel receive-only head coil. A 10-second, 3-plane localizer image was first acquired for positioning of subsequent scans. A coronal T 1 -weighted, magnetization prepared rapid gradient echo  (Mugler and Brookeman, 1990)  sequence was used to acquire a high-resolution scan for MRS voxel positioning and tissue segmentation (repetition time (T R ) = 2530 ms, echo time (T E ) = 3. 65 ms, inversion time (T I ) = 1100 ms, flip angle = 7°, number of slices = 240, matrix size = 256 × 256, field of view (FOV) = 256 mm × 256 mm, slice thickness = 1 mm).
Structural MRI data processing A high-resolution structural scan was acquired to position the voxel during data acquisition and to determine the tissue composition of the voxel through segmentation. The T 1 -weighted scan was processed using the standard FreeSurfer pipeline (http://surfer. nmr. mgh. harvard. edu) for tissue segmentation and anatomical parcellation  (Fischl et al. , 2002). Further details related to the FreeSurfer processing can be found online, and in one of our previous publications  (Porter et al. , 2011). In-house software was used to compute the transformation matrix from the scanner coordinates to the FreeSurfer-processed T 1 -weighted image. A mask representing the spectroscopy voxel in the anatomical image space was then created using tools from the FMRIB Software Library  (Smith et al.
Quantification The acquired spectra were analyzed using LCModel 6. 1-4A  (Provencher, 1993 (Provencher, , 2001 )(Stephen Provencher, Inc. , Oakville, Ontario, Canada), with the basis set generated using in-house programs based on the density matrix formalism  (Henry et al. , 2006)  in Matlab (The MathWorks, Inc. , Natick, MA, USA) using the known chemical shifts and J-couplings  (Govindaraju et al. , 2000; Kaiser et al. , 2010). The simulated spectra of the following twenty metabolites were included in the basis set for LCModel: alanine (Ala), ascorbate (Asc), aspartate (Asp), Cr, GABA, glucose (Glc), Gln, Glu, glycerophosphorylcholine (GPC), glycine (Gly), glutathione (GSH), lactate (Lac), mIns, NAA, N-acetylaspartylglutamate (NAAG), phosphocreatine (PCr), phosphorylcholine (PCho), phosphorylethanolamine (PE), scylloinositol (sIns), and taurine (Tau).
Experimentally measured metabolitenulled macromolecular spectra from 41 subjects were also included in the basis set (T I = 827 ms, N S = 64). No baseline correction, zero-filling or line broadening were applied to the in vivo data prior to the analysis. The LCModel fitting was performed over the spectral range from 0. 5 to 4. 2 ppm. Subjects were excluded from the analysis if their NAA line-width was greater than 8 Hz. Further, criteria for selecting reliable metabolite concentrations were based on Cramér-Rao lower bounds (CRLB), which are estimates of the %SD of the fit for each metabolite  (Provencher, 1993). Only results with a CRLB ≤ 30% were included in the analysis. Concentrations with CRLB > 30% were classified as not detected. Only metabolites that had a CRLB below 30% in more than 75% of the spectra were included in the neurochemical profile.
Acknowledgments This study was supported by grant  R01DA017843  awarded to  M. Luciana  by the  National Institute on Drug Abuse , by grant  R01AA020033  awarded to  M. Luciana  by the  National Institute on Alcohol Abuse and Alcoholism , by the  University of Minnesota's Center for Neurobehavioral Development , by grants  P41 RR008079  (NCRR),  P41 EB015894  (NIBIB) and  P30 NS057091  ( NINDS)  awarded to the  University of Minnesota's Center for Magnetic Resonance Research , and by the  Minnesota Supercomputing Institute. We are grateful for the contributions of the research staff who helped with subject recruitment and data acquisition, including  Zach Grice-Patil ,  Daniel Johnson ,  James Porter ,  Ann Schissel ,  Brittany Schmaling , and  Sasha Sommerfeldt. We would also like to thank the participants who partook in this research.
Study Design and Data Source This is a prospective observational cohort study from the research database of the «Observatoire Sommeil de la Federation de Pneumologie'' (OSFP). This database is a large, well maintained database administered on a not-for-profit basis by the French Federation of Pneumology. It contains anonymized medical records from respiratory physicians in private practice, general hospitals and university hospitals  [29]. The OSFP registry is a standardized web-based report including longitudinal data from patients complaining about sleep symptoms and being treated for sleep breathing disorders. Participating staff are trained in the use of computerized medical records and appropriate software. Periodic quality control checks are made to ensure up-to-standard data recording.
Ethical committee approval was obtained by ''Le Comite ćonsultatif sur le traitement de l'information en matiere de recherche en sante'' (C. C. T. I. R. S nu 09. 521) and authorization from the ''Commission Nationale Informatique et Liberte'' (C. N. I. L), the French information technology and personal data protection authority. The OSFP Independent Scientific Advisory Committee approved data use for this study. Patients included in the database gave written informed consent. Inclusion criteria. Between March 2009 and December 2011, adult patients ($18 years old) fulfilling the following criteria were identified in the OSFP database: -A baseline medical visit reported in the database that included the diagnosis of obstructive sleep apnea syndrome exclusively (i. e: patients with central or mixed apnea syndrome were not selected) and the prescription of CPAP treatment.
-A follow-up medical visit, within a 1 to 24 month window after CPAP prescription, reported in the database. Patients treated with other modalities of pressure therapy such as bi-level positive airway pressure were not selected. Outcome measures. Clinical information collected for the analysis included anthropometry, medical history with cardiometabolic co-morbidities, severity of sleep apnea (established by inlab attended or unattended polysomnography and respiratory polygraphy (minimum 3 cardiopulmonary channels)), daytime sleepiness (Epworth Sleepiness Scale; (ESS))  [30] , fatigue (Pichot fatigue Scale), depressive status (Pichot depression scale  [31] ) and treatment modalities (auto-adjusted CPAP versus fixed pressure, pressure level, interface type, use of humidifier). A specific questionnaire targeting CPAP-related side effects was filled-in by each patient.
Data Management and Statistical Analysis Data were analyzed using Statistical Analysis System (SASH) software version 9. 1. 3 (SAS Institute, Cary, NC, USA). Continuous data were expressed as mean (SD), and categorical data as percentage. Univariate conditional logistic regression models were used to compare all the variables between CPAP adherent and non-adherent patients (threshold 4 h/night). The delay to the follow-up visit was the matching factor (conditional factor): four strata were defined: ]1-6 months], ]6-12 months], ]12-18 months] and ] 18-24 months]. When log-linearity of a continuous variable was not respected, the variable was converted to dichotomous data (. or,the median value). Variables which were associated with the risk of being nonadherent to CPAP in univariate analysis (p,0.
Results Figure  1  shows the flow chart of the study. Two thousand three hundred and eleven OSAS patients with complete information regarding symptoms, comorbidities, CPAP technology, interfaces, CPAP adherence and side effects were analyzed. The mean delay between CPAP initiation and the follow-up visit was 4. 563. 6 months. Seventy-seven and nineteen percent of the follow-up visits occurred between 1 to 6 months and 6 to 12 months respectively. For 2475 patients with OSA in the database, CPAP adherence was not known (2122 patients) or the type of interface was not recorded (353 patients) and they were not included in the analysis. Table  1  shows a comparison between patients who were analyzed versus patients excluded from the analysis. There were some statistically significant differences that actually had no clinical relevance.
Acknowledgments The authors thank  Mrs Line Mounier  and  Mr D Leclercq , who analyzed the data of the regional associations of the  ANTADIR observatory (AADAIRC (Rochefort), ADIRAL (Nancy/Dijon), ADIR Assistance (Rouen) ,  AGIR aDOM (Grenoble) ,  AIR Basse Normandie (Caen) ,  ALISEO (Nantes/Angers) ,  ARAIRCHAR (Reims) ,  SADIR Assistance (Toulouse) ,  SANTELYS (Lille) ). Dr Alison Foote  for English editing. Members of the scientific council of The Sleep registry of the  French Federation of Pneumology :.
Immunocytochemistry and Fluorescent Linked ImmunoSorbent Assay (FLISA) Fixed cell monolayers were rehydrated by overnight incubation in phosphate buffered saline solution without Ca++ and Mg++ (PBS0). Incubations of primary antibodies were carried out overnight at 4uC in PBS0 containing 0. 2% bovine serum albumin (weight/volume; fraction V, Eurotech). After 3 washing cycles with PBS0, cell preparations were incubated with Hoechst 33258 and secondary antibodies (Goat-anti-rabbit-Alexa-568, Molecular Probes) during one hour at 37uC in PBS0 containing 0. 2% bovine serum albumin. After 3 washing cycles with PBS0, cellular preparations were mounted into ProLong Gold (In Vitrogen) and visualized under Leica videomicroscope (x 40 magnification) or Zeiss apotome microscope (x 63 magnification). For quantification, preparations were observed under a Leica fluorescent video microscope (x 40 magnification) with Metamorph software.
Quantitative RT-PCR Experiments We designed forward and reverse primers with Beacon Designer or PerlPrimer  [36]  software; the specificities were assigned independently on line with the Blast software. Total RNA was extracted from tryptophan and saline treated fibroblasts collected at 0, 6, 12, 18, 24, and 30 h after a serum shock using the Trizol reagent (Invitrogen, Cergy Pontoise, France), treated with DNAse (RNAse free) for 30 min at 37 uC (Promega). The quality was checked on agarose gels and the quantity determined using a NanoVue TM Plus Spectrophotome-terGE Healthcare at 260 and 280 nm. Afterwards, 1 mg of purified RNA was reversed-transcribed using the reverse transcription system (Promega) according to the manufacturer's instructions. Real-time PCR was performed to measure relative mRNA expression in a Bio-Rad iCycler iQ system using the iQ SYBRGreen Supermix PCR kit (Bio-Rad Laboratories) and specific primers.
Data and Statistical Analyses On data obtained from immunofluorescence imaging, normality of distribution of the intensity of a specific labeling was tested on 30 cells at a 5% level according to Kolmogorov test. Concerning, FLISA values, experimental results are expressed as means +/2 s. e. m. With n = four-six cell monolayers per time point. Each cell monolayer was from a different rat. To test for the presence of circadian rhythms, time series data were first analyzed by three-ways ANOVA using the on line available R package  [38]  based on classic statistics  [39]. All tests were two-tailed and the significance level was set at 5% level. There is no consensus to analyze rhythms, however we have used the methodology of Exploratory Data Analysis (http://www. itl. nist. gov/div898/handbook/index.
Acknowledgments We thank  Thomas Moyon  ( UMR-PHAN-1280 ,  Nantes, France ) for helpful comments in statistical analysis. We thank  Isabelle Jiquel ,  Frank Doulay  and  Guillaume Poupeau  for animal care. The authors gratefully acknowledge the assistance of  Philippe Hulin  from the  Cellular and Tissular Imaging Core Facility of Nantes University (MicroPICell). The work was in part reported in the Master of  Science of ND (Sanh, University of Nantes ). We are grateful to  Dominique Darmaun  for careful reviewing of manuscript.
Data analysis WHO criteria  [28]  were adopted to define the resistance status of the mosquito populations. When less than 80% mortality was observed the population was considered 'resistant', between 80% and 97% mortality the population was considered 'tolerant' (or 'suspected of resistance') and when the mortality was above 98% the population was considered 'susceptible'. Biochemical assay data (amounts of MFO and enzymatic activity per mg protein of NSE and GST) of Kisumu and the An. gambiae populations from the sentinel sites were compared using Kruskall-Wallis nonparametric test (Statistica software). Conformity of L1014F and L1014S kdr and ace-1 mutations frequencies with Hardy-Weinberg equilibrium was tested for An. gambiae population from the sentinel sites using the exact probability test  [29].
Discussion A nation-wide survey of insecticide resistance in An. gambiae s. s. in Cote d'Ivoire was conducted in order to have an overview of the resistance status and establish a baseline dataset that would guide the National Malaria Control Programme. The bioassays data showed high resistance levels of An. gambiae s. s. to organochlorides, carbamates and pyrethroids but at a lesser extent toward deltamethrin. The resistance levels to the organo-phosphate (pyrimiphos-methyl) varied greatly from susceptibility to resistance across sites. The kdr and ace-1 R mutations were highly expressed in all An. gambiae s. s. populations with the exception for the ace-1 R in samples from Abengourou and San-Pedro. The ace-1 D duplication was present in Bingerville and Port-Boue ¨t respectively in S and M molecular forms confirming previous finding highlighting its presence in Cote d'Ivoire  [31, 32].
Acknowledgments Sincere thanks go to  Dr Koffi San , former Head of the  National Malaria Control Programme of Cote d'Ivoire  and the research unit of  NMCP  ( Dr Serge B Assi. ,  Dr Florence-Judith Kadjo ). We are very grateful to all the staff at the  Institut Pierre Richet, Bouake, Cote d'Ivoire , particularly Aboubacar Kone, for his hard work during the field and laboratory experiments. We also thank to  Aboubacar Sidick  from the  Institut de Recherche pour le Developpement (IRD) / Centre de Recherche Entomologique de Cotonou (CREC) ,  Benin , for his technical assistance in biochemical and molecular experiments. We are very grateful to  Dr Pierrick Labbe  ´for his crucial help on the ace-1 D frequency calculations. Special thanks also go to  Bamoro Coulibaly  for mapping the study site.
Methods: We searched three online databases to systematically identify publications testing temozolomide in animal models of glioma. Median survival and number of animals treated were extracted and quality was assessed using a 12-point scale; random effects meta-analysis was used to estimate efficacy. We analysed the impact of study design and quality and looked for evidence of publication bias. Results: We identified 60 publications using temozolomide in models of glioma, comprising 2443 animals. Temozolomide prolonged survival by a factor of 1. 88 (95% CI 1. 74-2. 03) and reduced tumour volume by 50. 4% (41. 8-58. 9) compared with untreated controls. Study design characteristics accounted for a significant proportion of between-study heterogeneity, and there was evidence of a significant publication bias.
Although temozolomide was being used clinically for melanoma in the mid-1990s, supported by substantial experimental data  (Bleehen et al, 1995) , there were no such animal data on temozolomide for brain tumours. The efficacy of temozolomide in glioma was actually first reported in patients somewhat anecdotally  (O'Reilly et al, 1993) , with a more structured study by the same group published in 1996  (Newlands et al, 1996). The first animal work in the area was published in 1994  (Plowman et al, 1994) , and by 1998 and 2000 the first patients were recruited for phase II (van den  Bent et al, 2003)  and III  (Stupp et al, 2005)  trials, respectively. A preliminary literature search of PubMed suggests that by the end of 2000, only seven relevant experimental studies had been published. The basis for the decision to proceed to clinical trial is therefore not clear: possible factors include those experimental data, which did exist; the non-randomised clinical evidence of O'Reilly  (O'Reilly et al, 1993)  and Newlands  (Newlands et al, 1996) ; and evidence from phase II studies testing temozolomide with other cancer types  (Bleehen et al, 1995; Woll et al, 1995).
Data collection. We extracted data relating to experimental design and for each comparison, we recoded median survival or tumour volume in treated and control groups. When data were not clearly or fully described we contacted authors seeking the data; if no reply was received after 1 week these were excluded. Data were entered to the Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies (CAMARADES) data manager application in Microsoft Access 2003. For experimental design we extracted data for species, strain and sex of animals; glioma cell type, tumour implantation site, tumour implantation method, number or volume of implanted tumour cells and method of tumour volume measurement (where relevant). Glioma models were stratified into groups (Table  1 ).
In addition, we recorded the total dose of temozolomide (in mg kg À 1 ), delay to treatment (in days) and route of administration. When dose was given in mg m À 2 , we converted it to mg kg À 1 using Freirich's  (Freireich et al, 1966)  conversion factor; where animals received continuing treatment courses, we calculated the total dose received by the time of median survival or the last time at which tumour volume was measured. Experimental starting point (i. e. , day 0) was defined as the day of tumour implantation. When the delay from implantation to treatment was not given, we contacted authors seeking this information. For each comparison, we extracted data for the number of animals in each group and either median survival or mean tumour volume, relative tumour volume or percentage change in tumour volume.
ACKNOWLEDGEMENTS The authors acknowledge financial support from  MRC Trials Methodology Hub  ( KJE, MRM) CCBS Studentship  (HMV), the  MRC (IRW) ,  CRUK (IRW)  and the  Melville Trust (IRW). This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3. 0 Unported License.