dataesr/scientific-paragraphs-categorization huggingface dataset:
s. l. , 30 • S, 70 • W), during two hydrologically contrasting years (i. e. , dry vs. wet). The model is calibrated and forced with meteorological data from nine Automatic Weather Stations (AWSs) located in the watershed and atmospheric simulation outputs from the Weather Research and Forecasting (WRF) model. Results of simulations indicate first a large uncertainty in sublimation-to-melt ratios depending on the forcing as the WRF data have a cold bias and overestimate precipitation in this region. These input differences cause a doubling of the sublimation-to-melt ratio using WRF forcing inputs com-pared to AWS. Therefore, the use of WRF model output in such environments must be carefully adjusted so as to reduce errors caused by inherent bias in the model data. For both input datasets, the simulations indicate a similar sublimation fraction for both study years, but ratios of sublimation to melt vary with elevation as melt rates decrease with elevation due to decreasing temperatures.
, 2017). However, an energy balance model requires a larger input dataset that is often not available in Andean catchments due to the logistical difficulty of Automatic Weather Station (AWS) installation and maintenance. Therefore, the evaluation of alternative methods for acquiring distributed meteorological information is required. Options include the use of interpolation-extrapolation strategies (e. g. , MicroMet;  Liston and Elder, 2006b) , reanalysis (NCEP;  Kalnay et al. , 1996)  or atmospheric model outputs (e. g. , Weather Research and Forecasting (WRF) model;  Skamarock and Klemp, 2008). For the semiarid Andes, both Mi-croMet extrapolation based on AWS data  (Gascoin et al. , 2013)  and atmospheric models (e. g. ,  Favier et al. , 2009; Mernild et al. , 2017)  have been used to force snow models.
However, none of these studies have quantified the uncertainties related to forcing data. The relative importance of melt and sublimation to total ablation has been studied at both the point scale  (Mac-Donell et al. , 2013a)  and catchment scale  (Gascoin et al. , 2013)  in one catchment in the semiarid Andes. MacDonell et al. (2013a)  estimated that the sublimation fraction was 90 % at high altitude (> 5000 m a. s. l. ) in an extreme environment with predominantly subfreezing temperatures and strong local wind speeds. Using a distributed snowpack model  Gascoin et al. (2013)  found that the total contribution of sublimation (static-surface and blowing snow sublimation) to total ablation in the Pascua-Lama area (29. 3 • S, 70. 1 • W; 2600-5630 m a. s. l. ) was 71 %. However, this value was obtained for one snow season, and the precipitation was estimated from snow depth measurements as precipitation gauge data were unreliable.
The sensitivity of sublimation to meteorological forcing and in particular to precipitation uncertainties was not evaluated. The objective of this study is to assess the uncertainties related to modeling snow evolution in the semiarid Andes using AWS and WRF-model-generated meteorological datasets during two contrasting years. From this analysis, the snow mass balance for one relatively wet and one dry year will be compared, and an evaluation of the impacts of model choices on sublimation and melt rates in dry mountain areas will be discussed. To address this aim, the model SnowModel described in  Liston et al. (2006b)  will be applied to the La Laguna catchment in the semiarid Chilean Andes during 2014 and 2015. These two years were selected because in this region, 2015 was considered to be a strong El Niño event, associated with warm and wet conditions, whereas 2014 was drier and colder and considered a neutral year (CEAZA-Met; http://origin.
Meteorological data (a) Automatic weather station measurements Meteorological data from nine Automatic Weather Stations (AWSs) are available in the catchment (Fig. 1b ) over the study period 2014-2015. La Laguna, Tapado and Paso del Agua Negra AWSs are scientific-grade permanent stations maintained by CEAZA (http://www. ceazamet. cl, last access: December 2019) with hourly measurements. In addition, five HOBO ® weather stations (Colorado Bajo, La Gloria, Llano de Las Liebres, Colorado Alto and Vega Tapado) were installed in March 2014 and set to record meteorological data every 30 min. The Tapado Cubierto station was installed in 2013 on the debris-covered part of the glacier and provides measurements at hourly intervals. Although the lower accuracy of HOBO weather sensors compared to the permanent stations represents a source of errors in the forcing data, errors resulting from the spatial interpolation of forcings are likely to be much greater than these.
More details regarding available measurements and the time periods for which these are available are provided in Fig. 2. Tapado records are reported in Fig. 3  as an example of the weather conditions in this catchment. Due to the complexity of precipitation measurement (e. g. ,  MacDonald and Pomeroy, 2007) , datasets were postprocessed. First, filters were applied to eliminate outliers (i. e. , negative values and values larger than 30 mm h -1 ). Second, satellite images (MODIS Aqua and MODIS Terra) were used to remove recorded precipitation events on sunny days, which were probably due to wind transport. These precipitation events were only removed if five cloud-free images were available (i. e. , the two for the day: one the afternoon before and one the next morning). In total, three precipitation events lower than 2 mm w.
e. were removed with this method. At Tapado, measurements are recorded at two Geonor weighing precipitation gauges, of which one is shielded (alter shield) and one is unshielded. After being filtered, the cumulative difference at the two gauges was 9. 1 mm for 2014 (i. e. , 10 %; 97. 1 mm for unshielded gauge 1 and 106. 2 mm for shielded gauge 2) and 5. 4 mm for 2015 (i. e. , 1 %; 457. 5 mm for gauge 1 and 462. 9 mm for gauge 2) with a maximum hourly difference of 1. 1 mm; however the relative bias between the sensors is neither constant nor unidirectional. As the difference between the two sensors was relatively small, the mean of the two datasets was used as the reference precipitation value, and a maximum uncertainty of 10 % was estimated.
Local snow depth data In 2014, three AWSs provided snow depth measurements at an hourly time step (Vega Tapado, Tapado and Tapado Cubierto; Fig. 2 ). Over the 2015 winter, snow depth measurements were available at five stations (La Gloria, Colorado Alto, Tapado, La Laguna and Tapado Cubierto). The snow depths were measured with ultrasonic sensors and require post-treatment because they are particularly prone to measurement errors and typically produce a noisy signal  (Lehning et al. , 2002). Therefore, the control procedure described in  Lehning et al. (2002)  was applied to clean the signal and in particular to eliminate spikes, check for outliers and physical limits.
Simulations Two types of simulations have been performed over the entire catchment for the period 1 January 2014-1 January 2016 on a 100 m resolution DEM. The first simulation was forced with input from the nine automatic weather station measurements (referred to as AWS forcing), whereas the second simulation was forced with the WRF data (referred to as WRF forcing). After indicating the differences observed for these two forcing sets, the model is primarily validated at local points. Results for the two simulations were first compared to local snow depth measurements at each AWS (described in Sect. 2. 2. 1). The performance was evaluated using a kappa statistic coefficient  (Cohen, 1960)  denoted k to measure the agreement between the simulation and the observation, considering the percentage of time with and without snow.
g. , March 2015), a timing bias of a few days (e. g. , March 2014) and/or additional events (during both 2014 and 2015 winter). The simulated SCA evolution over winter and spring of 2015 shows strong variation over the entire catchment, which is not observed in the MODIS record. Here again, for both forcing datasets, better performances are observed for 2015 (NSE AWS = 0. 79, RMSE AWS = 93. 2 km 2 , i. e. , 19 % of the total area; NSE WRF = 0. 61, RMSE WRF = 125 km 2 (25 %)) than for 2014 (NSE AWS = 0. 41, RMSE AWS = 117 km 2 (23 %); NSE WRF = 0. 23, RMSE WRF = 133 km 2 (27 %)).
This gauge undercatch uncertainty (see Sect. 5. 4. 1) could increase the difference between the precipitation simulated by WRF and that measured at the AWSs. In addition, questions arise regarding the representativity of point measurements compared to the grid cell considered in the model. The spatiotemporal variability observed in the difference between AWS and WRF precipitation data highlights that it would be inappropriate to use a constant correction factor to adjust WRF data with measurements. More studies comparing WRF output to AWS among other datasets are required to determine a realistic correction method.
Consequences of the forcing used Biases between the two forcing datasets cause significant differences in the energy and associated mass balances during both study years. In 2014 there are lower RH and P biases (Fig. 4c, d ), but larger air T differences, especially at high elevations (Fig. 4b ). Wind speed is on average higher in the WRF forcing, whereas biases in incoming LW and SWi and air pressure are low for both years (results not shown). The larger RH bias in 2015 indicates an overestimation of the dryness for this year (compared to 2014) and would lead to larger differences in sublimation rate for 2015 than for 2014, which is the opposite of the results observed. Likewise, the larger overestimation of precipitation amount observed in 2015 (Fig. 4d ) does not explain the larger difference in sublimation, as a deeper snow depth should result in more persistent snow cover during the warm period and hence a lower sublimation ratio related to larger melt rate.
Precipitation uncertainties directly impact snow on the ground and therefore indirectly impact sublimation rates. Uncertainties in wind speed were likely the second source of error in sublimation results, which need to be better constrained in future studies. Therefore, this study highlights that this uncertainty has a strong impact on sublimation and further work is suggested to (i) improve measurement uncertainties, (ii) increase the number of sensors over the catchment, and (iii) incorporate AWS measurements into the WRF model and use data assimilation to improve model outputs. CEAZA is currently working on point (iii) to provide improved WRF outputs for the semiarid Andes of Chile. This study has highlighted the current difficulties in using standard WRF model outputs in a semiarid Andean catchment.
Moving forward, it would be highly advantageous to improve WRF model performance in mountainous areas where high relief and difficult access often limit AWS distribution to valley floors, therein limiting the accuracy of interpolation techniques for terrain-sensitive variables, such as precipitation and wind speed and direction. Data availability. Part of the data used in this paper (automatic weather station data) can be accessed at https://www. ceazamet. cl (CEAZA, 2020). Satellite images are available by contacting Simon Gascoin. Data were processed using the snowpack model SnowModel and this algorithm can be accessed by contacting the administrator, Glen E. Liston. For any other access to the data presented in this study, please contact the corresponding author. Supplement. The supplement related to this article is available online at: https://doi.
org/10. 5194/tc-14-147-2020-supplement. Author contributions. MR and SM designed the study. MR and SG designed the modeling strategy. MR ran the numerical experiments and conducted data preparation and analyses. SG provided the MODIS data. MR, SM and CK collected and provided the field meteorological data. All authors contributed to the preparation of the paper. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. The authors thank  Glen E. Liston  for providing SnowModel code and for the interesting and useful discussions and suggestions. We are also grateful to  Pablo Salinas  and  Arno Hammann  for providing the WRF simulation outputs and for providing useful assistance related to these data. Finally we acknowledge  Jonathan Conway  and  Kay Helfricht  for their detailed comments and the helpful suggestions which significantly improved the quality of the paper. Financial support. Marion Réveillet  and  Shelley MacDonell  were supported by  CONICYT-Programa Regional   R16A10003 , and the  Coquimbo regional government FIC-R ( 2015 )  BIP 30403127-0. Christophe Kinnard  was supported by a  Coopération bilatérale -Québec-Chili Ministère des Relations Internationales et Francophonie.
In the mature cochlea, each inner hair cell (IHC) is innervated by multiple spiral ganglion neurons of type I (SGNI). SGNIs are morphologically and electro-physiologically diverse. Also, they differ in their susceptibility to noise insult. However, the molecular underpinnings of their identity and physiological differences remain poorly understood. In this study, we developed a novel triple transgenic mouse, which enabled the isolation of pure populations of SGNIs and the analysis of a 96-gene panel via single-cell qPCR. We found three distinct populations of Type I SGNs, which were marked by their exclusive expression of Lmx1a, Slc4a4, or Mfap4/Fzd2, respectively, at postnatal days P3, P8, and P12. Our data suggest that afferent SGN subtypes are established genetically before the onset of hearing and that the expression of key physiological markers, such as ion channels, is heterogeneous and may be underlying the heterogeneous firing proprieties of SGNIs.
LOD Ct values for each primer pair were determined in dilutions of bulk cochlear cDNA (neonatal and P21) over 16 orders of magnitude. Primers that did not meet the three above stated validation requirements were eliminated. A list of 96 genes (Supplementary Table  S1 ) was used for gene expression profiling, and the Log2Ex values were used for downstream analysis. Before clustering, the data set was cleaned by removing cells with low expression of housekeeping genes Gapdh and Actb, as well as removing any non-neuronal contaminants by selecting cells, which expressed Map2 and Tubb3. We used HDBSCAN, implemented in python, to cluster single cells obtained from P3, P8, and P12 cochlea, and obtained six clusters. In selecting our clustering method, we sought to find a method that would agree with our underlying assumptions about the cell populations, namely that: (1) the variance between different cell populations might not be the same; (2) the size of each subpopulation may be different; (3) some rare cell populations might not be sufficiently sampled; and (4) variance in the data can be introduced by additional factors, such as RNA degradation.
05/96), and then each significant ANOVA was tested using the post hoc Tukey test. Statistical analysis was performed in Python and Prism (Graphpad). We also repeated the same clustering process using PCA analysis plus K-means clustering and hierarchical clustering. K-means clustering was applied in each data sets using the algorithm ''Hartigan-Wong'' with one thousand iterations (iter. max = 1,000) in R. Hierarchical clustering was computed by Ward's minimum variance method (Ward. D2). The numbers of stable clusters generated were assessed by gap statistic  (Tibshirani et al. , 2001).
Confocal Imaging and Signal Quantification Images were acquired on a confocal microscope (Zeiss, LMS700) as previously described  (Mendus et al. , 2014). A 0. 5 µm z-stack of images was collected. The signals for specific genes such as Cacna1a, Mfap4, and Kcnd2 in each cell were visualized in Velocity 3D image analysis software (PerkinElmer, Inc. , Waltham, MA, USA). The boundary of a cell was defined by merging fluorescent images with bright-field images and manually tracing cell borders. The numbers of fluorescent signals in each defined boundary were quantified manually.
Validation of Type I SGNs in the Mouse Cochlea After identifying IA, IB, and IC to be the main three subpopulations arising from our single-cell dataset, we sought to validate these three populations in vivo. First, we designed in situ probes against Cacna1a, which we observed to be enriched in both the IA and IB subtypes. Indeed, we see a strong signal of Cacna1a in cochlear tissues at both P3 and P8 (Supplementary Figures  S6D, E ). However, due to the low resolution of traditional in situ, we were not able to localize the expression of more than one gene to individual populations of neurons. Therefore, we opted to use RNA-scope technology, an in situ hybridization approach with low background signal, that allows us to visualize and/or co-localize two or more probes  (Wang et al. , 2012).
Comparison to a Previously Published Dataset of P3 SGNs We next sought to compare our study to the previously published results from whole-genome single-cell studies  (Petitpré et al. , 2018; Shrestha et al. , 2018; Sun et al. , 2018). Of these, two focused primarily on adult SGNs, and therefore were not suitable for comparison with our study, as neuronal properties are known to change from neonate to adult  (Crozier and Davis, 2014). We thus chose to compare our work to the Pvalb-cre P3 neurons sequenced by  Petitpré et al. (2018)  although the two studies use different transgenic mouse models for sorting. In both the adult and P3 data,  Petitpré et al. (2018)  found three subtypes of SGNs, characterized by their expression of Calb1, Runx1, and Calb2 (Type IA), Lypd1, Grm8 and Runx1 ±13. 9%) as a percentage of all cells scored per image.
Finally, Slc4a4 and Cacna1a were broadly expressed across all subtypes (Supplementary Figures  S7J, K ). These discrepancies in the two datasets may suggest that transcripts like Lmx1a and Mfap4 may be low-abundance and therefore need to be pre-amplified to be detected. The limited detection of low-abundance transcripts is a known problem with both the 10× and Smart-seq2 pipelines, as these transcripts are not efficiently converted into cDNA. Thus, our dataset complements previous whole sequencing studies that set the outlines of SGN subtypes with the detection of these low abundance transcripts.
This step facilitated the construction of single-cell transcriptomes of SGNIs and SGNIIs neurons. Using our single-cell dataset of SGNI cells from P3, P8 and P12 cochlea's we identified three distinct populations: Type IA, marked by Lmx1a expression, Type IB marked by Slc4a4 and Type IC marked by Mfap4/Fzd2. Although, we observed all three subtypes of Type I SGNs at each developmental age, their relative sizes varied throughout development. This might be due to the differentiation of progenitor-like SGNs at earlier time points, or selective loss of certain subtypes that are more sensitive to stress (Figure  7C ). Previous work has highlighted the existence of three electrophysiological categories whose proportions changed between early postnatal (P0-P3) and adult  (Crozier and Davis, 2014).
Intriguingly, although a previous study also found three subtypes of SGNs at postnatal day 3 (P3;  Petitpré et al. , 2018) , we found little overlap between the marker genes defining the populations. In particular, Lmx1a expression was absent from this dataset. This discrepancy between the datasets may be due to the qPCR-based technique allowing pre-amplification of low abundance transcripts, the technique of cochlear dissociation, or the choice of transgenic mouse models. These differences, however, highlight the importance of using several orthogonal methods to investigate complex biological systems, as each technique can reveal unique and complementary features of the tissue. Previous studies have established that type I SGNs have a variety of physiological phenotypes, corresponding to the so-called low, mid and high threshold neurons  (Adamson et al.
ACKNOWLEDGMENTS We sincerely thank  Drs. Stefan Heller  and  Marta Milo  for help with data analysis, and  Dr. Walter Marcotti  for helpful discussion. We also thank  Dr. Kathleen J. Millen  for generously providing Lmx1a-cre mice and  Dr. Theresa Zwingman  for answering the many questions we had concerning these mice ( Seattle Children's Research Institute Center for Integrative Brain Research, The University of Washington ). We thank  Dr. Joseph P. Sarsero  ( Murdoch Children's Research Institute, Australia ) for sharing Peripherin-GFP mice with us and  Dr. Lin Gan  ( Flaum Eye Institute, University of Rochester School of Medicine ) for sharing Bhlhb5-cre line.
Moreover, most of robotics algorithms are not easy to be parallelized, e. g. due to the dependencies between successive smaller paths calculations, and exploiting heterogeneity by shaping computing cores or by optimizing the execution on the different available ones, according to the involved operations, is not trivial. This article focuses on the analysis of the Damped Least Squares (DLS) algorithm for IK solution, looking to a prospective hardware implementation on FPGA of a DLS-based hardware controller for a robotic arm. More in details, the contributions of this article are: • the open-source sequential and parallel MATLAB implementations of the DLS  [12]. These implementations have been released as open data of the CERBERO project together with a trajectory generator  [13]  for the workspace described in Section II.
1 + 2 5 ρ < z < 29 - 2 5 ρ (1) 10 < ρ < 35 (2) ρ 2 = x 2 + y 2 (3) According to the described workspace, we have derived, and made available as an open-data  [13]  of the CERBERO project, a trajectory generator. The trajectory generator is a MATLAB script that generates 100 possible trajectories for the WidowX Robotic Arm. For each of them the initial and final position of the end-effector are defined as spatial coordinates within the workspace. By default, the initial point is fixed to A(x 0 , y 0 , z 0 ) = {35, 0, 15} that is the idle resting position of the arm. Along with the script itself, a trajectories. csv file is also provided. It contains the set of trajectories that are used for the algorithm explorations reported in this article in Section V. They can be classified in three groups, short, medium, and long, on the basis of their length.
3) Data-Driven methods: learning-based techniques are the foundation for these methods that require having available large datasets and pre-learned postures to match the position of the end-effector. These characteristics make them suitable for complex problems such as human-like structures. 4) Hybrid methods: they are based on the simplification of the IK problem, by decomposing it in a combination of analytical and numerical components and exploiting the related algorithms for the solution. As data-driven methods, the hybrid methods are suitable for complex problems, such human-like structures or multiple end-effectors ones. Due to the peculiarities of the numerical solutions, that are highly suitable for the problem we address in this article, we focus our attention on this family. Indeed, given the chosen target (see Section II-A), where multiple end-effectors or body-like mechanisms are not there, complex data-driven or hybrid methods can be excluded.
V. DAMPED LEAST SQUARES ALGORITHM: ANALYSIS The goal of this analysis is to verify features, pros and cons of the DLS algorithm, when parameters vary. In particular we analyze two different MATLAB implementations that we made available as an open-data  [12]  of the CERBERO project. We will refer to these implementation as: • classic -intended as fully sequential, where prior to start a new iteration of the algorithm the current computation of Equation 7 should be completed. In this case, programmers can play with two parameters: the damping factor and the number of iterations. • parallel -This implementation is composed of two steps. In the first one the DLS is used to obtain coarse points over the whole trajectory; the outcome is a segmentation of the whole trajectory. In the second step, the DLS is run again over each segment, using as starting and final points the sequence of adjacent points calculated in the first step.