dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulations show improved IBS performance after pre-screening Supplementary Fig. 16  shows the C-index obtained for the simulated models, before pre-screening and with optimal pre-screening. The C-index performance Fig. 4  C-index obtained after pre-screening for the 16 cancers and the convex penalization methods studied (i. e. ridge, lasso, elastic net, adaptive elastic net). In each case, we computed the C-index by 10 repetitions of a nested 5-fold cross validation (section Suggested screening algorithm) strongly depends on the simulation runs (with a median C-index being 0. 8, 0. 73 or 0. 65 for simulations 1 to 3). It suggests that the vector of β values strongly impacts the predictability of a given dataset. Similarly to the real TCGA dataset (Fig. 5 ), the pre-screening does not improve nor degrade the C-index of the model learnt.
Supplementary Fig. 17  shows the IBS obtained for the simulated models, before pre-screening and with optimal pre-screening. Also, the IBS performance strongly depends on the simulation runs (with a median IBS being 0. 21, 0. 11 or 0. 12). Unlike the C-index, IBS is improved by the pre-screening procedure, also in line with what we observe for the real TCGA dataset (Fig. 5 ). On each of the 3 simulated datasets, we also pre-filtered correlated genes, to keep only one gene in a group of correlated ones (with a correlation coefficient above 0. 5 in absolute value, we kept the most correlated to survival in a single variable Cox model). This led to a reduction of the number of genes, from 17,249 to 4,890. Some of the suppressed genes were in the ground truth set, used to simulate the data. However, the performance of the models were equivalent with both sets of genes, with and without pre-screening, in the two metrics investigated (C-index and IBS, not shown).
Pre-screening without any cross-validation procedure Previous sections show the interest of the cross-validation pre-screening procedure to decrease in a controlled manner the number of variables before further selection with lasso like procedures. If the number of Fig. 5  Performance of the predictions for ccRCC patients, test on two different datasets. In gray without pre-screening, in blue after optimal bi-dimensional pre-screening performed on the TCGA dataset. In both case, the model is tested on an independent set not used for learning, either TCGA or ICGC. (A) C-index metric, higher is better. (B) IBS metric, lower is better. In each case, we computed the C-index by 10 repetitions of a nested 5-fold cross validation (section Suggested screening algorithm). Paired Wilcoxon tests. ***: p ≤ 0.
We applied the pre-screening procedure only on transcriptomic data. When performing a supervised prescreening on combined clinical and transcriptomic data, it could be interesting to test on both individual genes and clinical data, in order to pre-select only genes that have additional prognostic values than clinical data. In this study, we have not investigated how to group genes following current knowledge, for example clinically relevant genes. Also, other penalizations could be used to account for pathway information  [71]. In three simulated datasets, we have pre-filtered the to keep only one in a group of correlated genes, choosing an arbitrary threshold of 0. 5. This procedure is interesting as it highly decreases the number of genes with low computation time, except if one performs a cross-validation procedure to fix the threshold.
In order to apply predictions to patients in clinics, it is important to pursue research on how to normalize the data. While in the cross-validation procedure we provide a fair standardization, using the mean and standard-deviation of the expression of each gene in the learning set, this procedure did not work for the ccRCC independent dataset: the average and standard deviation were too different between the two experiments, probably due to the difference in protocols. Using the learning set parameters did not affect the C-index, but it clearly decreased IBS performance (not shown). To reach good performance, we had to use the mean and standard deviation of the test set. This is not practical in a clinical context. Thus, further improvements are required on the normalization and standardization of protocols, but this is beyond the scope of the present article.
e. C-index or IBS in this study). We also provide a benchmark of different penalization methods (i. e. ridge, lasso, elastic net, adaptive elastic net) after our bi-dimensional pre-screening procedure. First, we showed that integration of mRNA-seq data with clinical data allows to improve predictions over clinical data alone. Second, we showed that our bi-dimensional pre-screening algorithm allows to improve predictions for some cancers regarding both the C-index (LGG, KIRC, BLCA, LAML and BRCA) and the IBS (UCEC, BLCA, LAML, BRCA, and PAAD), while removing irrelevant genes for prediction and for clinical and research applications. For the other cancers, the improvement depends on the chosen penalization, but can only be beneficial. Third, we demonstrated that the different penalization methods reached similar prediction performances, with slight differences among cancers.
Fourth, our bi-dimensional prescreening procedure allows to get higher C-indices than ISIS algorithm for 12 of the 16 cancers studied, and comparable for the 4 others. Finally, we have proved that the proposed methodology is valuable on an independent dataset. However, we have to acknowledge that the improvements remain modest. Other procedures than ISIS have been recently proposed, but are not yet implemented, nor the code provided with the original article  [72, 73]. A benchmark among all these procedures would be an interesting perspective. As practical conclusions, we first advise that the genes are pre-screened with both supervised and unsupervised approaches, with several (e. g. , 10 in our study) repetitions of K-fold cross-validation (K=5) to calculate the optimal thresholds. If this procedure is not feasible, for example when integrating various omics data, selecting the top 200 to 2,000 mRNAs in terms of single variable Cox model p-value works well for all the datasets and methods we have investigated and allow to get effectively rid of irrelevant genes.
Figure5shows the performance of the model on the same TCGA dataset through cross-validation, and on an independent ICGC:RECA dataset. First, we observe on the independent dataset a strong reduction in performance on the C-index, which we interpret by the small proportion of patients with aggressive cancer in ICGC:RECA (14% of clinical stage III and IV patients in ICGC, compared to 39% in TCGA). Interestingly, even though there is almost no improvement in the C-index after prescreening procedure is applied on TCGA (from C = 0. 71 [0. 702 ; 0. 72] to C = 0. 716 [0. 698 ; 0. 730]), the improve- ment is clear when the model learned on TCGA datase,t after the pre-screening procedure, is applied on the ICGC dataset ( (from C = 0. 545 [0. 535 ; 0. 553] to C = 0. 566 [0. 560 ; 0. 573]), Fig. 5A). Besides, the improvement is clear and strong when measuring the performance with the IBS, both on TCGA as a test set, and on ICGC (Fig.
The main SN luminosity parameters for the two sibling SNe are obtained by using SNooPy, a light curve fitting code based on templates. Using a hierarchical bayesian approach, we build the Hubble diagram with a sample of 96 SNe Ia obtained from the Combined Pantheon Sample in the redshift range z = 0. 02-0. 075, and calibrate the zero point with the two sibling type-Ia SNe in NGC 4414. We report a value of the Hubble constant H 0 = 72. 19 ± 2. 32 (stat. ) ±3. 42 (syst. ) km s -1 Mpc -1. We expect a reduction of the systematic error after a new analysis of the Cepheids period-luminosity relation using the upcoming Gaia DR4 and additional Cepheids from the HST and JWST.
2020; Biswas et al. 2022). In this paper, we present BVRI photometry of SN 2021J in eight epochs obtained with the CCDT150 camera mounted on the 1. 5-m telescope at the Observatorio de Sierra Nevada (OSN), and UBV photometry from Swift-UVOT. Moreover, we report multi-epoch spectra observed with the OSIRIS spectrograph on the Gran Telescopio Canarias (GTC) at La Palma. From the LC analysis of the 2 siblings SN 1974G and SN 2021J in NGC 4414, we determine the Hubble constant by means of a hierarchical bayesian approach using a SNe Ia sample drawn from the Combined Pantheon Sample in the redshift range z = 0. 02 -0. 075. In Sect. 2 we present our SN 2021J photometric and spectroscopic data, and describe the data used for SN 1974G. In Sect. 3 we focus on the analysis of both SN light-curves, and derive the Hubble constant.
The optical images for SN 2021J were obtained with the CCDT150 camera mounted on the 1. 5-m telescope at the Observatorio de Sierra Nevada (OSN) located in Granada, Spain (see Fig. 3 ). We used the Johnson-Cousins BVRI filters to obtain the SN photometry in eight epochs during an observing interval of 60 days from the first observation. Two epochs were observed before the SN peak brightness, which allows to sample the peak luminosity and build a reliable light curve (LC). The data have been reduced by using a Python-based 3  pipeline developed by us to automatically reduce the data obtained for the SN2 observing program. Bias subtraction and flat fielding were implemented using the ccdproc and astropy Python packages  (Craig et al. 2017). Subsequently plate-solve calibration was applied to single images using astrometry.
in preparation), and the pipeline codes will be available in the website of the project. The photometry of SN 2021J is presented in Table  B. 1. We also reduced the Swift data available for SN 2021J using the most updated HEASOFT version (v6. 30) and calibration database (CALDB). After correcting sky coordinates with uvotskycorr, we combined all images for each single observation epoch and performed the photometry measurements using the calibration presented in  Breeveld et al. (2010)  with uvotsource, using a 5 aperture centered on SN 2021J and an annulus background of 8 radius. The results of our measurements are given in Table  B. 2.
The basic parameters of these observations are given in Table  1. We performed the reduction of the spectra using IRAF standard tasks  (Tody et al. 1986) , and GTCMOS pipeline A13, page 2 of 10 ). The full details of the data reduction and its analysis will be presented in a subsequent publication. Here we present a brief summary. Bias and flat-field corrections were applied to the object and standard images. The 2D spectra were calibrated in wavelength using the HgAr+Ne+Xe lamps from the GTC instrument calibration module. The 1D spectra were extracted using an optimal extraction aperture, and the flux calibration was done using spectro-photometric standard stars, observed the same night and with the same gratings than SN 2021J. The atmospheric extinction was corrected using the ORM extinction curve at the observed airmass.
The light curves of SN 1974G and SN 2021J The SNe light curves were fitted with the SNooPy (SuperNovae in object oriented Python) package  (Burns et al. 2010). The software generates uBVgriYJH light-curve templates to perform a LC fitting to determine the main luminosity parameters. We parameterized the SNe LCs using the decay-rate parameter ∆m 15 introduced by  Phillips (1993) , and defined as the difference in magnitude between the peak and 15 days after peak of SN emission in the B band. We selected the max-model implemented in SNooPy for the fits to determine in each filter the maximum magnitude and ∆m 15. This model does not assume any intrinsic extinction of the SN, neither in the host galaxy nor in the intergalactic medium. The model only corrects for the Milky Way extinction using the Schlegel maps  (Schlegel et al.
1998) , and it uses the spectral template of  Hsiao et al. (2007)  to compute Kcorrections 6. We considered a value of cz = 991 km s -1 for the recessional velocity of NGC 4414 with respect to the CMB rest frame 7   (Carrick et al. 2015). Table  2  shows the LC parameters for both SNe. The first four rows correspond to the maximum brightness in the different filters. We note that SN 1974G has photometric data only in B and V bands. The fifth row indicates the MJD day of B-maximum, and the last row shows the decay-rate parameter. Figure  3  shows the LCs fits for SN 1974G and SN 2021J, respectively. The solid black lines are SNooPy fits for the different filters. Dashed lines in LC fits indicate the 1-σ errors.
The variance for each of the two SNe is: σ 2 calib,i = σ 2 B,i +σ 2 µ NGC 4414 +(P 1 σ ∆m 15 ) 2 +2R(σ 2 B,i +σ 2 V,i )-2Rσ 2 B,i. (3) We used Eq. (  11 ) in K21 to calculate the variance of each SN in the cosmological sample. This equation includes the term σ int,cosmo which accounts for any unknown scatter in the sample. For the priors, we considered a Half Cauchy distribution for σ int,cosmo and normal distributions for the rest of the parameters. We obtained the best-fit parameters of the model P 0 , P 1 , R, H 0 , and σ int,cosmo by performing a Markov chain Monte Carlo (MCMC) sampling implemented in Python with the PyMC3 A13, page 4 of 10 probabilistic programming framework  (Salvatier et al. 2016). Figure  4  shows the posterior probability distribution (PPD) of the parameters inferred with the No-U-Turn Sampler (NUTS) algorithm 8.
Acknowledgements. We thank the anonymous referee for the helpful comments. We thank  Barry Madore ,  Wendy Freedman ,  Radek Wojtak ,  Eoin Ó Colgáin , and  Adam G. Riess  for useful comments and discussions. The authors acknowledge financial support from the  State Agency for Research of the Spanish MICINN  through the  "Center of Excellence Severo Ochoa" award  to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ). EGC thanks the incentive grant by the  Ministry of Economic Transformation, Industry, Knowledge and Universities REF. P20_00880 , project co-financed by 80% by funds from the operational program FEDER de Andalucía 2014-2020. LI and NK were supported by two grants from  VILLUM FONDEN  (project number  16599  and  25501 ). CDT, FP and EP thank the support of the  Spanish Ministry of Science and Innovation funding  grant  PGC2018-101931-B-I00.
Historically associated with pathological grade  [8]  and size, and depth  [9] , the prognostic characterization is being refined, thanks to the contribution of genomic signatures such as CIN-SARC  [10]  or the immunologic constant of rejection (ICR) signature  [11]. Clinical trials assessing prospectively the clinical utility of CINSARC (NCT03805022, NCT04307277) are ongoing. The second issue relies on the identification of novel therapeutic targets. The limited development of new chemotherapy drugs and targeted therapies during the last decade has not modified the overall prognosis, and doxorubicin remains the backbone of systemic treatment. Chondroitin sulfate proteoglycan 4 (CSPG4), also called neural-glial 2 (NG2), is a cell surface proteoglycan, overexpressed in certain human cancers, with low expression in normal tissues and oncogenic roles in tumor growth and metastatic dissemination  [12]  via the promotion of cell proliferation, cell survival and drug resistance, angiogenesis, cell migration and invasion  [13].
To fill this gap and given the potential relevance of CSPG4 as a target for immunotherapy, we analyzed its expression in 1,378 localized STS clinical samples. We searched for correlations between expression and clinicopathological data, including disease-free survival (DFS), but also the components of the tumor immune landscape. gene expression profiles had been generated using DNA microarrays or RNA-sequencing. The data sets were selected if the clinical and expression data, including CSPG4 expression level, were available. The final data set included 1,378 clinical samples. The study was approved by our institutional board. We also analyzed cancer cell line data from the Dependency Map (DepMap) portal (https:// depmap. org/ portal; accessed on 05 April 2022) to compare the RPPA-based protein expression levels versus the RNA-seq-based mRNA expression levels of CSPG4.
Ontology analysis of the resulting gene list was based on GO biological processes of the Database for Annotation, Visualization and Integrated Discovery (DAVID; david. abcc. ncifcrf. gov/). We tested the robustness of the resulting gene list in the remaining data sets defined as validation set (N=1068) by computing the Pearson correlation distance between each sample and the CSPG4high profile defined in the learning set as the average expression of each significant gene in the CSPG4-high class. Validation samples with negative correlation were classified as "CSPG4-low-like" and those with positive correlations were classified as "CSPG4-high-like". Supervised analyses also searched for differentially altered genes between "CSPG4-high" and "CPG4-low" STS at the DNA level using the copy-number alteration (CNA), mutation and methylome public data available on TCGA portal (https:// portal.
Several sensitivity analyses were performed. To account for a potential residual confounding, the main model was further adjusted for HPCs. To evaluate the impact of missing values on the results, the analysis was repeated after imputing missing data for explanatory variables using the multiple imputation by fully conditional specification (FCS) method. 23 The FCS method is iterative and well-adapted for large data sets including both categorical and continuous variables. Based on variables related to clinical expressions of asthma, age, sex, smoking habits, BMI, education level, FDep, and any asthma treatment, we generated ten data sets. As there is no consensus on eosinophil and neutrophil cut-offs, associations between the clinical expressions of asthma and the inflammatory phenotypes defined according to the other cut-offs were studied: (a) 0¢23 £ 10 9 /L for eosinophil and 4 £ 10 9 /L for neutrophil corresponding to the 75th percentiles in the 160,272 adults from the CONSTANCES cohort; (b) optimal cut-offs estimated by Receiver-Operating Characteristic (ROC) curves using Youden's index (Supplementary methods); and (c) EOS at 0¢3 £ 10 9 /L corresponding to the eosinophil cut-off often used in clinical studies and 5 £ 10 9 /L for neutrophil.
Acknowledgments The authors thank the " Caisse nationale d'assurance maladie" (CNAM)  and the  "Centres d'examens de sant e" of the French Social Security  which are collecting a large part of the data, as well as the "Caisse nationale d'assurance vieillesse", ClinSearch, Asqualab and Eurocell in charge of the data quality control. The authors also thank  S Le Got ,  S Lemonnier ,  A Ozguler ,  C Ribet  from  Inserm   UMS11. The authors are also grateful to Groupe Respiratoire CONSTANCES:  MC Delmas ,  O Dumas ,  V Giraud ,  Y Iwatsubo ,  B Leynaert ,  N Le Moual ,  T Perez ,  R Varraso. The CONSTANCES cohort receives grants from  ANR  ( ANR-11-INBS-0002 ), the  Caisse nationale d'assurance maladie-CNAM  and the Ministry of research. CONSTANCES also receives funding from  MSD ,  AstraZeneca ,  Lundbeck  and L'Or eal, managed by  INSERM-Transfert.
Funding The CONSTANCES cohort receives grants from  ANR  ( ANR-11-INBS-0002 ), the  Caisse nationale d'assurance maladie-CNAM  and the Ministry of research. CONSTANCES also receives funding from  MSD ,  AstraZeneca ,  Lundbeck  and L'Or eal, managed by  INSERM-Transfert. T. Tsiavia is supported by a PhD grant from the  Fondation pour le Recherche M edicale  ( ECO202006011654 ).
The TestGen component generates vulnerable code from configurable templates across various vulnerable attributes allowing easy changes through the templates. Measuring the attack and exploitation is with the DefEval component, serving as an attacker at runtime and can be updated with new exploitations. We present a comprehensive evaluation of compiler defenses and sanitizers on RecIPE with gcc and clang, showing the strengths and weaknesses of the various defenses. The results show pros and cons which may not be well known, including gaps between the in-principle guarantee and practical implementation. Our results also point out directions for further improvement in defenses.
A more systematic approach is through a benchmark suite. Two widely used suites are RIPE  [30]  and Juliet  [18]. Existing memory error benchmarks share the following limitations: (i) fixed test cases that are difficult to update and extend; (ii) lack of an accurate evaluation of attack and exploit prevention; (iii) lack of adaptability to newer architectures; and (iv) lack of customizability to newer defenses. We propose RecIPE to support extensible and accurate evaluation of memory defenses. 1 RecIPE focuses on spatial memory errors 2 on Intel architectures, in particular, the 64-bit X86_64 and AMD64 processors. For extensibility and customizability, we design RecIPE as two components, the component for test case generation and the component for attacks and result analysis. In test case generation, RecIPE generates C source code with vulnerabilities from templates and configuration files.
The template makes it easy to extend or adjust the creation of vulnerable code, while the configuration makes it easy to customize the details. As an example, we easily create a 32-bit version, RecIPE32, to evaluate against RIPE. Moreover, RecIPE mimics an attacker interacting with a vulnerable program to exploit it. With integrity checks, it collects the result from both attack and exploit, which is analyzed to give comprehensive and accurate effectiveness measurement. We present a comprehensive evaluation using RecIPE on the gcc/clang compilers with relevant security compilation flags for lightweight defenses and more state-of-art systems for finding memory errors such as sanitizers and dynamic binary instrumentation. The effectiveness results from RecIPE on attacks and exploits illustrate intended design features and details that may not be well known.
Softbound uses a form of fat pointers while LowFat leverages a special LowFat memory allocator to encode bounds information into native pointers indirectly. The use of fat pointers in SoftBound is known to have compatibility issues with existing C/C++ code  [25]  while LowFat using native pointers has good compatibility. EffectiveSan extends the ideas in LowFat to do runtime type checking and, as a byproduct, also has strong bounds checking as it is able to detect precise spatial memory errors, including subbounds errors, going beyond the bounds of a sub-object. This is at the cost of increased overheads. Valgrind Memcheck  [22]  is a well-known runtime bug-finding tool using the Valgrind dynamic instrumentation framework  [17]. We will simply refer to it as Valgrind. Before ASan, Valgrind Memcheck was probably the most popular tool for finding memory errors.
Memory-Error Benchmarks. A more comprehensive approach to evaluation is to have a test suite which has good coverage against a range of vulnerabilities involving memory errors. Two well-known benchmarks are: Juliet: Juliet  [18]  is a security benchmark proposed by NIST. It is a collection of C/C++, C#, and Java programs with known flaws. The latest version released in 2017 covers 64099 C/C++ cases, 28942 C# cases, and 28,881 Java cases. Juliet covers hundreds of different kinds of flaws documented by the Common Weakness Enumeration (CWE). The benchmark dataset includes real-world, synthetic test cases from various sources. However, Juliet does not include attacks and exploitations. This is because it is designed more for a program analysis setting where the analyzer is supposed to find vulnerabilities in the test cases.