dataesr/scientific-paragraphs-categorization huggingface dataset:
098 ± 0. 03), but their relative FIGURE 2 | Phylogenetic diversity rarefaction curves. Phylogenetic diversity (mean ± s. e. m. ) was measured as the sum of branch lengths of the 16S OTU phylogenetic tree for each rarefied sequence pool. Sequence datasets were rarefied to 10,000 sequences and were sampled using a step of 50 sequences; rarefaction sampling was reiterated 1000 times. Sample color code is as in Figure  1. abundance was nearly 10-fold lower in the bottles, especially in the control (0. 014 ± 0. 008) and CO 2 -treated (e. g. , mid-CO 2 : 0. 013 ± 0. 002) conditions.
ACKNOWLEDGMENTS The  Catlin Arctic Survey  was funded by  Catlin Ltd. and coordinated by  Geo Mission Ltd. Helen S. Findlay  was supported by the  PML Lord Kingsland Fellowship  and received the  Ralph Brown Expedition Grant  from the  Royal Geographical Society  (with IBG) and a  Royal Society   Travel Grant. The authors are extremely grateful to the  Ice Base and Geo Missions  staff for their support in carrying out field work, and the other field scientists for assistance in the field. The authors thank  J. Gilbert ,  Argonne National Laboratory , for discussions on experimental design and for sequence analysis. Sample processing, sequencing and core amplicon data analysis were performed by the Earth Microbiome Project (www. earthmicrobiome. org)and all amplicon and metadata have been made public through the data portal (www.
Detecting diagonal blocks can be seen as a particular 2D segmentation issue. The 2D segmentation has been widely investigated for the detection of contour with arbitrary shape in images (see, for example,  Darbon and Sigelle, 2006a, b; Hochbaum, 2001). From a computational point of view, image segmentation is an open problem because no predefined ordering exists that could be used to provide exact and efficient algorithms. Compared with contour detection, it is worth noticing that Hi-C data segmentation displays a specific pattern that did not receive any special attention from the image processing community. One of our contributions is to prove that this 2D segmentation problem boils down to a 1D segmentation problem for which efficient dynamic programming algorithms apply  (Bellman, 1961; Lavielle, 2005; Picard et al.
, 2005). Our formulation of the problem also allows us to solve some nonblock diagonal segmentation problems (see the end of Section 2. 2). The article is organized as follows. In Section 2, we define a general statistical model for Hi-C data, which can deal with both raw and normalized data. We prove that the maximum likelihood estimates of the block boundaries can be efficiently retrieved. In Section 3, we first present an extensive simulation study to assess the performance of our approach on both simulated and resampled data. We then apply the proposed methodology to the data studied by  Dixon et al. (2012) , which are publicly available, and compare our results with their regions. The package implementing the proposed method is presented in Section 4 where some open problems are also discussed.
Fixed block design To evaluate the performance of our methodology in the negative binomial framework, we generated block diagonal matrices according to Model (5) (B) where ð i;j Þ is defined by (4). More precisely, we generated 50 block diagonal interaction matrices of size n = 300 with a structure inspired by the one found by  Dixon et al. (2012)  for the interaction matrix of Chromosome 19 of the mouse cortex. The different parameters * k ; * 0 and are estimated from this matrix. This resulted in matrices including five diagonal blocks such that * 1 =2:87; * 2 =4:85; * 3 =7:92; * 4 =4:33; * 5 =11:99; * 0 =0:09 and =0:67. Then, for each simulated dataset, new matrices were derived by multiplying the * k s by a constant c 2 f0:1; 0:2; 0:3;. ; 1g to reduce the signal-to-noise ratio. For each simulated dataset and each constant, we computed K and the corresponding tk s using the procedure described in Section 2.
This strategy is repeated 100 times to obtain 100 interaction matrices. Compared with the previous simulation design, one can observe that the change-point positions now change from one dataset to the other, and that the data are not anymore simulated according to a negative binomial distribution. While the statistical analysis of datasets generated from this second simulation setting is more difficult, it allows one to visit more realistic data configurations closely similar to real data. We report here the results obtained when the simulated data are analyzed with Model (5) (B), the results obtained with Model (5) (P) being similar. Figure  3  (left and center) displays two log-likelihood curves (up to some constants) as a function of the number of changepoints. The solid and dotted lines indicate locations of the true and estimated number of change-points, respectively.
Application to real data In this section, we applied our methodology to the raw interaction matrices of Chromosomes 13-22 of the hESCs at resolution 40 kb, and we compared the estimated number of blocks and the estimated change-points found with our approach to those obtained by  Dixon et al. (2012)  on the same data, as no ground truth is available for those datasets. From Figure  4 , we can first see that the approach of  Dixon et al. (2012)  tends to produce, in general, more change-points than our strategy except for Chromosome 22. This can also be seen in Figure  5 , which displays the log-likelihood curves (up to some constants) with respect to K as well as the number of change-points proposed by  Dixon et al. (2012)  (dotted line) and our approach (solid line). We also compared both methodologies by computing the two parts of the Hausdorff distance defined in (  12 ) and (  13 ) for Chromosomes 13-22.
HiCseg R package In this article, we propose a new method for detecting cis-interacting regions in Hi-C data and compare it with a methodology proposed by  Dixon et al. (2012). Our approach described in Section 2 is implemented in the R package HiCseg, which is available from the Web page of the corresponding author http://www. agroparistech. fr/mmip/maths/essaimia/_media/equip es:membres:page:hicseg_1. 1. tar. gz and from the Comprehensive R Archive Network. In the course of this study, we have shown that HiCseg is an efficient technique for achieving such a segmentation based on a maximum likelihood approach. More precisely, HiCseg package has two main features, which make it attractive. Firstly, it gives access to the exact solution of the maximum likelihood approach. Secondly, as we can see from Figure  9  and Table  1 , which give the computational times on synthetic data following Models (5) (G), (P) or (B), HiCseg is computationally efficient, which makes its use possible on real data coming from Hi-C experiments.
Recent evidence suggests that some seizures are preceded by preictal changes that start from minutes to hours before an ictal event. Nevertheless an adequate statistical evaluation in a large database of continuous multiday recordings is still missing. Here, we investigated the existence of preictal changes in long-term intracranial recordings from 53 patients with intractable partial epilepsy (in total 531 days and 558 clinical seizures). We describe a measure of brain excitability based on the slow modulation of high-frequency gamma activities (40-140 Hz) in ensembles of intracranial contacts. In prospective tests, we found that this index identified preictal changes at levels above chance in 13. 2% of the patients (7/53), suggesting that results may be significant for the whole group (p , 0.
We assessed the sensitivity of cross-frequency coupling changesdefined as the ratio of correctly predicted seizures to the number of seizures investigated -and also their specificity from long-term interictal data  3. Analyses were made from a large database of 53 patients with continuous multi-day intracranial EEG, collected in three European epilepsy centers (Freiburg, Germany; Paris, France; Coimbra, Portugal)  40. Our analysis focused on interactions between the phase of low frequency rhythms (slow waves and theta) and the amplitude of different sub-bands of gamma rhythms. This analysis of spatial fluctuations in the coupling phase of ensembles of intracranial contacts revealed preictal changes, occurring at levels greater than chance in a small but significant number of patients. To validate our results, we compared them with predictions based on the power in individual frequency bands (delta, theta and gamma).
1e ). Projected on post implantation MRI scans, contacts with these specific preictal phase fluctuations were distributed over spatially broad regions both within and outside the focal region (Fig. 1f ). We investigated long-term coupling between fast and slow oscillations (see Methods for a detailed description of the strategy). First, the proportion of intracranial contacts at a specific mean phase interval was estimated over multi-day recordings. Second, an alarm was raised when this proportion exceeded a critical threshold. The threshold was adjusted and parameters including preictal durations and frequency bands were optimized on a first training part of the data obtained from each patient. For the selected optimal parameters, the last part of data was used to test prospectively discrimination between preictal from non-preictal periods.
Recently, using a chronically implanted device running an algorithm which was trained on the basis of each patient's EEG signals, Cook et al. 7  demonstrated that prospective seizure prediction is possible on long-term recordings of several months. The algorithms used in this work relied purely on classification of power measures in different frequency bands (8-128 Hz) and derived from subdurally recorded neural activity. During the prospective evaluation, the warning device worked to a level better than would be expected by chance in 8 over 11 patients. Using the same implanted device, but in dogs with naturally occurring epilepsy, a similar seizure forecasting algorithm performed significantly better than a random predictor in all investigated cases  46. In our study, we used data from a large group of patients obtained during clinical monitoring for epilepsy surgery.
Based on this database, we were only able to find a small group of patients having seizures that can be forecasted better than chance. Our observation seems to contrast with Cook's optimistic observations using longterm outpatient ''real life'' recordings and reporting high prediction performances in most of the investigated patients. One reason of this discrepancy could be the known limitations related to use of inpatient intracranial EEG data to evaluate prediction algorithms, including relatively short duration of EEG recordings (in particular for training purposes) and the effects of drug changes or surgery. Also, differences in statistical thresholds could explain different perfor-mances. Nevertheless, both studies support the feasibility of longterm seizure forecasting. Statistically significant results were obtained in a small group of patients, which does not facilitate a full identification of pathophysiological mechanisms.
Methods Database. Long-term intracranial EEG recordings from 53 epilepsy patients (26 males; age range, 3-63 years; mean age: 32 years) suffering from medically intractable partial epilepsy were analyzed. The data were recorded in three different epilepsy units: Epilepsy Center, University Medical Centre of Freiburg, Germany; Unite d'E ´pilepsie of the Pitie-Salpetriere Hospital, Paris, France; Hospitais da Universidade de Coimbra, Portugal. Data acquisition and database were performed in accordance with the approved guidelines of the local ethic committees (Ethik-Kommission der Albert-Ludwigs-Universita ¨t Freiburg; Comiteconsultatif sur le traitement de l'information en matiere de recherche dans le domaine de la sante, Pitie-Salpetriere University Hospital; and Ethics Committee of the Coimbra University Hospital, respectively).
Patients gave a written consent for a research use of these data. Funded by the European Union, this database was established to compile high-quality, longterm continuous EEG data, enriched with clinical metadata  40  (http://www. epilepsiae. eu/). A standardized EEG annotation protocol was developed to ensure the comparability and reliability of seizure onset times at all project sites  56. A total of 531 days of EEG records (,10 days/patient) included 558 seizures (,11 seizures/ patient). Records include at least 5 days of continuous EEG from each patient with at least 5 clinically manifest epileptic seizures separated by more than 1. 5 hours. EEG data were recorded using different digital video EEG systems with different sampling rates in the three different centres; Nicolet, 256 Hz; Micromed, 400 Hz; Compumedics, 512 Hz and Neurofile NT, 1024 Hz.
First, the range of the phase signal Q x (t), with values within the interval (2p,p), was divided into bins Q i with i 5 1,…,40. Then, the time indexes k i when the phase fell in the interval Q i # Q x (k i ) , Q i11 , were determined. For each bin, amplitudes at time k i of the high frequency time series were averaged (,A x (k i ). ). The distribution of mean amplitudes versus phase defined the phase of the slower activity, where high frequencies tend to appear (Fig. 1b ). We quantified this tendency by approximating the distribution to a Von Mises function (circular Gaussian), and extracting the mean phase representing the preferred phase of coupling Q c. A statistical threshold (based on a phase randomization procedure for instance) was not used  41. We thus maintained computationally efficiency in treating data from data epochs of 1 minute.
Codes are freely available in the EPILAB platform  55  at: http://www. epilepsiae. eu/project_outputs/epilab_software/. Identifying fluctuations of mean coupling phase patterns. We followed the time course of mean coupling phases of multiple contacts, during consecutive nonoverlapping time windows of 1 minute, by determining the proportion of contacts with a mean coupling phase at a specific phase interval [Q c1 , Q c2 ]. Figure  1c  shows an analysis for a representative patient during interictal and preictal periods. Long-term trends of coupling phase were determined by applying a first-order Kalman filter to the proportion of electrodes within [Q c1 , Q c2 ]. Thus short-time fluctuations were smoothed, as previously proposed  43. A variable threshold was then set to produce an alarm when the number of intracranial contacts exceeded a defined value (Fig.
2a, b ). After an alarm, a new alarm could be generated only after a refractory period corresponding to the defined preictal time. Prospective evaluation of preictal changes. Application of highly optimized methods to small and preselected data may result in overestimating the results that are poorly reproducible on unselected, longer continuous data  3. While algorithm parameters may be adjusted to data from an individual patient, this optimization must be performed only on one part of the data (the training set) excluded from the data set used subsequently to assess algorithm performance (the testing set). For training, we used continuous data including the first 4 seizures and at least 10 hours of recording. This training data permitted parameter selection to optimize sensitivity and specificity.
FPR was defined as the number of false alarms during the test set divided by the duration in which false alarms could be triggered (with refractory periods excluded), which is obtained by subtracting the time under false warning from the total interictal period  3. Mathematically, the FPR is expressed as: FPR~# false alarms interictal duration{(#false alarms : preictal duration) Comparison with a random predictor. Once the sensitivity and specificity of a prospective prediction algorithm has been assessed on the test dataset, it is necessary to ask whether it is indeed superior to the chance level. We evaluated statistical significance by comparing results to chance established by a random predictor, which produced alarms in the absence of information from the EEG signal  42. With random alarm generation, the probability to predict at least k out of K seizures was given by a binomial distribution: P binom fk;K;Pg~X j §k K j P j (1{P) K{j ð3Þ Where P indicated the probability (Poisson process) of that a single alarm was triggered during the preictal time.
To evaluate whether our observations based on cross-frequency coupling did not simply reflect power fluctuations related to different states of vigilance, a similar strategy was developed to generate alarms based on the relative power of different bands: delta (0. 1-4 Hz), theta (4-8 Hz) and gamma (30-140 Hz). After filtering the raw signals, the power spectral density was computed by Burg method over consecutive windows of 5 seconds. Then, the power of each band relative to the power in the whole frequency range was extracted. The averaged relative power over all channels was used to determine an optimal threshold above which an alarm of preictal period was triggered. As in our cross-frequency coupling strategy, the predictor based on the individual bands was first optimized on the training data (in sample definition of preictal times and thresholds).
Acknowledgments This work was supported by the  European Union - FP7 Project EPILEPSIAE (Evolving Platform for Improving Living Expectation of Subjects Suffering from IctAl Events , Grant No  211713 ) and by the  program Investissements d'avenir   ANR-10-IAIHU-06 , France. C. A. R thanks the  Administrative Department for Science, Technology and Innovation (COLCIENCIAS), Colombia , for financial support. V. N. was supported by  Contrat d'Interface INSERM. We also thank  Richard Miles  for critical reading of the manuscript.
Definitions and concepts Authors should describe the variables and units used in field measurements or in data processing and of the output estimates from allometric equations. Units need to be stated using the International System of Units (SI). Clearly defined variables aid in understanding which tree components were considered in developing the allometric equations. For example, it is not always obvious if a basic variable such as "tree height" refers to total tree height or to commercial tree height (e. g. Feldpausch et al. 2010 or Ribeiro et al. 2011 ). Information about the height where the diameter measurement was taken is needed. For example, if trees were buttressed or were irregular at the standard point of measurement (1. 3 m), how were diameters measured? Similar details on volume and tree components included in the model need to be clearly stated.
Sampling and scope of the study It is essential that much thought and precise documentation exist for the sample design and actual sampling needed to construct robust allometric equations. This includes information on the number of trees considered, the range of diameter, the tree components measured (e. g. were the belowground components measured?), the area investigated (e. g. trees were harvested in a single plot or in several randomly located within a landscape), and whether trees were chosen randomly, based on diameter classes, vertical stratification, or floristic composition. Such information helps potential users to gauge the robustness of models and determine the limits of their application. We recommend that the tree partitioning proposed by Henry et al. (  2011 ) be adopted as a minimum standard for partitioning tree components during biomass harvests and data processing: stump, trunk, bark, small roots (D<5 mm), medium roots (5<D<10 mm), big roots (D>10 mm), thin branches (D<7 cm), large branches (D>7 cm), dead branches, leaves, and fruits.
Metadata on raw data Raw data and its accompanying metadata help understand the scope and limitations of available datasets, calculate uncertainties, validate models, and construct new improved allometries. Properly documented volume data, for example, are very useful to develop biomass expansion factors, which are used by most countries to assess national forest biomass (FAO 2010). In contrast, poorly documented raw data or missing metadata preclude validation or further development equations based on expanded datasets. Authors are encouraged to share their raw data to facilitate future efforts and to build collaborations within the scientific community. Data repositories such as Dryad (http://datadryad. org/), DataONE (http://www. dataone. org/), and others are now available for storing ecological and biological data.
Checklist of recommended guidelines for documenting allometric equations in scientific or technical publications Storage and rights of use or additional datasets. Several instruments are used to measure forest variables, and it is important to mention them so the reader can gauge their accuracy. For example, different biases can be associated with tree height measurement according to the instrument used(Hunter et al. 2013; Larjavaara and  Muller-Landau 2013). The manuals developed by Anderson.
Acknowledgments UN-REDD, FAO, and the  SilvaCarbon Program  provided funding for the " Regional Technical Workshop on Tree Volume and Biomass Allometric Equations in South and Central America ", where ideas for this paper were first discussed. The BEF unit supported by the  French National Research Agency (Agence Nationale de la Recherche, ANR)  through the  Laboratory of Excellence (Labex) ARBRE  ( ANR-12-LABXARBRE-01 ). This work is part of the  QLSPIMS  project. MRM was supported by two " Investissement d'Avenir " grants managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-2501 ;  TULIP, ref. ANR-10-LABX-0041 ) and by the  CoForTip  project ( ANR-12-EBID-0002 ). We also acknowledge the input of the 30 participants in this technical workshop. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Prepared by the  National Greenhouse Gas Inventories Programme. Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds) IGES ,  Hayama Jenkins JC ,  Chojnacky DC ,  Heath LS ,  Birdsey RA  (2003) Comprehensive database of diameter-based biomass regressions for North American tree species. USDA Forest service,  Delaware Laar AV , Akça A (2007) Forest mensuration, vol 13. Managing forest ecosystems, vol 13, 2nd edn. Springer,  Dordrecht Larjavaara M, Muller-Landau HC  (2013) Measuring tree height: a quantitative comparison of two common field methods in a moist tropical forest. Method Ecol Evol 4: 793-801. doi: 10. 1111/2041-210x. 12071  Ngomanda A, Engone Obiang NL, Lebamba J, Moundounga Mavouroulou Q, Gomat H, Mankou GS, Loumeto J, Midoko Iponga D, Kossi Ditsouga F, Zinga Koumba R, Botsika Bobé KH, Mikala Okouyi C, Nyangadouma R, Lépengué N, Mbatchi B, Picard  N (2014) Site-specific versus pantropical allometric equations: which option to estimate the biomass of a moist central African forest?For Ecol Manage 312:1-9.
Methods: We provide here both a DNA-barcoding reference for groupers, and a new phylogenetic reconstruction based on five genes and a comprehensive taxonomical sampling. We analyse the correlation between geographic range of species and their susceptibility to ciguatera accumulation, and the co-occurrence of ciguatoxins in closely related species, using both character mapping and statistical methods. Results: Misidentifications were encountered in public databases, precluding accurate species identifications. Epinephelinae now includes only twelve genera (vs. 15 previously). Comparisons with the ciguatera incidences show that in some genera most species are ciguateric, but statistical tests display only a moderate correlation with the phylogeny. Atlantic species were rarely contaminated, with ciguatera occurrences being restricted to the South Pacific.