dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the technique is robust to over-fitting, noise and outliers  (Breiman, 2001; Marston et al. , 2014). Two-thirds of the data is used to train the RF with the remaining third, the 'out of bag' (OOB) samples, retained for RF prediction error testing, removing the need for cross-validation via a separate test dataset  (Perdiguero-Alonso et al. , 2008). Variable importance rankings are generated by comparing increases in OOB error when a variable is randomly permuted while all others remain unchanged (Abdel-Rahman, Ahmed, & Ismail, 2013), enabling importance ranking of the variables in relation to Ochotona spp. presence (Fig. 3 ). Random forest analysis was performed in the R statistical environment using the randomForest package  (Liaw & Wiener, 2002). In addition, to measure the strength and direction of the association between Ochotona spp.
Fig. 4  identifies four phases of varying point-biserial correlation strength. Period A corresponds to winter when vegetation biomass is very low with frequent snow cover, resulting in no clear distinction between degraded grassland and other habitats within the EVI dataset. During period B intensive vegetation growth occurs resulting in clear contrasts between lowbiomass degraded and other non-degraded habitats. During period C vegetation has also grown in the degraded areas (with some delay as low biomass areas are likely to be slower to produce a measureable increase) which, coupled with reduced grazing due to the departure of yak to their summer pastures, results in decreased contrasts between degraded and non-degraded areas. Period D corresponds to vegetation growth ending due to the onset of autumn/winter and increased grazing as yak return from their summer pastures.
, 2004). If overgrazing continues to increase grassland degradation, expansions in Ochotona spp. distributions resulting in closer proximity to villages are likely to result in greater infection risk to domestic dogs. Although the role of Microtine species in disease transmission in this area cannot be discounted, the extension of Ochotona spp. colonies closer to villages containing dogs increases potential human exposure to parasite eggs, and infection risk to local communities. Here, multi-temporal landscape characterisation using VIs have been applied within an epidemiological context, with results showing that the MODIS 16-day composite VI data product has sufficient sensitivity to track the seasonal vegetation phenology at this study site. Although the 250 m resolution of this imagery means multiple land cover types could potentially be contained within a single pixel, the relatively homogenous grassland dominated areas within this study site suggest that the use of MODIS time-series VI datasets is appropriate for capturing the landscape and vegetation dynamics at an ecologically meaningful scale.
Acknowledgements Special thanks to  F. Raoul ,  JP Quéré ,  D. Rieffel ,  N. Bernard ,  R. Scheifler  and  A. Vaniscotte  who collected small mammal transect data with  P. Giraudoux , and  Yang Wen , from the  Sichuan Center for Disease Control  for his logistical support in the field. This research was supported by the  Wellcome Trust  ( 094325/Z/10/Z ), and  US National Institutes of Health and National Science Foundation  ( EID TW001565-01 & 05 ) from the  Fogarty International Center. The paper has been written in the framework of the GDRI "Ecosystem health and environmental disease ecology" (http://gdri-ehede. univ-fcomte. fr). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  Wellcome Trust ,  Fogarty International Center  or the National Institutes of Health.
Although certain taxa are known to associate with different lake states, they have lake-and time-specific ecological functions in the community. Higher-level analysis seems also to present limited opportunities for interpreting changes in community structure. Biodiversity alone informs only on numbers of species not relative prevalence of functional categories. Here we show that we can make robust inferences about changes in functional structure by correlating biodiversity with compositional disorder, which measures the degree of unpredictability in composition of sequential samples. Our objectives are to (1) analyze population dynamics of species that compete for similar resources; (2) build the dynamics into a stochastic simulation of a community under environmental forcing, to generate predictions for structural changes in community composition; (3) test predictions on replicate paleo datasets of diatoms and chironomids (non-biting midges) from lakes that have undergone critical transitions to significantly different states.
To achieve the mapping, the simulation of exogenous forcing needed only to force changes in the relative abundances of the different functional categories, not to create a critical transition per se. In a first period, we toggled the community-wide death rate (d in equation (  1 ) above) in order to induce the short-term fluctuations in biodiversity and ºdisorder characteristic of empirical data. In a second period, we raised mean d in order to intensify the forcing. These stresses on the community induced a sequence of steady-state responses in functional composition, from which we could test for community-level signals in ºdisorder-biodiversity correlations. The simulation developed on  Doncaster (2009)  as detailed in Appendix S1, with the code in Matlab 7. 8. 0 given in Metadata S1 and Data S1.
Empirical datasets for testing predictions Predictions from the simulation model were tested against empirical datasets on diatoms and chironomids from lake Erhai (Yunnan Province) and lake Taibai (Hubei Province) in China, which both underwent transitions. We have insufficient information on drivers or evidence of hysteresis to identify the type of transition sensu  Capon et al. (2015). Instead, we construct our analysis on two reasonable assumptions: (1) The data show transitions of some kind which were critical in taking the ecosystem to a new and sustained state of changed biodiversity, evidenced in statistically significant break points (sequential t-tests plus arima forecasting) and corroborated by other studies; (2) structural changes within the system will precede any such critical transition in biodiversity (a generally accepted view).
Pearson's product-moment coefficient was calculated on consecutive ºdisorder-biodiversity pairs through the time series using first differences between values. For example, Erhai diatoms had dated values of ºdisorder and biodiversity: {41. For each dated core section, ºdisorder was correlated with biodiversity on two datasets: (i) the set of 15 points comprising that section and the 14 sections immediately preceding it, to give a coefficient for a putative 'pre-transition' period; (ii) the complementary set of all earlier sections than these, to give a coefficient for a putative 'lead-in' period. A sensitivity analysis indicated that 15-row incidence matrices maximize the magnitude of correlation for the putative pre-transition period (Appendix S2: Fig. S1 ). Neither the non-independence of sequential observations, nor the compaction of the sediment core over time (Appendix S2: Fig.
S2 ), compromise the correlations, which require independence only amongst residuals (Appendix S2: Fig. S3 ). Early-warning signals of state change were identified from differences in the sign and magnitude of the correlation coefficient measured in discrete segments of time series, not from year-on-year evolution of the coefficient. For dated sections of the Erhai core it was nevertheless possible to consolidate the more recent and less compacted samples in order to create a reduced dataset of diatom abundances at 30 time points approximately equally spaced by 3-5 years. This dataset yielded 16 consecutive time points from 1944 to 2005 with ºdisorder and biodiversity values, for comparison with the complete dataset.
From 1966 until 2001, a significantly negative correlation for the putative pre-transition period steadily strengthens in magnitude and in difference to the weakly positive correlation for the putative lead-in period (Fig. 4a , left-hand graph). Multiple regression analyses of  1898-1966 'lead-in', 1966-2001 'pre-transition' and 2001-2009 'transitioned'  time partitions found that only the pre-transition negative correlation had a significant coefficient (Appendix S2: Figs  S3  and S4 ). These patterns depend very little on species abundances, given the similarity between biodiversity and species richness. The dataset at equally-spaced time points showed a similar relationship to that of the full dataset. Its most parsimonious time-series model was arima(0, 1, 0) for both ºdisorder and biodiversity, indicating stationary time series in the mean of first differences.
For communities of aquatic organisms competing for similar nutrients in well-mixed lakes, this model becomes increasingly appropriate as environmental stressors further reduce the spectrum of available resources. The expression of the diagnostic signal may also depend on the type of transition (e. g. , fold, gradual or no bifurcation:  Boettiger et al. 2013). Future models could explore this possibility by directly linking critical transitions to ºdisorder-biodiversity correlations. The lake datasets show signals prior to both eutrophication and hypereutrophication, which may involve different types of transition. Further empirical analyses in progress on time series of oceanic phytoplankton suggest a potential wider applicability to different communities and dynamics from those modeled and tested here.
Material and Methods: Five female patient datasets (ages ranging from 3 to 18 years) were used for this retrospective study. The planning target volume (PTV) included total lung volume with a 1 cm margin (and adjacent vertebrae for three patients). Organs at risks included were heart, breast bud/tissue, liver and thyroid. 6 MV AP-PA (with segments) and RapidArc (2 or 3 full arcs) plans were created using the Eclipse treatment planning system (Version 11). Plans were calculated using the anisotropic analytical algorithm (AAA). The prescribed dose was either 15 Gy in 10 fractions or 18 Gy in 12 fractions based on the patient's age. PTV D2%, D98% and D50% and mean and maximum doses for heart and breast were obtained. The absolute excess risk (AER) of cardiac mortality at 15 years post treatment was calculated for each plan based on an age-at-exposure adjusted relative risk per Gy obtained from published data (1,2,3,4) combined with contemporary UK population-based absolute risks.
Material and Methods: The CXRT machine Papillon 50™ installed in Centre Antoine Lacassagne (Nice, France) delivers a 50 kV X-ray beam with a dose rate close to 15 Gy/min, allowing treatment delivery more comfortable for the patients [2]. The system is currently used for treating skin and rectal cancers. The detailed geometry of the Papillon 50™ machine [3] was fully generated in Monte-Carlo code PenEasy based on PENELOPE [4] and the resulting simulations were validated against measurements in water (depth dose curves and transverse dose profiles) for all applicators used for rectum cancer. For 10 patients with T2-T3 nodes smaller than 3 cm, dose distributions were calculated to irradiate the high risk target volume. For each patient, 30 Gy CXRT dose was computed with Monte-Carlo simulation in 3DCT patient data acquired in a position close to the rectal cancer CXRT position (genupectoral position).
1% Nonidet-P40)  11  and incubated at 37 °C for 2 h. Ethanol (70%) was used to remove chlorophyll after GUS staining. Morphological and cellular analysis. Measurements of leaves, petals (stage 14) and roots were conducted by scanning to generate a digital image and then calculating by ImageJ software. To measure cell number and cell size, leaves, petals and roots were mounted in the clearing solution (30 ml water, 80 g chloralhydrate, 10 ml glycerol). A Leica DM2500 microscope with differential interference contrast optics was used to observe samples and a SPOT Flex cooled charge-coupled device digital image system was employed to photograph cells. Petal cell sizes were measured on the adaxial side of petals. Leaf cell sizes were measured from palisade parenchyma cells in the middle region of the leaf.
1% Triton X-100, 1 mM EDTA and 1 Â Complete protease inhibitor cocktail) and further washed three times with 25 mM NH 4 HCO 3 (pH 7. 4). The proteins binding on agarose beads were resuspended with 8 M Urea in 25 mM NH 4 HCO 3 (pH 7. 4). The proteins were reduced with 10 mM dithiothreitol at 37 °C for 1 h and alkylated with 25 mM iodoacetamide at room temperature for 1 h in the dark. In-solution trypsin digestion was performed at 37 °C for 18 h using a trypsin:substrate ratio (1:50). The peptides were desalted and then analysed by liquid chromatography-tandem mass spectrometry using LTQ-Orbitrap elite mass spectrometer. The proteins were identified by searching the UniProt database using the software MaxQuant (version 1. 4) with a false discovery rate 1%. Yeast two-hybrid assay. The Matchmaker Gold Yeast Two-Hybrid system (Clontech) was used to conduct yeast two-hybrid analysis.
Acknowledgements We thank  Michael Lenhard  for the pCYCB1;1:CDB-GUS seeds and the  Arabidopsis Stock centres ABRC  and  NASC  for sod3 mutants. This work was supported by the  National Natural Science Foundation of China  (Grants  31425004 ,  91417304 ,  91017014 ,  31221063  and  31100865 ) and the  Ministry of Agriculture of China  (Grant  2013ZX08009-003 ).
The accuracy and reliability of the predictions are demonstrated in large-scale comparisons using well annotated alignment databases, where the homologous sequence segments are detected with very high sensitivity and specificity. Conclusions: LEON-BIS uses robust Bayesian statistics to distinguish the portions of multiple sequence alignments that are conserved either across the whole family or within subfamilies. LEON-BIS should thus be useful for automatic, high-throughput genome annotations, 2D/3D structure predictions, protein-protein interaction predictions etc.
Another recent method, OD-seq  [13] , is designed to find outlier sequences by examining the average distance of each sequence to the rest and represents a useful, fast method for checking very large alignments containing thousands of sequences. In general, these methods work well for multiple alignments of proteins that are homologous over their full lengths and allow the accurate detection of regions that are conserved in all members of a sequence family. However, large multi-domain proteins are becoming more and more prevalent in the sequence databases, with the arrival of numerous new genome sequences, in particular from eukaryotic organisms. In addition, badly predicted sequences mean that there are numerous fragments, spurious insertions/deletions and 'incoherent' segments in any set of sequences retrieved from the generalist databases  [1].
Taken together, this means that only a small proportion (18 %) of the conserved 'core blocks' in the alignments are present in most (>90 %) of the aligned sequences, while most of the blocks are only conserved within specific sub-families. These 'rare' segments or patterns are often characteristic of context-specific functions, e. g. substrate binding sites, protein-protein interactions or post-translational modification sites. To create a suitable test set for the LEON-BIS evaluation experiments, we used the unaligned sequence sets corresponding to each reference alignment in BAliBASE Reference 10. The first sequence of each set was arbitrarily defined as the query sequence. For each set, we then added up to four sequences which were considered to be "unrelated" to the query sequence, by selecting sequences from the other reference alignments with two criteria: (i) the selected sequence shared less than 50 % percent residue identity with the query sequence and (ii) no shared domains were identified in the PFAM protein family database  [24].
Algorithm overview Given a multiple sequence alignment, LEON-BIS predicts, for each sequence in the alignment, the regions that are homologous to a specified query sequence. The algorithm uses a similar workflow to LEON  [16]  and consists of five major steps, as outlined in Fig. 1. In the first step, the protein sequences are clustered into subfamilies using the Secator program  [17]  and any highly divergent or 'orphan' sequences in the alignment are identified and excluded from the subfamily clustering. In the second step, locally conserved segments or 'core blocks' are defined for each subfamily, based on Bayesian column scores and a sliding window analysis. At this stage, the SIBIS algorithm  [22]  is used to identify any 'inconsistent' sequence segments that may represent badly predicted sequences.
Sequence-level homology analysis To evaluate the accuracy of LEON-BIS for the detection of related and unrelated sequences, we constructed a large scale test set, based on the latest multiple alignments in the BAliBASE benchmark suite  [23]. These alignments represent large complex protein families and include multi-domain proteins, transmembrane proteins, fragments, badly predicted sequences, etc. For the purpose of these experiments, we then added to each protein family a number of sequences known to be unrelated. Finally, the resulting sequence sets, containing both divergent sequences and unrelated sequences, were re-aligned using the MAFFT multiple alignment program  [25]. Given a specific query sequence, LEON-BIS was used to predict the related and unrelated sequences in each alignment, and we estimated the accuracy of our approach in terms of sensitivity (=true positives/(true positives + false negatives)) and specificity (=true negatives/(true negatives + false positives)), shown in Table  1.
Detection of conserved regions and comparison with PFAM domains To further evaluate the quality of the conserved regions predicted by LEON-BIS, we extracted the known structural or functional domains from the PFAM database  [24]  for all the sequence sets used in the previous evaluation. We then compared the related regions identified by LEON-BIS and by LEON with these PFAM domains, as shown in Fig. 3. The precision (true positives/(true positives + false positives)) and recall (true positives/(true positives + false negatives)) of the two algorithms were then calculated (Table  2 ). Precision is defined as the fraction of retrieved instances that are relevant, i. e. the fraction of predicted regions that overlap with PFAM domains with respect to the total number of regions predicted by the method.
MAT1/Tfb3 is a component of the general transcription and DNA repair factor IIH (TFIIH), and contains two conserved domains from the PFAM database, including the C3HC4 RING finger domain in the N-terminal region and the MAT1 domain, corresponding to a central coiled-coil domain with a long helical fibrinogen-like structure. Although the C-terminal region (positions 250-309 in the human sequence, Uniprot:P51948) does not have any PFAM annotation, LEON-BIS identified a core block that is conserved in all the metazoan, plant and fungi sequences. This block corresponds to part of the hydrophobic C-terminal domain (amino acids 229-309), identified in structural studies  [28]  and shown to be required to assemble and activate CAK. In yeast, truncation of the 22 C-terminal amino acids of Tfb3/ Rig2 (MAT1 counterpart) was shown to be lethal  [29].
Nevertheless, there are a number of shortcomings in estimating scores for amino acids based only on their observed frequencies, especially when the number of observed sequences is small. First, unseen amino acids are assigned scores based on an amino acid substitution matrix that is generally chosen arbitrarily. Second, an arbitrary threshold for profile scores must be defined to distinguish related from unrelated or erroneous sequences. Therefore, the profile scoring scheme, originally developed in the context of the ClustalW multiple alignment program  [18] , has been replaced by a Bayesian statistical framework. This allows the definition of conservation based on background knowledge (amino acid frequencies extracted from alignments and represented by Dirichlet mixture models), combined with observed amino acids in alignment columns.
In a first large-scale evaluation, the ability of LEON-BIS to distinguish between related and unrelated sequences was compared to existing methods, including the original LEON method and a recent algorithm, ODseq, for the detection of outlier sequences in multiple alignments. The sensitivity and specificity of LEON-BIS were shown to be slightly higher than LEON. OD-seq had very high sensitivity and detected most of the related sequences, but the specificity was low, meaning that a large number of unrelated sequences were retained in the alignments. It should be noted ODseq is designed specifically for very large alignments containing thousands of sequences and it therefore represents a complementary approach to the method developed here. Then, in a second experiment, the regions predicted by LEON-BIS were compared to known domains from the PFAM database and both the precision and recall of LEON-BIS were shown to be about 92 %.
Funding This work was supported by the  Agence Nationale de la Recherche  ( BIPBIP :  ANR-10-BINF-03-02 ;  ReNaBi-IFB :  ANR-11-INBS-0013 ), the  Région Alsace and Institute  funds from the  CNRS , the  Université de Strasbourg  and the  Faculté de Médecine de Strasbourg.
Availability of data and materials All of the software, as well as the datasets supporting the conclusions of this article are freely available at http://www. lbgi. fr/~julie/LEON-BIS/. Java code is available for the LEON-BIS algorithm. The method also requires the cluspack software for sequence clustering and the chain_blocks program from the LEON suite, both written in 'C'. A UNIX shell script is provided that calls all the programs. LEON-BIS takes multiple alignments in either MSF or FASTA format as input and outputs a refined alignment in an XML format file, annotated with the predicted core blocks and the conserved regions. The refined alignment contains only those sequences predicted to contain homologous regions. Software is provided to convert the XML format alignment file to MSF or FASTA formats, if required.
In order to have a calculation for the right and left river banks separately, a value of -100 is affected on the middle of the river. Then the difficulty is to choose the starting and ending points for the least cost path analysis as any algorithms or database give the approximate location of river banks. As the least cost path analysis is not sensitive to the choice of those points, they have been located on the river bed. Two sets are drowned, one for the right side and another for the left side. They have to be closed enough especially when other rivers are flowing to the main river (see Figure  7 ). and its modified curvature (green lines) Figure  7  shows the result for the example of the Aude river. It can be seen that river banks are sometimes interrupted by other rivers flowing in the main river.
Statistical analysis Data are presented as mean ± SEM (n= number of individual measurements). The Student's t-test was used to compare two means and to determine significant differences. Differences between values were considered significant (*) when p< 0. 05; very significant (**) when p< 0. 01 and highly significant (***) when p< 0. 001. All calculations were performed using the GraphPad Prism 6 software.