dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Catherine Glaux  for her support in preliminary experiments, Benoı ˆt Moury for the R code to build the rarefaction curves,  David Baltrus  for his suggestions for core genome analysis, the technicians in the Plant Growth facilities at the  INRA Plant Pathology Research Unit in Montfavet (France)  for their support in preparing plant materials, our team mates  Luciana Parisi  and  Jay Ram Lamichhane  for reading and improving the manuscript and to the colleagues who have provided us with strains for this work.
The authors confirm that all data underlying the findings are fully available without restriction. All 764 files containing partial cts sequences are available from the genbank database (accession numbers: KJ877792 to KJ878556). The metadata of strains are available from the PAMDB data base (http:// genome. ppws. vt. edu/cgi-bin/MLST/home. pl). However, all relevant data are within the paper and its Supporting Information files (Table  S1 ).
Calibration, quality control, and data processing Carbon dioxide mole fractions are referenced to a working high standard (WH) and a working low standard (WL). Additionally, a calibrated cylinder filled with compressed ambient air is used as a target gas (T) to check the precision and stability of the system routinely. All standard gases are pressurized in 29. 5 L treated aluminum alloy cylinders (Scott-Marrin Inc. ) fitted with high-purity, two-stage gas regulators. The standard gases are calibrated with cylinders assigned by the WMO/GAW CO 2 Central Calibration Laboratory operated by NOAA/ESRL. All data are reported on the WMO X2007 scale  (Zhao and Tans, 2006; Zhao et al. , 1997). Calibration of the working standards and target gases is done at CMA's central laboratory in Beijing before and after use at the four stations.
Because of the dead volumes in the in situ sampling system, the response of the analyzer is usually not stable until 1 min after switching to a different gas source using the multiposition valve. Every gas cylinder (WH, WL, or T) was measured for 5 min and the data processing routine used the last 3 min of each 5 min segment to compute CO 2 mole fractions. The same is done for the ambient air data after switching in 5 min intervals from one sampling height to the other and after the reference gas measurements. The average of the last 3 min is used to represent the CO 2 concentration in current 5 min segment. The ambient measurements were calibrated using a linear two-point fit through the most recent standard gas measurements (WH and WL). Ambient CO 2 measurements were retained only during periods when the measurement of the most recent target gas (T) using this same calibration procedure was within ±0.
1 ppm of its assigned value. More than 97 % of the total 5 min average data points were retained for meeting this criterion at the four stations and treated as valid data. After computing the CO 2 mole fractions, the valid data were manually inspected. Occasionally, analytical or sampling problems -including local influences such as nearby fires, vehicles, and cattle identified according to the station logbook entries made by the local operators -affected the observations. Those events were eliminated prior to the next processing steps. More than 97 % of valid 5 min data remained for each station after this filtering step. The 5 min data selected in that manner were then aggregated to hourly averages. To evaluate the seasonal cycle and peak-to-peak amplitude, we used the curve-fitting method described by  Thoning et al.
Carbon dioxide data Figure  2  shows the hourly averages of the processed ambient air CO 2 concentration at the four stations. The data gaps at WLG are due to a malfunction of the instrument. Valid flask sample pair measurements at WLG during the same period are also shown. To test the measurement quality of the Picarro system, differences between discrete flask measurements from NOAA ESRL and corresponding hourly CO 2 mole fractions from the Picarro instrument at WLG are studied. For the greater part of the time, differences between flasks and in situ systems vary by ±1 ppm. The mean value of the difference (flask minus in situ) is 0. 1 ± 0. 1 ppm (95 % CI) for 168 sampling events (336 flasks). This result suggests that NOAA flask and CMA in situ measurements at WLG are in good agreement and the CMA calibration and quality control procedures are well implemented without generating systematic biases.
The differences between the two data sets are likely due to the sampling height and method  (Zhou et al. , 2005). The discrete flask sampler collects air 5 m above the ground, while the in situ system is measuring air at 80 m. Due to unique topography at WLG station (located at the top of Mt. Waliguan in the Tibetan Plateau), the flask samples are collected at 08:00 LT (local time) before the updraft in the daytime may bring the local contaminations from the valley. The NOAA flask sampler collects air samples almost instantaneously (sampling period < 1 min), whereas the Picarro system generally has a continuous coverage during each hour. Thus, a part of the differences can be most likely explained by the atmospheric CO 2 fluctuations.
Impact of local or regional sources on CO 2 concentration In this study, we mainly focus on CO 2 concentration that is not obviously affected by very local sources and sinks (≤ ∼ 10 km). As the three regional stations are located in an area of complex terrain, the observed CO 2 mole fractions are likely affected by very local sources for certain wind directions. At LAN, the town of Lin'an is located to the southwest (SW-SSW sector). As a result, observed CO 2 mole fractions are higher than the other directions in the four seasons (data not shown). Especially in autumn, the maximum enhancement relative to the seasonal average (407. 5 ± 0. 4 ppm) is 7. 4 ± 1. 6 ppm. Additionally, the charcoal factory located at 1. 4 km north of the station may emit a large amount of CO 2 during manufacturing.
Acknowledgements. We express our great thanks to the staff at Lin'an, Longfengshan,  Shangdianzi , and Waliguan station who have contributed to the system installation and maintenance at the stations. This work is supported by the  National Natural Science Foundation of China  (program no. 41175116 ), the  National Key Basic Research Program  (no. 2010CB950601 ), the  International S & T Cooperation Program of the MOST  (no. 2011DFA21090 ), and the CMA operational fund (2012Y003). The data used in this study will be available to the public within the  China Meteorological Administration (CMA)  policy. Martin Steinbacher  acknowledges funding through the  GAW Quality Assurance/Science Actitity Centre (QA/SAC) Switzerland , supported by  MeteoSwiss. We thank  NOAA ESRL , the  Japan Meteorological Agency (JMA) , and the  University of Bern  for providing monthly CO 2 data from  Mauna Loa (Hawaii)  in the US,  Ryori in Japan , and Jungfraujoch in Switzerland from 2009 to 2011.
Results: We present a new automatic algorithm to segment and simultaneously classify cell nuclei in 3D/4D images. Segmentation relies on training samples that are interactively provided by the user and on an iterative thresholding process. This algorithm can correctly segment nuclei even when they are touching, and remains effective under temporal and spatial intensity variations. The segmentation is coupled to a classification of nuclei according to cell cycle phases, allowing biologists to quantify the effect of genetic perturbations and drug treatments. Robust 3D geometrical shape descriptors are used as training features for classification. Segmentation and classification results of three complete datasets are presented. In our working dataset of the Caenorhabditis elegans embryo, only 21 nuclei out of 3,585 were not detected, the overall F-score for segmentation reached 0.
Objects are then segmented using a classical watershed approach and the training is used to fuse separated regions. In the present paper, we introduce a learning-based method to segment nuclei in 3D/4D images of early embryos. This work was developed to compensate for the lack of robust alternatives to segment our working dataset of the C. elegans embryo imaged with a spinning disk confocal microscope. Our method distinguishes itself from previous works in three ways. Firstly, we propose a segmentation technique that can be applied to different image acquisition conditions and various embryo models. Secondly, the number of parameters has been minimized to include just a few biological parameters. Lastly, our algorithm segments and classifies nuclei simultaneously, while other approaches first segment and then classify nuclei.
Specifically, we chose the threshold leading to the object that is most similar to one in a set of training samples. In this paper, we present the results of applying our novel algorithm to three different datasets containing embryo nuclei from C. elegans, Drosophila and 3D simulated data  [11]. The algorithm has been implemented as an open-source plug-in for ImageJ  [12]  and is publicly available for download along with a tutorial and sample data  [13].
Sample preparation of working dataset The C. elegans early embryo dataset was acquired with a spinning-disk confocal microscope. The dataset is composed of a developing embryo from the 4-cell stage to the 90-cell stage with 160 time points. The signal consists in Histone-GFP expressed in the ovary and transmitted to the future embryo at the time of oocyte formation. At each cell division during embryo development, this maternal load is incorporated into the chromatin of an increasing number of cells, thus leading to a dilution of the fluorescence signal over time.
Overview of the method The main idea of the algorithm is to use machine learning to improve the segmentation results of cell nuclei. The learning step is an interactive process. The user first selects representative nucleus samples of each cell cycle by clicking on their locations inside the 3D or 4D dataset. The objects at the clicked positions are then segmented through an iterative thresholding procedure using the user-supplied volume estimates as a reference. After that, the user has to validate the proposed segmented nuclei, and 3D descriptors are computed from this set of validated nuclei. A predefined classifier is then trained to assign cell cycle phases to nuclei based on that set and those descriptors. The main joint segmentation/classification procedure is subsequently applied to all time points.
Choosing and setting up the classifier Before setting up the segmentation procedure based on classification, it was necessary to choose and set up a classifier. A first training set of manually-validated nuclei were extracted from the image using the procedure presented in the Supervised learning section below. Each object was represented by a feature vector of 3D descriptors calculated as described in the following section, and was assigned its corresponding cell cycle phase or class by the user. To find the most suitable learning algorithm, we made experiments across our datasets after dividing our samples into a training and a test set (66% and 33% of the data, respectively). For this purpose, we used the experimenter of the Waikato Environment for Knowledge Analysis (WEKA) suite  [14]  and the BIO-CAT program  [15].
Using a priori biological knowledge In our procedure, branches with higher thresholds are favored. This provides good separation of nuclei, even when they are close (as in anaphase). In some cases, like in our C. elegans dataset, some prophase nuclei display very condensed chromosomes that look similar to metaphase nuclei. Due to our strategy, whole nuclei will be segmented using low thresholds and classified as prophase. However, for higher thresholds, condensed chromosomes inside may be segmented and classified as either prophase or metaphase. Therefore, we implemented a generic inclusion model that does not allow particular classes (prophase, in this case) to include other classes (prophase and metaphase).
Datasets The algorithm was applied to three different datasets: the first two were acquired from two different biological models and the third one was a synthetic dataset that simulated dividing cells (Table  1 ). Our original motivation was to automate the segmentation of nuclei in the C. elegans dataset where none of the available methods had provided satisfactory results. The Drosophila dataset was obtained from an existing reference and hence represents an additional validation dataset  [6]. In their study, the authors used a Histone-GFP labeling method with confocal microscopy. For our C. elegans dataset, a slightly different labeling procedure was used, with Histone-GFP expressed in the ovary and transmitted to the future embryo. The fluorescence signal was thus gradually diluted with mitosis, leading to an overall decay in fluorescence intensity and, consequently, of image quality over time.
Another difference between the two datasets was that Drosophila embryos were imaged with a conventional confocal microscope  [6] , whereas C. elegans embryos were imaged with a spinning disk microscope. The third dataset was a synthetic dataset generated using CytoPack  [20] , a program that models dividing cells and creates synthetic datasets that simulate 4D video-microscopy acquisition. Although the aspect of nuclei was realistic, it was not completely natural, and could present high intensity variation, thus providing a difficult test case for our algorithm.
Supervised learning Before using the classifier in the main classificationbased segmentationtion algorithm, we had to validate it. For this purpose a first test was performed using objects segmented by the procedure presented in the Supervised learning section. This procedure is based on a MSER algorithm and uses the volumes given by the user as inputs. For each dataset, between 100 and 200 sample nuclei, with approximately equal number for each class, were chosen from different time points according to shape variability, and then assigned a class by a human expert. For all of the datasets, the confusion matrix for the classifier showed satisfactory results, with a classification rate of approximately 90% for the two real embryo datasets and 80% for the synthetic dataset (Table  2 ).
Segmentation The efficiency and accuracy of the proposed algorithm was tested on the three complete datasets (see Table  3 ). The segmentation results were visually compared with original data for all time points and taking the 3D information into account. We used a classification based on five classes (interphase, prophase, metaphase, anaphase and telophase) for all three datasets. Between 10 and 30 sample nuclei per class were chosen for the initialization (supervised training of the classifier). The values used as volume limits for the C. elegans dataset were (in μm 3 ): 9. 8-24. 3 (interphase), 14-36. 6 (prophase), 5. 9-19 (metaphase), 3. 6-7. 3 (anaphase) and 6. 3-9. 9 (telophase). For the Drosophila dataset, the minimum and maximum volumes were set to (in μm 3 ): 45-100, 120-150, 36-40, 25-39 and 20-28.
For the C. elegans dataset, there was a decay of intensity with time due to the particular labeling technique and acquisition system, and the image quality was therefore not as high as in the Drosophila dataset. Furthermore, due to the embryo depth and imaging conditions, the slices farthest away from the objective lens were quite noisy. The selected thresholds corresponding to segmented nuclei varied between time points as well as within each time point image. The threshold values varied between 883 and 1,757 (with an average of 1,180) for time point 40, between 754 and 1,200 (with an average of 900) for time point 86, and between 652 and 1,064 (with an average of 781) for time point 122. However almost all of the nuclei could be correctly detected (correct position) for all time points (Additional file 1).
Nevertheless, after time point 128, the image quality was too low to accurately delineate the actual shape of the nuclei. Figure  4  shows the results obtained for some of the time points. At an advanced time point (122), all nuclei were correctly segmented despite the low quality of the image (Figure  5 ). For the time points to 128, only 21 out of 3,585 (0. 3%) were not detected, with zero false negatives. In the Drosophila dataset, which has a higher signal quality, nuclei were generally present in a 2D layer. All nuclei were correctly segmented, only 15 out of 4,820 (0. 3%) were not detected, and an additional 77 spurious noisy objects were incorrectly identified as nuclei (1. 6%) (Figure  6 ) (Additional file 2). The selected threshold values did not vary much, which was coherent with the homogeneous intensity throughout the entire 4D dataset.
The difference between the lowest and highest threshold value used was 28 for time point 5, 34 for time point 9, and 36 for time point 25. For the simulated dataset, all synthetic nuclei were correctly segmented despite high intensity variance within nuclei (Additional file 3). The results obtained with our method on C. elegans data (our target dataset) were compared with the Hierarchical K-Means method (HK-Means)  [21]  of ICY  [22]. This method implements a slightly modified version of the MSER technique and is therefore probably one the best available methods to compare to. With the same volume parameters as for our algorithm, HK-Means was globally less accurate. Several detected objects were actually noise, some nuclei were not detected, and several others were merged. For example, at time point 86, there was one noisy object, three nuclei were missing, and there was one fusion of two nuclei; for time point 122, there were six noisy objects, two nuclei were missing, and two were merged (Figure  7 ).
Since HK-Means method favors low threshold values, the objects tended to be larger, and the delineation of nuclei was globally not as accurate as with our algorithm. The time to segment and classify 128 time points for the C. elegans dataset (160 frames, 512 × 512 × 31 voxels) on a Xeon bi-processor with 12 cores/24 threads (Xeon X5660@2. 80 Ghz), running Ubuntu Linux 12. 04 with 24 Gb of RAM and using Java 1. 7, was between 4 and 6 hours, corresponding to approximately 3 minutes per time point.
Classification Although the purpose of the classification procedure in our method is related to the selection of the best threshold value for segmentation, it can also be used collaterally as a classification algorithm per se to predict  cell-cycle phases. The information about the cell-cycle stage given by the algorithm can be used for cell-cycle analysis and to help further tracking. We visually analyzed the classification results on a frame-by-frame basis, using temporal information to determine the actual cell-cycle stage. For the C. elegans dataset, 165 out of 3,583 nuclei (for time points 1-128) were not correctly classified (4. 6%), and classification errors occurred uniformly in all time points. This error rate was less than the training error (∼10%, see Table  1 ) because of the high number of interphase nuclei in the entire dataset, and mainly resulted from shape similarity between classes, especially at the anaphase and telophase stages.
For the Drosophila dataset, 225 out of 4,820 nuclei were not correctly classified (4. 7%). 186 of these classification errors occurred in the three last time points. This error rate was similar to the one previously reported  [23]. For the synthetic dataset, where all nuclei were correctly segmented, the classification results were similar, with 96% of correctly classified nuclei. Due to the non-natural aspect of the simulated nuclei, the distinction between some stages was not obvious: the prophase and telophase stages were difficult to discriminate from other stages, whereas the interphase, metaphase and anaphase stages were correctly classified (Additional file 3).
The major advantage of our method is its ability to robustly adapt to different imaging conditions and different species by reporting the introduction of the required knowledge on the initial learning stage from user-specified samples. The only assumption that is made is that an object can be segmented by one threshold value and has a class-specific shape. Our algorithm was successfully tested on two embryo models, the C. elegans dataset (our work dataset), and the Drosophila dataset (our validation dataset). The overall segmentation results were comparable or better than those of existing algorithms, without the necessity of finetuning parameters. The only required parameters are the volumes of nuclei that can be easily learned from a set of trials and errors at the supervised learning step, where all descriptors values are displayed to the user.
The user can take some time in training the system but it only needs to be done once per embryo model and imaging conditions. The same set of training descriptors can be re-used in different experiments. The implemented 3D descriptors summarize the shape of the object and do not take quantitative variable aspects such as object volume, surface or intensity into account since these values can be extremely variable over time. In fact, in the first time points of the series, nuclei are usually very large and very bright, contrasting with the numerous darker small nuclei at end time points. For the Drosophila validation dataset, only a few nuclei were not detected (0. 3%), and generally corresponded to very crowded telophase nuclei that displayed very unusual shapes. However, it seems that we detected more nuclei than the original paper  [6]  (4,820 compared to 4,166; it is possible that some border objects were not counted).
We were also able to achieve an overall acceptable classification with an error rate of less than 5%, where classification errors were mainly present at end time points for very crowded interphase nuclei. For our working dataset, C. elegans, the results for segmentation and classification were quite satisfactory, even in a context of low signal-to-noise ratio. We can then validate the acquisition and labeling procedure for further studies to improve automated lineage reconstruction. For the synthetic dataset, we observed significantly better classification results in the entire data than in the training samples because of the over-abundant interphase nuclei (1,586 out of 1,974), which were easier to classify than the other ones. Classification is a very important step in our thresholding procedure.
Acknowledgements The authors thank the different people for providing 3D/4D datasets so we could test and improve our algorithm. JGM thanks  Campus France  and the  Regional Government of Kurdistan (KRG)  for providing him a scholarship for his thesis. IAC was funded by the  French National Research Agency  ( ANR) Infrastructure d'Avenir TEFOR  ( ANR-11-INBS-0014 ). VG wishes to thank: the  Centre National de la Recherche Scientifique  ( ATIP Grant ), the  University of Pierre and Marie Curie , the  French National Research Agency  ( ANR 07-BLAN-0063-21  and  ANR 12-BSV2-0018-01 ), the  Association pour la Recherche contre le Cancer , and the  Bettencourt-Schueller Foundation  (Coup d' élan pour la recherche 2012).
1  Although RCTs provide high-level evidence on the efficacy and safety of therapeutic interventions, they generally involve well-defined study populations that exclude complex patients and have standardized protocols for management with closer monitoring and stricter follow-up than is typical of routine clinical practice. Thus, their results are not always directly applicable to the general population or routine practice. Traditional observational studies, often limited to small patient populations and performed at a single institution, are giving way to multicenter and national registries, supported by the transfer of information to large databases. Structured data collection can inform the generation of new hypotheses and help to test established ones. Registries are also subject to limitations as well as potential confounding factors related to the population selected, number, and scope of tracked variables and prevailing concepts of the disease under investigation.