dataesr/scientific-paragraphs-categorization huggingface dataset:
The pressure dependency of the geothermometer  (Tomkins et al. , 2007 ; Section 2. 1 for details) is illustrated in Fig. 5  for a detrital rutile dataset (from  Pereira et al. , 2021)  plotted using the Kohn (2020) mixed model calibration at 0. 7 and 2 GPa. Temperatures estimated using either 0. 7 or 2 GPa are outside calibration and analytical uncertainties of each other (Fig. 5 ). The variation of using either pressure estimate can have an impact of about 50 to 75 • C and with implications for interpreting possible rock sources and metamorphic facies. It is important that detrital rutile U-Pb-T Zr studies provide more constraints regarding choice of pressure estimates (and propagate this uncertainty), and include a separation of metamafic-from metafelsicderived rutile estimates, to allow an easier evaluation of possible α-quartz and t-ZrO 2 activities impacting on the resulting temperature estimates (e.
g. Pereira et al. , 2021). While some studies use what they consider an appropriate pressure estimate, occasionally they do not provide many arguments to support such choices (e. g. Zoleikhaei et al. , 2021). Often, the practice is to assume 1 GPa pressure (e. g. Rösel et al. , 2018) , taken as a reference of a common rutile stability pressure, or use the  Watson et al. (2006)  calibration which is equivalent (e. g. Flowerdew et al. , 2019; Zhou et al. , 2020). Knowing how much more abundant rutile is in eclogite-facies rocks than in amphibolites (as discussed in Section 2), these low-pressure estimates applied to detrital rutile are i) skewing datasets to lower temperature estimates, outside the Zr-in-rutile thermometry uncertainties; ii) resulting in overinterpreting rutile as sourced from amphibolite-facies rocks.
Assuming a pressure estimate to detrital datasets is often a huge source of uncertainty as the mineral is out of petrological context. This is where mineral inclusions and trace element-based discrimination proxies are very promising. As discussed in Section 2, mineral inclusions found in rutile are robust monitors of rutile growth P-T conditions (e. g. kyanite, coesite, omphacite inclusions). Despite some efforts in testing datasets to discriminate rutile, the best advances have been made on discriminating metamorphic, from magmatic and fluid-related rutile (e. g. Agangi et al. , 2019; Pereira et al. , 2019)  and from other TiO 2 polymorphs  (Triebold et al. , 2011) , but not between different metamorphic conditions. Yet, an important aspect when discussing thermometry data and considering a calibration and related assumptions is to take rutile stability into account, in either metamafic or metapelitic rocks/compositions (e.
For ID-TIMS work, concentration data becomes increasingly important as smaller grain fractions are required, i. e. if single grains or parts of single grains are required. Another consideration, often overlooked, is the presence of mineral inclusions. As discussed in Section 2. 2, these can be common and overlooked because rutile is commonly dark or semi-opaque. BSE imaging should be performed prior to analysis, as a first-order approach, just as should be the case for all minerals. Mineral nano-to micro-inclusions could add to the U, Th or Pb budget and lead to spurious ages. These can be screened in in-situ time-resolved analysis (i. e. in LA-ICP-MS), monitoring for isotope spikes in one or more of the isotopes being measured that may highlight the presence of inclusions, and discarding some segments of the analysis from the final integration.
6B , smaller circles with higher f 206 Pbc). Plotting rutile data in a T-W diagram may enable the correct determination of age and initial 207 Pb/ 206 Pb, but this approach should not be used for detrital rutile datasets. In cases with high f 206 Pbc and potentially with evidence for Pb loss (Pb isotopic zoning across the grains;  Kooijman et al. , 2010)  the problem becomes unsolvable and these data should be discarded. For instance, in  Kooijman et al. (2010) , the authors decided to apply common Pb corrections only when f 206 Pbc was <0. 01, while in detrital rutile studies, higher f 206 Pbc (0. 2) grains are considered for corrections and to be included in the dataset  (Avigad et al. , 2017). Alternatively, data exclusion can be made after common Pb corrections, discarding data with large uncertainties, with variable filters for different age ranges (e.
, 2002; Zack et al. , 2004b). Since Cr is more diffusive than Nb in rutile, thermal diffusion can affect Cr more readily than Nb, leading to alteration of an original signature. This decoupling may explain why at amphibolite facies conditions this discrimination occasionally fails (e. g. Meinhold et al. , 2008) , as Cr is diffusive while Nb is retained. Additionally, as more petrogeochemical tools are applied to rutile, this post-rutile growth modification can amplify the uncertainties in applying these tools to discriminate magmatic-from metamorphic-related and from other settings as discussed later in Section 6. One of the main impacts of these potential element decoupling occurrences is our ability to probe largescale phenomena affecting the crust, that require handling larger datasets (Fig.
In Fig. 11 , a compilation    S2  of Supplementary data) is overlain on the distribution of detrital zircon ages through geological time. While the main difference between detrital rutile and zircon datasets arises from an incomplete coverage of terranes and ages concerning detrital rutile (e. g. orogenic sources from Pangea or Rodinia assemblies), some general comments can be made. Archean detrital rutile ages have a similar distribution to Archean zircon ages, with a main age peak at ca. 2. 7 Ga and another one at 2. 5 Ga. Between ca. 1. 9 and 1. 6 Ga there is a large overlap between detrital rutile and zircon ages with a discernible main peak shift of rutile in respect to zircon ages (Fig. 11 ). These differences may be linked to the different Pb closure temperatures of these geochronometers.
Ages corresponding to the assembly of Rodinia are largely lacking in the detrital rutile dataset (Fig. 11 ). These rutile ages come from a single region (NW Scotland), that had sources from East Grenville terranes during Rodinia's collision stage. A main rutile age peak is found between 0. 7 and 0. 5 Ga, not nearly as pronounced as the detrital zircon equivalent. These ages correspond to the tectonometamorphic events that took place during the Panafrican orogeny, and that are very pronounced in the source areas of Cambro-Ordovician quartzites from Gondwana-affinity terranes and some modern sands. Differences between the detrital zircon and rutile records in this time slice may be due to an underrepresentation of Pangea-related rutile in the current dataset, which may explain a lack of a single zircon age peak, but rather a cumulative peak together with those of Pangea and of modern orogens (Fig.
11 ). Since this is still a limited dataset, care must be taken in interpreting any trends. However, similarly to what the detrital monazite age record has shown  (Mulder and Cawood, 2022) , significant correlation between Fig. 10. Cumulative probability detrital rutile age curve fields for rift to passive margin type sequences (3; 4-affecting older continental blocks; 5-developed immediately after continental collision) and for convergent to collisional type sequences (1,2) from  Pereira et al. (2020). Detail on the particular scenario of rift sequences developed immediately after collision and in close proximity to suture zones, that despite plotting close to the fields of convergent-collisional type sequences, the cumulative probability age curves change up sequence, into typical rift-type cumulative probability age curve shapes (sigma-shape).
However, in the absence of access to such instruments, trace element-based discrimination diagrams using V-Cr and Cr-Al-Fe have been developed  (Triebold et al. , 2011; Plavsa et al. , 2018; Fig. 12). While they might be equivocal, they can provide a first-order approach to filter out possible anatase and brookite from a detrital dataset, and reduce polymorph-induced bias. Elevated W contents (W > 1000 ppm) in rutile from gold mineralised rocks has long been recognised as a distinctive feature  (Clark and Williams-Jones, 2004; Rice et al. , 1998) (Fig. 13A ), but other studies have shown that W alone can be misleading  (Agangi et al. , 2019; Plavsa et al. , 2018). Indeed, these studies have shown that rutile coming from barren rocks can also yield elevated W values, namely magmatic-derived rutile that is derived from evolved, peraluminous granites.
e. excluding potential zircon formed after Zr-exsolution, ilmenite lamellae and other alteration-type secondary minerals). Although this is only a limited dataset, it illustrates one of the main challenges of applying inverse, mineral equilibria P-T modelling using mineral inclusions in detrital rutile, which is finding inclusions. Indeed, in  Hart et al. (2016)  the ubiquity of mineral inclusions was impressive, as nearly all rutile grains yielded several mineral inclusions per grain. The abundance of inclusions in rutile may not always be as shown in their study. As we move forward, understanding what controls mineral entrapment by rutile during metamorphism will become paramount, as well as placing better constraints on the survivability of inclusionbearing rutile grains during weathering.
It is encouraging that the limited detrital rutile U-Pb age database correlates closely with the expansive zircon one. More detrital rutile data are needed to test the veracity further on a global scale. In particular, the current rutile database lacks many younger-aged orogenies associated with Pangaea formation, which is primarily a consequence of the available studies, which have so far been regional in extent. We do also encourage further regional studies across specific orogenic age profiles to further test the KDE approach to resolving tectonic settings. We have demonstrated the potential of mineral inclusion studies to unravel hidden parts of the P-T evolution of orogenies, particularly the prograde path but also UHP and UHT metamorphism. We have also demonstrated that mineral inclusions in rutile can survive intact in sedimentary deposits.
Acknowledgements We want to thank continued support from a wonderful group of colleagues that contributed to the current manuscript either through technical support or fruitful discussions throughout the years:  Emilie Bruand ,  James Darling ,  Robin Strachan ,  Joseph Dunlop  and  Geoff Long. We also would like to thank the contributions of the two anonymous reviewers and the efficient editorial handling by  Greg Shellnutt. The work presented here has benefited from multiple sources of funding, namely the  University of Portsmouth , from  NERC  grant  NE/I025573 , the  French Government Laboratory of Excellence  initiative n •  ANR-10-LABX-0006 , and the  Portuguese National Science Foundation (FCT)  I. P. / MCTES through a fellowship contract under the  Individual Call to Scientific Employment Stimulus  (2021.
Numerical results The code that is used for the numerical results is available here https://github. com/mehrenbe/InghamWaveBeam. On Figure  3 , we represent the gap z 2 n+1 -z 2 n with respect to √ n, for n ∈ {1,. , 10 4 }, for different values of > 0 belonging to L, and taking A = 1. We remark that the gap oscillates between a value γ min 2 and π for large values of n. The gap is almost always around π, but almost periodically, with a period proportional to 1/ , it falls down near γ min. The behavior at the beginning is different. In particular, when is large, the gap becomes very small, even if it still remains strictly positive. These results are in agreement with the gap result in Proposition 4. 1. On Figure  6 , we represent now the gap ω m+1 -ω m with respect to √ m. The results are then quite different, and we can see on these numerical results that we no longer have a gap.
Data Availability Statement We use in this study a Landscape Evolution Model (FastScape S2S; Yuan, Braun, Guerit, Rouby, &  Cordonnier, 2019; Yuan, Braun, Guerit, Simon, et al. , 2019; https://fastscape. org); The version of the program we use is the one published online on 26 April 2021 (release v0. 1. 0beta3; fastscapelib-fortran; public access) available on GitHub: https://github. com/fastscape-lem.
9 Overall, the milling time was 15-24 min to extract $15 pillars from a specimen. This demonstrates the possibility of using fs lasers to define features and extract ROIs in fixed, metal stained, resinembedded biological tissue. To apply this for the preparation for synchrotron x-ray tomography, we sought to obtain near-cylindrical samples with smooth surfaces as these result in the highest resolution and most-efficient data acquisition for tomography. Figures  2(g ) and 2(h) show such an example where again a region containing a genetically labeled glomerulus is marked on a lab x-ray CT (LXRT) tomogram and subsequently milled on the above-mentioned fs-SEM system. The resulting trimmed volume can be readily imaged at a parallel beam SXRT beamline and it contains the target region of interest, resulting in high-resolution tomograms where individual dendrites of experimentally relevant neuronal circuits are readily visible as expected [Figs.
Both two-photon (a) and LXRT (b) datasets were warped to the same space making it possible to identify the MOR174/9 glomerulus in the stained and embedded sample (c). (d) A targeted region of interest (magenta circle) was defined so it would contain the MOR174/9 glomerulus (crosshairs) and their associated projection neurons. Other non-targeted regions of interest could be also programmed in the remaining specimen (blue circles). (e) and (f) The sample was then mounted on a standard SEM pin (e) and the excess tissue was milled with the fs laser, leaving the carved pillars (f). (g) The milled pillar (orange) was mounted individually and imaged with parallel-beam synchrotron x-ray computed tomography with propagation-based phase contrast (SXRT) at the ID19 beamline of the European Synchrotron Radiation Facility.
This x-ray tomography technique provides near-lm detail nondestructively in heavy metalstained, epoxy resin-embedded samples of the biological soft tissue. The CMI approach allowed warping the 2p data to the LXRT (gray volume) and to this new SXRT dataset. This panel shows the actual position of the milled pillar (orange, segmented from SXRT data) within the original sample volumes before milling (gray, segmented from LXRT data). (h)-(i) Warping the 2p data onto the SXRT dataset revealed the position of the fluorescently labeled MOR174/9 glomerulus in the resin-embedded pillar. (i) As previously reported, the SXRT data can provide a subcellular biological context across the imaged landscapes, such as an apical dendrite of a mitral cell (black arrowheads) evolving in straight trajectory toward the genetically identified MOR174/9 glomerulus (circled with a dashed yellow line).
Unlike mechanical trimming that relies on a flat blade trimming 2D faces, laser ablation operates from one single direction, ablating tissue in a line. This makes it possible to extract multiple target sample regions from one tissue specimen, including regions that are separated by only $100 lm (Fig. 3 ). Such a dense array of volumetric ROIs (in this case pillars) minimizes any loss of material during the cutting process and enables parallel downstream processing and high resolution imaging of samples from neighboring regions. While this packing density may not be sufficient to follow individual biological features across neighboring pillars on their own, these can be mapped to previously obtained SXRT datasets in a correlative multimodal imaging workflow, thereby enabling inter-pillar matching of biological features at the lm scale (Fig.
DATA AVAILABILITY The datasets presented in this study can be accessed through https://wklink. org/2389 (C417_LXRT), https://wklink. org/8293 (C417_SXRT) and https://wklink. org/2803 (C427_SBF-SEM). The data that supports the findings of this study are available in the repository https://github. com/cboschp/fsLaser_CMI. The landmarks and warping engine linking the LXRT and SXRT datasets are available in the repository https://github. com/FrancisCrickInstitute/warpAnnota tions, Ref. 32.
For the Western blot assay, proteins were transferred onto a polyvinylidene difluoride membrane and detection was performed using the His probe (right). (B) Mass spectrometry identification of the high-molecular-weight complex identified in panel A. Accession numbers and protein descriptions are labeled according to the fully sequenced genome of R. gelatinosus S1. (C) Graphic description of the coxSLM operon encoding the three subunits of the putative CO dehydrogenase. fhuE is a gene encoding an outer membrane receptor for iron uptake, coxD encodes an accessory protein belonging to the CO dehydrogenase complex, coxE encodes a protein with a CoxE-like motif, and coxX encodes a protein of unknown function. sodC encodes a putative Cu,Zn superoxide dismutase. strong dark-purple staining, in sharp contrast with the SOD, was also detected.
Altogether, these data showed that Cu 1 directly inhibits the activity of the assembled pCOD complex. Cadmium also inhibits the pCOD complex activity. As copper is biologically and redox active, we questioned whether a redox-nonactive toxic metal such as cadmium could inhibit the activity of the pCOD complex of R. gelatinosus. To address this question, we used the mutant devoid of the Cd 21 -efflux ATPase CadA to assess the effect of cytoplasmic accumulation of Cd 21 on the pCOD activity. While WT R. gelatinosus can tolerate up to 3 mM CdCl 2 in its growth medium, the DcadA mutant is limited to low concentrations of up to 200 mM  (26). In subsequent assays, the WT and DcadA mutant cells were grown in the presence of 100 mM CdCl 2. The activity of pCOD in the soluble fractions of these cells was then analyzed by non-denaturing PAGE as described above.
7B ). These data clearly showed that Cu 1 and Cd 21 inhibit the activity of different enzymes in the bacterial XDH/XOR family. We also sought to verify whether mammalian XDH activity could be compromised by exposure to Cu 1 or Cd 21. In mammalian cells, xanthine oxidase (XO) and xanthine dehydrogenase (XDH) are interconvertible forms of the same enzyme, known as xanthine oxidoreductase (XOR)  (28, 37). The xanthine oxidoreductase activity of bovine milk XOR was tested either in liquid or by non-denaturing PAGE in the presence of xanthine with increasing concentrations of CuSO 4 or CdCl 2. During the in-liquid reactions, as expected the untreated samples showed the formation of a dark blue color formed due to the interaction of superoxide ions with NBT (Fig. 8A ). Low concentrations of CuSO 4 in the reaction buffer generated a light blue color, demonstrating a decrease in the enzyme activity.
ACKNOWLEDGMENTS This work has benefited from the facilities and expertise of the  Proteomic-Gif (SICaPS)  platform of I2BC,  CEA ,  CNRS ,  Université Paris-Saclay , and  ProGénoMix IBISA  platform in Bagnols-sur-Cèze. We are grateful to  James Provan  for his critical review of the manuscript. S. O. conceived the study. A. -S. S. , M. B. , M. -L. B. , A. D. , S. L. , G. M. , J. A. , and S. O. acquired, analyzed, and interpreted data. M. B. , A. -S. S. , and S. O. wrote the manuscript. We declare that we have no conflicts of interest with the contents of this paper.
(B) Mass spectrometry identification of the high-molecular-weight complex identified in panel A. Accession numbers and protein descriptions are labeled according to the fully sequenced genome of R. gelatinosus S1. (C) Graphic description of the coxSLM operon encoding the three subunits of the putative CO dehydrogenase. fhuE is a gene encoding an outer membrane receptor for iron uptake, coxD encodes an accessory protein belonging to the CO dehydrogenase complex, coxE encodes a protein with a CoxE-like motif, and coxX encodes a protein of unknown function. sodC encodes a putative Cu,Zn superoxide dismutase.
Analyses of molecular datasets A manual check of misreads in chromatograms was performed with BioEdit v. 7. 0. 5. 3  (Hall, 1999). Sequences of the mitochondrial coding cox1 gene were translated into amino acids with ExPASy Translate Tool to detect stop codons and pseudogenes. GenBank accession numbers for newly generated sequences are listed in Table  S1 , as well as those for publicly available cox1 and 28S sequences of T. pullus and T. pullus azorica included in the datasets. Representatives of the family Phasianellidae were chosen as outgroups for the phylogenetic reconstructions: Hiloa variabilis (Pease, 1861) (AB365219, AM048723) for the analysis with cox1, 28S, and concatenated dataset; Agathistoma viridulum (Gmelin, 1791) (AY68209) for the ITS2 phylogeny. The cox1 dataset was aligned with Clustal Omega  (Sievers et al.
, 2011) , whereas the 28S and ITS2 datasets were aligned with MAFFT v7 online server  (Kuraku et al. , 2013; Katoh et al. , 2019). Default settings were used to align the 28S dataset, whilst the Q-INS-I iterative method was set for ITS2 to account for secondary structures. Datasets were reduced to haplotypes using the web-based software ALTER  (Glez-Peña et al. , 2010). GBlocks Server v0. 91b  (Castresana, 2000)  was used to eliminate poorly aligned positions and divergent regions of the 28S and ITS2, as these often hinder the phylogenetic signal  (Talavera and Castresana, 2007). Estimates of the raw (p) distances (i. e. , proportion of fixed differences between two sequences) among Tricolia species were performed in MEGA11  (Tamura et al. , 2021) , with one representative of each species per locality sampled.
Species delimitation and phylogenetic analyses An iterative approach to species delimitation was used  (Yeates et al. 2011) , where species are considered as hypotheses to be subsequently tested by independent evidences  (Puillandre et al. 2009 (Puillandre et al. , 2012a,b),b). Morphological identification of the specimens represented the starting point to identify putative morpho(sub)species. These morphology-based Preliminary Species Hypotheses (PSH) were contrasted with the results of a series of analyses of the molecular datasets. Haplotype subnetworks at the 95% connection limit often coincide with Linnaean species-level names, constituting a useful tool to delineate putative species-boundaries using nucleotide datasets in poorly known groups  (Hart and Sunday, 2007; Chen et al. , 2010).
Therefore, statistical parsimony haplotype networks based on individual cox1 sequences were generated with the software TCS v1. 21  (Clement et al. , 2000) , regardless of species category, to enlighten the relationships among samples. The program tcsBU  (Santos et al. , 2016)  was used to annotate the resulting haplotype networks, facilitating the visualization of intricate relationships. One sample arbitrarily chosen from each haplotype subnetwork was used in a search for matching sequences in the Barcode of Life Data System v3 database (BOLD;  Ratnasingham and Hebert, 2007) , in February 2023. Then, PSH were tested against a molecular approach, with two methods based on pairwise genetic distance on the cox1 dataset: Automatic Barcode Gap Discovery (ABGD;  Puillandre et al. , 2012a)  and Assemble Species by Automatic Partitioning (ASAP;  Puillandre et al.