dataesr/scientific-paragraphs-categorization huggingface dataset:
The greatest error in the rain rate retrievals is observed for low RR values: for RR < 2 mm/h the root mean square error  RR = 2. 5 mm/h. The exclusion of clear sky data from consideration leads to the increase in the retrieval accuracy. Thus, when we used accurate estimation of the atmospheric absorption at 10. 65 GHz as a criterion to identify the rain pixels, we obtained  RR of 0. 7 mm/h. Also it can be seen that the information from 4 brightness temperature differences is not sufficient to properly resolve the similarity between cloud liquid water and rain drop influence upon the brightness temperature. Large scatter of the retrieved rain rates from in situ ones for the whole dataset is inherently caused by the geophysical model and use of low microwave frequencies: all kinds of liquid drops lead to brightness temperature increase.
The unique highest quality of all RSS products is ensured by permanent calibration and algorithm upgrading taking into account the newest knowledge and data available. The validation of the last (Version-7) rain rate products is given in  [49]. TMI rain rate estimates are also considered to be mostly reliable since TRMM mission was specifically planned for rain measurements. Beside TMI there is also an active microwave instrument, precipitation radar, onboard the TRMM. It measures radar backscatter from rain drops during pushbrooming instant measurements with high accuracy and much higher resolution (∼5 km) than that of the radiometer in a narrower swath  [7]. Since the TRMM has a nonsun synchronous orbit, TMI data are mostly appropriate for the creation of global collocated in time and space with AMSR2 measurements dataset.
Calibrated Level 1B brightness temperature data from AMSR2 are available starting from August 2012 through GCOM-W1 Data Providing Service (https://gcom-w1. jaxa. jp/). The spatial resolution depends on the channel frequency but the pixel size for C-and X-band channel measurements is the same (∼10 km). For the collocation with the gridded TRMM RR product, AMSR2 Level 1B swath measurement data for 2012-2014 were gridded onto the same grid using Delaunay triangulation and triangle-based linear interpolation  [50]. Both ascending and descending orbits were gridded within the geographical region 40S ÷ 40N at a pixel resolution of 0. 25 deg. After gridding, time collocation was applied to select only those pixels where AMSR2 and TMI measurement times were within 10 minutes. Tight time collocation criterion was set due to fast changing atmospheric state.
The strips of the collocated pixels move from day to day, during several months filling in the whole zone from 40S to 40N. Five-month coverage for 2012 is shown in Figure  5. Following the results of the numerical experiment (low performance of AMSR2 RR retrieval algorithm at low rain rates), we did not validate the algorithm for RR = 0. For the algorithm validation only those data were selected where RSS RR were not equal to zero. The resulting datasets included 90432 data for the 5 months of 2012, 200521 data for the whole year of 2013, and 73214 data for the first 4 months of 2014. Totally 364167 data with nonzero rain rates comprised the whole validation dataset. The maximum value of the TMI RR in the validation dataset was 23. 5 mm/h. 93. 4% of all data were less than 5 mm/h and only remaining 6.
6% exceeded 5 mm/h. TMI RR distribution in the validation dataset is presented in Figure  6. To compare the algorithm performance relative to TMI RR product with another algorithm performance we validated also AMSR2 RSS RR product against the same TMI dataset. It was expected that since all Remote Sensing Systems passive microwave products had been accurately intercalibrated the two different RSS RR products should demonstrate high correlation. At the same time we supposed it was more reasonable to use TMI RR product for the validation since this product is being constantly calibrated with the precipitation radar rain rates which can be considered as in situ RR data.
Results and Discussion The results of the comparison of the RRs, retrieved from AMSR2 data, with TMI RRs for the whole RR range are presented in the scatter plot in Figure  7. Color in the Figure  7  denotes the number of the data contributing to the scatter plots. The total retrieval error, calculated as a root mean square difference for the whole match-up dataset, is  = 1 mm/h. The correlation coefficient Rc = 0. 8; the equation of the linear trend is  = 0. 8 + 0. 3. It can be seen that most of AMSR2 retrievals (94%) do not scatter from TMI rain rates larger than ±2. Only 6% of all AMSR2 retrieved rain rates differ from those of TMI by more than 2 mm/h. These large differences can be due to many reasons. One of the most probable sources of the errors is the difficulty for the algorithm to distinguish between high total cloud liquid water contents and rain rates, following the simplest parameterization of used liquid cloud, rain model and the absence of any ancillary data.
The corresponding scatter plot of RSS AMSR2 RR versus TMI RR is shown in Figure  10(a). In Figure  10 (b) the scatter plot of AMSR2 RR, retrieved with the new algorithm, versus TMI RR is shown again to stress the difference. It can be seen that RSS AMSR2 rain rates are unbiased compared with TMI rain rates. No underestimation of high RR values is observed. The total retrieval error for the whole dataset is a little bit lower, 0. 9 mm/h as compared to 1 mm/h, ensured by the usage of the new algorithm. The correlation coefficient Rc = 0. 8; the equation of the linear trend is  = 1 + 0. 1. We found that for RSS AMSR2 product the root mean square error for the range of rain rates less than 5 mm/h was 0. 8 mm/h, and for the rain rates less than 2 mm/h  was 0. 6 mm/h, 2 times higher than with the usage of newly developed AMSR2 algorithm.
Conclusions The new algorithm for rain rate (RR) retrievals from the data of Advanced Microwave Sounding Radiometer 2 (AMSR2) onboard GCOM-W1 is developed using 6 low frequency channel measurements at 6. 9, 7. 3, and 10. 65 GHz. The algorithm is based on the numerical modeling of brightness temperatures with their following inversion into RR using neural network-based method. The general approximations of forward modeling include a simplified form of the radiative transfer equation, not taking into account ice clouds and precipitation and a simple uniformly distributed rain profile. The validation of the new algorithm is carried out through the comparison of the results of its application to AMSR2 Level 1B brightness temperature data with Remote Sensing Systems (RSS) rain rate product, based on Tropical Rain Measurement Mission (TRMM) Microwave Imager (TMI) measurements for the period of August 2012-April 2014.
Spatial collocation is ensured by gridding AMSR2 data onto TMI RR grid; time collocation is within 10-minute interval. The root mean square error , calculated for the whole dataset, is about ∼1 mm/h which is 0. 1 mm/h greater than  for RSS AMSR2 product. RSS AMSR2 RR product is also validated against the same TMI RR product for the comparison between the performances of RSS and newly developed algorithms. It is found that the new algorithm outperforms the RSS algorithm for RR < 2 mm/h. For this low RR range the error of the new algorithm is 0. 3 mm/h compared to 0. 6 mm/h for RSS algorithm. At the same time the new algorithm significantly underestimates rain rates for RR > 10 mm/h, whereas RSS AMSR2 rain rates are highly correlated and unbiased compared with RSS TMI rain rates for the whole RR range.
To screen further for the possibility of contaminants, we aligned the Illumina reads of the DNA library to the reference mitochondrial genome sequence of Homo sapiens (GenBank accession number NC_012920. 1  [7] ) using BWA version 0. 5. 7  [8]. Out of 1 billion DNA reads, we found 11 reads with a minimal length of 30 bp that perfectly matched the mitochondrial genome sequence of Homo sapiens, five of which also perfectly matched the reference mitochondrial genome of Bison bison (GenBank accession number NC_012346  [9] ). By contrast, the same alignment parameters yielded 1,825 reads that perfectly matched the Bison bison reference mitochondrial genome sequence.
Illumina sequencing data analysis Reads were trimmed for adapter sequences, N's and low quality stretches on the 3' end, using a software based on the FASTX-Toolkit package (http://hannonlab. cshl. edu/fastx_toolkit) and designed by Genoscope. After this step, sequences shorter than 20 nucleotides were discarded using an in-house Python script, yielding a dataset of 1,033,460,536 DNA reads (543,346,926 reads from the HiSeq 2000 sequencing lanes, and 490,113,610 reads from the HiSeq 2500 sequencing lanes). The DNA reads of this study have been deposited at EBI under accession number ERP006376.
Assembling the Bison priscus mitochondrial genome sequence The sequence assembly was performed in three steps. First the Illumina reads ranging in size between 20 and 101 nucleotides were aligned to the mitochondrial genome of Bison bison and to the mitochondrial control region of Bison priscus isolate BS212 (GenBank accession number AY748539. 1, [2] ) using BWA with default options except for-o (gap opening) and-n (maximum edit distance), which were set to 0 and 2, respectively. This process yielded a set of 3,816 unique reads providing a 9. 8-fold coverage of the Bison bison reference sequence and left 87 positions without coverage. A provisional consensus sequence was then derived from the 3,816 unique selected reads. In this study a consensus for a given position in the genome was based on at least two concordant unique sequences (either Illumina reads or consensus PCR sequences) with a minimal mapping quality of 25 for Illumina reads.
Sequence annotation The annotation of SGE2seq was derived from the annotation of the Bison bison reference mitochondrial genome. Briefly, SGE2seq and the Bison bison reference mitochondrial genome were aligned using the LAGAN software  [12]  with the annotation data of the Bison bison genome (http://www. ncbi. nlm. nih. gov/nuccore/NC_012346. 1). LAGAN produced a list of the aligned genomic features with their coordinates in the two genomes, which was manually checked. The occurrence of the expected start and stop codons at the ends of the coding sequences and the absence of internal stop codon were verified using an in-house Python script. The SGE2seq annotated sequence has been deposited in the GenBank database (accession number KM593920).
Illumina reads mapping to other Bos and Bison sequences To confirm the origin of the SGE2 sample, all the Illumina reads of the library with a minimal length of 30 bp were aligned to the reference mitochondrial genomes of Bison bison, Bison bonasus (GenBank accession number NC_014044. 1  [13] ), Bos primigenius (GenBank accession number NC_013996. 1  [14] ) and Bos taurus (GenBank accession number NC_006853. 1  [15] ) using BWA with the options-o 0 (no gap opening) and-n 0 (perfect match). To evaluate the coverage of the nuclear genome, the Illumina reads were aligned to the 18S rDNA sequence of Bos taurus (extracted from the sequence with GenBank accession number DQ222453. 1) using BWA with the options-o 0 (no gap opening) and-n 1 (one mismatch). The Illumina reads were also aligned to the complete Bos taurus nuclear genome generated by inflating and combining the bt_ref_Bos_taurus_UMD_3.
ncbi. nlm. nih. gov/, last accessed August 4, 2014) along with the reference mitochondrial genomes of Bos primigenius, Bos taurus and Bubalus bubalis (swamp buffalo, Gen-Bank accession number NC_006295. 1) were inferred using the Maximum Likelihood, the Minimum Evolution and the Neighbor-Joining methods. The Maximum Likelihood method was based on the General Time Reversible model. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories). The analysis involved a total of 16,315 positions.
All samples, but none of the negative controls (mock extracts and PCR blanks) yielded amplification products of the predicted size that were characterized through the sequencing of cloned PCR fragments (SGE1: 1 PCR, 13 clones; SGE2 extract 1: 4 PCR, 47 clones; SGE2 extract 2: 2 PCR, 39 clones; SGE3: 1PCR, 12 clones; SGE5: 1PCR, 14 clones). SGE1 and SGE3 DNA extracts yielded sequences that all displayed a perfect match with Bos (Bos taurus, Bos primigenius) reference mitochondrial genomes, and SGE5 returned a mixture of sequences that perfectly matched either Bos or Bison bison genomes (Fig  2 ). The PCR data may indicate that SGE1 and SGE3 samples correspond to Bos bone fragments, but the observation that SGE5 contains Bos as well as Bison DNA challenges this interpretation and rather suggests that all samples collected from the ground surface had been contaminated by modern cattle DNA brought by modern human intrusions since the cave discovery.
The sum of the lengths of the 540 Illumina reads corresponding to the Bovidae hits represents a total of 26,856 nucleotides for one million Illumina reads analyzed, predicting 27,754,600 nucleotides for Bovidae sequences in the whole set of reads. To analyze the Bison mitochondrial genome sequence, we first retrieved from the library all the reads that aligned to the mitochondrial genome of Bison bison (which, as indicated above, turned out to be more relevant for this purpose than Bison bonasus) or to the mitochondrial control region of Bison priscus isolate BS212 (GenBank accession number AY748539. 1, [2] ). This alignment was performed under less stringent conditions than the previous ones and allowed for mismatches (see details in Materials and Methods). A provisional consensus sequence was derived from the selected reads and was used in turn to retrieve all reads aligning to it.
These substitutions correspond to 11 amino acid differences. Among the 114 differences, 93 correspond to polymorphisms that are specific to Bison priscus and are not found in any published Bison bison genome (S5 Table ). These 93 specific polymorphisms are mostly located in proteincoding genes (69%) and correspond to 7 amino acids differences. A phylogenetic analysis was performed using all the available complete mitochondrial genomes of Bison bison, Bison bonasus and Bos grunniens along with the reference mitochondrial genomes of Bos primigenius, Bos taurus and Bubalus bubalis (swamp buffalo). Phylogenetic trees were constructed from this dataset with the Maximum Likelihood (ML), Minimum Evolution (ME) and Neighbor-Joining (NJ) methods. As shown in In summary, we have presented here the analysis of a 19,000-year-old bone specimen that made it possible to obtain the first complete mitochondrial genome for the extinct steppe bison.
part of the D-loop region has been sequenced for a number of Bison priscus fossils [2,4], we analyzed the phylogenetic relationships between the orthologous portion of SGE2seq and of several Bison priscus fossils and Bison bison specimens. We also considered the D-loop region of several reference genomes of Bison and Bos species, including the Bos grunniens (yak) reference mitochondrial genome (GenBank accession number NC_006380). The phylogenetic reconstruction, carried out using the Maximum Likelihood method, positions SGE2seq among Bison priscus fossils sequences, outside the strongly supported clade of modern Bison bison sequences (S2 Fig). This result was observed when taking into account three specimens from each of the four Bison groups defined in [2], and similar results were obtained when including all published D-loop sequences of Bison priscus and Bison bison (data not shown).
To compensate for this variability, regulatory authorities recommend an ICR with several readers to mitigate potential bias resulting from variance among investigator sites  (2, (5) (6) (7) (8). The software solution used in this study was Lesion Management Solutions (LMS) developed by MEDIAN Technologies  (16, 17). LMS is an image analysis software application for evaluating CT images. It allows lesion identification, quantification, and comparison of successive CT scans of the same patient. The comparison is achieved by synchronous navigation between two scans and automatic pairing of the lesions. Using these tools, we made possible a cloud-based local-read paradigm for imaging evaluations which consists of implementing a cloud software solution and a quality control at investigator sites.
SOLTAGE provides storage, computing, and application services. The investigator sites provide the medical images and perform the analysis and interpretation of the medical images on Web-based MEDIAN Technologies Imaging Service. The analysis and interpretation results are centrally stored and available to the independent reviewer to perform its analysis and interpretation. This workflow is under the supervision of the data manager and data center that also have access to central information and progress status of the study. CT images of 10 lung cancer patients were acquired at three time-points (baseline, best response, and progression) in the course of treatment. Patients who were treated with EGFR tyrosine kinase inhibitors (EGFR-TKI) were randomly selected, since obvious changes in image were observed when EGFR-TKI was administered to lung cancer patients with EGFR activating mutations.
A well-trained radiologist was selected as a reviewer, and two medical oncologists who were experienced as specialists of more than 10 years were selected as investigators. CT scans were evaluated according to the RECIST 1. 1 criteria by two oncologists from Saga University and one radiologist from Nice University Hospital, independently, through the cloud-based software. The software was hosted by the data center (Canon IT Solutions, Tokyo, Japan). Readers and data managers (Canon Inc. and MEDIAN Technologies) were responsible for de-identification, quality control, and centralization of the images and evaluations. The study compared evaluations between the oncologists (investigators) and the radiologist (reviewer) and analyzed the reasons for discordance. The second part of the study, KUMO I Extension (Fig.
Imaging technique All images were taken with multi-detector CT scanners (LightSpeed VCT Õ , GE Healthcare Japan, Tokyo, Japan; SOMATOM Definition Õ , SIEMENS, Munich, Germany) at Saga University Hospital, or selected in MEDIAN images database. Slice thickness of the scans was 5 mm for KUMO 1 and 1-2. 5 mm for KUMO 1 Extension. Tube voltage was 120 kV, and field of view (FOV) was 300-500 mm. All images were properly anonymized and copied to a virtual server at the data center operated by Canon IT Solutions, Inc. The images were processed by a cloud-based prototype of Lesion Management Solutions (LMS) (MEDIAN Technologies, Valbonne, France). LMS is at the core of MEDIAN's Clinical Trial Imaging Services, which include image and workflow management, and image processing specifically designed for multi-site oncology clinical trials (Fig.
2 ). The image processing component of LMS offers software for detection, segmentation, and quantification of thoracic lesions (Fig. 2a ). The segmentation process, which is based on a three-dimensional (3D) region-growing algorithm, begins with a simple point-and-click on the lesion of interest. Readers can make manual adjustments to the contour of the lesion as necessary. After segmentation is complete, the longest axial diameter, short axis, and volume of each lesion are extracted automatically. In the follow-up evaluation, two scans from two time-points are displayed side by side while automatic registration points to the volume of interest in the newer scan (Fig. 2b ). A reader points to the corresponding lesions in the newer scan, which are then analyzed in the same manner as at the baseline evaluation.
Statistical analysis Kappa analysis was performed to evaluate interobserver agreements between the reviewer and each investigator using IBM SPSS Statistics 19 (SPSS, Inc. , IBM Company, Tokyo, Japan). The strength of agreement indicated with kappa values has been reported as: <0, poor; 0-0. 20, slight; 0. 21-0. 40, fair; 0. 41-0. 60, moderate; 0. 61-0. 80, substantial; and 0. 81-1. 00, almost perfect  (18).
Improvement of inter-reader agreement was observed after reaching consensus between a reviewer and investigator. Based on the results of KUMO I study, KUMO I Extension added a cloud-based quality control service to achieve a consensus on the selection of target lesions (Fig. 1 ). The KUMO I Extension, raised the agreement rate of response evaluations to 82% from 65% in KUMO I (Table  2 ). Kappa coefficient value of interobserver agreement for response evaluations between the reviewer and the investigators was 0. 724 AE 0. 17 (95% CI). The cloud software solution gives the possibility for a given clinical study that all investigator sites work with the same tools, on the same database. Thanks to the data centralization provided by this cloud configuration, an ongoing quality control is made possible.
Therefore, the total cost to set up such a cloud-based service is much lower than using locally installed software from both direct and indirect perspectives including maintenance. To secure the participants' privacy, all necessary deidentification processes are done before putting images to the LMS system. Personal information is not shared among readers. We also chose VPN connection and a highly secure data center. Some limitations of the study are the small sample size and the lack of evaluation of lymph nodes. These studies pave the way for further investigations such as the improvement of the automated segmentation tools to better address the lesions adjacent to the pleura and mediastinum as well as the lymph nodes, since discordance in response evaluation occurred mainly when segmentation was performed manually.
(2000). To determine the organic fraction of soil, the density profile of the soil carbon must be known for the entire soil grid. Using the HWSD database, the soil carbon densities in the first 0. 3 m, ρ top (kg m -3 ), and the remaining 0. 7 m below, ρ sub (kg m -3 ), are known: ρ top = S top d top and ρ sub = S sub d sub , (20) where S top and S sub (kg m -2 ) are the topsoil and subsoil organic carbon contents respectively, d top and d sub (m) the thicknesses of each observed soil horizon (0. 3 and 0. 7 m respectively). We extrapolate the density present below 1 m from this observed near-surface profile (Eq. 20). The extrapolation assumes that the carbon profile decreases sharply with soil depth according to a power function. The shape of this function is given by the observed profile if the topsoil organic carbon density is superior to the subsoil density.
22). Examples for the soil porosity, w sat , the soil saturated hydraulic conductivity, k sat , and the soil heat capacity, c, are given. Dotted lines represent vertical homogeneous mineral soil properties, dashed lines the idealized peat soil properties, and plain lines the resulting combined soil properties using the averaging method summed up in Table  1. from June to September each year (see  Morin et al. (2012)  for details). The Col de Porte data set includes many observations at a daily time step for evaluating land surface models. In this study, the observed snow depth, surface albedo, and soil temperature at 10 cm are used to evaluate model simulations over the entire period. The snow water equivalent (SWE) is also used for this model evaluation but daily values are only available from 2001 to 2011.