dataesr/scientific-paragraphs-categorization huggingface dataset:
The point sets can also be extensible in the number of points and number of dimensions. By "constructing the points" here we mean defining the set P n by selecting the parameter values for a general structure, by trying to miminize a figure of merit (FOM) that may represent a discrepancy D(P n ) or be an upper bound on it. Once this is done, other software can be used to randomize and generate the points for their utilization in applications; see  [28, 43]  for example. LatNet Builder is available in open source at https://github. com/umontreal-simul/latnetbuilder. It is a descendant of Lattice Builder  [34] , whose scope was limited to ordinary lattice rules. Another related tool is Nuyens' fast CBC constructions  [41]. The rest of this paper is organized as follows. In Section 2, we recall the types of QMC point sets covered by our software, namely ordinary lattice points, polynomial lattice points, digital nets, and their higher-order and interlaced versions, as well as the main randomization methods to turn these point sets into RQMC points.
The discrepancies that we consider often provide upper bounds on the mean square integration error when using these randomizations, for certain classes of functions. In Section 3, we give the general form of weighted RKHS used in this paper and the corresponding generalized Koksma-Hlawka inequality. We also recall the common types of weights, all supported by the software. In Section 4, we review and justify the various discrepancies that are supported by LatNet Builder and can be used as FOMs to select the parameters of point set constructions. In Section 5, we summarize the search methods implemented in our software. In Section 7, we compare FOM values obtained by various point set constructions and search methods. We also compare RQMC variance for simple integrands f. Section 8 gives a conclusion.
Usage of the Software Tool At the first level, LatNet Builder is a library written in C++ which implements classes and methods to compute FOMs and search for good point sets for all the construction methods and FOMs discussed in this paper. The source code and a detailed reference manual for the library are provided at https://github. com/umontreal-simul/ latnetbuilder. The library can be used directly from C++ programs and can be extended if desired. This is the most flexible option, but it requires knowledge of C++ and the library. At the second level, there is an executable latnetbuilder program that can be called directly from the command line in Linux, Mac OS, or Windows. This program has a large number of options to specify the type and number of points, number of dimensions, search method, FOM, weights, output file format, etc.
We think this is the most convenient way of using LatNet Builder in practice. In addition to giving the search results on the terminal, the program creates a directory with two output files: one summarizes the search parameters and the other one puts the parameters of the selected point set in a standard format designed for reading by other software that can generate and use the RQMC points in applications. There are selected file formats for ordinary lattice rules, polynomial lattice rules, Sobol points, and general digital nets in base 2. A tutorial on the command line and a summary of the options can be found in the reference manual. At a third level, there is a Java interface in SSJ, a Python interface included in the distribution, and a Graphical User Interface (GUI) based on the Jupyter ecosystem, written in Python.
These interfaces use the command line internally. With the GUI, the user can select the desired options in menus, write numerical values in input cells, write the name of the desired output directory, and launch a search. The LatNet Builder program with the Python interface and the GUI can be installed as a Docker container on one's machine. An even simpler access to the GUI is available without installing anything: just click on the "Launch Binder" black and pink link on the GitHub site and it will run the GUI with a version of the program hosted by Binder. This service provides limited computation resources but is convenient for small experiments and to get a sense of what the software does. We now give examples to illustrate how the command line works, how the results look like, and give some idea of the required CPU times for the search.
The timings were made in a VirtualBox for Ubuntu Linux running atop Windows 10 on an old desktop computer with an Intel i7-2600 processor at 3. 4GHz and 32 Gb of memory. The following command makes a search for a polynomial lattice rule with n = 2 16 points in 256 dimensions, with the default irreducible polynomial modulus, using the fast-CBC search method, the P2 criterion, q = 2, and order-dependent weights Γ 2 = 10, Γ 3 = 0. 1, Γ 4 = 0. 001, and the other weights equal to 0 (recall that Γ 1 has no impact on the selection). These weights decrease quickly with the order because the number of projections of any given order increases very quickly with the order. If they decrease too slowly, the total weight of the projections of order 2 in the FOM will be negligible compared to those of order 4, for instance.
Comparison with tabulated parameter selections We now give small examples showing how searching for custom parameter values with LatNet Builder can make a difference in the RQMC variance compared with pre-tabulated parameter values available in software or over the Internet. We do this for Sobol' nets, and our comparison is with the precomputed direction numbers obtained by Joe and Kuo  [25] , which are arguably the best proposed values so far. These parameters were obtained by optimizing a FOM based on the t-values over two-dimensional projections, using a CBC construction. With LatNet Builder, we can account for any selected projections in our FOM. For instance, if we think all the projections in two and three dimensions are important, we can select a FOM that accounts for all these projections.
Conclusion LatNet Builder is both a tool for researchers to study the properties of highly uniform point sets and associated figures of merit, and for practitioners who want to find good parameters for a specific task. It is relatively easy to incorporate new FOMs into the software, especially if they are in the kernel form  (9). Many questions remain open regarding the roles of the construction, the search method, the randomization, and (perhaps more importantly) the choice of the weights. It is our hope that the software presented here will spur interest into these issues.
Acknowledgements This work has been supported by a  NSERC   Discovery Grant  and an  IVADO Grant  to  P. L'Ecuyer , and by a stipend from  Corps des Mines  to  P. Marion. F. Puchhammer  was supported by  Spanish and Basque governments fundings through BCAM (ERDF, ESF ,  SEV-2017-0718 ,  PID2019-108111RB-I00 ,  PID2019-104927GB-C22 ,  BERC 2018e2021 ,  EXP. 2019/00432 ,  KK-2020/00049 ), and the computing infrastructure of i2BASQUE and  IZO-SGI SGIker (UPV). Yocheved Darmon  wrote code for producing the output files.
Among the first to invest in the technology was the CAD software company Autodesk which spearheaded the effort of developing affordable VR systems for the masses. In a seminal 1988 memo that cited both science and fiction, its co-founder and president urged the company to take a leading role in the development of 'cyberspace' (i. e. VR), which he discussed as a step beyond user interfaces and into illusionistic 3D interactive experiences. The memo was titled "Through the Looking Glass"alluding to Sutherland's reference to the world of Alice -and concluded with: "Cyberspace. Reality isn't enough any more". 45 The same year Autodesk established Project Cyberia to pursue that vision. Although most of its members left by the end of 1989, in this short period the team broke new ground producing a working prototype and applications that proved affordable VR was indeed possible.
90%, 8 associated with a reverse Trendelenburg position for obese patients. 9, 10 The brain target of remifentanil concentration was 2 ng/mL (according to Minto's model) at induction. Propofol was then started with a brain concentration at 2 mg/mL (according to Schnider's model) and, if necessary, a gradual increase of 0. 5 mg/mL (usual final brain concentration, 3-5 mg/mL) to achieve narcosis compatible with the surgical procedure. A nasal oxygen catheter was then inserted (oxygen flow between 6 and 10 L/min was temporarily stopped in case of laser). Procedural Failure Definition. Failure of the procedure was defined by the presence of at least 1 of the following criteria: hypoxemia (SpO 2 88%),  11  apnea episodes requiring face mask ventilation, or mechanical ventilation with OTI. Data Collection.
Most recent advances in computational metabolomics provide a long-awaited framework to systematically explore the above-described importance of the species x tissue SM variations  (4). These novel capacities to explore plant chemical spaces are further propelled by platforms such as, the MassIVE database (https://massive. ucsd. edu/) reaching 12000 metabolomics datasets in 2022. Despite the increasing amount of data that can be generated from modern MS instruments, the average annotation rate of most MS metabolomics studies remains at the order of a few percent of deconvoluted MS/MS features  (17). The number of computational tools to address this challenge of transforming spectral information into chemical knowledge is hence rapidly increasing and can be divided into two main approaches. One set of approaches relies on MS/MS spectral grouping, as embodied by the game-changing development of molecular networking and of the repertoire of network annotation/mining tools embedded within the GNPS ecosystem  (18, 19).
Additionally, we aimed via this pluri-tissue approach to explore tissue-level shifts in SM class prevalence across the focal species as a mechanism of organismiclevel chemodiversification. Noteworthy, amounts of leaf exudate material collected greatly differed among the focal species, with Nicotiana setchellii (2. 7 mg of exudate per g leaf fresh weight) and Nicotiana glutinosa (2. 5 mg/g) producing the largest amounts of dried exudates collected from leaf washes (Fig. S1  and Table  S1 ). All methanolic extracts were analyzed using a previously established UPLC-ESI + QTOFMS method with optimized settings for massive MS/MS data collection  (6). 17901 metabolite-derived MS/MS spectra (hereafter referred to as features) were, after a data redundancy and contaminant check using a custom script, deconvoluted and considered for Feature-based Molecular Networking (FBMN) processing with settings that were optimized to handle the species x tissue-exacerbated metabolic diversity in the dataset.
Deep metabolome annotation empowered by a multi-inference approach incorporating a 1 million natural product in silico spectral database and consensus substructure computations The previous analysis indicated a critical need not only for broadly increasing feature annotations beyond CANOPUS class predictions but also for gaining structural insights into core (sub)structures underlying molecular networks' topology. As outlined in a recent review  (40) , substructure annotation provides information on functional groups, building blocks, or scaffolds within a chemical structure. This level of information is complementary to compound class prediction, most commonly addressing biosynthetic origin and/or compound physico-chemical properties. To propel substructure identification in our dataset, we first optimized a multiinference annotation pipeline (Fig.
S2  and S5 ). Briefly, feature spectra were first queried against an in-house Nicotiana attenuata SM MS/MS database (NaMS, entries resulting from the analysis of purified SMs) and the GNPS library, the resulting hits being referred respectively to as annotation levels 1 to 2 according to the Metabolomics Standard Initiative nomenclature  (41). Interrogation of these two experimental spectral databases provided hits for 4% of the MS/MS features (Fig. 3A ). Level 3 of the annotation nomenclature regrouped class-based annotations mostly derived from manual inspection of network-level hits (5%). To circumvent limitations in the chemical space covered by these two experimental databases, spectral interrogations were conducted in parallel against in silico-predicted MS/MS spectral libraries using both molDiscovery which predicts MS spectra of small molecules on-the-fly and scores their probabilistic modeling  (24) , and a combination of CFM-ID and MatchMS.
The above-described multi-query approach of the 17901 features from our dataset retrieved annotations for 57 % of these features, with 9 % hits for priority levels 1-to-3 (Fig. 3A ). To maximize structural insights that could be gained from this deep annotation, we finally computed the top most common substructures (referred to as Network Consensus Structure, NCS) based on feature annotations for each of the FBMN molecular networks that did not contain any level 1-to-2 annotations. Consensus structure computational prediction relies on a new algorithmic approach that employs hits obtained from in silico MS/MS spectral databases (See description in the Method section and Code Availability and Description). The NCS strategy is illustrated in Fig. 3C-D  with top NCS hits for network #486 whose MS/MS features were initially classified as "Amino acids and derivatives" by CANOPUS.
The latter can typically be approached through MS2LDA, an unsupervised method to extract common patterns of mass fragments and neutral losses, referred to as mass motifs, from collections of fragmentation spectra  (22). From this analysis, we retained 76 mass motifs that best depicted the structural diversity within our dataset as confirmed by hierarchical clustering (resulting in clusters of co-varying mass motifs) and mapping of enriched CANOPUS predictions for each mass motifs (motif-level propagation of CANOPUS predictions) (Fig. 5A , Fig. S9 ). In analogy to the critical role of conserved domain/motif inferences in protein structure-activity studies, mass motif inference offers a dimensionality reduction perspective on recurrent fragmentation patterns derived from particular substructures.
This approach is however often limited by the scarcity of structurally annotated mass motifs in MS2LDA libraries. An asset of our approach is that it mutualizes the previously described SM cartography to mine most interesting mass motifs (Fig. 5B  and Data S5). For instance, we confirmed the presence in motif cluster 1 (MC1) of a mass motif (Strepsalini_110) which was characteristic of the O-acyl glycerols specific to Suaveolentes. MC1 also contained motif #631 and motif #254 characteristic of steroidal glycoalkaloids and that were strikingly specific to N. plumbaginifolia. Motif #646, present in the second cluster (MC2) captured the complete diversity of 17-HGL diterpene glycosides, allowing to efficiently explore tissue-specificity for this compound class. MC4 contained a motif (motif #37) with fragments indicative of hydroxycinnamic acid substructures derived from a network of O-phenolic glycosides.
MolNetEnhancer has been developed to more efficiently combine outputs from molecular networking, MS2LDA as well as in silico and chemical classification tools  (45). However, substructure discovery from MolNetEnhancer outputs is strongly hampered by the scarcity of annotated motifs in the Mass2Motifs database embedded into MS2LDA, many of which additionally translating from relatively unspecific fragmentations (e. g. water, methyl, hexose losses). Only 24 of the 76 mass motifs retained for further analysis had partial annotation hints in the MS2LDA Mass2Motifs database (Data S5). To significantly improve substructure discovery and annotation, we implemented two complementary approaches. On the one hand, we propagated CANOPUS predictions at mass motif level (MP-CANOPUS) by computing frequencies in "super-class/subclass/most specific class", and combined this information with mass motif co-regulation analysis (Fig.
5 ). The second approach implemented for substructure analysis involved advanced maximum common substructure calculations to integrate annotations from multiple tools on a network (NCS) or motif level (MCS). Overall, we obtained 349 NCS or 303 MCS predictions for the whole data-set (Data S3 and S6). Maximum common substructure computation for substructure prediction had been employed in one of our previous studies to cluster candidate structures obtained by the MetFrag searches among co-regulated herbivory-induced metabolites  (46)  and is also one of the processing steps within the Network Annotation Propagation tool of the GNPS web-platform  (47). Altogether, we advocate that the NCS/MCS approach implemented here has three main advantages: (i) it is an efficient mean of summarizing common substructure within the diversity of outputs from database queries as SMILES strings, (ii) it can used as input to reveal substructures statistically associated with intense chemodiversification in a given species, and (iii) it provides structural guidance during the manual interpretation of mass motifs or molecular network.
In this respect, our study led to the curation of 76 mass motifs (Data S5). Such effort is important to empower supervised search of mass motifs which is already possible in MS2LDA and which will be greatly facilitated with the recent release of the MS2QUERY tool  (48). A very important delivery of our work is the development and public sharing of the 1M-DB which is, to the best of our knowledge, the largest in silico spectral database. This approach resulted into 5-fold more hits (annotation of 57% of the total features), than experimental spectral database interrogation alone. Data of the 1M-DB can currently be accessed and interrogated from the GNPS platform. The size of this data-set can represent a challenge for MatchMS-based queries, which can nonetheless be locally implemented with reasonable computing capacity with the parallelized script (Code Availability and Description) provided with our study.
It is therefore foreseeable that the efficiency of the interrogation of the 1M-DB will strongly benefit from up-to-date optimization of MatchMS parallelization as part of future version releases. Multiple tools have been developed in recent years to produce hypothetical MS/MS spectra  (23) (24) (25) (26) 49). A more recent development in this area is that of QCxMS which provides, in our experience, very high-quality spectra. This program is currently too much computationally demanding and could not be transposed to the scale of this study, besides the computation of MS/MS predictions for the 429 structures of the Jassbi database and using a limited number of fragmentation trajectories (Zenodo link, https://doi. org/10. 5281/zenodo. 6536010). One promising direction for improving the confidence of such in silico fragmentation-based annotation is exemplified by the recently developed COSMIC workflow that incorporates a confidence score consisting of kernel density P-value estimation from a decoy library and a support vector machine algorithm  (50).
Plant material, growth conditions and treatment Nicotiana species with their origin and associated accession numbers are summarized in Table  S1. Seeds of all Nicotiana species were directly germinated on soil, with the exception of N. attenuata, for which smoke-induced seed germination was established as described previously  (Krügel et al. , 2002). For all species, glasshouse growth conditions were as described previously  (Krügel et al. , 2002). Six-to-eight weeks old elongated plants were used for all metabolomics analyses. In order to analyze the regulatory function of jasmonate signaling on metabolomicsinferred specialized metabolism classes, petioles of 2 elongated plants were treated with either 20 µL lanolin paste containing 150 µg methyl jasmonate (Lan + MeJA) or with 20 µL pure lanolin (Lan) according to  Heiling et al.
Ultra-high resolution MS imaging data acquisition and processing Freshly collected rosette leaves of N. nesophila were embedded into M-1 embedding matrix (Thermo Scientific) and frozen before cutting. Cuts were done on a transverse plane at 25µm thickness and -15°C using a cryotome FSE. Sections were deposited on indium-tin-oxide coated slides and sprayed with a-cyano-4-hydroxycinnamic acid (HCCA) matrix at 10mg/mL in 70% ACN, 0. 1% trifluoroacetic acid using the HTX M5 sprayer. Nozzle temperature was set at 75°C, flow rate at 0. 120mL/min, velocity at 1200 mm/min, pressure at 10 psi, gas flow rate at 3 L/min and nozzle height at 40mm. Four passes were applied with a track spacing of 3mm and a HH pattern. Samples were analyzed with a Burker SolariX 7T Fourier transform ion cyclotron mass spectrometer at resolving power R=120,000 at m/z = 400.
Acquisition was performed in positive ion mode on a 100-500 m/z mass range, with an accumulation of 0. 020 s, the transfer optics time of flight set at 0. 600 ms, frequency at 6 Hz and RF amplitude at 350 Vpp. The MALDI plate offset was set at 100 V, deflector plate at 200 V, laser power was set at 20%, laser shots at 100 and frequency at 1000 Hz with a small laser focus. The instrument was calibrated by multipoint correction using the peaks of the HCCA matrix (m/z = 379. 0924, 399. 0377, 401. 0744, 417. 0483). The regions of interest were determined in FlexImaging with a raster width of 50µm. Images of the ions of interest +/-3 ppm were displayed in MSiReader v1. 03  (66). The data was submitted to metaspace and is available at https://metaspace2020. eu/project/nicotiana_msi-2022.
Feature-based molecular networking of UPLC-QTOF MS data Raw data were converted to the. mzML format using MSConvert (Version 3. 0. 21112-b41ef0ad4, Chambers et al. , 2012). The resulting data files were then processed with the Batch Mode (See Code availability, Script S10) of MZMine 2. 53  (68)   and for spectral analyses in Sirius  (21). The resulting. mgf and. csv files were further filtered to exclude redundant none-biologically informative MS/MS features using newly developed Python Scripts S11 and S12 (see Code availability). The m/z signals that appear more than 5 times (± 3ppm) with a retention time coefficient of variation greater then 10 % were discarded. This filtering step excluded 11580 features (out of a total of 29481 retrieved from the MZMine-based processing), a vast majority of those corresponded to redundant features detected at high-level in solvent blanks.
MS/MS elemental formula and compound class predictions with Sirius Sirius (Version 4. 8. 2) was used to predict elemental formulas for MS/MS precursors as well as for the deep neural network-based compound class prediction as part of the CANOPUS pipeline  (20). Sirius commands are summarized as part of Script S13 (see Code availability). Elemental formulas by Sirius were further processed with Scripts S14 and S15 (see Code availability) to restore Feature IDs and calculate the degree of unsaturation of these formulas. A main strength of CANOPUS-based class prediction is that does not involve the interrogations of spectral libraries with fragmentation spectra, thereby allowing class prediction of MS/MS features for which no database hit is retrieved and circumventing the possible issue of error propagation when false class prediction is obtained by FBMN network-level propagation from feature-derived database hits.