dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We are grateful to the following colleagues from  WSL  who provided help on various aspects of this study:  Gabor Reiss  and  Ulrich Wasem  (experiment installations);  Lukas Bächli ,  Marie Dobbertin ,  Yann Vitasse , and  Charlotte Grossiord  (data collection);  Nikola Vasic  and  Stephanie Pfister  (laboratory measurement); and  Benjamin Dauphin  (participated in a discussion). We are also grateful to the local forester  Konrad Egger  for technical support in the field, the  Burgergemeinde Leuk  for long-term permission of the field experiment, and the  HYDRO Exploitation SA  for supplying irrigation water and electricity.
Eight subjects were dosed in each cohort, randomized to 6 MW189:2 placebo control. Matched placebo is 0. 9% sodium chloride (saline). Dosing within each cohort was staggered, with a sentinel pair (1 MW189:1 placebo) dosing at least 1 day before the remaining cohort subjects (5 MW189:1 placebo). Dosing of the remaining cohort subjects was contingent on an acceptable safety review of the sentinel pair's day 1 safety data and 24-hour postdose laboratory safety tests. Escalation from one cohort to the next was contingent on joint approval by the Investigator and sponsor following a comprehensive blinded safety review of the residential phase data. In the MAD study, subjects were screened as above, admitted to the clinical research unit on day -1, and remained in the phase 1 unit until discharge on day 8.
Safety Evaluations and Analyses Safety and tolerability were assessed during and following dosing by clinical staff and physician observation and spontaneous reporting of symptoms by subjects. Other safety and tolerability end points included reported AEs, changes in vital signs, physical and neurological examination results, 12-lead ECGs, and clinical laboratory tests including hematology, chemistry, and urinalysis. A safety monitoring committee evaluated the safety and tolerability in the dose-escalation cohorts. Safety data for each dosing cohort were reviewed prior to initiation of each subsequent cohort. Doses were escalated in successive cohorts unless 1 subject experienced a serious adverse event (except if clearly unrelated to the study product) or 2 participants reported the same adverse event of moderate intensity that was considered at least probably related to the study drug.
All subjects who received any study treatment were included in the safety analysis grouped by treatment received. The statistical analysis of safety data is descriptive in nature; no inferential hypothesis testing was performed on the safety variables. For all safety analyses, baseline was defined as the last evaluation before dosing. For continuous variables, summaries include sample size, mean, standard deviation, minimum, and maximum. For categorical variables, the summaries include frequencies and corresponding percentages. Repeat or unscheduled results were not included in the summaries, but are listed. Data from subjects who received placebo were pooled for data presentations. Statistical analyses of these data were performed using version 9. 1 (SAD) or version 9. 4 (MAD) of SAS (Cary, North Carolina).
The model assumed a monotonic nondecreasing function with balanced data. Any concentration below the quantification limit was assigned a value of zero for the PK calculations. For both the SAD and MAD cohorts, the peak drug concentration (C max ) and time of peak concentration (T max ) were calculated using the observed data for MW189. For the SAD cohort, AUC from time 0 to time of last nonzero concentration (AUC 0-t ), AUC from 0 extrapolated to infinity (AUC 0-inf ), and elimination half-life (t 1/2 ) were calculated. For the MAD cohort, AUC from 0 to 12 hours (AUC tau ) was assessed for MW189. AUC 0-t and AUC tau were calculated using the linear trapezoidal method. Trough concentration (C MIN ), defined as the concentration 12 hours after the last dose and before start of the next dose was calculated using the observed data for MW189 after doses 1 and 2 on day 1, dose 1 on days 2, 3, and 4, and dose 1 on day 5.
Using data from the last dosing on day 5, the following additional PK parameters were estimated: terminal elimination rate constant (λz), elimination half-life (t 1/2 ), clearance (CL), and volume of distribution during the terminal phase (V z ). λz was determined as the slope of a log-linear least squares of at least 3 concentration-time points judged after the last dose by visual inspection to be in the apparent terminal elimination phase. Half-life was calculated as t 1/2 = ln 2 / λz. Clearance at steady state (CL SS ) was estimated using CL SS = dose/AUC tau. V z was estimated using V z = dose/(λ z × AUC 0-inf ). Area under the concentration-time curve from time of last dosing to infinity (AUC 0-inf ) was estimated using AUC 0-inf = AUC LAST + C LAST /λ z , where C LAST was the last measurable plasma concentration after the last dose and AUC LAST was the AUC from the time of last dosing to the time of the last measurable sample.
Three custom arrays were used ([1] TNF-α/IL-6/IL-8/IL-10, [2] CCL2, [3]  IL-1ra), grouped according to their expected plasma concentrations. Analyte concentrations were quantified by comparison with the corresponding standard curves that had previously been qualified over the linear concentration ranges for each analyte. For each cytokine, the differences in mean cytokine levels were assessed for treatment by time (treatment × time) effects using the generalized linear model procedure (analysis of variance) in SAS on ln-transformed data. If the treatment × time effect was found to be statistically significant, then treatment levels were compared for each time separately using post hoc analysis (SLICE option from the least-squares means [LSMEANS] statement). The SLICE option provides a test of the equality of means for each level of each effect in an interaction.
025 to 0. 30 mg/kg). The clearance and volume of distribution were not appreciably different between dose groups, and there was no significant accumulation of MW189. Steady state was achieved by dose 3 for all dosing cohorts. Overall, the PK parameters support a twice daily intravenous dosing regimen in future clinical studies. Preclinical efficacy and PK studies of MW189 were performed in rats and permitted determination of PK parameters associated with an efficacious dose. Efficacy studies in a rat rheumatoid arthritis model of inflammation showed a significant reduction in inflammation and clinical disease with a minimum effective dose of 1 mg/kg (unpublished data). After dose-normalizing the PK data to the minimum effective dose of 1 mg/kg, mean C max and AUC tau over the dosing interval associated with an effective dose ranged from 513 to 741 ng/mL and 496 to 694 ng•h/mL, respectively.
Acknowledgments These studies were supported in part by  Alzheimer's Association  grant  PTC-17-445974. The authors thank the Data Safety Monitoring Board members, who volunteered their time in support of the phase 1b MAD study:  Richard J. Kryscio, PhD  (DSMB chair,  University of Kentucky), Val R. Adams, PharmD (University of Kentucky ),  Gregory Cooper, MD  ( Baptist Health ),  Edward D. Hall, PhD  ( University of Kentucky ), and  Roger L. Humphries, MD  ( University of Kentucky ).
Statistical analysis All variables were examined by univariate analysis using the chi-square or Fisher's exact test, as appropriate. Continuous variables were analyzed by Student's t-test. All statistical tests were two tailed and p < 0. 05 was considered statistically significant. Multivariate analysis was performed by logistic regression. A backward stepwise selection with an entry and stay level of p = 0. 2 was used to build the final multivariate logistic regression model. Statistical analyses were computed by SPSS 22. 0 (IBM, Armonk, NY, USA).
However, the diffusion of this clone in different hospitals in France and in other countries may suggest that this clone spread in both the community and hospital settings. Interestingly, heterogeneity in geographical distribution was observed in France with a prevalence of 3. 1 to 23. 5% according to the hospital  [8]. This heterogeneity could be explained by a greater endemic diffusion in some areas, possibly related to specific routes of transmission. Our study has some limitations. Firstly, the retrospective collection of data may have introduced information bias. Secondly, we did not determine the population structure of non-CC398, and the control group very probably consisted of genetically diverse S. aureus.
Study design We considered this substudy as an observational study and it is therefore reported in accordance with the STROBE guidelines. 9 The study is a secondary analysis of data from a subset of patients in the multicentre CAAM randomized clinical trial. The CAAM trial was conducted under European regulations where the boards waived the requirement for obtaining informed consent given the emergency setting. Deferred consent from the patient and/or relatives was subsequently obtained. Patients with OHCA were randomized to either BMV or ETI during CPR. In case of return of spontaneous circulation, the patients in the BMV group were subsequently intubated. Early-onset pneumonia was specified as secondary outcome in the study protocol but has not yet been explored (protocol version 2). 10  The population, design, and results of the primary analyses of the CAAM trial are reported in detail elsewhere.
A 5% significance level was used. A priori a multivariable logistic regression controlling for survival beyond 96 h, age, gender, systolic blood pressure, heart rate, arterial oxygen saturation, catecholamine administration, hypothermia, no flow time and shockable rhythm was built to compare the incidence of EOP between the two groups of randomisation was planned. Survival beyond 96 h was included to account for any bias that might occur if the entire time period for the symptoms to appear was not present. However, missing values were too extensive for this to be feasible. Instead, a series of analyses were conducted, including one covariate at a time in addition to the intervention, in order to assess the robustness of the bivariate analysis. In a multivariable regression analysis, we subsequently chose to exclude variables with more than 10% missing data in order to avoid reflection of random variation in the data rather than true association.
Here, we provide a detailed and multi-temporal study of geometric changes in El Morado Glacier and its adjacent proglacial lake using an ensemble of topographic information obtained from archival aerial and terrestrial photography, multispectral satellite imagery, aerial and terrestrial Light  Detecting and Ranging (LiDAR)  surveys data and satellite Synthetic Aperture Radar (SAR) data, which was not previously included in Farias-  Barahona and others (2020). Through analysis of these remotely sensed datasets, we quantify both ice area and surface elevation changes for the entire glacier  and the glacier tongue. Particular focus was given to the El Morado Glacier calving front, where a proglacial lake has been progressively increasing in size as a consequence of glacier retreat. The availability of the topographic information described together with bathymetric survey data obtained during a field campaign in 2017 has allowed us to conduct an assessment of the evolution and the volume of water storage in this lake, the first of its kind in the Central Andes.
Quantifying glacier and lake area changes from satellite images and aerial photographs To quantify areal changes in El Morado Glacier and its adjacent proglacial lake, satellite images and aerial photographs were orthorectified and geo-referenced and manually digitised (Table  1 ). The 1955 photogrammetric survey data were provided by the Chilean Military Geographical Institute (IGM) and represents the oldest large-scale geospatial information source for the glacier. Aerial photographs from 1983 by Chile-80 and 1996 by GEOTEC were obtained from the Chilean Air Force's Aero-photogrammetric Service (SAF). Each of these aerial photographs  (1955, 1983 and 1996)  was photogrammetrically scanned at a resolution of 1200 DPI prior to processing. The 1967 declassified stereo Corona KH-4A satellite imagery was acquired in digital format from the US Geological Survey (USGS).
Additionally, several Landsat Thematic Mapper (TM), Operational Land Imager (OLI) and Google Earth images were also used, this imagery having been captured during the ablation season between  2000 and 2018. PlanetScope data (Planet Team, 2018)  with a pixel resolution of 3 m were acquired for 2019 through the Planet Labs Research and Education Initiative. Once orthorectified and geo-referenced, the manual digitisation of glacier and lake outlines from the satellite/aerial imagery was performed using standard procedures (Table  1 ). For Landsat images, we used the approach outlined by  Paul and others (2002). To improve consistency and accuracy of crosscomparisons, all glacier/lake outlines were digitised by the same expert user, with high-resolution satellite (SPOT 6: 2015) and aerial drone imagery (DJI Phantom 4: 2017) providing a detailed topographic reference point.
1). This process was aided by GCPs collected from stable non-glacier locations visible in the terrestrial laser scanning (TLS) and airborne LiDAR surveys. Based on these GCPs, the SfM workflow in Pix4D executes an iterative routine of block bundle adjustments (BBA), camera self-calibration and exterior parameter adjustments (Table  2 ). Subsequently, a coarse 3D point cloud is created from the extraction of numerous image tie points, and further multi-view stereo (MVS) methods are applied to derive a denser 3D point cloud (see  Smith and others, 2016  for details). The resulting dense 3D point clouds (average 1-2 points per m 2 ) were filtered for erroneous points using a static outlier removal tool available in Cloud Compare version 2. 10 software (Girardeau-Montaut, 2019) and gridded at 1 m resolution using a triangulated irregular network method.
TLS and airborne LiDAR and GNSS data Four LiDAR scans of the glacier front were obtained for the years 2015,  2018 and 2019  (2 scans) using a REIGL VZ-6000 longrange TLS (Table  3 ). The TLS instrument obtains ranging measurements by actively transmitting short pulses of electromagnetic radiation towards a target object and then recording its return. Distances between the sensor and targets are then calculated using time-of-flight principles. For each of the observation periods, the glacier front was surveyed from two overlapping stable-ground scan locations. To obtain the absolute position of each LiDAR measurement location, two overlapping stable-ground scanning locations were used, measured using a mounted double-frequency GNSS receiver. For all LiDAR measurements, we obtained a horizontal and vertical accuracy of a few centimetres.
To enable comparisons, the January 2019 scan was subsequently used to co-register the other three TLS point clouds in Reigl RiScan® software using point-to-point feature matching and a subsequent iterative closest point algorithm of stable bedrock locations (RIEGL, 2015;  Xu and others, 2017). The average alignment error of the point cloud co-registration between each scan period was calculated as ∼0. 04 m. Co-registered point clouds were subsequently interpolated using the kriging method and exported in raster format with a horizontal resolution of 0. 5 m and in the UTM 19S coordinate format. Assuming uncorrelated errors, the propagated error of the TLS differencing from GPS, point cloud and raster error sources was estimated as ±0. 20 m (stable rock). Additionally, a DEM covering the entire glacier was made available by the Water Directorate of Chile (DGA) (hereafter DGA-2015 DEM).
SRTM and TanDEM-X The Shuttle Radar Topography Mission (SRTM) acquired nearglobal bi-static C-band Interferometric Synthetic Aperture Radar (InSAR) data between  11 and 22 February 2000 (Farr and others, 2007). The SRTM-C DEM is available in a gap-filled 1 arcsec resolution. However, the original SRTM-C DEM covers just the tongue of El Morado Glacier (Farías-  Barahona and others, 2020). TerraSAR-X and TanDEM-X (TDX) is an ongoing satellite constellation launched by the German Aerospace Center (DLR) that has been acquiring bi-static X-band InSAR data since 2010 (  Krieger and others, 2007). Here, the TDX scenes used were processed following methods described by  Braun and others (2019)  and Farías-  Barahona and others (2019) , utilising tools available within GAMMA software and elevation reference data from the SRTM-C DEM.
Lake bathymetry and glacier tongue flotation The bathymetry survey of the proglacial lake was conducted during a field visit on 11 February 2017 using a custom-built expanded polystyrene remote-controlled boat fitted with a 150 kHz thru-hull depth transducer connected to Actisense Dst-2 active depth sounder module. The control system for the sounder module included an ATMega 328 microcontroller, an uBlox NEO-6 GPS unit and an RFM69HW radio transceiver that was used during debugging. The boat itself was controlled manually using a standard 2. 4 GHz radio system and water depth measurements were acquired every 3 s. Further specifications for the bathymetry boat are available in  Wilson and others (2019). Using the lake depth data acquired in the field, a 2 m grid bathymetry DEM was interpolated using the kriging method.
The lake volume was calculated using the bathymetry DEM and the waterline with 0 m depth, which is derived from aerial photographs acquired on the same day as the bathymetry measurements (Section 3. 1). This bathymetry and the topography data were used to estimate and determine how close the glacier tongue was to flotation during our study period, using the flotation thickness estimation  (Benn and others, 2007 ;  Boyce and others, 2007). Regarding the bathymetry uncertainties, we estimated a depth uncertainty of ±1. 2 m. This uncertainty was calculated using error propagation, considering errors related to the depth sounder equipment, uncertainty of the acoustic velocity due to water temperature (0. 7% of the depth) (e. g. Haritashya and others, 2018 ) and depth differences obtained from overlapping track lines.
Due to the lack of ground-based mass-balance observations, we assume the same values by  Masiokas and others (2016)  for Echaurren Norte Glacier for the parameters α, μ and T melt (3. 9 (α), 89 mm °C(μ) and 0 °C). The model requires as forcing, monthly data of air temperature and precipitation. These input data were taken from the CR2MET dataset (Alvarez-  Garreton and others, 2018). The CR2MET is a new gridded climatology dataset available at daily and monthly time step with a grid size of 0. 005°(∼5 km). In the case of precipitation, the data was obtained using the ERA-Interim reanalysis and using transfer functions and statistical models that take account of the local topography and parametrisations estimated from observations. For air temperature, a grid was constructed again using the large-scale variables (ERA-Interim) as well as the local observations and topography.
1d  there is evidence in both the frontal change and climatic massbalance datasets that ice losses during this period were less than usual, with positive mass balances being estimated for both the 2002/03 and 2005/06 hydrological years. Similar patterns of decreased thinning rates were also observed by  Burger and others (2019)  for Bello Glacier and Yeso Glacier between 2000 and 2013, both of which are located in the Maipo River basin. Prior to the 2000-2009 period, sustained periods of positive mass-balance behaviour and frontal advance had also been observed for glaciers in the Central Andes during the 1980s, including Echaurren Norte Glacier, Universidad Glacier and glaciers in the Rio Plomo basin, Argentina, among others  (Espizua and others, 2008; Masiokas and others, 2016 ;  Wilson and others, 2016).
19 km 2 in 2019. Lake bathymetry data collected in 2017 reveal a maximum water depth of 68 m. Using these data, a total water storage of 3. 6 million m 3 (±5%) was calculated. Overall, the data presented reveal a general trend of retreat and frontal thinning at El Morado Glacier since 1932, with an acceleration of these ice losses occurring since 2010. Through comparison with data available for other glaciers in the Central Andes, this study highlights that such behaviour is not isolated to El Morado Glacier, and may indicate a wider regional trend. The most direct impact of these glacier losses on a basin-scale is the change in the availability of meltwater. A combination of factors such as the reduction in precipitation amounts and snow cover, and the occurrence of an extreme drought event, has put increased pressure on glaciers as a water resource in the Central Andes.
These observations measured on the surface of San Francisco Glacier were acquired by two Automatic Weather Stations, the first (AWS1) installed at 3806 m a. s. l. and the second (AWS2) installed at 3466 m asl. The AWS1 observations span the period from 1 December 2012 to 18 April 2013. The daily mean of these meteorological observations was used to transfer the CR2MET data to the 3806 m a. s. l. (Figs S2,.
Acknowledgements. We greatly acknowledge the  Aero-photogrammetric Service (SAF)  and the  Geographical Military Institute (IGM)  for the aerial photographs. Meteorological and LiDAR data were kindly provided by  DGA , TLS data from 2015 by Geocom S. A. , TanDEM-X data by  DLR  under project  AO XTI_GLAC0264  and Landsat TM by  USGS. CR2MET  gridded climatology (http://www. cr2. cl/datos-productos-grillados/) was provided by  CR2. We acknowledge  Mark Neal  ( Ystumtec Ltd. ) for designing and building the bathymetry boat used in this study, and the  Deutscher Andenverein (DAV) -Club Andino Alemán, Chile  for the mountaineering report (in http:// www. dav. cl). D. F-B, C. B and A. C acknowledge the support of  ANID  through the  doctoral scholarships program. D. F-B thanks the  Fränkischen Geographischen Gesellschaft  for funding a field campaign and  F413.
The aim is to provide a global perspective, measure predictive risks, and evaluate the use of biogeography on predicting diseases outbreaks. We also proposed disease biogeography as a tool for identifying the potential hotspots for Disease X listed in the BPDs. Methods and materials: Data of the observed outbreaks  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018)  were obtained from promed mail and WHO archives. Bioclimatic covariates and altitude data were extracted from 'worldclim' (R package dismo) and the 2017 land cover data (MODIS satellite imagery). We constructed species distribution models including bioclim, maxent and Bayesian models with absence data generated to map the predictive risk of future outbreaks.
Conclusion: Biogeography is a robust tool in forecasting hotspots of EIDs outbreaks. We will also complete the analysis by aggregating the common risk factors of the predicted EIDs to characterize hotspots for an unknown Disease X. Methods and materials: We collected demographic and clinical data and nasopharyngeal aspirate (NPA) samples from 502 children with suspected ARTI admitted to the General Hospital, Kegalle, Sri Lanka from March 2016 to July 2018. The study sampled children less than 5 years of age with ≤4 days history of ARTI. Climatic data of the Kegalle region within the study period was obtained from the World Weather Online API (application programming interface). IFA (D3 UltraTM ® , USA) was performed on NPA to detect seven viruses including RSV. Viral RNA was execrated (Qiagen, Germany) from RSV positive NPA samples and performed a real-time RT-PCR (Altona-Diagnostics EN) for typing.