dataesr/scientific-paragraphs-categorization huggingface dataset:
We found that BulkSignalR inferred an average number of LRIs which were significantly more reliable according to two neutral quality criteria. The number of LRIs found by stLearn, CellPhoneDB and SpaTalk varied substantially, and SpaTalk computing times were very long (up to hours on four processors). We also observed limited overlap between the four tools for each dataset. Using a new Visium™ dataset that included four CRC-LM samples, we used two of these samples to experimentally challenge selected inferences obtained with BulkSig-nalR. The CDH1-EGFR, CDH1-cMET, DCN-EGFR and DCN-cMET interactions were supported by protein colocalization in both samples assessed by IF. The activation of receptors, and especially receptor tyrosine kinases, such as EGFR, is complex and involves multiple agonist and antagonist ligands, as illustrated by our analysis that unraveled several, spatially-dependent ligands besides CDH1 and DCN.
The complete analysis of the net results of all these interactions goes beyond the scope of this article, but the presented data showed the potential of our ST data analysis pipeline. The analysis of spatial data did not require any modification of BulkSignalR, only the adaptation of few parameters. However, we added dedicated graphical functions. The BulkSignalR R library was designed to be easy to use by scientists with basic knowledge of R. Few functions are sufficient to prepare a dataset, infer the LRIs, and generate informative plots. Although our tool can only infer what is documented in reference databases, users can add to or replace the list of putative LRIs entirely opening the door to testing new LRIs.
In the SDC and GTEx brain cortex datasets, no individual L-R correlation was strong enough such that it reached 5% statistical significance or better without considering pathways. A simple dot at (0,0) was drawn to indicate this fact. (E) Performance of simple single-cell-inspired scores when samples display clear differences in cellular composition. We considered clusters A vs. B∪C and A versus B to look for increased scores compared with the randomized expression data in (A) or in both directions. (F) Principle of constructing of a gene signature for a L-R-pathway triple, and computation of the weighted average of z-scores (left). Multiple L-R-pathway signature scores can be compared among samples, e. g. in a heatmap (right). (G) In BulkSignalR, the reference database is of human origin, but an integrated ortholog mapping tool allows using it for virtually any species.
Correct cases include interactions involving 1, 2 or 3 cell types and increasing Gaussian noise N(0, σ ). Random cases were obtained by shuffling 10, 25 or 50% of data. Synthetic models were generated using a small (SDC) and a large (TCGA breast cancer, BRCA) dataset. In total, 100 synthetic models were constructed for each configuration (correct/random, , shuffling rate, #CTs = number of cell types).
The color code indicates the number of tools that found one LRI (BSR and BSR. N as well as STL and STL. S count for one tool). The Venn diagram represents the overlap between tools and selections. (F) Statistical associations between tissue regions and concomitant expression of the ligand and the receptor captured by the product L × R yielded Q-values (Kruskal-Wallis test followed by Benjamini-Hochberg multiple-hypothesis corrections due). The boxplots compare the strengths of association (the Q-values) obtained by each tool. (G) Spearman correlation between ligand and receptor in the whole tissue. (H) Number of significant LRIs in the HER2 + breast cancer dataset. (I,J) Same as (F,G) for the HER2 + breast cancer dataset. (K) Number of significant LRIs in the dorsolateral prefrontal cortex dataset.
ACKNOWLEDGEMENTS The authors acknowledge the experimental help of  Mr Cyril Degletagne ,  Cancer Genomics Platform  ( Centre Léon Bérard, Cancer Research Center of Lyon, France ) for NGS library preparation and sequencing. The authors are thankful to  Dr Virginie Georget  ( Montpellier Imaging Platform, Biocampus, Montpellier ) for the help with IF data acquisition. FUNDING J. C. was supported by grants  INCa   R17080FF-CT (PLBIO-2017 ),  Labex EpiGenMed   ANR-10-LABX-12-01 ,  Fondation ARC pour la Recherche sur le CancerPJA 20141201975 ;  Région Occitanie , programme Recherche et Société(s);  European Union (FEDER)  to the project biomarqueursMETCP; A. T. is supported by the  Labex MabImprove Starting Grant and SIRIC Montpellier Cancer Grant INCa Inserm   DGOS 12553. Funding for open access charge: Grants INCa  R17080FF-CT (PLBIO-2017 ),  Labex EpiGenMed   ANR-10-LABX-12-01 ,  Fondation ARC pour la Recherche sur le Cancer   PJA 20141201975 ;  Région Occitanie , programme Recherche et Société(s);  European Union (FEDER)  to the project biomarqueursMETCP.
BulkSignalR and companion scripts (BulkSig-nalR companion) are available with documentation from https://github. com/jcolinge and Zenodo (doi. org/10. 5281/zenodo. 7762645 & doi. org/10. 5281/zenodo. 7762632). The CRC-LM spatial transcriptomics data are available from GEO (accession code GSE217414). The other data were made public by their authors.
02), STL. S = stLearn second selection with a score threshold set to have the same number of LRIs as with BulkSignalR, STL = stLearn first selection. The color code indicates the number of tools that found one LRI (BSR and BSR. N as well as STL and STL. S count for one tool). The Venn diagram represents the overlap between tools and selections. (F) Statistical associations between tissue regions and concomitant expression of the ligand and the receptor captured by the product L × R yielded Q-values (Kruskal-Wallis test followed by Benjamini-Hochberg multiple-hypothesis corrections due). The boxplots compare the strengths of association (the Q-values) obtained by each tool. (G) Spearman correlation between ligand and receptor in the whole tissue. (H) Number of significant LRIs in the HER2 + breast cancer dataset.
(I,J) Same as (F,G) for the HER2 + breast cancer dataset. (K) Number of significant LRIs in the dorsolateral prefrontal cortex dataset. (L,M) Same as (F,G) for the cortex dataset. *P < 0. 05, **P < 0. 01, #P < 1E-3, ##P < 1E-5, P < 2. 2E-16 (Wilcoxon two-sided).
Data set synthesis and analysis preparation We synthesized a data set of temporal trends in detrital nutrients (N and P) and stoichiometry (C:N, C:P, N:P) to understand the nutrient dynamics of submerged leaf litter decomposing in streams. On 29 April 2020, we conducted a systematic literature search for aquatic decomposition experiments using the databases Academic Search Complete, Agricola, and Web of Science and the keywords "(litter OR leaf OR leaves OR detrit* OR wood OR cellulose OR 'coarse particulate') AND (freshwater OR wetland* OR stream* OR river* OR lake*) AND (C:N OR C:P OR N:P OR C:nutrient OR 'nutrient content' OR 'nitrogen content' OR 'N content' OR 'phosphor* content' OR 'P content' OR 'nutrient content' OR minerali* or immobili* or 'nutrient release') AND (decompos* OR decay).
" This search resulted in 3075 publication records, which were systematically scanned for data adhering to the following criteria: 1. Data were time series of decomposing detrital nutrient content or stoichiometry from a single litter cohort (at least initially and one other time point of detrital C:N, C:P, N:P, %N, %P, N mass, or P mass). 2. Studies included a measure of mass loss. 3. Litter was submerged in freshwater during the entire decomposition assay and not buried in sediment. 4. Decomposition occurred under relatively natural conditions in natural systems or mesocosms. We also extracted ancillary data, including water nutrient concentrations, temperature, geographic F I G U R E 1 Conceptual model of the processes controlling detrital nutrients in leaf litter. (A) Nutrient accrual controlled by microbial immobilization of external nutrients as well as abiotic adsorption of nutrients onto litter surfaces.
The majority of these data sets originated from temperate regions of North America and Europe, but all continents were represented (excluding Antarctica), including multiple subtropical and tropical data sets from South America and Asia (Appendix S2: Figure  S1 ). Nutrient mass was calculated as percent nutrient content multiplied by percent litter mass remaining and was then normalized to initial nutrient content, so each litter had a value of 1 when the mass loss was 0. Thus, values greater than 1 are net sinks of nutrients and less than 1 are net sources of nutrients. While terrestrial studies often interpret this scaled representation as indicating immobilization and mineralization processes  (Manzoni et al. , 2010; Parton et al. , 2007) , nutrient losses in streams are affected by flow-driven physical processes in addition to microbial nutrient use and excretion, complicating the interpretation of exact drivers of temporal modulation of litter nutrients (Figure  1 ).
01% to 0. 23%. First, we modeled normalized nutrient mass or stoichiometry as a tensor interaction between mass loss and initial nutrient content or stoichiometry using the function "gam" in package mgcv  (Wood, 2011). We included a random slope smoother for each time series to better identify global relationships. Models assumed a gamma distributed response (positive, continuous data), and for N and P mass models, we selected the link function (among identity, log, and inverse) for the model based on AIC and homoscedasticity. For stoichiometric ratio responses, we modeled log-link functions only  (Isles, 2020). To determine where normalized nutrients and stoichiometry changed significantly with mass loss, we calculated instantaneous rates of change ("first derivative" functions) and their confidence intervals (CI) along each predicted initial nutrient curve  (Simpson, 2018).
We first used our models to predict values of the mean response for each initial nutrient curve at every 0. 2% mass loss units. Then, we re-sampled the model's posterior distribution on these levels 10,000 times using the "fitted_samples" function in the gratia package  (Simpson, 2022). For each posterior draw and nutrient level, we calculated first derivatives by subtracting predicted values from previously predicted values and dividing by the mass loss step (0. 2%), which created a distribution of derivatives for each initial nutrient curve and mass loss step. We used the median value of each distribution as the central tendency, and 95% CI were selected as the 97. 5% and 2. 5% quantiles of the derivatives at each mass loss step. We used the CI to indicate significant points of curve changes.
Rates of change in normalized nutrient curves indicated nutrient increases (positive rate of change) or decreases (negative rate of change), and rates of change different from -0. 01 (g nutrient/% mass loss) indicated nonproportional changes of nutrient with mass loss, signifying preferential nutrient retention or loss. We modeled ergosterol with GAMMs using only mass loss as a predictor. Ergosterol data were collected in the original data synthesis when available. Fungal biomass is frequently estimated using ergosterol quantification on leaf litter  (Gulis & Bärlocher, 2017). When studies reported fungal biomass, we transformed the values to ergosterol based on the transformation equation reported in the original publication's methods. Similar to the above nutrient and stoichiometry GAMMs, we modeled ergosterol assuming a gamma distributed response, choosing a link function based on residual plots and AIC, and including a random intercept and slope smoother for the time series from which the data originated.
Exploratory analysis of environmental covariates Second, we explored the influence of environmental covariates by adding a three-way tensor interaction to the fixed effects of the nutrient curves, which allowed predicted normalized nutrients to be modified by the environmental covariate. Insufficient data were available to test three-way interactions in models in which P mass, C:P, or N:P was the response variable. We tested N mass and C:N models with temperature, NO 3 -N, and PO 4 -P as environmental covariates. Because most studies only reported one central value per litter deployment for these values, we used the average value where the measurements varied across time. When N mass was the response variable, we analyzed the raw nutrient mass responseas opposed to the normalized nutrient mass (previous sections)-for ease of interpretation of three-way tensor interaction effects.
For example, if there were 100 time series comprising a data set for a particular response variable, 100 separate GAMMs were fitted each using a data set of 99 time series. We visualized the sensitivity of GAMM outputs for both the predicted response curves for each GAMM analysis (see Appendix S3: Figures  S6-S14 ). Codes used for all data tidying, analyses, and visualizations are archived publicly with Zenodo  (Robbins, 2023).
ACKNOWLEDGMENTS We are indebted to  Cristiane Biasi  for her contributions to the data synthesis. We thank  Colin Smith  at  Environmental Data Initiative  for assistance in archiving our main data set. Caleb J. Robbins  was supported by  NSF   DEB-1442595. David W. P. Manning was supported by  NSF   DEB-2106075. Halvor M. Halvorson  was supported by  US   NSF EPSCoR Track-2 OIA-2019596. Ada Pastor  was supported by  Generalitat de Catalunya  ( Beatriu de Pin  os  BP-2020-BP-00237 ).
DATA AVAILABILITY STATEMENT We synthesized a data set of temporal trends in detrital nutrients (N and P) and stoichiometry (C:N, C:P, N:P) by completing a literature search in Academic Search Complete, Agricola, and Web of Science using queries specified in Appendix S1: Section S1. The synthesized data set  (Robbins et al. , 2022)  is archived with the Environmental Data Initiative at https://doi. org/10. 6073/ pasta/f53d35244db9a38da0cd7d2e37503270. The code  (Robbins, 2023)  used to generate the analysis and presentations in this manuscript is archived with Zenodo at https://doi. org/10. 5281/zenodo. 7770607.
Intervention The enhanced intervention consists of six sessions of exposure to EE spread over 9 days (depending on the inclusion date). These sessions take place in addition to the standard intervention and are planned with the patient so as not to interfere with other activities (workshops or therapeutic meetings). The EE is produced using two innovative tools combining different types of stimulation: The practice of mindfulness in multisensory VR A total of six virtual environments (VEs) have been developed in collaboration with Sensiks (Amsterdam, Netherlands) using Unity software. An Oculus headset and two joysticks are used to interact with the VEs. To mimic real life and guarantee the participant's immersion, the following interactions are possible in the VEs using the joysticks: moving around the environment by teleportation, catching and throwing virtual objects and ordering a virtual drink using a menu.
This device (the Sensory reality pod) was designed by Sensiks as a modular framework that includes electronic modules and programmable actuators to generate the defined stimulations at the desired times. The modules and actuators are linked through a central device. The VR cabin measures 119×119×224 cm. This tool provides a multisensory experience that adapts to the VE presented. The appropriate smells (notably forest, beach, alcohol, tobacco, coffee, gasoline), sounds, airflow and heat are programmed for each VE and evolve according to the exploration of the environment or as the session progresses (eg, when moving in the sun in the VE, heaters are switched on). For each of the six sessions, patients complete 20 min of guided mindfulness while exploring a VE. The first two sessions aim to teach participants the practice of mindfulness through immersion in relaxing environments (VEs 1 and 2).
Data analysis Data analysts will be blind to the condition: the condition will be letter coded rather than explicitly 'control' or 'intervention'. All analyses will be conducted using both RStudio and SPSS V. 23. 0 software. A descriptive analysis of the study population will be performed. Qualitative variables will be expressed as a proportion with 95% CI. Quantitative variables will be expressed as mean and SD or as median and IQR. A value of p<0. 05 (two-tailed) will be considered statistically significant. In intention-to-treat analyses, missing data will be simulated using a multipleimputation technique with interim values. The initial comparability resulting from the randomisation will be checked using tests appropriate to the distribution (parametric or non-parametric) and type (quantitative/qualitative) of the variables studied.
, 2019). The Ecopath with Ecosim (EwE) model from the EEC was not included in this study because the parameters of the model are not available in the EcoBase database (https://ecobase. ecopath. org). Moreover, the time series data used to calibrate the EwE model  was from a different period in comparison to Atlantis and OSMOSE and it relied mainly in information from surrounding ecosystem. Therefore, in this study, we focused on Atlantis and OSMOSE ecosystem models that were previously calibrated and validated  (Girardin et al. , 2018; Travers-Trolet et al. , 2019). These models were used to compute network-derived indicators through the application of ecological network analysis (ENA).
e. the inflow from low trophic level compartments is unlimited, we were more concerned about preserving the outflows balance. Thus, we used the input or donor method  (Allesina and Bondavalli, 2003)  to balance the systems, whenever necessary Since the detritus, primary producers and invertebrates were considered as energy donors, like OSMOSE, the balancing method used was input or donor method M. Ito et al. considered as a single outflow  (Fath et al. , 2007). Moreover, the energy flows mobilized by fisheries were not allocated to specific compartments; instead, these flows were incorporated indistinctly to the exports. Once the compartments and weighted flows were set, we applied ENA to the networks using the package enaR  (Borrett and Lau, 2014 ; htt ps://github. com/SEELab/enaR) in R environment  (R Core Team, 2021)  to analyze the models' outputs and compute the network-derived indicators.
In summary, a user of Coqlex writes a lexer in a familiar syntax close to standard lexer generators, and gets a human-readable description of the lexing rules in Coq. They should review the lexing rules in Coq syntax to ensure that it corresponds to their intent. Coqlex then provides a function that takes those lexing rules and interprets them into a lexing function, with a formal proof that this function is correct. 2 1 the source code of Coqlex can be found on https://gitlab. inria. fr/wouedrao/coqlex 2 Our lexer functions are correct with respect to a standard specification discussed in Section 5.
3:2 After having finished this work, we discovered the independent research on the Verbatim++  [6, 7]  formally verified lexer. For this reason, we will here also include: 3. A comparison with other lexer generators. More precisely, we compare Coqlex to OCamllex and flex (because these are standard tools) and to Verbatim++ (the state of the art for verified lexers in Coq), with respect to execution time and usability. In OCamllex, lexical rules are compiled into a non-deterministic automaton represented by a compact table of transitions, with backtracking and semantic actions. The generated lexer code simply follows the automaton transition by repeated table lookups. In contrast, Coqlex contains no such compilation, it interprets the userprovided regular expressions against the input by using Brzozowski derivatives  [4].
This allows to have a simpler formalization, and leads to surprisingly good performance: roughly 100x slower than OCamllex, but 100x faster than Verbatim++. This is reasonable for a pure program extracted directly from Coq, compared to an efficient implementation, and is more than fast enough in practice. For example, the lexer of the Coqlex generator is implemented in Coq, using the Coqlex data structures and functions. That generator is used without any noticeable slowness. The Verbatim++ lexer (also verified in Coq) implements regexps using Brzozowski derivatives and then compiles those regexps into deterministic finite automata  [3]  for fast regexp matching. Even though significant emphasis in the work  [6, 7]  is put on optimization, Verbatim++ remains substantially slower than Coqlex in our experiments (which used the benchmarks provided by Verbatim++).
Using the fuel technique: this technique consists of ensuring the termination of lexers using a natural number (nat) that decreases at every recursive call. Requiring that each semantic action discards at least one input character is too strict in practice. Studying lexers in the wild, we have found many cases of lexers designed to "skip" an optional part of the input, that accept the empty string if nothing needs to be skipped. For example, the lexer of the OCaml compiler contains the following: Forcing users to write termination proofs for potentially non-terminating lexers would force users to review and complete the generated Coq code and so alter the user experience. We thus chose to express general, potentially non-terminating lexers using fuel. Consequently, the type of one step of lexing becomes lex1(T) := nat -> lexbuf -> Result(T * string * position) To make it simple to call a lexer from a semantic action, we replace the separate arguments string and position by the more informative lexbuf type already used with semantic actions.
Coqlex in Practice Coqlex comes with a Coq library that allows to write lexers using sets of lexical rules. It also provides a text processor that will convert a markup language (. vl syntax), which is similar to the OCamllex  [20]  specification language (. mll syntax), into its equivalent Coq code (. v file). Figure  1  presents the. vl version of the mini-cal (a micro language for arithmetic expressions: numbers, idents, + * -/ and parentheses) lexer and its equivalent in. mll. This. vl definition has four parts: 1. The header section: The header section is arbitrary Coq text enclosed in curly braces. If present, the header text is copied as it is at the beginning of the output file. Typically, the header section contains the Coq Require Import directives, possibly some auxiliary functions, and token definitions used for lexer definitions.
2. The regexp definition section: This section allows to give names to frequentlyoccurring regular expressions. This is done using the syntax let id ent = r e to associate the name id ent to the regexp r e. The syntax of regexp is defined in Figure  2. 3. The lexer definition section: This section allows to define lexers using sets of rules. A rule is defined using the syntax | p {a} (the '|' symbol is not mandatory for the first rule) to associate the pattern p to the Coq text representing a semantic action a. This pattern is either a regexp or a string -> bool function (defined using the syntax $( f ) where f is the Coq code of this function). Typically, this kind of pattern is  used to detect situations in which the lexing must stop (e. g when the input string is empty). When the pattern is a regexp, the semantic rule is said to be regexp based.
However, a lexer running out of fuel for a given input does not necessary mean that this lexer loops. In Figure  3 , we compare the description mini-cal lexer in Coqlex syntax to the code it generates. Typically, the generator translates the regexp written in. vl syntax into the Coqlex regexp data type. The generated lexing function also calls Coqlex functions such as generalizing_elector, longest_match_elector, and exec_sem_action. It also recurses over the fuel explicitly; we could instead generate a call to a fixpoint combinator, but this would be difficult to scale to mutually-recursive lexers. Similarly to OCamllex, Coqlex also allows to choose the semantic action by matching the shortest prefix. In that case, the user should use the keyword shortest instead of parse in the. vl file. In the generated file, the function longest_match_elector is then replaced by the function shortest_match_elector.