dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment criteria Receiver Operating Characteristic (ROC) curve together with Area Under Curve (AUC) is always used as criterion for accuracy  [73]. We define the accuracy of a set of proteins by averaging accuracies of all proteins. We suggest the weighted arithmetic mean of AUC (wAUC) and mean of AUC (mAUC) as two criteria of accuracy for a set of proteins: wAUC ¼ P AUCðiÞ Â lenðiÞ P lenðiÞ ð1Þ mAUC ¼ AUC ¼ P AUCðiÞ N ð2Þ For a protein i, AUC(i) is its AUC value and len(i) is length of the protein, while N is the number of proteins in a dataset. We call the AUC that compare all the residues in a dataset together as total AUC (tAUC) and use it as a reference for comparison. We define standard deviation of AUC of a data set as sAUC to show the accuracy stability varying the data sets: sAUC ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P AUCðiÞ Â AUCðiÞ À N Â AUC 2 N À 1 s ð3Þ Other binary criteria include specificity, sensitivity, precision, accuracy, F1 score and Matthews correlation coefficient: specificity ¼ TP TP þ FN ð4Þ sensitivity ¼ TN FP þ TN ð5Þ precision ¼ TP TP þ FP ð6Þ accuracy ¼ TP þ TN P þ N ð7Þ F1 ¼ 2TP 2TP þ FP þ FN ð8Þ MCC ¼ TP Â TN þ FP Â FN ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ðTP þ FPÞðTP þ FNÞðTN þ FPÞðTN þ FNÞ p ð9Þ TP is true positive, TN is true negative, FP is false positive and FN is false negative.
Use of prediction programs Li lab and Xiaoyong Pan provided RNAProSite and xypan prediction results respectively, with default parameter. aaRNA was used by inputting the structure file with default parameters. RNABindR and RNABindRPlus were predicted with default parameters, while removing 95% sequence identity in RNABindRPlus. KYG was predicted with command line using "method_type = 8" option. RBscore_SVM was based on the training set of R246. DBS-PSSM, DBS-Pred, RBRIdent, PPRInt, PRBR, DNABind, RBRDetector and ProteDNA were used with default parameter. The DISPLAR program was provided by Sanbo Qin and was run with default parameters. BindN and BindN+ were used by default parameters, while suffix "_RNA" and "_DNA" are RNA mode and DNA mode respectively. PRNA and Predict_RBP were trained on PRNA_R205 and BindN_R107 respectively, without cross-validation and applied with default parameters.
Obviously, dataset bias is a very severe problem for some predictors and require special attention. A successful prediction program should demonstrate stable predictive ability on all the criteria of the assessment. Three criteria (MAVR, MAV and CAVR, described in Methods), stand for distance cutoff dependent accuracy variances (wAUC) were used to assess the stability. It can be deduced from Fig 3A that the prediction KYG, RNAProSite, RNABindR, ProteDNA, RBscore, DISPLAR and DNABINDPROT are less dependent on distance cutoff (mAUC and tAUC based results could be found in S3 Fig and S4 Fig). Generally, programs tend to favor the distance cutoff used during training (Supplementary Note 11 in S1 Text). In terms of data set, standard deviations of AUC (sAUC) were measured as criteria to assess the stability and is shown in Fig 3B.
The accuracies of DBS-PSSM, RBscore, aaRNA, DBS-Pred, ProteDNA and DNABINDPROT are more stable when varying the assessment data sets. Considering both prediction accuracy and stability, the 'barrel effect' applies to the predictions with dataset bias and a program can best be assessed by its minimum accuracy value in all the datasets. In Fig 3C, RNA binding site predictors are assessed on RBP while DNA binding predictors on DBP, the minimum wAUC measured by all distance cutoffs is taken as their prediction ability. Fig 3D shows the minimum wAUC applied on all the datasets. A more complete list can be found in Table.
Acknowledgments We gratefully thank  Pascal Auffinger  for helpful discussions on the manuscript,  Drena Dobbs  and  Rasna R. Walia  for valuable discussions on machine learning methods and tests of  RNA-BindR and RNABindRPlus ,  Shandar Ahmad  for beneficial discussions on assessment criteria and tests of DBS-PSSM, DBS-Pred, SRCPred and SDCPred,  Carmay Lim ,  Yao-Chi Chen  and  Jon Wright  for the small scaled test of DR_bind1, aaRNA developers for scripts in running aaRNA in batch mode,  Piero Fariselli  for helpful discussion of machine learning theories and techniques,  Sanbo Qin  for program package of DISPLAR,  Xiaoyang Pan  for providing prediction results of his method,  G. P. S. Raghava  and  Manish Kumar  for technical details in PPRInt,  Haipeng Gong  and  Xiong Dapeng  for discussion of RBRIdent,  Kei Yura ,  Yaping Fang ,  Zhiping Liu  for technical details in KYG, Predict_RBP and PRNA respectively.
918 0. 395 0. 866 0. 415 0. 341 aaRNA 6 New_D31 0. 804 0. 813 0. 060 0. 834 0. 552 0. 891 0. 381 0. 854 0. 451 0. 378 RNAProSite 6 Luscombe_D129 0. 785 0. 803 0. 081 0. 790 0. 760 0. 696 0. 290 0. 705 0. 420 0. 329 DNABind 6 RBscore_D381 0. 765 0. 798 0. 152 0. 774 0. 614 0. 898 0. 492 0. 859 0. 546 0. 467 KYG 6 DNABINDPROT_D54 0. 707 0. 703 0. 074 0. 712 0. 444 0. 802 0. 281 0. 749 0. 344 0. 206 RBP datasets Sequence-based DBS-Pred 6 aaRNA_R141 - - - - 0. 359 0. 790 0. 190 0. 738 0. 248 0. 116 ProteDNA 4. 5 Sungwook_R267 - - - - 0. 001 0. 999 0. 140 0. 888 0. 002 0. 003 RNABindRPlus 6 RNABindR_R111 0. 738 0. 725 0. 113 0. 720 0. 321 0. 914 0. 316 0. 848 0. 319 0. 233 RNABindR 6 New_R15 0. 733 0. 756 0. 093 0. 737 0. 661 0. 681 0. 279 0. 678 0. 393 0. 258 RBscore_SVM 6 New_R15 0. 687 0. 697 0.
Although the original dataset consists of two separate experiments, these experiments are identical for the purposes of the analyses reported here. We will therefore discuss the original data as one, collapsed, dataset of 32 participants that were tested in a setup as approved by the Ethical Committee Psychology of the University of Groningen. The outline of a task is depicted in Fig. 1. The participants were asked to produce the 2. 5 s interval by pressing the spacebar twice using the right hand index finger. Visual feedback was presented after each trial indicating the deviation from the standard duration. During the entire interval a small circle served as a fixation point. Before the first keypress, the circle was shown in light gray on a black background. The first keypress changed the color of the circle to white, as a visual cue that the interval had started.
2 s). On average 149. 7 trials with a response between 2. 2 and 2. 8 s were analyzed per participant, and 212. 5 time production trials ranging from 1. 8 to 3. 2 s. Time-frequency analysis was performed using FieldTrip  (Oostenveld. , 2011) , an open source Matlab toolbox, using FieldTrip's default FFT methods. The 1/f property was removed from the data time domain prior to the calculation of spectral power by computing the first order derivative as implemented in FieldTrip. Removing the 1/f trend ensures that all frequencies of broadband averaged signal contribute equally to beta power estimates and power estimates are dominated by lower frequencies due to the 1/f property when absolute baseline normalization is applied  (Cohen, 2014). To analyze oscillatory power we used a single Hanning taper with an adaptive time window of 6 cycles per frequency in 15 ms steps for frequencies from 4 to 40 Hz.
g. ,  Haegens et al. , 2011; Fig. 2. Time-frequency decomposition of the first keypress-locked data measured at FCz for the pseudo-experimental Short (2. 2-2. 4 s), Correct (2. 4-2. 6 s), and Long (2. 6-2. 8 s) conditions with corresponding topographies. Topographical plots are based on a 15 to 30 Hz frequency band and focus on the time windows that showed significant differences in the cluster based analysis. The bottom-most spectrogram depicts the power differences for Long minus Short categories. The spectrograms and three top-most topoplots depict power in uV 2 /Hz, as depicted in the legend to the right of the second topoplot. The topoplot at the bottom depicts the results of cluster based permutation test in F-values. F-values for all significant time windows were averaged. For more details, see the main text.
Jenkinson and Brown, 2011). As depicted in Fig. 2 , post-first-keypress oscillatory beta power falls within the selected frequency range. To ensure that any effects observed for beta power are not an artifact of a general modulation of oscillatory power, we also assessed the power per pseudo-experimental condition in theta (4-7 Hz) and alpha (8-14 Hz) bands. For display purposes the data for beta band frequency was smoothed using an eight-element kernel, but all reported analyses are based on raw, unsmoothed data.
, 2013) , and thus any preparatory beta effects could influence the observed post-keypress effects. On the other hand, the beta power measured before the subject-paced onset could influence subsequent behavior independently from the motor-response evoked beta, mimicking the correlations between pre-stimulus oscillatory activity and stimulus processing  (Busch and VanRullen, 2010;  for reviews see,  Deco and Romo (2008) ,  Engel et al. (2001) ). Therefore, we also measured standardized beta power over the 0. 5 s interval prior to the first keypress for all "green" trials (i. e. , productions larger than 1. 8 and smaller than 3. 2 s) and subjected this data to similar linear mixed effect analyses. (Similar analyses of the restricted dataset showed numerically similar effect sizes, but these failed to reach significance.
Acknowledgments This work has been partially supported by the European project  COST ISCH Action TD0904 "Time In Mental activity: Theoretical , behavioral, bioimaging, and clinical perspectives" (TIMELY: www. timely-cost. eu). We thank  Virginie Van Wassenhove  for critical comments on this work. Preliminary data were presented at the final Timely Cost meeting (Kononowicz and van Rijn, 2014b).
a A typical torticollis is shown. These symptoms normally occur 7 to 10 days after a complaint of high mortality is reported. b In severely affected birds, mild swollen head and dyspnea with profuse secretions in the trachea were found. c & d Hemorrhagic & necrosis of intestines especially the caecal tonsils & peyer's patches were found. e Upon PM, the trachea was severely congested and late in the disease stages pericarditis, perihepatitis and caseous air sacculitis were observed. f Proventicular hemorrhages were consistent. g, h & i Bursa atrophy was also commonly found in the outbreak. The cut surfaces of the bursa were hemorrhagicquite atypical from ND infection which prompted us to look for other infectious agents. Not shown above was atrophic thymus by using the Absolute Quant Software provided with the instrument.
Sequencing, amino acid sequence analysis, phylogenetic construction of NDV from the 2011 outbreak The same primer sets for amplifying NDV as described above  [1, 26]  were used for sequencing. The PCR products of the expected amplicon sizes were purified by using the PCR clean-up gel extraction kit according to the manufacturer's protocol with slight modifications (Analytik Jena, Germany). Sequencing of the fusion protein gene of NDV from the 14 farms was done in a commercial sequencing facility using the BigDye® Terminator v3. 1 cycle sequencing kit. In order to confirm that all positive cases were true Newcastle disease virus, a Basic Local Alignment Search Tool (BLAST) search of the sequence was done in the Genbank® database (Data not shown). The sequence editing and assembly were done by using BioEdit® Sequence Alignment Editor version 7.
0. 5. 2 (Tom Hall, US). Sequences were aligned by using ClustalX™. The phylogenetic tree was constructed by using the distancebased neighbor joining method by using Mega™ 5 software (Biodesign Institute, Tempe, Arizona) and evaluated using the bootstrapping method calculated on 1000 repeats of the alignment. The sequence identity matrix was generated with BioEdit® Sequence Alignment Editor version 7. 0. 5. 2 (Tom Hall, US). All sequences used for constructing the phylogenetic tree are listed in Table  1.
Therefore, based on the findings, the presence of IBD and MDV suggest a significant negative impact on the immune system and growth of these broiler chickens. NDV itself was not immunosuppressive, and vaccination with killed-NDV vaccines failed to reduce incidences of NDV outbreaks, because the core underlying factors such as IBD and MDV that were causing sub-clinical infections were not addressed, thus reducing the protective effect of the NDV vaccination programs. Sequencing, amino acid sequence analysis, phylogenetic construction BLAST® analysis showed that all samples were true NDV cases when compared with other sequences on Genbank®. Amino acid sequence analysis of the 535 bp fragment of the fusion protein (F) gene of the fourteen Malaysian NDV isolates showed that eleven of the isolates were categorized as velogenic virus and three were lentogenic.
Nucleotide sequence accession numbers The complete genomic sequences of the 14 NDV isolates reported in this paper were deposited with the GenBank® database under accession numbers JN613112, JN613113, JN613114, JN613115, JN613116, JN613117, JN613118, JN613119, JN613120, JN613121, JN613122, JN613123, JN613124 and JN613125. These sequences are downloadable from Entrez™ Pop Set data as a group of sequences.
Africa Genbank® 22 ZW3422/95 AF109877 VIIb Zimbabwe Genbank® 23 NDV05-055 DQ439910 VIIc China Genbank® 24 TW/2000 AF358786 VIIc/d Taiwan Genbank® 25 F8 JN613119 VIId Malaysia Genbank® 26 Ch/2000 AF358788 VIId China Genbank® 27 MB064/05 GQ901893 VIId Malaysia Genbank® 28 MB016/07 GQ901894 VIId Malaysia Genbank® 29 F1 JN613112 VIId Malaysia This study 30 F2 JN613113 VIId Malaysia This study 31 F3 JN613114 VIId Malaysia This study 32 F4 JN613115 VIId Malaysia This study 33 F9 JN613120 VIId Malaysia This study 34 F10 JN613121 VIId Malaysia This study 35 F11 JN613122 VIId Malaysia This study 36 F12 JN613123 VIId Malaysia This study 37 F13 JN613124 VIId Malaysia This study 38 F14 JN613125 VIId Malaysia This study 39 MB043/06 GQ901896 VIId Malaysia Genbank® 40 MB091/05 FJ008916 VIId Malaysia Genbank® 41 MB093/05 FJ008917 VIId Malaysia Genbank®.
65 μg of at least 6 pmol Cy3/ng of each RNA sample was fragmented using the Agilent Gene Expression Hybridization Kit. Sample hybridizations were performed on an Agilent Bovine (V2) Gene Expression Microarray for 17 h at 65°C. After gentle washing, according to Agilent's protocol, microarrays were scanned on a C scanner (Agilent). Agilent Feature Extraction software (v10. 7) was used to read out and process the microarray image files. The software determined feature intensities and background, rejected outliers, and generated validated data files for further analyses. Validation of each array consisted in the analysis of additive error, spatial distribution of outliers on the array, histogram of signal plots, background sub-signal value and linear range spike-in statistics. All assays were satisfactory for these parameters.
Microarray data normalization and analysis All validated scan data files were processed using the Agilent GeneSpring GX (v11. 5. 1) software for normalization with the 75 percentile model, with baseline transformation based on the median of all samples. All entities with flag values present in at least 100% of the values in any one out of the two status conditions (non-infected versus infected) were considered. Statistical analysis (paired t-test) using the volcano plot filter was used to generate a unique list of up-or down-regulated entities with associated Benjamini Hochberg False Discovery Rate corrected pvalue and fold change (FC) and for hierarchical clustering. Using Ingenuity Pathway Analysis (IPA) software, differentially expressed entities were separated into unmapped and mapped entities.
A deduplication exercise on mapped entities resulted in a final list of unique identified genes (p-value < 0. 05 and absolute fold change 2), which was then used to perform IPA bio-function analysis. IPA categorized and subcategorized modulated genes according to p-values (calculated by the Fisher exact test) and z-scores. The z-score predicts the direction of change of a function: a function is increased when z-score is 2 and decreased when z-score 2. IPA calculated a bias-corrected z-score to correct dataset bias (i. e. when there are more up-than down-regulated genes in a bio-function or vice-versa).
Primer pairs were selected on the same gene sequences (same GenBank accession numbers) as microarray probes (Table  1 ). The PCR thermal cycling program consisted in one step at 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min, and a final dissociation step, which confirmed single specific PCR product amplifications. A negative control (no-template control) was included in each primer assay. FC of expression levels between infected and control groups were calculated using the relative expression software tool (REST)  [20]. Correlation between the microarray and RT-qPCR results for the six regulated genes was performed by the Pearson's correlation calculation (www. socscistatistics. com/tests/pearson/) and the statistical significance of the correlations was determined.
All microarray data can be downloaded from NCBI's Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) with the GEO series accession number GSE70059. It must be noted that there was no evidence of differential response between the four animals that had to be sacrificed before the end of the experiment and the two animals that survived until the end, although it was not possible to perform a statistical analysis with such a small number of animals.
An absolute z-score 2 was considered to be significant. A bias-corrected z-score* was calculated by IPA to correct dataset bias, i. e. , when there are more up-than down-regulated genes or vice-versa. "genes" indicates the number of genes that are associated with each function. A function is predicted to be decreased (DOWN) if the z-score (or the bias corrected z-score) 2 and increased (UP) if the z-score2. doi:10. 1371/journal. pone. 0139678. t002.
We developed an approximated model  (Andrieu et al. , 2015d ) that has the capability of being able to model a layer of ice covering a surface with radically different optical properties, for instance a different refractive index, unlike its predecessors. It was originally designed for the study of Martian seasonal ice deposits, using massive spectro-imaging datasets, such as the OMEGA  (Bibring et al. , 2004)  or CRISM  (Murchie et al. , 2007)  datasets. For this purpose, it is semi-analytic and implemented to optimize the computation time. However, the algorithm was built to be adaptable to any other spectroscopic data, from terrestrial water ice laboratory measurements, as is the case of this work, to the study of SO 2 ice on Io or N 2 ice on Pluto using the NIMS  (Carlson et al. , 1992)  and RALPH  (Reuter et al.
, 2009)  datasets respectively. In the present article, we test the accuracy of this approximated model on laboratory spectroscopic measurements of the bidirectional reflectance distribution function (BRDF) of pure water ice on top of snow. At the same time, we present an innovative Bayesian inversion method that was developed to retrieve the properties of solar system compact ice using satellite spectro-imaging data. In this paper, the term "inversion" is used and means "solving the inverse problem". The slabs that are studied contain no impurity, and the surface properties we seek to retrieve are the thickness of the ice, the roughness of the ice-air interface and the grain diameter of the underlying snow. The main goals of this work are (i) to test the ability of the model to reproduce reality and (ii) to propose an inversion framework able to retrieve surface ice properties, including uncertainties, in order to demonstrate the applicability of the approach to satellite data.
Method We designed an inversion method aimed at massive data analysis. This method consists of two steps: first, the generation of a synthetic database that is representative of the variability in the model, and then the comparison with actual data. To generate the synthetic database, we used optical constants for water ice at 270 K. The 7 K difference between the actual temperature of the room and the temperature assumed for the optical constants has a negligible effect. We combined the datasets of  Warren and Brandt (2008)  and  Schmitt et al. (1998) , making the junction at 1 µm, the former set for the shorter wavelengths and the latter for the wavelengths larger than 1 µm. In order to validate the model on the specular reflection from the slab, we chose to use the reflectance at 1. 5 µm, where the ice is very absorptive.
We first generated a synthetic database (lookup table), using the direct radiative transfer model. We simulated spectra in the same geometrical conditions, for a 12. 5 mm thick ice layer over a granular ice substrate constituted of 1000 µm wide grains. These two last parameters are not important since the absorption is so high in ice, such that the main contribution is from the specular reflection, and the diffuse contribution is negligible (the penetration depth inside a water ice slab at the 1. 5 µm wavelength is lower than 1 mm). The sampling of the parameter space, i. e. , the lookup table, must correctly represent the variability of the model according to its parameters. For this study, we sampled the roughness parameter from 0. 1 to 5 • with a constant step d θ = 0. 01 •. We used a likelihood function L defined in Eq.
(  8 ), where d sim and d mes are n geom -element vectors, with n geom the number of angels (63 in this study). They respectively represent the simulated and measured reflectance at a given wavelength in every geometry. C is a n geom × n geom matrix. It represents the uncertainties of the data. In this case, we considered each wavelength independently, thus generating a diagonal matrix, containing the level of errors given by the technical data of the instrument described by  Brissaud et al. (2004). It corresponds at this wavelength to 2 % of the signal. The roughness parameter θ returned by the inversion is described by its normalized PDF: P θ (i) = L (i) d θ j L (j ) d θ = L (i) j L (j ). (9) F. Andrieu et al. :  Retrieving the characteristics of slab ice covering snow by remote sensing The value θ (i) in the database with the highest probability (maximum likelihood) corresponds to the sampling step that is closest to the maximum of the a posteriori PDF.
dp 2 (j ). For the inversion, we used the same method as previously described, with a likelihood function L that is written as in Eq. (  8 ). Two different strategies were adopted. First, we inverted each spectrum independently. Thirty-nine geometries were sampled (described in Sect. 3. 2), and thus we conducted 39 inversions for each sample. This time d sim and d mes are thus respectively the simulated and measured spectra. Therefore d sim and d mes are n b -element vectors, where n b is the number of bands (61 in this study) and C is a n b × n b matrix. As previously done (see Sect. 4. 2), we considered each wavelength independently, thus generating a diagonal matrix, containing the level of errors given by the technical data of the instrument given by  Brissaud et al. (2004). The error is a percentage of the measurement, and thus C is different for every inversion.