dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we show that the fluorescent signal of GFP-expressing chlamydiae allows monitoring of the progression through the developmental cycle in a quantitative manner. This has several advantages over current methods. Thousands of events can be rapidly analyzed by flow cytometry, generating highly quantitative data even on rare events. Calibration of the instrument allows detection of individual fluorescent EBs and thus direct counting of the particles. The fluorescence level in individual EBs is also sufficient for detection by microscopy, and we show that automated tools allow for rapid quantification of bacterial entry into cells. Using these quantitative tools, the action of anti-chlamydial compounds on different steps of the chlamydial developmental cycle can easily be assessed.
Pictures of fields with 5-10 cells were acquired using an Axio observer Z1 microscope equipped with an ApoTome module (Zeiss, Germany) and a 636Apochromat lens. Pictures were taken with a Coolsnap HQ camera (Photometrics, Tucson, AZ) using the software Axiovision. A minimum of 80 bacteria was analyzed per condition. We designed an automatic, ready-to-use analysis protocol for the Icy software  [16]  to perform the quantification on entire image folders without manual intervention (The Chlamentry protocol will be made publicly available on the Icy website upon publication). First, a wavelet-based detection module  [17]  was used to detect all objects in the green and red channels. Then, an object-based colocalization module was used to visualize and quantify the colocalization between the two detection sets.
Two detections were considered colocalized under a distance threshold of 4 pixels (i. e. 400 nm) between their center of mass, accounting for the chromatic aberration of the imaging setup. Finally, the protocol produced a comprehensive result sheet containing the number and location of detected objects in each channel, the number of colocalized detections (i. e. number of extracellular bacteria), and a final script calculated the ratio of [greencolocalized detection] to [green detection] (i. e. ratio of internalized bacteria). Of note, the light sonication procedure preceeding infection can lead to the appearance of red-and-not-green dots. These red dots are also usually not visible in the blue channel (DNA), and presumably correspond to bacterial wall debris. We used conditions where such events represented less than 10% of the total red staining.
The FACS Gallios parameter FSC collection angle N (Narrow FSC angles 1 -8u) was used, triggering on the FSC channel during acquisition. A minimum total of 10,000 gated events were collected for each sample. Data were analyzed using the Kaluza 1. 2 software (Beckman Coulter) and FlowJo. Calibration beads were purchased from Spherotech. For analysis of transfected cells, after fixation the cells were centrifuged at 1500xg, washed in PBS, centrifuged again, and incubated for 1 hr with home-made rabbit anti-histidine tag antibodies in 1 mg/ml BSA, 0. 05% (w/v) saponin in PBS. The cells were washed and incubated for one hour in the same buffer with anti-rabbit antibodies conjugated to Cy5. The cells were washed, resuspended in PBS and analyzed by flow cytometry in the FL-1 (green) and FL-4 (far-red) channels.
Fluorescence of infectious particles is sufficient for tracking entry by automated microscopy tools We next asked whether the GFP signal could be used to monitor bacterial entry into cells. For future applications, we anticipated that co-localization with host proteins, or other information on the spatial organization of the entry step, would be sought. We therefore used a microscopy-based approach, which, in contrast to flow cytometry, gives access to spatial information. Cells were incubated with bacteria at 4 uC to allow bacterial attachment but not entry, and then transferred to a 37 uC incubator for variable times. The cells were then fixed with paraformaldehyde, and extracellular bacteria were stained with anti-MOMP antibodies followed with Cy5-conjugated secondary antibodies. For each time points, 10 pictures in randomly selected fields were acquired in the green and the far-red channels and analyzed using the ICY software, as detailed in the Methods.
Consequently, EB internalization rates are mostly absent from the literature, with, to the best of our knowledge, a single report on the kinetics of C. pisttaci entry  [24]. In addition, using FITC-coupled bacteria, we had noticed that some of the internalized bacteria were not accessible to antibodies, even after saponin permeabilization, possibly because actin polymerization around nascent inclusions limits antibody access. The green fluorescence of the EBs reduces the time needed to process the sample (one single labeling step), and eliminates underscoring of intracellular bacteria due to limited antibody access. Also, the green fluorescence is strong enough to allow automated detection by the ICY software, which drastically reduces the analysis time. Provided that enough images are collected, the procedure can be applied to very low MOI.
Acknowledgments We thank  Dr. S. Ouellette  and  Dr. A. Dautry  for critical reading of the manuscript,  Dr. I. Clarke  and  Dr. I. Derre  ´for providing the SW2::GFP and p2TK2-SW2 IncDProm-RSGFP-IncDTerm plasmids, respectively,  Clemence Taisne  for generating stably transformed bacteria,  P-H Comme  `re and  Dr. M. Nguyen-de Bernon  ( Plateforme de Cytometrie, Institut Pasteur ) for help in flow cytometry.
FV is funded by the  Ministere de l'Education Nationale, de la Recherche et de la Technologie  and by the  Fondation pour la Recherche Medicale   ú282046 ), the  ANR  ( Menage a trois ,  ANR-12-BSV2-0009-02 ), the  Institut Pasteur  and the  Centre National de la Recherche Scientifique.
In this paper we investigate the efficiency of the novel term weighting algorithm for opinion mining and topic categorization of articles from newspapers and Internet. We compare the novel term weighting technique with existing approaches such as TF-IDF and ConfWeight. The performance on the data from the text-mining campaigns DEFT'07 and DEFT'08 shows that the proposed method can compete with existing information retrieval models in classification quality and that it is computationally faster. The proposed text preprocessing method can be applied in large-scale information retrieval and data mining problems and it can be easily transported to different domains and different languages since it does not require any domain-related or linguistic information.
A novel term weighting method  (Gasanova et al. , 2013)  is also considered, which has some similarities with the ConfWeight method, but has improved computational efficiency. It is important to notice that we use no morphological or stop-word filtering before text preprocessing. It means that the text preprocessing can be performed without expert or linguistic knowledge and that the text preprocessing is language-independent. In this paper we have used k-nearest neighbors algorithm, Bayes Classifier, support vector machine (SVM) generated and optimized with COBRA (Co-Operation of Biology Related Algorithms) which has been proposed by  Akhmedova and Semenkin (2013)  For the application of algorithms and comparison of the results we have used the DEFT ("Défi Fouille de Texte") Evaluation Package 2008 (Proceedings of the 4th DEFT  Workshop, 2008)  which has been provided by ELRA and publically available corpora from DEFT'07 (Proceedings of the 3rd DEFT  Workshop, 2007).
Corpora Description The focus of DEFT 2007 campaign is the sentiment analysis, also called opinion mining. We have used 3 publically available corpora: reviews on books and movies (Books), reviews on video games (Games) and political debates about energy project (Debates). The topic of DEFT 2008 edition is related to the text classification by categories and genres. The data consists of two corpora (T1 and T2) containing articles of two genres: articles ex-tracted from French daily newspaper Le Monde and encyclopedic articles from Wikipedia in French language. This paper reports on the results obtained using both tasks of the campaign and focuses on detecting the category. All databases are divided into a training (60% of the whole number of articles) and a test set (40%). To apply our algorithms we extracted all words which appear in the training set regardless of the letter case and we also excluded dots, commas and other punctual signs.
, , d β α Then for each individual constrained modification of COBRA is applied for finding vector w and shift factor b. And finally individual that showed the best classification rate is chosen as the designed classifier. We can see from the Tables 3-7 that the best F-scores have been obtained with either ConfWeight or novel Term Weighting preprocessing. The algorithm performances on the Games and Debates corpora achieved the best results with ConfWeight; however, we can see that the F-scores obtained with novel Term Weighting preprocessing are very similar (0. 712 and 0. 720 for Games; 0. 700 and 0. 714 for Debates). Almost all best results have been obtained with SVM except the Games database where we achieved the highest F-score with k-NN algorithm. This paper focuses on the text preprocessing methods which do not require language or domain-related information; therefore, we have not tried to achieve the best possible classification quality.
Background: Cardiac fibrosis disrupts the normal myocardial structure and has a direct impact on heart function and survival. Despite already available digital methods, the pathologist's visual score is still widely considered as ground truth and used as a primary method in histomorphometric evaluations. The aim of this study was to compare the accuracy of digital image analysis tools and the pathologist's visual scoring for evaluating fibrosis in human myocardial biopsies, based on reference data obtained by point counting performed on the same images. Methods: Endomyocardial biopsy material from 38 patients diagnosed with inflammatory dilated cardiomyopathy was used. The extent of total cardiac fibrosis was assessed by image analysis on Masson's trichrome-stained tissue specimens using automated Colocalization and Genie software, by Stereology grid count and manually by Pathologist's visual score.
Results: A total of 116 slides were analyzed. The mean results obtained by the Colocalization software (13. 72 ± 12. 24%) were closest to the reference value of stereology (RVS), while the Genie software and Pathologist score gave a slight underestimation. RVS values correlated strongly with values obtained using the Colocalization and Genie (r > 0. 9, p < 0. 001) software as well as the pathologist visual score. Differences in fibrosis quantification by Colocalization and RVS were statistically insignificant. However, significant bias was found in the results obtained by using Genie versus RVS and pathologist score versus RVS with mean difference values of: -1. 61% and 2. 24%. Bland-Altman plots showed a bidirectional bias dependent on the magnitude of the measurement: Colocalization software overestimated the area fraction of fibrosis in the lower end, and underestimated in the higher end of the RVS values.
Stereology Stereology is an interdisciplinary field for volume estimation of three-dimensional structures by their planar sections. We performed our study on tissue sections of 3 μm, thus the stereology was performed on a projection rather than on ideal 2D plane. A point counting grid was used to estimate the fraction area  [42]. "Stereology toolkit 4. 2. 0" from ADCIS (Saint Contest, France) was used in this study. This stereology module allows defining a ROI and a grid that overlay an area of a virtual slide. Then the type, the spacing and the pattern size of the grid must be adjusted (Figure  3A ). 150-200 test points are recommended for acceptable analysis precision  [43, 44]. The grid of point counting, with the sampling interval of 200 pixels and a pattern size of 20 pixels was chosen to evaluate the area fraction of myocardial tissue and cardiac fibrosis.
Statistical analysis Data are presented as means and standard deviation (Mean ± std. deviation). For the statistical comparison of data, the Pearson's correlation coefficient, Friedman's test with post hoc (Wilcoxon signed-rank with a Bonferroni correction applied) and scatter-dot graphs (with r 2 , intercept and slope) were used. To enable a standard approach to the data, a natural logarithmic transformation of all measurements was performed before drawing scatted-dot plots. The agreement between fibrosis measurement methods was tested with Bland-Altman plots  [45] , using the stereology estimation as a reference method for the X axis  [46]. All statistical analysis was performed using the SPSS package (version 19. 0 for Windows; SPSS Inc. , Chicago, IL, USA) at 5% significance level.
Results A total of 116 slides were analyzed digitally, by visual scoring and using stereology grids. The mean result of fibrosis obtained by Colocalization software was 13. 72 ± 12. 24% being closest to the reference value of stereology (RVS: 13. 21 ± 15. 25%). The mean values obtained by the Genie software (11. 60 ± 15. 41%) and the pathologist's score at week 0 (11. 20 ± 15. 53%) and week 2 (10. 76 ± 17. 37%) indicated a slight underestimation relative to RVS. However, the range of Colocalization software was 73. 79% being the lowest of all tested methods with a difference of around 20%. The range of the Genie software was 88. 22% and the pathologist's score had the highest range of 100%. These results were comparable to the range of the RVS (96. 50%), Table  1. Both the Colocalization and Genie methods correlated very strongly with the RVS cardiac fibrosis estimates, yielding r = 0.
639, p = 0. 000 with a variance bias of 2. 11% (Table  3 ). Single linear regression model plots demonstrated some advantage of Genie software over the Colocalization software with noticeably better values in both original raw and log-transformed measurements for rsquare 0. 896 and 0. 804 (log) versus 0. 861 and 0. 707 (log); slope 0. 956 and 1. 222 (log) versus 0. 745 and 0. 639 (log); intercept -1. 033 and -0. 860 (log) versus 3. 875 and 0. 972 (log) (Figure  4 ). The pathologist's mean score correlation with RVS was similar: r-square 0. 864 and 0. 684 (log), slope 0. 994 and 0. 838 (log), intercept -2. 155 and 0. 062 (log); the inter-observer variation at week 0 and week 2 was negligible: r-square 0. 931 and 0. 824 (log), slope 1. 079 and 0. 939 (log), intercept -1. 328 and -0. 020 (log).
Surprisingly, both digital methods did not correlate as well as expected with still acceptable r-square values (0. 947 and 0. 794 (log)), but high intercept (4. 744 and 1. 500 (log)) and slope far from ideal (0. 773 and 0. 486 (log)) (Figure  5 ). Bland-Altman plots showed a bidirectional bias dependent on the magnitude of the measurement: Colocalization software overestimated the area fraction of fibrosis in the lower end, and underestimated it in the higher end of the RVS scale (Figure  6A ). Meanwhile, Genie software as well as the pathologist's mean score showed more uniform results throughout the complete scale with a slight underestimation in the mid-range for both (Figure  6B, C ). Presented histograms indicate a normal distribution of the differences for each plot (Figure  6 ).
Particularly since a stereology test grid was not used for RVS. Hadi et al. [16]  quantified cardiac fibrosis by automated analysis using ImageJ software and traditional polarization microscopy, with subsequent validation of the results, using stereology data as criterion standard. To our knowledge, it is the only study of cardiac fibrosis that applied stereology procedures to obtain RVS; however, the validation was performed on rat cardiac rather than human samples (the analysis was then tested on a postmortem tissue samples from a 78 year old man). In our study, we have tested several methods to evaluate the extent of human cardiac fibrosis, which can be  readily implemented in clinical practice today. We used stereology as the most independent and objective RVS available and a modified Bland-Altman plot as the best statistical tool to measure agreement between the tested method and a RVS.
The initial data were somewhat in favor of the Colocalization software: it demonstrated the closest fibrosis mean value to a reference and resulting difference of 0. 50% was statistically insignificant. However, the Colocalization software had a noticeably narrower variation, which was 20% behind the RVS and the pathologist's range, and also 15% behind the Genie software. This drawback may be not of great importance in clinical practice, as the range limitation was only evident in the higher range and myocardium fibrosis hardly reaches these values, whereas the lower range was acceptable. Further analysis revealed the superiority of Genie software: the higher correlation with RVS, the better values in single linear regression against the reference and, most importantly, more uniform results in Bland Altman analysis.
While the Colocalization software was overestimating at the lower end and underestimating at the higher end, Genie software was only slightly underestimating in a mid-range with the results still exceeding those of the pathologist's mean score. Of note, both digital algorithms produced slightly different results, a fact that might appear surprising. Despite both algorithms are aimed to measure the same feature, namely, the proportion of connective tissue in the myocardium, they are still based on different principles and may result in different measurement errors. While Colocalization classifies each pixel according to its color characteristics, the Genie software is based on a far more complex pattern recognition system, which also refers to spatial aspects of the image. Probably, the only relevant drawback of Genie was the underestimation bias of 1.
61% from the RVS. Overall, the Genie classifier performed best in our study, being closest to the RVS, with almost perfect correlation, adequate range and uniform results throughout the whole scale. Potential limitations of the Genie software are related to the necessity to train the system to identify the various structures of interest, which is time-consuming and based on the inherent subjectivity of the "human trainer". This fact also makes the Genie software sensitive to inter-laboratory reproducibility issues. However, after the adaptation of Genie software to the clinical needs it can be run fully automated and as a result it can be equally as time-efficient as the Colocalization software is. The Genie software has the possibility of tuning the algorithm, which makes it more flexible in practical maintenance.
Even if 2% is an acceptable error for cardiac fibrosis estimate in clinical sense, this algorithm may require further adaptation to potential sources of slide quality variation. The Colocalization software has also proved to be a fully acceptable method for cardiac fibrosis measurement. In clinical practice, the Colocalization software should provide similar precision and accuracy as the Genie tool, because cardiac fibrosis values are rarely exceeding 40%, and a slight overestimation in the lower range may be acceptable. The Colocalization software is less complex, simpler to use and calibrate, and less expensive. Furthermore it can be run fully automated from image scanning to the final results and it is very time efficient. The Colocalization algorithm is less dependent on human investigator input at any point of the process (except initial settings for color deconvolution), making it more transparent and manageable for users.
The first on-sky results obtained by CANARY, the Multi-Object Adaptive Optics (MOAO) demonstrator, are analysed. The data were recorded at the William Herschel Telescope, at the end of September 2010. We describe the command and calibrations algorithms used during the run and present the observing conditions. The processed data are MOAO-loop engaged or disengaged slope buffers, comprising the synchronised measurements of the four Natural Guide Stars (NGS) wavefront sensors running in parallel, and near Infra-Red (IR) images. We describe the method we use to establish the error budget of CANARY. We are able to evaluate the tomographic and the open loop errors, having median values around 216 nm and 110 nm respectively. In addition, we identify an unexpected residual quasi-static field aberration term of mean value 110 nm.
Considering the open loop operation, typical DM errors such as creep and hysteresis have been studied earlier  (Morzinski et al. 2008; Kellerer et al. 2012). For certain DM types including that in CANARY, they have been demonstrated to be small compared to the fitting error. In addition specific open loop procedures have to be developed as presented in section 3 for CANARY. In Sect. 2, we present the CANARY instrument with a hardware and software description and in section 3 the control and calibration algorithms. The data reduction approach is described in Sect. 4. In particular, the error budget computation is detailed. Section 5 presents the on-sky results, the observing conditions and introduce the three selected NGS asterisms. The data are processed to retrieve a 15 layer C 2 n (h) profiles in order to quantify the tomographic and open loop errors.
Phase B will use, in addition, four Rayleigh LGS to augment the turbulence tomography in open loop (see Fig. 1 ). Phase C will introduce the full configuration proposed for EAGLE. We will use a woofer DM in closed-loop (in fact the 8x8 actuator DM), as a first stage simulating M4 of the E-ELT  (Vernet et al. 2012 ) with an open-loop tweeter DM as a second stage simulating the MOAO channel. The tweeter DM in Phase C will be a higher-order DM from ALPAO with 17 × 17 actuators (241 useful). In order to reconfigure the system between the three phases, the optical design is build around a set of interchangeable optical modules. The Rayleigh LGSs used for these demonstrations have variable range-gate height and extension in order to simulate many of the LGS effects that will be encountered at the E-ELT.
The Learn & Apply tomography algorithm The linear tomographic reconstruction matrix is derived using the Learn & Apply (L&A) algorithm from  (Vidal et al. 2010b) , where the minimum mean square error (MMSE) reconstructor is computed from an atmospheric model directly identified on-sky from the WFS measurements. Engaging the open-loop compen-sation is preceded by the acquisition of an on-sky WFS data set, from which the optimized estimator is deduced. We detail hereafter the implementation. We define notations as follows. The set of local wavefront slopes along x and y directions, measured by a WFS i with p sub-apertures, is collected into a slope vector S i S i =                             sx 1. sx p sy 1. sy p                             -S re f i After acquisition of number of frames nframe of an on-sky WFS data set we build a matrix M o f f axis by concatenating horizontally the column-vectors formed by synchronous measurements t = [1,.
During a real scientific observation the number of timeindependent realisations is limited, which leads to covariance matrices that have partly converged. Moreover, the general case is that it is impossible to acquire the data from the central direction with the truth sensor because of the faintness of the scientific target. We have therefore developed a method where we fit a model to the off-axis covariance matrices in order to extract the essential parameters of the turbulence and WFS configurations. Therefore, we are able to re-generate the modelled covariance matrices and compute the tomographic estimator from Eq. 3. We call C O f f O f f raw the real on-sky measured off-axis covariance matrix. In the CANARY case during this first run, we also use the TS data to fit the C OnO f f raw matrix.