dataesr/scientific-paragraphs-categorization huggingface dataset:
Patient samples OncoNeuroTek (ONT) is a local brain tumor tissue bank collecting samples from patients operated at the University Hospital La Pitié-Salpêtrière. All samples were collected with informed consent from patients. The inclusion criteria of the discovery local cohort (47 patients) were as follow: (i) newly diagnosed and histologically veri ed GBM, (ii) age at diagnosis is below 70 years, (iii) KPS above 70%, (v) known MGMT promoter methylation status, (vi) known IDH status, (vii) treated with the standard rst-line therapeutic regimen including maximal safe surgery, chemoradiation and adjuvant temozolomide and, (viii) a documented clinical follow-up. The validation cohort (121 patients, TCGA cohort) clinical information and RNA-sequencing data (read counts) were downloaded from the National Cancer Institute's Genomic Data commons (GDC) Data portal and from the NCBI GEO GSE62944, respectively.
05 were considered signi cant  (20, 21). Furthermore, we have used TCGA database to evaluate and pro le tumor in ltrating immune populations and whether it differ among the highly expressed C86 tumor cells. TCGA immune data (i. e. , CIBERSORT calculated immune populations) was retrieved from https://cavei. github. io/exampledatasets/panCancerAnnotation. RData. Comparisons were performed by two-side Wilcoxon-test and p-values were corrected for multiple comparisons using FDR method.
Prognostic value of CD80 and CD86 expression Our patient's cohort was used as a discovery cohort, while the TCGA dataset was used as a validation cohort. In a univariate analysis, mRNA expression of CD80 and CD86 was not signi cantly correlated with OS in both the ONT cohort and TCGA dataset (Table  1 ). On the other hand, CD80 and CD86 mRNA high expression was signi cantly associated with shorter PFS (p = 0. 04 and p=0. 005, respectively; Figure  3, A-B ). Moreover, these ndings were validated using the TCGA cohort; higher CD80 and CD86 expressions were correlated with shorter PFS (pvalue; 0. 0428, 0. 00283; Figure  3 , C and D). Interestingly, higher CD86 protein expression was associated with shorter PFS in the ONT cohort (P<0. 005; Table  2 ). CD80 and CD86 protein expression were not available in the TCGA dataset for validation purposes.
However, we have used TCGA database to pro le tumor in ltrating immune cells in the selected cohort. As expected, MGMT promoter methylation was associated with longer PFS and longer OS in the ONT cohort (p<0. 05 and p<0. 05 respectively) and TCGA dataset (p<0. 05 and p<0. 05 respectively) (Tables  1  and 2 ). Furthermore, IDH mutations were also associated with better OS and PFS in the TCGA database (p<0. 05 and p<0. 05 respectively); however, in the ONT cohort, the limited number of IDH-mutant GBM did not allow a robust analysis (n=2). In multivariate analysis, CD80 mRNA expression did not provide additional prognostic information to MGMT promoter methylation in the ONT cohort. On the other hand, multivariate analysis of CD86 mRNA expression was an independent prognostic factor for PFS in the TCGA dataset only (p<0.
Not to mention, there is a possible error and noise in protein quanti cation and mRNA extraction that could in uence mRNA stability and protein expression  (20). In addition to DAB staining intensity used in our study, quanti cation of protein using the number of positive cells should be also evaluated in future IHC analyses to better understand expression of proteins and mRNA of interest. Number of patients (n=47) in the ONT cohort is lower than the number of patients in the TCGA dataset (n=121). The higher number of TCGA GBM samples could be one reason that affected the statistical analysis and provided a better prognostic value than the ONT cohort. Indeed, GBM samples' availability with comprehensive clinical and biological annotations and ful lling the inclusion criteria is a limitation for a larger cohort.
Larger patient cohort is needed to evaluate the prognostic value of CD86 expression in the TME of GBM patients. Using TCGA data to pro le immune cell populations interestingly revealed that CD86 expression is associated with an immunosuppressive tumor microenvironment with low activity of cytotoxic T cells however protein analysis of immune cell populations is needed to validate our ndings from TCGA immune cell population pro ling. Indeed, high CD86 expression is associated with a cold immune microenvironment with a limited anti-tumor immune response promoting tumor growth and poor prognosis. The expression of 50 immune checkpoint molecules was investigated in breast cancer. The study showed that high expression of costimulatory immune checkpoint molecules was associated with better PFS. However, no signi cant effect on prognosis was associated with CD80 and CD86 expression in the selected cohort  (22).
Feng et al. reported that low expression of CD80 is a predictive biomarker for poor prognosis in gastric adenocarcinoma  (23). Furthermore, CD80 and CD86 were found to be potential biomarkers for better prognosis survival in nasopharyngeal carcinoma  (24). Additionally, the molecular characterization of PDL1 expression was correlated with other checkpoint proteins, i. e. , CD80, highlighting that higher levels of immunosuppression are associated with GBM than lower-grade gliomas (LGG)  (25). In myeloma cell lines, silencing the CD28-CD86 pathway resulted in myeloma cells' signi cant cell death  (26). A recent study constructed a more robust model, using GBM and LGG data from the TCGA and CGGA (Chinese Glioma Genomic Atlas), and identi ed that low expression of CD86 molecules is a good prognostic indicator for OS.
In line with these ndings, our study supports that the expression of immune checkpoint inhibitors may inhibit Tlymphocyte and anti-tumor reaction. A recent integrated analysis of the prognostic value of CD86 reveals that CD86 is heterogeneously expressed in gliomas and is an independent unfavorable prognostic value in LGG  (31)  CD86 molecular status could be explored as predictor of response to immunotherapies in the setting of future clinical trials dedicated to glioblastoma patients. Our study suffers from the limitation of retrospective studies with a limited number of patients. Nonetheless, our results were validated in an independent dataset and support investigations of immune checkpoint molecules as potential prognostic biomarkers and potential predictive biomarkers of response to immunotherapies in GBM.
CIBERSORT calculated tumor in ltrating immune populations in TCGA database. Immune cell populations represented fraction of the X-axis immune cells to the whole gene expression mixture. Box plots depicting the estimated relative fractions of immune cell types by GBM category according to CD86 expression. The Y-axis here show the relative proportion which can range from 0 to 1. Relative fraction estimates the percentage of a given cell population in the total tumor in ltrate. In our analyses immunosuppressive M2 macrophages and lymphocytes were the most frequently observed immune phenotypes.
The MaNGA final data release (DR17;  Abdurro'uf et al. 2021 )  includes observations of about 10 000 galaxies: the DR17 MaNGA Morphology Deep Learning Value Added catalogue (DR17-MMDL-VAC) provides morphological classifications and the DR17 MaNGA PyMorph photometric Value Added Catalogue (DR17-MPP-VAC) provides S érsic (Ser) and S érsic + Exponential (SerExp) fits to the 2D surface brightness profiles of these objects, along with a detailed flagging system for using the fits (see Dom ínguez S ánchez et al. 2022 for details). Bernardi et al. (in preparation)  describe how to combine the photometric and flagging information to determine reliable B/T values for these objects, and how the B/T correlate with morphology. Because the MaNGA selection function is complicated, and because the MaNGA sample is much smaller than the SDSS , we use the SDSS to determine the shape of the stellar mass function and how f ellipticals varies with stellar mass, but MaNGA to determine how B/T correlates with stellar mass.
8  the major merger pair fraction as predicted by DECODE for Models 1, 2, and 2a. We compare our results with the data from UKIDSS UDS, VIDEO/CFHT-LS, UltraVISTA/COSMOS, and GAMA surv e ys, presented in  Mundy et al. ( 2017 ). The pair fraction in DECODE is calculated as the number of infalling satellites with stellar mass ratio abo v e 1/4 living within 5 and 30 kpc from the centre of the central galaxy. We assume that the distance of the satellite galaxies scales proportionally to its dynamical friction time-scale  (Guo et al. 2011 ). We make use of the projected 2D distances computed following the recipe in  Mundy et al. ( 2017 )    stochastically a polar angle in spherical coordinates and projecting the 3D distances on to the z-axis. Analogously to what inferred for the number of major mergers, Model 1 tends to produce more pair fractions with μ > 1/4 than Model 2.
Although the total SMF is an input in DECODE , the satellite SMF is an actual prediction of the model, as it depends both on the satellite evolution after infall and on the rate of galaxy mergers, which in turn depend on the highz SMHM relation and on the dynamical friction time-scales. Fig. 9  shows the results of the SMF for all galaxies (centrals + satellites) and only satellites (red and blue lines, respectively). The different types of line distinguish the four SMHM models, and the blue and red dots with error bars are, respectively, the satellite 9  and total galaxy SMF as measured in SDSS using, for consistency with our Models, the data from  Bernardi et al. ( 2017 ). As one can see from the red lines, the total SMF is well reproduced by DECODE , as expected by construction via the abundance matching relation given in equation (  8 ).
9. Fig. 9  shows that only Model 2, the one Figure  10. Fraction of elliptical galaxies as a function of the galaxy stellar mass as predicted by DECODE. Panels A, B, and C show the predictions for the four models described in Section 4. 1 at redshifts z = 0. 1, 1, and 2, respectiv ely. P anel D shows the predictions for Model 2 at redshift z = 0. 1, along with the impact of assuming different mass ratio for the major mergers (denoted as MM in the legend), as well as the prediction for Model 2a. The data from the SDSS surv e y, GALICS semi-analytical model and the TNG100 hydrodynamical simulation are included, as labelled, for comparison. characterized by an evolving SMF and constant scatter, better lines up with the observational data of satellites, especially at stellar masses M * 3 × 10 10 M.
We will investigate this possibility in this Section. For each SMHM model, we follow the merger history of galaxies in the mock from redshift z = 4, and we label each galaxy that has undergone a major merger as an 'elliptical', and then compute the fraction of ellipticals at different redshifts. We show the results in Fig. 10. Panels A, B, and C show the fraction of ellipticals predicted by DECODE using the four Models of SMHM described in Section 4. 1 for redshifts z = 0. 1, 1, and 2, respectively, along with the predictions from the TNG simulation and from the semi-analytical model GALICS , as well as with the SDSS data (see Section 2. 4 ), only shown in the panels at z = 0. 1. We note that, as discussed in Section 2 , GALICS and TNG define elliptical galaxies in a somewhat different way than a simple cut in μ, but we still include their predictions in Fig.
We compare our predictions with data on the mean B/T as a function of galaxy stellar mass from MaNGA (black dots with error bars), as detailed in Section 2. 4. The fact that the shaded areas in SMHM Model 2 are slightly broader at high stellar masses than those in Model 1 is an artefact of the number of mergers predicted by these Models, as shown in Figs  6  and 7. In particular, at fixed number of DM mergers, Model 2 predicts less major mergers, i. e. more minor mergers than Model 1. This leads to a higher bulge/disc mass in the cases where all the mass from minor mergers goes to the bulge/disc, leading, therefore, to a larger bound. The MaNGA data clearly indicate that all local galaxies have a B/T ratio that is confined within 0. 2-1, with a mean value slightly rising with increasing stellar mass, from ∼0.
In particular, we find that, irrespective of the exact input SMHM relation, to broadly match the data we need to assume that a fraction of the disc mass is transferred to the bulge at each disc instability event, in line with some previous cosmological models (e. g. Bower et al. 2006  ). In the bottom panels of Fig. 12  , we show both the cases where clumpy accretion (following  Bournaud et al. 2011  ) and 'classical' disc instabilities  (Efstathiou et al. 1982 )  are implemented. We find that Model BT1, 12 the one without disc regrowth, with SMHM Model 2 broadly matches the SDSS local average B/T ratios when some level of disc instabilities is included in the model, while it fails to match the data at low masses with SMHM Model 1. In particular, disc instabilities implemented following  Bournaud et al.
agreement with our predictions and the data. In addition, they also include bulge formation via disc instabilities (see e. g. Tacchella et al. 2019 ; Cattaneo et al. 2020 ) , predicting indeed B / T > 0. 2 at low masses in line with the data, though the mean B/T predicted by GALICS tends to be larger than the one measured in MaNGA to wards lo wer stellar masses (we provide in Appendix D a detailed comparison between the B/T ratio found in MaNGA and those from other samples/studies). Our Model 1 is roughly consistent with the predictions of GALICS at all masses but not with the observational data and the TNG simulation at low masses, while Model 2 behaves in nearly the opposite fashion, highlighting once again the dependence of galactic properties, this time the mean B/T ratios, on the input SMHM relation.
Thus, the final set of manuscripts that reported primary datasets of SARS-CoV-2 wastewater surveillance via passive sampling totaled 11 publications as summarized in Table  1. Four publications reported paired data derived from passive samplers of various materials and more traditional grab or composite samples. Four publications reported on the use of passive samplers for building-level wastewater surveillance of COVID-19 in university residence halls and the Olympic Village in Tokyo, Japan. One study reported both a method comparison and COVID-19 surveillance with passive sampling of wastewater on a university campus. Lastly, two studies reported empirical observations of passive sampler kinetics and calibration for microbiological analytes from wastewater. Herein, we synthesize the observations from these studies into the following topical considerations: (i) passive sampling performance versus traditional sampling methods (composite and grab) for SARS-CoV-2 RNA in wastewater; (ii) passive sampling performance for COVID-19 surveillance; (iii) the theoretical and mechanical basis for passive sampling of microorganisms such as SARS-CoV-2 from wastewater; (iv) prospects of passive sampling to produce quantitative data; and (v) fabrication, deployment, and processing of passive samplers.
, 2022). For PMMoV, HAdV 40/41, and enterovirus, electronegative membranes demonstrated linear accumulation for 48 h of exposure with estimated sampling rates of 1 mL/h, 33. 1 mL/h, and 0. 3 mL/h, respectively  (Li et al. , 2022). Tampon swabs and cotton buds demonstrated non-linear uptake with saturation in 8 h or less whereas cotton gauze demonstrated rapid uptake followed by declines in viral analytes up to 48 h suggesting desorption or decreased recovery efficiency with increasing exposure duration  (Li et al. , 2022). Applying the linear uptake model derived for electronegative membranes to published SARS-CoV-2 RNA datasets indicated a sampling rate of 0. 1 mL/h to 4. 2 mL/h for SARS-CoV-2 in wastewater  (Schang et al. , 2021; Habtewold et al. , 2022; Li et al. , 2022). These two studies highlight several important implications of passive sampling theory for SARS-CoV-2 and wastewater.
Passive sampling to generate quantitative SARS-CoV-2 RNA data The uptake of SARS-CoV-2 and several other viruses from wastewater indicate passive samplers, especially electronegative membranes, may be capable of producing time-weighted average or semi-quantitative data. Despite the promising qualitative and kinetic results, studies considering the ability of passive samplers to produce accurate SARS-CoV-2 RNA quantitative data from wastewater are limited. Schang et al. (2021)  found that SARS-CoV-2 RNA concentrations on passive samplers demonstrated a statistically significant correlation with concentrations in wastewater and suggested that passive samplers could yield semi-quantitative data, but replication is needed. To assess the performance of passive sampling to produce quantitative data across five different settings, we considered SARS-CoV-2 RNA concentrations on various types of passive samplers as implemented on three university campuses in combination with datasets extracted from the work of  Habtewold et al.
(2022)  and  Schang et al. (2021). Universities have played a key role in piloting wastewater surveillance methods to manage COVID-19 among campus residents  (Harris-Lovett et al. , 2021). Wastewater surveillance programs at the University of Maine (UMaine), University of Massachusetts Amherst (UMass), and University of Connecticut (UConn) have used passive samplers to measure SARS-CoV-2 RNA in wastewater. The methodological details pertinent to each of these three programs are reported in the Supporting Information. From these five unique studies (UMaine n = 20; UMass n = 7; UConn n = 48; Schang et al. n = 33; Habtewold et al. n = 3) we compiled a dataset of paired measurements (n = 111) of SARS-CoV-2 RNA in wastewater (GC/L by composite sampling) and on passive samplers (GC/sampler), Fig.
1. In the case of  Habtewold et al. (2022) , which compares three different passive sampler types to a single paired composite, we averaged across all three replicates of each passive sampler type to produce three paired samples. The concentration data was log transformed, with non-detects (n = 20) removed from the regression analyses. The non-detects reported in the datasets (not displayed on the graph) highlight a key consideration: a difference in the analytical sensitivity of passive samplers versus composite samplers. In the aggregated dataset, there were 17 instances in which passive samplers yielded non-detection while SARS-CoV-2 RNA was detected in the paired composite sample and three pairs consisting of passive sampler RNA detection and composite sample non-detection. These results suggest for qualitative data passive samples were 84% accurate in detecting SARS-CoV-2 RNA compared with composite samples.
When aggregating all five datasets, we observed a weak linear relationship between SARS-CoV-2 RNA concentrations from passive and composite samples (y = 0. 40 x + 3. 0, r 2 = 0. 41; Fig. 1 ). Since the passive sampling methods used by each sampling campaign were unique, we divided the analysis into individual campaigns to further examine the relationship across methodological approaches. The lowest r 2 value for a linear model was observed with the UConn dataset (y = 0. 37 x + 3. 2; r 2 = 0. 27) while UMaine demonstrated the strongest linear fit (y = 0. 71 x + 1. 4; r 2 = 0. 76). The data from Schang et al. (y = 0. 40 x + 3. 2; r 2 = 0. 31) demonstrated a weak linear fit while the data from UMass demonstrated a moderate fit (y = 0. 71 x + 1. 4; r 2 = 0. 56). We did not fit a model to the data from Habtewold et al.
The University of Massachusetts Amherst (UMass, gold), University of Maine (UMaine, red), and University of Connecticut (UConn, blue) datasets (unpublished) were provided by co-authors. Brief descriptions of these sampling campaigns are provided in the Supplemental Information. The concentrations were log transformed, and linear regressions were fit to each series individually (except for  Habtewold et al. , 2022)  and to the aggregated data with the resulting fit and r 2 shown. 2021). In sampling locations subject to high flow rate wastewater, the tensile strength of fishing line may not be sufficient and some teams have made use of materials such as Rexlace (Pepperell Brading Company, Pepperell, MA, USA), a sturdier plastic line, to deploy passive samplers. Passive samplers have been left deployed in wastewater systems for durations from 3 to 96 h with exposure durations of 24 to 48 h being most common.
Ethics and study registration The protocol was approved by the "Comitede Protection des Personnes Ile de France XI" (#2010-A00908-31-10056) on September 13, 2010 and registered on clinicaltrials. gov (NCT01378169) https://clinicaltrials. gov/ct2/show/NCT01378169. According to French national regulations, written consent of the patients was required but waived for the unarousable ones, and obtained if the study still required specific samples when the patient awoke.
Data transformation For all considered biomarkers, we determined the maximum value of the blood levels (Cmax) obtained at day 0 and day 1 of inclusion. We chose peak values because we were unable to ascertain the precise date of sepsis onset, due to the variability of the syndrome and the high number of biomarkers used, leading to missing values in several biomarkers. These values were natural log-transformed (ln(marker+0. 0001) to normalize their distribution (convert the skewed distribution of these variables to approximate normality) and further standardized to have 0 mean and unit variance.
Subclass Mars3, with a better survival, displayed increased expression of adaptive immune or T-cell functions. Davenport et al. identified two sepsis subclasses named SRS1 and SRS2  [32] , with SRS1 characterized by a higher mortality and decreased expression of immune functions. Finally, based on gene activation profiles, Sweeney et al. identified three sepsis subclasses named inflammopathic, adaptive and coagulopathic  [33]. The adaptive subgroup was associated with lower severity and mortality, and the coagulopathic subgroup with higher mortality and clinical coagulopathy. In our study, we also derived clusters from endotypes, but these were assessed with biomarkers made of molecules previously documented to play a role the pathophysiology of sepsis. We used unsupervised statistical approaches to set up clusters, because they explore data without a priori classification  [34] : principal component analysis, to determine different patterns, hierarchical clustering to group the patients and k-means algorithm to assess the internal validity of the clusters.
Consequently, we consider that only the endotypes (and not the phenotypes) that we found may be useful for this purpose. In future use of these endotypes, a selection of 2 or 3 biomarkers for each immune pathway would be sufficient, limiting the risk of missing values. Future external validation of our results could be based on a minimal dataset based on both the endotypes and phenotypes we found. This data set could be the following: confirmed infection at day 3: yes/no; blood values of CRP and ferritin; use of RRT and/or vaso-pressive drugs; blood levels of TNF-α, IL-6, and IL-8 (pro-inflammatory); blood levels of IL-1Ra and IL-10 (anti-inflammatory); HLA-DR and CD74 (adaptive profile). Our study has limitations. First, several biomarkers, including some recently described, were not assessed in this cohort, and should be assessed in similar conditions.
Soil BD was calculated by dividing the dried soil mass (g) of each sample by its 60-cm 3 volume when sampled. Soil water content and BD data for each soil pit were plotted against depth to find obvious outliers. There were 300 total soil samples. Only five outliers were observed in dry-site samples and eight outliers observed in wet-site samples. With outliers removed, mean water content and BD values could be calculated for each depth and access tube. Neutron probe count ratio values at each depth and access tube were calculated for each neutron probe by dividing the reading (count) at each depth by the mean of all standard counts taken for that neutron probe.