dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the effect of competition between growth of existing crystals and nucleation during intervals of zirconium oversaturation  [ 74 ]  is not included in our model. All the zircon mass available for precipitation contributes to crystallization of new zircon grains (i. e. varying nucleation rates but instantaneous growth;  [ 78 ] ). Moreover, in natural datasets, the inherent mixing of age domains within whole grains produced by CA-ID-TIMS analyses may result in slightly shorter crystallization ranges than expected (i. e. averaging effect;  [ 22 , 68 , 78 ] ), adding a degree of uncertainty in comparisons between time-resolved natural and synthetic zircon crystallization temperature histories ( Fig. 8  ).
Concluding remark The method developed in this study allows users to simulate zircon crystallization trajectories in upper crustal magma reservoirs for a variety of thermal histories. The outputs of this model are tailored to allow a direct comparison with high-precision U-Pb zircon geochronology (CA-ID-TIMS) datasets. Specifically, the stochastic sampling of synthetic zircon age populations mimics the random process of selection of a limited number of zircons from a rock sample, while the uncertainty on the model output is parametrized based on a comprehensive dataset of high-precision zircon ages. The model is inclusive of statistical tools that allow the user to quantitatively compare simulation results and data from natural magmatic systems, thus informing on the dominant thermomechanical processes taking place in a crystallizing magma batch.
EXTREAM algorithm Our previous study used the E-flux algorithm  20  to accurately predict the maize root phenotype under nitrogen starvation based on transcriptomic data. This work involved photosynthetic tissues, having additional metabolic complexities compared to roots. Thereby, the efficacy of the E-flux algorithm was re-assessed. In a previous study, the maize leaf was divided into 15 cross-sections, and generated transcriptomics data for each of these sections  21. For this study, we collected transcriptomics data of cross-sections 1, 5, and 15 based on the greenness of the leaf blade (less green to greener). These data were integrated in the leaf GSM  22  using E-flux algorithm. The highest growth rates in maize leaves occur at the base of the leaf, where new cells are actively dividing and elongating.
As the cells grow toward the tip, they undergo expansion and maturation, leading to a decrease in the growth rate. Eventually, the cells at the tip stop elongating altogether. The contextualized leaf model correctly predicted higher biomass growth rate of leaf base compared to leaf tip (Fig. 1F ). The model also predicted rate of carbon fixation correctly, however, it incorrectly predicted the starch distribution profile along the leaf (Fig. 1F ). Thus, modification of the E-flux algorithm was required to fit the starch distribution profile. To achieve this, we proposed the EXTREAM algorithm, which equally distributed transcript levels for each gene based on the number of cognate biochemical reactions. Once we contextualized the leaf model with the EXTREAM algorithm, it predicted the correct biomass growth rate, carbon fixation, and starch distribution profiles along the selected leaf sections (Fig.
1F ). We hypothesized that the solution space of the E-flux algorithm was overly permissive, resulting in an inaccurate carbohydrate profile. Thus, we further restricted the feasible solution space by equally distributing the transcript of each gene based on the number of reactions the gene participated in (Supplementary Note). The objective function was to minimize the sum of reaction fluxes compared to a reference condition, calculated from the transcriptomic data. A similar objective function was used in MOMA  36. However, MOMA used wild-type flux distribution as the reference to estimate flux distribution after gene knockouts. In our case, the purpose of the reference condition was to maximize agreement between transcriptomic data and reaction flux. After these modifications, the new algorithm, EXTREAM, predicted the correct carbohydrate profile across leaf crosssections.
EXTREAM algorithm In this work, we proposed the EXTREAM, where transcript of each gene was equally divided based on the number of reactions the gene participated. We also changed the objective function which is the minimization of sum of reaction fluxes compared to a reference condition, calculated from the transcriptomics data. The formulation of EXTREAM is the following: ݉݅݊ ൫ห‫ݒ‬ ห െ ‫ݒ‬ ,௦ ൯ Subject to, ܵ , ‫ݒ‬ ൌ 0, ‫݅‬ ‫א‬ ‫ܫ‬ ሺ1ሻ ‫ݒ‬ , ‫ݒ‬ ‫ݒ‬ ,௫ , ‫݆‬ ‫א‬ ‫ܬ‬ ሺ2ሻ ‫ݒ‬ ௦௦ ݂ ൈ ‫ݒ‬ ௦௦, ௫ ሺ3ሻ Here, ‫ݒ‬ is the flux to be calculated for reaction ‫,ܬ‬ ‫ݒ‬ ,௦ is the reference condition calculated from the gene-protein-reaction association for reaction ‫,ܬ‬ ܵ , is the stoichiometric matrix for metabolite ‫,ܫ‬ and reaction ‫,ܬ‬ ‫ݒ‬ , and ‫ݒ‬ ,௫ are the upped and lower bound of reaction ‫,ܬ‬ ‫ݒ‬ ௦௦ is the desired biomass growth rate, ‫ݒ‬ ௦௦,௫ is the maximum possible biomass growth rate, and ݂ is fraction between 0 to 1.
DATA AVAILABILTY iZMA6517, EXTREAM, and MBA codes are available in this GitHub directory: https://github. com/ssbio/iZMA6517. SI-݇ ௧ codes are available in this GitHub directory: https://github. com/ChowdhuryRatul/kcat_iZMA6517. RNAseq project is deposited in Gene Expression Omnibus (GSE235654). All steps of the experiment, from growth conditions to bioinformatic analyses, were detailed in CATdb: http://tools. ips2. u-psud. fr. fr/CATdb/; Project: NGS2021_19_Rhizophagus according to the MINSEQE. Nutrients for the plant growth can be accessed in Supplementary Data 13.
To this end, we confirmed the presence of SQOR in human umbilical vein endothelial cells (HUVECs) and quantified the direct functional effect of GYY4137 on SQOR by measuring mitochondrial oxygen consumption. Finally, we confirmed the ability of GYY4137-derived H 2 S to directly increase intramitochondrial H 2 S levels by trapping H 2 S with zinc. Collectively, these data demonstrate that GYY4137 influences mitochondrial function in endothelial cells depending on the presence of SQOR.
Analysing the Presence of SQOR by Western Blot Protein lysates were obtained using RIPA lysis buffer (50 mM Tris-Cl pH 8. 0, 150 mM NaCl, 1% Igepal Ca 630, 0. 5% Sodium Deoxycholate, 1. 0% SDS, 0. 4% protein inhibitor cocktail, 1 mM sodium orthovanadate, 10 mM NaF, 10 mM β-mercaptoethanol). Next, protein concentrations were measured with a Bio-Rad protein assay on a Bio-Tek Synergy H4 plate reader. Samples were loaded to 4-20% sodium dodecyl sulfate-polyacrylamide pre-casted gels (Bio-Rad TGX gels #4568096, Hercules, CA, USA) and transferred to a nitrocellulose membrane. A stain-free picture was captured to allow post-hoc normalisation for protein load. Membranes were blocked with 5% skimmed milk for 30 min and incubated with the primary antibody anti-SQORDL (Sigma-Aldrich #HPA017079, St. Louis, MI, USA) (1:1000, v/v) overnight at 4 • C.
(E) Incubation of HUVEC with 10 mM GYY4137 (blue line) followed by 80 µM and 800 µM zinc chloride (zinc) inhibited oxygen consumption in the presence of GYY4137. (F) Oxygen consumption of HUVECs treated with rotenone (Rot) followed by incubation with GYY4137 or solvent (arrow), finally antimycin A (AMA) 5 µM was added to inhibit mitochondrial oxygen consumption. (G) In SH-SY5Y cells administration of GYY4137 10 mM (blue line), followed by adding 80 µM and 800 µM zinc inhibited oxygen consumption in the presence of GYY4137, finally AMA 5 µM was added to inhibit mitochondrial oxygen consumption. (H) In the absence of cells, GYY4137 10 mM was added and oxygen consumption was measured. AMA; antimycin A. HUVECs; human umbilical vein endothelial cells, Rot; rotenone, SY5Y; SH-SY5Y. Data are represented as mean ± SEM, * means p < 0.
The deluge of genomic data raises various challenges for computational protein annotation. The definition of superfamilies, based on conserved folds, or of families, showing more recent homology signatures, allow a first categorization of the sequence space. However, for precise functional annotation or the identification of the unexplored parts within a family, a division into subfamilies is essential. As curators of an expert database, the Carbohydrate Active Enzymes database (CAZy), we began, more than 15 years ago, to manually define subfamilies based on phylogeny reconstruction. However, facing the increasing amount of sequence and functional data, we required more scalable and reproducible methods. The recently popularized sequence similarity networks (SSNs), allows to cope with very large families and computation of many subfamily schemes.
Still, the choice of the optimal SSN subfamily scheme only relies on expert knowledge so far, without any data-driven guidance from within the network. In this study, we therefore decided to investigate several network properties to determine a criterion which can be used by curators to evaluate the quality of subfamily assignments. The performance of the closeness centrality criterion, a network property to indicate the connectedness within the network, shows high similarity to the decisions of expert curators from eight distinct protein families. Closeness centrality also suggests that in some cases multiple levels of subfamilies could be possible, depending on the granularity of the research question, while it indicates when no subfamily emerged in some family evolution. We finally used closeness centrality to create subfamilies in four families of the CAZy database, providing a finer functional annotation and highlighting subfamilies without biochemically characterized members for potential future discoveries.
The main advantage of SSNs compared to phylogeny is their ability to cope with large families for which the alignment computation and results are problematic. Their main limitation is that (i) they require the exploration of the many possible cutoffs and (ii) the critical step for the determination of the optimal solution fully relies on human expertise. While expert knowledge is arguably one of the very principles of science, it lacks the scalability required to deal with nowadays amount of data and the reproducibility for sharing scientific advances. For experts, SSN tools do not necessarily produce humanfriendly visualization of the results, making the selection of a suitable network, out of all possible SSNs, a real challenge. As curators of a specialist resource, the Carbohydrate-Active enZyme (CAZy) database, one of our objectives is to enhance the predictive power of our protein annotation, since our community relies on our annotation and expert knowledge to build hypotheses leading to future discoveries  [7].
Due to the large amount of CAZy families (currently >300) and the existence of very large and multifunctional families, there is an urgent need to make the subfamily investigations more performant, as well as reproducible. We therefore decided to investigate currently available methods and indicators from which the optimal SSN can easily and reproducibly be derived by expert curators. We investigated various clustering methods, dimensionality reduction techniques and various network properties, which indicate grouping and spread of nodes within the network. We applied these methods to artificial datasets, as well as to the recently published SSN of glycoside hydrolase family GH16  [8]  and to various families from the structure function linkage database  [9]. We show that the closeness centrality criterion is a fast and relevant indicator for optimal subfamily delineations.
Closeness centrality supports findings by expert curators To confirm the relevance of closeness centrality, we applied this criterion to determine the optimal cutoffs in published SSN analysis by several independent research groups, based on real protein family datasets, to compare with the cutoff determined by expert curators. We created the SSNs of one Interpro family, three SFLD families, and two CAZy glycoside hydrolase families. We first compared the closeness centrality performance to an analysis that used the popular SSN tool EFI-EST  [5]. We selected one among its most recent citations in PubMed (September 2020), and with easily reproducible methods  [10]. In this publication about the domain IPR037434, Travis et al. identified three relevant cutoffs including 10 -55 corresponding to 5 subfamilies with more than 3 members.
We generated SSNs from 10 -5 to 10 -100 (steps of 10 -5 ), on which closeness centrality computations show a local optimum for an e-value of 10 -60 (difference possibly influenced by the increase in the database size; 228 sequences in Travis et al. , 376 in this investigation), which corresponds to 10 families with more than 3 members (51 subfamilies total). A consensus probably resides between those two. For SFLD family 159 of tautomerases  [11] , the authors concluded first on an e-value of 10 -11 for subfamily assignment, but due to the great diversity in their sequences also further investigated an e-value of 10 -18 to define 18 subfamilies. The closeness centrality computations show local optima at e-values of 10 -9 and 10 -18 , the latter displaying 15 subfamilies with >100 members. However, an absolute maximum was obtained at 10 -34 , although this would result in 12 more subfamilies (with >100 members), and more than 2000 smaller networks.
This would allow (i) a better coverage of the sequence space, as many sequences cannot be assigned to a subfamily at lower thresholds; and (ii) finer functional prediction, associated to the diversity of the functions in the subdivision, hence broader at higher thresholds. In some cases, this might also depend on the scope of the database. For the specialized GH19 database  [18] , the authors chose to annotate multiple levels of subfamily, corresponding to first functional and later structural differences. The CAZy database is primarily interested in the functional differences, and we therefore decided that one subfamilylevel annotation is sufficient, although this might depend on the families or enzyme classes. Therefore, we only implement one subfamily hierarchy level, as do many other specialized databases like MEROPS  [19] , ESTHER  [20] , or SulfAtlas  [21].
The subfamily division is however not applicable to all families in a systematic/automatic manner, as it requires in-depth analysis to ensure long-term stability of the proposed classification, the availability of enough characterized members and human decision when several optima are found. This highlights the need to support such expert databases, which are dedicated to mine the functional annotation of (meta)genomes in the literature, as such effort can no longer be supported by universal databases. For GH55, we found an agreement to prior analysis  [15]. Bianchetti et al. identified, in a large-scale screening of GH55, three major subfamilies. Two of these families contained functional enzymes, and were divided into a fungal and a bacterial family, with the third family containing non-functional bacterial proteins.
g. assigning a value of 0 as for singlets. While this might be necessary in case many different networks are built at the same time, in most circumstances a manual evaluation by the curator with the amount of networks in mind should suffice. An unexpected limitation which we encountered is the usage of the e-value for the generation of SSNs. Most researchers decrement the e-value to generate their SSNs  [4, 5]. The e-value is a floating-point value, where reaching 0. 0 is considered to be the most significant value, indicating a very good match, most often resulting in perfect matches. The e-value is dependent on database size and query length though. In case of too large families, or very long protein domains (e. g. PL26s average is 867 AA), the e-value will often result in 0 (see  S5 Fig, with  an overview of e-values per percentage identity), due to the limit of 10 -308 for a 64-bit floating point value with double precision (IEEE 754 standard for floating point arithmetic,  [28] ; the equivalent limit for 32-bit is 10 -38 ).
Generation of artificial data Artificial data was generated with the Python networkX 2. 4/5 library  [32]. We first used the network generators to create different subnetworks (to be connected during the second step): (i) two perfect networks (all possible edges between all nodes exist) of size 50 and 200, using the nx. complete_graph function; six random networks of size 200 with 300, 500, 1000, 2000, 3000 and 4000 edges (representing from 1. 5% to 20. 1% of a complete graph) using the nx. gnm_random_graph function; and two networks consisting out of 50 or 200 single nodes, or singletons. Secondly, we generated network collections. A collection consists in multiple networks build upon the same initial pair of subnetworks (described above; see S2 Table ). A collection starts with a first network simply consisting in the two disconnected subnetworks.
For the artificial data, all these properties were calculated, while CAZy families GH16  [8] , GH19  [18] , GH45, GH51, GH54, GH55, GH68, GH130  [14]  and PL26, as well as different families of the Structure Function Linkage Database (SFLD)  [3) , SFLD families 19, 122 and 159 and Interpro IPR037434, were investigated with the closeness criterion only (see results). An overview can be seen in S3 Table.
A closeness centrality of a network thus suggests that the defined subfamilies are well-packed, while lowering values reflects the drift of some (groups of) proteins, away from the rest of their relatives. The closeness centrality will raise up when these (groups of) proteins finally separate to create another connected component. An example is given in S7 Fig. The parameter wf_improved in networkX was set to False, to prevent the Wasserman and Faust correction, which was designed to consider the network as a whole, and thus penalizing closeness centrality value if the graph is composed of several subcomponents, which is exactly the objective of our study. A script allowing the computation of closeness centrality from a given network file has been made available on GitHub under https://github.
Generation of SSNs Sequence similarities on SFLD and CAZy families were computed with Blastp v2. 5  [35]. Standard parameters were used, besides the parameter max_target_seqs, which was scaled to the size of the family/target database. The Blastp pairwise hits, based on e-value cutoff, were transformed into a digraph with the python library networkX v2. 4/2. 5  [32] , while sequences without any hit were added as single nodes. We generated SSNs for each e-value cutoff, from 10 -5 up to 10 -160 , by step of 10 -1. Exceptions to these start-and end-points were made when either the network did not form any more subnetworks at high cutoffs, or when the network disintegrated into too many subnetworks (typically >1000) at low cutoffs. All networks were visualized with Cytoscape version 3. 7. 2  [31].
EFI-EST The EFI-EST website  [40]  was used to reproduce the published IPR037434 family analysis, similarly to  [10]. An initial e-value cutoff of 10 -5 was used, and subnetworks were produced based on this network by decreasing the e-value cutoff by steps of 10-5 until the maximum selectable e-value of 10 -100. Steps of 10 -1 were prohibitive in an online-tool without the possibility of automation, and edge values were not included into EFI-EST SSNs during our first tests. For reading xgmml files, networkxgmml v0. 1. 6. was used (https://pypi. org/project/ networkxgmml/).
Extraction of domain sequences SFLD sequence data for families 19 of glutathione transferase  [12] , family 159 of tautomerase  [11] , and family 122 of nitroreductase  [13]  were downloaded from http://sfld. rbvi. ucsf. edu/ archive/django/superfamily/index. html  [9]. For these families the full protein sequences were used. Data for glycoside hydrolase families GH16, GH19, GH51, GH54, GH55, GH68, GH130 and polysaccharide lyase family PL26 were directly extracted from the CAZy database  [41] , and were further supplemented with data by collaboration partners. Domain sequences were extracted from the full proteins using in-house pipeline based on the results of hmmscan function in HMMer3. 3  [42]  as follows. HMM searches using the in-house CAZy HMM profiles were performed and only matches with an e-value <1-4 were further processed.
Section 5 is devoted to the implementation of the SGS algorithm for the optimization of persistent homology-based objective functions f. In Section 5. 1, we provide empirical evidence that SGS behaves better than GD and GS with a simple experiment about minimization of total persistence. In Section 5. 3 and Section 5. 2, we consider two novel topological optimization problems which we believe are of interest in real-world applications. On the one hand, the Topological Template Registration of a filter function x defined on a complex K, is the task of finding a filter function x 1  over a smaller complex K 1 that preserves the barcode of x. On the other hand, given a Mapper graph G, which is a standard visualization tool for arbitrary data sets  [67] , we can bootstrap the data set in order to produce multiple bootstrapped graphs G i.
Section 5. 2 and Section 5. 3 introduce two novel topological optimization tasks: that of topological registration for translating filter functions between two distinct simplicial complexes, and that of topological Fréchet mean for smoothing the Mapper graphs built on top of a data set. Implementation Our implementation is done in Python 3 and relies on TensorFlow  [1]  for automaticdifferentiation, Gudhi  [56]  for TDA-related computations (barcodes, distances W q , Mapper graphs), cvxpy  [32]  for solving the quadratic minimization problem involved in Algorithm 4. Our implementation handles both ordinary and extended persistence, complexes of arbitrary dimension, and can easily be tuned to enable general objective functions (assuming those are provided in an automatic differentiation framework).
, 2017; Tully et al. , 2021). Both modes are based around a common sample environment that enables switching between them with a simple software command that changes the position of a valve. In SC mode, operating at the maximal throughput, up to 500 measurements can be collected per day. The measurement throughput for SEC-SAXS is lower, limited by the length of time taken for a dedicated chromatography column run. The operation of BM29 relies on a high level of software development for beamline control, automated dataacquisition software, real-time data display and automatic data processing  (Pernot et al. , 2013) , which allows experimental users with no, or very little, prior knowledge of synchrotron-based BioSAXS beamlines to benefit from automatic sample loading/unloading, data collection, processing (conversion of a 2D image to a normalized 1D X-ray scattering profile) and analyses in both SC and SEC-SAXS online modes.
Users obtain a normalized 1D scattering profile containing the raw data and automatically processed data sets that include the size (radius of gyration, maximum dimension and volume), molecular weight and in the near future a rough envelope of each sample within a minute from when a sample is measured. The results are logged, stored and displayed in the modified ISPyB/ExiSAXS database  (De Maria et al. , 2015)  where they can be followed (e. g. during remote access or mail-in experiments) and also downloaded. This allows, where necessary, for data collection strategies to be optimized 'on the fly'. Although many of the beamline's instrumentation and software developments have been directed and produced inhouse, BM29 also benefits from international collaborations. The automation of sample loading through the SC is the result of a collaboration between the EMBL (Grenoble and Hamburg outstations) and the ESRF.