dataesr/scientific-paragraphs-categorization huggingface dataset:
In this last case, all possible classifications are given (separated by a pipe symbol ''|''). We present an example of PASTEC output in table  S1. PASTEC output is a tabular file, with the columns from left to right indicating the name of the TE, its length, the orientation of the sequence, chimeric/non-chimeric status (OK indicating that the element is not potentially chimeric), class (class I in this case), order. In the first line of the example provided, the TE is a LTR. We presume that the element is complete because we have no evidence to suggest that it is incomplete, and the confidence index is 71/100. The last column summarizes all the evidence found: coding sequence evidence, such as the results of tblastX queries against the Repbase database (TE_BLRtx evidence), blastX queries against the Repbase database (TE_BLRx evidence) and profiles.
Results and Discussion We compared the results of PASTEC with those of the two other classification tools, REPCLASS and TEclass, for three datasets: (i) the A. thaliana consensuses found in Repbase update version 15. 09 (referred to hereafter as Repbase-atha), (ii) the TEs present in Repbase update 15. 09 but not in Repbase 13. 07 (referred to hereafter as Repbase-diff), and (iii) the whole Repbase update 15. 09 from which we removed redundant TEs, i. e. , those with strictly identical sequences (referred to hereafter as Repbaseall). For RepClass and PASTEC, which require a database for blast analyses, we used RepBase update 15. 09 from which we removed the A. thaliana TEs in the first case, RepBase 13. 07 in the second case and no blast database in the last case. The second case (Repbase-diff) mimics standard use: we wanted to classify TEs obtained from a new genome and, therefore, not yet known in the Repbase database.
Indeed, Repbase-diff contains the TEs present in Repbase Update 15. 09, but not in Repbase Update 13. 07, and we provided Repbase Update 13. 07 for sequence similarity searches by REPCLASS and PASTEC. So, as is usually the case in such analyses, we have unknown TEs (Repbase-diff) and a database of known TEs (Repbase Update 13. 07). This made it possible to check whether the classification results for Repbase-diff were consistent with the classification given in Repbase Update 15. 09. The last case is the most difficult, with attempts to classify TEs as if no known TEs are present in the database. We compared the classification performance, in terms of the percentages of TEs well classified, misclassified relative to their annotation in the Repbase database or not classified at the level of class (Table  2 ) or order (Table  3 ), for each of the three tools.
It was not possible to compare sensitivity and specificity for each order, because the three tools do not use the same classification. Nevertheless, sensitivity and specificity data are presented in the supplementary data for the orders for which this was possible. We mapped the PASTEC classification onto those used by TEclass or REPCLASS, to improve comparisons. Table  3  shows the performances obtained with PASTEC after mapping. PASTEC misclassified very few TEs, regardless of the dataset tested (Table  2  and 3 ). These rare ambiguities mostly concerned closely related TEs: essentially MITE/TIR (427/605 270. 6% -TEs misclassified at order level in the Repbase subtraction dataset). For Repbase-diff, PASTEC was frequently more specific than the other two tools (see suppl. Data: Venn diagram and table of sensitivity and specificity of the three tools for this second dataset Tables  S2, S3 , S4, S5 S6, Figures  S1, S2 , S3, S4, S5 S6).
However, it failed to classify many TEs due to a lack of useful evidence. PASTEC HMM profile detection was found to be particularly useful if the database contained no similar TE to that for which the search was carried out. For Repbase-all without blast searches, we had profile information for 3488 TEs, making it possible to classify these elements at least partially. For classification to order level, PASTEC gave better results than REPCLASS. PASTEC correctly classified more TEs than REPCLASS for the Repbase-all library (31. 9% of the TEs were well classified by PASTEC on the basis of REPCLASS order, whereas REPCLASS classified only 12. 4% of TEs well, and PASTEC misclassified fewer TEs for all datasets (see Table  2  and 3 ). REPCLASS performed well on the Repbase-atha dataset, particularly for helitrons, thanks to its helitron_scan tool, if no TSDs were found.
It was efficient, but required the genome sequence. This tool searches for 59 and 39 termini, including the flanking sequences of all copies found in the genome, and a subterminal hairpin-like GC-rich motif. For the other datasets, we provided no genome sequence because the TEs came from many different genomes. In such conditions, the TSD module of REPCLASS cannot be used, but good results were nevertheless obtained with this tool for the Repbase-diff dataset (67% of TEs well classified to order level, see Table  3 ). For this dataset, REPCLASS was more sensitive (83. 6% vs 52. 7%) but less specific (89. 7% vs 99. 9%) than PASTEC for classification to order level. By contrast, for the Repbase-all dataset (without blast homology searches), this tool gave the worst results (12. 4% of TEs well classified to order level).
TEclass classified the A. thaliana dataset well, but despite the number of TEs correctly classified with each of the three test datasets, the percentage of TEs misclassified remained high. TEclass misclassified between 40% and 60% of TEs from the Repbase-diff and Repbase-all datasets, misclassifying more than half of the TEs at order level for these two datasets. This suggests that this tool does not recognize effectively the situations in which it cannot give a correct classification. For interpretation purposes, a lower level of misclassification with more TEs unclassified and requiring manual checking would be preferable. It should also be noted that, in this study, TEclass was at an advantage over the other tools, as we could not train TEclass with a database containing none of the TEs in the test set.
Conclusions In conclusion, TEclass misclassified TEs more frequently than the other tools for the two largest datasets (more than 50% of TEs for classification to order level). Moreover, the order classification of this tool is highly simplified (only four potential classifications). REPCLASS also has a simplified order classification (only four potential classifications). For these four orders, this tool performs well in conditions in which a homology search is possible (86% of Repbase-atha TEs well classified, with only 3% misclassified, 67% of Repbase-diff TEs well classified, with only 11% misclassified). However for the Repbase-all dataset, for which no homology search was possible, it performed poorly (12% of TEs well classified for 33% misclassified). PASTEC was found to have many advantages over the other tools.
First, it can classify TEs into no less than nine orders, according to Wicker's classification (versus only four for TEclass and REPCLASS). It performs well for classification into these nine orders, particularly for the dataset for which blast homology searches were not possible, due to the possibility of carrying out HMM profile searches (22% of TEs are well classified and only 16% are misclassified). This is particularly useful when looking at a species phylogenetically distant from the species from which the known TEs were obtained. It performed well for the Repbase-diff dataset, which simulates a standard dataset: 51% of TEs were well classified and 11% were misclassified for classification to order level (for the nine available orders). If we consider only the four REPCLASS levels of classification, PASTEC classified 59% of TEs well and misclassified only 3% of TEs.
(TIF) Figure S4 Venn diagram (Repbase-diff dataset) for helitron TEs. The number of well classified helitron TEs is shown in brackets. The numbers within the Venn diagram are the numbers of TEs well classified by each tool, with the overlaps indicating those well classified by several tools. Note: TECLASS does not classify helitron TEs to order level. (TIF) Figure S5 Venn diagram (Repbase-diff dataset) for LTR TEs. The number of well classified LTR TEs is shown in.
Public Health Action South Pacific Operational Research Supplement S6 course organisers several weeks before Module Three. More recently, participants had to start creating their EpiData files and 'dummy' tables and figures (draft versions with no data) during the module and send them 2 weeks after Module Two. 44 We piloted a new way of running this module: time was allocated for helping participants to finish (or almost finish) their EpiData files and dummy tables and figures during the module. Participants were also required to present them for discussion at the plenary on the last day. This allowed for more intense and personalised technical support during the week, as well as peer review and dis-cussion about analysis for each project individually at the plenary. It also helped participants to start thinking early on about how to prioritise and present their data so that decision makers would pay attention to their findings and recommendations.
Intensified support for Module Two milestones In the Union/MSF courses elsewhere in 2012, the first of two Module Two milestones was submission of EpiData files and draft tables and graphs 2 weeks after Module Two, although completion during the module was not required. In our courses, we allocated time during the module to help participants prepare these files and draft tables and graphs in class and then to present these for discussion in plenary on the last day of Module Two. The intensified support and immediate feedback on files and documents during the course helped participants to prepare for sending their documents 2 weeks later for the first Module Two milestone.
Introduction of a new session into the curriculum on Excel We introduced a short session on Microsoft Excel into the Module Two curriculum in the Pacific course. Although we do not recommend using it for data entry, we were aware that some participants had existing data sets in Excel that required some modification before importing into EpiData. The session also recognised the fact that many health data sets in the region use this software and that participants' skills in it were generally poor. Any upskilling might benefit programmes that use Excel for their routine data collection. The Union = International Union Against Tuberculosis and Lung Disease; MSF = Médecins Sans Frontières.
Host based IDS In  [4] , the authors presented an Unsupervised Behavior Learning (UBL) system for predicting performance anomalies in virtualized cloud systems. UBL is a host-based IDS, implemented at the hypervisor level which uses a set of continuous VM behavior learning modules to capture the patterns of normal operations of VMs relying on system metrics (CPU, MEM, TX, RX). To that aim, it leverages Self Organizing Map (SOM), an unsupervised learning method, to predict anomalies by looking at early deviations from the normal system behaviors. This work is related to ours since, it considers the same metrics as our system. However, the limit of UBL relies in monitoring computer activities on a single host. Thus, it does not enable the building of a global view of intrusions and is not effective in detecting fast-spreading attacks such as DDoS ones.
Learning automata has many applications in computer science. Notable examples are the use in model checking  [15]  and testing  [3]. See  [26]  for an overview. While active learning of regular languages is meanwhile well understood and is supported by freely available libraries such as LearnLib  [27]  and libalf  [10] , extensions beyond plain regular languages are still an area of active research. Recently, automata dealing with potentially infinite data as basis objects have been studied. Seminal works in this area are that of  [1, 20]  and  [19]. While the first two use abstraction and refinement techniques to cope with infinite data, the second approach learns a sub-class of register automata. Note that session automata are incomparable with the model from  [19]. Thanks to their closure and decidability properties, a conservative extension of Angluin's classical algorithm will do for their automatic inference.
Yet, to date, the effect of changes in autumn plant phenology on overwinter juvenile survival remains unexplored. Our first goal was to identify the annual variation of plant primary production and phenology among mule deer population summer range, measured using NDVI curves of the growing season. Second, with annual plant phenology characterized, we assessed both direct and indirect (through fawn body mass) effects of these key periods on overwinter survival of mule deer fawns. We used a uniquely long-term (1998-2011) and large-scale dataset to disentangle plant phenology effects on mule deer survival, encompassing 13 different populations spread over the entire southern half of Idaho, USA, while most previous studies have focused only within one or two populations. These populations represent diversity of elevations, habitat quality and climatological influences.
(c) Defining population ranges of mule deer We used the mule deer winter and summer ranges for each GMU as the main spatial units of analysis, and we extracted NDVI data from summer range and winter weather from winter range for each year from each population. We combined relocation points for all individuals and years in a single study site to estimate a 95% adaptive kernel home range for both summer and winter  [76]  for mule deer captured within a population. All deer populations were migratory with an average winter range size of 430 km 2 and average summer range size of 3360 km 2. Migratory periods, 1 April to 1 June and 1 October to 15 November, were excluded from the home range estimates, and remaining animal locations between 1 June and 30 September were used for summer, 1 December to 31 March for winter.
e. portions of the curve) and not due to few independent NDVI values. FPCA produces eigenvalues (measuring variation explained by each dimension) and principal component scores for sampling units (summarizing similarities among NDVI curves). However, eigenvectors are replaced by eigenfunctions (harmonics) that show the major functional variations associated to each dimension. + - A M J S N M J S N J A O D spring autumn A M J S J A O D A M J S N J A O D A M J S N J A O D 0. 2 0. 6 0. 2 0. 6 0. 2 0. 6 A J S N J A O D A M J S N J A O D (a) To facilitate the application of FPCA by ecologists and remote sensing scientists, we have provided in the electronic supplementary materials the data and the full R code (based on the fda package) to reproduce the analysis performed in the paper. As these methods are poorly known in ecology and remote sensing, we have also provided an expanded description of the mathematical theory, but the reader could consult the original books  [51, 82]  for additional information.
g. axis 1 versus senescence date; electronic supplementary material, table  S3 ). Functional analysis provides a novel and powerful approach for studies of the ecological effects of plant phenology, and arose out of the productive collaboration between remote sensing scientists and ecologists. We anticipate the benefits of functional analyses to extend far beyond NDVI, to ecological analyses of variation in the other remotely sensed vegetation indices (e. g. fPAR, EVI), MODIS snow and temperature datasets, and aquatic measures such as sea surface temperature, chlorophyll and other important ecological drivers. In conclusion, in large parts of the world that are semiarid or deserts, our results strongly show that it may not be just spring phenology that matters to ungulate population dynamics.
Our new approach using functional analysis of the entire NDVI curve provides a powerful method to identify first key periods within the growing season and then disentangle their respective roles on demographic traits when combined with hierarchical path analysis. Our approach thus allowed us to determine the most likely pathways by which plant growth influenced mule deer overwinter survival of fawns. Finally, and perhaps most importantly, we demonstrated a novel approach to first identify different temporal components of remote sensing datasets that are the key drivers of large-scale population responses, aiding the broad objective of enhancing our ability to monitor responses of biodiversity to environmental change at global scales. Mule deer capture and handling methods were approved by IDFG (Animal Care and Use Committee, IDFG Wildlife Health Laboratory) and University of Montana IACUC (protocol no.
Acknowledgements. J. Unsworth ,  B. Compton ,  M. Scott ,  C. White ,  J. Shallow  and  C. McClellan , and  M. Elmer  provided guidance and logistical support without which this project would not be possible. We thank  Idaho Department of Fish and Game Wildlife technicians , biologists and managers for quality data collection and support. We thank  S. Running  and  M. Zhao  for valuable discussions about remote sensing, and  M. Mitchell ,  W. Lowe ,  P. Lukacs ,  N. Pettorelli ,  A. Mysterud  and one anonymous reviewer for helpful discussion and comments on previous drafts of the paper.
(58) One can show that U ijkl = 0 whenever the resonance condition is satisfied, i. e. , i = j + k + l + d, in other words, the + --terms do not arise. One can construct a proof that U ijkl = 0 essentially repeating the procedure we employed above for the +++ terms, except that the roles of i and l become interchanged. Performing appropriate integrations by parts using (49-50), one arrives at the following representation: U ijkl ∼ 1 -1 P ( d 2 -1, d 2 ) i (ξ) U jkl (ξ)(1 -ξ) d 2 -1 (1 + ξ) d 2 dξ, (59) where U jkl (ξ) is a polynomial of degree j + l + k + d + 1 = i + 1. Substituting explicit expressions for the mode functions, one finds that the coefficients of the two highest powers in U jkl (ξ) are in the same ratio as in P ( d 2 -1, d 2 ) i+1 (in practice, we have used a FORM script to perform this polynomial evaluation, and the output of the script is given at the end of Appendix B).
B FORM-based analysis of secular term coefficients In this section we present our FORM script dealing with the +++ secular terms, which retraces the derivation steps given in section 3. The script, given below, is extremely straightforward, and should be reasonably easy to interpret even for readers completely unfamiliar with FORM programming. It starts with declaring an expression matching (47) up to normalization, and then applying a sequence of symbolic substitutions that implement integration by parts, re-expression through Jacobi polynomials, substituting the two highest order terms from each Jacobi polynomial, extracting the two highest-order terms of the entire (polynomial) expression, and finally, simplifying and factorizing the result. The lines starting with * are comments that do not affect the execution of the script.
Acknowledgments We would like to thank  Piotr Bizoń ,  Alex Buchel ,  Luis Lehner  and  Andrzej Rostworowski  for useful discussions. The work of B. C. and J. V. has been supported by the  Belgian Federal Science Policy Office  through the  Interuniversity Attraction Pole   P7/37 , by  FWO-Vlaanderen  through project  G020714N , and by the  Vrije Universiteit Brussel  through the.
Application of continuous alignment to actin patches We collected data on the time course of the accumulation and disappearance of the actin cross-linking protein fimbrin in endocytic actin patches to test our alignment method on a dynamic process in live cells. We imaged fission yeast cells expressing Fim1p-mEGFP at 25°C in EMM5S medium on five consecutive confocal sections spaced at 360 nm along the z-axis. These imaging conditions allowed us to track patches during most of their lifetimes and verify that their entire fluorescence signal was collected and did not overlap with other patches. We restricted our FIGURE 1: The continuous-alignment method. (A) Example of a signal that is sampled twice (red and blue vertical lines) at a regular interval but starting at different times delayed by t offset.
(A and B) A sinusoidal signal is measured and the data sets are realigned with (A) the discrete-alignment method on peak values or (B) the continuous-alignment method. Dots of the same color are from the same data set. (B) Inset, comparison of offsets in the original data sets with offsets estimated by the continuous-alignment method. The estimates are accurate and allow reconstruction of the original signal with a higher temporal precision than the sampling time. (C and D) Noise representing biological variability (40% Gaussian noise proportional to the data) and the measurement variability (20% white noise) was added to the sinusoidal signal used in A and B. Data were collected in 20 independent simulated experiments with sampling times of 1 s. Data are realigned with (C) the discrete-alignment method or (D) the continuous-alignment method and then averaged.
After the initial lag of ∼6 s up to the peak number of fimbrin molecules, patches moved progressively faster, and the distance from their origin increased on average, even if individual tracks FIGURE 3: The time course of fimbrin and capping protein appearance and disappearance in endocytic patches is highly reproducible. (A) Example of three patches tracked with fimbrin (Fim1p-mEGFP) in wild-type cells (1 patch per line with the color code for each patch on the right). Each image represents the sum of Fim1p-mEGFP fluorescence intensities from five confocal sections at 1-s intervals. The horizontal gray lines represent the approximate position of the plasma membrane, and the vertical gray ticks mark the horizontal positions of each patch in the first image. Fim1p-mEGFP fluorescence intensity is color-coded from low to high intensities: black-blue-orange-red-yellow-white (ImageJ "fire" lookup table).
Strains and microscopy We tagged the fim1+ gene on the C-terminus of the protein with mEGFP at its native locus in a wild-type S. pombe strain  (Bahler et al. , 1998). We also used the strain QC255  (Chen and Pollard, 2011) , which expresses fim1p-mEGFP from its native locus and where the cofilin gene has point mutations (cof1-M2). Cells were grown at 25°C in liquid EMM5S medium at exponential phase (OD at 595 nm between 0. 1 and 0. 6). Cells were imaged on 25% gelatin pads diluted in imaging media, containing EMM5S and 100 μM antioxidant n-propyl-gallate (P-3130; Sigma-Aldrich, St. Louis, MO) to limit photobleaching and phototoxicity. Live cells were imaged with an Olympus IX-71 microscope with a 100×/ 1. 4 NA Plan-Apo objective (Olympus) coupled with a 1. 6× magnifier, and a CSU-X1 spinning-disk confocal system (Yokogawa) equipped with an iXON 897 EMCCD camera (Andor Technology) and Andor iQ imaging software.
Tools for patch tracking and quality control Analysis of microscopic images was performed with new plug-ins and macros developed for ImageJ  (Schneider et al. , 2012) , bundled in the tool set PatchTrackingTools, and postprocessed with Matlab. Before any analysis, raw movies were corrected for camera offset noise and for uneven illumination  (Wu and Pollard, 2005; Wu et al. , 2008).
The photobleaching rate is estimated by fitting a single exponential to the temporal evolution of the intensity of predefined ROIs in random positions in the cytoplasm. Intensities are converted into a number of molecules using a calibration curve  (Wu and Pollard, 2005; Wu et al. , 2008). In each frame, the two-dimensional position of a patch is estimated as the center of mass of fluorescence of its ROI on the z-sum projection image or on the z-slice with the highest intensity in the ROI. Intensity and position data are saved as text files and postprocessed with Matlab to produce statistics and figures. The displacement of a patch refers to the ratio of the distance and the time delay between two consecutive positions (1 s) and can be seen as an average speed over 1 s.