dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Validation on independent data On the four validation datasets, each time rfPred is in par with the best performing predictors (Figure  2 ). The rfPred AUC is 0,90 on EXOVAR dataset (vs 0,84 for Polyphen2 and 0,86 for Mutation Taster), of 0,86 on COL7A1 (vs 0,85 for Polyphen2), of 0,95 on COL4A5 (vs 0. 946 for SIFT). On the ClinVar-1KG validation set, rfPred is clearly more accurate than the logistic regression framework. Although the most reliable predictor seems to be Mutation Taster on the EXOVAR dataset, on the two specific gene-based validation datasets, SIFT and Polyphen2 give better results (Table  4 ). The strength of rfPred is that in all these datasets, it is the only one to remain among the most accurate ones. If we consider now the partial AUC for high sensitivity or high specificity, the rfPred method is especially effective to detect disease causing variants with a minimum of false positives (high specificity).
The resulting rfPred model seems not only to be more accurate than the logistic regression framework already proposed and the CADD score for missense variants, but is also easily available to the scientific community. rfPred is built with 5 000 trees, which was found to be the best compromise between complexity and accuracy on our training dataset; with 10 000 trees, the classification errors do not decrease and the forest stability does not increase anymore significantly (data not shown). Concerning the learning dataset we have deliberately chosen to work with the neutral variants which have a frequency < 1% in the 1000 Genomes Project. Indeed, more frequent variants are easily excluded from sets of possible causal variants in monogenic diseases without resorting to any statistical tool. According to us, the added-value of prediction methods for monogenic diseases appears precisely when a high number of variants remain potentially pathogenic even after filtering on the minor allele frequency in the general population.
Our model is intentionally built on the more challenging dataset in term of classification; the implicit hypothesis is that the genetic variants with a frequency higher than 1% in the population are more often correctly classified as neutral by function alteration predictors. We have checked this hypothesis in comparing the rfPred scores distribution according to allele frequency of neutral variant (Supplementary data) and seen that the neutral variants with a MAF > 1% have globally lower rfPred scores than the rare neutral SNVs. rfPred has been compared with other classification models coming from machine learning or statistical learning communities as Support Vector Machine (SVM) or boosting technique  20. A recent example of such a tool is the CADD method 6 , and rfPred seems really more performant on our different datasets.
In the unified likelihood model of the tool, the variant prioritization is taken into account through an approach based on a conservation measurement PhastCons; a variant prioritization model like ours could further improve this tool, whatever its use in common diseases or in rare diseases. Finally, to decrease the request time, rfPred scores have been pre calculated for each possible variation in the human exome and are available in tabix files downloadable at http://www. sbim. fr/rfPred. A R package downloadable on Bioconductor. org queries the data files stored directly on the server website, or locally downloaded. For a query of 200 variations scores, the request time is about 1s if the data files are stored locally on the computer (versus 6s if the data files are on the website). It is also possible to download the random forest model (in R.
A novel algorithm delivering high resolution soil moisture maps is developed by merging active (SAR) and passive microwave. -MAPSM is based on the concept of Water Change Capacity. -A case study using MAPSM is presented by using the RADARSAT-2 and SMOS retrieved soil moisture data products over Berambadi watershed, Karnataka, India. -The algorithm parameters show scalability from the spatial resolution of 20 m to 2000 m.
g. ,  [12] [13] [14] [15] [16] [17] ) or by assimilating coarse scale soil moisture into a Land Data Assimilation System (LDAS) (e. g. ,  [18] ). However, few studies have demonstrated merging the active and passive microwave soil moisture  [4, 19]  to improve the spatial and temporal availability at a moderate spatial resolution. The spatial downscaling using physical approaches are based on the assumption that the evaporative flux is controlled by the amount of water available in the top surface layer  [12, 15]. The spatial distribution of evaporative flux is computed using the visible/infrared satellite products available from the satellites such as MODIS and Landsat. These approaches were tested in arid and semi-arid environments and were successfully able to spatially downscale coarse scale soil moisture.
RADAR SATellite-2 (RADARSAT-2) is an active microwave satellite mission operating in C-band, capable of retrieving surface soil moisture at a spatial resolution of less than 100 m and with a temporal resolution of 24 days  [31]. Launched in December 2007, RADARSAT-2 provides a long term dataset for the validation studies. Since the MAPSM uses only microwave satellite data, it can be applied in all weather conditions. In the following parts of the paper a description of the study area and the used satellite datasets are provided. The MAPSM model is presented in Section 3 along with the validation strategy. Sections 4 and 5 contain the results and discussion, respectively. Section 6 contains a summary of the conclusions of the study.
Satellite Soil Moisture Datasets For the active microwave, 30 RADARSAT-2 retrieved soil moisture maps were available over the study area based on the previous study from  [30]. Details of the RADARSAT-2 maps are given in Table  1. They approximately cover the entire study area. Soil moisture was retrieved using the Cumulative Density Function (CDF) transformation at a spatial resolution of 20 m. The details of the CDF transformation along with the multi-scale validation of the retrieved soil moisture using measured soil moisture in 50 plots (1262 data) and SMOS soil moisture are given in  [30]. Comparison of the retrieved soil moisture with the field data showed a Root Mean Squared Error (RMSE) ranging from 0. 02 to 0. 06 m 3 /m 3 for the majority of plots. SMOS (ESA Level 2 User Data Product v 551) soil moisture products for ascending and descending orbits are used for the passive microwave soil moisture data.
Merging Active and Passive Microwave Soil Moisture This section contains the details of the Merging Active and Passive microwave Soil Moisture (MAPSM) algorithm. Figure  3  shows the spatial and temporal resolution of soil moisture products commonly available from active and passive microwave satellites. MAPSM attempts to combine the strength (high spatial resolution) of active microwave with the strength (high temporal resolution) of passive microwave to obtain a high spatio-temporal resolution soil moisture. Hereafter, the fine (active microwave) and coarse (passive microwave) data are represented respectively by an A and a P subscripts. The soil moisture at the fine scale at time t (SM t M ) can be expressed in terms of the most recent fine scale soil moisture (SM t-1 A ), the WCC at time t (WCC t A ), the fine scale spatial heterogeneity index (SH A ), the coarse scale soil moisture at time t (SM t P ) and the most recent coarse scale soil moisture (SM t-1 P ) in the following manner: SM i,t M = SM i,t-1 A + WCC i,t A SH i A SM j,t P -SM j,t-1 P (1) Here, the superscript i represents a RADARSAT-2 pixel within a SMOS pixel.
Validation Strategy Validation strategy for the MAPSM model is shown in Figure  8. MAPSM is applied when data from both RADARSAT-2 and SMOS are available, but without using the SM i,t A. Then, the model is tested by comparing the computed SM i,t M with the RADARSAT-2 retrieved soil moisture (SM i,t A ) at the same date. Performance of the model output is assessed in terms of correlation coefficient and RMSE.
Bias Correction at Coarse Scale The comparison of soil moisture at coarse scale from the two sensors showed a negligible bias, however there was a significant difference in the range i. e. , soil moisture is varying from approximately 0. 10 to 0. 21 m 3 /m 3 for RADARSAT-2 while it is varying from 0. 01 to 0. 3 m 3 /m 3 for SMOS. This difference in range is corrected using the quantile matching. A total of 18 soil moisture data are available concurrently from SMOS and RADARSAT-2 data (see Table  1 ). Twelve data are used for the calibration and the remaining six are used for the validation. The comparison for the calibration and validation period is shown in the Figure  9. The RMSE was 0. 021 m 3 /m 3 and 0. 018 m 3 /m 3 for the calibration and validation period, respectively, which is much lower when compared to the uncorrected values of approximately 0.
As an example, the relationship at a spatial resolution of 1000 m along with the fitted sigmoid model is shown in the Figure  10. The estimated value of k is found to be 96. 8 with a standard error of 12. 98 at a spatial resolution of 1000 m. Based on the result obtained in Figure  10 , it can be inferred that not all fine scale pixels follow the same sign of change (drying/wetting) observed at the coarse scale. The fraction of area is dependent on the magnitude of change at the coarse scale. A higher magnitude shows a larger fraction. The behaviour of the k parameter with respect to the spatial resolution is shown in Figure  11. Parameter k showed an exponential behaviour with the spatial resolution. The fitted exponential curve is also shown in the figure. Parameter k was also estimated using the soil moisture data measured in the field plots.
RISAT presents a low revisit frequencies which increases the interest for MAPSM. It is the case also for Sentinel-1 mission outside the European continent. The algorithm can also be used to merge SMOS / SMAP and Sentinel-1 data. Nevertheless these applications will need specific updates of the algorithm to take into account for example the angle sampling and resolution of Sentinel-1 and RISAT data or the mismatch between SMOS and SMAP.
Acknowledgments: This research work was funded by  TOSCA  ( Terre Océan Surfaces Continentales et Atmosphère) CNES program. The SMOSL2 data provided by  European Space Agency  is duly acknowledged. RADARSAT-2 data procured under RISAT-1 ( Radar Imaging SATellite) utilization programme of ISRO  is gratefully acknowledged. The authors are also thankful for partial financial support received under the IISc- STC  project  (ISTC/CCE/MS/214 ). This work was also supported by the  VIGISAT-CLS  project which provided the RADARSAT-2 satellite dataset from 2012 to 2014. The funding from  CEFIPRA  project  4700-W1  and  ISRO0198  for the salary of project personnel, instruments and field studies is also acknowledged. The authors would like to thank the filed team and the help provided during the field campaigns by the farmers.
Results We used an integrated strategy to sequence and assemble the gorilla Y Chromosome. In short, as the first step, the Y Chromosome was flow sorted. Next, Illumina paired-end (PE) and mate pair (MP), as well as Pacific Biosciences (PacBio), libraries were constructed. From the resulting Illumina reads, we extracted the Y Chromosome-specific reads using a novel algorithm developed in-house, RecoverY (see below). Next, such reads were assembled into contigs with SPAdes  (Bankevich et al. 2012 ) and scaffolded with SSPACE  (Boetzer et al. 2011). PacBio reads were used to further scaffold the assembly with SSPACE-LR  (Boetzer and Pirovano 2014)  and to close assembly gaps with PBJelly  (English et al. 2012). The resulting assembly was additionally improved by creating super-scaffolds based on transcript information.
This assembly was evaluated with an independent scaffolder, BESST  (Sahlin et al. 2014) , which resulted in a similar insert size distribution of MP data (Supplemental Fig. S8B, C ). We also evaluated the best assembly with REAPR  (Hunt et al. 2013) , which reported a relatively low proportion of assembly errors (Supplemental Table  S6 ). A total of 55 scaffolds in the best assembly aligned to gorilla PAR (Supplemental Table  S7 ). We also performed two additional assemblies using only PacBio reads and the HGAP  (Chin et al. 2013)  and MHAP  (Berlin et al. 2015)  software, but found the results to be inferior to our best assembly (Fig. 2 ; Supplemental Note S2; Supplemental Table  S3 ). The improvement in PacBio data analysis tools (which are undergoing active development) could make PacBioonly assemblies a more attractive option in the future.
Preprocessing raw read data We trimmed adapters in raw reads using Trimmomatic version 0. 32 (http://www. usadellab. org/cms/index. php?page=trimmomatic) with the following settings: ILLUMINACLIP: ${adapter_file}:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:30. PE reads were trimmed in "palindrome" mode, and MP reads were trimmed in "simple" mode. All FASTQ files were checked with fastQValidator (http://github. com/statgen/fastQValidator). We additionally preprocessed the MP reads using NxTrim (http:// github. com/sequencing/NxTrim), which classified reads into three categories based on the location of biotinylated adapters, e. g. , MP (long fragment size), PE (short fragment size), and unknown (adapter absent, but validation suggests mostly long fragment size). The "PE contamination" was eliminated, retaining approximately two-thirds of the MP data.
Repeat masking Repeats on the gorilla Y were identified with RepeatMasker version open-4. 0. 5 (http://www. repeatmasker. org), search engine NCBI/RMBLAST [2. 2. 27+] and database RepeatMaskerLib. embl (20140131) with parameters "-species Primates -s. " The repetitive element content for the ampliconic and X-degenerate regions was computed from scaffolds containing exons of the ampliconic and X-degenerate genes, respectively. In order to make the comparison of repeat content consistent between different genomes, we re-ran RepeatMasker on human Y Chromosome, chimpanzee Y Chromosome, and gorilla female genome using the same parameters as for the gorilla Y.
repeatmasker. org) and combined to generate gene consensus sequences with TGICL  (Pertea et al. 2003). We then scaffolded the TGICL contigs using SSPACE (version 3. 0)  (Boetzer et al. 2011). We next mapped male and female genomic reads to the gene scaffolds with Bowtie 2 (v. 2. 1. 0) (Langmead and Salzberg 2012)  and retained only male-specific gene scaffolds (with at least 80% of the sequence covered by male-specific reads and no more than 20% of the sequence covered by female-specific reads). Following  Brawand et al. (2011) , we utilized a threshold of <20% (we also tested 10%) of a transcript covered by female genomic reads to retain the Y-specific transcripts as some regions of the Y Chromosome are almost identical to the X Chromosome, particularly the gametologous genes. The mapping of RNA-seq and genomic reads was performed with the local alignment, and the read threshold was equal to 1.
Lastly, the RNA-seq reads were mapped back to the final transcripts to evaluate coverage and gene sequence reconstruction. Annotation of the final transcripts was performed using nucleotide and protein databases. The transcripts generated here, the gorilla transcripts from  Cortez et al. (2014)  and the gorilla X-degenerate genes from  Goto et al. (2009) , as well as human Y genes/cDNAs (http://www. biomart. org), were aligned to the best assembly with BLAT  (Kent 2002). Focusing on the matches with identities >95% for gorilla and >90% for human, we determined the level of completeness for each gene in the best assembly and performed additional ordering of the scaffolds based on exon connectivity of certain genes that spanned several scaffolds, resulting in super-scaffolds.
None to small manual changes in the human primers enabled targeting the homologous gorilla amplicon (Supplemental Table  S11 ). The sequence specificity of primers used was determined via BLASTn against the appropriate speciesspecific databases in GenBank (i. e. , nucleotide collection, reference genomic sequences, NCBI genomes, reference RNA sequences, and transcriptome shotgun assembly). All ddPCR primers and amplicons were confirmed through forward and reverse Sanger sequencing of a PCR product consisting of the amplicon ± 200 bp, and then alignment of these sequences to each other and the respective primers, amplicon, and the best assembly. Quantification of gene copy number was performed by ddPCR using a Bio-Rad QX200 Droplet Digital PCR system  (Hindson et al. 2011; McDermott et al. 2013).
The QuantaSoft droplet reader software (v1. 4. 0. 99; Bio-Rad) was used to cluster droplets into distinct positive and negative fluorescent groups and fit the fraction of positive droplets to a Poisson algorithm to determine the starting concentration (copies/µL) of the input target DNA molecule  ([Miotke et al. 2014] , Droplet Digital Applications Guide). Copy number was determined by calculating the ratio of the target (unknown) concentration to the reference concentration and then multiplying this ratio by the number of copies the reference gene has in the genome (  [Miotke et al. 2014] , Droplet Digital Applications Guide). Two reference genes used simultaneously were the single-copy Y-Chromosomal SRY and the two-copy (diploid) autosomal RPP30. Mean copy number are reported with the Poisson 95% confidence interval calculated by QuantaSoft (Supplemental Tables  S11, S12 ).
Detection of microsatellites and development of a fluorescent assay Microsatellites in the best assembly were detected with PHOBOS v3. 3. 12 (http://www. rub. de/spezzoo/cm/cm_phobos. htm). We searched for uninterrupted tri-and tetranucleotide microsatellites because they are easier to score than dinucleotide microsatellites  (Eckert et al. 2002; Ananda et al. 2013). BLAST analyses of the designed microsatellites have been performed against the NCBI nucleotide database to check for their gorilla Y specificity. Subsequently, seven gorilla Y-specific microsatellite amplifications were performed in each of the 14 wild-born gorilla males in GeneAmp PCR system 9700 (Applied Biosystems) using the following thermal conditions: initial denaturation at 94°C, 30 or 35 cycles of: denaturation for 1 min at 94°C, annealing at primer specific temperature for 45 sec, extension for 45 sec at 72°C; followed by a final extension for 5 min at 72°C.
The forward primer of each of the seven primer pairs was labeled with one of the fluorescent dyes compatible with Applied Biosystems 3730XL sequencer: FAM, HEX, NED, or ROX (Supplemental Table  S16 ). PCR reaction mixtures consisted of 20 ng DNA, 1 unit of ChoiceTaq DNA polymerase (Denville Scientific), 10× PCR buffer, 1. 5 µM MgCl2 (Denville Scientific), 500 µM dNTPs (Roche), 1. 25 µM of each primer, 1. 25 µL DMSO (Sigma), and water to a final volume of 25 µL. All fluorescently labeled PCR products were electrophoresed for each gorilla male in one assay on an Applied Biosystems 3730XL Sequencer. The collected data were analyzed by the Peak Scanner Software v1. 0 (Life Technologies).
Software, assembly, and alignment availability All scripts are available in Supplemental File S4, which represents the content of the code repository (http://github. com/makovalabpsu/GorillaY_project/) as of January 20, 2016. Readers are encouraged to download the latest versions of the scripts directly from the GitHub repository. The gorilla Y assembly and alignments are available at https://usegalaxy. org/u/rsharris/p/gor-hum-chi-y.
Acknowledgments We thank  Michael DeGiorgio  and  Raquel Assis  for useful discussions of the results and the  San Diego Zoological Society and Smithsonian Institution  for providing us with samples used in this study. Kristoffer Sahlin  provided assistance with running BESST. All HiSeq  and Sanger sequencing were performed at  Pennsylvania State Genomics Core Facility, University Park , Pennsylvania; PacBio sequencing was performed at  Johns Hopkins University Deep Sequencing and Microarray Core Facility. This study was funded by  National Science Foundation (NSF)  awards  DBI-ABI 0965596  (to K. D. M. ),  DBI-1356529  (to P. M. ), IIS-1453527, IIS-1421908, and  CCF-1439057  (to P. M. ). Additionally, this study was supported by the funds made available through the  Penn State Clinical and Translational Sciences Institute , and through the  Pennsylvania Department of Health (Tobacco Settlement Funds).
M. C. was supported by the  National Institutes of Health (NIH)-PSU funded Computation, Bioinformatics and Statistics (CBIOS)   Predoctoral Training Program  ( 1T32GM102057-0A1 ). O. A. R. acknowledges financial support from the  John and Beverly Stauffer foundation and the Alice B. Tyler Charitable Trust. M. A. F. -S. thanks the  Leverhulme Trust  for the Emeritus Fellowship award, which provided support for his contribution to the project.
Study Design and Population Subjects were recruited for the present study from Perm Region Oncological Dispensary using procedures approved by the Local Ethics Committe  (Gileva et al. , 2012). Patients gave informed consent to participate in this study via recording IR thermograms and X-ray mammograms of their two mammary glands: the cancerous breast and the contralateral unaffected breast. The thermography (resp. mammography) database included 33 (resp. 30 among these 33) females, aged 37-83 (average 57 years), who all went through surgery to remove a histologically-confirmed malignant tumor (invasive ductal and/or lobular cancer) a few weeks after thermo-(mammo-)grams were recorded. The tumors were found at depths varying from 1 to 12 cm into the tissue, with a size (diameter) varying from 1.
Software and Documentation The numerical procedure to perform the WTMM analysis of 1D signals is avaliable at http://perso. ens-lyon. fr/benjamin. audit/Last Wave LastWave is an open source software written in C. We recommend interested users read the LastWave C-Application Programming Interface documentation and to contact the corresponding author to be directed to the part of the code of most relevance to them. For 2D WTMM analysis, we adapted home-made Xsmurf software written in C, for specific application to mammograms.
CONCLUSIONS To summarize, we showed that the wavelet-based multifractal analysis of X-ray mammograms was able to detect the presence of an internal malignant tumor via some loss of correlations in the breast density spatial fluctuations. We demonstrated that this important change in mammogram roughness fluctuations strongly correlated with some drastic simplification of temperature dynamics recorded with an IR camera, from multifractal to homogeneous monofractal temporal fluctuations. The nature of this study was exploratory, with a data set limited to females who all went through surgery to remove the histologically confirmed malignant tumor of rather large size (diameter between 1. 2 and 6. 5 cm). To our knowledge, our study is the first to report on the observation of some physiological alteration and architectural disorganization of the microenvironmet of breast tumors using classical screening techniques, including the currently, most used X-ray mammography.
FUNDING This work was supported by  INSERM ,  ITMO Cancer  for its financial support under contract  PC201201-084862  " Physiques, mathématiques ou sciences de l'ingénieur appliqués au Cancer , " the  Russian Foundation for Basic Research  (grant  16-41-590235 ), the  Perm Regional Government (Russia)  with the contract "Multiscale approaches in mechanobiology for early cancer diagnosis" and the  Maine Cancer Foundation.