dataesr/scientific-paragraphs-categorization huggingface dataset:
However, this does not provide detail concerning the statistical distribution under consideration. We also contacted the technical support group at Onset Computer (Massachusetts, USA) about their definition of accuracy, to which the leader of the support group stated "I can say that the probability of the logger being within the advertised accuracy is very high. NIST testing can confirm that. " (NIST is the U. S. Commerce Department's National Institute of Standards and Technology, which provides calibration services for temperature recording equipment). The statement from Onset Computer confirmed that a statistical distribution underlies what temperature data logger reports but again provided no specifics about the exact distribution. We assume that in both cases, the distributions of values generated by the data loggers were either a Gaussian or a uniform distribution.
The experimental procedure used for Engineering Technology Open Access Journal data logger calibration is sometimes described with detail in publications  [13, 14]  but often the published procedure is reported as a simple comparison with a mercury thermometer. Furthermore, even when the experimental procedure was clearly stated, the mathematical method used to correct data logger temperatures when more than 2 control temperatures are used is rarely indicated. Regular calibration testing of data loggers is important because the drift of temperature accuracy can be as large as 0. 1°C/year (for UA-001-08, pers. comm. from technical support group at Onset Computer). Our standardized method of calibration uses both a precise experimental procedure and a precise mathematical procedure.
Uncertainty of a Measurement We generated a simulation to measure the impacts of the accuracy, the resolution and the sampling rate on the quality of the average temperature data that are reported in published studies. To do this, we generated 10,000 time series of temperatures gradually changing at a rate chosen in a uniform distribution from -0. 002 to +0. 002°C per minute with the initial temperature being chosen in a uniform distribution from 25 to 30°C. We then retained only those records of the time series that corresponded to a specified sampling rate (e. g. , for a sampling rate of 60 minutes, we retained only temperature data that occurred at the completion of every 60 minutes time bin). To incorporate errors associated with data logger accuracy, we added to each retained temperature a random number obtained from a uniform distribution centered on 0 and with minimum and maximum corresponding to the reported accuracy of each data logger.
We truncated the recorded temperatures to mimic the impact of the limitations of the resolution of each data logger (the resolution effect). From a mathematical point of view, the resolution effect can be obtained using this formula: With int being the closest allowable value based on the assumed level of resolution. This formula ensures that the truncation effect is well centered in the interval. As an example, assume a dataset has the following temperatures: 30, 30. 1, 30. 2, 30. 3, 30. 4, and 30. 5°C, with assumed data logger resolution being 0. 5°C. Applying this formula, the dataset is converted to what the data logger should report according to its resolution: 30. 0, 30. 0, 30. 0, 30. 5, 30. 5, and 30. 5°C. ( ) ( ) ( ) / 2 * Next, the uncertainty is defined as the 95% confidence interval of the difference between the true mean value and the recorded mean value during the relevant interval of time calculated for all the replicates.
For the purpose of this test, we have created a function in the R package embryogrowth (version 7. With sample. rate being the sample rate in minutes, accuracy being the accuracy of the data logger in °C, resolution being the resolution of the data logger in °C, and max. time being the total time period in minutes over which an average temperature is estimated. This function will generate replicates values of the average temperature for the whole period, and the uncertainty is defined by the range of 95% confidence interval of the difference between true and estimated mean temperature. Optional parameter method is used to control the output estimate as described in the help page of the function that can be displayed using ?uncertainty. datalogger.
The data logger should be programmed to record temperatures every minute, with the time of data recording noted by the researcher. At each minute the data logger records a temperature value, the researcher should also record the temperature from the certified thermometer. Begin with the water heated to the maximum anticipated temperature the data loggers will be recording during future research studies, and lastly the minimum anticipated temperature. It may be necessary to add colder water to the stirred bath if the lower end of the anticipated temperature range is below room temperature. The temperatures recorded with the certified mercury thermometer will serve as a reference to correct the temperatures recorded with the data logger. To make this calibration simpler, we have created a function: calibrate.
Uncertainty of a Measurement The uncertainty of measures obtained with two different data logger models were estimated using uncertainty. datalogger() function with 10,000 replicates and average for 10 days. *The IP is an international standard published by the International Electrotechnical Commission (IEC). It uses a 2-number code, the first one designates dust resistance (0 to 6) and the second one designates water resistance (0 to 8); the higher the number, the more resistant is the product. The results for iButton DS1921G-F5# (Table  1 ) were generated using: uncertainty. datalogger (sample. rate=c(30, 60, 90, 120), max. time = 10 * 24 * 60, accuracy=1, resolution=0. 5) The results were shown in Table  2. and For Tinytag Talk 2 TK-4014 (Table  1 ), the results were generated with: uncertainty.
datalogger (sample. rate=c (30, 60, 90, 120), max. time = 10 * 24 * 60, accuracy=0. 5, resolution=0. 05) The results were shown in Table  3. The uncertainty returned by this function corresponds to the 95% confidence interval width of the difference between true average temperature and recorded average temperature: the uncertainty of the Tinytag Talk 2 TK-4014 for the average temperature recorded every 60 minutes is around 0. 07°C whereas it is only around 0. 15°C for the iButton DS1921G-F5#. It should be noted that the response was not linear and uncertainty increased as the time interval between samples increased (Figure  1 ). The uncertainty is more dependent on the accuracy and second on the resolution. The measures were obtained with a large range of temperatures and temperature variations, thus the estimated uncertainty can be considered as being dependent only on the data logger characteristics and the sampling rate of temperatures.
Calibration of Data Loggers Water was heated to 40°C in a microwave oven and a UA-001-08 data logger was immersed in the water as well as a certified mercury thermometer. Temperatures were recorded with the data logger and read in thermometer every 5 minutes until water temperature reached air temperature +5°C (Figure  2A ). Then the calibration procedure has been run using the function calibrate. datalogger() in R package embryogrowth. The corrected temperature recorded by data logger and the Engineering Technology Open Access Journal temperature recorded using certified mercury thermometer are shown in Figure  2B.
The correct calibration and adequate uncertainty of data loggers according to the analyzed temperature-dependent phenomenon seem quite logical but they are not always correctly done or at least reported not in publications. When data loggers are used to record temperatures to be analyzed in the context of temperature change due to climate-change, it appears crucial that temperatures are recorded with known uncertainty that is at least smaller than the supposed effect of temperature. For publication purpose, the function uncertainty. datalogger() can be used to evaluate the uncertainty of one measurement taking into account both accuracy and resolution effect. It can be used also to evaluate the uncertainty of series of measurements done during H hours at a h sampling rate.
Mapping lake area dynamics Imagery from the Landsat archive (5, 7 and 8; 16 day repeat cycle) and China's Huanjing satellites (1A and 1 B; 2 day repeat cycle) were combined to improve the temporal coverage of cloud-free observations through data blending and fusion. All imagery have the same resolution (30 m) in visible and near-infrared bands (see  [3]  for details), and were collected from September to December during which lake extents on Tibetan Plateau are generally stable  [2, 3, 13, [25] [26]. Combined imagery from Landsat and Huanjing were used to extract annual extents in 871 lakes larger than 1 km 2 from 2009-2015. Among them, lake areas from 2009-2014 were directly obtained from our previous mapping  [3]. For the period before 2009  (2002) (2003) (2004) (2005) (2006) (2007) (2008)  when Huanjing imagery were unavailable, images from Landsat 5 and 7 were fused using the planetary-scale platform Google Earth Engine (GEE)  [27].
Specifically, we used the SRTM DEM version 3. 0  [22]  and the ASTER GDEM version 2  [23] , both under the same resolution (1 arc second or ∼30 meters). The inundation area of a lake in the SRTM DEM was obtained from the SRTM Water Body Dataset (SWBD)  [35]  while that in the ASTER GDEM was detected based on surface flatness as in Fujisada et al  [36]. Such a lake area (hereafter referred to as 'base area') corresponds to the water level (hereafter referred to as 'base level') at the acquisition time of the DEM (figure  2 ). Given higher vertical accuracy and quality consistency  [37, 38] , the SRTM DEM was prioritized: it was chosen if (1) the base area is lower than the minimum lake area during the study period, or (2) the base area is lower than that in ASTER GDEM. Otherwise, the ASTER GDEM was applied.
Accuracy assessments DEM-derived LWS trends from 2002-2015 were validated against the trends during the same period but calculated using available hypsometric curves provided in the LEGOS Hydroweb database (http://hydroweb. theia-land. fr; hereafter Hydrowebderived trends)  [49]. Many hypsometric curves provided by Hydroweb were constructed from multidecadal records of water levels observed by radar altimeters and water extents mapped from satellite imagery  [18, 49]. A total of 18 lakes on the CP with Hydroweb hypsometry were used for the validation. Volume of these lakes varies from ∼0. 2 to ∼100 gigaton (see table  S1 ); collectively, they account for approximately 70% of the total lake volume across the CP  [4]. For a comparison purpose, we also validated the LWS trends from 2002-2015 using hypsometric curves constructed from ICESat altimetry and Landsat imagery by Zhang et al  [16]  (hereafter ICESat-derived trends).
97). This implies that volume change estimates using extrapolated hypsometry may need to be interpreted with cautions. Given such improved spatiotemporal coverage and reduced estimation biases, this study advances our understanding of recent variations in lake water budget across the remote Tibetan Plateau. Our produced lake area and storage change data set is freely available at PANGAEA (https://doi. pangaea. de/ 10. 1594/PANGAEA. 888706). From 2002-2015, the net LWS increased at an average rate of 7. 34 ± 0. 62 Gt yr -1 (cumulatively 95. 42 ± 8. 06 Gt), manifested as a dramatic monotonic increase of 9. 05 ± 0. 65 Gt yr -1 before 2012, a deceleration and pause in 2013-2014, and then an intriguing decline after 2014. Using TWS anomalies from GRACE observations and a water balance model, we quantified that ∼70% of the monotonic LWS increase before 2012 was attributed to the increase in net precipitation (P-ET).
Acknowledgments We acknowledge the  USGS  and  NASA  for providing the Landsat imagery and the  SRTM DEM , the Google Earth Engine for processing Landsat image fusion,  NASA Jet Propulsion Laboratory  for providing GRACE mascon data, and  China Center for Resources Satellite Data and Application  for providing Huangjing satellite imagery. Constructive comments on uncertainty analysis were given by  Dr. Chunqiao Song  from  Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences  and  Dr. Arnaud Temme  from the  Department of Geography, Kansas State University. Publication of this article was funded in part by the  Kansas State University Open Access Publishing Fund.
Data are expressed as means ± SD. N = 3 per group. *P < 0. 05, **P < 0. 01, ***P < 0. 005 dose of 60 mg/kg/day orally for 1 month and compared muscle function tests with 10 months old non-treated mdx mice (from now on mdx mice; n = 5) and 10 months old C57 wild-type mice (from now on WT mice; n = 5). We first performed a DigiGait analysis, which allows identifying differences in the walking pattern of healthy and dystrophic mice. We did not find differences between WT mice, mdx mice, and nintedanib-treated mdx mice, suggesting that the severity of weakness involving muscles of the paws in this murine model at this age is not enough to find differences using this technique (Fig  S4 ). We then used electrophysiological tests to analyze compound muscle action potential (CMAP) amplitude and morphology of the motor unit action potentials (MUAPs).
8 mM CaCl 2 and incubated in recovery solution consisting of proliferation media at two time points: 90 s and 10 min. After that cells were exposed for 2 min to propidium iodide (Sigma) (20 µg/ml in HBSS) that stains nuclear DNA only if when the cell membrane is injured. Finally cells were washed with HBSS, fixed with 4% of paraformaldehyde (Sigma) in phosphate-buffered saline (PBS) and stained with Hoechst (100 µg/ml in PBS) (Invitrogen, Carlsbad, CA). The total number of nuclei and immunoprecipitation-positive nuclei were counted using Fiji software.
Migration was quantified using Image J software. Inhibition of migration was expressed considering 100% as the amount of migration in the conditions without nintedanib. Three pictures of each replicate (n = 3) and each condition were taken using an inverted microscope (Olympus). Transwell assays were performed in fibroblasts treated with or without 0. 4 μM nintedanib for 2 h in a T75 flask. Then, cells were seeded into 8-μm pore-size transwell filters (Corning Incorporated, Corning, NY) at 4 × 10 4 cells/well in 200 μl of OPTIMEM. In total, 600 μl of OPTIMEM with 10 ng/mL PDGF-AA or vehicle was added to the lower chamber. After 24 h of treatment at 37 °C, cells on the topside of the filter were removed by scrubbing twice with a cotton swab, and cells on the bottom side of the filter and cells in the well (migrating cells) were counted.
Motor functional analysis: Digigait assay Locomotion analysis was performed using the Digigait Imaging system (Mouse Specifics, Quincy, MA). In brief, digital video images of the underside of the mouse were collected with a high-speed video camera (80 frames/s) from below the transparent belt of a motorized treadmill. Each mouse was allowed to explore the treadmill compartment, with the motor speed set to zero, for 5 min. Then, the motor speed was set to 20 cm/s to collect the videos. A minimum of 200 images was collected for each walking mouse so that five to seven strides were captured in each run. Video images of 12. 5 ms duration were digitized and the area (in pixels) of the paws was calculated with the DigiGait software.
Since the force imposed by the filament is fairly constant, two different VF monofilaments were used to evoke responses, one with a known force of 10 g and 5. 07 size and the other with a force of 26 g and a size of 5. 46. The EMG responses recorded from the tibialis anterior muscle in response to those mechanical stimuli were recorded and analyzed to measure the area under the curve of each response using Chart software. At least, three different responses for each Von Frey filament were assessed per mouse.
Acknowledgements We thank the people of the  Neuromuscular Disorders Unit laboratory  and the animal room staff at Sant  Pau Hospital. We thank  Anna Molins  for the technical support to the experiments. We also thank  Marisol Montolio , from  DPPE , for her comments and continuous support. This study has been funded by  Duchenne Parent Project Spain, Fundación Isabel Gemio and Boehringer-Ingelheim  and a grant from  Fondo de Investigaciones Sanitarias (FISS)   PI15/ 01822  (J. D-M. ). L. M. E. , and P. G. -G. are supported by the  Ramón y Cajal program  ( PI13/01347 ); L. M. E. , and P. G. -G. are supported by the  Ramón y Cajal program  ( PI13/01347 ); L. M. E, and P. G. -G. work is funded by the  Ministry of Economy, Industry and Competitiveness  grant  BFU2016-74975-P  co-funded by  FEDER  funds.
Admixture between populations provides opportunity to study biological adaptation and phenotypic variation. Admixture studies rely on local ancestry inference for admixed individuals, which consists of computing at each locus the number of copies that originate from ancestral source populations. Existing software packages for local ancestry inference are tuned to provide accurate results on human data and recent admixture events. Here, we introduce Loter, an open-source software package that does not require any biological parameter besides haplotype data in order to make local ancestry inference available for a wide range of species. Using simulations, we compare the performance of Loter to HAPMIX, LAMP-LD, and RFMix. HAPMIX is the only software severely impacted by imperfect haplotype reconstruction.
Loter is the less impacted software by increasing admixture time when considering simulated and admixed human genotypes. For simulations of admixed Populus genotypes, Loter and LAMP-LD are robust to increasing admixture times by contrast to RFMix. When comparing length of reconstructed and true ancestry tracts, Loter and LAMP-LD provide results whose accuracy is again more robust than RFMix to increasing admixture times. We apply Loter to individuals resulting from admixture between Populus trichocarpa and Populus balsamifera and lengths of ancestry tracts indicate that admixture took place $100 generations ago. We expect that providing a rapid and parameter-free software for local ancestry inference will make more accessible genomic studies about admixture processes.
LAI can be used to study patterns of introgression  (Hufford et al. 2013; Suarez-Gonzalez et al. 2016; Medugorac et al. 2017) , to map genes involved in reproductive isolation (Corbett-Detig and Nielsen 2017) and phenotypic variation  (Lindtke et al. 2013; vonHoldt et al. 2016) , and to decipher past admixture processes  (Brandvain et al. 2014; Liu et al. 2014). Although various LAI software are now available, they have been mainly tuned to human data in order to map disease-associated variants  (Patterson et al. 2004; Seldin et al. 2011). In this paper, we introduce the software package Loter for Local Ancestry Inference, which does not require specifications of statistical or biological parameters in order to make LAI available for a wide range of species. Several software packages for LAI have been developed including HAPMIX, LAMP-LD, and RFMIX  (Price et al.
Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons. org/licenses/by-nc/4. 0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals. permissions@oup. com Open Access  (Maples et al. 2013). Except for the genetic map, default parameters values can also be provided when using RFMIX  (Maples et al. 2013). Other less-important differences between LAI software packages are also provided in table 1. Except for LAMP-LD that uses statistical parameters only, RFMIX and HAPMIX require biological information such as genetic map, which can be difficult to provide for nonmodel species.
The software package Loter is based on the copying model introduced by  Li and Stephens (2003)  and already used in HAPMIX to perform LAI  (Price et al. 2009 ). The copying model assumes that given a collection of "parental" haplotypes from putative source populations, haplotypes from admixed individuals are modeled as a mosaic of existing parental haplotypes (fig. 2 ). The main difference with HAPMIX is that Loter is not based on a probabilistic formulation that requires specifying several parameters. Instead, Loter is based on an optimization problem parametrized with a single regularization value k that penalizes switches between parental haplotypes. Solutions of the optimization problem are found by using a dynamic programming algorithm, whose computational complexity is linear with respect to the number of markers and the number of individuals from the source populations.
Simulated Human Admixed Individuals We consider simulated admixed individuals resulting from admixture between Africans (YRI population in HAPMAP 3) and Europeans (CEU population in HAPMAP 3). Accuracy obtained with several LAI software packages varies depending on time since admixture occurs (fig. 4 ). For recent admixture where admixture occurred five generations ago, LAMP-LD and RFMix obtain the best result with median diploid accuracies of 99. 6% and 99. 8% whereas median diploid accuracy of Loter is equal to 99. 3%. For ancient admixture where admixture occurred 500 generations ago, Loter obtains the largest median diploid accuracy of 86. 7% followed by LAMP-LD with a diploid accuracy of 80. 6% and RFMix with a diploid accuracy of 72. 0%. For the smallest admixture times (l 20 generations), diploid accuracy obtained with the three approaches is >95% and RFMix and LAMP-LD outperform LOTER.
By contrast, for the largest admixture times (l ! 150 generations), Loter is the most accurate software for LAI (fig. 4 ). To evaluate these LAI software packages in a different context, we consider an additional setting where haplotypes of the reference panel are not drawn from the source populations but from populations that have diverged from the FIG. 3. Graph that represents the optimization problem of equation (  1 ). An optimal solution for ðs 1 ;. ; s p Þ is found by finding the shortest path from node a to node b. We assume that there are n individuals in the source populations resulting in 2n haplotypes denoted by ðH 1 ; Á Á Á ; H 2n Þ. The value (0 or 1) of the ith haplotype at the jth SNP is denoted by H j i. A vector ðs 1 ;. ; s p Þ describes the sequence of haplotype labels from which the haplotype h of an admixed individual can be approximated.
The loss of accuracy of LAMP-LD for ancient admixture times is less pronounced but Loter still provides more accurate reconstructions than LAMP-LD when l ! 100 generations. We evaluate the benefit of bagging and of averaging local ancestry values obtained with different values of the regularization parameter, which are implemented by default in  Loter. First, diploid   S2, Supplementary Material online). Additionally, we vary the number of haplotypes in the reference panels to investigate to what extent Loter is sensitive to the reference panels. When using the TSI and LWK reference samples, the diploid accuracy varies substantially from 41% with a single haplotype in each population to 98% when using 100 haplotypes in each population. We additionally evaluate the diploid accuracy of HAPMIX, which is another LAI software package based on the copying model.