dataesr/scientific-paragraphs-categorization huggingface dataset:
The results were obtained using the open-source simulator WiLabV2Xsim  [30] , freely available at https://github. com/V2Xgithub/WiLabV2Xsim (accessed on 25 November 2022). The main simulation settings are listed in Table  1. The suitable PER value β was derived following the approach described in Section 3. 1. As an example, Figure  3  compares the communication performance in the highway LOS scenario, with 100 vehicles/km, LTE-V2X MCS 7, and a 350-byte packet size. As observable from the curves and confirmed by the minimization of the MAE, the best comparison with the solid line (i. e. , the one obtained with the PER vs. SINR curve) is achieved when β = 0. 5, which corresponds to the orange dashed curve. A number of additional results, assuming different technologies, MCSs, packet sizes, and vehicle densities, are also evaluated and reported in Table  2.
Furthermore, the choice of primary endpoint in randomized studies is problematic as the power for detecting differences in HG/CIS recurrence and progression will be low. Given these difficulties, the next step is to conduct a longitudinal study assessing ADXBLADDER at each follow-up cystoscopy performed according to current recommendations. Besides providing estimates of the NPV over time and data on the safety of reducing follow-up cystoscopies, this will give information about the possible anticipatory positive effect of ADXLADDER in cystoscopy-negative patients  [27]. After the 3-mo follow-up cystoscopy, other possibilities exist. For patients with low-risk disease it has been proposed that the next follow-up cystoscopy be performed at 12 mo if the 3-mo cystoscopy is negative  [12]. ADXBLAD-DER could be assessed at 6 and 9 mo to rule out HG/CIS recurrences before 12 mo.
Prognostic factors for HG/ CIS recurrence other than ADXBLADDER status could not be identified. To predict the probability of HG/CIS recurrence in patients with LG pTa disease, institutions should keep an updated database of follow-up data. Health care costs, which vary across countries, and the cost effectiveness of ADXBLADDER have not been considered in detail  [28]. The NPV depends on the prevalence of recurrence. In this study of patients with LG pTa without CIS at their previous diagnosis, recurrence rates were low at 12. 6% for overall recurrence and 1. 9% for HG/CIS recurrence. In other recent studies involving patients with wider variations in stage and grade distributions and follow-up  [15] [16] [17] [18] [19] [20] , recurrence rates were also low, varying from 10. 4% to 22. 6% for overall recurrence and 3.
The recent advances in neural networks have led to the development of the deep learning survival models  7, [9] [10] [11]. These models are feed-forward artificial neural networks of the densely connected layers which transform the inputs into more predictive lower dimension disease or biological features. A fundamental challenge in deep learning is to find the best network hyperparameters, i. e. the network design that provides the best prediction accuracy. Other common issues of the deep learning techniques are the over-fitting and generalization failure. When applied to high-dimensional transcriptomic data, the deep learning survival models show good performance on the traininig datasets and fail to generalize well on the test datasets or transfer the learned features to the independent dataset.
Training and comparing deep survival networks Our previous experiments  13  let us demonstrate the ability of deep survival models to benefit from training with data from multiple cancer types. In this work the survival models have been trained using all five TCGA datasets of the "pan-gyn" group  15  : high-grade serous ovarian cystadenocarcinoma (OV), uterine corpus endometrial carcinoma (UCEC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), uterine carcinosarcoma (UCS), and invasive breast carcinoma (BRCA). The survival networks were evaluated for their accuracy in predicting OV outcomes and the transfer learning strategy used is shown in Fig. 1. The deep survival models use the negative log-likelihood to adapt the weights of the neural network to transform molecular features into lower dimension latent variables to explain survival.
Validating with the external dataset We explored further the prognostic accuracy of our proposed model N-MTLR-Rank on an external independent dataset. For this purpose, we selected twelve High Grade Serous Ovarian Carcinoma (HGSOC) patients treated within the Institut de Cancérologie de l'Ouest (ICO), we will refer to this dataset as ICO-OV. The results of this comparison are presented in Fig. 5  and Fig. 6. We observed that our model N-MTLR-Rank can generalize rather well on the new unseen data as the resulting performance on the ICO-OV dataset stays acceptable, especially for C-index metric. We hypothesize as well that poorer IBS results could be due to the small dataset size of ICO-OV. In order to compare the clinical characteristics of TCGA-OV and ICO-OV datasets we generated the descriptive statistics (see Table  1  and Table 2 ).
The measure of how much the input dimensions contribute to the output through the layers in this method is referred to as the attribution. We generated the attributions for all the input gene expressions for each patient in the TCGA-OV and ICO-OV datasets in order to measure how the input features contribute to different outputs of our networks, i. e. survival probability mass function predictions. To investigate the molecular pathways related to ovarian prognosis, we performed the attributions Gene-Set Enrichment Analysis (GSEA)  19  of our N-MTLR-Rank model using the Molecular Signatures Database (MSigDB)  20. The GSEA focuses on coordinated differential expression of annotated groups of genes, or gene-sets, and produces results that can more easily be interpreted in terms of the relevant biological processes.
We analyzed the attributions obtained for the endpoints close to 1 year (372 days) and close to 5 years (1919 days) and run the GSEA for all the patients in TCGA-OV and ICO-OV datasets. The hallmark collections (H) used for GSEA contains 50 gene-sets and the oncogenic signature collection (C6) -189 gene-sets, the Table  3  presents the overall number of significantly enriched pathways (p-value < 0. 05) found for each collection in different datasets. We report as well the most frequent pathways found for patients of the TCGA-OV (> 5% at 5 years endpoint) and the presence of the corresponding pathways in ICO-OV dataset (see Table  4  and Table 5 ). We noted that 4 pathways out of 6 most frequent pathways in TCGA-OV are also detected in ICO-OV dataset. Finally, these molecular pathways can be used to identify the high-risk or low-risk individuals based on their molecular pathway attribution enrichment scores.
Thus, this pathway could still be an alternative method of targeting oncogenic KRAS-driven cancers. Finally, all of the ICO-OV individuals were reported as ALLOGRAFT REJECTION and IL2 STAT5 SIGNALING enriched at 5 years endpoint, our deep survival network "see" them as low-risk group, which is in line with the overall better survival in ICO-OV dataset. The absence of the ICO-OV individuals attributions enriched in MTORC1 SIGNALING, G2M CHECKPOINT and E2F TARGETS is rather consistent as well with overall survival difference in TCGA-OV and ICO-OV datasets. Understanding and advancing the treatment of the ovarian cancer is conditioned by the identifying the underlying biology and molecular pathogenesis of this disease. Although our study extends the insights into the use of deep learning for survival modeling, the found prognostic associated molecular pathways of ovarian cancer patients represent an interesting point for future research, it is worth underlying that these pathways should be considered hypothesis generators and more detailed in vitro experiments and follow-up clinical studies are required.
TCGA gene expression and clinical data We used the data obtained in our previous work as described in  12, 13. TCGA RNA-sequencing and clinical data were downloaded from Genomics Data Commons (GDC) portal using the pipeline of the R/Bioconductor package TCGAbiolinks  28. Supplemental survival data were downloaded from the standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR)  29. We merged the survival data from TCGA-CDR with the GDC clinical data. We performed our tests on overall (OS) endpoint. The corresponding TCGA-CDR columns included OS for status and OS. time for time-to-event data. OS column contained the value 0 encoding for alive (censored) status and 1 for deceased (failure) and OS. time contained numbers of days from the date of diagnosis to either the date of last follow up if OS was 0 or time to death if OS was 1.
We downloaded the RNA-seq data for the following TCGA projects: TCGA-OV, TCGA-BRCA, TCGA-UCEC, TCGA-CESC, TCGA-UCS. The harmonized GRCh38 aligned RNA-seq data (HTSeq-counts  30  ) were normalized per TCGA project the TCGAbiolinks normalization function which is recommended for differential expression analysis. After merging RNA-seq and clinical data and discarding cases without survival information, we obtained 372 samples for OV, 1087 for BRCA, 549 for UCEC, 291 for CESC, 55 for UCS. All the datasets contained 16673 gene expression features in common.
External validation dataset ICO-OV We curated 12 ICO patients diagnosed with HGSOC between 2007 and 2016, their retrospective electronic records data were collected: date of birth, date of pathologic diagnosis, clinical stage, histological grade, date of death or date of last follow-up, the age at pathological diagnosis, OS and OS. time were derived. We extracted RNA from the corresponding archival FFPE slides with COVARIS ME220 Focused-ultrasonicator, to allow a high quantity and high quality of RNA extracted. RNA libraries were prepared with the SureSelect XT HS2 RNA Reagent kit and the SureSelectXT Human All Exon V6 +UTR probes from Agilent. All libraries were sequenced on an Illumina NextSeq550 in paired-end mode (2 x 75bp) with a target depth of 20 million fragments per sample. Sequenced reads were trimmed with fastp v0.
20. 1 and mapped to GRCh38 using HISAT2 v2. 1. 0 both with default parameters. Reads overlapping genomic features were counted with featureCounts v2. 0. 0 31 from the Subread package and Ensembl v99. Only uniquely mapped and not duplicated reads were counted. Multiple overlaps of unique genomic feature were not counted. The obtained raw featureCounts  31  of the ICO-OV dataset were further normalized using the TCGAbiolinks normalization function which resulted in 15521 genes in common between ICO-OV and TCGA "pan-gyn" group. In order to account for "batch effect", we used the method ComBat-seq  32  particularly suited for RNA-seq data. Our study was approved by the ethics committee of the university hospital center of Angers (2021-102) and done in accordance with ethical standards of the 1964 Helsinki Declaration and its later amendments.
The plate was read at 405 nm using a plate reader (Tecan Infinite â M200 PRO microplate reader; Morrisville, NC, USA). Where AS and AC are the absorbance values of the sample and the control, respectively. The results are expressed in terms of IC 50 , representing the concentration of test extract required to cause 50% inhibition of the enzyme. Calculations were carried out using the GraphPad Prism 5 software. All determinations were made in triplicate.
The α-glucosidase control (20 mL enzyme and 120 mL PBS) without any inhibitor represented 100% enzyme activity. Appropriate test extract controls containing the reaction mixture with and without the enzyme were used to correct for color interference. The α-glucosidase inhibition (%) of the samples was calculated in the same way as described previously for the α-amylase assay. Acarbose, a prescribed drug for a a-glucosidase inhibition, was also used as a positive control. The results are expressed as the concentration of extract inhibiting the enzyme activity by 50% (IC 50 ), as calculated by the GraphPad Prism 5 software. All determinations were made in triplicate.
Finally, the cells were washed with 50 μL 1% acetic acid and solubilized with tris(hydroxymethyl)aminomethane (10 nM). The optical density of the solution was measured at 560 nm using a spectrophotometer bioassay reader (BioRad, USA). Results are expressed as the concentration of agent that reduces cell growth by 50% (CC 50 ), calculated by GraphPad Prism 5 software. Docetaxel was used as a positive control. All determinations were performed in triplicate. In addition, the level of harmfulness on normal cells was evaluated by determining the selectivity index (SI)  [16].
Bioassay of antiproliferative activity The sulforhodamine B (SRB) assay was carried out according to the method reported by Skehan et al. [15] , using D-MEM medium with 10% FBS to induce cell proliferation. After 48 h of incubation, the medium was discarded and 100 μL of icecold 40% trichloroacetic acid (TCA, Aldrich) were added to fix the cells, incubating for 1 h at 4 °C. The cells were washed five times with water, left to dry, and then 50 μL of SRB stain (10 mg, 1% acetic acid, Sigma) were added to each well and left to stand for 30 min. Finally, the cells were washed with 50 μL of 1% acetic acid and solubilized with tris(hydroxymethyl)aminomethane (10 nM). The optical density of the solution was measured at 560 nm using a spectrophotometer bioassay reader (BioRad, USA). Results are expressed as the concentration of agent that reduces cell growth by 50% (IC 50 ), calculated by GraphPad Prism 5 software.
Identification of polyphenolic metabolites using Ultraperformance liquid chromatography and mass spectrometry (UPLC/MS) Analyses of major individual phenolic compounds present in freeze-dried fruits and plant extracts were performed according to a previously reported method  [17]. All analyses were conducted using an ultra-pressure liquid chromatograph (Model Waters Acquity CHA, Waters Corp, Milford, MA, USA) coupled with Micromass Quattro Micro API tandem mass spectrometry (UPLC-Volume 7; Issue 04 J Diabetes Treat, an open access journal ISSN: 2574-7568 MS/MS) system and controlled with Mass Lynx V4. 2 data analysis system (Micromass, Cary, NC, USA). The column used was an Aquity BEH C 18 (100 × 2. 1 mm, 1. 7μm) (Waters, Milford, MA, USA), and the mobile phase consisted of 0. 1% formic acid in water (solvent A) and 0.
Statistical analysis The experiments were performed in a completely randomized design in triplicates, and data are expressed as mean ± standard error (± SE). The significant difference between, TPC, TCC, FRAP, DPPH, α-amylase, and α-glucosidase inhibition values were tested using one-way analysis of variance (ANOVA) and the multiple mean comparison using LSD test (IBM SPSS Software â ). Significant levels were defined as probabilities of 0. 05 or less.
A DNA fragment comprising the V3 and V4 hypervariable regions of the 16s rRNA gene was amplified for sequencing analysis  23. Amplicons were sequenced on the MiSeq Illumina platform (Illumina, San Diego, CA). Sequence results were analyzed using Quantitative Insights into Microbial Ecology (QIIME2) v2019. 7  24  and were processed with DADA2 for quality control  25. The classify-sklearn method was used for taxonomy assignment  26  with an in-house customized classifier based on the SILVA reference database  27, 28. To construct the customized reference database, we extracted the sequences according to our primers (forward primer sequence: CCTACGGGNGGCWGCAG, reverse primer sequence: GACTACHVGGGTATCTAATCC) from the SILVA 132 database clustered at 99% identity  29. We trained the classifier using our tailored reference reads and SILVA 7-levels for reference taxonomy, including the species probability (weights) likely to be observed for human stool (downloaded from https://github.
1% formic acid for C18 analysis or 100 µL of a mixture of 10 mM ammonium carbonate buffer (pH 10. 5) and acetonitrile (40:60, v/v) for HILIC analysis (see below). A volume of 95 μL of the supernatant obtained upon centrifugation was transferred to 0. 2 mL-vials and mixed with 5 µL of a mixture of external standards ( 13 C-glucose,  15  N-aspartate, ethyl malonic acid, amiloride, prednisone, metformin, atropine sulfate, colchicine, imipramine) to check for consistency of analytical results in terms of signal and retention time stability. Quality control samples were obtained by mixing aliquots of each sample and were injected every 5 to 10 samples throughout the analysis for further data normalization/standardization purposes. Untargeted metabolomics experiments were performed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) using a combination of two complementary chromatographic methods  32, 33  , consisting of reversed-phase chromatography (C18 chromatographic column) and hydrophilic interaction chromatography (HILIC) for the analysis of hydrophobic and polar metabolites, respectively.
Metabolomic data processing and metabolite annotation Metabolomic data processing and metabolite annotation were performed essentially as described  31. Data processing and statistical analysis were achieved using the Workflow4Metabolomics (W4M) platform  34. Annotation of metabolite features was first performed using our spectral database according to accurate measured masses and chromatographic retention times  32. Verification of metabolite annotation was then conducted by performing additional LC-MS/MS experiments using a Dionex Ultimate chromatographic system combined with a Q-Exactive mass spectrometer (ThermoFisher Scientific) operated under non-resonant collision-induced dissociation conditions. Obtained MS/MS spectra were both manually and automatically matched using MS-DIAL software  35  to the spectra included in our in-house spectral database, as described  36.
Effect of chronic cocoa supplementation on the fecal metabolome (untargeted metabolomics approach). LC-HRMS with a combination of two complementary chromatographic methods, reversed-phase chromatography C18 (+) and HILIC (-), was used to analyze the effect of the 10 weeksconsumption of cocoa on the fecal metabolome. Under these conditions, 10,866 and 6795 metabolite features were detected, of which 191 metabolite features from the C18(+) and 330 from the HILIC(-) analysis matched the accurate masses and retention times of the metabolites included in our chemical database, yielding 411 unique annotated metabolites in total. The score plot of the PCA and PLS-DA did not reveal any distinctions between T1 (CO) and T3 (CO), and T1 (CT) and T3 (CT) groups (data not shown). Clustered heatmap analysis of the whole set of annotated fecal metabolites (top 50 according to ANOVA p-values) highlighted poorly consistent metabolic profiles across the population of athletes when studying the effect of cocoa consumption: T1 (CO) and T3 (CO); T1 (CT) and T3 (CT) using HILIC (-) and C18 (+) analyses (Figure  4  and Figure  S3 ).
Also, no significant metabolites were found when considering p-values after Benjamin-Hochberg correction. These results might reflect highly variable inter-individual effects of cocoa consumption and/or the difficulty in obtaining homogenous and representative fecal samples. Overall, these data indicate that cocoa does not exert any robust changes in the fecal metabolome after chronic consumption (10 weeks). and T3 (after dietary intervention) and in A) cocoa (CO) and B) maltodextrin (CT) groups. Effect of cocoa on the serum metabolome after 10 weeks of chronic consumption. An experimental set-up similar to that described for untargeted fecal metabolomics was used for the analysis of the serum metabolome. A total of 3195 and 1600 metabolites features using C18 (+) and HILIC (-), respectively, were found, of which 98 and 163, respectively, matched the accurate masses and retention times of the metabolites included in our chemical database, yielding 238 unique annotated metabolites in total.
supplement. All measurements were recorded before and after the 10-week endurance training intervention. The Ethics Committee for Clinical Research of the Community of Madrid Spain approved the study (Ref: 07/694487. 9/17), and all procedures were in accordance with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all the volunteers. The study was registered in the US National Library of Medicine (http://www. clinicaltrials. gov) with the accession number NCT04444388. A schematic of the study design is shown in Figure 2.
The second and less commonly used approach is to incorporate intervals directly into the training phase. In this case, layer weights and bias values are determined as interval quantities so that (uncertain) input data lead to interval bounds on the system outputs which themselves enclose uncertain but bounded (measured) data. In this section, we give references and examples for the first approach in Sec. 2. 2 and for the second in Sec. 2. 3 and Sec. 2. 4 to highlight the specific combination of NN modeling with interval methods suggested in this paper.
Here, W 1 ∈ R L×m and W 2 ∈ R n×L are weighting matrices of suitable dimensions with the bias vectors b 1 ∈ R L and b 2 ∈ R n. In the frame of training feedforward NNs with the aim of function approximation, the vector g(x) of activation functions is usually chosen either as the so-called ReLU function g x ( ) g x 1 ( ). g x L ( ) ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ max 0, x 1 ( ). max 0, x L ( ) ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ (4) or as a sigmoid-type function g x ( ) σ x 1 ( ). σ x L ( ) ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦. ( 5 ) A typical choice for the latter is the hyperbolic tangent σ x i ( ) tanh x i ( ), (6) (implemented in MATLAB by the function tansig). Throughout this paper, we use the representation σ x i ( ) 2 1 + e -2xi -1 ( 7 ) to avoid overestimation caused by a dependency of the numerator and denominator terms on the same variable in the alternative representation of the hyperbolic tangent function.
As mentioned in Remark 1, the use of a sigmoid hidden layer ensures bounded outputs for inputs with large absolute values. For both kinds of activation functions, the comparison of different mesh sizes for the input intervals reveals the problem of multiple interval dependencies that leads to overestimation in the computed output ranges. For that reason, it is necessary to apply techniques for the reduction of overestimation during the interval evaluation of NNs with interval-valued input data q k. This might include a (brute force) subdivision of input intervals 2 as shown in Figure  2  or algorithmic means such as centered form or affine representations and slope arithmetic  (Cornelius and Lohner, 1984; Chapoutot, 2010). Due to the restriction to globally defined (and, for the sigmoid case, differentiable) functions in the hidden layer, these methods can be implemented using existing interval libraries such as INTLAB   (Rump, 1999)  or JULIAINTERVALS  (Ferranti, 2021 ).
10) are determined in the second stage. In Figure  4  and all subsequent block diagrams of NN models in this paper, the parameters indicated by solid arrow heads are determined during the training and optimization phases of the proposed procedure while the non-filled arrow heads denote connections with fixed weights that are set to the constant value one. We suggest the following interval procedure for static NN modeling. Stage A Train the NN N (q k , W 1 , W 2 , b 1 , b 2 ) according to (Eq. 3) by means of a standard algorithm (e. g. , trainbr in MATLAB)  to minimize the quadratic cost function J N k∈T n i 1 y i,k -y m,i,k 2 , ( 11 ) 2 ; Δ b 2 y m,k -k kmax k 1 N q k , W 1 , W 2 , b 1 , b 2 (12) so that all measured ouput samples y m,k are included in the interval-valued NN outputs. In (Eq.