dataesr/scientific-paragraphs-categorization huggingface dataset:
Further, we applied JCM to two cell signaling datasets. First, we used it to obtain multi-parametric characterization of different T cell subpopulations upon T cell receptor (TCR) stimulation in a time course phosphorylation experiment. This illustrates how a complex multi-class and multi-sample experiment can be systematically analyzed in a fully automated and reproducible manner to generate precise and objective profiles for every class. Importantly, it is based on a comprehensive list of rigorously estimated model parameters for each population, which is output by JCM. As illustrated by our next application, such unsupervised, thorough approach can also reveal new or subtle expression phenotypes in specific subpopulations, which might otherwise go undetected in manual gating that generally follows a pre-determined sequence of 2D visualization steps.
Novel overlay plots are produced for visual comparison of all class-templates. To illustrate the different capabilities of JCM, we applied it to two sets of experiments involving multiple markers, time points (or stimulations), staining panels, and sample classes. Also, we allow two modeling options: the default using mixtures of multivariate skew t-distributions and its symmetric counterpart using a mixture of multivariate t-distributions, and tested both models on two cell signaling datasets.
Through automated analysis of multiplexed data, JCM identified a nuanced signature for signaling alterations in high-dimensional marker-space that further improved the distinction between the two FL patient classes. We analyzed 28 pre-processed patient samples for two time points, 0 min and 4 min (i. e. pre-and post-BCR stimulation respectively). At every timepoint, and for all patients, the data consisted of 8 panels, each with 4 markers, including two B cell markers CD20 and BCL2 that were common to every panel. Signaling responses were measured in terms of phosphorylation of 16 phospho-proteins from the BCR signaling network. By multiplexing panels, the signaling for all these network components could be measured in every sample. Each sample's phenotype (or class label), LNP lo (18 samples) or LNP + (10 Samples), was assigned by human expert analysis (Supplemental Methods of Irish et al.
Performance and Comparactive Analysis Although in general flow analysis methods do not compute cluster correspondence, we compared JCM with FLAME  [7]  and pooled metaclustering, and another published method flowClust  [14]. Based on four real-world benchmark datasets from flowCAP1 contest (N. Agheepour et al. in preparation), we first created templates for each panel and class. Then every sample was classified with the most similar template using the empirical (sample) version of the Kullback-Leibler distance. A comparison of the classification error rates for all methods (see Supplementary Table T1) demonstrates the effectiveness of the JCM approach. The JCM approach is based on the assumption that a sample can be modeled as an instance of its parent template, and hence the accuracy of the JCM approach for classification purposes depends on how distinctive the class templates are.
Methods Following is the description of the JCM workflow and details of the models and methods, also continued in Supplementary Information. Step 1: JCM input: The input to JCM is a batch of m flow cytometric samples in the form of either a zipped folder of m. fcs files or a R flowSet object of m flowFrames. We assume that the flow data have been acquired, quality-controlled and preprocessed (such as live cell gating) properly. Commercial software and freely available BioConductor packages (e. g. flowCore[26]) are highly useful for such purposes. If q (>1) multiplexed panels of markers were used, then q such zipped folders or flowSet objects matrices must be provided, such that every sample is represented by q panels. The user can also specify which mixture model (MVT or MST) to fit, and an optional range for the expected number of populations (g) in the batch template.
This means that the random-effects terms are distributed as a hijk ∼ N (1, ξ 2 1hi ) and b hjk ∼ N (0, ξ 2 2h ) (i = 1,. , p) (9) for a given h, j, and k (h = 1,. , g; j = 1,. , n k ; k = 1,. , m). The E-and M-steps, as described in Appendix A (Supplementary Information), are alternately repeated until the relative change in log likelihood is smaller than 10 -6 , or the number of iterations reaches 1000, whichever happens earlier. If a range for the number of populations (g) in the template is not specified, JCM precomputes one using the average silhouette width (implemented by pamk from the R package fpc). The selection of the optimal number of components in the mixture model is made by default on the basis of the Bayesian Information Criterion (BIC), although other criteria can be used. Step 3: Output files: A list of all the input parameters ("input parameters.
An overlay plot of all templates across different classes ("template overlay. pdf") and one for each class showing within-class variation ("class overlay. pdf") are produced. Fluorescence intensity heatplots are produced for side-by-side spatial or temporal comparison of individual populations that are matched across all samples. The option of pairplots to illustrate the mixture model based clustering for each sample is also available ("sample pairplot. pdf"). Finally, JCM outputs a zipped folder (cluster labels. txt") containing the cluster-membership labels for each point in every sample of the batch. Step 4: Downstream analysis: JCMs output formats allow easy application of visualization and other analytical routines in R, GenePattern, and other platforms. In the present analysis, for 2-class BCR signaling data, we used the Gene Set Enrichment Analysis module of GenePattern to identify the enriched meta-features (or feature-sets) across multiplexed staining panels.
Cell signaling datasets Details are described in the Supplementary Information. [27] McLachlan, G. J. & Peel, D. Robust cluster analysis via mixtures of multivariate tdistribtuions. in Lecture Notes in Computer Science 1451, 658-666 (Springer-Verlag, 1998). [28] Sahu, S. K. , Dey, D. K. & Branco, M. D. A new class of multivariate skew distributions with applications to Bayesian regression models. Can. J. Stat. 31, 129-150 (2003). [29] Dempster, A. P. , Laird, N. M. & Rubin, D. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. , B, 39,  1-38 (1977). plus the lineage markers CD3, CD5, CD20, and BCL2 and cell light scatter properties. Using 8 panels allowed us to measure 16 phospho-proteins, but meant that on a given cell we only measured two phospho-proteins simultaneously.
Svalgaard (2014)   has recently pointed out that the calibration of the Helsinki magnetic observatory's H component variometer was probably in error in published data for the years 1866-1874. 5 and that this makes the interdiurnal variation index based on daily means, IDV(1d), (Lockwood  et al. , 2013a), and the interplanetary magnetic field strength derived from it (Lockwood et al. , 2013b), too low around the peak of solar cycle 11. We use data from the modern Nurmijarvi station, relatively close to the site of the original Helsinki Observatory, to confirm a 30 % underestimation in this interval and hence our results are fully consistent with the correction derived by Svalgaard. We show that the best method for recalibration uses the Helsinki Ak(H ) and aa indices and is accurate to ±10 %.
This makes it preferable to recalibration using either the sunspot number or the diurnal range of geomagnetic activity which we find to be accurate to ±20 %. In the case of Helsinki data during cycle 11, the two recalibration methods produce very similar corrections which are here confirmed using newly digitised data from the nearby St Petersburg observatory and also using declination data from Helsinki. However, we show that the IDV index is, compared to later years, too similar to sunspot number before 1872, revealing independence of the two data series has been lost; either because the geomagnetic data used to compile IDV has been corrected using sunspot numbers, or vice versa, or both. We present corrected data sequences for both the IDV(1d) index and the reconstructed IMF (interplanetary magnetic field).
M. Lockwood et al. : Improved representation of solar cycle 11 magnetic field B at all times before the space age was as similar as possible to that in modern times. Lockwood et al. (2013b)  subsequently used this index to reconstruct B with the first full analysis of reconstruction errors, carried out using a Monte Carlo technique. This homogeneous construction was achieved by using just three magnetic observatories, in the same latitude band and the same longitude sector, to generate the IDV(1d) composite and by using a model of the secular change in the geomagnetic field to allow for the drift of the observatories in geomagnetic coordinates. The problem with this approach is that unknown errors in the data from a station used would be reproduced in the composite. As a check against this possibility,  Lockwood et al.
(2013a)  compared the IDV(1d) composite against similarly derived values from other nearby stations. This check was necessarily poorer for the data from the 19th century when fewer stations were available for comparisons. Specifically, agreement was found to be very good in comparisons with data from  Wilhelmshaven (commencing 1883 ), Parc St Maur (commencing 1883)  and Ekaterinburg (commencing 1887). In addition, after 1880 agreement was shown to be close with the IDV index, derived from a global network of a variable number of observatories  (Svalgaard and Cliver, 2010). Before 1863, the IDV(1d) composite data were compared with observations from St Petersburg and the agreement was again very good. Between 1863 and 1883 the only two data sets available for comparisons were from Greenwich and Bartels' interdiurnal daily mean variability index u which, like IDV, was compiled from a variable mix of stations.
Agreement between IDV(1d) and u was good after about 1880 but  Lockwood et al. (2013a)  noted that before then it was partly based on Greenwich data for which both they and Bartels had noted considerable stability problems and could only be readily corrected for temperature variations in a statistical manner. In addition,  Lockwood et al. (2013a)  were concerned about the u index before 1872 because it was based on diurnal range proxies and so was not a homogeneously constructed index. As a result, despite good correlations with IDV over a period of overlap, it could have different general responses to solar wind and IMF (interplanetary magnetic field) properties. This concern was shown to be well founded. Because of the stability issues with the Greenwich data, Bartels gave them half weighting and so his interdiurnal u data largely came from the Colaba Observatory, Bombay (now Mumbai), and daily mean interdiurnal variation data for modern stations at such a low geomagnetic latitude station show a (BV SW ) n dependence on solar wind speed V SW with an estimate of n of -0.
4 ± 0. 9  (Lockwood et al. , 2013a) , the uncertainty being at the 95 % significance level, derived by applying Fisher's Z test to the correlograms with n (as described by  Lockwood, 2002). A result of a negative n is that u would be depressed in intervals of enhanced solar wind speed and this is particularly clear during the declining phases of solar cycles 9 and 10 when u is compared to  IDV(1d)  which was designed to depend on the IMF strength only (i. e. n ≈ 0). This was confirmed to be the case for cycle 9 by the IDV(1d) variation derived from the St Petersburg data, IDV(1d) SPE (the three-letter IAGA observatory code for St Petersburg is SPE). In the light of this finding,  Lockwood et al. (2013a)  concluded that the difference between u and IDV(1d) at the peak of solar cycle 11 could have arisen for a lower than average solar wind speed at that time and so was not necessarily due to an instrumental error in either data series.
However,  Svalgaard (2014)  noted that, although the diurnal range in annual means of the Helsinki horizontal (H ) component data was similar to that from other stations for most of years for which that station operated (giving usable annual means for 1845-1897), for 8. 5 of those years (1866-1874. 5) it was lower. Furthermore, using a linear regression of group sunspot number with IDV(1d), values were found to be low at this time, whereas for all other years the agreement was considerably better. He concluded that poor calibration of the "horizontal force" variometer at Helsinki was causing the H component (and hence the IDV(1d) values based on it) to be low in these years. Previously,  Nevanlinna (2004)  had noted that the range index Ak(H ) HLS , scaled from the three-hourly range k index derived from the H data from Helsinki (the IAGA code for Helsinki is HLS) were lower than the linearly regressed aa range index (derived from a Northern Hemisphere and a Southern Hemisphere station) at this time which could also have been caused by the same calibration problem.
The fact that correlations are almost as high for IDV(1d) as for IDV is, at first sight, surprising because IDV is based on a number of stations that varies with date from 1 to over 60, whereas IDV(1d) is based on just one station at any one time. However, IDV uses just one hourly mean value per day, whereas IDV(1d) uses all 24. If we regard data taken by the same station at different Universal Time (UT) as differing only in their UT-dependent calibration, we have a suppression of geophysical and instrument noise by an additional factor in IDV(1d) of 24 0. 5 = 4. 89, which is only achieved using 24 stations for IDV. Hence although noise suppression in IDV(1d) is better for later years (when more than 24 stations are available), it is poorer in early years. In addition, as discussed in Paper 1, the point of IDV(1d) is that it is, unlike IDV, homogeneously constructed to ensure its responses to solar wind parameter variations were as similar in past epochs to those in the space age as it is possible to make them.
The peak correlations r in Fig. 1  for post-1872 data are all in the range 0. 821-0. 848 and for n in the range 0. 5-0. 75. The middle panels (c and d) show the variation of the significance S of the difference between the peak correlation and that at general n, as determined by  Lockwood (2002). It can be seen that the differences between the S(n) variations are not statistically significant after 1872. However, for neither IDV(1d) or IDV is the same true before 1872. For (uncorrected) IDV(1d), the peak r is 0. 806 at n = 0. 29 (the black line in Fig. 1a ). For IDV, however, the peak r is 0. 942 at n = 1. 18 (the black line in Fig. 1b ). These discrepancies could denote problems with either the geomagnetic indices or with the sunspot number record, or both. However, it is noticeable that the error is in the opposite sense for the two geomagnetic indices, with peak correlation at somewhat lower n for IDV(1d) but considerably higher n for IDV.
The black lines in Fig. 1e  and f  show the probabilities S that the true n is the same before 1872 as after it: for IDV(1d) this peaks at 87 % whereas for IDV it peaks at just 62 %. Figure  1  therefore shows that neither index is behaving consistently before 1872. In the case of IDV, the correlation with R is far higher than for after 1872 (peaking at 0. 942) and is for an optimum value of n that is close to unity. Hence it is clear that the sunspot number and the IDV geomagnetic index are no longer independent data before 1872, making IDV far too similar to R in this interval. This may be because the sunspot number has been used to correct and/or calibrate the geomagnetic data, or vice versa, or both. Hence the concerns about using R to correct geomagnetic indices expressed in Paper 2 are well founded.
That having been said, the IDV(1d) index before 1872 is also not behaving in the same way with R as it does in later years, indicating that it too is in error. In the present paper, we derive a correction that does not depend on sunspot number, thereby avoiding the inhomogeneity that Fig. 1  shows that the IDV index suffers from. For completeness, the green lines in Fig. 1a, c  and d  show the results for IDV(1d) after that correction has been applied. In this case, the n giving a peak correlation of 0. 68 is not significantly different to that for the intervals after 1872. The peak correlation coefficient ris lower (0. 782), which is to be expected given the increased uncertainties in both the geomagnetic and sunspot data for the earliest years. The green line in Fig. 1e  shows that the probability that the behaviour with R for the corrected IDV(1d) is different before 1872 to that after 1872 is just 0.
It is important to note that, because it is at a higher latitude, there is an auroral component to the response of Helsinki, indeed that response is important in the development of IDV(1d) as it gives a dependence on IMF B rather than the BV -0. 4 of lower latitude stations (see Paper 1). Thus at least some of the ±19 % uncertainty arises from the latitude of Helsinki. Importantly, Svalgaard's (2014) estimate of a 30 % error during the period 1866-1874. 5 is outside the ±19 % uncertainty inherent in the recalibration procedure he adoptedconfirming the Helsinki data are indeed too low in this interval and potentially correcting the error in IDV(1d) also identified in Fig. 1.
85 (significance > 99. 99 %). However, this contains the interval for which  Svalgaard (2014)  finds the calibrations of the horizontal component instrument to be poorly calibrated (1866-1874. 5), and removing these data causes the correlation to rise to 0. 92 (however the significance falls to 99 % due to the lower number of data points). The best-fit linear regression for this second fit is [Ak(H ) HLS ] fit = 0. 843 × aa C -0. 039. (1) Note that for the modern Nurmijarvi data (1953-2011) the corresponding slope is 1. 026 with an intercept of zero to within seven decimal figures. The ratio of the regression slopes with aa C for the modern Nurmijarvi data and the historic Helsinki data (0. 843/1. 026) implies that the sensitivity of the 19th century Helsinki instrument is 82 % of that for the modern Nurmijarvi instrument and this is consistent with the intercalibrations used to generate IDV(1d), as described in Paper 1.
The ratio [Ak(H ) HLS ] fit /Ak(H ) HLS from Eq. (1) gives us a correction factor which we can apply to the data from 1868 (when aa C is available) and Fig. 4c  indicates that this recalibration can be expected to be accurate to within about ±10 %. The black line bounding the filled orange area is the IDV index, scaled using the regression for the period 1880-2013, as shown in Fig. 12  of  Lockwood et al. (2013a). IDV(1d) derived from various early data sets are also shown: from St Petersburg, IDV(1d) SPE (black triangles); from Ekaterinburg, IDV(1d) EKA (cyan stars); from Parc St Maur, IDV(1d) PSM (mauve squares); from Wilhelmshaven, IDV(1d) WLH (yellow diamonds); and from Greenwich, IDV(1d) GRW (white circles). The blue diamonds are derived using the D component observed at Helsinki, IDV(1d) HLS,D.
The Greenwich values are compiled using the daily means of H that are uncorrected for temperature variations, with annual IDV(1d) GRW means subsequently corrected (giving the shown uncertainty bands caused by the temperature effects). Solar cycle numbers are shown across the base of the figure. The green-and-black dashed line in Fig. 6  is the variation of IDV(1d) derived Paper 1 from the uncorrected Helsinki magnetometer data. The mauve line shows the data series with the 30 % upward recalibration for the period 1866-1874. 5 derived by  Svalgaard (2014).
The length of the data series means that the upper limits to n are 2, 1. 5 and 1 for the IMF, geomagnetic and model data series; for all three cases the lower limit is near n = 0. 2. Given this large uncertainty in the peak n estimates (at the 90 % confidence level), the difference in the behaviour of the reconstructed IMF and the other two is not significant. the modelled variation after 1845 all show a linear variation. This appears to show a limiting minimum value or "floor" in the IMF of around 4 nT when the group sunspot number R G falls to zero. The only data in Fig. 9  which fall below such a floor is the modelled data for before 1845 and so one might be tempted to dismiss these modelled values as an error, even though the modelling is very successful after 1845 (the cyan squares match the reconstructed values well, giving r = 0.
Thus, making a relatively small and simple allowance for the prior history of the sunspot number explains how the modelled values can be below the apparent floor value before 1845, whilst matching the data and reconstruction after 1845. Making this simple allowance for the prior R G history makes no significant difference to the fits to data after 1845 (for which we have geomagnetic reconstructions) or after 1964 (for which we have in situ observations). This directly demonstrates that the available IMF data and reconstruction do not extend into a wide enough range of conditions to differentiate between the two sunspot number fits and hence allow detection of the dependence on the prior history of R G. Thus one has to look very carefully at the assumptions inherent in a linear regression fit before using it to derive an estimate of any floor value to the near-Earth IMF.
Conclusions We find that the correction of  Svalgaard (2014)  to IDV(1d) for solar cycle 11 is largely correct. We do this in three ways, firstly using daily Ak(H ) indices and comparing to the corrected aa index, aa C. Secondly we use the newly digitised St Petersburg data which is conveniently close to Helsinki and so provides a very valuable test. Finally we derive IDV(1d) HLS,D from the declination observations at Helsinki. We show that the Ak(H ) test and recalibration is roughly twice as accurate as methods using the average diurnal range, comparing to other stations or to group sunspot number. The strong dependence of diurnal range-based proxies on solar wind speed means that, as yet, we do not have any digitised geomagnetic data on which we can reconstruct the IMF before the start of the Helsinki data midway through 1844.
This analysis indicates that estimates giving a high-level floor to the near-Earth IMF arise from the use of data that does not extend back into the Dalton or Maunder minima and fail to recognise the role of prior solar activity. One important implication of the role of the prior history is that it means predictions of future solar activity have a sound physical basis in addition to the statistical predictability found in heliospheric parameters (such as open solar flux, the interplanetary magnetic field and the cosmic ray modulation potential) over several solar cycles  (Lockwood et al. , 2010). These predictions have ranged from half a cycle in advance  (Svalgaard et al. , 2005)  to several cycles in advance  (Lockwood, 2010; Barnard et al. , 2011). The applications of such predictions in the area of space weather are well documented and include satellite orbit prediction, integrated radiation dose prediction for electronics on spacecraft and aircraft, corrosion maintenance planning on long pipelines, and many more.
Acknowledgements. We thank a great many scientists who made, recorded and digitised the many geomagnetic observations employed in this paper. We also thank all those who have contributed to almost half a century of observations of near-Earth space and the staff of the  Space Physics Data Facility at Goddard Space Flight Center  for compiling them into the Omni2 data set. We thank the  UK Natural Environment Research Council  ( NERC ) for the provision of a PhD studentship for  L. Barnard , and subsequent support under  NERC  grant  NE/J024678/1. Topical Editor  M. Temmer  thanks  S. Macmillan  and one anonymous referee for their help in evaluating this paper.