dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS The general characteristics of the study population are shown in Table  1. The data concerned 205 participants: 134 women (65. 4%) and 71 men. The mean age was close to 81 years and 61. 6% (125/203) had history of hypertension. During follow-up, the median number of MMSE was 3 (mean 3. 31; range: 1 to 10, 29 patients had only 1 MMSE). The median follow-up period for the 176 patients who had two MMSEs was 14. 5 months (range: 2 days to 5 years). The patients were divided into groups according to their final diagnosis: no NCD (11 cases), mild NCD (41 cases), AD (85 cases), mixed dementia (45 cases), vascular dementia (12 cases), LBD or PD (7 cases) and "other/non determined" (4 cases). Given the small sample sizes, "no NCD", "LBD or PD", and "other/non determined" groups were not considered in the analyses.
ACKNOWLEDGMENTS The authors thank  Drs. Anne Mialon ,  Murielle Lemoine ,  Ludivine Aujas ,  Lorine Cailler ,  Thomas Biel ,  Marie-Anne Bourquard , and  Maël Roojee  for their participation to this work. They also thank  Jean Iwaz  ( Hospices Civils de Lyon ) for the revision of the final draft of the manuscript. Authors' disclosures available online (https:// www. j-alz. com/manuscript-disclosures/18-0533r2).
Pathogenicity bioassay The aphids were immersed for 10 s in each fungal suspension, which was prepared at the concentration (1. 10 7 conidia/ml) of sterile distilled water and supplemented with a drop of Tween 80 (0. 05%). Dishes were kept at room temperature of 25 °C and 60% R. H. The mortality rates of the aphids were evaluated on 1st, 3rd, 5th, and 7th day after inoculation. All the treatments were repeated six times. Obtained data were analyzed. Tukey test was applied after one-way ANOVA for numbers of live individuals, using SPSS ® for data analysis  (version 17. 00 Software. 2006 SPSS. Inc. Chicago. il, USA). The percentage effects of the three fungal isolates were calculated and corrected by  Abbott's (1925)  formula: Abbott ' s corrected mortality% ¼ mortality in control-mortality in treatment ð Þ mortality in control Â 100 Enzymes' essays Screening for protease enzyme production The three EPF (B.
Acknowledgements The authors thank  Süleyman Demirel Üniversitesi ,  Ziraat Fakültesi ,  Bitki Koruma Bölümü ,  32260 ,  Isparta-Türkiye ,  Mentouri Brothers Constantine, Faculty of SNV, Constantine, Algeria , and the  National Institute of Agronomic Research Constantine-Algeria (INRA. Constantine) , for their kind and great help.
Endobronchial coil treatment (ECT) is a minimally invasive procedure developed for palliative care of patients with severe emphysema. ECT has demonstrated a decrease in hyperinflation, an improvement in quality of life, and an acceptable safety profile in randomized controlled trials (RCTs). Because alpha-1 antitrypsin deficiency (AATD) is a classical exclusion criterion in RCTs, there is no available data for ECT in AATD. In this post hoc analysis of the REVOLENS study (Réduction volumique endobronchique par spirales; ClinicalTrials. gov Identifier: NCT01822795), a multicenter 1:1 RCT which compared bilateral ECT with usual care in severe emphysema, we analyzed the efficacy and safety results at 1 year in six patients with AATD (five males, one female; mean age: 52±9 years) who underwent ECT.
Introduction Despite optimal medical management including intravenous alpha-1 antitrypsin (AAT) augmentation therapy, many patients with AAT deficiency (AATD) with severe emphysema remain symptomatic. 1 Compared with non-AATD-related emphysema, lung volume reduction surgery is associated with inferior outcomes and lung transplantation is associated with similar outcomes in AATD emphysema. 2 Endobronchial lung volume reduction (ELVR) treatments including valves, coils, and thermal vapor ablation have demonstrated efficacy and safety in randomized controlled trials (RCTs) including highly selected emphysematous patients. 3 However, AATD emphysema is a usual exclusion criterion in RCTs, which has led to the absence of robust data in this population. The REVOLENS study (Réduction volumique endobronchique par spirales; ClinicalTrials.
Acknowledgments The members of the REVOLENS Study Group include all authors and  Hervé Vallerand MD ,  François Lebargy MD PhD ,  Sandra Dury MD ,  Claire Launois MD  ( Hôpital Universitaire de Reims ),  Johanna Pradelli MD ,  Matthieu Buscot MD ,  Celine Sanfiorenzo MD ,  Sylvie Korzeniewski MD ,  Andrea Mazzetta MD ,  Jennifer Griffonnet ,  Ariane Guillemart ,  Demosthenes Makris MD  ( Hôpital Universitaire de Nice ),  Armelle Marceau MD ,  Marie Christine Dombret MD ,  Frédérique Servin MD ,  Marie Pierre Debray MD  ( Hôpital Universitaire Bichat, Paris ),  Sophie Laroumagne MD ,  Fabienne Bregeon MD PhD ,  Carine Gomez MD ,  Philippe Astoul MD PhD  ( Hôpital Universitaire de Marseille ),  Nathalie Lesavre MD  ( Center d'Investigation Clinique 1409, AP-HM, Marseille ),  Jean Pierre Mallet MD ,  Anne Sophie Gamez MD ,  Philippe Tarodo MD ,  Christophe Brousse MD ,  Clément Broissin MD  ( Hôpital Universitaire de Montpellier ),  Yoann Thibout MD ,  Fabrice Di Palma MD ,  Frédéric Costes MD  ( Hôpital Universitaire de Saint Etienne ),  Amandine Briault MD ,  François Arbib MD ,  Emilie Reymond MD  ( Hôpital Universitaire de Grenoble ),  Romain Kessler MD PhD ,  Michele Porzio MD ,  Benjamin Renaud-Picard MD  ( Hôpital Universitaire de Strasbourg ),  Vincent Jounieaux MD PhD ,  Bénédicte Toublanc MD ,  Géraldine François MD  ( Hôpital Universitaire d'Amiens ),  Mathieu Salaün MD PhD ,  Luc Thiberville, MD PhD ,  Antoine Cuvelier MD PhD ,  Samy Lachkar MD  ( Hôpital Universitaire de Rouen ),  Margaux Bonnaire ,  Delphine Gras PhD  ( Monitoring Team, Hôpital Universitaire de Reims ),  Philippe Benoit MD  ( Pharmacie, Hôpital Universitaire de Reims ),  Sylvain Dukic PhD ,  Annick Leclainche ,  Damien Jolly MD PhD  ( Pôle Recherche-Innovations, Hôpital Universitaire de Reims ).
These collaborators did not receive any compensation for their contributions. The  REVOLENS Study  was supported by an academic grant from the  French Ministry of Health (Direction Générale de l'Offre de Soins ,  PSTIC-2012 ). The coils were purchased from the manufacturer ( BTG/PneumRx ,  Mountain View ,  CA, USA)  which had no involvement in the study design and analysis of the data. PneumRx/BTG  provided a financial support for the follow-up assessment after 1 year. The  French Ministry of Health  had full access to the results of the study, but had no involvement in the design and conduct of the study, collection, management, analysis, and interpretation of data, preparation, review, or approval of the manuscript, and decision to submit the manuscript for publication.
Simulated productivity during the observational period is influenced at least in some models (for instance those having C-N interactions) by historical climate change and CO 2 changes since the preindustrial period. Thus, instead of assuming that productivity was in equilibrium with current climate, historical reconstructions of meteorological variables from gridded CRUNCEP data at half-hourly time step  (Wei et al. , 2014)  were combined and bias corrected with site observations to provide bias corrected historical forcing time series from 1901 to 2013 (CRUNCEP-BC). In addition to the observed current climate defining the ambient simulation, nine altered rainfall forcing datasets were constructed by decreasing or increasing the amount of precipitation in each precipitation event by -80, -70, -60, -50, -20, +20, +50, +100 and +200 % during the time span of productivity observations at each site, leaving all other meteorological variables unchanged and equal to the observed values.
Modelers performed all simulations described below based on the same protocol (see below) and the model output was compared with measured ecosystem productivities (GPP; NPP; ANPP; and BNPP, belowground NPP), whenever available. Simulation S0 spin-up: models simulated an initial steady state spin-up run for water and biomass pools under preindustrial conditions using the 1901-1910 CRUNCEP-BC climate forcing in a loop and applying fixed atmospheric CO 2 concentration at the 1850 level. Simulation S1 historical simulation from 1850 until the first year of measurement  (1986 for SGS, 1982 for KNZ and 2009  for STU): starting from the spin-up state, models were prescribed with increasing atmospheric CO 2 concentrations and dynamic historical climate from CRUNCEP-BC. Because there is no CRUNCEP-BC data for 1850-1900, the CRUNCEP-BC climate data from 1901 to 1910 was repeated in a loop instead.
Simulation SC1 ambient simulation for the measurement periods  (1986 -2009 for SGS, 1982 -2012 for KNZ and 2009 -2013  for STU): starting from the initial state in the start year of the period and run with observed CO 2 concentrations and meteorological data corresponding to site observations at the hourly or half-hourly scale. Simulations SP1-SP9 altered precipitation simulations for the measurement periods  (1986 -2009 for SGS, 1982 -2012 for KNZ and 2009 -2013  for STU): starting from the initial state in the start year of the period and run using the nine altered rainfall forcing datasets with observed CO 2 concentration.
Metrics of the response of productivity to precipitation changes In the analysis, we begin with testing our first specific objective, i. e. , if the productivity-P sensitivities of spatial relationships are greater than the temporal ones in the models as found in the observations. We calculated the temporal slopes and spatial slopes between productivities and precipitation from multi-year ambient simulations (SC1). Temporal slopes are site based and relate inter-annual variability in precipitation to inter-annual variability in the productivities using linear regression analysis. Spatial slopes relate mean annual precipitation to mean annual productivity across the three sites. We then calculated two indices to analyze the asymmetric responses of primary productivity to precipitation simulated by ecosystem models and derived by observations whenever data were available.
, 2017; Wilcox et al. , 2017). (3) There still exist large gaps between changes of precipitation occurrence and intensity in reality and how we simulated them in the current work, i. e. , the altered rainfall forcing datasets were constructed by decreasing or increasing the amount of precipitation in each precipitation event by a fixed percentage during the time span of productivity observations at each site and not by modifying precipitation structure or reproducing the real treatment. Further studies need to consider better different scenarios of precipitation occurrence and intensity under climate change  (Lauenroth and Bradford, 2012) , which will likely help to better understand the responses of productivities to altered precipitation in the next decades. In addition, modelers will need to simulate the control experiments corresponding to the real local precipitation manipulations applied by field scientists, e.
Acknowledgements. This study was supported by  National Natural Science Foundation of China  ( 41530528 ). PC was supported by the  European Research Council  Synergy project SyG-2013-610028 IMBALANCE-P. The field work at Stubai was funded by the  EU   FP7  project  Carbo-Extreme  and the  Austrian Science Fund (FWF) ; the synthesis and contribution to the manuscript was supported by the  Austrian Academy of Sciences (ClimLUC). We also acknowledge support from the  ClimMani COST action  ( ES1308 ). Sara Vicca  is a postdoctoral fellow of the  Fund for Scientific Research -Flanders. Markus Kautz  acknowledges support from the  EU   FP7  project  LUC4C , grant  603542. We thank  Jeffrey S. Dukes ,  Shiqiang Wan  and the organizers of the conference for the modelexperiment interaction study in Beijing.
was supported by funds from the  European Research Grant information: Council  ( ERC-2015-STG RCSB 679980 ), the  Mairie de Paris 'Emergence(s)' program , and the  ANR 'Investissement d'Avenir Programme'  ( 10-LABX-62-IBEID ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statistical analysis For the validation study (LOD determination), the assessment of the effect of L20B cell passages on poliovirus secondary viral suspension titers was performed using a Cochran's test. The comparison of the variability of data obtained with PMKC and L20B cells was performed using a Fisher-Snedecor's test. Assessment of the difference in titers between PMKC and L20B cells was performed using a Student's test  (Dixon and Massey, 1957; Snedecor and Cochran, 1989). Statistical analysis was carried out on raw data to define the LOD of the test for inactivation with L20B cells with different test designs. The LOD can be defined as the last common positive dilution of the six experiments (experimental LOD) and by calculation of the mean and 95% confidence interval of the mean of the six last positive dilutions (calculated LOD).
Processing pyrosequencing data All sequence processing was done using the QIIME pipeline software  (Caporaso et al. , 2010). Poor quality sequences (score <10 on a 50 bp sliding window) or sequences shorter than 300 base pairs for bacteria and 230 for fungi were discarded. De novo and reference-based chimera detection, as well as clustering in OTUs were performed using USEARCH  (Edgar, 2010)  and the greengenes database (V. 05/2013) for 16S rRNA and UNITE database for ITS. The identity thresholds were set at 97% for both 16S and ITS data. For 16S and ITS data, representative sequences for each OTU were aligned using PyNAST  (Caporaso et al. , 2010)  and a 16S phylogenetic tree was constructed using FasTree  (Price et al. , 2009). Taxonomy was assigned using UCLUST  (Edgar, 2010)  and greengenes database  (McDonald et al.
Microbial diversity Sequencing and quality filtering resulted in 44,481 high quality sequences for the 16S rRNA gene and 37,969 sequences for ITS with an average of 2965 and 2531 sequences per sample for the bacteria and fungi, respectively. The dataset comprised 1756 bacterial OTUs and 1338 fungal OTUs at 97% genetic identity. Whatever the treatment, the bacterial diversity was distributed among 4 dominant bacterial phyla (Fig 1A ), which represented 94% of the overall bacterial diversity; Firmicutes (37%), Proteobacteria (25%), Actinobacteria (18%) and Acidobacteria (13%). For the fungal community (Fig. 1b ), the number of representative phyla was low, as only two phyla represented 72% of the overall fungal biodiversity, namely Ascomycete (60%) and Basidiomycetes (12%), but 26% of fungal reads were unclassified at the phyla level (Fig.
Acknowledgements This research was founded by the  ANR (Agence Nationale pour la Recherche,)  " Heveadapt " project, the  French Institute of Research for Development (IRD)  through the  International joint laboratory LUSES (Land Uses and Soil Ecosystem Services) , the  French Institute for Natural Rubber (IFC)  and the companies SIPH, SOCFIN and  MICHELIN  within the program "HeveaBiodiv". The authors gratefully acknowledge,  Josiane Abadie  ( INRA ) and  Anne Laure Pablo (IRD)  for their technical support and the  Land Development Department, Thailand  for technical and logistical support.
70, which could perhaps be mistaken for an explosive volatility. In other words, the emphasis on a particular dynamic of the drift or of the volatility as the diagnostic of a bubble may depend on the chosen time scale and on whether one focuses on transient (i. e. "explosive") dynamics. As a transient drift contributes to volatility at longer time scales, this might contribute to misunderstanding between these apparently fundamentally distinct approaches. Recently,  Vogel and Werner (2015)  compared the historical and implied volatility time series of the S&P500 from September 2004 to June 2014 and suggested that implied leads historical volatility and that a rise in the implied volatility foreshadows a bubble or crash condition. However, this conclusion is based on a very limited dataset, and may not be generalisable, as we discuss below.
The arrow plot in the bottom panel of figure 32 confirms this observation by showing a gradual rise in the green arrows in the months before the crash. To understand the credit conditions during and after this period of boom and bust, figure ?? shows the credit from banks to the private non-financial sector as a percentage of the GDP of Hong Kong together with its two-yearly growth rate. The historical time series are plotted starting five years before and ending five years after the peak of the Hang Seng Composite index in January 1994. The data are from the Bank for International Settlements (2017). It can be seen that the level of credit to GDP reached a low right at the end of the bubble. From this data it is clear that credit growth was no driver of the bubble. Figure  31. The credit conditions in Hong Kong between 1982 and 1992, five years before and after "Black Monday".
The historical time series are plotted starting five years before and ending five years after the peak of the SSE index in October 2007. The data are from the Bank for International Settlements (2017). The credit as a percentage of GDP and its growth rate were decreasing and at low levels around the time of the end of the bubble. We will classify this stock market bubble, ending at the end of October 2007, as a fearless, nonleveraged bubble, the former meaning that there was a significant decrease in volatility towards the end of the bubble, starting approximately four months before its peak, the latter summarising the fact that the credit level and its growth rate were at historically low levels during the bubble build-up phase. This section focusses entirely on the Shanghai Composite index.
There is absolutely no increased volatility that could be interpreted as a warning signal for the investors who were facing a 60% loss in the following months. Figure  41  is similar to figure 39 from the previous section; the observations are only shifted nine months forward in time. The plot shows the credit from banks to the private non-financial sector as a percentage of the GDP of Argentina together with its two-yearly growth rate, starting five years before and ending five years after the peak of the Argentinian stock market in June 1992. The data are from the Bank for International Settlements  (BIS, 2017). The credit as a percentage of GDP has stabilised over the time interval of this bubble with a growth rate converging to zero. We will classify this stock market bubble, ending at the end of June 1992, as a fearless, nonleveraged bubble, the former meaning that the volatility decreased during the ramp up of the bubble, the latter summarising the fact that the credit levels were stable over that same period.
During the bubble buildup phase, only a momentary rise in the historical volatility can be observed around November 1993. This coincided with a minor transient correction in the stock market. However, in the following three months, until the peak in February 1994, the volatility declined again. Figure  43  is similar to the figure 41 from the previous section; the observations are only shifted twenty months forward in time. The plot shows the credit from banks to the private non-financial sector as a percentage of the GDP of Argentina together with its two-yearly growth rate, starting five years before and ending five years after the peak of the Argentinian stock market in February 1994. The data are from the Bank for International Settlements  (BIS, 2017). In contrast to 1992, the credit as a percentage of GDP has resumed its growth with a two-year average growth rate of close to 60%  (compared to -24% in 1992).
We will classify this stock market bubble, ending in February 1994, as a fearless, leveraged bubble, the former meaning that the volatility was stable over the development of the bubble, the latter summarising the fact that the credit level had been increasing significantly over the period of this bubble. Figure  43. The credit conditions in Argentina between 1988 and 1998, five years before and after February 1994. The two filled circles are the last observations from the data source (BIS, 2017) before the peak and the subsequent crash.
Consequently, We will classify this stock market bubble, ending in February 1994, as a fearful bubble. The database of the Bank for International Settlements (BIS, 2017) does not contain the amount of credit from banks to the private non-financial sector in that period for Chile. Therefore we will not evaluate the impact of leverage on this bubble. Figure  49. The Chilean stock market bubble ending in February 1994: price and historical volatility of the IPSA index (see section 4 for a detailed methodological explanation).
To understand the credit conditions during this bubble and its aftermath, figure  51  shows the credit from banks to the private non-financial sector as a percentage of the GDP of Mexico, together with its two-yearly growth rate. The historical time series are plotted starting five years before and ending five years after the peak of the Mexican BOLSA index in October 1997. The data are from the Bank for International Settlements (BIS, 2017). We can see from the decreasing credit levels that, in the aftermath of the "Tequila" crisis, the economy was still deleveraging. We will classify the stock market bubble, ending in the October 1997 crash, as a fearful, nonleveraged bubble, the former meaning that there was a significant rise in volatility during the bubble period leading up to the crash, the latter meaning that the bubble could not be linked to any rise in credit in the Mexican economy during that same period.
This observation is clearly confirmed by the arrow plot where we see the green arrows confined in the low volatility, high return region, and the red arrows in the high volatility, low return region. So, we will also classify this bubble as fearless. The database of the Bank for International Settlements (BIS, 2017) does not contain the amount of credit from banks to the private non-financial sector in that period for Venezuela. Therefore we will not evaluate the impact of leverage on this bubble. 6. 30. Indonesian stock market bubble ending in January 1994 In section 6. 16, we discussed the regime-switch that happened in the Hong Kong stock market in January 1994. It was mentioned that this was part of what  Lowell et al. (1998)  named "a rash of crises", which impacted, between January and June 1994, stock markets in Chile, Hungary, India, Indonesia, Malaysia, Philippines, Poland, South Africa, Turkey, Venezuela, Germany, Hong Kong, Singapore, the UK and the US.
The data are from the Bank for International Settlements  (BIS, 2017). We can see that credit creation had been stable during the build-up period of the stock market bubble. We will classify this case as a fearful, non-leveraged bubble, the former meaning that there was a significant rise in volatility during the bubble period, the latter meaning that the bubble could not be linked to any rise in credit in the Indonesian economy during that same period. Figure  56. The credit conditions in Indonesia between 1988 and 1998, five years before and after January 1994. The two filled circles are the last observations from the data source (BIS, 2017) before the peak and the subsequent crash.
To understand the credit conditions during this bubble and its aftermath, figure  58  shows the credit from banks to the private non-financial sector as a percentage of the GDP of Indonesia, together with its two-yearly growth rate. The historical time series are plotted starting five years before and ending five years after the peak of the JCI index in July 1997. The data are from the Bank for International Settlements  (BIS, 1997). We can see that credit to the private sector had been growing strongly during the build-up period of the stock market bubble. We will classify this event, as a fearless, leveraged bubble, the former meaning that there was no  increase in volatility during the bubble period, the latter meaning that the bubble was clearly linked to a rise in credit in the Indonesian economy during that same period.