dataesr/scientific-paragraphs-categorization huggingface dataset:
III. EXPERIMENTS AND RESULTS We acquired the T1-weighted images (of size 182×218×182) of 102 drug-addicts and 10 healthy volunteers. For convenience of training, the image intensity was normalized to the range [0, 1]. The images of 10 healthy volunteers and 94 randomly selected addicted brains were used for training, and the images of the rest 8 addicts were used for testing. To evaluate the performance of the proposed registration method ADMIR, we compared it to the SyN implemented in ANTs  [34]  software package, and the VoxelMorph  [24] , which represent the state-of-the-art algorithms in traditional and deep learning methods. Since the VoxelMorph is used for deformable registration, it requires preregistering the images with an affine transformation. To fairly compare the above-mentioned methods, the moving images were firstly registered to the fixed images with an affine transformation using FSL software.
B. QUANTITATIVE COMPARISON To quantitatively evaluate the performance of different registration methods, we selected some critical brain regions, including thalamus (red), lateral ventricle (green), caudate nucleus (pink), lentiform nucleus (purple), and the third ventricle (light blue), as illustrated in Fig. 6 , to calculate the corresponding average Dice score, Hausdorff distance, and average symmetric surface distance. In Table  1 , for each metric: the first row represents the corresponding values between the non-preprocessed moving image and the fixed images. From second to fifth rows, the moving images are firstly warped by affine transformation using FSL software, and such pre-aligned moving images are then registered by the three methods. We observe that using the coarsely pre-aligned moving images, the registration results with the proposed ADMIR mode are the best: the average Dice computed over all structures is about 10% higher than ANTs and about 3% higher than VoxelMorph; the HD is about 39% smaller than ANTs and about 22% smaller than VoxelMorph; the ASD is about 28% smaller than ANTs and about 20% smaller than VoxelMorph.
7. Note that, when the pre-aligned images are taken as the moving image, not only the median Dice score of the proposed ADMIR model (purple box) is much higher than the other models (blue and green boxes), but also the interquartile range is much narrower, which verifies the stability of ADMIR. When the pre-alignment is not performed, registering the original images with the ADMIR model (orange box) achieves even better performance than VoxelMorph, with a higher median Dice score, a narrower interquartile range and less isolated points, which demonstrates that the proposed end-to-end registration model is more effective. Concerning the computation time for different registration models, on a platform with CPU of Intel Xeon (E5-2650) and a GPU of NVIDIA Tesla V100, registering the whole testing data including 8 subjects takes only 15 seconds with our proposed model, which is much faster than using ANTs that takes about 10 minutes, or VoxelMorph that takes about 3 minutes since it involves an extra affine transformation that takes much computation time.
This can be explained by the affine module used in this work. With the convolution neural network, as illustrated in Fig. 2 , the rotation, scaling, shearing, and translation information between the fixed and moving images can be accurately learned from a large number of image pairs. Such data-driven learning will be better than traditional optimization methods based on a single sample. This can be demonstrated in Fig. 8 , in which, we selected five landmarks (Fig. 8(a) ) to evaluate the performance of FSL and ADMIR for affine registration. We calculated the following L1 distance between the landmarks in the fixed and the warped images, which is defined by: dis i = loc fi -loc wi with i = 1, 2,. , 5 (8) where loc fi is the location of the i th landmark in the fixed image, and loc wi indicates the location of the i th landmark in the warped image.
The boxplot of this L1 distances for all the testing samples is given in Fig. 8(b ). We found that the landmarks in the images registered with the proposed method are closer to those in the fixed image in terms of L1 distance than those in the images registered by FSL. This illustrates that the proposed model outperforms the traditional registration method FSL in terms of affine registration. Besides, compared to the VoxelMorph model, which focuses only on the deformable registration; therefore, the DVF learned from this model is only related to the relationship between the pre-aligned and fixed images. If the pre-aligned image suffers from errors, which is inevitable in practice, the final registration results will inevitably be influenced. In contrast, as to the ADMIR model proposed in this work, the error caused by the affine registration module can be corrected further by the deformable registration module, as can be observed in Fig.
V. CONCLUSION We have proposed an unsupervised end-to-end registration neural network ADMIR model for drug-addicted brain images. The method consists in using an affine registration module and a deformable registration module, and does not require supervised information or extra pre-alignment operation. The results showed that the proposed method outperforms the state-of-art method VoxelMorph, even when the latter uses pre-aligned images as inputs while our method uses the original moving image as input. Such end-to-end registration makes it possible to implement medical images registration quickly and accurately.
Materials and Methods National obstetric specialists and researchers from 20 tertiary hospitals were included in the KOrean Preterm collaboratE Network (KOPEN) registry (Supplementary Figure  S1 ). We recruited pregnant women between 16 and 34 gestational weeks who had symptomatic preterm labor and were admitted into a tertiary care center from March to November 2016. Data collection was completed in September 2017, regardless of whether the admitted pregnant women had given birth. Only data from women who delivered at the participating hospitals were considered. Pregnant women who had symptomatic preterm labor, cervical incompetence, and premature rupture of membranes were included, and quadruplet multiple pregnancies were excluded. Data were recorded in an electronic case report form (eCRF) using the internet-based clinical research and trial management system at each tertiary hospital.
Collected data included demographics, obstetric and medical histories, and basic laboratory test results, including blood tests and vaginal discharge findings for clinical and basic characteristics. In the questionnaire, sleep quality was evaluated using the Pittsburgh Sleep Quality Index. A pelvic examination was performed to assess the cervical condition, such as presence of bleeding, ripening, opening, and water leakage. A speculum examination was used for fetal fibronectin and vaginal swab culture of microorganisms, if possible. An ultrasound examination was conducted to assess the cervical length and shape, fetal gestational age and weight, presence of anomaly, presentation, amniotic fluid volume, and presence of maternal anatomical abnormalities. Blood serum samples were obtained for assessing the blood count and C-reactive protein (CRP) level.
Results In total, 879 pregnant women in preterm labor were registered at the 20 participating tertiary perinatal centers between March 2016 and November 2016 (Figure  1 ). Of these registered patients, 152 pregnant women had missing birth data such as, no delivery records present due to withdrawal from the participant agreement (22 patients), delivery at another undesignated hospital, or delivery had not yet taken place when data collection was concluded in the eCRF system. Data from the remaining 727 pregnant women who gave birth at a designated institute were analyzed, and the rates of early preterm, late preterm, and term births were 18. 16%, 44. 02%, and 37. 83%, respectively. The significant factors in maternal characteristics at admission that are associated with preterm delivery are shown in Table  1.
Acknowledgments This work was supported by the  Research Program  funded by the  Korea Centers for Disease Control and Prevention  ( 2016-ER 630800 ) and the  Ministry of Health& Welfare of the Republic of Korea  ( HI18C0378 ). The statistical analysis of this research was developed using the data analysis software program supported by the  Korea Health Industry Development Institute. The authors also thank  Dr. Junguk Hur , from the  University of North Dakota, USA ,  Dr. Kyung-sook Yang , from the  Department of Medical Statistics, Korea University, Seoul, Korea for critical comments, and Editage Inc. for their professional language editing service.
Notably, the role of B-cells and, in particular, TLS in response to ICB remains unclear. We recently conducted a phase 2 clinical trial of neoadjuvant treatment with ICB in patients with high-risk resectable (clinical stage III or oligometastatic stage IV) melanoma to assess the safety and feasibility of this treatment in this patient population (NCT02519322)  17. Importantly, longitudinal tumor samples were taken in the context of therapy, and molecular and immune profiling was performed to gain insight into mechanisms of therapeutic response and resistance. In these studies, known and novel biomarkers of response were identified, and targeted protein expression profiling (via Nanostring Digital Spatial Profiling) revealed significantly higher expression of B-cell markers in baseline and on-treatment samples of responders to ICB  17.
Importantly, findings of high B-cell lineage scores in responders were replicated in samples from an additional cohort of melanoma patients treated with neoadjuvant versus adjuvant checkpoint blockade (NCT02437279, OpACIN trial (n=12 R and 6 NR)  33  (Extended Data Fig. 1d  and 2c , Extended Data Table  2 , Supplementary Tables  4  and 7 ). B-cell signatures alone were predictive of response in univariable analyses (OR 2. 6, p=0. 02 for our trial, and OR 2. 9, p=0. 03 for combined melanoma cohorts), but not in multivariable analyses when considering other components of the immune cell infiltrate, suggesting that B-cells are likely acting in concert with other immune subsets and not acting in isolation; however these analyses were limited due to the low sample size (Extended Data Table  4  and 5 ).
To evaluate the validity of these findings across additional cancer types, we next assessed the expression of these immune cell gene expression signatures in a pre-surgical ICB trial for patients with metastatic renal cell carcinoma (RCC) (NCT02210117, PD-1 blockade monotherapy versus combined CTLA-4 and PD-1 blockade versus combined PD-1 blockade and bevacizumab) (Extended Data Table  3 ). Gene expression profiling by microarray and subsequent MCP-counter analysis of baseline tumor samples was performed, demonstrating significantly higher expression of B-cell related genes in R vs NR to therapy (p=0. 0011, n=17R and 11 NR) (Fig. 1d , Extended Data Fig. 1c , 2b, and 3, Supplementary Table  5 6 7 ). As in the case of melanoma, B-cell signatures were predictive of response in univariable analysis in the RCC cohort (OR 61.
7 for B-cell low, p=0. 05) in comparison to B-cell high group (Extended Data Table  7 ). Similar analyses were performed to assess the expression of immune-related genes in clear cell RCC from the TCGA (TCGA-KIRC, n=526)  37. In these studies, similar immune clusters were observed; however, immune infiltrate was not associated with survival in these patients (p=0. 24) (Extended Data Fig. 5 , Supplementary Tables  11 12 13 ), possibly owing to the heterogeneous nature of this disease and other driving mechanisms of patient outcomes. Based on these insights from gene expression profiling data, we next assessed tumor samples histologically to gain insight into the density and distribution of B-cells as well as their relationship to TLS in patients treated with neoadjuvant ICB. The density of CD20 + B-cells, TLS, and ratio of TLS to tumor area was higher in R versus NR in our neoadjuvant melanoma cohort, particularly in early on-treatment samples (p=0.
We first assessed differences between intra-tumoral B-cells and those in the peripheral blood of patients. In these studies, unique clusters of CD45 + CD19 + (B-cell) populations including naïve (CD19 + , CD27 -, IgD + ), transitional (CD19+, CD24 ++ , CD38 ++ , CD10 + , CD27 -, IgD + ), unswitched and switched memory (CD19 + , CD27 + , IgD +/-), double-negative (CD19 + , CD27 -, IgD -), and plasma(like) cell (CD19 + , CD20 -, CD22 -, CD38 ++ , CD27 ++ ) populations were demonstrated in peripheral blood and tumor samples, with distinct profiles in the tumor compared to peripheral blood samples (Fig. 3e , Extended Data Fig. 17   We next compared the phenotypes of B-cells in Rs as compared to NRs to ICB in both tumor and peripheral blood. Though B-cell subsets (naïve, memory and transitional B-cells and plasma cells) in the peripheral blood had a similar distribution in R and NR (Extended Data Fig.
While the distinct mechanisms through which B-cells contribute are incompletely understood, our data suggests that the same properties of memory B-cells and plasma cells desirable for acquired immune responses may also be contributing to For the validation melanoma cohort, we used samples of 18 patients enrolled in the OpACIN trial (NCT02437279). In the phase 1b OpACIN trial 20 patients with palpable stage III melanoma were randomized 1:1 to receive ipilimumab 3 mg/kg and nivolumab 1 mg/kg, either four courses after surgery (adjuvant arm), or 2 courses prior to surgery and two courses post-surgery (neoadjuvant arm). Co-primary endpoints were safety/feasibility and tumor specific T-cell expansion. For this current correlative study, response was defined as not having disease relapse. These patients were treated at the Netherlands Cancer Institute (Amsterdam, the Netherlands).
The study was conducted in accordance with the Declaration of Helsinki and approved by the medical ethics committee of the Netherlands Cancer Institute. All subjects provided informed consent prior to their participation in the study. Patients underwent a pretreatment tumor biopsy (1x formalin fixed and paraffin-embedded (FFPE) and 2x fresh frozen) obtained as a core biopsy performed by a radiologist. RNA was extracted from one frozen biopsy for RNA-Sequencing analysis. We only included 18 patients in our analysis because the tumor purity in the frozen pretreatment biopsy of two patients was too low, therefore no RNA could be isolated and these patients could not be included in this analysis. The clinical responses of this cohort has been previously described  33. The renal cell carcinoma (RCC) trial was an open-label, randomized, pre-surgical/pre-biopsy trial RNA-seq data processing and quality check.
Gene expression quantification and normalization. HTSeq-count (v0. 9. 1)  42  tool was applied to aligned RNA-seq BAM files to count for each gene how many aligned reads overlap with its exons. The raw read counts generated from HTSeq-count (v0. 9. 1)  42  were normalized into fragments per kilobase of transcript per million mapped reads (FPKM) using the RNA-seq quantification approach suggested by the bioinformatics team of NCI Genomic Data Commons (GDC)  43. Briefly, FPKM normalizes read count by dividing it by the gene length and the total number of reads mapped to protein-coding genes using a calculation described below: " RCg, number of reads mapped to the gene; RCpc: number of reads mapped to all protein-coding genes; L, length of the gene in base pairs (calculated as the sum of all exons in a gene).
The FPKM values were then log2-transformed for further downstream processes. RNA Sequencing and Analysis for OpACIN trial. RNA sequencing and data analysis were performed as previously described  33. Affymetrix Microarray for RCC. The Affymetrix microarray data were created using the Affymetrix Clariom TM D Assay (Human). There are 28 available pre-treatment samples from 3 arms: Nivolumab (n= 6), Nivolumab+Bevacizumab (n= 14) and Nivolumab+Ipilimumab (n= 8). The raw CEL files were normalized using the built-in SST-RMA method of the Affymetrix Transcriptome Analysis Console (TAC, v4. 0) software. The cell lineage scores were calculated using the R package MCP-counter algorithm (v. 1. 1. 0). The Limma R software package  44  was used to identify DEGs from normalized microarray data for the RCC cohort.
TCGA SKCM and KIRC data downloading and patient selection. The normalized RNA-seq expression data of TCGA skin cutaneous melanoma (TCGA-SKCM) and Kidney Renal Clear FPK M = RC g *10 9 RC pc *L Cell Carcinoma (TCGA-KIRC) was downloaded from NCI Genomic Data Commons (GDC, https://portal. gdc. cancer. gov) and the relevant clinical data was downloaded from recent TCGA PanCancer clinical data study  45. The information of SKCM genomic subtypes was obtained from the TCGA-SKCM study  36. To achieve a uniform cohort of patients with Stage III (non-recurrent) melanoma for analysis, we applied an appropriate set of sequential filters: The TCGA-SKCM cohort was filtered to include patients with biospecimen tissue sites that included regional lymph node or regional subcutaneous metastases. We excluded patients presenting with Stage IV disease.
Then, to exclude patients with recurrent Stage III disease, we excluded all patients for whom the number of days from the diagnosis of the primary to the accession date was > 90 days. Additionally, for a patient to be included, their tumor must also have had a defined melanoma driver type. Finally, we eliminated those lacking sufficient gene expression data, yielding a final Stage III TCGA-SKCM cohort of n=136. Survival data missing for 9 or 136 samples, so n=127 for overall survival analyses. For TCGA-KIRC, the cases without available expression data were excluded and a total of 526 cases were taken into subsequent analysis.
Identification of differentially expressed genes. The HTSeq normalized read count data for all expressed coding transcripts was processed by Deseq2 (v3. 6)  46  software to identify differentially expressed genes (DEGs) between two response (R versus NR) groups. A cut-off of gene expression fold change of ≥2 or ≤0. 5 and a FDR q-value of ≤0. 05 was applied to select the most differentially expressed genes. The Limma R software package  44  was used to identify DEGs from normalized microarray data for the RCC cohort.
Deconvolution of the cellular composition with MCP-counter. The R package software MCP-counter  18  was applied to the normalized log2-transformed FPKM expression matrix to produce the absolute abundance scores for 8 major immune cell types (CD3 + T-cells, CD8 + Tcells, cytotoxic lymphocytes, NK cells, B lymphocytes, monocytic lineage cells, myeloid dendritic cells, and neutrophils), endothelial cells, and fibroblasts. The deconvolution profiles were then hierarchically clustered and compared across response and treatment groups. Pathway Enrichment Analyses. The network based pathway enrichment analysis was performed using differentially expressed genes across responder and non-responder groups in the bulk-tissue RNA sequencing data from melanoma neoadjuvant cohort and single-cell RNA sequencing data from metastatic melanoma cohort.
In the bulk-tissue, the differentially expressed genes which had a q-value <0. 05 and log2foldchange >1. 5 were & < -1. 5 were selected as input for network based pathway enrichment analysis using ReactomeFiViz  47  application in Cytoscape  48, 49. In single-cell, the differentially expressed genes with q-value<0. 1 were selected as input for pathway enrichment analysis. Pathway enrichment was calculated using several biological databases (KEGG, NCBI, Reactome, Biocarta, and Panther) with hypergeometric test false discovery rate (FDR) <0. 01.
Survival analyses. In TCGA cohort, survival data was not available for 9 samples and these were excluded from survival analysis. As described previously  36  , the survival time for each patient was "Curated TCGA survival (i. e. , from time of TCGA biospecimen procurement). The were plotted using Kaplan-Meier method. The statistical comparison of the survival curves was done using the log rank test. The analysis was done using R package survival  50.
Quantitative analysis of IHC staining was conducted using the image analysis software ImageScope-Aperio/Leica. Five random areas (1 mm 2 each) were selected using a customized algorithm for each marker in order to determine the number of positive cells at high power field (HPF). The data is expressed as a density (total number of positive cells/mm 2 area). IHC staining was interpreted in conjunction with H&E stained sections.
05% sodium azide (Sigma-Aldrich) to a final concentration of 0. For each antibodies or isotype control, 0. 4 µg per tube was added to each tube. The samples were allowed to incubate at RT for 1 to 3 hours, in the dark. 300µl 0. 1% BSA/PBS was added to the samples and the samples were placed on a magnet (1min incubation). The supernatant was discarded and the beads (and bound exosomes) were washed once with 400 µl 0. 1% BSA/PBS. The beads were visible on the magnet at each step of the procedure described above. The supernatant was discarded and the beads were resuspended in 200 µl of 0. 1% BSA/PBS and transferred into flow cytometry (FC) tubes for FC analysis. The FC data were captured within 24 hours of completing the staining of the beads-exosomes samples. If not read immediately after completing the staining, the FC tubes were stored at 4°C in the dark.
All incubations were carried out at RT and covered from light, and beads were visible at each step when placed on the magnet. Rabbit anti-human GPC1 antibody was used (Sigma, SAB2700282, 3µl per tube), and Alexa Fluor 488 conjugated goat anti-rabbit IgG (Invitrogen, A-11008, 2µl per tube) were used. The samples were analyzed by FC and positive signal was gated on the secondary only-paired sample. Nanoimager analyses. Beads with exosomes stained for FC analysis for CD63 (Alexa Fluor 647 anti-human CD63) or isotype control described above (Flow cytometry analyses of exosomes) were evaluated by using the on the Nanoimager S Mark I from ONI (Oxford Nanoimaging) with the lasers 405nm/150mW, 488nm/200mW, 561nm/300mW, 640nm/1W and dual emission channels split at 560nm. Data was processed on NimOS (Version [1.
Though findings in these cohorts are provocative, further studies need to be performed in additional cohort and pre-clinical models to better understand the mechanisms through which B-cells and TLS may favorably impact response to immunotherapy. Nonetheless, this represents an important insight into therapeutic responses to ICB and will likely stimulate further research in this area. Materials and Methods: Patient Cohort(s) and Sample CollectionFor the melanoma neoadjuvant cohort (NCT02519322) 17  , 23 patients enrolled in a phase II clinical trial of neoadjuvant ICB. Twelve patients received nivolumab monotherapy with 3 mg/kg every 2 weeks for up to 4 doses, and 11 patients received ipilimumab 3 mg/kg with nivolumab 1 mg/kg every 3 weeks for up to 3 doses followed by surgical resection. These patients were treated at the University of Texas MD Anderson Cancer Center and had tumor samples collected and analyzed under Institutional Review Board (IRB)-approved protocols (2015-0041, 2012-0846).
NCT02210117) whereby adults with metastatic RCC without prior immune checkpoint therapy and anti-VEGF therapy were enrolled and randomized 2:3:2 to receive nivolumab (3mg/kg q2wks x3 doses), nivolumab+bevacizumab (3mg/kg q2wks x3 +10mg/kg x3) or nivolumab+ipilimumab (3mg/kg q2wks x3 1mg/kg x2), followed by surgery (cytoreductive nephrectomy or metastasectomy), or biopsy at week 8-10, and subsequent nivo maintenance therapy up to 2 years. Response was assessed at 8 weeks and then at ≥12 weeks by RECIST 1. 1 criteria. Clinical response data collection is still ongoing at this time. For this current correlative study, response was defined as achieving a complete or partial response. Pre-and post-treatment blood and tumors were obtained for correlative studies by IRB-approved lab protocol PA13-0291.