dataesr/scientific-paragraphs-categorization huggingface dataset:
From each point, we get the timestamp and just at the end of the sensing process, we also get the colour from the combination of the sensors' output. Table  1  shows the combination of each colour based on the sensors' output. At each of the five points mentioned, the KG receives the data points. A simulated system has been created in the AnyLogic tool (https://www. anylogic. com/). The system sends the workpiece ID to the simulation model, which simulates the operations of the actual system for that workpiece providing the expected result. This simulated result is compared with the real-time status of the Festo sorting station for validation of its performance. In case of discrepancy, a suitable corrective measure may be applied to the physical system.
Detection of malfunction A test scenario has been given below to demonstrate the reasoning to detect the malfunctioned component for a system error. The machine monitoring status in GraphDB is compared with the simulation result. It can be seen that several workpieces are missing from collection point 1 as the number of workpieces in collection point 1 (CQ 5) is less than the number of workpieces simulated for the same collection point. Therefore, the system-level error is marked by the comparison script as shown in Figure  5 , where the workpiece with ID 004 is one of the missing workpieces from collection point 1. Figure  5  -malfunctioning of Red Sorting Unit When the rules given from (  4 ) to (  7 ) are run on the KG, rule (  5 ) is triggered because the ActOfCarryingRed realises the function of the Conveyor as given by the state description in Figure  6  (Workpiece 004 carried from s_out to w).
Collected from the water column surrounding the sponges without altering their biomass, such specialized EMs could become a new source of inspiration in drug discovery. However, studying the chemical and biological potentials of sponge's EMs requires a variety of technical challenges to be addressed, the first one being the concentration of molecules in a diluted aqueous environment. The second encompasses the identification and quantification of molecules in a complex mixture. Moreover, due to the lack of available mass spectrometry (MS) data from marine organisms in public databases, less than 10% of marine metabolites can be annotated. 20, 21  Such situations require targeted isolation of already known metabolites to confirm their structural identity and/or the use of in silico structural annotation tools such as SIRIUS MS/MS or CFM-ID.
Proportion of Brominated Features in A. cavernicola's Exometabolome. To determine the proportion of features that are shared between crude and EM extracts in the entire dataset, we evaluated the general distribution of chemical features detected in crude and in each of the three EM extracts for CAPT1 and CAPT2 by analyzing the MS 1 data matrix. Features detected in controls were discarded from the analysis. As represented in Figure  2C , features present in both crude and EM extracts, possibly encompassing sponge-specialized brominated metabolites, accounted for 6% of the total detected features in CAPT1 and 7% in CAPT2. Interestingly, the proportion of shared features between crude and EM extracts was very similar for both CAPT1 and CAPT2, as opposed to the proportion of features specifically detected in EM or crude.
MS 2 dereplication enabled further annotation of brominated EMs by analytical propagation from node to node and comparison of data with those already published whenever possible (Figure  3C ). The identity of aerothionin A3 was further confirmed with the publicly shared MS 2 data on GNPS. 57  Compound A1 was annotated as aerophobin 1 (M477T9. 91 [C 15 H 16 Br 2 N 4 O 4 + H] + )  58, 59  and A4 was annotated as purealidin L (M496T11. 75 [C 15 H 21 Br 2 N 5 O 4 + H] + ). 52  Connected to purealidin L with a cosine score of 0. 9, A10 was attributed to aplysinamisine II (M510T13. 19 [C 16 H 23 Br 2 N 5 O 4 + H] + ). 52, 60  The annotated MS spectra are available in the Supporting Information. The compound A8 ([C 14 H 19 Br 2 N 3 O 4 + H] + ) was found to be closely related to both A3 and A4 (cosine scores 0.
74 and 0. 80, respectively). Both its molecular formula and exact mass could match the structure as found in PubChem (CID: 101765254), N-(4 aminobutyl)-2-[(E)-hydroxyimino]-3-(2-hydroxy-3,5-dibromo-4-methoxyphenyl) propenamide but has not been reported to be isolated from any natural source. Table  2. 1  H and  13  C NMR Data for A8 Acquired in CD 3 OD (600 MHz) at 300 K a Calculated coupling constants by quantum mechanical analysis through 1 H iterative full spin analysis, using Cosmic Truth (https://ctb. nmrsolutions. fi/). 66,67 p = pseudo. 52  (Figure  4 ). We further proceeded with the purification of A8 from the sponge crude extract to confirm its identity by NMR (one-dimensional (1D) 1 H/  13  C and two-dimensional (2D) gCOSY, HSQC, and HMBC). This compound was isolated after two steps of Vacuum Liquid Chromatography (VLC) fractionation followed by purification by preparative high-performance liquid chromatography (HPLC) (Figure  S5 ), along with other abundant bromo-spiroisoxazolines, notably, A1, A4, and A10.
The structural identity of such bromotyrosine spiroisoxazolines was confirmed by comparing their NMR data with those already published. 34, 37, [58] [59] [60] [61]  Both 1 H and  13  C NMR spectra of A8 showed characteristic chemical shifts of an ortho methoxy bromo-cyclohexadiene spiroisoxazoline, also observed for all isolated congeneric spiroisoxazolines (Table  2  and Figure  4 ). Briefly, two doublets characterized by a small coupling constant (J = 0. 9 Hz), often described as singlets in the literature, were observed in the  1  H NMR spectrum at δ H 6. 42 (1H, H-5, d, J H5-H1 = 0. 9 Hz) and δ H 4. 07 (1H, H-1, d, J H5-H1 = 0. 9 Hz). A singlet corresponding to a methoxy group is present at δ H 3. 73. Finally, the protons at 3. 78 (1H, H-7a, d, J = 18. 2 Hz) and δ H 3. 09 (1H, H-7b, d, J = 18.
Using the CFM-ID spectra prediction module, we also obtained a predicted MS 2 spectrum similar to the acquired one at 40 eV. 69  Accordingly, A2 was annotated aplysinamine 1 in this study with a level 2b of confidence. 62  The third brominated spectral family (Figure  5B ) includes the MS 2 spectra of A11 (M340T4. 66, monoisotopic m/z 337. 9022 [C 9 H 9 Br 2 NO 3 + H] + ), whose calculated exact mass and molecular formula match aeroplysinin-1. Analysis of the MS 2 fragmentation pattern using MetFrag and interrogating several databases (e. g. , COCONUT, 70 ChEBI 71 ) also suggested aeroplysinin-1 for A11, along with two other isomers: 3,5dibromo-L-tyrosine and 3,5-dibromo-2-hydroxy-4-methoxyphenylacetamide. Both of them were already recorded in other Verongiida sponges. [72] [73] [74] [75] [76]  Hence, aeroplysinin-1 and 3,5dibromo-L-tyrosine were purchased to determine their MS 2 spectra and retention time under our analytical conditions.
1 mm, Phenomenex) using a Thermo Scientific Dionex Ultimate 3000 UHPLC system equipped with a diode array detector and connected to an electrospray-ionisation-quadrupole time-offlight (ESI-Q-TOF) IMPACT II mass spectrometer (software Bruker Otof Control version 4. 1). LC-MS data processing was performed on the Compass DataAnalysis software (Bruker Version 5. 0) and with Mzmine 2 (version 2. 53). Preparative HPLC was performed on a GILSON PLC 2020 instrument (Gilson Inc. ) using a Luna Omega Polar C18 column 5 μm (250 mm × 21. 2 mm, Phenomenex). NMR analyses were performed on a Bruker Avance II+ spectrometer at 600 MHz equipped with a TCI Cryoprobe at 300 K. The acquired spectra were processed using Topspin 4. 1. 1 NMR software package (Bruker BioSpin). Iterative full spin analyses were performed with the β version of Cosmic Truth (CT) (https://ctb.
02 Da, and RT range for MS 2 scan pairing 0. 03 min), (4) isotope peak grouper (m/z tolerance 10 ppm, RT tolerance 0. 2 min, monotonic shape, representative isotope: most intense), (5) join aligner (m/z tolerance 10 ppm, weight for m/z 75%, RT tolerance 0. 2 min, weight for RT 25%), and (6) feature list rows filter (RT 1. 00-20. 00 min and keeping only peaks with MS 2 scans). Features detected only in MeOH were deleted. The MS1 aligned feature list contained 2508 detected signals, and the MS 2 feature list contained 2411 signals. Multivariate Data Analysis. The MS1 feature list exported as. csv file was processed on the MetaboAnalyst 5. 0 web application (https://www. metaboanalyst. ca/home. xhtml) as follows: data integrity check was set as default, log-transformed, and normalized by Pareto scaling.
The selection of main chemical features related to brominated metabolites between the three groups (EM, crude, and control) was achieved by selecting the first 30 variable importance in projection (VIP) for component 1 (19. 9% of variance) of the Partial Least-Square Discriminant Analysis (PLS-DA) plots. A permutation test (999 permutations, PPLSDA models) based on a double crossvalidation followed by pairwise post-hoc permutational test was performed for the differentiation of each group of chemical fingerprints. The classification error rate (CER, 0-1) was calculated to estimate the strength of the generated models, using R software (version 4. 1. 0. ) with the RVAideMemoire (version 0. 9-80) package. Feature-Based Molecular Networking. The feature quantification table and the corresponding list of MS 2 spectra linked to the MS 1 features (.
57  The resulting network was visualized and interpreted using Cytoscape 3. 8. 56  Computational Analysis of Exometabolite Molecular Diversity. A total of 83 non-brominated chemical signals detected in all exometabolite (EM) extracts from CAPT1 and CAPT2 were analyzed in SIRIUS computational annotation tools (version 4. 9. 9). 28  De novo molecular formulas were computed with SIRIUS by matching the experimental and predicted isotopic patterns and following analysis of the fragmentation trees. Parameters were set as follows: molecular formula candidates retained = 10, profile (Q-TOF), m/z maximum deviation = 10 ppm, all adducts were considered, Sirius score threshold = 100, and all databases and fallback adducts were selected for CSI/FingerID structure elucidation. CANOPUS module was also selected enabling the assignment of exometabolites in different structural classes.
5 min up to 30% in 30 min. The applied flow rate was 20 mL/min, and 350-500 μL of fractions at ∼50 mg/mL were injected each time. Aplysine 1 (1 mg) eluted at 19 min, closely followed by aerophobin 1 (0. 9 mg), purealidin J (1. 4 mg) at 22. 5 min, purealidin L (4. 2 mg) at 24 min, aerophobin 2 (3. 8 mg) at 25. 5 min, and aplysinamisine I (2. 5 mg) at 27 min (Figure  S5 ). Likewise, aerothionin (4. 1 mg) was purified by preparative HPLC from VLC fraction four. The identity of each purified bromo-spiroisoxazoline alkaloid derivative was confirmed by corroborating NMR (1D, 2D) and MS 2 data to previously published studies. 34, 37, [58] [59] [60] [61]  The structural characterization of aplysine 1 was performed by 1D NMR ( 1 H, 13 C) as well as 2D NMR analyses using nonuniform sampling pulses (2D gCOSY, gHSQC, and gHMBC) under quantitative conditions on a Bruker Avance II+ 600 MHz equipped with TCI Cryoprobe.
Logarithmic calibration curves were established by measuring the area under the curves of extracted ions chromatogram (EIC) for each of the nine concentrations ranging from 0. 024 mg/mL to 0. 094 μg/mL for aplysine 1, from 0. 033 mg/mL to 0. 13 μg/mL for aerophobin 1, from 9. 87 to 0. 038 μg/mL for purealidin L, and from 0. 12 mg/mL to 0. 46 μg/mL for aerothionin (Figure  S12 ). The limits of detection (LOD) and quantitation (LOQ) were determined at a signal-to-noise ratio superior or equal to 3 and 10, respectively. The Supporting Information is available free of charge at https://pubs. acs. org/doi/10. 1021/acsomega. 2c05415. Photos of A. cavernicola (S1); comparative adsorption capacities of XAD resins (S2); PLS-DA score plot (S3); feature-based molecular network of the entire Dataset (S4); preparative HPLC chromatogram of fraction 2 used for the purification of aplysine 1 (S5); annotated MS 2 spectra of identified bromotyrosine spiroisoxazolines (S6); assigned NMR spectra of aplysine 1 (S7); ECD analyses of aplysine 1 (S8); GIAO NMR and DP4+ analyses of aplysine 1 (S9); IR spectrum of aplysine 1 (S10); assigned 1 H NMR spectra and  13  C NMR data of isolated bromo-spiroisoxazoline alkaloids (S11 and S12); MS-based calibration curves used for the quantification of bromo-spiroisoxazoline EM (S13); raw MS 2 data (*mzxml) are freely available at the UCSD Center for Computational Mass Spectrometry database with the MassIVE identifier MSV000089502; all MS 2 spectra and NMR raw data of purified bromotyrosine spiroisoxazolines are made freely available at https://doi.
Multidimensional surfaces of quantum chemical properties such as potential energies and dipole moments are common targets for machine learning, requiring the development of robust and diverse databases extensively exploring molecular configurational spaces. Here we composed the WS22 database covering several quantum mechanical (QM) properties (including potential energies, forces, dipole moments, polarizabilities, HOMO, and LUMO energies) for ten flexible organic molecules of increasing complexity and with up to 22 atoms. This database consists of 1. 18 million equilibrium and non-equilibrium geometries carefully sampled from Wigner distributions centered at different equilibrium conformations (either at the ground or excited electronic states) and further augmented with interpolated structures.
Since then, a great effort has been undertaken to develop increasingly complex machine learning potentials (MLPs)  [9] [10] [11] [12] [13] [14]  , which are nowadays capable of fitting nonlinear PES of organic molecules within the so-called chemical accuracy (1. 0 kcal mol -1 ) or better  [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]. As a standard protocol to probe their performance, the newly developed MLPs are benchmarked against existing compound databases 14 spanning the configurational space, the compositional space  24  , or both  [25] [26] [27]. The MD17 database, for instance, is widely used for benchmarking MLPs across configurational space  28. It comprises ten independent datasets of small to medium-sized molecules with geometries, potential energies, and atomic forces extracted from AIMD simulations performed at a temperature of T = 500 K, using a van der Waals corrected PBE functional.
This database was recently revised by tightly converging energies and forces for a randomly selected subset of 100,000 geometries to reduce numerical inaccuracies in the original data  29, 30. Despite the notable improvements in data quality, there are still limitations in the MD17 database from the perspective of assessing the MLPs' ability to quantitatively describe quantum effects that require a much broader sampling of the PES in terms of energies and configuration space. This issue has gained increasing attention, and recent databases such as VIB5  31  and QM-22  32  are targeting global PESs without holes and much broader energy distribution. However, both VIB5 and QM-22 focus on energies and forces of relatively small molecules with up to 15 atoms. The surfaces of other quantum chemical properties such as dipole moments are also important targets of ML  33.
The availability of extensive and high-quality quantum chemical data is of paramount importance to advancing the development and application of ML models. With this in mind, we have developed the WS22 database, which aims at complementing previously published datasets in three main aspects: (i) introduce new datasets for molecules of increasing complexity in terms of chemical composition and accessible conformations; (ii) provide a broad and statistically robust representation of PESs with high numerical precision; (iii) provide an extensive set of QC properties that can be used as a target for many different ML tasks. To accomplish goal (i), we selected ten molecules of increasing complexity (see Fig. 1  and Table  1 ), most of which contain flexible functional groups giving rise to different conformations.
In fact, some of the molecules in the database are photoactive compounds that can undergo significant structural changes such as cis-trans isomerization, proton transfer  34, 35  , or both  36, 37  when exposed to light. Then, to ensure the configurational diversity required in (ii), we adopted a composing strategy to construct the WS22 database by combining a Wigner sampling approach  38  together with a geometry interpolation scheme  39. The former method enables us to effectively explore the vibrational degrees of freedom with a dense sampling of non-equilibrium geometries near the local minimum, characterizing different molecular conformations. Complementary, the interpolation scheme allows connecting multiple configurational spaces through a non-linear path that explores internal molecular rotations.
A similar strategy has been used to build effective and compact initial training data to perform ML-driven excited-states molecular dynamics  40. Finally, we accounted for goal (iii) by extending the standard quantities required to fit the PES (i. e. , potential energy and forces) to several other chemical properties such as dipole moments, polarizabilities, and HOMO-LUMO energies (Table  2 ), all computed at a tightly converged DFT level. In total, the WS22 database contains 1. 18 million equilibrium and non-equilibrium molecular geometries with associated quantum chemical properties equally distributed over ten independent datasets corresponding to molecules consisting of 8 to 22 atoms. Owing to its configurational diversity and chemical complexity, we believe that the WS22 database will be helpful in probing the performance and guide the development of advanced MLPs and other ML models by raising the challenge of the learning task to a higher level.
Geometry optimizations & frequency calculations As a first step, the equilibrium geometry of each molecule in the database (Fig. 1 ) is determined either for different conformers or the minima of the two lowest electronic-state surfaces (ground-state, S 0 , and first excited-state, S 1 ) depending on the molecule considered to build the dataset. The only exception is the toluene molecule, for which no relevant distinct conformations are available, so the equilibrium structure was obtained only for the ground-state surface. This molecule was included in our database for comparison purposes with the MD17 dataset. All geometry optimizations were performed without symmetry constraints using density functional theory (DFT) with the hybrid density functional PBE0  41  in conjunction with the 6-311G* basis set  42.
The Gaussian 09 program  43  was used to perform all electronic structure calculations in this Data Descriptor. Following the protocol described in ref. 29 for the revised MD17, here we used the SCF=VeryTight and Integral(Grid=UltraFine) keywords of Gaussian to tightly converge the electron density and total energy, aiming to achieve a noiseless description of the chemical properties included in our database. To ensure that the final geometries correspond to a (local) minimum in the PES, a tight convergence criterion was also adopted for optimization. The lowest-energy nature of the optimized structures was further confirmed by frequency calculations in which only positive values were found. In the case of excited state calculations, the linear-response time-dependent DFT  44, 45  approach was used for geometry optimizations and frequency calculations with the same theory level, PBE0/6-311G*.
Frequencies are provided in our database and additional information (zero-point energies as well as internal energies, enthalpies and Gibbs free energies at 298 K) is given in Table  3  for all equilibrium geometries. For urea, 2-nitrophenol, DMABN (4-(dimethylamino)benzonitrile), and thymine, the geometry optimization calculations were carried out for both S 0 and S 1 states. The equilibrium structures of 2-nitrophenol and DMABN in the S 1 state are highly distorted with a twisting angle of 90 • for the nitro and dimethylamino groups, respectively, in relation to the planar ground-state geometry (Figure  3 ). In the case of urea, the most significant difference between the S 0 and S 1 equilibrium geometries is the pyramidalization of the carbon atom in the excited state conformation. This optimization strategy using the first excited state allows us to sample a much broader region of the configurational space, as will be described in the next subsection.
The remaining five molecules composing our WS22 database (acrolein, alanine, SMA (2-(methyliminomethyl)phenol), urocanic acid, and o-HBDI(4-(2-hydroxybenzylidene)-1,2-dimethyl-1H-imidazol-5(4H)-one)) are characterized by multiple conformations accessible in the electronic ground-state. Thus, in this case, we search for the minimum energy structures by performing geometry optimizations starting from different conformers. Four low-lying conformers corresponding to different local minimum structures (M1 to M4) were found for alanine with a maximum energy difference of 2. 24 kcal mol -1. The molecular geometries of acrolein and o-HBDI were optimized for single cis and trans conformations, which differ in energy by -1. 63 kcal mol -1 and 7. 50 kcal mol -1 , respectively. Concerning the other two molecules completing the database, several conformations are energetically accessible in the ground-state.
In the case of the Schiff base SMA, we have selected and optimized the four lowest energy structures reported in ref. 46. These structures are named in our work as cis_i and trans_i, i = 1, 2, where the maximum energy difference of 14. 82 kcal mol -1 is observed between the two trans conformers. Finally, urocanic acid is the compound in the database with the largest number of conformers. A total of eight molecular structures, four cis and four trans isomers were selected from ref. 47 to perform the geometry optimizations. The ground state energies of these structures span a range of about 11. 53 kcal mol -1 in the following energetic order: cis_1 < trans_1 < cis_2 < trans_2 < trans_3 < trans_4 < cis_3 < cis_4. Each of the eight urocanic acid isomers can be inter-converted into the other by rotations of the carboxylic and imidazole groups attached to the central carbon-carbon double bond and cis-trans isomerization around this double bond.
Wigner sampling A central motivation to build a dataset beyond MD17 is that the quantum mechanical distribution of the configurational space is much broader than the one provided by classical AIMD at 500 K  48. The zero-point energy stored in each degree of freedom is usually much bigger than the classical thermal energy, meaning that the vibrational amplitudes are much more prominent in the quantum than in the classical picture. A straightforward way of sampling the configurational space (or, more generally, the phase space) to match the zero-point energy requirement is through a quantum-harmonic-oscillator Wigner distribution of the nuclei  38. The optimized molecular geometries and their corresponding harmonic frequencies described in the previous section form the basis for generating an ensemble of non-equilibrium structures sampled from a Wigner probability distribution function  38, 49.
This function maps the nuclear wave function-written as a product of ground-state harmonic oscillator wave functions, one for each normal mode-on the classical phase space  50. Within this approach, each of the N F = 3N at -6 normal mode coordinates and momenta are randomly sampled according to the probability distribution function P W (Q, P) = N F ∏ i=1 1 πℏ exp - 1 ℏω i ω 2 i Q 2 i + P 2 i ( 1 ) where Q i = µ 1/2 i q i and P i = µ -1/2 i p i are the mass-scaled coordinate and momentum for each normal mode i with coordinate q i and momentum p i , reduced mass µ i , and angular frequency ω i. After the sampling, the normal-mode coordinates and momenta are converted to Cartesian coordinates and momenta. Using the normal mode coordinates of the PBE0 equilibrium structures as input for the Wigner distribution, we generate a total of 100,000 geometries for each molecule in the WS22 database.