dataesr/scientific-paragraphs-categorization huggingface dataset:
Map validation The figure  8  illustrates the final binary map for forest disaster detection with intact and damaged areas. Reference data were collected from orthophotos of 15 cm resolution, that were available after the storm. The Overall accuracy was of 87. 2%. Some small changes or intact areas were not extracted due to the limited spatial resolution of Formosat-2. Besides, in our method, the shadows are not taken into account, therefore the segmentation was not robust to shadow changes. The INRA 1 inventory data layer of the Nezer site references ages of all forest stands. It was confronted to the obtained classification. The overall accuracy and the percentage of damaged pixels (damaged rate) were computed for all available ages. The obtained results are depicted in figure  9. One can observe that the classification accuracy increases significantly with the class age.
When the initial structure of GLIC was solved at 2. 9 A resolution, residual electron density was observed in the pore and interpreted as a bundle of six dodecyl-maltoside molecules (DDM) with the maltoside moiety pointing inward for the central detergent and outward for the five other detergents  (Bocquet et al, 2009). However, the GLIC_2. 4 structure reveals that the locus where the polar head of the central detergent was previously assigned is in fact occupied by a tight self-stabilized water pentagon (Figures  1  and 2 ). In order to confirm the presence of detergents in the upper half of the pore, GLIC was co-crystallized in selenium-derived DDM and single-wavelength anomalous data were collected at the peak value of selenium. This experiment unequivocally confirmed the presence of a detergent bundle in the pore of GLIC with the maltoside moieties pointing outward (Figure  3 ).
Acknowledgements We thank the  French National Agency for Research for funding  (Grant  ANR-2010-BLAN-1534 ). Molecular simulations were performed using HPC computing resources from  GENCI-/TGCC/ CINES/IDRIS (Grant 2011-072292  to MD). We thank  Damien Laage  and  Fabio Sterpone  for helpful discussions on water structure and  Hugues Nury  for help in early crystallographic work. We acknowledge the help of the Proteopole platform at  Institut Pasteur. We thank the staffs of Proxima1 at Soleil and of  ID23-1 at ESRF.
Analysis of climate change velocity We used four climate scenarios combining two contrasted emission scenarios (A2A and B2A, higher and lower greenhouse gas emissions respectively) and two climate models [HADCM3 and CCCMA, from Hadley Centre (U. K. ) and the Canadian Centre for Climate Modelling and Analysis, respectively]. Temperature and precipitation from climate scenarios downscaled at 2. 5-arc-minute resolution were taken from the WorldClim database  (Hijmans et al. 2005). On average over the region, climate scenario HADCM3 had the highest temperature increase of 2. 2-3. 2°C in 2050 (depending on the emission scenario), whereas CCCMA showed a maximum increase of 2. 0-2. 1°C. Future scenarios of precipitation, with CCCMA (in 2050), showed increased annual precipitation between 445 and 539 mm on the Pacific watershed and southern countries (Costa Rica and Panama) and a reduction of 399-679 mm on the Caribbean watershed depending on the emission scenario (HADCM3-B2A showed similar changes).
Analysis of the impacts of climate change with and without corridors We mapped the impacts of climate change with and without corridors to analyze whether corridors reduce significantly the impacts of climate change on PA. Two regional conservation policy scenarios were evaluated: (i) strong conservation policies: vegetation is restored or conserved in BC and PA to its natural (pristine) state (and therefore providing suitable habitat), whereas vegetation outside BC or PA is conserved in its current state (suitable if covered by natural vegetation and unsuitable otherwise); (ii) weak conservation policies: vegetation is restored or conserved in PA and fully degraded outside PA (to unsuitable habitat), including in BC. Spatial data on current land use, PA, and BC were taken from existing regional maps (CCAD-UNDP/GEF 2002;  Vreugdenhil et al.
CKD requiring dialysis 3 Cirrhosis (Child-Pugh A) 2 Non-bladder metastatic cancer 1 Blood transfusion, in the absence of gross hematuria, was not an exclusion criteria for our study. Statistical analysis was performed using XLSTAT V 7. 5 software  (Addinsoft, 2004). Two-sided p-values were obtained using Mann-Whitney non--parametric bilateral tests. Results were considered significant when the p-value was below 0. 05.
Duchesne et al. (  4 ) compared two hypofractionated EBRT schedules in a prospective trial, with no difference between a 35 Gy-10 fraction and 21 Gy-3 fraction concerning hematuria improvement (55% improvement vs. 50%, p = 0. 198). Clinical evaluation was performed at 3 months. Symptoms such as nocturia, dysuria, and urgency were also improved after EBRT, with no significant difference between the two schedules. Our retrospective study focused on the hemostatic effect of EBRT on bladder cancer. Other urinary symptoms were not taken into account in our data analysis. Limitations include the retrospective nature of this study and the limited patient cohort. Two EBRT schedules were proposed: 30Gy in 10 fractions (Group A -2 weeks) or 20Gy in 5 fractions (Group B -1 week). ECOG PS defined which schedule would be assigned: More severe patients (ECOG > 2) underwent the hypofractionated group B procedure.
Link to ex-post analysis Although the selected studies aim at produce damage functions to assess ex-ante evaluation of flood damage, most of them were based on ex-post analysis of past events. This link varies between studies, and is not always explicitly mentionned. Ex-post analysis may be used to precise some mechanisms explaining damage, or to estimate, based on expert knowledge, some costs or others figures needed to assess loss. Förster et al. (2008)  was based on the construction of empirical damage function from a database of damage given by insurance companies (HOWAS). Poirée and Ollier (1973)  and  Duthion (1982)  make reference to experiments made explicitly to assess crop loss due to flood.
The review also highlights the lack of data (experimental data or ex-post data collection) to construct damage functions. Specifically concerning crop damage, the review revealed that existing data to construct damage functions is limited and not up to date  (Poirée and Ollier, 1973; Duthion, 1982). Simplifications may also be related to the adaptation of methodologies to fit the availability of land use data. For instance, to evaluate crop damage, damage functions must correspond to the crop typology used in the land use description. But, most land use databases do not provide a detailed crop typology. The same problem can be encountered concerning the location and typology of agricultural buildings. Another difficulty may also be the attribution of a value to each type of agricultural building.
Acknowledgements. This work has benefited from the support of  Plan Rhône (FEDER funding) ,  Syndicat d'Aménagement de la Touloubre ,  French Ministry of the Environment (DGPR, CGDD). This paper received especially helpful and detailed comments from  J. Morris  and one anonymous referee. Edited by: L. Bouwer Reviewed by:  J. Morris  and one anonymous referee.
Flow cytometric analysis of the sub-G0/G1 cell population. The sub-G0/G1 cell population (hypodiploid cells: dying and dead cells) was analyzed by labeling cells with propidium iodide. Cells were harvested by trypsinization after 24, 48 and 72 h of treatment and washed with phosphate-buffered saline (PBS; 0. 1 M; pH 7. 4). Cells were fixed in 70% ethanol at -20˚C for ≥30 min, washed twice with PBS and re-suspended in 200 µl PBS containing 0. 25 mg/ml RNase A and 0. 1 mg/ml propidium iodide (Sigma-Aldrich Chemie GmbH). Following incubation in the dark at 37˚C for 30 min, the fluorescence of 10,000 cells/sample was analyzed by flow cytometry, and histograms were analyzed using the CellQuest software (FACScan, BD Biosciences, Belgium).
Study cohort Of the 720 participants, 549 completed the study, and DNA was available for 512 participants. To reduce heterogeneity in genetic background, 412 individuals of white European ancestry, based on self-reported ethnicity, were included in the analysis. All other ethnic sub-groups were excluded from these analyses due to their limited size. Following genotyping quality control procedures (see below), 405 participants were available for analysis at baseline, and 376 completed the dietary intervention. Data from the IVGTT was available for 377 participants at baseline and for 354 of those who completed the study. Of these participants, 48% had fasting glucose [5. 6 mM (impaired fasting glucose), and no participants had fasting glucose [7 mM (T2D). The characteristics of the participants included in these analyses are presented in Table  1.
This exploratory analysis was conducted despite the assumption that we would have low power to detect small effects of individual SNPs to illustrate the contribution to the effects of GPS. Statistical analysis was conducted using Stata 11 (StataCorp, TX, USA). A Bonferroni correction was applied to the six tests between T2D-GPS and the three traits at baseline, and to change in response to the intervention (P = 0. 008). We did not correct the associations of individual SNPs as we decided to only report the summary Characteristics of participants at entry into the study, who were included in the analysis of association of T2D-GPS with (1) traits at baseline and (2) the change in traits following the dietary intervention. Data are presented as mean (SD) or per cent statistics for future research, rather than to interpret them on their own due to the limited statistical power to detect small individual effects when corrected for false positive chance.
2011 ). However, also in participants of the DPP (n = 2843), insulin sensitivity indices were studied using a genetic risk score (analogous to our GPS and with 23 of 34 SNPs covered in our study). Similar to the current study, they found a trend for a lower estimated insulin secretion (insulinogenic index) and oral DI (from a 2 h post-oral glucose load value) at baseline, but there was also no overt effect of GPS on change in insulin sensitivity or secretion indices following 1 year of intensive lifestyle modification or metformin treatment  (Hivert et al. 2011 ). It appears that SNPs that are more strongly associated with a given trait in cross-sectional data may not be the most important SNPs in terms of change in trait, especially when improvements are seen in the trait that underlies the association.
Acknowledgments The RISCK dietary intervention trial was funded by the  Food Standards Agency  project number  NO2031. Genetic analyses were funded by the participating centres including  UK Medical Research Council  (grant codes  U105960389 ,  U1052. 00. 014 ). The  RISCK Study Group comprised of: Carmel Moore ,  Mark Chatfield ,  Christine Williams ,  Hannah Farrant ,  Claire Lawrence ,  Edel Magee ,  Kit Tsoi ,  Darren Cole ,  Steve Austin ,  Hanneke Mfuni ,  Kate Guberg ,  Anna Gent ,  Celia Greenberg ,  Caroline Stokes ,  Mario Siervo ,  Rosemary Hall ,  Louise Goff ,  Claire Howard ,  Namrata Dhopatkar ,  Bushra Siddiqui ,  Anne Dornhurst ,  Fiona Lewis ,  Samantha Bowen ,  L Chen ,  Robert Gray ,  Nuala Booth ,  Gary Moore ,  Roy Sherwood ,  Anthony Leeds ,  A Shah ,  G Saran ,  J Niehuser-Saran ,  JA Cockburn ,  Rachel Gitau ,  Katie Newens ,  Sean Lovegrove ,  Ana Rodriguez-Mateos ,  John Wright ,  Margaret Griffin  and  Nicola Harman.
The reaction mixture (20 mL) contained 10 mL SYBR Premix Ex Taq (2x), 0. 4 mL of ROX Reference Dye (50x), 0. 2 mL of each SfMNPV primer (10 pmol/ mL) (Table  1 ) and 1 mL of DNA template. qPCR was performed under the following conditions: 95uC for 30 s, followed by 45 amplification cycles of 95uC for 5 s and 60uC for 30 s and finally a dissociation stage of 95uC for 15 s, 60uC for 15 s and 95uC for 15 s. Data acquisition and analysis were handled by Sequence Detector Version 2. 2. 2. software (Applied Biosystems). Known dilutions of SfMNPV CsCl-purified DNA (10 25 -1 ng/ ml) were used as internal standards for each qPCR reaction. Melting-curve analysis was performed to confirm specific replicon formation in qPCR. Mean ODV content within OBs was determined by end point dilution in Sf9 cells  [25].
ODVs were released from 5610 8 OBs in a volume of 500 mL by incubation with Na 2 CO 3 0. 1 M at 28uC for 30 minutes. This suspension was filtered through a 0. 45 mm filter and serially diluted 1:5 in TC100 medium. Sf9 cells were infected with 10 mL of each ODV dilution in 96-well plates. Twenty-four wells containing 1610 4 cells/well were inoculated with each dilution in triplicate. Dishes were incubated at 28uC for 7 days and then examined for signs of virus infection. Results were analyzed by the Spearman-Ka ¨rber method  [26]  to estimate 50% tissue culture infectious dose (TCID 50 ). TCID 50 values were subsequently converted to infectious units per 5 10 8 OBs and compared by t-test in SPSS 15. 0 (SPSS Inc, Chicago, IL).
Virus induced mortality data were subjected to probit analysis with the POLO statistical program  [28]. In order to determine speed-of-kill, groups of 24 recently-molted second instars were starved overnight and allowed to drink an OB concentration estimated to result in 90% mortality, namely, 2. 19610 5 OB/mL for Sfbac and 2. 24610 5 OB/mL for Sf32null. Larvae that drank OB suspensions within 10 min were reared individually on diet at 25uC and mortality was recorded at 8 hour intervals until larvae had died or pupated. The experiment was performed on four occasions. Time-mortality results were subjected to Weibull analysis using the generalized linear interactive modelling (GLIM) program  [29]  and to Kaplan-Meier survival analysis using SPSS 15. 0. OB production was determined in larvae that died in the MTD assays.
Electron Microscopy Scanning electron microscopy (SEM) was used to determine OB diameter. OBs in suspension were fixed overnight by mixing with an equal volume of fixative (4% formaldehyde and 1% glutaraldehyde in 0. 1 M phosphate buffer, pH 7. 4) and then washed twice with 0. 1 M phosphate buffer. Samples were then partially dehydrated with ethanol 70%, dried, placed on aluminum mounts using carbon tags, sputter-coated with gold-palladium and photographed at magnifications of 6,0006 and 25,0006 using a scanning electron microscope (Philips SEM 550). Images were analyzed with the ImageJ software (National Institutes of Health) and Feret's diameter (the longest distance between two parallel tangents) was taken as a measure of OB diameter. A total of 500 OBs were analyzed for the Sfbac virus and 345 were analyzed for the Sf32null virus.
OB measurements were normalized by squareroot transformation and compared by t-test using SPSS 15. 0. The number of ODVs occluded within OBs and the number of nucleocapsids per ODV were determined by examination of OB sections by transmission electron microscopy (TEM). OBs in suspension were fixed for 2 h at 4uC with 1. 5% glutaraldehyde. Samples were then concentrated in 0. 4% agar, washed with phosphate buffer (0. 2 M, pH 7. 3), post fixed with 2% osmium tetroxide for 2 h, followed by 1 h treatment with 2% uranyl acetate. Samples were then embedded in epoxy resin, sectioned, stained with lead acetate and observed under TEM at 100 KV (JEOL JEM 1010). Different fields of each sample were photographed at a magnification of 640,000. Images were analysed using ImageJ software for each sample, the number of ODVs was counted in 30 OBs.
Gene and Protein Sequence Analysis To determine the nature of the putative SF32 protein, nucleotide and amino acid sequences homologs were searched in the updated Genbank and EMBL databases using BLAST  [30]. Protein properties were studied using the Peptide Property Calculator (Center for Biotechnology, Northwestern University). PSIPRED and JPRED3 tools were used to predict protein secondary structure  [31]  and signal sequences were screened using SIGNALP v3. 0  [32]. Cellular location was predicted by TargetP 1. 1 server  [33]. The presence of transmembrane domains was detected using TMHMM v2. 0 and MEMSAT3  [34]. Finally, post-translational modifications were predicted using PROSITE  [35].
Magnetoencephalographic (MEG) recordings are a rich source of information about the neural dynamics underlying cognitive processes in the brain, with excellent temporal and good spatial resolution. In recent years there have been considerable advances in MEG hardware developments and methods. Sophisticated analysis techniques are now routinely applied and continuously improved, leading to fascinating insights into the intricate dynamics of neural processes. However, the rapidly increasing level of complexity of the different steps in a MEG study make it difficult for novices, and sometimes even for experts, to stay aware of possible limitations and caveats. Furthermore, the complexity of MEG data acquisition and data analysis requires special attention when describing MEG studies in publications, in order to facilitate interpretation and reproduction of the results.
Introduction Recent methodological and technological developments in the field of magnetoencephalography (MEG) have led to a new level of sophistication for performing state-of-the-art data acquisition and analysis. Thanks to the unique features of MEG data in combination with improved analysis techniques, a steadily increasing number of researchers have realized the potential of MEG to answer neuroscientific questions. These developments pose specific challenges, both to the methods developers and to the empirical scientists. Given the breadth of expertise required (time-resolved paradigm designs, multidimensional time series analysis, source reconstruction, statistical analysis, etc. ), even experienced researchers in the field may find it difficult to keep up with new developments. New methods and technologies may have been introduced without rigorous testing, validation and comparison with existing techniques.
In addition, the level of experience and the sophistication of data acquisition and analysis are highly variable, not only within but especially between groups, and there is often little room left for exchange of data, ideas and people across the MEG community. Similar procedures are often developed several times independently in different labs (or even in the same lab); sometimes with little awareness of existing procedures/toolboxes. We realize a genuine need for the development of good-practice guidelines, the implementation of validated analysis pipelines and the sharing of practical knowledge across the MEG community. Such an endeavor could have several dimensions: documentation of best practice (recommended procedures for a certain type of analysis), quality control (how to ascertain that the analysis step was successful/meaningful), reproducible research (how to ascertain that the result of an analysis can be reproduced by the same or another researcher), report of results (recommendations for reporting MEG analysis and results suitable for peer-reviewed publications), and dissemination of source code implementing recommended analysis pipelines.
Seventh, it will facilitate training and sharing of expertise and knowledge in the field. Here, we take the first step 1  in this direction and propose research guidelines for the acquisition and analysis of MEG data. These guidelines are intended to be taken as general recommendations and to provide a basis for further discussion and improvement by the entire MEG community. These recommendations may be useful as a guide for those new to the field of MEG by providing the novice with information and references to guide them through their initial MEG data recording and analysis (see, for example, recent guidelines for fMRI by  Poldrack et al. , 2008). In addition, they may serve as a basis for ongoing discussions and improvements of 'best practice' for MEG data acquisition and analysis. The paper also highlights common pitfalls, specific to MEG, which may confound the interpretation of results.
Background Data acquisition is a critical component of a MEG study as it affects data quality directly. To ensure optimal data quality, the following elements need to be taken into account: • Operating status of the system and set-up (sufficient helium level, optimum calibration/tuning of the sensors, proper functioning of the interference suppression system including magnetic shielding, sufficiently low ambient magnetic interference level). • Preparation of the participant (removal of magnetic materials that may distort the measurements), explaining the task and giving instructions to avoid movements, eye-blinks and eye movements during the trials. • Experimental design (e. g. , allowing for sufficient number of data trials after rejection of artifacts). • General acquisition setup (sufficiently high sampling rate and filter pass-band, recording of supplementary data such as electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG), eyetracking, digitized head shape).
Recommendations and caveats • Before the participant enters the MEG laboratory: We recommend that the condition of the MEG system is recorded by performing an "empty room" recording of about 2 minute duration before and after the experiment. These records are snapshots of the noise level (and their changes) at the time of measurement, allowing for the identification of problems with the system. We suggest simple spectral analysis and storage of original data and spectra for later comparison. These datasets allow monitoring the noise level and system status over longer periods of time. Tuning of individual sensors may be required for optimal performance. A log book is helpful to record and communicate the status of sensors and the system in general. • Subject preparation: Simultaneous acquisition of EOG (horizontal and vertical) and ECG is recommended for several reasons: • Eye movements/blinks may be induced by experimental conditions, task and instructions in a non-trivial way  (Picton et al.
If possible, software which shows the head position with respect to the dewar immediately after activation of head localization coils should be used to ensure correct positioning. Additional mechanical head support can be provided to minimize head movements during the experiment; such movements are detrimental to data quality. If possible, head position should be recorded continuously to allow for offline control or correction of data but even then head movements should be minimized. Empirical evidence from online head position recordings suggests that on average the largest head movements occur downward within the first few minutes of the recording. To minimize this effect, it may be useful to provide arm support with a laptop table or armrests and to have the participant seated under the dewar for a few minutes before the experiment starts.
This time can be used to attach peripheral electrodes, instruct the participant or have him/her complete training trials. Alternatively, recordings can be performed in supine position, which can be more comfortable for prolonged recording sessions, except when EEG is recorded simultaneously as occipital electrodes may cause discomfort. • It is advisable to perform at least 2 minute 'resting state' recordings before and after the experiment with the subject fully prepared and positioned in the MEG system and all required equipment (microphone, camera, projector, etc. ) switched on. This is valuable baseline data in the absence of stimulation and can also be useful to identify more subtle stimulation artifacts on the MEG signal. • Data acquisition: The usual configuration is to have an anti-aliasing (low-pass) filter cut-off at 1/3 or 1/4 of the sampling frequency.