dataesr/scientific-paragraphs-categorization huggingface dataset:
Quadratic Program for Cross Modality Mapping on Meshes A central goal is to imbue the atlas with molecular or cellular information by estimating a cross-modality mapping between the atlas and a finer scale, single-cell or subcellular dataset, such as those emerging particularly from imaging-based spatial transcriptomics technologies. To compute this mapping, we model each modality as an image varifold, a product of measures over physical and feature space, by instantiating each measurement as a triangulated or simplex mesh following  [27]. Each mesh carries a collection of vertices x = (x i ∈ R 2 ) i∈I. From the vertices we construct the simplex triangles γ j (x) and their centroids m j (x) for j ∈ J, with vertex numbers |I| and simplices |J| determined by the resolution selected. We denote the target mesh as τ throughout the paper; see Section 4.
The simplex law is determined by the contribution of each ontology region to the vertex for j ∈ J 0 , given by the mixture distribution p j (ℓ), L p j (ℓ) = 1. The atlas has appended the molecular feature space estimated from the target (α π , ζ π ) and is given as: µ π τ0 = j∈J0 α π j |γ j |(δ mj ⊗ ζ π j ) (4a) with ζ π j = 1 α π j ℓ∈L p j (ℓ)π ℓ α π j = ℓ∈L p j (ℓ)π ℓ (F). The group action carrying the atlas onto the target becomes φ • µ π τ0 = j∈J0 α π j |Dφ| mj |γ j |(δ φ(mj ) ⊗ ζ π j ). (4b) Here, |Dφ| mj is the Jacobian determinant of φ at m j. Figure  1  shows a mesh-based image varifold for two coronal sections (Z = 385, Z = 485) of the Allen Atlas, with finest granularity ontology (|L| ≈ 700) and with meshes rendered at 50 µm resolution. The right panel of Figure  1  illustrates the physical densities α j , j ∈ J of mRNA transcripts per mm 2 in coronal sections of MERFISH from the Allen Institute  [33].
Throughout, we take α min to be the 5th percentile of values of (α j ) j∈J in the target. The variational problem maximizes the overlap of homogeneous regions in the atlas (e. g. each partition in the ontology) with those in the target (e. g. regions where conditional feature distributions are stationary over space) by deforming coordinates using LDDMM to optimize the vector field v t , t ∈ [0, 1]. The quadratic programming calculations solve for π ℓ , ℓ ∈ L for the atlas to gene expression and cell-type problem and are described in the methods section 4. 3. Figure  2  illustrates the results of mapping the Allen Atlas coronal sections to Allen MERFISH spatial transcriptomics sections, shown in Figure  1. Allen Atlas sections were chosen based on correspondence through visual inspection. Estimated mRNA densities, α π j = πj (F), as depicted on left and middle columns, were achieved through solution of the quadratic program as defined in  (9) , and reflect total mRNA densities from a full set of 702 genes as features.
This is particularly relevant, here, in the context of mapping spatial transcriptomics to atlases where we aim to estimate distributions over genes for each region in our atlas that we assume is homogenous within the region. Various methods have been described for identifying which genes in a spatial transcriptomics dataset are more spatially varying than others, some examples being Gaussian process registration, Laplacian Score,  [35]  and Moran's I  [37]. In order to score genes which are most spatially varying we introduce Mutual Information scoring which assesses the differential expression of genes in space in a cell-independent manner. Specifically, we score each gene with the mutual information between the two random variables X, M which capture, respectively, an orientation in space and a relative density of mRNA expression for that gene (see Section 4.
In line with expectations, 75% of the genes comprising those with scores in the bottom 25% of the total 700 genes were decoy genes (e. g. 'BLANK') without biological meaning but used as controls for assuring the quality of the dataset. A Universal Method for Crossing Molecular and Atlas Modalities Shown in the bottom row of Figure  3  is the estimated probability, (ζ π j ) j∈J0 , for each of the three genes with highest mutual information score (Gfap, Trp53i11, Wipf3 ), shown for each region on the Allen atlas section. For calculating these estimates we solve the variational algorithm with LDDMM and quadratic programming estimation of the gene feature distributions to map the Allen atlas section, Z = 485 to the Allen MERFISH target image-varifold. For this result, the smaller feature space of 20 total gene types corresponding to those with the highest mutual information scores are used for the mapping algorithm.
Mapping of Cell Distributions to Atlases The two previous sections presented results solving the mapping problem between atlas and MERFISH based on the mRNA reads directly. Alternatively, these raw mRNA reads can be segmented into discrete cells as a mode of data reduction followed by downstream analyses clustering the cells into discrete cell types. The mesh-based image varifold framework is ideal for taking the measure representation directly on the aggregated cells and solving the variational problem of mapping to atlas coordinates. Figure  4  shows the results of mapping an Allen atlas section at Z = 675 to a section of cell-segmented MERFISH transcriptional data (courtesy of the JEFworks Lab, Johns Hopkins University). The total gene set measured is ≈ 500 genes, with each transcript assigned to a single cell.
Transcriptional profiles per cell are clustered into 33 distinct clusters using Leiden graph-based clustering  [38]  and annotated as cell types based on known marker genes. This gives a cell-based dataset analogous to the transcript-based dataset discussed in Section 2. 2 in which densities, α j (•), reflect the spatial density of data points (here, # cells mm 2 ), and conditional probability distributions, ζ j (•), are defined over the feature space of cell types, |F| = 33. The essential part of the model for estimating the atlas distribution over cell types is the stationarity of the model across each atlas partition. It is therefore natural to examine the entropy of the distribution within each atlas compartment as a measure of the multiplicity of cell types within a compartment. Shown in the right column is the entropy of the estimated probability distribution over cell types for each simplex in both target and atlas, with nonzero probabilities assigned to ∼ 3 -5 distinct cell types in each simplex of the target versus ∼ 1 -20 cell types in each simplex of the atlas, varying per region in the original ontology.
Discussion We have introduced, here, a universal method for mapping tissue scale, 'cartoon' atlases to molecular and cellular datasets arising in the context of emerging transcriptomics technologies. We root our method in the modeling of each object as a mesh-based image varifold, as previously described  [27] , and outline an alternating algorithm that simultaneously incorporates the classical deformation tools of LDDMM  [29]  with quadratic programming to jointly estimate an optimal geometric transformation and conditional feature law that maps atlas onto target. A Universal Method for Crossing Molecular and Atlas Modalities As presented, our method fills a current need, as highlighted previously  [35] , for universal tools that can integrate the diverse types and large quantities of data emerging from the evolution of both transcriptomics and imaging technologies over the last decade.
With each technology generating a slightly different perspective and different set of animal or human samples to compare, a method that can stably handle the format of past, current, and future datasets will be paramount to integrate both new findings with the vast number of datastores currently available across institutes. The image varifold framework used here is general enough to model emerging transcriptional data from both image-based and spot-resolution technologies and classical imaging data (as demonstrated in our atlas-to-atlas mappings). Therefore, it provides a gateway for comparing data historically curated through immunohistochemistry, MRI, and other techniques in addition to the emerging transcriptomics methods. In parallel to the development and dispersion of diverse molecular datasets, there has been continued development on the side of reference atlases to reflect trends in these new measures and integrate these trends across even more samples of particular species.
The Allen MERFISH data showcased here is part of an entire set of serial sections that span the whole brain. Consequently, we are optimizing our method to compute mappings of atlas to molecular target in 3D, where both added dimensions and added magnitudes of data contribute to the theoretical and computational complexity of the problem. Indeed, with ≈ 6 billion individual transcripts measured across the span of the brain, treatment of this data as a regular lattice image would require on the order of 1000 billion voxels at 1 µm resolution, which is coarser than that needed even to resolve two molecules of mRNA. Hence, it becomes even more vital to treat such data in the particle setting, as presented here, where we capture the sparsity and irregularity of the data in modeling it effectively in its lowest dimension, as 6 billion individual particle measures.
Second, though we have highlighted both gene-based and cell-based datasets achieved with image-based MERFISH technologies, we are currently investigating the use of our method to map data from spot-resolution technologies such as SlideSeq  [47]  and additional image-based technologies such as BarSeq  [48] , which introduce variations in both the number of genes measured and the scope of tissue (whole versus hemi-brain) measured. Finally, we emphasize that central to the model posed here is the underlying assumption that each compartment has a homogeneous distribution over molecular features that is stationary with respect to space. This assumption holds in many settings, as we might expect, given the inherent construction of atlases often to delineate regions of particular cell types and thus, where we see a set of predominant cell types or gene types consistently across the region in the molecular scale data, as in Figures  4  and 5.
Meshes were constructed using Delauney triangulation  [49]  on a grid defined over the support of the starting dataset with the size of each square dictated by the input resolution. Varifold measures, α, ζ, were associated to the simplices of the mesh following assignment of each individual data point (e. g. mRNA or cell read) into its single nearest simplex. Meshes were pruned of simplices that both contained fewer than 1 data point and existed outside the largest connected component of simplices containing at least one data point. In this manner, both for atlas images and transcriptomics data sets, resulting simplex meshes spanned the entire tissue foreground.
J o u r n a l P r e -p r o o f Journal Pre-proof outer PCB and design a more rigid MM PCB  [3, 4] , as can be seen in Fig. 4. To avoid the mechanical connection of the MM PCB to the housing, a new design was made which includes a 6 mm thick outer PCB screwed to the aluminum housing. The outer PCB was connected with the MM PCB with spring-loaded pins. To choose more rigid MM PCB material and its proper thickness, structural simulations of the board deformations under mesh tension were performed using open-source tools: FreeCAD, Gmsh and CalculiX  [5]. Based on the experience from the first 19-channels prototype, the main objective for the 10×10 cm 2 module was to minimise the deformations below 10 µm throughout the whole active area. The calculations included analysis of how the mesh tension and contact pressure influence the planarity.
Nano-GISAXS experiments Nano-GISAXS experiments were performed at the European Synchrotron Radiation Facility (ESRF) beamline ID13 (Grenoble, France). Mesoglea samples were prepared 2-3 d before experiments and deposited on a 100 nm-thick Si 3 N 4 window surrounded by a Si frame (SPI Supplies PA, US). Experiments were performed on dehydrated samples and at T = 20 C and ambient pressure. Mesoglea samples were probed with nano-focused beam (diameter: 200 nm) with a wavelength of 0. 81 A ˚(15. 3 keV) and at an incident angle of 0. 46. Data was collected with a 2D detector (Maxipix, ESRF, France). 74 Silver behenate (C 21 H 43 COOAg) was used for calibration.
Nano-GISAXS data analysis After background subtraction and deadtime correction satellite peak positions were determined using 2Dgaussian fit. The error was estimated by the half width at half maximum. The angle was determined from the dot product of reciprocal space vectors according to  1 ) cos g Ã = a Ã ! $ b Ã ! ja Ã ! j b Ã ! (Equation where a* and b* represent the reciprocal lattice vectors with an angle of g*. The angle in real space, g, was calculated as g = p À g Ã (Equation  2 ) The real lattice vectors were determined using a ! $a Ã ! = 2p and b ! $ b Ã ! = 2p.
8 V relative to the baseline. At each position along the Hydra body axis 4 points with a distance of 5 mm were indented. Each spot was indented two times. Subsequent measurements at the same spot with a standard deviation larger than 10% were discarded. Local elastic moduli at each position on mesoglea were determined by averaging over the 8 measurements. Data points with standard deviation larger than 40% were discarded. The relative measurement position was determined using ImageJ. 75 mple preparation for mass spectroscopy Isolated mesogleas (n R 20) from AEP, ActP:Wnt3a, Alp-treated AEP (4 days after treatment) and DMSOtreated AEP hydras (4 days after treatment) were purified for protein content by trichloroacetic acid (TCA) precipitation. For SP3 and TMT labelling, TCA pellets were mixed with 50 ml of 1% SDS (in 50 mM HEPES, pH 8.
In situ hybridization A customized LNA digoxygenin-labeled RNA probe was designed and produced by Qiagen corresponding to the antisense strand of the HAS-10 cDNA (TACTGTACCAAGTCGCAAGCAA). The whole-mount ISH procedure was performed as described previously in Ziegler et al. 45  Samples were finally mounted in PBS containing 90% glycerol, or in Mowiol 4-88 (Carl Roth), and images were acquired with a Nikon Digital Sight DS-U1 camera mounted on Nikon Eclipse 80i and imaging software NIS Elements (3. 10, SP3, Hotfix, Build645). Further image processing was performed with Adobe Photoshop CS6 and Fiji. 73.
To avoid the thickness-dependent mis-estimation of the Young's modulus,  79  the fitting range was kept within 10% of the total thickness of the mesoglea (Figure  S7. Thickness of isolated Hydra mesoglea determined from confocal images, related to Figure  3. ). Data in the figures are presented as means and standard deviations. The onset point of each force curve was chosen as the center of the marching range. This point was determined roughly by fitting linear functions to the outermost parts of the noncontact and contact region of a force curve given by following relations, respectively:  6 )  7 ) r noncontact = ðz; dÞ ˛data ðz min + z max Þ 2 < z (Equation r contact = ðz; dÞ ˛data ðd min + d max Þ 2 < d (Equation where z and d corresponds to the piezo and deflection of a datapoint, z max and z min corresponds to the maximum and minimum piezopositions and d max and d min represent the maximum and minimum cantilever deflection in the dataset.
Mass spectroscopy data analysis For LC-MS/MS, Liquid Chromatography (LC) was performed on an UltiMate 3000 RSLC nano LC system (Dionex) fitted with a trapping cartridge (m-Precolumn C18 PepMap 100, 5 mm, 300 mm i. d. x 5 mm, 100 A ˚) and an analytical column (nanoEaseä M/Z HSS T3 column 75 mm 3 250 mm C18, 1. 8 mm, 100 A ˚, Waters). For trapping, a constant flow of 0. 05% trifluoroacetic acid at 30 mL/min onto the trapping column for 6 min was used. Peptide elution was carried out on an analytical column with a constant flow of solvent A (0. 1% formic acid in water) at 0. 3 mL/min, and an increasing percentage of solvent B (0. 1% formic acid in acetonitrile) from 2% to 4% in 4 min, from 4% to 8% in 2 min, then 8%-28% for a further 66 min, and finally from 28% to 40% in another 10 min. The outlet of the 33 analytical column was coupled directly to a QExactive plus (Thermo) mass spectrometer using the proxeon nanoflow source in positive ion mode.
The peptides were added to the QExactive plus via a Pico-Tip Emitter 360 mm OD x 20 mm ID; 10 mm tip (New Objective) with a spray voltage of 2. 2 kV. Full mass scan was acquired with mass range 375-1200 m/z in profile mode with a resolution of 70000, a capillary temperature of 275 C and a filling time of maximal 100 ms with a limitation of 3x106 ions. Data dependent acquisition (DDA) was carried out with the resolution of the Orbitrap set to 17500, with a fill time of 50 ms and a limitation of 2x105 ions. A normalized collision energy of 32 was used. Dynamic exclusion time of 20 s was applied. The peptide match algorithm was set to 'preferred' and charge exclusion 'unassigned', charge states 1, 5-8 were excluded. MS2 data was acquired in profile mode. The raw output files of IsobarQuant (protein.
ACKNOWLEDGMENTS We thank  ESRF  for the synchrotron beamtime. M. V. thanks  M. Sontag Gonza  ´lez and  F. Gebert  for experimental assistance. We thank  M. Rettel  for support in the proteome analysis and  X. Zhang  for providing monoclonal antibodies mAb52 raised against Hydra laminin and mAb39 raised against HCol-I. This work was supported by the  German Science Foundation (DFG)  through the  Collaborative Research Centers   CRC 1324  ( A5  to T. W. H. and M. T. ,  B7  to S. O ¨. ),  CRC 873  ( A1  to T. W. H. and  B7  to M. T. ), and  OE 416/8-1  to S. O ¨. , the  CHS Foundation  (T. W. H. and H. P. ), and the  Germany's Excellence Strategy   -2082/1-390761711  (to M. T. ). M. V. thanks the  DFG  for fellowships ( GRK1114  and  EcTop2 ). R. S. and M. T. thank the  JSPS  ( JP22H03939  to R.
Statistical Analysis The efficacy of the product was calculated using Abbott's formula  [44] : Efficacy (%) = 100 × [mean count in control group] -[mean count in treatment group] [mean count in control group] Efficacy was calculated in the two groups at each time point, and in each group between D0 and either D14, D30, D60, D90, D120, and D150. Flea counts were compared with a t-test for two independent samples. The significance threshold was set at 0. 05. The software used for calculations was XLSTAT, Addinsoft, v. 2020. 4. 1).
In addition, implant length will be calculated at each timepoint for both groups (SDS: Spring length, OWSER: Notched rod length) so that implant growth velocity over time can be graphed and calculated. The number of outpatient axial traction events of OWSER patients, if these are performed, will also be reported. All radiographic measurements will be performed by a trained assessor. To ensure data quality, radiographic measurements are performed semi-automatically in Surgimap v2. 3. 2. 1 software (Nemaris, New York, USA), after which all measurements are audited by the assessor. At the end of the data collection phase, all radiographic measurements are Fig. 1  Spring Distraction System. The SDS consists of three components that are added to standard growing rods. It provides a continuous distraction force during follow-up, without the need for repeated lengthenings.
In case there are missing EOSQ-24 item scores or questionnaires, multiple imputation with parcel summary scores will be performed using a previously published statistical method  [29]. For all statistical tests, statistical significance will be set at p < 0. 05. All statistical analyses will be performed in IBM SPSS statistics v. 26. 0. 0. 1 (Chicago, USA). Fig. 2  One Way Self-Expanding Rod. The OWSER is a growing rod that passively lengthens one way as the spine grows. In the BiPOWR trial, the distal anchors are iliosacral screws instead of pedicle screws. Blue: The proximal fixation consists of hooks positioned in a claw configuration. Two crosslinks are added for torsional stability (green). Red: The growing domino, combined with a rod that is serrated across its distal length, allows for lengthening.
Bibliometric data were extracted from the Scopus database using the search strings "casticin" and "cancer" and analyzed using VOSviewer software to generate network maps to visualize the results. Overall, more than 50% of the articles were published since 2018 and even more recent studies have expanded the knowledge of casticin's antitumor activity by adding interesting new mechanisms of action as a topoisomerase IIα inhibitor, DNA methylase 1 inhibitor, and an upregulator of the onco-suppressive miR-338-3p. Casticin counteracts cancer progression through the induction of apoptosis, cell cycle arrest, and metastasis arrest, acting on several pathways that are generally dysregulated in different types of cancer. In addition, they highlight that casticin can be considered as a promising epigenetic drug candidate to target not only cancer cells but also cancer stem-like cells.
Literature-based quantitative research on casticin in cancer To gain a general overview and characterize the papers published until 10 December 2022 on the anticancer properties of casticin, a bibliometric analysis was performed by sequentially using Scopus (an online abstract and citation database) and VOSviewer software  (v. 1. 6. 18). A Scopus search was performed (search within article title, abstract, and keywords) using the keywords (Search documents): "casticin" AND "cancer. " The 103 retrieved documents were subsequently "cleaned" of papers that did not contain the searched keywords in the title, abstract, or author keywords; furthermore, the cleaning process also applied to retraction-or erratum-type documents. As a result of this manual inspection, 75 documents were collected and analyzed with the Scopus function "analyze search results" to first characterize the obtained bibliographical dataset ("documents by year," "documents by subject area," and "documents by country or territory").
Interestingly, the bar chart (Supplementary Figure  S1C ) shows that among the top ten countries by number of publications, seven were from Asia, as were the top five, and by far the largest was China, with a total of 40 documents indexed in Scopus. That is not surprising because casticin is extracted from several Vitis species, have have long been used in traditional Chinese medicine  (Ramchandani et al. , 2020). The bibliographical dataset of 75 documents obtained from the cleaned Scopus query was further analyzed by calculating, plotting, and exporting the keyword distance network maps based on keyword co-occurrence from VOSviewer. Starting from this dataset, the software extracted 2,101 keywords, of which 183 met the default threshold of five minimum occurrences. The authors arbitrarily excluded the keywords "article," "priority journal," and "review" from the final calculation to make the topology of the network more relevant for the purpose of analysis.
All other VOSviewer parameters were left at their default settings. The network visualization of VOSviewer shows the keyword cooccurrence network graph, with the keywords being represented by nodes and the co-occurrence in papers between two keywords by a connection (or link). The different diameters of the nodes represent the difference in the total number of occurrences of every single keyword extracted by the software from the dataset (the larger the diameter, the higher the number of occurrences). VOSviewer extracted four clusters (see Supplementary Table  S1  for cluster keyword composition), each plotted with a different color in the network map and the cluster density map (Figures  1A, B ). The graph network visualization shows an overall general topology of highly interconnected nodes among the four clusters, which suggests a highly interconnected relationship of the keywords in the activity of casticin in cancer research, where the central node is represented by the keyword "casticin," which scored the highest number of occurrences (70), being fully interconnected with nodes in all the four clusters.