dataesr/scientific-paragraphs-categorization huggingface dataset:
General Approach At the core of a zero-dimensional box model is the chemical mechanism, which describes the chemical system that is being modelled. At a mathematical level, the chemical mechanism is a system of coupled ordinary differential equations (ODE) which can be solved versus time using an appropriate numerical integrator. A number of open sources, free to use modelling toolkits designed to be used with the MCM are available, include the following: • AtChem Online  (Sommariva et al. 2020 )-https://atchem. leeds. ac. uk/webapp/. • AtChem2  (Sommariva et al. 2020 )-https://github. com/AtChem/AtChem2. In general, the following parameters need to be defined to run a basic chemical box model: • model variables and constraints and solver parameters; • environmental variables and constraints; • photolysis rates; • initial concentrations of chemical species and lists of output variables.
Radiation Effects Radiation effects have been discussed in Chap. 2. All the calculated photolysis processes apply chamber-specific scaling factors (F x ) in order to take into account radiation effects of transmission through the chamber walls, backscatter from the aluminium chamber floor and cloud cover  (Bloss et al. 2005; Sommariva et al. 2020 ). In addition, the photolysis rate of nitrogen dioxide, j(NO 2 ), is routinely measured in chamber A at EUPHORE and these data are available for the experiment above. Variations in actinic flux from day to day and during the experiment resulting from short temporal-scale variations in cloud cover are accounted for by considering the difference between the measured and clear sky calculated j(NO 2 ) at any given time during the experiment. This variable scaling factor, JFAC, is applied to all calculated photolysis rates along with F x.
The simulated data were obtained using the standard glass model described in the previous section, combining MCM propene chemistry and the CESAM and standard-glass modules determined for the NO x -air-light system. All kinetic parameters pertaining to NO x heterogeneous chemistry were kept unchanged. In spite of a very good ability of the initial model to capture the concentration of NO, NO 2 , HONO and ozone, the propene loss remained constantly underestimated as shown in Fig. 7. 16. This means that propene undergoes some degree of heterogeneous photocatalytic decomposition in the presence of TiO 2 , probably triggered by the presence of hydroxyl groups formed from adsorbed water vapour molecules following photocatalytic site activation at the interface. As the formaldehyde and acetaldehyde buildup was also significantly underestimated, a simplified surface conversion reaction was added to the TiO 2 -glass module accordingly: C 3 H 6 + hv → CH 3 CHO + HCHO The kinetics rate constant for this photocatalytic process was found to lead to the best fits when set to (8.
Here the data evaluation is completely different and is based on the steady-state approach  (Minero et al. 2013; Toro et al. 2016). Since the simulation chambers during the EUROCHAMP projects were used as static reactors, the CSTR concept will not be further considered here. For larger chambers, like, for example, EUPHORE, the CSTR concept is practically not possible, due to either the extremely high air exchange rates necessary or the long duration of experiments, for which stable conditions (e. g. photon flux) are not available. If a simulation chamber is used in photocatalysis to quantify the initial uptake, there is a kinetic limitation caused by the transport of the reactants to the active surfaces. Here, in heterogeneous chemistry, a high surface-to-volume (S active /V ) ratio is recommended, for which unwanted side reactions in the gas phase (e.
, 1971)). They also revealed the mechanism of tropospheric ozone build-up  (Weinstock 1971; Niki et al. 1972; Westberg et al. 1971). The indefatigable work of Pitts, Winer, Atkinson, Niki, Becker, Moortgat, Schurath, and others led to the development of a huge database of kinetic reaction parameters that laid the foundations for the first chemical transport models. Subsequently, the use of simulation chambers has led to many other significant breakthroughs in atmospheric chemistry research. Simulation chamber experiments on the atmospheric oxidation of unsaturated hydrocarbons led to the identification of the essential role of biogenic VOCs in rural ozone formation  (Abelson 1988). During the 1990s, chamber experiments contributed to the elucidation of the relation between gasoline composition and secondary organic aerosol formation  (Odum et al.
The availability of this information is indeed critical for any attempt of re-using previous datasets, especially if a modeling approach is planned (which is generally the case). One of the key steps in the long journey of the scientific community toward a precise understanding of the chemistry at work in atmospheric simulation chambers is the rise of the concept of "chamber chemistry". According to this concept, when the initial precursor concentration is low enough and when the light energy is atmospherically relevant, the observed behavior of chemicals during chamber experiments would be the result of the interplay between the atmospherically relevant chemistry and the chamber effects. Refusing to consider the chambers as black boxes and hence starting to study the chamber-dependent processes themselves with scientific rigor has led to two complementary efforts.
The first of these is the study of wall reactions using the tools of microphysics and surface chemistry. While some aspects of wall reactions are relevant for the understanding of atmospheric processes (see, e. g. ,  Pitts et al. 1984; Rohrer et al. 2005) , the true motivation for characterizing them was a different one: the authors were already building what is now known as an "auxiliary mechanism". Thirty years ago,  Jeffries et al. (1992)  were already recommending that those chamber-dependent reaction sets should be available for each chamber dataset to be simulated. They were pointing out that when evaluating a reaction mechanism in a given chamber, the auxiliary mechanism is combined with a core mechanism which is asserted to be chamber independent, and that misrepresentations in the auxiliary mechanism could induce compensating errors in the core mechanism and so in the derived atmospherically relevant knowledge.
This goal has never been that close to being attained, as not only this information has been made available for most of the atmospheric simulation chambers installed in Europe, but also because the present guide, for the first time, provides clear guidance for the building of such auxiliary mechanism (see Chaps. 2 and 3). The second aspect of chamber-dependent processes is related to the simultaneous exploitation of a large number of datasets arising from various chambers. Based on a quasi-statistic interpretation of the previous concept, it assumes that deconvolution of chamber-dependent processes from atmospherically relevant processes can be significantly enhanced by the parallel analysis of comparable experiments carried out in different simulation chambers. This approach has been proposed as early as in 1999  (Jeffries 1999) , but until now it has never been applicable due to the lack of diversity among datasets.
In most of the centers developing an experimental atmospheric simulation activity, datasets are indeed carefully stored, forming several databases comprising generally hundreds to thousands of experiments. Nevertheless, the community was missing coherent datasets investigating the same chemical systems by means of very different installations. Thanks to the "multi-chamber experiments" initiative developed in the framework of EUROCHAMP-2020, this approach for three important "standard" chemical systems (propene oxidation, toluene/xylene oxidation, and a-pinene oxidation) may receive its first full-scale validation. Further, the release of the EUROCHAMP database (https://data. eurochamp. org/) has made freely available around 3000 datasets of chamber experiments, generated in more than 20 fully characterized chambers (including an auxiliary mechanism for 16 of them), which exhibit a high diversity of size, type, material, and irradiation.
• DSMACC (Emmerson and Evans 2009)-http://wiki. seas. harvard. edu/geoschem/index. php/DSMACC_chemical_box_model. • (O'Meara et al. 2021)-https://github. com/simonom/PyCHAM. The AtChem online website contains tutorial material and a number of examples. Any chemical mechanism can be integrated by these tools, as long as they are in an appropriate format.
Kintecus-http://www. kintecus. com/. • Chemistry with Aerosol Microphysics in Python (PyCHAM) box model • DSMACC (Emmerson and Evans 2009)-http://wiki. seas. harvard. edu/geoschem/index. php/DSMACC_chemical_box_model. • (O'Meara et al. 2021)-https://github. com/simonom/PyCHAM. The AtChem online website contains tutorial material and a number of examples. Any chemical mechanism can be integrated by these tools, as long as they are in an appropriate format.
The stimuli were recorded in a sound-proof studio by a phonetically trained female native speaker of Cypriot Greek. The experiment was conducted online using Qualtrics survey software. 5 Participants, after confirming they were wearing headphones, were presented with the auditory stimuli. On each trial, participants were simultaneously presented with one nonword to each ear, with the members of each nonword pair differing only in the initial consonant, for example, povi, lovi. The nonword pairs were drawn from the set of options defined by the major-class permutations listed in Table  10. 2. Participants were presented with four trials per nonword pair, counterbalanced for left versus right ear (for example, povi twice in one ear, twice in the other), to control for right-ear dominance in the perception of verbal stimuli  (Kimura 1961).
In particular, I maintained that, given that eNs are more complex than ENs, the former can properly govern the latter even if it is itself properly governed. Provided we accept the extra condition concerning the governing domain established by the two consonants flanking the EN preceding an eN, this crucially allows for accounting for sequences of silent nuclei without departing from strict adjacency and binarity. Notes 1. This work is supported by the FWO Marie Skłodowska-Curie Actions -Seal of Excellence Postdoctoral Research Grant Nr. 12Z7520N. I would also like to thank Jean Lowenstamm, Radwa Fathi, and Tobias Scheer for useful comments. 2. See  Polgárdi (1999) ,  Harris and Gussmann (2002) ,  Cavirani (2015)  for attempts at translating GP mechanisms into constraints, and Brandão de Carvalho (  2021 ) for a discussion of this possibility.
Methods Our data were collected from a female native speaker originating from Leitir Móir (Lettermore), a rural Connamara Gaeltacht in West Central Ireland. While residing in Boston, she makes frequent trips back to Galway to visit family and friends and has taught the Irish language and the traditional song repertoire for many years. The data for this study were recorded in a sound proof booth using professional equipment. Our corpus consists of c. 300 nouns elicited in list format over the course of three months in 2014. Many nouns were recorded as pairs with the citation form followed by the inflected form with stem-final palatalisation as reflected in the orthography. Several sets of words were constructed to investigate the questions noted above. Our data replicate most of the restrictions on the VRC sequences found to govern the svarabhakti vowels in SG and earlier studies of Irish.
set 1 The first set of words were designed to investigate the duration and vowel quality correlates of the organic versus (orthographic) epenthetic stems. Twenty-five nouns of each type were recorded in citation and palatalised inflected forms. The data were analysed in Praat  (Boersma and Weenink 2019). Praat textgrids were constructed to segment the VCV intervals based on visual inspection of the wave forms and the spectrograms. Praat scripts collected duration measurements for the vowels (V1 and V2) as well as the average values for their first and second formants. ( The notch plots in Figure  17. 1 show the distributions for the duration measures of the V1 and V2 vowels as a function of their status as organic (CVCVC) versus svarabhakti (CVCC) sequences. Comparing the two plots it is obvious that the durations of the stressed V1 vowels are significantly greater than V2 for both stem types.
The TNF superfamily members BAFF (TNFSF13B) and TRAIL (TNFSF10) were consistently upregulated in the areas with severe tissue damage. We used published spatial and single-cell SARS-CoV-2 data sets to validate our findings in the lung tissue from additional cohorts of patients with COVID-19. The resulting model of severe COVID-19 immune-mediated tissue pathology may inform future therapeutic strategies.
05) of gene ontology (GO) BP terms related to T cell activation and differentiation, antigen presentation, cytokine production, cytotoxicity, and response to IFN-γ (Figure  2B ). By contrast, genes enriched in areas of mild damage (n = 40; BH adjusted P < 0. 05; |fold change| >1. 5) had a significant overrepresentation (BH adjusted P < 0. 05) of pathways associated with wound healing, regeneration, and hemostasis (Figure  2B ). Network analysis implicates CD8 + T cells, mononuclear phagocytes, and active TLR, IFN, and IL-1 signaling in COVID-19 lung inflammation. To perform an unbiased exploration of the cellular and phenotypic variations present in the set of the profiled AOIs, we used weighted gene correlation network analysis (WGCNA)  (13). This analysis identified 17 distinct modules of co-expressed genes (n = 27-266 genes per module; median, n = 88) (Supplemental Figure  4 , A-D, and Supplemental Table  4 ).
Next, we identified BPs and pathways overrepresented among the gene members of each WGCNA module (Figure  3B  and Supplemental Table  5 ). On the basis of these analyses, we named each module according to its cell-type associations and biological pathway enrichments. As expected for lung tissue, we found a set of stromal modules representing (a) epithelial cells (containing EPCAM), (b) type 2 pneumocytes (containing the surfactant-encoding genes SFTPB, SFTPC, and SFTPD), (c) fibroblasts ("fibroblast phenotype," containing COL1A1, COL3A2, COL5A1, and THY1), and (d) vasculature (containing CDH5, THBD, and ENG), which all showed corresponding pathway and celltype associations (Figure  3 , A and B, and Supplemental Figure  4B ). We found 3 modules that showed a high correlation with the presence of CD68 + cells (Figure  3A  and Supplemental Figure  4, B  and C ).
The vasculature and IL-1 response: IL-6/IL-8 modules contained genes associated with mature neutrophils (namely, ELANE, MME,   6 ) and WGCNA module eigengene expression (all AOIs; positive Spearman's correlation values are shown). (B) Selected GO BP, KEGG, and reactome pathways significantly overrepresented in the detected modules (BH adjusted P < 0. 05; 1-side Fisher's exact tests; see also Supplemental Table  5 ). (C) WGCNA module eigengene expression is shown for each AOI (see also Supplemental Table  4 ). Sampled AOIs are annotated with patient identity, the severity of damage, adjacent virus antigen presence, and the percentage of MPO, and CTSG) (Supplemental Figure  4B ). The vasculature module was also associated with the GO BP platelet degranulation pathway (Figure  3B ). Finally, we found 3 modules that were associated with more general cellular processes.
The presence of these modules, which we termed cell cycling, chromatin remodeling, and hypoxic response, was indicative of immune cell proliferation and oxygen stress. Severe alveolar damage in COVID-19 is linked with myeloid cell antigen presentation, T cell cytotoxicity, and expression of the CXCL9/10/11 IFN response genes We next sought to better understand the variation in cellular and immune phenotypes that was present across the tissues and sampled AOIs. To do so, we hierarchically clustered the AOIs by expression of the WGCNA module eigengenes. This analysis revealed 5 groups of AOIs with different transcriptional signatures and associations with severity, patient, and cell composition (Figure  3C  and Supplemental Figure  8 ). This clustering broadly split patients A and B into 2 distinct groups with differences in the cytotoxicity and T cells module expression, whereas sample AOIs of patient C had a consistent transcriptomic profile.
Initial applications of spatial proteomics and transcriptomics have started to reveal the spatial landscape of lung damage in COVID-19  (6, 7) , but molecular details of the signaling circuits that perpetuate pathology remain to be fully elucidated. In this study, we sought to generate new insights by applying network-based analysis approaches to the analysis of rich spatial transcriptomics data generated with the Nanostring GeoMx Digital Spatial Profiler (DSP) platform. To do so, we used correlation networks to integrate WGCNA module eigengenes, cytokine gene expression levels, and computationally predicted cell-type abundances. Use of this flexible JCI Insight 2023;8(2):e157837 https://doi. org/10. 1172/jci. insight. 157837 and extensible cellular phenotype network analysis approach uncovered new links among the cell types, biological pathways, and cytokines that are associated with lung tissue damage in severe COVID-19.
5 LS Probe V-nCoV2019-S and checked for quality against slides treated with the positive control probe Hs-UBC and negative control Probe DapB. IHC for SARS-CoV-2 N protein. Deparaffinization and heat-induced epitope retrieval were performed on the Leica BOND-RXm using BOND ER2 (pH 9. 0) for 30 minutes at 95°C. Staining was conducted with the Bond Polymer Refine Detection kit and a rabbit anti-SARS-CoV-2 N Ab (Sinobiological; clone 001; dilution: 1:5000), and counterstaining was with hematoxylin. Whole-slide image analysis and QuPath software were used to quantify virus  (53). Lung tissue was distinguished from empty space by applying the Create thresholder function on hematoxylin-stained areas, and then virus-positive pixels were quantified using the detect positive staining function (downsample factor: 10; Gaussian σ: 5 μm; hematoxylin threshold: 0.
Tissue sections were then loaded into the GeoMx platform and scanned for an immunofluorescent signal. JCI Insight 2023;8(2):e157837 https://doi. org/10. 1172/jci. insight. 157837 After selection of AOIs, UV light directed at each AOI released oligonucleotides that were collected and prepared for sequencing. Illumina i5 and i7 dual-indexing primers were added during PCR (4 μL of collected oligonucleotide per AOI) to uniquely index each AOI. AMPure XP beads (Beckman Coulter) were used for PCR purification. Library concentration as measured using a Qubit fluorometer (Thermo Fisher Scientific), and quality was assessed using a Bioanalyzer (Agilent). Sequencing was performed on an Illumina NextSeq 2000, and FASTQ files were processed by the NanoString DND pipeline, resulting in count data for each target probe in each AOI.
Analysis of immunofluorescent images for cell counts. The number of nuclei and the CD3 + and CD68 + cell counts were determined using CellProfiler software. A pipeline was designed to quantify circular objects within RBG files for each AOI  (54). Global manual intensity thresholds were set for object identification of nuclei and CD3 + cells, and CD68 + cells were identified by adapted intensity thresholds. The efficacy of object identification for each AOI was visually confirmed. Quality control and preprocessing of GeoMx transcript expression data. Quality control and initial data exploration were conducted using the GeoMx DSP Analysis Suite. Sequencing quality per AOI was examined and an under-sequenced area (B_04) with zero deduplicated reads was excluded. Expression of each transcript was measured by 5 or more probes; outlier probes (defined as when the probe geomean in all AOIs divided by the geomean of all probes for a given target was <0.
We investigated the influence of known technical and biological factors on the variance structure of the data set by performing PCA of the log2(n+1) transformed quantile-normalized expression values. This analysis revealed that the first component in the data was associated with the aligned read-depth statistic. We therefore corrected the quantile normalized expression values for this technical factor using the Limma removeBatchEffect function. Finally, to distinguish gene expression from background noise, we modeled the expression distribution of the set of negative probes, defining the median of the negative probe expression values plus 2 times the median absolute deviations as a robust detection threshold. In total, we retained 1631 genes that passed this detection threshold in at least 2 AOIs.
05 (R library: clusterProfiler; ref. 55)  with the following parameters: pvalueCutoff = 0. 05 and qvalueCutoff = 0. 10. Redundant and duplicated pathways were removed for Figure  2B ; for a full list of pathways, see Supplemental Table  3. WGCNA. WGCNA was applied to the log2(n+1) transformed, quantile-normalized, aligned read-corrected and -filtered expression values. The parameter values were set as follows: the minimum fraction of nonmissing samples for a gene to be considered good was 0. 5; the minimum number of nonmissing samples for a gene to be considered good was 4; the minimum number of good genes was 4; the cut height for removing outlying samples was 100 (no samples removed); the minimum number of objects on a branch to be considered a cluster was 2; network type equaled signed hybrid; soft power equaled 4; adjacency correlation function was bicor; adjacency distance was calculated by euclidean distance matrix computation; topological overlap matrix type was signed; minimum module size (number of genes) was 10; and the dissimilarity threshold used for merging modules was 0.
25. The analysis identified 17 distinct modules labeled with colors, plus a module of 3 unassigned genes (grey module). JCI Insight 2023;8(2):e157837 https://doi. org/10. 1172/jci. insight. 157837 The WGCNA analysis was performed using pipeline_wgcna. py (https://github. com/sansomlab/cornet; commit ID 196637b04d11682bc06670595f0ec1298a9aa1f4). The expression patterns of the modules were summarized by calculation of module eigengenes using the WGCNA package (which defines a module's eigengene as the first principal component of the expression of the module's gene members). AOIs were hierarchically clustered by expression of the module eigengenes (Pearson's correlation distance; optimized leaf ordering) from which 5 distinct groups were observed and highlighted using the R cutree function. The overrepresentation of GO categories, KEGG pathways, and reactome pathways in module gene members was tested using 1-sided Fisher's exact tests (https://github.
com/sansomlab/gsfisher; commit ID 3ad1d79293c6891cb23575e0e080fb61c74310b1), using the union of gene members from all the modules as the background gene set. Representative gene sets and pathways that had significant (BH adjusted P < 0. 05) overrepresentations are displayed in the figures. WGCNA modules were further characterized by assessing the correlation of the module eigengenes with (a) histological severity (Pearson's correlation), (b) predicted cell abundances (as estimated using SpatialDecon; Spearman's correlation), and (c) that of selected genes (Spearman's correlation; R library Hmisc). Cell deconvolution of GeoMx transcript expression data. Deconvolution of cell types from the gene expression data was performed using the SpatialDecon R library  (14). For this analysis, the full matrix of quantile-normalized gene counts (without correction), mean negative probe counts, and the cell profile matrix Lung_plus_neut were applied as input.