dataesr/scientific-paragraphs-categorization huggingface dataset:
Multiobjective learning is utilised since the learning process could potentially be biased toward a particular level if a unique objective function is employed  [99]. In addition, we found that multiobjective optimisation typically reduces the number of epochs required to train the network by half. Our method was applied successfully to the SHREC'19 dataset, and is applicable to any kind of shape. In addition to the Fokker-Planck and the Dirac-Kähler equations, one may alternatively consider the Schr ödinger equation. The shape descriptor associated with Schr ödinger's equation is the celebrated wave kernel signature (WKS)  [8]. The Schr ödinger equation, like the Fokker-Planck equation, has a fractional counterpart  [100] ,  [101]  which is given by i ∂ψ ∂t = -η α ( ∇) α ψ (63) where ∇ α is the fractional gradient operator, which is defined by Eq.
What We Found: LNI contralateral to the prostatic lobe with worse tumor characteristics is rare and depends on the presence of cancer contralateral to the dominant side, its grade, and extent. Our findings provide the grounds for evaluating unilateral ePLND in future studies. Limitations: The multi-institutional nature of our data might harbor a certain degree of unaccounted heterogeneity, especially concerning MRI acquisition, reporting, and biopsy together with the lack of external validation. Interpretation for Patient Care: In the era of modern PCa imaging, ePLND can be omitted contralateral to the prostatic lobe with the worse tumor burden in selected patients, especially in the absence of high-risk clinical features. We propose a model for the prediction of LNI contralateral to the dominant prostate lobe that can help avoid contralateral ePLND in almost onethird of cases.
Study Population We relied on prospectively maintained, Institutional Review Boardeapproved databases. Data for the study were provided by 19 tertiary referral centers for a total of 2,359 individuals. Due to missing data, 106 patients were excluded from the study cohort. The study was endorsed by the Young Academic Urologists Working Group on Prostate Cancer of the EAU. Centers were invited to participate if they could provide data on patients with cN0 disease who had been diagnosed and/or staged by preoperative multiparametric (mp) MRI, who had undergone radical prostatectomy with ePLND, and for whom detailed information on the LNI site was available. Overall, cN0 status was documented through conventional imaging (79%) or PSMA PET (21%) along with mpMRI information.
U N C O R R E C T E D P R O O F All patients underwent mpMRI prior to biopsy. Patients with a sole midline lesion where a dominant lobe could not be clearly identified were not eligible for the study. Imaging was performed and reported according to the Prostate ImagingeReporting and Data System (PI-RADS) recommendations. 14 All men underwent systematic plus targeted biopsy of PI-RADS 3 lesion(s) as well as radical prostatectomy between 2016 and 2021 in each tertiary referral center. Distant metastatic disease was ruled out by conventional imaging (CT scan and bone scan) or PSMA PET according to local recommendations. Men with suspicion of metastatic or nodal disease were excluded. An ePLND was performed in patients at risk of nodal invasion according to preoperative models based on baseline characteristics.
Since the database encompassed data from different institutions, we included institution clustering in the logistic regression using a generalized estimating model through the cluster function in Stata. A nomogram was then built based on the coefficients of the logit function. We performed internal validation through the leave-one-out cross-validation. The predicted probability, after internal validation, was used to calculate the area under the receiver operator curve (AUC), to assess calibration, and the net benefit associated with its use applying a decision curve analysis. We performed a systematic analysis of different thresholds of contralateral LNI risk, starting from 1% with increments of 0. 5%. Additionally, to confirm our hypothesis that the presence of tumor contralateral to the index lesion along with its extent and features are the major drivers of contralateral LNI, we added to the contralateral LNI model U N C O R R E C T E D P R O O F ISUP grade on the dominant side as covariate.
Its implementation in practice could allow avoiding bilateral ePLND in almost onethird of cases. Reducing the rate of bilateral ePLND could potentially lower the operative time and rate of complications while reducing the length of hospital stay, leading ultimately to lower overall costs. These outcomes along with recurrence data need to be evaluated in prospective series and/or a randomized controlled trial. Indeed, the implementation of our approach needs to be weighted relative to recurrences that are associated with high overall costs for health systems. Notably, the 1 patient in the development cohort who was below the 1% cutoff with a theoretically missed contralateral LNI had organ-confined disease on imaging and pathology with ISUP 2 on biopsy and pathology. LNI in this context is a rare occurrence, especially if contralateral, as our data demonstrate.
EDITORIAL COMMENT Although extended pelvic lymph node dissection (ePLND) during radical prostatectomy is considered the gold standard staging tool, it is associated with increased morbidity. 1 Therefore, Martini et al investigated reducing bilateral ePLND to unilateral ePLND in selected cases, without compromising short-term oncologic outcomes. 2 In their novel nomogram, based on a multi-institutional data set from 19 tertiary referral centers, the rate of lymph node invasion (LNI) was 13% in both development and validation cohorts. Very well according to the authors' hypothesis, LNI was predominantly found on the ipsilateral side of the prostatic lobe with the worst disease features and yielded an impressive area under the curve of 84%. Nevertheless, it is noteworthy that LNI was also found contralaterally.
| Statistical analysis Effectiveness and safety analyses were performed on all included patients who received at least one iGlarLixi dose. The changes in HbA1c and FPG from baseline to weeks 12 and 24 of iGlarLixi treatment were analysed using a mixed model for repeated measures (MMRM) adjusted for baseline HbA1c and FPG, respectively. Based on this MMRM, which used an unstructured covariance matrix, we estimated the least-squares (LS) mean changes in HbA1c and FPG in the two age subgroups and the corresponding 95% confidence intervals (CIs). All other variables were assessed descriptively, with categorical variables presented as counts and percentages, and continuous variables as mean ± standard deviation or as median (quartile 1-quartile 3). No imputation of missing data and no adjustment for multiple testing were made.
We propose a methodology for monitoring an artificial intelligence (AI) tool for breast cancer screening when deployed in clinical centers. An AI trained to detect suspicious regions of interest in the four views of a mammogram and to characterize their level of suspicion with a score ranging from one (low suspicion) to ten (high suspicion of malignancy) was deployed in four radiological centers across the US. Results were collected between April 2021 and December 2022, resulting in a dataset of 36,581 AI records. To assess the behavior of the AI, its score distribution in each center was compared to a reference distribution obtained in silico using the Pearson correlation coefficient (PCC) between each center AI score distribution and the reference. The estimated PCCs were 0. 998 [min: 0.
Data Reports were anonymously collected from 4 radiological centers across the US between April 2021 and December 2022, resulting in a dataset of 36,581 AI records. Table  1  summarizes the characteristics of the data set extracted for this study. AI versions 1. 2 and 1. 3 were developed to process FFDM images (2D), while version 2. 0. 1 was developed to process DBT images (3D). To assess the score distribution of each center, a reference distribution is required. Data to create each reference come from a retrospective evaluation (RE) conducted for regulatory purposes prior to the deployment of the AI in a clinical setting. The main purpose of the RE is to run the algorithm on an external dataset representative of the target population on which the AI will be deployed. These data have not been used for algorithm development to ascertain its generalizability  [8, 13, 14].
In this study, a reference is a distribution of scores obtained in the RE for a specific version of the AI, manufacturer and modality. Therefore, a reference represents the behavior expected from the AI and constitutes the control point from which any deviation could indicate a malfunction of the tool. Some of the characteristics of the dataset used to create the references are summarized in Table  2. In total, 13,433 exams were used to create the reference distribution for the 1. 2 AI version, 25,330 exams were used for the 1. 3 AI version and 14,187 exams were used for the Life 2023, 13, 440 3 of 11 2. 0 AI version. Each of these distributions is then used for statistical analysis (explained in the next section).
Statistical Analysis To assess the behavior of the AI, its score distribution in each center was compared to a reference distribution matching the AI version, image modality (i. e. , FFDM or DBT) and the manufacturer used by the center (e. g. , Hologic, Fuji or GE). Data from each center were analyzed globally (over the entire duration of the data extraction) and locally (over each month). Similarity between distributions was estimated using the histogram correlation which consists of calculating the Pearson correlation coefficient (PCC) between each center AI score distribution and the reference. The coefficient from a given distribution is computed as explained in the following paragraph. Let be p a histogram representing a data distribution. To construct a histogram from a score distribution (ranging from 1 to 10), we split this feature into 10 consecutive and nonoverlapping bins p i.
In order to catch and quantify these types of deviations, we added the Wasserstein distance (W). This metric is widely applied in statistics and machine learning for its ability to quantify the distance between two distributions as the displacements to be made between the bins to pass from one histogram to another  [18]. Statistical calculations were performed in Python 3. 8. 8 [19]. For the Pearson correlation calculation (PCC) and the chi-squared test, Python bindings for the OpenCV library were used  [20]. Wasserstein distance (WD) was estimated using the Python module scipy. stats [21].
Background: Esophageal cancer (EC) is one of the most common cancers worldwide. The prognosis for patients with the same stage of EC can vary substantially. Progress of single-cell analysis technology has furthered the understanding of tumor heterogeneity. This paper aimed to apply single-cell analysis to explore the characteristics of the tumor environment of EC and provide inspiration for personalized treatment. Methods: The latest gene expression data and clinical follow-up information of single-cell sequencing results of EC samples were downloaded using from The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) Application Programming Interface (API). A differential gene function analysis of the immune in ltration signature agents in the tumor microenvironment was undertaken using bioinformatics analytic methods, and potential molecular targets were sought.
In addition, single-cell sequencing analysis has also been applied to a variety of cancers, such as multiple myeloma, lung adenocarcinoma, and stomach adenocarcinoma. In this study, we downloaded public data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to perform scRNA-seq on large-scale cells derived from ESCC tumor samples. We discovered some interesting facts that may help deepen our understanding of the immune microenvironment and immunotherapy in EC and provide some new inspiration for EC patients.
Methods Data download and preprocessing. We used TCGA Genomic Data Commons (GDC) Application Programming Interface (API) to download the latest gene expression data and clinical follow-up information for EC on 2 February, 2022. The GSE145370 dataset was downloaded from the GEO database as GSE145370_RAW. tar. In processing TCGA data, only normal and primary tumor samples were retained. For GSE145370 data processing, we used R (version 3. 6. 4, https://www. r-project. org/) and the Seurat R software package (version 3. 6, https://satijalab. org/seurat/) to combine single-cell sequencing results for each sample from the GSE145370_RAW dataset to build a single-cell dataset. Quality control of single-cell data. As above, we used R and the Seurat R package for the analyses. The lter criteria of cells were determined after referencing previous studies  [15, 16].
According to the median number of genes and the percentage of mitochondrial genes in the samples, cells with < 200 and > 4,000 genes (potential cell duplets) and a mitochondrial gene percentage of < 20% were ltered. After quality control (QC), 103,223 high-quality cells were obtained. The relationship between the percentage of mitochondrial genes and the messenger RNA (mRNA) reads was detected and visualized as the relationship between the number of mRNAs and the reads of mRNA. After data normalization, all highly variable genes in single cells were identi ed after controlling for the relationship between average expression and dispersion. Subsequently, we used principal component analysis (PCA) with variable genes as the input and identi ed signi cant principal components (PCs) based on the JackStraw function.
A total of 20 PCs were selected as the input for uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE) when statistically signi cant. With a resolution of 0. 5, cells were clustered by the FindClusters function and classi ed into 24 different cell types (clusters 0-23). Next, we used the FindAllMarkers function to nd differentially expressed genes (DEGs) between each type of cell. Cell cycle bias analysis. Cell cycle analysis was performed using the Seurat program (CellCycleScoring function). We used a previously de ned core set of 43 G1/S and 54 G2/M cell cycle genes. Cells were classi ed according to the maximal average expression ('cycle score') in these 2 gene sets. When the cycle scores of G1/S and G2/M were both less than 2, we considered these cells noncycling; otherwise, we considered the cells proliferative.
After the cell cycle analysis, no bias induced by cell cycle genes was observed. Notes for cell subgroups. Cell type assignment was performed based on the marker genes reported in previous studies  [17] , the CellMaker database, and the SingleR package. Subclustering analysis of B cells, monocytes, effector CD8 + memory T (Tem) cells, CD8 + T cells, CD4 + T cells, and naive CD8 + T cells was performed using the SubsetData function in the Seurat program. Following data normalization and scaling, all highly variable genes in single cells were identi ed. Subsequently, 20 PCs were selected as the input for t-SNE when statistically signi cant. The FindClusters function was used to classify cells into different clusters. Next, the FindAllMarkers function was used to nd DEGs between each subtype.
Next, to assign pathway activity estimates to individual cells, the R package gene set variation analysis (GSVA) with the standard settings was implemented in the single-sample gene set enrichment analysis (ssGSEA), and a gene set containing all human pathways was downloaded from the Molecular Signatures Database (MsigDB), including the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We identi ed signi cantly enriched pathways de ned by the Benjamini-Hochberg-corrected P-value < 0. 05. Feature-rich analysis. The KEGG pathway enrichment analysis systems were applied to the R package, clusterPro ler[18], to identify over-represented KEGG pathway genes. In addition, we used the Signal Pathway Impact Analysis (SPIA) software package to activate and inhibit the KEGG pathway based on the different genes, and used GSEA to analyze the KEGG pathway enrichment of cells and samples from different sources, using the gene expression spectrum.
Results Single-cell data set quality control. We downloaded the GEO dataset GSE145370 and analyzed it using the Seurat R package. The number of minimal information units (MIUs) was not signi cantly correlated with mitochondrial gene percentage (Fig. 1A ), but positively correlated with the number of mRNAs (Fig. 1B ). We also analyzed the number of mRNAs, the readings of mRNA, and the distribution of mitochondrial genes (Fig. 1C ), which revealed that most genes were distributed between 0 and 2,500 bp, mRNA readings were distributed between 0 and 1,000 bp, and mitochondrial percentages were below 15%. Further, we removed cells with more than 20% expression originating from mitochondrial genes and those with fewer than 200 genes. The expression spectrum of 33,538 genes from 103,223 cells was obtained, and the number of cells in each sample was counted (Table  1 ), including the number of ltered mRNAs, the readings of mRNA, and the distribution of mitochondrial genes (Fig.
2C ). Further, applying rank and testing methods to identify differences in genes using the cell type annotation based on the CellMarker software package (diff_genes_wilcox_fdr. txt per cell group), these 24 clusters were eventually annotated to 16 cells (all. marker. anno. txt), which we de ned as: Cell Cluster 0, cell monocytes from tumor tissue; Cell Cluster 1, Paneth cells; Cell Cluster 2, B cells; Cell Cluster 3, monocytes 1; Cell Cluster 4, exhausted CD8 plus T cells; Cell Cluster 5, CD8 + plus effector memory T (Tem) cells; Cell Cluster 6, natural killer (NK) cells; Cell Cluster 8, monocytes 2. In addition, we observed different proportions of tumor samples in different monocyte populations, with B cells derived mainly from normal samples and Paneth cells mainly from tumor samples (Fig.
3A. We selected the genes common to the three types of cells to include in the R software package clusterPro ler for KEGG Functional enrichment analysis which used a total of 29 pathways (step3_A_KEGG_enrich. txt), with the most signi cant top 10 shown in Fig. 3B. In addition to our analysis of the genetic differences between the CD8 + effector memory T (Tem) cell, B cell cancer, and cancer side sample tissues, we observed 2 types of genetic differences with a large intersection (Fig. 3C ). Similarly, we identi ed common differences in genes using the R software package, clusterPro ler, for the KEGG functional enrichment analysis, which used a total of 22 pathways (step3_B_KEGG_enrich. txt), with the top 10 shown in Fig. 3D. We observed a signi cant overlap between the different gene functions in cells in the cancer and cancer side sample tissues (Fig.
The B cell count was signi cantly higher in Stage II than in Stage III, and the monocyte count was signi cantly higher in Stage II than in Stage III, which suggested unique immune cell immersion in the different stages. We compared the differences in gene expression between cancer and cancer side samples of the B cell and the monocyte, and observed that most of the degenerated genes in the monocyte intersected with 324 genes in the B cell (Fig. 4A ). Further, using the R software package SNP Panel Identi cation Assay (SPIA), we analyzed differences in the pathways of gene participation between the 2 types of cells. In B cells, we observed a total of 40 pathways with disorders (e. g. , step4_spia. A. txt), of which 24 were activation pathways (Fig. 4B ) and 16 pathways were inhibition pathways (Fig.
7A ). In addition, they enriched the molecular functions of the structural constituents of the ribosome (Fig. 7B ), and we observed that these genes were enriched into a large number of biological processes and cellular components, such as nuclear-transcribed mRNA catabolic processes and nonsense-mediated decay (Fig. 7C ). In cellular components, such as cell-substrate-adhesin junctions and focal adhesions (Fig. 7D ), these biological pathways are closely related to tumor proliferation and metastasis. We mapped these genes to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) version 11. 0b (www. string-db. org) database to obtain protein interoperability networks (ppi. network. txt) of these genes, and analyze the interrelationships of these genes to the network degree distribution (ppi.
TCGA dataset validation. To verify the prognostic correlation of CXCL8, we downloaded the TCGA-Expression Spectrum Data Set (TCGA-ESDS; TCGA-ESCA. exp. txt) and the corresponding clinical follow-up data (TCGA-ESCA. exp. os. txt), from which the expression spectrum of CXCL8 was extracted, and differences in expression between the cancer and cancer side tissue samples (Fig. 8A ) could be observed. In the tumor samples, the expression of CXCL8 was signi cantly higher than in the cancer side samples. In addition, we compared the relationship between CXCL8 expression and prognosis, using the maxstat package of R, to group CXCL8 expression, and analyzed the prognosis by considering differences of overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) in patients with high expression and low expression.
Immunohistology of ileum Paraffin-embedded ileum was cut in 4-μm-thick sections, and cells expressing CD3, CD4, or CD8 markers were visualized using the Ventana Ultraview DAB Detection System. For quantification of immune cells, sections were digitally scanned (Nanozoomer 2. 0, Hamamatsu) and imported into QuPath 0. 2. 3 software (Suppl. Figure  2 ).
Statistical analysis Values are expressed as the mean ± SEM. GraphPad Prism version 5. 0 software was used for statistical analysis. Comparison between values of AIA and control groups was assessed using unpaired Student's t test or Mann-Whitney test when data were not normally distributed. A non-parametric multivariate analysis of variance (PER-MANOVA) approach was used to compare dissimilarities between bacterial communities. A compositional analysis  [17]  of the gut microbiota was performed to distinguish differentially abundant taxa between AIAs and controls. The relationship between two quantitative variables was investigated using Spearman's correlation coefficient. P values less than 0. 05 were considered significant.
in rheumatoid arthritis  [20]. Among the cytokines studied, IL-8, IL-17A, and IL-33 exhibited the earliest increase in the ileum. IL-8 is known Fig. 3  Ileal epithelial intestinal integrity in AIA rats. Plasma iFABP levels were measured in control and AIA rats at the preclinical (A), onset (B), and acute (C) stages. Values expressed as means ± SEM (n = 14/15 per group). ***(p < 0. 0005), n. s. : not significant to induce transendothelial migration of neutrophils to sites of inflammation, including joints  [21] , and circulating levels of IL-8 are strongly correlated with disease and inflammatory activities in AS patients  [22]. Our data showing that IL-17 upregulation preceded that of IL-23 and TNFα support the fact that IL-23 and IL-17 are at least partly uncoupled in SpA and suggest that IL-17 plays an early role in the pathophysiology of the disease.