dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION Characterizing the hypoxic state of pancreatic tumors and how it is interacting with the immune microenvironment could provide an alternate strategy in predicting patient survival and in guiding treatment trajectories, which could potentially alleviate the dismal prognosis of this disease. Increasing evidence has implicated hypoxia reversal with alleviation of resistance to chemo-and radio-therapy in PDA  (11) (12) (13) (14) (15). As such, various clinical trials are being pursued to determine the impact of combination treatments with hypoxia-targeted approaches on patient response and survival  (6). Among the completed clinical trials, a phase III trial found no survival advantage to the addition of a hypoxia targeting agent to gemcitabine in advanced pancreatic cancer (NCT01746979).
In this way, we managed to capture a common response to hypoxia, which we then applied in PDA patient samples. The relevance of the signature in PDA was confirmed by showing that tumors with high hypoxia exhibited significantly increased expression of hypoxia response genes pertinent in this cancer type. The presence of hypoxia as reflected by our signature was associated with worse OS in two independent datasets of PDA. This provides for a more robust signature than what has been previously published in this cancer  (9, 38). Indeed, while a recent thirty-gene hypoxia signature was reported to be associated with worse OS and PFS in the TCGA dataset  (38) , this was not tested in an independent patient cohort. Moreover, when we compared survival parameters in tumors scored based on that signature, to those scored with our eightgene signature, only ours maintained a significant association with OS and DSS.
Given that the authors did not report the list of genes, we could not test their signature in PAAD Data-2. With respect to the other reported signature, it again was only tested in one dataset and the survival association found was lost in multivariate analysis, negating the need to test it in our cohorts  (9). On the other hand, our signature was not only associated with OS, but was also independent of clinical covariates, namely grade and stage, in two independent cohorts of primary PDA. This points to a key conglomerate implication of the genes in our signature and the phenotype that they represent, in determining patient prognosis. Moreover, our evaluation of the signature in vitro allowed for the inclusion of only genes that can be quantified, which intensifies prospects of successfully scoring patient tumors using direct RT-qPCR based assays.
On the other hand, the lowest hypoxia scores were present in the immune classical subtype, which was reported as having the most protective effect on survival  (50). This finding highlights the positive impact the absence of hypoxia can have on the tumor's immune microenvironment in pancreatic cancer and how that can directly affect patient prognosis. Moreover, our signature maintained significant associations with both DFS and OS in the same pancreatic cancer dataset even in the presence of the molecular subtypes, suggesting that hypoxia, as captured by our signature, is representing additional factors that are independently affecting survival. It is widely admitted that hypoxia in PDA deters the establishment of an effective anti-tumor response  (4, 16, 17, (39) (40) (41). Using a validated, tissue agnostic eighteen-gene tumor inflammation signature  (52) , we showed that tumors with high hypoxia scores had significantly lower immune scores as indicated by that signature.
In line with this notion, less hypoxic tumors had significantly higher fractions of CD8+ Tcells, while more hypoxic tumors showed higher fractions of M0 macrophages in two independent datasets of pancreatic cancer. The presence of M0 macrophages in PDA has recently been shown to be associated with worse OS and relapse free survival, while the presence of CD8+ T-cells alluded to improved survival  (61). In that respect, a large meta-analysis has also revealed that among tumor infiltrating lymphocytes, CD8+ T-cells show the strongest association with OS, and high infiltration improves patient prognosis  (62). While we also found significantly higher fractions of certain immunosuppressive immune cell types in tumors with low hypoxia, namely M2 macrophages, resting mast cells, monocytes, and Tregs, these observations seem to be dataset-specific, and could point towards the involvement of other regulatory mechanisms that supersede hypoxia.
Given the complex interplay between the immune and hypoxic microenvironment, we delineated pancreatic cancer subgroups that could better predict survival considering both those elements of the TME. We stratified patients for the first time based on both their hypoxia and immune scores and observed significant differences in survival endpoints. While immune score alone was not associated with survival in the groups with high hypoxia, the hypoxia score was strongly associated with survival in the immune low groups. Indeed, hypoxia in these groups imparted increased risk, suggesting that in immune low patients especially, the presence of hypoxia has a detrimental impact on patient survival and could serve as an enhanced predictor of prognosis. It is important to note here that in the TCGA dataset, patients with hypoxia low and immune high tumors experienced worse survival than patients with hypoxia low and immune low tumors, which could be an indication of the distinct impact of the immune contexture on prognosis in tumors with low hypoxia.
This difference was only seen in PAAD Data-1 and could also be due to inherent differences between the two datasets in terms of degree of tumor heterogeneity as well as sample collection and processing. The importance of investigating the immune background based on the hypoxic status of pancreatic tumors is reflected in the failure to date of single agent immune checkpoint inhibitors from achieving the success experienced in other solid tumors  (2) (3) (4) (5). Indicators of ICI application and efficacy, including TMB, Tcell infiltration, neoantigen load, and PD-L1 expression  (47) , do not seem sufficient to achieve improved response. With this notion in mind, a four-chemokine signature has recently been proposed as an alternate indicator of response for immunotherapeutic strategies  (48).
The cutoff for this score is 0. 4, making all the PDA tumors analyzed as microsatellite stable  (70). Nonetheless, there was a significant increase of the MSI MANTIS score and mutation count in tumors classified as high hypoxia. This is in line with previous findings that hypoxia is associated with increased genetic instability and tumor mutational burden  (71, 72). Therefore, it seems that the presence of hypoxia could be impacting various ICI relevant parameters and the signature could reflect an aspect of the TME that is worth pursuing in future ICI trials in PDA. As with other studies with retrospective data, our findings require prospective validation. Regarding analysis conducted in a single dataset, they need additional replication in independent PDA cohorts. Given that the results were produced using data from patients with early-stage PDA, whether similar associations exist in late stage and metastatic disease warrants further investigation.
In addition, the associations made between our signature and the immune microenvironment were based on transcriptomic data and could benefit from further in vivo validation using proteomic analysis and flow cytometry. Another caveat could be that the sample size in the comparison of survival parameters after grouping based on both the immune and hypoxia scores of the tumors was low and could have inflated the significance of the associations. We believe this to be unlikely, however, given the reproducibility of the finding for OS in two datasets. Taken together, our findings point to the ability of the eightgene signature to distinguish to some degree between immune active and deprived microenvironments that could be useful in predicting response to immunotherapy. In this context, our signature could potentially be used to determine the degree of tumor hypoxia, serving as a translational predictive marker to guide treatment trajectories.
Some studies have focused on power-law regression analysis from a degradation perspective, considering field or experimental measurements. Romanoff published a thorough analysis of underground pipelines based on 47 different soil types, with an exposure time greater than ten years and different soil aeration levels (i. e. , good, fair, and poor)  [2]. Similar power laws have been reported in the work of Kucera and Mattsson  [3] , Southwell et al. [4] , and Velázquez et al. [5]. Kucera and Mattsson evaluated the general corrosion at atmospheric pressure. Southwell and co-workers fitted the data of pitting corrosion from 52 metal and alloys exposed to five different tropical environments for 16 years  [4]. Finally, Velázquez et al. investigated the corrosion rates during three years in different soils in the south of Mexico, showing that corrosion aggressiveness could be shown to be in the following order: clay > loam > sand  [5].
These categories were selected based on the recognized leak state limit of 85%t, where t is the pipeline thickness. Further criteria using the failure probability could also be implemented. The correspondence between the corrosion categories and the J soil features were evaluated following three stages (Figure  4 ). The first stage evaluated each feature individually to inspect if it tended to be independent of the corrosion category using contingency tables and the χ 2 test. The second stage implemented a multiple correspondence analysis (MCA) from the entire dataset to identify links among soil categories and correlations with the corrosion depth. The third stage considered a K-mode approach that clustered the combination of features with more profound corrosion defects, aiming to identify possible aggressive features of the soil.
Multiple Correspondence Analysis of the Soil Features Multiple correspondence analysis (MCA) is an exploratory analysis analogous to a principal component analysis (PCA) using categorical variables. Let I and J be the number of individuals and variables, respectively, as it would be in a survey with I responses of J multiple-choice questions. Each of the J "questions" has a specific number of possible answers denoted by K j that are known as categories; for instance, the corrosion variable has four categories, as depicted in Table  2. The objective of the MCA is to determine how similar the individuals (i. e. , responses) are to identify possible relationships between variables (i. e. , questions) and categories (i. e. , possible answers). We denote as x ij the category selected by the ith individual of the jth variable and X = [x ij ] as the I × J matrix dataset.
This dataset can be expressed in a I × K indicator matrix, where K = ∑ j K j , as depicted in Figure  5. This figure shows the indicator matrix, in which instead of having the category selected by the individual (x ij ), an indicator vector of length K j is used for each variable with components y ik , defined as follows  [30] : y ik = 1, if x ij = k; the k th category of the jth variable 0, Otherwise, In this matrix, two types of variables are separated: active and supplementary. The active variables allow the determination of the leading associations and the supplementary variables as descriptors of the active ones; therefore, the corrosion variables were considered supplementary and the soil features as active variables for this work. The individuals and the variables are in two clouds, denoted as N I and N K , which have R K and R I dimensions, respectively.
The projections occur in the direction of the eigenvectors. This contribution describes the ratios of the inertia projected by an individual/category and the inertia projected from the entire cloud (in a given component). The quality of representation corresponds with the ratio of the inertia projected and the total inertia of the individual/category. This can also be described with the square cosine of the angle formed between the two inertias. It indicates the level of association between the categories and a given component  [30]. In this work, the package of "FactoMineR" in R is implemented to evaluate the MCA results  [33].
Correspondence Results with the Soil Categories The initial stage evaluated the independence assumption between each soil feature and the corrosion severity labels low (0-24%), moderate (25-49%), high (50-74%), and very high (75-100%). For this purpose, contingency tables and a χ 2 -test were implemented, obtaining the results depicted in Table  6. This table presents the χ 2 statistic in Equation (  1 ) and the p-value under the null hypothesis of independence, considering 2000 Monte Carlo replicates for each soil feature and dataset. Based on a level of significance of 5%, the null hypothesis is rejected for the following features: The obtained results suggest some interesting patterns. For the inner wall at the second inspection, the results indicate that the corrosion labels are almost independent of the soil features, as expected, except from a weak dependence with the fertility with a p-value of 0.
2% and 51. 7% of the total variance of the ILI1-Inner and ILI1-Outer datasets, respectively. Thus, almost half of the individual and variable clouds' variability could be explained by projecting the clouds to the plane formed by the first two components. The remaining inertia was distributed in lower proportions in four additional components at the inner wall and three for the outer dataset; however, only the first two components were evaluated. As an initial result, the correlation of the variables with the first two principal components for both pipe walls was compared, obtaining the results shown in Figure  7. In this figure, the corrosion depth is depicted in blue color because it is a supplementary variable, whereas the remaining variables were active, so they are represented with a red color.
For the outer wall, Figure  7b  shows that the soil depth and the soil acidity could also provide additional descriptions of the corrosion depth. Regarding the MCA factor results, Figure  8  depicts the quality of the category representation obtained from both datasets. This figure shows a pattern known as the Guttman effect, where the distribution of factors looks like a parabola. According to Husson et al. [30] , this effect indicates that some active variables (i. e. , soil features) may be redundant, and the cloud of individuals may be constructed in a higher proportion for the first principal component. The authors also pointed out that this effect is favored for categorical variables following a given order, where one axis repels the smaller and most significant categories and another axis separates some extreme categories.
As noted before, the lithology, texture and fertility are closely related primarily at the extreme value at the upper right, where a cluster formed by L i3 (sandy clastic rock and clay silt), T x4 (medium to fine), F e1 (low fertility) and A c2 (extremely to strongly acid) is recognized in both datasets, corresponding with the records reported in soil S3. Acidity categories A c2 and A c5 can be identified for the inner wall comparing the two extremes of the parabola, which are associated with a pH from 3. 5-6 and 5. 1-6, respectively. Similarly, for the outer wall, the extremes discriminated against the fertility categories F e1 (low) and F e4 (moderate to low), which would explain this pattern in both datasets.
Corrosion and Soil Characteristics: Result of K-Mode Analysis The last stage of the correspondence analysis between the corrosion categories and the soil features was the K-mode clustering, following the methodology reported by Huang  [34]. As mentioned in Section 4. 3, this algorithm requires an initial number of modes where the individuals will be clustered depending on their nearest neighbor. This procedure continues until there is no change in the way individuals are clustered. Therefore, sensitivity analyses with different modes were implemented to identify the number of modes, minimizing the within-cluster distance. Following an elbow (point of maximum curvature), we found that two modes were sufficient to handle this algorithm, except for low corrosion in the ILI2-Outer dataset, which required an additional mode.
To model species assemblages, recent statistical advances yield to Joint Species Distribution Models (JSDMs)  [43, 9, 37, 52] , which are multivariate extensions of generalized linear regression models (GLM) [other approaches can be found in  21, 51]. In JSDMs, the regression coefficients are related to the response of species to the environment, as in SDMs, while the correlation among the residuals describe the pairwise-species dependencies not explained by the environment. Since JSDMs were created to deal with community data, they are gaining popularity with the everincreasing developments of novel methods for community assessment, such as environmental DNA (eDNA) metabarcoding  [47]. However, their application to large datasets still faces strong limitations such as computational costs and the interpretation of the residual covariance matrix.
Indeed, JSDMs are computationally expensive because the number of estimated parameters in the residual correlation matrix grows quadratically with the number of species. There are several approaches to address this problem. For JSDMs that are based on the multivariate probit model, computational reduction can be achieved by efficient parallel sampling  [6, 39]  to fit a full covariance matrix in a frequentist framework. Another common solution relies on dimension reduction through latent variable models (LVM)  [52] , where the effective dimension of the model is reduced by a low-rank approximation of the residual covariance matrix  [52, 35, 48, 24]. While the approximation with low rank values could capture the residual associations in the covariance matrix for a large number of species and significantly improve convergence and computational time  [52] , their wide applications to large dataset is still prohibited  [39].
Implementation and specification of the models We applied our DP c and PY c models together with the original DP model in dimension reduction with the default settings on the Bauges plant data. We fitted the models using Bayesian inference via MCMC using a Gibbs sampling scheme. For the original DP model, we used the R package GJAM  [48]. We implemented the DP c and PY c models in R by extending the original GJAM R package. In particular, we implemented an additional adaptive Metropolis-Hasting step (for DP c ) and the multi-step algorithm proposed by  [27]  to sample from PY c (see details in Appendix Section S. 6). Our code (an extension of the GJAM package) can be found at the first author's Github repository (https://github. com/dbystrova/GJAM clust). The prior on the number of clusters was set using the number of plant functional groups (PFGs) as described above (see Appendix Section S.
However, we did check that the model fitted well the data both on training and test set. The dataset was randomly partitioned into a training and a testing dataset, using 70%/30% ratio. We fitted models on the training dataset and then predicted species occurrences on the testing data, comparing the predicted and the actual occurrences, similarly to  [32, 53]  (cross-validation is not a doable task due to the computational costs of the models). For each species, we measured the predictive performances by calculating the area under the receiver operating characteristic curve (AUC) on both training (AUC in ) and testing datasets (AUC out ).
FUNDING DB, GP & WT were supported by the GAMBAS project funded by the French National Research Agency (ANR-18-CE02-0025). JA was supported by the Grenoble Alpes Data Institute, funded by the French National Research Agency (ANR-15-IDEX-02). JSC and WT also acknowledged support from the Programme d'Investissement d'Avenir under project FORBIC (18-MPGA-0004). This work also received funding from the ERA-Net BiodivERsA -Belmont Forum, with the national funder Agence National pour la Recherche (FutureWeb: ANR-18-EBI4-0009) to WT and the National Science Foundation (NSF grant No 1854976) to JSC. This work has been partially supported by MIAI @ Grenoble Alpes, (ANR-19-P3IA-0003).
Coupling procedure of Fluid-Solid interaction The coupling between the fluid and the solid models requires a transfer of data from one model to another. In our case, the fluid model uses meshes while the solid model is meshless (see Fig. 3   23 )). It is specified that in this fluid-solid coupling, the transfer is retroactive, where the fluid transfers to the solid the pressure force, a force considered external to the solid. f i = p i d 2 ; f j = p j d 2 ( 22 ) where p i and p j are the fluid pressures at nodes i and j, and d is the distance of the finite element edge from node i to node j. Finally, the global forces (F 1 , F 2 ) acting on the DDA block can be written as: F 1x = n i=1 f i n x ( d 2 d 1 + d 2 ), F 1y = n i=1 f i n z ( d 2 d 1 + d 2 ) F 2x = n i=1 f i n x ( d 1 d 1 + d 2 ), F 2y = n i=1 f i n z ( d 1 d 1 + d 2 ) ( 23 ) where n is the total number of nodes in the edge, d 1 and d 2 are the distances from the given node to vertices 1 and 2, and n x and n y represent the directions along x and y respectively.
However, current methods of MD estimation require manual muscle definition of CT slices and have to be performed by a trained operator using dedicated software. These constraints limit its use in daily practice  [7]. A solution was found in the automation of segmentation by using Deep Learning. This machine learning allows computer models, using artificial neural networks, to autonomously learn abstract representations from large amounts of data. For the learning process, the data are provided to the algorithm in the form of "ground truths", corresponding to an ideal expected result. Among these models, convolutional neural networks (CNN) led to major advances in image and video recognition  [8]. U-Net, launched in 2015, is one such CNN developed for segmentation of biomedical images  [9].
Patients Selection To increase diversity and reduce overfitting, the training set consisted of 190 patients included in 2 prospective trials of the Lymphoma Study Association (LYSA) group (GAINED and LyMa trials). In brief, GAINED (NCT01659099) was a prospective phase III trial carried out from 2013 to 2015  [26]  that compared Obinutuzumab vs. Rituximab (R) in combination with CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) or ACBVP (idem with bleomycin and vindesine replacing vincristine) chemotherapy in newly diagnosed untreated DLBCL patients under 60 years of age. The other main inclusion criteria were an age-adjusted International Prognostic Index (aaIPI) ≥1; eligibility for autologous stem cell transplantation (ASCT); life expectancy ≥3 months; and normal liver, renal and hematological function unless abnormalities were related to DLBCL.
The therapeutic strategy was guided by an interim metabolic evaluation by FDG-PET (PET) after cycle 2 (PET2) and 4 (PET4), leading to a therapeutic intensification with (ASCT) in case of insufficient response. The results showed no difference between the 2 antibodies. LyMa (NCT00921414) was a prospective trial which evaluated rituximab maintenance following ASCT in patients with previously untreated mantle cell lymphoma  [27]. All included patients performed a baseline PET-CT prior to the beginning of treatment. A total of 100 patients from LyMa and 90 randomly selected patients from GAINED were included in the training set. The algorithm's training validation process was performed on a testing set including 49 patients randomly selected from GAINED and different from the training cohort. The exploratory set consisted of the 670 patients included in the GAINED trial.