dataesr/scientific-paragraphs-categorization huggingface dataset:
Data treatment and response variables To test our hypotheses that humpback whales would approach the source of HFKW sounds and avoid the source of MEKW sounds, we computed the horizontal tracks of the focal whale during the playback experiments. We followed the method de scribed by  Wensveen et al. (2015)  to construct the horizontal track of focal whales, which consists of a dead-reckoning track anchored to visual positions (and GPS fixes when available). We had no tag data for whale mn16_018a (Table  1 ), so we constructed the horizontal track of this whale with linear interpolation between visual surfacing positions and times. We quantified the approach or avoidance response of humpback whales by calculating a horizontal reaction score (HRS) for each playback. (1) HRSs measure the difference, relative to the sound source, between the actual position of the focal whale and its extrapolated position based on its movement patterns during the pre-exposure period.
We calibrated the pressure sensor data of the tags to compute dive profiles for the focal whales, from which we measured the vertical response variables. We defined a dive as any duration spent deeper than 5 m. We selected this threshold based on the size of the humpback whales and the position of the tags; if the tag reached 5 m underwater, the focal whale was most likely diving. We described the vertical component of the responses with the maximum dive depth and maximum dive duration reached during each experimental period. We used maxima for dive depth and dive duration because the low number of dives within each experimental period (15 min) reduced the efficiency of descriptive statistics such as mean and median and because we judged that maxima would be efficient at detecting outlier response Fig.
In addition to classification, the random forest provides the possibility to assess the importance of each variable in the decision process. We did so by randomly permuting the values of each variable and computing the resulting reduction in accuracy of the classifier. We centred and scaled all variables before running the random forest to avoid size effects in the classification or the calculation of variable importance  (Strobl et al. 2008 ). We used the 3 most important variables to build a 3-dimensional graphical representation of the behavioural responses to playback. We used MATLAB R2017a (MathWorks) to calibrate tag data, compute dive profiles and horizontal tracks, and draw the 3-dimensional plot. We used R v. 3. 4. 1 (R Core Team 2017) to run the random forests with the 'randomForest' package  (Liaw & Wiener 2002)  and the Dunn's procedure with the 'dunn.
Acknowledgements. This research has been part of the  3S (Sea mammals, Sonar, Safety ) project funded by the  US Office of Naval Research , the  Royal Norwegian Navy , the  Norwegian Ministry of Defence , the  Defence Research and Development Department of the Netherlands Ministry of Defence , and the  French Ministry of Defence (DGA). We thank all members of the fieldwork crew for their effort over the years. We are grateful to  Dr. Valerie Dufour  for her critical reading of the manuscript.
, 2019) , or other unknown interferences, could lead to the formation of OH radicals inside the detection cell. Removal of ambient OH through external reaction with a scrubbing agent, such as perfluorpropylene (C3F6)  (Griffith et al. , 2016; Rickly and Stevens, 2018) , allows quantification of all OH formed within the detection cell. The remaining OH signal would be a measurement of the interference that can be subtracted from the total OH signal when the ambient OH is not removed. The LP/LIF instrument is automated using a National Instruments multifunction DAQ board (NI USB 6024) and a customized LabView interface program that controls the 355-nm laser shutter and monitors the power of both the 355-nm fragmentation laser and the 308-nm excitation laser. The 308-nm laser is controlled by the Indiana University Laser-Induced Fluorescence -Fluorescence Assay by Gas Expansion (IU-FAGE) instrument as described previously  (Dusanter et al.
Acknowledgements. We would like to thank  James Flynn  ( University of Houston ) for the spectroradiometer used to obtain the J(NO2) measurements. Financial support. This study was supported by the  National Science Foundation, Directorate for Geosciences  (grant nos. AGS-1012161  and  AGS-1827450 ) and the  Alfred P. Sloan Foundation, Chemistry of Indoor Environments Program  (grant nos. G-2017-9944  and  G-2018-11061 ). https://doi. org/10. 5194/amt-2021-102Preprint. Discussion started: 23 April 2021 c Author(s)  2021. CC BY 4. 0 License.
Indeed, samples with less than 10 7 microbes result in biased microbiome analysis. Whole genome shot-gun sequencing is therefore less suitable for the analysis of very low biomass microbiota. 16S rRNA gene amplicon sequencing benefit from PCR amplification, which makes it particularly adapted for analysis of low biomass microbiota samples. Whereas PCR can overcome the limitations associated with sample biomass, successive PCR amplification cycles may severely bias the analysis. Finally, bioinformatic pipelines also introduce variations. Indeed, taxonomy assignment (UPARSE  24  , MOTHUR  25  , DADA2  26  ), reference databases (Greengenes, SILVA, RDP and NCBI), depth normalization  27, 28  and distance algorithms are non-consensual and therefore introduce additional discrepancies between studies.
Results 16S rRNA gene sequencing is limited by sample biomass. Stools from two healthy donors were selected to assess sample biomass limit, below which 16S rRNA gene sequencing loses the ability to correctly represent microbiota composition. To this end we conducted serial dilutions to prepare samples of 10 8 , 10 7 , 10 6 , 10 5 and 10 4 microbes, which were extracted with three different extraction protocols. 16S rRNA genes were then amplified using two different PCR protocols, a semi-nested PCR (coined Nested) and a standard PCR (coined Standard). From the three different DNA extraction protocols, only the Zymobiomic's Miniprep (MP) kit extracted genomic DNA from which the V3-V4 16S rRNA gene fragment could be amplified for all microbial dilutions. Chemical precipitation (CP) and Magbeads (MB) reached their limits for microbial quantities below 10 7 and 10 5 microbes, respectively (data not shown).
PCR amplicon libraries were sequenced on a MiSeq Illumina platform (Genotoul, Toulouse, France and Biofidal, Vaulx-en-Velin, France) producing 2 × 300 bp paired-end reads. The quality of the sequence run was checked internally using PhiX, and then each paired-end sequence was assigned to its sample with the help of the previously integrated index. Bioinformatics for 16S rRNA gene analysis. Further processing of demultiplexed sequence reads followed the DADA2 workflow for Big Data (https:// benjj neb. github. io/ dada2/ bigda ta. html26) and employed R software (version 4. 0. 4  39 ) and the DADA2 package (v. 1. 5. 2). Briefly, sequences were quality filtered, trimmed and assembled. Lengths of 240 bp and 240 bp were chosen for hard trimming of forward and reverse reads, respectively, because these were the lengths beyond which median quality scores decreased below 20 for the lowest-quality library and to ensure that paired sequences contained sufficient information for merging (overlapping 3′ ends).
Individual reads were further truncated at the base, where a quality score of 2 was observed, and filtered to contain no ambiguous bases. Additionally, the maximum number of expected errors in a read was set to 2. Reads were assembled only if the overlap between forward and reverse reads, which occurs in the conserved region between V3 and V4, was 100% identical. Chimeras from combined runs were removed by the DADA2 protocol. Amplicon sequence variants (ASVs) generated by DADA2 analysis of the quality-filtered sequence data were taxonomically classified using the RDP naïve Bayesian classifier  41  , trained with the Silva 16S rRNA gene taxonomic training data formatted for DADA2 (Silva version 132; https:// zenodo. org/ record/ 11727 83/ files/ silva_ nr_ v132_ train_ set. fa. gz). Read counts for ASVs assigned to the same taxonomy were summed for each sample.
Data analysis. Microbiome composition was studied with the Phyloseq R package (version 1. 28). Alpha diversity was assessed as the Shannon diversity index and beta-diversity as the Bray-Curtis distance. The distance matrix was subjected to a Principal Coordinate Analysis (PCoA). PERMANOVA test was conducted in the vegan R package (2. 5. 6) with the adonis function. Statistical analysis was performed in R (version 3. 6. 1) using non-parametric Mann-Whitney Wilcoxon test (paired and unpaired), parametric Student paired test and Spearman correlations. Heatmaps were generated with ComplexHeatmap R package (2. 0. 0). Plots were generated using ggplot2 (3. 2. 1), corrplot (0. 84) and ade4 (1. 7. 13). Primers pairwise alignment against mitochondrial 16S rRNA gene and other single alignments were performed using r package Biostrings (2.
Images We selected MRI sequences from 25 patients who had underwent surgery between 10/01/2018 and 20/11/2019 from the Institut Curie Picture Archiving and Communication System (PACS). Among the MRI sequences of operated patients, 14 had a mastectomy and 11 had a lumpectomy performed. Two additional sequences corresponded to healthy control cases for patients not requiring surgery. All MRI sequences were performed as part of a preoperative staging. The standard breast MRI protocol included T2 sequences, T1 gradient-echo sequences, and injected dynamic 3D sequences with a gadolinium-containing contrast agent. Only the T1-weighted contrast-enhanced MRI examination was used in this study. MRI data were collected from the computer server of the radiology department and anonymized with subject identification codes.
Relevance In DIVA, we demonstrate a method to potentially significantly improve surgeon proficiency in reading MRI examinations without the need for massive retraining. The reported increase in efficiency of medical image reading has the potential to improve surgical planning and greatly improve communication between surgeons and radiologists. MRI examination using the radiologic slice-based interface common to PACS frontend software and by using DIVA in its VR mode. For the slice-based modality, we used the image viewer available at the Institut Curie, which is developed by Carestream Health (Rochester, New York). Surgeon participants were free to use all software features available in the PACS image viewer. Figure  1  illustrates the two image analysis modalities. Additionally, a recording of the use of DIVA in the context of this study is available in the Data Supplement.
DISCUSSION In this study, we show that the DIVA system improves the speed and accuracy of BC image analysis for surgeons, notably for residents. The immersive 3D breast and tumor representations provided by the DIVA system, moreover, have the potential to enhance the surgical approach and could greatly assist surgeons in the preparation of complicated cases. It is important to emphasize that 3D rendering of medical images is not new. In fact, most PACS software tools contain 3D rendering or reformatting tools, although they are highly underexploited by surgeons. In an online survey, we performed on 59 French obstetrics and gynecologic surgeons, we found that 48 (81%) seldom or never use 3D volume rendering tools with existing PACS software (Data Supplement). From our accounts, this is because of a lack of training to take advantage of 3D reformat tools and a lack of interactivity in these typically static renderings.
The authors acknowledge funding from the  Institut Pasteur  (J. -B. M. ), the sponsorships of CRPCEN,  Gilead Science , and foundation EDF (J. -B. M. ), the  ANR-17-CE23-0016  TRamWAy (J. -B. M. ), the  INCEPTION  project ( PIA/ANR-16-CONV-0005 , OG) (J. -B. M. ), and the  programme d'investissement d'avenir supported by L ' Agence Nationale de la Recherche   ANR-19-P3IA-0001  Institut  3IA  Prairie (J. -B. M. ).
Design of experiments Correlation models between pH, T and E/S and kinetic parameters (kXp and kDH) were obtained by DoE. To do so, a full factorial design was used. The matrix was composed of three factors (pH, T and E/S) at 3 coded levels (Table  2 ). Hence, the matrix consisted of 30 experiments (27 combinations and 3 replicates at the center point). The response considered were kXp (Y1) and kDH (Y2) obtained by regression of experimental kinetics with second order kinetic models (Eq. 1 and 2). Correlation models were polynomial equations with the three operating conditions as variables (X1, X2 and X3) and intercept (b0), linear (bi), quadratic (bii) and interaction (bij) coefficients: = + ∑ + ∑ ² + ∑ (7) MODDE 12. 0. 1 software (Umetrics, Sartorius Stedim Biotech, Sweden) was used to fit the data to the polynomial equation with adapted transformation if necessary.
The regression coefficients were obtained by multivariable regression. An ANOVA was used to evaluate the statistical significance of these coefficients. Final correlation models were obtained by suppressing the non-significant terms (p-value > 0. 05). The coefficients R² and Q², the relative standard deviation (RSD), the reproducibility and the lack of fit were analyzed using the MODDE software to characterize the goodness of fit for each model (Y1 and Y2). Response surface plots were also drawn with this software.
, 2015; Tinker et al. , 2008a). The sedimentary rocks in the Karoo Supergroup are dominantly siliciclastic so have a lower chemical weathering potential than basalt but also could weather chemically under favorable conditions with sufficient time. Cretaceous erosion took place under climatic conditions much warmer than today, which could have promoted chemical weathering (e. g. ,  Cohen et al. , 2004; Jenkyns et al. , 2004). A rough compilation of precipitation records based on paleobotanical data suggests a sharp change around 85 Ma from semi-arid to very humid conditions on both the Atlantic and Indian margins favoring intense silica weathering up to 40 Ma  (Braun et al. , 2014; Ponte et al. , 2019; Sandersen, 2007). Deep weathering surfaces are found throughout southern Africa (e. g. ,  Summerfield, 1983)  and were forming throughout Africa from the Late Cretaceous through the Neogene  (Guillocheau et al.
Data Availability Statement All data included in the modeling have been previously published and is available in  Amante and Eakins (2009) ,  Baby et al. (2020) ,  Belton and Raab (2010) ,  Brown (1992) ,  Brown et al. (1990 Brown et al. ( , 2002 Brown et al. ( , 2014)) , M. C. J. De Wit (1988),  Flowers and Schoene (2010) ,  Green et al. (2017) ,  Kounov et al. (2009 Kounov et al. ( , 2013)) ,  Raab (2001) ,  Raab et al. (2002) ,  Stanley et al. (2013 Stanley et al. ( , 2015)) ,  Stanley and Flowers (2020) ,  Tinker et al. (2008b) , and  Wildman et al. (2015 Wildman et al. ( , 2016 Wildman et al. ( , 2017)). Data are summarized with publication information specified for individual data points in the supplemental materials. The code used to generate all model results is archived with Zenodo  (Braun & Stanley, 2020)  as is all of the input data, configuration, and results  (Stanley, 2020).
However, very little information on electronic architecture is available on these models. Tesla is known to embed NVidia GPUs in their vehicles to perform processing. Google remains relatively discreet regarding the architectures used. Therefore, we have chosen to give particular focus to the experimental vehicles dedicated to AV related research. AV research is essentially structured in two folds: data-driven research and experimental platform design. Data-driven research usually exploits referenced databases to develop new perception and inference algorithms. The experimental platform design is well-suited for studying navigation methods and evaluating perception under thorough use-case scenarios. KITTI introduced a well-known example of data driven research where an experimental vehicle was devoted to the creation of sensor databases  [1].
This platform was employed to collect a great amount of data that is now a reference to the research community. It carries two pairs of stereoscopic cameras, a Velodyne LiDAR and an GNSSS/IMU unit. All of the sensor data are recorded on a central computer. The architecture is a centralized high level architecture that only considers perception functions. Other experimental vehicles, such as Broggi et al. [2] , were intended for extensive testing perception and control functionalities under thorough conditions over a 13,000 Km long trajectory. The robotcar platform  [3]  was recently developed from a Nissan LEAF. It carries several laser rangefinders and cameras. The data are processed by a central computer that collects and processes it. The platform is strongly based on the use of laser rangefinders to produce 3D maps.
A dataset from this platform has been made publicly available. Taking all of these experimental platforms and their wide functional range into consideration, a trade-off design should meet the architecture of a classical vehicle and the innovative sensing properties of an autonomous prototype. The architecture of a classic vehicle is very well known. It is based on the use of the CAN network, which has many advantages for this use. Regarding autonomous vehicles, there are no standards, since they are still under development. For us, an AV architecture can be structured into two parts. The low level architecture is composed of the Engine Control Unit (ECU) in a commercial car. It manages all the functions of the car, such as the engine driver or the steering. It mainly operates under real-time constraints (with low latency).
• A publicly available dataset of laser rangefinder experiment. To the best of the authors' knowledge, the proposed design is innovative and original, since it efficiently combines high and low-level functionalities in a scale model vehicle. This architecture concept is a fundamental basis of an embedded real-time robotics system, and it constitutes key training support for engineering students. This design can certainly also facilitate the knowledge transfer between AV research and teaching programs. In Section 2, the remaining of this paper presents a synthesis of state-of-the-art robotics teaching solutions and scale-model vehicles. Subsequently, Section 3 describes the context and objectives that are addressed by our scale-model vehicle design. Section 4 is devoted to formally detailing the scale-model architecture, electronics components, and onboard sensing modalities.
This type of product is mainly differentiated by its industrial manufacturing quality. Many of existing robotic platforms are adapted to the study of low level functionalities, such as the study of sensors, interfacing, and embedded programming. These robotic platforms do not have vehicle dynamics. On the other hand, platforms that are based on radio-controlled vehicle chassis almost satisfy teaching needs regarding low level functionalities. However, exiting platforms limit their application to high level functions. They focus on high level sensors (camera and laser) by simplifying the low level as much as possible and, therefore, are especially not representative of the electronic architecture of an autonomous car. Table  1  summarizes all of the platforms presented. The advantage of our proposed platform in comparison to the existing ones can be emphasized, as follows: the platform is completely customizable and it can be adapted to the teaching needs; -inspired from a real car, our platform has a distributed electronic architecture and a CAN network to communicate between the processing different nodes; -the platform has pre-configured nodes, including the technologies used by an autonomous cars; -thanks to its modular architecture, the platform allows working at different levels (sensors, electronics, and algorithms) while taking advantage of the rest of the car; and, -structural elements of the proposed platform design and a sample dataset are publicly available, which enable replication, validation, or use in teaching applications.
Students Profile The platform has been designed to be usable for all levels of high school students. It is possible to use the platform using only the very high level functions and to only program one of the microcontrollers. This is perfectly sufficient for discovering the problems that are related to embedded systems and AV. We apply a processor used very commonly today (Atmega368p from Microchip, Chandler, AZ, USA). The use of this processor made us compatible with high level languages, like mBlock or Scratch. This allows us to use the platform to discover embedded systems. It is then possible to discover the electronic part of the platform. It seems possible to discover some functions with an average level in electronics. Indeed, the car's functions are separated in each function block.
Programming Several programming levels are possible. The first level is the lowest level, and it is possible to reprogram all of the basic modules. This can be done with the arduino software. Numerous examples are available for creating additional sensor nodes. It is then possible to program the two processing units that are available on the platform: the Raspberry PI4 card and the Lattepanda card. We were able to install ROS on the Raspberry PI4 card. Indeed, many modules are available to directly interface the sensors that we have chosen: RPlidar, RealSense camera, and Marvelmind indoor localization system. ROS allows us to concentrate on the high level programming of our platform. The Lattepanda card was mainly used to create applications that are not compatible with Linux. In particular, some of our students may only have skills on Graphical programming language (e.
Perspectives Currently, our platform is complete and fully functional. We wish to develop two axes of perspectives. The first axis concerns communication between several vehicles and between a vehicle and its environment. For the moment, our vehicle is equipped with all of the sensors that are necessary for the perception of its environment. However, it does not have radio communication, which allows for data exchange between vehicles. The simplest solution would be to use the WIFI communication that is available on the Raspberry PI4 card which is already on board our model. However, the use of WIFI seems to be unrealistic in the context of autonomous vehicles. Therefore, a radio module will have to be developed and interfaced to our CAN bus. It will allow V2V and V2X communication. The second axis concerns the experimentation environment.
Data Availability Statement: Dataset -Scaled-model vehicle -Paris-Saclay University (2020-06-04-00-22-50) https://doi. org/10. 17632/6xs7pzpmz6. 1 (accessed on 31 May 2021), AutonomousVehicle. https://github. com/BastienV-SATIE/AutonomousCar/ (accessed on 31 May 2021).
[8]. The amplified products were used for Illumina paired-end library preparation and cluster generation, followed by 250 bp paired-end sequencing on an Illumina MiSeq instrument (INRAE, GeT-PLaGE platform, Toulouse, France). The raw data, provided by sequencing, were processed with the pipeline rANOMALY  [45] , which relies on the dada2 R package  [46]  to produce amplicon sequence variants (ASV) as taxonomic units. A step of decontamination was carried out, based both on the prevalence of contaminant ASVs, as identified in the blank samples, and on the DNA concentration, as described by Theil et Rifa  [45]. The filtered ASV count table was used to perform statistical analyses.