dataesr/scientific-paragraphs-categorization huggingface dataset:
The central furrow displays a fine structural arrangement and progressive chemical inhomogeneity To decipher the specific mechanical properties of the central furrow and its relationships with the chemical composition and  as mean (solid lines) AE SD (shadowed area) (n > 40) spectra of the area sampled. Chemical profiles of CPM components were obtained on area-mean normalized data (i. e. the area under each spectrum is scaled) to avoid biases due to differences in cross-section thickness (see Fig. 2 ) and expressed as Raman relative intensity with arbitrary units (a. u. ). For each component, a specific band was selected according to a homemade reference database  (Reynoud et al. , 2022) : cutin (1440 cm À1 ), crystalline cellulose (380 cm À1 ), pectin (852 cm À1 ), polysaccharides (1375 cm À1 ), p-coumaric acid (1605 cm À1 ), flavonoid 1 (547 cm À1 ), flavonoid 2 (1550 cm À1 ), and phenolics (1170 cm À1 ).
The '//' indicates that values were filtered-out as detailed in experimental subsections. Bold values in charts represent Pearson correlations (*, P-value < 0. 05; **, P-value < 0. 01; ***, P-value < 0. 001) between elastic modulus and Raman intensity see Fig. S4  for full spectra. structure of the CPM, a multimodal approach was conducted combining Raman imaging and Infrared mapping at three developmental stages, that is, 20, 30, and 40 DPA. Two areas of the CPM were compared, the central furrow, and the sides of the furrow (Figs  3e, S5 ). At 20 and 30 DPA, PCA from Raman data showed no strong specific spectral fingerprints of the central furrow until the 40 DPA stage, whereas it was more progressive for OPTIR data (Fig. 5a ). Principal component analysis loadings of OPTIR data (Fig. S6a ) evidenced that the central furrow was rather composed of cutin, whereas polysaccharides were progressively excluded to the sides.
Loading of Raman data (Fig. S6b ) indicated changes in the relative accumulation of phenolic compounds in the furrow sides along with crystalline cellulose, whereas pectins concentrated in the central furrow. These multimodal spectroscopic approaches indicated that the central furrow adopts a slight but distinct chemical composition than the furrow sides along tomato fruit development. Then, the specific molecular orientation of CPM components was further assessed by Raman spectroscopy using a linear polarized laser. By following changes in band intensity due to different settings of polarization, parallel (0°), and orthogonal (90°) to the cuticle surface, specific molecular orientations could be inferred (Fig. 5b ;  Gierlinger et al. , 2010; Reynoud et al. , 2022). We focused on lipid bands, as the central furrow was mostly composed of cutin.
The laser polarization direction was modified from parallel (0°) to orthogonal (90°) to the cuticle surface. For each polarization, two maps were acquired at 20 and 40 DPA (Fig. S7 ). The light image (upper left) represents the area of the cuticle that was sampled to assess the molecular orientations within the central furrow (pink) and the furrow sides (blue) with the corresponding elastic modulus (lower left). Bar, 5 lm. Data are expressed as mean (solid lines) AE SD (shadowed area) (n = 80). For both central furrow and furrow sides, mean Raman spectra for each polarization direction were calculated (right). The parallel (solid lines) to orthogonal (thin dotted lines) ratio of intensity was illustrated by vertical bars for three bands related to lipids: two bands attributed to CH 2 deformations, that is, s(CH 2 ) at 1305 cm À1 and d(CH 2 , CH 3 ) at 1440 cm New Phytologist polysaccharides embedded in the cuticle  (Reynoud et al.
Acknowledgements Raman and AFM were performed at the  PROBE research infrastructure, Biopolymers Interactions, Structural Biology (BIBS) facility , and Nantes. OPTIR was performed at the  Institut de Chimie Physique. NR was supported by  PhD fellowship (SeaS-CAPE)  granted by  INRAE  and the  Region Pays de la Loire. This work was also supported by the  ANR (Agence National de la Recherche)  grant  COPLAnAR  ( ANR-21-CE11-0035 ).
In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretraining of JT VAE on a large dataset and further optimization with a regression model led to a latent space that can solve several tasks simultaneously: prediction, generation, and optimization. We use the ZINC database as a source of molecules for the JT VAE pretraining and the QM9 dataset with its HOMO values to show the application case. We evaluate our model on multiple tasks such as property (value) prediction, generation of new molecules with predefined properties, and structure modification toward the property. Across these tasks, our model shows improvements in generation and optimization tasks while preserving the precision of state-of-the-art models.
Vector initialization in the embedding space We had to choose a method of initialization of the embedding vector that is to be fed to the gradient descent procedure described above. A good initialization of an embedding vector is of crucial importance to the result of the algorithm since the embedding space can have multiple minima-say optimal molecules-or no minimum for a given output value. Also, there is no intrinsic dependability that the local extreme must correspond to the molecule with the best fitting property. Fortunately, due to the inherent properties of VAE architecture, similar structures tend to locate close to each other in the embedding space. In our work, we studied different approaches to initialize a molecule (and thus the corresponding embedding vector). We introduce the following notion: let REGR TR and REGR VAL be datasets used in the regression training procedure, CTV-a molecule from REGR TR closest by target property value, CHV-a molecule from REGR TR closest by hidden vector in the latent space to a given one.
Datasets We use the ZINC  [30]  and QM9  [31]  datasets in our work. The QM9 dataset  [31]  is a widely used benchmark for the prediction of physical properties of molecules in equilibrium state. It consists of around 130k small organic molecules with up to nine heavy atoms of C, O, N, or F with the properties computed using DFT calculations. The QM9 contains additional information, from which we also obtain the HOMO properties of molecules to perform the regression task and test our structure optimization approach. The ZINC dataset  [30]  is a subset of a free database  [32]  of commercially available compounds for virtual screening. A dataset mixture to train the encoder-decoder pair. Originally in  [27] , JT VAE was studied by utilizing the ZINC dataset  [30]. It was discovered that ZINC lacks a variety of molecule types to the extent that the models trained on it lack efficiency in real tasks  [8].
To avoid the influence of a bias of a particular dataset on the training, we have extended the previous approach to a mixture of QM9 and ZINC datasets (we call it MIX) which helped us improve the model's generalization properties and general accuracy of reconstruction. JT VAE method decomposes every molecule into building blocks. One of the central assumptions that we used in our work is that the dictionary of simple structures forming the molecules is large enough for decomposing any possible molecule. This assumption is rarely satisfied when we change from one chemical database to another. Therefore, for the MIX dataset we expanded the vocabulary of simple compounds as much as possible. In particular, we grow the dictionary from 780 original building blocks covering the ZINC database in JT VAE to 2327 basic objects spanning MIX.
The total number of molecules in MIX is 355796. Datasets used to train the regression. For the property of interest -e. g. , molecular HOMO energy levels -merging two different databases is often a challenging task since there is no unique way to correctly join  the data from various sources due to variation in theoretical calculation techniques, conditions of measurements, etc. To overcome the consistency issues, we performed the regression training on QM9 dataset only. First, we have partitioned the QM9 datasets on the train, validation and test subsets ( QM9 TR , QM9 VAL , and QM9 TEST , respectively), so that the validation part con- tains 2500 molecules, and the test part contains 5000 molecules. For the consistency reasons, VAE was trained on the combined ZINC + QM9 TR dataset, and QM9 VAL was used for validation; for the training procedure of regression, QM9 TR and QM9 VAL were used; QM9 TEST was used as test data across HOMO prediction and reconstruction of molecules.
Regression and multi-step auto encoding Table  6  represents results mentioned in  [8]  for different datasets and methods of HOMO energies prediction, which were trained and tested on the indicated datasets. More complete description of the methods can be found in  [8]. The last four lines colored in gray correspond to our results. The lines Ridge Regr. and Elastic Net correspond to the training of the eponymous regression from latent space into the property of interest (HOMO). The last two lines correspond to the training of the unfrozen encoder (pretrained during the basic JT VAE training) jointly with two layers feed forward neural network with ReLu activations. We measure the HOMO accuracy (HOMO Acc columns in Tables  6  and 7 ) by MAE loss and the VAE accuracy (VAE Acc column in Table  7 ) by finding the percentage of SMILES strings that represent the molecule after reconstruction that are the same as initial SMILES strings.
Molecule reconstruction For Table  8  the methods of the columns Initialization and Type of search are described in Subsection Vector initialization in the embedding space, and the column Reconstruction displays the rate of molecules predicted from QM9 TEST dataset. The low values of the reconstruction percentage column in Table  8  are due to the fact that the model performed the unguided structure search for the HOMO values of the molecules from the QM9 TEST dataset, and also since multiple molecules can have close (or equal) HOMO values. We are unable to compute the real HOMO values of all the molecules suggested by our model because of high costs of DFT computations, but we attest that our model generated correctly the "reconstructed" molecules according to their HOMO values. Given that the QM9 TEST dataset was never exposed to our model during the training, we argue that the implementation of our algorithm warrants both the validity of predicted molecules and the high accuracy of HOMO value prediction.
In Fig. 10  we give several molecules from the QM9 TEST dataset that were suggested by our algorithm and thus we know that their HOMO values were predicted perfectly. In addition, we tested method B with a combination of CHV and Gaussian initialization on independent data. To this purpose, we took a molecule from  [33]  that is not in any way connected to MIX or QM9 datasets. The proposed method has successfully reconstructed the selected molecule c1c2ccccc2co1 from  [33] , which corroborates the reliability of our method. In Fig. 11.
, 1999). Sequencing of this isolate revealed a small Borrelia genome containing a chromosome (incomplete draft version in GenBank) and seven plasmids: cp26, lp17, lp25, lp28-9, lp36, lp32-10, and lp54  (Casjens et al. , 2011). A remarkable feature was the absence of cp32 plasmids, which are found in all other Borrelia species  (Casjens et al. , 2018; Casjens et al. , 2017; Schwartz et al. , 2021). We hypothesized that the small genome described for Far04 may be an adaptation to marine hosts and/or vectors. In order to gain additional insights into the genome diversity of seabird-associated B. garinii, I. uriae were collected in Norway from marine hosts in 2017 and 2018. Several new cultured isolates from marine B. garinii were obtained and were sequenced for genome exploration.
Multilocus sequence data analysis Multilocus sequence typing was done according to standard procedure (see pubmlst. org/borrelia/) for details. We used PHYLOVIZ 1. 1  (Francisco et al. , 2012)  to conduct goeBurst analyses  (Francisco et al. , 2009)  using ST numbers, allelic profiles and meta information employing the triple locus variant (TLV) setting and to generate a minimum spanning tree. New data were submitted to the Borrelia MLST database (https://pubmlst. org/borrelia/) via the curator. Data available for North American seabird isolates (n = 19), Eurasian terrestrial (n = 291) and human (n = 48) B. garinii isolates were downloaded from the PubMLST database and included in our goeBURST analyses.
20150226  (Darling et al. , 2004)  for contig sorting and comparison of the newly sequenced genomes with the genomes of Far04 (GenBank accession number NC_011875. 1, isolated from an I. uriae tick), 20047 (GenBank accession number NZ_CP028861. 1, isolated from a questing I. ricinus) and PBr (GenBank accession number GCA_000172275. 2). To concatenate chromosomal contigs the multi-draft based scaffolder MeDuSa was used  (Bosi et al. , 2015). PacBio read assembly was done at the NSC as part of the sequencing contract. The PacBio genome assembly of 17-63N1 was uploaded into the CLC genomics workbench v. 20 for Illumina read mapping and extraction of a consensus genome assembly. To determine the structure of plasmids (linear or circular), we employed NCBI BLASTN searches  (Altschul et al. , 1990).
, 2007)  or host invasion (  (Grimm et al. , 2004; Tilly et al. , 2007) ) and cspA, a member of the PFam54 gene array that has been shown to play a role in deflection of human and animal host complement by Borrelia  (Kraiczy and Stevenson, 2013; Lin et al. , 2020; Muhleip et al. , 2018; Wywial et al. , 2009). The PFam54 gene array is located on plasmid lp54 and consists of 5 clades with clade IV being diverse even within Borrelia species. Apart from the function of cspA, the function of other genes of the gene array is unknown although they may be important in host association  (Hart et al. , 2018; Hart et al. , 2021; Patton et al. , 2013; Wywial et al. , 2009). To download homologues of genes previously suggested to be potentially involved in host and vector association (cspA, ospA, ospC), protein sequences were used for BLAST searches within the Rast annotation server using default settings or GenBank (see Table  S5  for accession numbers of included isolates in addition to the samples from seabirds).
Data repositories Assembled genomes and sequence reads are available in GenBank via the following accession numbers under BioProject PRJNA327303: CP119374-CP119381 (SAMN32860459, 17-29Z1); CP118290-CP118297 genome, OQ450372, OQ450373 part of vls locus (SAMN32860460, 17-56Z1); CP119358-CP119365 genome, OQ450374, OQ450375 part of vls locus (SAMN32860461, FNG-1Z1); CP118298-CP118305 genome, OQ450376, OQ450377 part of vls locus (SAMN32860462, 17-58N4); CP119383-CP119390 genome, OQ450378 part of vls locus (SAMN32860463, 17-59N1); CP117887-CP117893 (SAMN32860464, FNG-2Z14); CP117894-CP117901 (SAMN32860465, 17-54Z3); CP117902-CP117908 (SAMN32860466, FNG-1Z4); CP117909-CP117917 (SAMN32860467, NG-Z6); CP117839-CP117847 (SAMN32949868, 17-63N1).
org/borrelia/ID2798), and one, ST804, is a single locus variant (SLV) of ST575. The other five isolates are STs ST801, ST802, ST803, ST921 and ST923 (ST921 is a SLV of ST802) (Table  2 , Figs. 2  and 3 ). Two STs, ST801 and ST921, were unique, appearing only once in the whole dataset (n = 388) (Fig. 2 ). Apart from the seven STs that came from our study, we downloaded B. garinii samples from the MLST website (www. pubmlst. org/borrelia/) available at the time representing the variety of B. garinii that is currently known. They included 19 North American seabird isolates from Canada, 291 Eurasian terrestrial bird and 48 human samples. The Canadian strains originate from I. uriae ticks collected from Atlantic puffin, black-legged kittiwake, Common Murre and razorbill in Newfoundland and Labrador  (Munro et al.
Acknowledgements The PacBio sequencing service was provided by the  Norwegian Sequencing Centre  (www. sequencing. uio. no), a national technology platform hosted by the  University of Oslo  and supported by the  "Functional Genomics" and "Infrastructure" programs  of the  Research Council of Norway  and the  Southeastern Regional Health Authorities. We would like to thank technical personnel from the  NRZ Borrelia ,  Cecilia Hizo-Teufel ,  Sylvia Stockmeier  for expert in vitro cultivation of Borrelia, DNA extraction and PCR, and  Nicholas Alig  for assistance with figures. The authors are also grateful to  Céline Toty ,  Tanguy Deville ,  University of Montpellier , for sample collection and  Konstantin Kuleshov ,  Central Research Institute of Epidemiology, Moscow, Russia , for very helpful advice on bioinformatics tools.
Funding information Financial support for sampling was provided by the ' Institut Polaire-Paul Emile Victor'  ( IPEV, Program  No. 333) and the  Agence Nationale de la Recherche (ANR) , grant  ESPEVEC  ( ANR  blanc  ANR-13-BSV7-0018-01 ),  France. Financial support was provided by the  Bavarian Health and Food Safety Authoriy  and by the  Robert-Koch Insitute, Berlin, Germany , via the  National Reference Center for Borrelia.
This ensures we are probing the same physical scales with our comparison. We also smooth the data to the same 10 km s -1 velocity resolution as the final VERTICO data cubes. Science ready data products such as flux, gas surface density, and signal-to-noise maps are produced using an identical procedure to the VERTICO data products described in  Brown et al. (2021). For this work we apply the selection criteria described in Section 2. 1, yielding a final field control sample of 10,817 pixels from drawn from 10 HERACLES survey galaxies.
The VERTICO rSFMS, rMGMS, and rKSR The rSFMS, rMGMS, and rKSR for the VERTICO H I- normal (blue) and H I-poor (red) subsamples as well as the HERACLES field sample (gray) are shown in the top-left, bottom-left, and bottom-right panels of Figure  2 , respectively. The corresponding best-fit relations for the H I-normal and H I-poor subsamples are shown by the solid lines in each plot. The fits are calculated using the LTSFIT Python package described in  Cappellari et al. (2013, their Section 3. 2) which accounts for measurement uncertainties in both axes to determine the best-fit parameters and scatter. The fit parameters for each relation are provided in the Appendix. The rSFMS (top right) and rMGMS (bottom left) in the H I- normal cluster galaxies closely follow the field relations although there is a small deviation to lower gas densities at low stellar mass density in the rMGMS.
Future work will focus on outstanding questions such as: what are the physical drivers of the variation in SFE? How does environment regulate molecular gas kinematics? By answering these questions, VERTICO will give us a more complete understanding of the gas-star formation cycle in cluster galaxies. Notes. The units are given in Figure  2. Pivot is in the same units as x. The rms scatter, σ, of the data about the fitted relation is calculated as the standard deviation of y fity pix. For the rKSR, we fit for Σ mol as the uncertainties are larger than for Σ SFR and rearrange the fit result for y = c + mx where c = -a/b + pivot and m = 1/b.
Acknowledgments The majority of this work was conducted on the traditional territory of the T'Sou-ke and Lekwungen peoples. The authors acknowledge and respect the  T'Sou-ke , Songhees, Esquimalt and  WSÁNEĆ Nations  whose historical relationships with the land continue to this day. We thank the anonymous referee for a considered review of the manuscript. This work was carried out as part of the VERTICO collaboration. T. B. acknowledges support from the  National Research Council of Canada  via the  Plaskett Fellowship  of the  Dominion Astrophysical Observatory. I. D. R. acknowledges financial support from the  ERC Starting Grant Cluster  Web  804208  (PI: van Weeren). N. Z. is supported by the  South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation.
C. D. W. acknowledges support from the  Natural Sciences and Engineering Research Council of Canada  and the  Canada Research Chairs program. L. C. acknowledges support from the  Australian Research Council  via the  Future Fellowship  and discovery Project funding schemes ( FT180100066  and  DP210100337 ). Parts of this research were conducted by the  Australian Research Council Centre of Excellence  for All Sky Astrophysics in 3 Dimensions ( ASTRO 3D ), through project number  CE170100013. A. C. acknowledges support by the  National Research Foundation of Korea (NRF) , grant Nos. 2022R1A2C100298211  and  2022R1A6A1A03053472. T. A. D. acknowledges support from the  UK Science and Technology Facilities Council  through grant No. ST/W000830/1. B. L. acknowledges the support from the  Korea Astronomy and Space Science Institute  grant funded by the  Korea government (MSIT ; project No.
Data Availability The molecular gas, SFR, and stellar mass surface density maps for all VERTICO galaxies are publicly available for download from the Canadian Advanced Network for Astronomical Research VOSpace cloud storage (Brown & The  VERTICO Collaboration 2023). Note that the online maps are not corrected for galaxy inclination. The data set and the Jupyter notebook used to perform this analysis are publicly available on Zenodo at doi:10. 5281/zenodo. 8253768 and on GitHub. 26.
For each sample, 520 uL of lysate was loaded into the sample chamber of the Xpert ® BC STRAT4 cartridge which was then placed into the GX machine to perform the assay (RNA extraction, purification, and RT-qPCR analysis). BC STRAT4 assay assesses ESR1, PGR, ERBB2, and MIKi67 mRNA expression levels. GX DX Software was used to analyse results from GeneXpert BC STRAT4 assay. Cytoplasmic FMR 1-interacting protein 1 (CYFIP1) is used as an internal reference gene and all target genes (ESR1, PGR, ERBB2, MKI67) mRNA measurements are normalised against CYFIP1 mRNA measurements. Cycle threshold (Ct) values for the reference gene (CYFIP1) and target genes (ESR1, PGR, MKi67, and ERBB2) are measured simultaneously. Delta Ct (dCt = reference gene Ct minus target gene Ct) for each marker is calculated using the CYFIP1 Ct as reference.
Statistical analysis The data was introduced and treated using Excel Software, version  16. 43. Result concordance between STRAT4 and IHC/FISH methods was accessed by estimating the percentage of concordance as well as the Cohen's Kappa (K) statistics. The Kappa (K) statistic numeric values are categorised into slight agreement (≤0. 2), fair agreement (between 0. 21 and 0. 40), moderate agreement (between 0. 21 and 0. 40), substantial agreement (between 0. 61 and 0. 80) and almost perfect agreement (between 0. 81 and 1. 00). All measurements were associated with 95% confidence intervals (95% CI), compared using Fisher's exact test and considered significant for p < 0. 05.
Acknowledgments and funding The authors would like to thank  Calouste Gulbenkian Foundation, Portugal  for funding this research via  ENVOLVE CIÊNCIA PALOP Program. ( ENVOLVE CIÊNCIA PALOP Program Promoter and Funder :  Calouste Gulbenkian Foundation Area of expertise: Health Sciences. ) The authors would also like to thank  Instituto Português de Oncologia-Porto  who hosted this program and project;  Pathology and Oncology Services of the ANUH, and IMP Laboratory  for helping to obtain the data and samples;  Prof. Carlos Lopes  from  Laboratório de Anatomia Patológica do Porto (LAP)  for helping with IHC and  Dr. Maria da Conceição Barbosa Leal  from  Instituto Português de Oncologia-Porto  who helped with the analysis of the IHC results. We would also like to thank the  Microbiology Lab and Experimental Pathology and therapeutics Labs  for helping us to perform BC  STRAT4  assays.