dataesr/scientific-paragraphs-categorization huggingface dataset:
This neatly confirms that this bubble was fearful. The Malaysian and the Philippine bubbles had common underlying drivers (the belief in the "dragon economies"), developed over the same period (the year 1993), accelerated in the final phase together (December 1993) and burst at exactly the same moment  (around January 5, 1994 ). Yet, in Malaysia the volatility went down right before the crash, whereas in the Philippines, it went up. This observation  strongly counters the assumption that the volatility can be used to diagnose bubbles and forecast their end. 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 the Philippines. Therefore we will not evaluate the impact of leverage on this bubble.
One can see the volatility increasing somewhat during that interlude. However, it clearly drops again during the final rally in the month before the crash. This is confirmed by the arrow plot in the lower panel of the figure. It can be seen from that figure that the green arrows, representing the volatility before the crash, are clearly confined in the low volatility, high return region of the plot. To understand the credit conditions during and after this period of boom and bust, figure  63  shows the credit from banks to the private non-financial sector as a percentage of the GDP of Russia together with its two-yearly growth rate. The historical time series are plotted starting five years before and ending five years after the peak in the stock market. The data are from the Bank for International Settlements  (BIS, 2017).
Oil bubble ending in July 2008 In a research paper entitled "1980-2008: The Illusion of the Perpetual Money Machine and What It Bodes for the Future",  Sornette and Cauwels (2014)  put the Oil bubble ending in July 2008 in a longterm historical perspective. They argue that, since the early 1980s, consumption has been increasingly funded by smaller savings, booming financial profits, wealth extracted from house price appreciation and explosive debt. They describe that this resulted in a transition, in the 1970s, from a productivity-  fuelled growth towards a regime which they call the "illusion of the perpetual money machine". This regime was further supported by a climate of deregulation and a massive growth in financial derivatives designed to spread and diversify the risks globally. The result has been a succession of bubbles and crashes, including the worldwide stock market bubble and great crash of October 1987, the savings and loans crisis of the 1980s, the burst in 1990 of the Japanese real estate and stock market bubbles, the emerging markets bubbles and crashes in 1994 and 1997, the Long-Term Capital Management (LTCM) crisis of 1998, the Dotcom bubble bursting in 2000, the 2008 house price bubbles and subsequent credit, stock market, commodities, oil and debt bubbles and crashes, all developing jointly and feeding on each other until 2008.
Because of the qualitative approach of this type of analysis, the full reasoning behind the classification was explained in all transparency. When the data from the Bank for International Settlements (BIS, 2017) are available, the credit conditions before and after the crash were also estimated. Bubbles were identified as leveraged or nonleveraged depending on whether the credit level and growth rate were increasing significantly during the bubble build-up. For 29 bubbles, the data are available to do this type of analysis, 16 of these were identified as leveraged, 13 as non-leveraged. This result suggests that the formation and development of a majority of the bubbles can be linked to a period of credit expansion. Our main finding is that volatility is neither a reliable indicator of the maturation of a bubble nor of its impeding ending in a crash.
PCR analysis Real time PCR (qPCR) was performed as previously described by Fyfe et al. [21]. Three primers pairs with probes targeting sequences of two insertions sequences (IS2404/IS2606) and Ketereductase B-domain gene (KR-B) present on the M. ulcerans virulence plasmid pMUM001(GenBank accession no. BX649209) were used (Table  1 ). These targets were chosen because they were reported to be present in multiple copies in the M. ulcerans genome and are absent in the closely related species Mycobacterium marinum  [19, 21]. To confirm the presence of M. ulcerans in an environmental sample, three consecutives qPCR runs were realized.
Acknowledgments We would like sincerely thank the  Institut National d'Hygiène  through its Director for supporting the logistic of this investigation. We also express our acknowledgments to the  National Program  for the Control of  Buruli Ulcer , Leprosy and Pian through its Coordinator for authorizing the survey. We acknowledge all the efforts provide by the personal from the program for Buruli ulcer control and the national center of Buruli ulcer patients treatment.
Methods Both patients were enrolled in clinical protocol 76-HG-0238 (identifier: NCT00369421), approved by the NHGRI Institutional Review Board, and gave written informed consent. Whole exome sequencing was undertaken using SeqCap EZ Exome+UTR Library using a HiSeq 2500 (Illumina). Alignment, genotype calling, and annotation were undertaken using the Illumina aligner BELAND( Efficient Large-scale Alignment of Nucleotide Databases), Samtools, and Annovar. Sanger confirmation was undertaken of the DDX58 variant. Primer sequences are available on request. Structural analysis was undertaken based on the structure of RIG-I  [7, 8]. RIG-I wild-type and mutant plasmid constructs were devised and transiently co-transfected with an IFN-b promoter-driven firefly luciferase reporter plasmid into a HEK293 cell line.
Funding Information This report presents independent research funded by the  National Institute for Health Research (NIHR)  ( NIHR   Transitional Research Fellowship ,  Dr. Tracy Briggs ,  TRF-2016-09-002 ). The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR , or the  Department of Health. Funding is also acknowledged from the  Intramural Research Program of the National Human Genome Research Institute  (WAG),  NIH Intramural Research Fund ZIA-AI001220 (RGM) ,  RO1-N5021328-030  (WAG),  NIH Common Fund   RO1OD010944-05  (WAG),  NIH   R01 grant  ( AI106912  and  AI111784 ) (SH), the  European Research Council  ( GA 309449 ) (YJC), and a state subsidy managed by the  National Research Agency (France)  under the BInvestments for the Future^program bearing the reference  ANR-10-IAHU-01  (YJC).
Patients This is a retrospective cohort study using longitudinally recorded data produced within the usual practice. Adult patients on chronic hemodialysis present in one of the five selected European facilities in the years 1995-1999 were considered for inclusion. Patients from Seville (latitude of 37. 4°N, Spain), Montpellier (43. 6°N, France), Ottignies (50. 7°N, Belgium) and Umea (63. 8°N, Sweden) participated in the study up to five years  (1995) (1996) (1997) (1998) (1999). In Santa Cruz de Tenerife (28. 5°N, Spain), patients were seen up to three years  (1997) (1998) (1999). A map is available in Fig. 1. Additional inclusion criteria were a history of 6 months of hemodialysis and a follow-up of 6 months or more. Patients gave oral informed consent to participate in the study.
Data collection Age, gender, diabetes, cause of renal failure and dialysis vintage were obtained at study entry. Predialysis BP, pre-and post-dialysis body weight (BW pre , BW post ) were recorded at each HD session. Blood pressure was measured in the supine position according to local clinical practices. Intra-dialytic weight loss (IDWL) was defined as the difference between pre-and post-dialysis weights expressed as a percentage of post-dialysis weight (IDWL = 100 x [BW pre -BW post ] / BW post ). Monthly means of BP were calculated for each patient with 6 or more measurements during the given month. Clinical data were anonymized and centralized. Climate records from the closest meteorological stations and the moment of the study was obtained from the European Climate Assessment & Dataset and local meteorological offices  [11] [12] [13].
Exposure Patients were considered exposed to temporal, geographical, seasonal and climatic effects. For each patient, the first year of observation was set to 1 and observation dates were then defined as the month and the order of the year. Geographical exposure was defined as the city and the latitude of the city. Seasonal exposure was successively defined by seasons (spring: March, April, May; summer; June, July, August; autumn: September, October, November; winter: December, January, February) or months of the year. Climate exposure was defined by climate data from the actual month and year of observation. Patients were considered climate-sensitive if they had minimal BP in summer and maximal BP in winter or autumn.
In addition, the present study included a limited sample of prevalent patients from each facility and it is consequently unlikely that these samples were representative of the local HD population, in contrast with the EURODOPPS study  [8]. However, because we obtained many longitudinal records for each patient, our study is well designed to assess the longitudinal changes in BP and the effect of climate on intra-individual change in blood pressure. The data were collected some time ago and one may think that patients do not reflect current HD patients. However, results were confirmed across groups of patients with different characteristics (age, diabetes). Hence, we believe that the results we present are relevant to current HD populations. Comfortingly, the results from our recent EURODOPPS study are very much in line with the present article.
Participants A group of pre-pubescent (9-10 y, n = 13), pubescent (14-15 y, n = 13) and post-pubescent females (20-24 y, n = 13), who had not performed any regular resistance training in the past 1 year, participated in this study. The sample size was estimated using the data from our previous study  (Chen et al. , 2014)  in which pre-pubescent (9-10 y), pubescent (14-15 y) and post-pubescent males (20-25 y) were compared for muscle damage after the first and second bouts of maximal eccentric exercise of the elbow flexors. Based on the effect size of 1 for a possible difference in muscle strength changes after eccentric exercise between pre-pubescent and post-pubescent groups, with the alpha level of 0. 05 and power (1-β) of 0. 80, it was estimated that at least 9 participants per group were necessary by G * Power (G * Power 3.
Maximal Concentric Strength of the Elbow Flexors and Extensors Maximal concentric strength of the elbow flexors and extensors were measured at the angular velocity of 60 • •s -1 for the range of motion of 140 •. Three continuous maximal voluntary concentric contractions were performed in both directions  (Chen et al. , 2016 (Chen et al. , , 2017a;; Tseng et al. , 2016). The torque, position (joint angle) and displacement signals of each contraction were recorded to a computer connected to the isokinetic dynamometer. The raw data were filtered and smoothed, and peak torque was analyzed by the software of the Biodex Medical Systems, and the highest value of the three trials was used for subsequent analysis  (Wang et al. , 2014; Chen et al. , 2015; Shih et al. , 2016).
Statistical Analyses Data were assessed by a Shapiro-Wilk test for the normality and a Levene test for the homogeneity of variance assumption. Baseline values of the criterion measures were compared amongst groups by a one-way analysis of variance (ANOVA). There were two main comparisons in the study; (1) comparison between the pre-pubescent, pubescent and post-pubescent groups for the changes in each dependent variable after the first bout of eccentric exercise, and (2) comparison between the first and second bouts for each group separately for the repeated bout effect. For these, a three-way ANOVA (group × bout × time) was performed first to identify whether a significant interaction effect existed for changes in each variable before and after the first and second bouts of eccentric exercise.
, 2014). The index for each measure was compared among the groups by a oneway ANOVA, and a Tukey's post-hoc test was performed when a significant main effect was found. By comparing the values of EC1 and EC2 used for the protection index, effect size for the difference between EC1 and EC2 for each variable was calculated by Cohen's d, and was considered 0. 2, 0. 5, and 0. 8 as a small, medium, and large effect, respectively  (Cohen, 1988). Statistical significance was set at P ≤ 0. 05. Data are presented as mean ± SD, unless otherwise stated. Statistical analyses were performed using the SAS 9. 4 software (https://www. sas. com/en_us/software/sas9. html).
Cells were mounted on coverslips with Prolong (Life technologies). Images were acquired with a TCS SP5 confocal microscope (Leica) using a ×63/1. 4 oil immersion objective driven by LAS AF Lite software. Alternatively, images were acquired with an LSM780 confocal microscope (Zeiss) using a ×63/1. 4 oil immersion objective and a PMT GaAsP camera, driven by ZEN software. Mid-focal plane images were selected and they were processed with ImageJ and with Canvas Draw software. Figures were compiled in Canvas Draw. Yeast cells were grown to post-diauxic shift (24 h) in selective media and imaged directly on glass slides. Images were acquired at room temperature with a DMI8 (Leica) microscope, equipped with an oil immersion plan apochromat 100 objective NA 1. 4, an sCMOS Orca-Flash 4 V2+ camera (Hamamatsu), and a spinning-disk confocal system CSU-W1 (Yokogawa -Andor) driven by MetaMorph software.
The average of these five maxima was taken as the maximum LD fluorescence intensity. Similarly, the maximum cytoplasmic fluorescence was determined from the five maxima of line-based profiles going from the nucleus to the cell exterior, with lines drawn manually and not crossing any LDs. The average protein fluorescence in the nuclear area was determined by placing three squares in the nucleus of each cell and calculating their average fluorescence. Data was processed in Microsoft Excel. To determine the diameter of LDs in Drosophila S2 cells, 3D-representations of S2 cells were generated from z-stacks and LDs were identified using spot detection with Imaris image analysis software (Bitplane, Oxford). Diameters of all spheres representing single LDs were recorded. LDs that were very close together and could not be identified by the software as individual spheres were manually eliminated.
Therefore, the total number of LDs identified in each cell is lower than the actual number. LDs in all isolated cells transfected and non-transfected in a selected image were quantified. Data were processed in Excel and in KaleidaGraph. Protein purification. Purification and labeling of full-length α-synuclein were as previously described  17. The protocol for purifying the various Plin4 constructs was adapted from that used for α-synuclein  50  , and included two steps: boiling to precipitate most proteins except Plin4, which is heat resistant, followed by ion exchange chromatography. In contrast to α-synuclein, Plin4-AH has a net positive charge at neutral pH (pI = 9. 56 for the amphipathic region [95-985]). Therefore, Plin4 fragments were purified by cation chromatography. E. coli cells BL21DE3 transformed with expression plasmids were grown to O.
190 µl of each solution was pipetted into a 600 µl glass tube, and a 10 µl drop of triolein (Sigma) was added to the top. They were vortexed for three cycles of 30 s on 30 s off at 25 °C under argon atmosphere. Images of resulting emulsions were taken with a phone camera. Measurements of the mean hydrodynamic radius of the Plin4-oil droplets by dynamic light scattering were performed on a sample taken from the middle of the tube, avoiding any unreacted oil that remained at the top of the emulsion, at least 3 h after vortexing to prevent the interference of gas bubbles with the measurement. Measurements were performed on a Zetasizer nano ZS machine (Malvern) at 25 °C, and data were processed using the CONTIN method. For analysis by electron microscopy, samples of emulsion were deposited on glow discharge carbon-coated grids and negatively stained with 1% uranyl acetate.
Acknowledgements We thank  Karine Eudes  and  Joel Polidori  for technical support,  Romain Gautier  for help with bioinformatic analyses, the  ImagoSeine  facility at the  Institut Jacques Monod , member of  IBiSA  and the  France-BioImaging  ( ANR-10-INBS-04 ) infrastructure, and in particular  Xavier Baudin  and  Vincent Contremoulins  for help with imaging and image analysis,  Florent Carn  for assistance with dynamic light scattering,  Sebastien Léon  for yeast plasmids and reagents, and  Jean-Marc Verbavatz ,  Veronique Albanèse ,  Luc Bousset ,  Yvon Jaillais  and members of our labs for comments and discussions. This work was supported by the  French National Research Agency (ANR) , grant  LDsurfDynamics  ( ANR-13-BSV2-0013 ) to C. J. and B. A. , " Fondation pour la Recherche Médicale ", grant number  DEQ20150934717  to C.
To the best of our knowledge, no data have addressed the potential role of PV variations on BP improvement in response to training. According to data in trained adults, HIT may result in significant variations in PV. Therefore, evaluating the potential benefits derived from HIT on PV and its eventual link to BP adaptations might provide additional support for recommending HIT among sedentary individuals. Hence, this study examined the effects of HIT on PV variations among sedentary wide age range subjects and its relationship to BP. For this purpose, we measured PV before and after 6 weeks of HIT, as well as acute variations to PV after warm-up and at the end of supramaximal cycling exercise. We hypothesized that a short HIT program would increase PV and reduce the age-related effect on PV decreases in response to exercise.
Statistical analysis Data are presented as means (standard error). Normality was tested using the Kolmogorov-Smirnov test. Paired t-tests were used to determine whether significant changes occurred in our primary variables (i. e. , fitness, anthropometry, BP and PV) between pre-and post-HIT. The relationship between variations in PV, BP and age between visits were assessed via Pearson correlations. Multiple linear regression with an extended-model approach was subsequently used to document the effects of the variables on PV variations. As a result, a series of multiple linear regression models were developed to determine the relationship between each variable and PV variations. A value of P < 0. 05 was considered statistically significant. Analyses were performed using IBM SPSS Statistics 19 software (IBM SPSS Statistics for Windows, Version 19.
Interestingly, after training, both DBP and SBP were reduced (~5 and ~6 mmHg), which may have clinical and biological relevance in reducing the risk of heart disease. Because of the dependence of BP on PV, when PV decreased, and total peripheral resistance increased  (Julius et al. 1971). Therefore, any variations in PV could impact blood pressure values. In fact, our results showed a significant relationship between systolic and diastolic BP improvements and PV variations after warm-up and SCT following HIT. In the present work, some limitations should be considered. In fact, the dehydration indexes (urine and blood osmolality) were not assessed; as a result, further data are needed to explore the effect of this phenomenon on PV variations in response to HIT among older individuals. Moreover, studying the effect of HIT on PV variations based on age categorization will be necessary to clarify how age in different categories (i.
Semi-thin sections (500 nm thick) were cut with a Leica Ultracut UCT ultramicrotome, stained with Toluidine Blue for 30 s at 60°C, washed with distilled water for 5 s, 100% ethanol for 10 s, and again with distilled water for 20 s. They were dried at 60°C and embedded in Epon resin that was allowed to polymerize for 48 h at 60°C. These sections were used for the correct preparation of the analysis zone during the ultrastructural study. The serial ultra-thin sections (70 nm thick) were cut with a Leica Ultracut UCT ultramicrotome, stained with 5% Uranyl Acetate (20 min) and placed on electron microscopy one-slot grids coated with Formvar film. The sections were observed at 100 kV with a JEM 1011 transmission electron microscope (JEOL, Tokyo, Japan) connected to a Gatan digital camera driven by Digital Micrograph software (Gatan, Pleasanton, USA) for image acquisition and analysis.
Three-dimensional reconstruction We recently described the use of serial EM sections for three-dimensional reconstruction  (Depla et al. , 2010; Ferraris et al. , 2013). We used a similar approach here, to generate three-dimensional reconstruction centrioles. Photoshop CS3 software was used to align images from consecutive serial ultrathin section stacks (70 nm thick). Contours were drawn with IMOD software and then arranged into objects. The contours of each object were then joined using the IMOD mesh feature, to form a three-dimensional model.
Acknowledgements We would like to thank  Jean Michel Drezen  for providing Cotesia congregata. Our data were obtained with the assistance of the  IBiSA Electron Microscopy Facility of University of Tours  and the  University Hospital of Tours. We thank  Proof-Reading-Service. com Ltd (Devonshire , company registration no:  8391405 ) for checking the English language. Many thanks to  Jadranka Loncarek ,  Greenfield Sluder ,  Giuliano Callaini  and  Monica Bettencourt-Dias  for critical reading of the manuscript and helpful comments.
Acknowledgements The authors are grateful to  Pr Fatima Ailal  and  Pr Bouchra Slaoui  for patient management and  Mrs Evelyne Crème ,  Désirée Duhem ,  Catherine Lelorne  and  Nadine Houriez  for skillful technical assistance. They are also grateful to  Dr Laura Ravasi  for help in writing and editing the English version of the manuscript on behalf of the  University of Lille , and  Mike Howsam  for proofreading.
We used anti glyceraldehyde-3-phosphate dehydrogenase (GAPDH, 1:1000, rabbit-IgG, Sigma-Aldrich) as loading control. Blots were rinsed three times in PBS with 0. 5% Tween-20 buffer (PBST), and then incubated with the corresponding horseradish peroxidase (HRP)-conjugated secondary antibody (1:1000, Bio-Rad) for 2 h at RT. Immunoreactive bands were detected using an ECL TM Western Blotting Analysis System (Amersham TM , GE/Healthcare). Films were scanned and the optical density of protein bands was quantified using Gel Pro Analyzer software 3. 1. 00. 00 (Media Cybernetics).
Fluorescence was acquired on a commercial inverted confocal SPT-5 microscope (Leica Microsystems), with magnification of 63X oil DIC with excitation at 405, 488, 561 nm. Percentage of reactive area and colocalization analysis were estimated using Image J Program (Image J1. 41o, NIH, United States). Briefly, images were obtained separately for each channel and analyzed. Single-channel images were thresholded and a binary image was created from each thresholded singlechannel image. We measure the percentage of pixels in the selection that have been highlighted in red as an indicator of the percentage of reactive area. For colocalization analysis, binary images were then added together to display overlap, and a third binarized image of strict pixel overlap (colocalization) was used for analysis.