dataesr/scientific-paragraphs-categorization huggingface dataset:
This might be a real-world phenomenon: perhaps in the long run, the phonetic and lexical change processes average out and start looking very similar). It could also be an artefact of our algorithms for estimating similarity: as all computational-historical-linguistic algorithms, they inherently tend to be more accurate for more closely related languages, so it might be that we see significant mismatches between Phonetic and Lexical only where our estimates can be sharp enough. We leave solving this question to future research. We conclude this case study with the following strong thesis: qualitative linguistic research into small sets of linguistic features should refrain from generalising its results to the overall workings of different types of change. The statistical mismatch between Phonetic and Lexical that we found was subtle and required a large dataset to be discovered.
The column for oss has no significant cells. The column for slv has significant cells up to k = 23 and non-significant ones higher than that. like religion, prosociality, openness to out-groups, justifiability of homosexuality, political engagement and trust in national institutions. There are 104 countries shared between these data that represent our subjects for this case study. The 'Cultural' dissimilarity matrix, Figure  8a  is constructed from a dimensionality-reduced dataset of nine cultural factors from WEVS from circa 2000CE, as described by  [Ruck et al. , 2018]. For 'Economics', we retained the 284 indicators of the World Bank dataset with less than 40% missingness, standardised to unit variance, capped extreme values at 10 s. d. -s, mean imputed, and computed the 'Economic' pairwise distance matrix.
Future research may thus want to first confirm that the cultural unusualness is not spurious (based, e. g. , on coding errors and the like in the datasets as processed), and second, to study what could be driving these anomalous profiles. There are countries that share residual structure. Countries in Latin America, such as Argentina, Uruguay and Puerto Rico, have large European descended populations  [CIA, 2018, Putterman and Weil, 2010] , so are culturally similar to Europe because cultural values percolate along linguistic and religious pathways  [Matthews et al. , 2013 , Matthews et al. , 2016 , Spolaore and Wacziarg, 2013]. However, Latin American countries have a relatively smaller GDP and have high economic inequality; they are therefore more culturally similar to Europe than Economics predict.
The Cultural data contain associations that are perhaps surprising; for example, Japan is culturally similar to the Czech Republic. Correcting for Economics, Figure  8c , reveals that this cannot be fully explained away by Economic similarity (the relevant cell is light blue, though not significant). In contrast, Poland and the Czech Republic are neighbouring countries that cluster very differently in Culture, and have large persistences. In this case, Economics may be playing an even smaller role than in the comparison Japan-Czech Republic: the residual Poland-Czech Republic is significant and shows they are much farther from each other on Culture than we could expect from Economics. To conclude, we have described how CLARITY analysis identifies interesting anomalies in one dataset that stand out when making predictions based on the other dataset.
In this case, the prediction is poor, so considerable follow up analysis would be needed to draw strong conclusions. The assessment of statistical significance was of secondary importance: we concentrated on systematic patterns in the CLARITY residuals that are likely to be of scientific significance, and the presence of statistically significant individual cells only confirmed the insights. This strategy is particularly useful for datasets based on a relatively small number of features, which will often be the case in social sciences: the statistical power of such data would be in the general case limited.
Summary of the examples We summarise the lessons from the four case studies above. In Section 2. 4, we show how CLARITY behaves on data from a simulation where we purposefully manipulated the 'structure' of the compared datasets. This case study builds intuitions about what CLARITY output would look like in different real-world scenarios. In another simulation study in Section 2. 5, we demonstrate that CLARITY correctly identifies anomalous links between two datasets generated by an existing epigenetic model, that did not conform to our definition of Structure. In the linguistic study on real-world data in Section 2. 6, we report the first-of-its-kind quantitative finding that phonetic and lexical language change operates in subtly different ways, though both occur in the same human communities and are subject to similar constraining factors.
This example is the only one in this paper where explicit hypothesis testing is of primary importance. Finally, in the example on culture and economics in Section 2. 7, we illustrated how CLARITY residuals can be explored to detect interesting anomalies that can then be selected for further in-depth study. Overall, we intend this set of examples to show how CLARITY can be a versatile tool for both exploratory data analysis and for explicit hypothesis testing, allowing us to make subtle and fine-grained comparisons between paired datasets stemming from the same subjects, but consisting of very different features, sometimes generated within different scientific disciplines. In the next section, we further discuss best practice and provide advice for interpretation of CLARITY results.
Whether to perform null hypothesis statistical testing CLARITY is in general a hypothesis generating tool and much of the value can be obtained without any use of statistical testing. For many datasets, statistical power will dominate (such as the Culture example) and many persistences are expected to be significant. We then care about practical significance and hypothesis testing is somewhat spurious. Similarly, in the Methylation example, clear persistent residuals were observed and these require no testing to confirm anomalies. Conversely, especially with small datasets, statistical power is limiting (such as the Language example), in which case we wish to check that a given persistence is significant. The statistical test that we provide compares Y 2 to the distribution of left-out data from Y 1.
It does so by first mean centering and scaling each dataset to the reference, and then applying the standard CLARITY correction of learning independent Relationships. This encodes an implicit assumption that the signal and noise are of the same scale, and therefore should only be used if this is plausible. Null hypothesis testing is a non-trivial process to implement because it requires that the user is able to either create independent features, or able to provide a set of bootstrapped samples generated on pseudoindependent samples, for both the reference and the test dataset. This comes with a computational cost, as of the order of 200 seudo-independent replicates are required to confidently reject at the 0. 05 level. We emphasise that the principal use case is the identification of large persistent residuals which CLARITY will only create where structure has changed, and does not require testing.
Conclusion CLARITY can be applied to any pair of datasets in which subjects are matched and so can be used in a wide variety of situations. We demonstrated this in very different fields: epigenetics, linguistics and bridging sociology and economics. In these examples we have recovered differences, supported by well-documented evidence, and generated new hypotheses. The software requires very little technical knowledge to employ, there are no tuning parameters, and the output can be presented in a simple, interpretable chart we called the residual persistence. This identifies the clusters and subjects that are poorly predicted, and allows interpretation of which other clusters they may share additional structure with. We suggest that the same approach may yield valuable insights when applied to other fields of interest, and that the results will generate hypotheses for further investigation through the application of additional, statistically robust, methods.
Simulation details For Section 2. 4 we generate a coalescent tree T 1 using 'rcoal' from the package 'ape'  [Paradis et al. , 2011]  for R  [R Core Team, 2018]. The 'true' A is a vector of zeroes except for the cluster membership k of i, for which A ik = 1. We then simulate a matrix D 0 consisting of k rows (clusters) and L columns (features) by allowing features to drift in a correlated manner under a random-walk model using the function 'rTraitCont' from the package 'ape'. This generates a 'true' X = Dist(D 0 ). To generate a feature d for a sample with mixture a, we simulate features d N (a T D o , σ 2 0 ), from which we can compute Y = Dist(D). In Scenario A we make T 2 into a non-ultrametric tree by randomly perturbing the branch lengths of T 1 by multiplying each by a U (0. 1, 2) variable.
Language example details We compute phonetic similarities using the Information-Weighted Distance with Sound Correspondences (IWDSC) method  [Dellert, 2018]. First, we estimate global sound similarity scores, based on the whole NorthEuraLex 0. 9 dataset with 107 languages. This provides us with an idea of which sounds in the data generally tend to be close. Implicitly, the employed inference method makes those sounds close that appear in words that are likely historically related. In other words, global sound similarities are not directly about articulatory or auditory similarities (i. e. how humans produce and perceive different sounds), but rather estimate "historical similarity", thus implicitly tracking processes of language change. After obtaining global sound similarities, we compute local sound similarity scores for each languagelanguage pair in our 36-language Indo-European subset of NorthEuraLex.
4. 6, dividing the data into two halves by meaning, and computing independently a similarity matrix from each half. When doing that, we always use the same global similarity scores, which represent the properties of a much larger sample of 107 languages, taken as a proxy for languages of the world in general. We use cross-validation to assess whether CLARITY decomposition at higher complexities k still captures signal rather than noise. For the pairs of matrices based on two halves of the data, we predict one based on the other (so Phonetic from Phonetic and Lexical from Lexical). This shows (Supp. Fig. 3 ) that even at the highest k we do not have overfitting to noise. Therefore the full range of k in the main analysis is interpretable. The CLARITY setup in this example implies that the diagonal values in our similarity matrices might not be amenable to successful modelling.
A second approach, which is less timeconsuming and observer-independent, uses supervised machine learning to analyze PET/CT images  (26). A research software prototype called PET Assisted Reporting System (PARS), based on convolutional neural networks (CNNs), has recently been developed by Siemens Healthineers to classify hypermetabolic foci into benign and malignant and to provide parameters such as TMTV, total lesion glycolysis (TLG), and Deauville score  (27). With this algorithm, PET volumes of interest are first segmented by using a fixed thresholding algorithm. Each volume of interest is then evaluated independently by using a combination of PET and CT multiplanar reconstructions, PET maximum intensity projections (MIPs), and atlas positions to predict the anatomic localization of FDG foci.
Research Cohort Concerning the research cohort, 119 patients followed up for DLBCL were included between November 2004 and September 2014, and their initial FDG PET/CT was analyzed. PET/CT scans were acquired on a Biograph 16 (Siemens Healthineers, Knoxville, TN, USA). Patients fasted for at least 4 h and were injected with FDG at an activity of 3. 5 MBq/kg of body weight. Images were acquired 60 min after injection at 2. 5 min per bed position. The manual segmentation of lesions was performed using semiautomatic software (Planet Onco, version 2. 0, DOSIsoft R , Cachan, France). A volume of interest was set around each lesion on the PET images. Then a fixed threshold value of 41% of SUV max was applied to define the volume for each segmented lesion. The volumes of all suspicious lesions in a particular patient were added to compute the TMTV.
Routine Cohort Concerning the routine cohort, 430 patients referred for cancer assessment underwent routine thoraco-abdomino-pelvic or whole body PET/CT (according to the indication), and with at least one tumoral uptake, were included between August 2018 and February 2020. PET/CT scans were acquired on GE 710 (General Electric, Milwaukee, WI, USA) or Biograph Vision 600 (Siemens Healthineers, Knoxville, TN, USA). Patients fasted for at least 4 h and were injected with FDG at a dose of 3. 0 MBq/kg of body weight. Images were acquired 60 min after injection at 2 min per bed position (GE 710) or by continuous bed motion (Biograph Vision). The manual segmentation of lesions was performed using another semiautomatic software (PET VCAR, General Electric R ) during routine clinical activity by two different nuclear medicine physicians (PD and PP).
In a recent study  (33) , the PARS software prototype was tested on a cohort of 280 patients with DLBCL. As with this study, we have established the ability to determine the prognosis of DLBCL using automatic segmentation. The authors however obtained a better lymphomatous lesion recovery coefficient (Dice) of 0. 73 and a better TMTV correlation of 0. 76. The automatically determined TMTVs were, as in our study, predictive of total and PFS with hazard ratios of 2. 8 and 2. 4, respectively. The difference in Dice coefficients and TMTV correlation could be explained by the difference in the populations. Our results are consistent with a recent study  (34) , in which the performances of a CNN model, based on nnU-Net, were investigated to automatically segment TMTV in patients with DLBCL. A first cohort of 639 patients with pretherapeutic FDG PET/CT was used to train the model.
In this cohort, the mean Dice score and Jaccard coefficients for manual and automatic segmentations were 0. 73 and 0. 68, respectively. There was a mean underestimation of automatic TMTV by 12 ml (p = 0. 27). An external validation was done on a second cohort of 94 patients. In this testing set, the mean underestimation of automatically determined TMTV was 116 ml, which was statistically significant (p = 0. 01). Concerning the clinical routine database, we chose to analyze the examinations of patients followed for any cancerous pathology, whereas the model was trained only on lung cancer and lymphomas. This approach corresponds well to the clinical routine where the pathology is variable, and the results remain consistent with those of the research cohort. Nevertheless, the results are more similar to those obtained for the research cohort, which is closer to the training conditions of the algorithm.
Image analysis Videos were processed and analyzed using Fiji software (ImageJ 2. 0. 0). Figures and videos based on two-photon microscopy are shown as 2D maximum intensity projections of 3D data. Tumor apoptotic events were manually quantified and scored as direct killing when a CAR T cell engaged the tumor cell before the detection of FRET loss. Indirect events corresponded to tumor cells undergoing FRET loss without any detectable interactions with a CAR T cell during the imaging period. To perform an unbiased analysis of the spatial localization of apoptotic tumors and CAR T cells, we developed an in-house Fiji script that computes a correlation score. Briefly, for each bone marrow mosaic of 2000x2000 µm, both CFP and GFP channels are first binarized using the Yen threshold. Then for each region of interest (ROI) of 100x100 µm (spaced 50 µm apart), the mean CFP and GFP values are calculated and considered as a good proxy for apoptotic tumor and CAR T cell presence.
Samples were sequenced by Macrogen using HiSeq X ten platform (Illumina). Reads were processed with the 10x pipeline Cellranger 3. 1. 0 and the count command. We used the mm10 genome and transcriptome annotations provided by 10X Genomics. We added two sequences to this assembly. First a consensus sequence of three fluorescent proteins (CFP, YFP and EGFP for identification of FRET-based caspase-3 reporter in tumor cells and GFP-expressing CAR T cells). We also added a segment of the CAR plasmid sequence. For these two additional sequences, we added in the annotation files features on both strands. We used R package Seurat 3. 1. 1 for analyzing the data  (64). From the raw Cellranger output, we selected cells with more than 1,000 unique molecular identifiers (UMIs) and less than 20% of mitochondrial UMIs (untreated (12,348 cells), CAR4-treatment (9,895 cells), CAR8-treatment (7,798 cells)).
We computed 2,000 variable genes and used 50 principal components (PCs) for neighbors finding and clustering. We also used 50 PCs for generating the Uniform Manifold Approximation Projection (UMAP) in this paper. For violin plots, Wilcoxon test was used. For volcano plots, Wilcoxon test and Bonferroni method for p value correction were used. Singlecell RNA sequencing data are available at the Gene Expression Omnibus (GEO) accession number GSE165797.
Multiplex assay for cytokine quantification Three days after CAR T cell transfer, bone marrow cells were isolated by flushing femurs and tibias from tumor-bearing mice, filtered through 70 µm cell strainers and washed with cold PBS. Cells were lysed using NP40 Cell Lysis Buffer (Invitrogen) following manufacturer's instructions and stored at -80°C until analysis. Multiplex assay was performed using 26-Plex ProcartaPlex Panel (Invitrogen) following manufacturer's instructions. Standards were reconstituted with Universal Assay Buffer diluted at 1:2 in assay diluent. For analyte capture, the plate was incubated overnight at 4°C under agitation on an orbital shaker. Analyses were performed using a Bio-Plex 200 system equipped with Bio-Plex Manager software (Bio-Rad). Figure legends.
Acknowledgments We thank  M. Lecuit  for providing IL-12p35-deficient mice and members of the  Bousso laboratory  for critical review of the manuscript. We acknowledge the mouse facility and  CB UTechS at Institut Pasteur  for support in conducting this study with the specific contribution of Valentina Libri. independent experiments. Each dot represents one mouse. One-way ANOVA was used for statistical analysis. ***, p<0. 001; **, p<0. 01; *, p<0. 05. ; ns, not significant.
Funding: The work was supported by  Institut Pasteur ,  Inserm  and an  Advanced grant (ENLIGHTEN)  from the  European Research Council  (P. B). M. Cazaux received financial support from  ITMO Cancer 'Alliance Nationale pour les Sciences de la Vie et de la Santé ' within the framework of the  Cancer Plan. Author contributions: M. B. , M. C. , R. T. , B. C. , F. L. , C. G. and Z. G. conducted the experiments. M. B. , M. C. and P. B. designed the experiments. Y. LM. performed the sequencing analysis. M. B. , M. C. and P. B. analyzed the data and wrote the manuscript.
1999). Integral field spectrographs with relatively large fields of view, such as the multi-unit spectroscopic explorer (MUSE), PCWI, and KCWI, have recently made significant advances in our understanding of the spatial extent, ionization state, and dynamics of the CGM in emission lines from ionized gas. Extended Lyα halos have recently been observed with MUSE around a large fraction of star-forming Based on observations obtained at the European Organization for Astronomical Research in the Southern Hemisphere under program 097. B-0323(B). galaxies at redshifts 3≤z≤6 in the Hubble Deep Field South and other regions  (Wisotzki et al. 2016; Leclercq et al. 2017 Leclercq et al. , 2020;; Bielby et al. 2020). Extended Lyα halos have also shown kinematic signs, suggesting large-scale rotation of accreting material  (Prescott et al.
ALMA observations The Atacama Large Millimeter/submillimeter Array (ALMA) cycle 3 observations in band 3 were carried out on UT 2016 March 6, with 38 min on-source integration and 38 operating antennas. The four 3. 875 GHz wide bands were tuned to include the [C i] 3 P 1 -3 P 0 ([C i](1-0) hereafter) and 13 CO J = 4 → 3 lines, covering  respectively. We calibrated the data with the Common Astronomy Software Applications (CASA) using the observatory-supplied calibration script. The data cube and moment-0 maps were also produced using CASA. A natural weighting (robust parameter of 2) was applied since the data has low signal-tonoise. This results in a continuum image with synthesized beam 1. 8"×1. 9" and a root mean square (RMS) of 14 µJy. To produce the cube and look for [C i](1-0) (ν rest =492.
MUSE observations The MUSE observations with the Very Large Telescope (VLT), Unit Telescopes 4 (program ID 097. B-0323(B)) were carried out over four nights between 2016 June 7 and 2016 September 2, with a total of five hours on source integration time during which the average seeing was ∼1. 2". The data were taken in five sets of two 30 min exposures rotated 90 degrees with respect to one another, resulting in a total of ten exposures. We reduced the data using the MUSE instrument pipeline version 1. 6. 2. First, we preprocessed the data with the standard settings to produce a calibrated and sky-subtracted combined data cube. But due to strong sky lines that were not optimally subtracted and thus created artifacts in the cube (especially the [O i]λ5577 night sky line which lies coincident with the redshifted wavelength of Lyα emission from 4C 19.
71), we decided to reprocess the cube. To overcome the problems with the previous reduction, we reduced each exposure individually without sky subtraction and put them on the same spatial and spectral coordinate grid by providing the first exposure (astrometry corrected) as the OUTPUT_WCS argument for the other nine exposures. Then, we sky-subtracted each exposure by using ZAP 2. 0 (the Zurich Atmosphere Purge;  Soto et al. 2016). We ran ZAP with the default parameters and since it was run on individual exposures, the contribution from the faint sources of emission in the data had a negligible impact on the sky subtraction. Finally, we merged all ten individual 30 min exposures into one combined cube using the python package MPDAF 2. 5 (MUSE python data analysis framework) developed by the MUSE Consortium 1.
Results Combining ALMA submillimeter (submm) observations with MUSE optical observations reveals extended line emission at larger scales around 4C 19. 71 than previously observed. Figure  1  shows X-ray 0. 5-8 keV continuum, K-band, IRAC 3. 6 µm, MIPS 24 µm, ALMA 100 GHz continuum, and VLA 4. 8 GHz images of 4C 19. 71. The AGN and its host galaxy are detected in all of these wavelength bands except with the VLA, where only the two synchrotron lobes extending toward the north and south were detected. The Lyα image constructed from the MUSE data cube shows bright emission at the position of the host galaxy extending out toward both the northern and southern radio hot spots (Fig. 2 ). Weak Lyα emission is also detected south of the southern lobe (region C, Fig. 3 ). Similar morphological structure is also seen in C iv, He ii, and C iii] (Fig.
e. , n H < ∼ 100 cm -3 ). This is consistent with the idea that these regions of the CGM are likely being photo-evaporated and eroded by the momenta of the photons striking and heating the dense gas observed in [C i]. Our data suggest that the CGM in 4C 19. 71 contains a multiphase region of ionized and molecular gas. This gas cloud can be explained as being ionized by the AGN. We are limited to low signal-to-noise and the source is at an unfortunate redshift, which makes the Lyα and He ii unusable for kinematic studies. We therefore rely only on the weak C iv emission. The [C i] has been shown to probe a larger amount of gas mass than what ionization lines can trace. This work shows the possibilities and power of observing galaxies using both ionized and molecular gas. We would need to observe 4C 19.