dataesr/scientific-paragraphs-categorization huggingface dataset:
A sampling plan, [θ 1 , θ 2 , θ 3 , θ 4 , θ 5 ] 80×5 , is created including different combinations of the UPs values, which are selected by the hybrid DOCE method within the known bounds [-150:150, -5:5, -6:6, -2:2, -1:1]. The quadratic optimization problem is solved 80 times using the "quadprog" optimizer of the Matlab optimization toolbox, based on the interior-point-convex algorithm, to yield the output data [Z * , x * 1 , x * 2 , x * 3 , x * 4 ] 80×5. Five MPMs, Ẑ* = f 0 (θ 1 , θ 2 , θ 3 , θ 4 , θ 5 ), x* i = f i (θ 1 , θ 2 , θ 3 , θ 4 , θ 5 ), i = 1, …4 , are fitted to approximate the optimal profit and prices as a function of the demand and the price escalation uncertainties. The MPMs performances are assessed relying on a new dataset, ( [ θ v 1 , θ v 2 , θ v 3 , θ v 4 , θ v 5 ] 400×5 -[Z *,v , x *,v 1 , x *,v 2 , x *,v 3 , x *,v 4 ] 400×5 ) , and their accuracies, in terms of NRMSE, are reported in Table  1.
As in the third case (Section 4. 3), a significantly high prediction accuracy of the MPMs is obtained (a maximum NRMSE of 0. 0035 %), which can be justified by similar reasons. Notice that despite the high dimensionality of the optimization problem (four decision variables) and the high number of the UPs (five) with respect to the other examples, the size of the training dataset required to achieve such high accuracy is on the same order of magnitude (80 points). Classical MPP approaches  [50]  provide a deterministic solution to this problem that is characterized by two critical regions over the UPs Fig. 7. Milk surplus problem: comparison between the solutions obtained by the classical MPP provided in  [50]  and the solutions provided by the proposed methodology. space. Fig. 7  shows a comparison between the deterministic solution and the approximate one provided by the proposed methodology, using the validation dataset.
Min x Z = x 3 1 + 2x 2 1 -5x 1 + x 2 2 -3x 2 -6 S. T : 2. 0x 1 + x 2 ≤ 2. 5 + θ 1 0. 5x 1 + x 2 ≤ 1. 5 + θ 2 0 ≥ θ 1 ≥ 1, 0 ≥ θ 2 ≥ 1 ⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭ (11) Three MPMs, Ẑ* = f 0 (θ 1 , θ 2 ), x* 1 = f 1 (θ 1 , θ 2 ) and x* 2 = f 2 (θ 1 , θ 2 ), are fitted using the input-output training dataset, [θ 1 , θ 2 ] 140×2 - [Z * , x * 1 , x * 2 ] 140×3 , generated as explained earlier for the other examples. The performance of the MPMs is evaluated using a new validation dataset, [ θ v 1 , θ v 2 ] 400×2 -[Z *,v , x *,v 1 , x *,v 2 ] 400×3 , and the results are shown in Table  1. Fig. 8  compares the approximated multiparametric solution obtained by the MPMs to the deterministic one obtain by  [9, 50]. In  [9] , different classical MPP algorithms have been applied to the problem, and the best one was the quadratic approximation algorithm that partitioned the UPs into four critical regions.
In this application, the size of the training dataset required to fit the MPMs is relatively high and, although the resulted accuracy is good (NRMSE of 0. 62 %, 1. 5 % and 1. 9 %), it is not as high as in the previous examples, where the NRMSE is less than 1 % in the worst cases. Again, this is because of the challenging discrete or piecewise characteristics of the multiparametric solution, which can be clearly noticed in Fig. 8.
The black-box simulation-based optimization problem (Eq. (  12 )) is solved 70 times (the "Fmincon" Matlab optimizer is used) to obtain the optimal values of the objective and decision variables[C * , S * F , S * T ] 70×3. Using this dataset, three MPMs are trained, which approximate the optimal behavior of the operational cost, steam flowrate and temperature as a function of the power demand and turbine efficiency: Ĉ* = f 0 (θ 1 , θ 2 ), Ŝ* F = f 1 (θ 1 , θ 2 ), Ŝ* T = f 2 (θ 1 ,θ 2 ). The validation is performed using another dataset including 400 samples, [ θ v 1 , θ v 2 ] 400×2 -[C *,v , S *,v F , S *,v T ] 400×3 , where the three MPMs are employed to  predict the outputs [ Ĉ*,v , Ŝ*,v F , Ŝ*,v T ] 400×3 and the NRMSE is calculated (Table  1 ). Table  1  and Fig. 10  show the high potential of the method: the optimal decisions are accurately predicted via simple interpolations using the MPMs, in a very short time (0.
05 and θ 2 = 0, as in  [19] , and exactly the same solution of the nominal problem (V 0 * = 978. 7 L, F * = 1 L/s, T * = 27. 7 s) is obtained. The proposed method is straightforwardly applied with the same steps previously illustrated. A sampling of 150 points is designed, the optimization problem (Eq. (  13 )) is solved (using the Matlab "Fmincon" algorithm) to obtain the input-output training data [θ 1 , θ 2 ] 150× 2 -[C * B , V 0 * , F * , T * ] 150×4 and, finally, four MPMs,C * B = f 0 (θ 1 , θ 2 ), V 0 * = f 1 (θ 1 , θ 2 ), F * = f 2 (θ 1 , θ 2 ) and T * = f 3 (θ 1 , θ 2 ) , are trained. The MPMs validation is accomplished in the same way as previously mentioned, considering a new input-output dataset [ θ v 1 , θ v 2 ] 300×2 -[C *,v B , V 0 *,v , F *,v , T *,v ] 300×4 different from the training one.
Hundreds of mouse cells were found in the tumor environment of PDX and were then removed from analysis. The top 200 differentially expressed genes were identified and organized by sample, cell class, and gene module to show gene signatures that define the relevant cell types examined (Fig. 3c ) with ToppCell (https://toppcell. cchmc. org/biosystems/go/index3/OncoMap) toolkit for each cell type (Methods). Further analysis, such as ToppGene enrichment analysis, as well as gene-interaction network, was also done in the integrated browser-able tool in the HB tumor. A heatmap of microenvironment cell gene expression demonstrates upregulation of cell activation, vascularity, and morphogenesis. Selected genes of cell-surface proteins, signaling  ligands, and transcription factors among differentially expressed genes (DEGs) in each cell type were used to build the interaction network in the tumor environment to show crosstalk with tumor cells.
Methods Human subjects. We have collected background liver and tumor samples from patients with HB under conditions appropriate for characterization and further generation of PDXs with institutional review board approval (IRB # 2016-9497) and informed patient consent. Data from 15 patient tumors and PDXs generated from five of these HB patient samples are reported. Primary tumor and background liver were used to examine gene expression by real-time PCR, protein expressionby western blot and histology, and a small subset of these samples were used for bulk RNAseq and scRNAseq, as well as the corresponding PDX tumor samples. PDX tumor samples were also examined by western blot and histology. The workflow of this methodology is depicted in Fig. 1a, b. Primary tumor and background liver were used to examine gene expression by real-time PCR, protein expression by western blot and histology, and a small subset of these samples were used for bulk RNAseq and scRNAseq, as well as the corresponding PDX tumor samples.
with addition of 0. 04% BSA/PBS in the final buffer  50. Three tumor samples (HB17, HB30, and HB53), two background liver samples (HB17 and HB53), and two PDX tumors (HB17 and HB30) were used for this analysis. Single-cell RNA sequencing of nuclei was done by 10X Genomics with chemistry v3. FASTQ files were aligned to GRh38 reference genome and processed with Cell Ranger version 3. 1. 0 to obtain the unique molecular identifier (UMI). Preprocessing, integration, and clustering of single-cell data. Cell ranger output of single-cell data was loaded using Seurat version 4 (3. 9. 9. 9010). Cells with fewer than 500 expressed genes or 800 UMIs, or greater than 10% mitochondrial counts were removed. Single-cell data of background liver sample from HB30 were removed because of low quality. In the end, 67,111 high-quality cells were harvested from seven samples for downstream analysis.
The total UMI counts per cell were normalized to 10,000 and the data were log 2 -transformed. Top 2000 highly variable genes were selected using the "vst" method of FindVariableFeatures function in Seurat. Data were scaled and principal component analysis (PCA) was conducted using highly variable genes with functions ScaleData and RunPCA, respectively. Principal components were used for integration of data from different samples using RunHarmony function of package Harmony 51. Neighbors were found with top 30 components of Harmony-corrected cell embeddings using shared nearest-neighbor (SNN) graph implemented in the function FindNeighbors. Clustering was conducted using Louvain algorithm. Different resolutions, including 0. 5, 1, and 2, were used to identify both general and fine-grained clusters.
Uniform Manifold Approximation and Projection (UMAP) was calculated in the PCA space for visualization in the reduced dimensions. Cell classes were annotated for each cluster based on gene expression levels of known markers and markers from a human liver cell atlas  52  (Supplementary Data 1-3). Enriched pathways of differential expressed genes were also used for further cell annotations. Scanpy was used for stacked violin-plot visualization of marker genes  53. Tumor cell clustering. Finer resolution of tumor subpopulations in the integrated data was achieved by extracting 52,629 annotated tumor cells from 5 HB tumor and PDX samples (HB17 tumor, HB17 PDX, HB30 tumor, HB30 PDX, and HB53 tumor). In order to remove batch effect between samples, we performed the reciprocal PCA procedure in Seurat, which is faster for large-dataset integration than the standard procedure.
Upregulated genes and enriched pathways were used to define tumor clusters (Fig. 5 ) (Supplementary Data 4-7). RNA velocity. Velocyto (PMID: 30089906) was used to measure spliced and unspliced transcripts in the sequencing data, which was further used for RNAvelocity computation in scVelo package  33. Data normalization was done using pp. filter_and_normalize. Then, firstand second-order moments among nearest neighbors (n_neighbors = 30) in PCA space (top 30 principal components) using pp. moments. Stochastic model of RNA velocities was built using tl. velocity. Then velocities were projected onto a lower-dimensional embedding using tl. velocity_graph. Velocity flows were drawn using pl. velocity_embedding_grid. Cell cycle scoring. Cell cycle scores, including S score and G2M score, were computed using the sum of scaled expression values of genes participating in S phase and G2M phase  54  (Supplementary Data 6).
Student t-tests were used to evaluate the significance of cell cycle score difference between cell types. Transcriptional profile correlation evaluation. Linear regression (lm function in R) was applied for mean-normalized expression values between cell types, including background hepatocytes and tumor cells in tumor and PDX (Fig. 4 ). Coefficient of determination (R 2 ) was computed after linear regression to evaluate transcriptional correlation between cell types. Differential gene expression and gene enrichment analysis. Gene expression signatures for each cell type and subtype within each sample group (Fig. 3 , Fig. 5 ) were computed from the Seurat-processed dataset using iteratively applied student t-tests embedded in the ToppCell portal (https://toppcell. cchmc. org/biosystems/go/ index3/OncoMap).
Besides, the same protocol was applied for tumor subclusters in each tumor sample for investigation of the individual tumor sample variation (Fig. S6 ). ToppCell provides easy user access to modularized representations of cell-type-specific gene expression signatures per sample group and provides a means of visualizing and carrying out post hoc analyses of the landscape of these modularized gene set signatures across all cell classes, subclasses, and samples and sample groups. Default ToppCell signatures correspond to the top 200 genes of each comparison per sample group, cell class, and subclass. Gene modules are arranged hierarchically according to the cell annotations, each of which can be combined and compared for downstream biological annotation feature analysis using ToppGene and ToppCluster.
More details can be seen in the tutorial of ToppCell. Additionally, the Scanpy function rank_genes_groups is used to compute differentially expressed genes between specific cell groups using student t tests (Fig. 4b )  53. Gene enrichment analysis was done by ToppGene to identify top enriched biological pathways, functions, and coregulatory associations. ToppCluster  55  was applied to compare different enrichment results of multiple gene modules across cell classes and tumor clusters. Various knowledge sources were used for enrichment, including gene ontology, as well as databases for interaction, drug, pathway, and human and mouse phenotype information. Adjusted p values of enrichment were calculated using the Benjamini-Hochberg procedure. They were used for the computation of gene enrichment scores (-log 10 (Padj)), which evaluate the strength of associations between a gene list and an enrichment term.
Volcano-plot visualization was generated using package EnhancedVolcano 56. Network analysis. ToppCluster allows researchers to draw both functional enrichment networks and interaction networks. In our analysis, genes from different pathways (Fig. 4e ) or various categories (Fig. 6d ) were sent to ToppCluster for functional enrichment. The associations between genes and pathways in different groups formed into a network, which shows shared and exclusive genes in each group. Apart from the gene-pathway association network, we also inferred gene-interaction network in ToppCluster using ToppCell gene modules (Fig. 7 , Fig. S2 ). Ligands and receptor genes were selected from gene modules and interactions were inferred based on curated-interaction database in ToppCluster. Further network analysis was supported by Cytoscape  57.
Statistical evaluation and reproducibility. Graphing and statistical calculations were performed using GraphPad Prism 8 software. Error bars denote standard error of the mean (SEM) and statistical significance was determined by unpaired, two-sided student t-test. Additional details are located in the figure legends. Data were confirmed in multiple human tumors, background liver, and PDX models to ensure robust and reproducible analyses. Biological material availability. Patient-derived xenograft tumor is available if sufficient quantities are available based on successful tumor growth in vivo.
Acknowledgements We would like to thank the CCHMC DNA sequencing and genotyping core, the  Potter lab , the  Confocal Imaging Core , and the Research pathology core for technical expertize and support. This work was supported by  NIH  grant  P30 DK078392  (Research pathology, DNA sequencing, Confocal, and genotyping core) of the  Digestive Diseases Research Core Center in Cincinnati. Funding sources-AASLD   Pilot award  ( SPR200430 ),  Markham award , and  Digestive Health Center: Bench-to-Bedside Research in Pediatric Digestive Disease  ( P30DK078392 ).
Data availability Sequencing data that support the findings in this study have been assigned Gene Expression Omnibus accession number GSE180666. Figures  1 2 3 4 5 6 7 and Figs. S1-S6 contain bulk or single-cell RNA sequencing data with associated GEO files. Single-cell RNA sequencing data analysis is in Supplementary Data 1-7. Raw data for real-time PCR and western blots (blots and analysis) are available in Supplementary Data 8-10.
The neighbourhood part of Eq. 4 is then modified, weighing the influence of all the particles by using the components of , as follows: ]  / :;" =  ×  / : +  " ×  " × I /,789: : -𝑿 / : J + 𝑐 # × 𝑟𝑎𝑛𝑑 # × ^𝑪/, 6 ,-" × _ /,789: :;" - / : X/ :;" =  / : +  / :;". ( 6 ) In this work, every particle will be a vector containing as much variables as the model contains parameters to be identified, and the objective function defined in Eq. 3 will be calculated for each of them. In the present study, the authors have made their own algorithm, coded with Matlab software  [27].
Over the last decade, single-particle electron cryo-microscopy has become one of the main techniques contributing to the growing library of high-resolution structures of macromolecules and their assemblies. For a full understanding of molecular mechanisms, however, it is important to place them into the broader context of a cell. Traditionally, this context can be visualized in 3D by electron cryo-tomography, and more recently, has also been studied by template matching of 2D images of cells and viruses. A current limitation of the latter approach is the high computational cost that limits the throughput and widespread adoption of this method. We describe here a GPU-accelerated implementation of 2D template matching in the image processing software cisTEM that allows for easy scaling and improves the accessibility of this approach.
We describe here an implementation of 2DTM in the software package cisTEM  (Grant et al. , 2018) , providing a user-friendly graphical interface and GPU-acceleration to speed up computation. We applied 2DTM to a set of images of Mycoplasma pneumoniae using a bacterial 50S large ribosomal subunit (LSU) as a template. M. pneumoniae cells are small and electron-transparent and do not require additional sample thinning  (Kühner et al. , 2009) , for example via controlled cell lysis  (Fu et al. , 2014)  or more commonly by using focused ion beam milling  (Marko et al. , 2007; Rigort et al. , 2012; Strunk et al. , 2012). We show that 2DTM is a versatile method with potentially broad applications for both in situ visual proteomics and in situ structure determination, including de novo structure determination.
GPU-accelerated 2DTM implemented in cisTEM The large search space required for 2DTM makes this method computationally demanding; a single 1850 x 1850 pixel image required 1000 CPU-hours for a search in the proof-of-principle MATLAB implementation  (Rickgauer et al. , 2017). To make 2DTM accessible to more users and a broader range of biological questions, we implemented 2DTM in cisTEM  (Grant et al. , 2018)  (Figure  1a ) using C++ and achieved a roughly 23x speed-up compared to the MATLAB implementation. The core 2DTM algorithm is unchanged from its original description as depicted in the flowchart in Figure  1  -figure supplement 1. The cisTEM implementation may be run as a standalone from the command line interface, or alternatively, using the cisTEM graphical user interface (GUI). In addition to a user-friendly interface, the GUI affords several advantages: Firstly, project metadata and image-specific information, such as CTF estimation, are tracked in a database; secondly, the search is easily divided over many CPUs or computers using cisTEM's MPI-like dispatch via run-profiles; and thirdly, the results are displayed in an interactive manner allowing for easy interpretation and comparison across multiple search conditions.
A screenshot showing the results of a template matching search in the cisTEM GUI is shown in Figure  1a. The GUI displays the image searched (Figure  1b ), maximum intensity projection (MIP) (Figure  1c ) and the plotted results (Figure  1d ), enabling rapid qualitative interrogation of the template matching results. Even with these improvements, searching a more typically sized image, like that from a Gatan K3 detector (5760 x 4092 pixels) requires ~7,100 CPU-hours when searching 13 focal planes, which are needed to resolve overlapping densities in a 150 -200 nm thick sample. Further substantial gains in the CPU-based code are not likely, given that about 85% of the computation for 2DTM is spent on calculating fast Fourier transforms (FFTs) using the already highly optimized Intel Math-Kernel library (MKL).
Detection of 50S ribosomal subunits by 2DTM The improved speed and increased throughput of 2DTM enabled us to perform an initial screen of template and search parameters that affect target detection in situ. To this end, we collected 2D images of plunge-frozen M. pneumoniae. M. pneumoniae lacks a cell wall and can be less than 200 nm thick, making it sufficiently thin to allow TEM imaging of whole cells in ice without the need for thinning  (Kühner et al. , 2009). We performed 2DTM using the M. pneumoniae 50S (PDB: in progress; EMDB: 11999)  (Tegunov et al. , 2021)  as a template (Figure  3a ) and identified 6,558 50S large ribosomal subunits in 220 2D images. This search did not distinguish between isolated subunits and subunits bound to the 30S small ribosomal subunit. The output from a 2DTM search are SNR values that depend on both the agreement between the template and the target as well as noise in the image  (Sigworth, 2004).
2DTM reveals species-specific structural features To test the specificity of 2DTM and to evaluate whether species-specific structural differences would preclude detection by 2DTM, we searched the same 220 images of M. pneumoniae with a 50S template derived from Bacillus subtilis (PDB: 3J9W). B. subtilis 50S is structurally closely related to M. pneumoniae 50S but displays differences in rRNA sequence and protein composition  (Grosjean et al. , 2014; Sohmen et al. , 2015)  (Figure  4a ). Comparing the two models by Fourier shell correlation (FSC) and taking 0. 5 as a similarity threshold (Figure  4  figure supplement 1a) suggests that the effective resolution of the B. subtilis template is limited to ~4. 7Å. Using B. subtilis 50S as a template, we identified 2,874 50S locations, less than half that were identified using M.
(  1 )) (Figure  6d ). The median in-plane distance of 12 Å is likely due to deformation of the sample under the electron beam, which is more pronounced with the higher electron dose used in tilt series, and to a lesser extent due to the lower resolution of 3DTM. In contrast to the x,y locations, which were calculated in steps of one pixel (1. 27 Å), the defocus values of 2DTM were sampled in 200 Å steps and, correspondingly the median out-of-plane distance between detected 2DTM and 3DTM targets was much higher at +/-84 Å (Figure  6e ). We used the program refine_template (see Methods) to perform a local defocus refinement, which reduced the median out-of-plane distance to +/-59 Å (Figure  6e ), less than 1/3 the diameter of the 50S. The 5-fold greater error in z relative to the x,y plane is consistent with the previously reported weaker dependence of the 2DTM SNR values on defocus  (Rickgauer et al.
Template optimization To realize an approach that combines 2DTM and 3DTM, a better understanding of how resolution affects 2D and 3D template searches will be needed. In this study, we found that applying a B-factor of 85 Å 2 to the 50S template maximized the mean detection SNR in this dataset (Figure  3  -figure  supplement 1a ). This value matches closely the B-factor of 86 Å 2 used to correct the in situ reconstruction of the M. pneumoniae 70S from subtomograms  (Tegunov et al. , 2021). Since this B-factor affects all atoms in the template equally, we expect that a more accurate method of generating templates that uses variable B-factors to account for local differences in mobility will further improve detection. We propose that given a sufficiently accurate method to calculate template densities, 2DTM could be used to investigate the relative impact of these and other, not yet considered, factors.
Future of template matching and conclusions We describe here the implementation of a GPU-accelerated 2DTM method into the graphical user interface of the open-source software cisTEM. 2DTM effectively detects ribosomes in 2D images of frozen M. pneumoniae cells at higher specificity, but lower sensitivity than 3DTM in samples thicker than 100 nm. We propose that an effective, high-confidence strategy for in situ visual proteomics would combine 2DTM and 3DTM and would not be limited to species for which high-resolution structures are available. We also propose that the search space of 2DTM could be further expanded to include a more detailed analysis of the signal present in an image, allowing for interrogation of conformational or compositional variability, by searching with a multi-template library.