dataesr/scientific-paragraphs-categorization huggingface dataset:
The incompleteness rapidly increases toward fainter magnitudes. For example, for H < 25, our model predicts that there are ;20 times more NEOs than currently known (i. e. , the known population is only ;5% complete). For H ; 25, we only know 1 in ;100 NEOs (see the differential distribution in the bottom-right panel of Figure  17 ). We discuss the NEO population completeness in more detail in Sections 8 and 10. To aid similar estimates, and help to plan future observations, we developed the NEOMOD Simulator. The code inputs the base (or any other) model from MultiNest, provided as an ASCII table, and generates a user-defined sample of NEOs with the smooth orbital and absolute magnitude distributions. The NEOMOD Simulator will be available from GitHub. 35  Figure  18  illustrates an example output from the NEOMOD Simulator, where the user requested to generate the full sample of H < 25 NEOs from the base model described here.
Statistically different NEO samples can be obtained by initializing the code with different random seeds. Potentially hazardous objects (PHOs) are defined as having a minimum orbit intersection distance (MOID) with Earth of < 0. 05 au (19. 5 lunar distances) and H 22 (D ; 140 m for p V = 0. 14). We used the code described in  Wiźniowski & Rickman (2013)  to estimate the number of PHOs as a function of orbital elements. Ten-thousand objects were placed into each orbital bin in a, e, and i, their nodal and perihelion longitudes were drawn from a uniformly random distribution, and MOID was computed for each orbit. We then evaluated the fraction of PHOs, following the definition above (MOID <0. 05 au), in each bin. The PHO fraction is the largest for orbits with q ∼ 1 au, Q = a(1 + e) ∼ 1 au, a ∼ 1 au, and/or i < 10°.
The differences for all other source weights and other parameters are smaller than 30%. For some reason, the new fitting procedure also gives N(17. 75) = 1010 ± 19-an ∼8% larger population than that obtained in the base model (and smaller uncertainty). This indicates that an accurate population estimate (also) depends on the details of the fitting algorithm. A detailed investigation of this approach is left for future work. Our preliminary results from 2013-2021 CSS observations, for Following  Granvik et al. (2016) , our base model accounted for the size-dependent disruption of NEOs at low perihelion distances. We extensively tested various NEO models where the disruption module in MultiNest was switched off. All modified models without disruption showed a strong excess of high-e and low-q orbits, and Bayes factors that strongly disfavored them (  ln 20 D > in favor of the base model).
Summary The main results of this work are summarized as follows. 1. We developed a new NEO model (NEOMOD). The model is based on numerical integrations of bodies from 12 sources (11 main-belt sources and comets). A flexible method to accurately calculate biases of NEO surveys was applied to CSS observations from 2005-2012  (Christensen et al. 2012). The MultiNest code  (Feroz & Hobson 2008; Feroz et al. 2009 ) was used to calibrate the model on CSS detections. The algorithms developed here can be readily adapted to any current or future NEO survey. 2. The methodology used in  Granvik et al. (2018)  was improved. We adopted the cubic splines to characterize the magnitude distribution of the NEO population. The cubic splines are flexible and can be modified to consider a broader absolute magnitude range and/or improve the model accuracy.
We used a large number of main-belt asteroids in each source (10 5 ), which allowed us to accurately estimate the impact fluxes on the terrestrial planets. Our model self-consistently accounts for the NEO disruption at small perihelion distances  (Granvik et al. 2016 ). 3. We used 10,000 test objects per orbital bin, 18,480 orbital bins, 56 absolute magnitude bins, and nearly 250,000 FOVs to compute the CSS detection probability as a function of NEO's a, e, i, and H. We considered different approaches to modeling the trailing loss of CSS. The trailing loss represents an important uncertainty in estimating the population of small NEOs, and we urge surveys to carefully characterize it. 4. Our base model is available via the NEOMOD Simulator (Section 6), a code that can be used to generate a userdefined sample of model NEOs.
This may imply that JFCs disrupt rather than becoming dormant (see  Nesvorný et al. 2010). 11. We estimate that the Mars-to-Moon ratio in the number of impacts is Ma/Mo ; 7. 1 for H = 15 and Ma/Mo ; 4. 7 for H = 25. In crater chronology studies, this is often normalized to the unit surface area on these worlds, giving the parameter R b , where the index b stands for bolides. We obtain R b = 2. 0 for H = 15 and R b = 1. 2 for H = 25. Both of these values are significantly lower than R b ; 2. 6 used for NEO impacts in previous works. Ďurech & Hanuš (2022, private communication) recently analyzed the Gaia data release 3 (DR3) to determine the obliquities for ;9500 asteroids. The distribution of obliquities confirms the trend predicted in Section 8 and item (9) above. The obliquities of main-belt asteroids immediately sunward of strong orbital resonances are θ < 90°, with a concentration for θ < 45°; capture in spin-orbit resonances is probably important here.
Acknowledgments The simulations were performed on the  NASA  Pleiades Supercomputer. We thank the  NASA NAS computing division  for continued support. The work of D. N. , R. D. , and W. F. B. was supported by the  NASA Planetary Defense Coordination Office  project " Constructing a New Model of the Near-Earth Object Population. " The work of S. N. , S. R. C. , and P. W. C. was conducted at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. D. V. acknowledges support from the grant  21-11058S  of the  Czech Science Foundation. We thank an anonymous reviewer for helpful comments.
The currently ongoing randomised studies COLLISION (NCT03088150), COLLISION-XL (NCT04081168) and NEW-COMET (NCT05129787) also aim to improve these shortcomings  [17]. Awaiting the results of these trials, the Cardiovascular and Interventional Radiological Society of Europe (CIRSE) initiated the multicentre prospective observational study CIRSE Emprint TM Microwave Ablation Registry (CIEMAR, NCT03775980). CIEMAR aims to study the long-term effectiveness of MWA in CRLM in the realworld clinical setting, including important parameters such as health-related quality of life (HRQOL), which provides a ''real-world'' counterpart to the COLLISION trial, to form a substantial evidence base for the use of MWA in CRLM. The study is sponsored and conducted by CIRSE.
Data collection and quality control Patients enter the study by accepting participation and signing the informed consent form. Baseline data are collected one month prior to the first treatment (see Fig. 1 ). Additionally, the relevant data to calculate the baseline Charlson Comorbidity Index (CCI) and the clinical risk score (Fong Score) are collected  [18, 19]. After each treatment, technical parameters are collected on how the treatment is performed, whether simultaneous resection of other lesions is performed, and whether concomitant systemic therapy is received. Follow-up data are collected 1 month after the treatment, then every 3 months for the first 12 months and then every 6 months until the study exit or study end, 36 months after the 500th patient has been included. Data for safety and adverse events are collected throughout the whole study.
HRQOL data are collected before the first treatment and at the first followup. Data validity, completeness, consistency, and uniformity are warranted by case report forms with programmed constraints on data types, ranges, and mandatory data fields. Furthermore, a regular remote monitoring scheme is employed to verify the data quality, inclusion of patients, and follow-ups. No on-site monitoring or source data verification is performed due to resource limitations. Remote monitoring is performed quarterly. Activities include monitoring calls with the sites, sending query reports and monitoring call reports in which all necessary action points are documented. Data are monitored regularly, and queries are raised via the electronic data capturing system. The non-interventional nature of the study means that the exact dates of the procedures and follow-ups are determined by the medical team.
Data sets For the main results analysis, a full analysis data set composed of all eligible patients who were enrolled in the registry and underwent MWA treatment will be used. Patients for whom the amount of data collected is too scarce to warrant meaningful analysis might be assessed for exclusion. The cut-offs for data scarcity will be evaluated during the final analysis, and the number of excluded patients as well as the reason for exclusion will be reported. Other data sets (e. g. to explore country or centre effects) might be created during the final analysis.
Missing data Missing observations will be presented as a percentage of the total. For the assessment of the primary outcome measure, a drop-out is defined as a lesion not followed up to the 12 months ? / -45 days mark. In the case of the death of a patient before the 12 months assessment, only lesions that had progressed will be regarded as not having achieved tumour control at 12 months and lesions where progression could not be determined before the death of the patient will be regarded as a drop-out. A sensitivity analysis will be done to assess potential bias due to drop-outs before the 1-year assessment. Depending on the differences between the primary analysis and the sensitivity analysis, a tipping-point analysis may be performed. The statistical analysis will be done using R Studio version 4.
Current status of the study Between October 2018 and June 2022, 100 hospitals that met the selection criteria were invited, of which 36 were activated (by means of training, access to the database, and eligibility to include patients). As of January 2023, 500 patients have been enrolled with 976 treated tumours, achieving the minimum sample size of 976 lesions for a precision between 0. 04 and 0. 06. Follow-up data will be collected until January 2026. The final clinical outcome results are expected to be published by 2027.
Acknowledgements This observational study was sponsored and conducted by  CIRSE , a medical society, with a grant from  Medtronic's   External Research Programme , the manufacturer of the Emprint TM and Emprint HP TM microwave ablation systems. Thanks to the financial support from  Medtronic ,  CIRSE  was able to act as a scientifically independent sponsor of the study and was the sole owner of the data. The electronic data collection system was developed and supported by  OpenClinica (LLC, Waltham, MA, USA ). The authors would like to thank  CIRSE headquarters  and the staff of the  CIRSE Clinical Research Department , in particular  Robert Bauer  and  Daniela Schweiger , for their support in designing and setting up the study and in writing the manuscript. The authors want to thank  Claire Poulet  for creating the visual abstract.
Funding The  CIEMAR study  was funded by an independent investigator-initiated research grant from  Medtronic's External Research Programme. CIRSE , the  Cardiovascular and Interventional Radiological Society of Europe , is responsible for the independent execution of CIEMAR and has sole ownership of the data. The funding body had no role in the design of the study, collection analysis and interpretation of the data, or writing of the manuscript. The funding body was allowed to review the manuscript for corrections related to the technical workings of the device.
In advanced cancer patients, tumor burden assessment relies on the Sum of the Longest Diameters (SLD) of the target lesions, a marker that lumps all lesions together and ignores intra-patient heterogeneity. Here, we relied on a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify the heterogeneity in lesion dynamics and measure their impact on survival. Using a nonlinear model of tumor growth inhibition, we estimated that dynamics differed greatly among lesions, and inter-lesion variability accounted for about 35% of the total variance of both tumor shrinkage and treatment effect duration. Next, we investigated the impact of individual lesion dynamics on survival. Lesions located in the liver and in the bladder had twice as much impact on the instantaneous risk of death as compared to those located in the lung or the lymph nodes.
, 2017b; Król et al. , 2018; Gavrilov et al. , 2020; Tardivon et al. , 2019) , while limiting the bias caused by informative censoring  (Desmée et al. , 2015; Mbogning et al. , 2015; Björnsson et al. , 2015). Performant algorithms have been developed, relying on softwares such as Monolix or NONMEM, allowing the diffusion of these methods in the statistical community  (Kerioui et al. , 2022). Joint models accounting for individual lesion dynamics have recently been developed, using existing softwares such as the R package rstanarm, but this approach remains limited to linear models for the longitudinal parts  (Brilleman et al. , 2019). Conversely, nonlinear models have been developed for individual lesion dynamics, but these models did not integrate the association with survival  (Mercier et al.
, 2020)  or remained limited to a two-stage inference approach  (Krishnan et al. , 2021). Developing nonlinear joint models for individual lesions is made complex by the fact that no existing softwares can readily be extended to incorporate another layer of random effects capturing intra-patient variability. In a frequentist approach, the curse of dimensionality may be a main limitation to its implementation. Beside the numerical complexity, using individual January 2022 lesions requires to have access to large dataset, where individual lesions are scrutinized and reported in a large number of patients. Here, we built on a previous works showing that the Hamiltonian Monte-Carlo No U-Turn Sampler (HMC-NUTS algorithm) implemented in Stan software could provide reliable estimates of nonlinear joint models  (Kerioui et al.
19. 2). We ran three chains in parallel of 500 iterations each, including 250 warmup iterations, leading to a posterior sample of P = 750 replicates. To compute the survival function, the integral of the hazard function was approximated by a Gauss-Kronrod quadrature method. Stan code is available in supplementary material. Convergence was assessed based on the observation of posterior trace plots, and classic Stan diagnostic tools, including R and effective sample sizes. The classic split-R compares the within and between-chain variances, and is expected to be close to 1 to ensure good convergence of the chains. The effective sample size is a measure of sample effectiveness, the larger the better.
Posterior Predictive Checks Posterior predictive checking (PPC) relies on the visual comparison of the original dataset with many replicated data generated under the selected model. We replicated B = 1000 datasets using the following procedure. For each replicated dataset b, we drew a vector of population parameters in the posterior distribution θ b ∼ p(θ|y, T , δ) and we sampled the random effects of each replicated lesion k from organ j of each replicated patient i in their distributions η b i ∼ N (0, Ω b 1 ) and ρ b i,j,k ∼ N (0, Ω b 2 ). The individual parameters vector of patient i ψ i b was then computed as function of θ b , η b i and ρ b i,j,k following equation (  3 ). Following study protocol, for each theoretical measurement time t l , we derived the simulated trajectory of the lesion, drawing y b i,j,k,l ∼ p L (y i,j,k,l |θ b , ψ b i,j,k ), and we sampled a time-to-death T b i ∼ p S (T i |θ b , ψ b i ).
In order to be consistent with study protocol, we assumed that SLD was no longer measured in case of progression defined by an increase of more than 20% and more than 5 mm from nadir. We also kept the same structure than the observed data, in terms of number of lesions per patient and location of the lesions. For each time t l , and depending on the lesion location, we computed the median of the lesions measurements over the N simulated patients. We used the 2. 5 th and 97. 5 th percentiles of the medians over the B replicated datasets to provide 95% prediction intervals and we compared this prediction with the median of the lesion measurements in the original dataset. Similarly, we compared the survival probability curve estimated by Kaplan-Meier on the original dataset with the 95% prediction interval computed over the B Kaplan-Meier estimates of the survival probability in each B dataset.
Data A total of 751 target lesions were followed over time, including 296 lesions in the lymph, 205 in the lung, 168 in the liver and 82 in the bladder. Each patient had 2 (1-5) target lesions in median (minimum-maximum). A total of 2726 target lesions measurements was collected, with a median (minimum-maximum) of 6 measurements per patient (1-35). Overall, 226 patients (66%) died during the follow-up. Of note, the proportion of death was larger in the 159 patients having at least one target lesions in the liver or the bladder (77%) as compared to the 183 patients without any liver or bladder target lesions (57%).
Discussion We here developed a multilevel nonlinear joint model for individual lesions kinetics and survival, and inference was conducted in a Bayesian framework using the HMC-NUTS algorithm in Stan software. The model could capture the large variability in individual lesion dynamics, caused by both different organ locations and intrinsic variability. Parameters such as the duration of the treatment effect could vary by more than 10-fold across the organs, and the shrinkage of the tumor size induced by the treatment was as twice as larger in the lymph nodes as compared to the organs with a poorer response, such as the liver. After adjusting for organ location, we estimated that intra-patient variability accounted for about a third of the total parameter variability in the population. Individual lesions also had a differential effect on survival, with lesions located in the liver and in the bladder having twice as much impact on the instantaneous risk of death as compared to those located in the lung or the lymph nodes.
Epimutations are rare alterations of the normal DNA methylation pattern at specific loci, which can lead to rare diseases. Methylation microarrays enable genome-wide epimutation detection, but technical limitations prevent their use in clinical settings: methods applied to rare diseases' data cannot be easily incorporated to standard analyses pipelines, while epimutation methods implemented in R packages (ramr) have not been validated for rare diseases. We have developed epimutacions, a Bioconductor package (https://bioconductor. org/packages/release/bioc/html/epi mutacions. html). epimutacions implements two previously reported methods and four new statistical approaches to detect epimutations, along with functions to annotate and visualize epimutations. Additionally, we have developed an user-friendly Shiny app to facilitate epimutations detection (https://github.
com/isglobal-brge/epimutacionsShiny) to non-bioinformatician users. We first compared the performance of epimutacions and ramr packages using three public datasets with experimentally validated epimutations. Methods in epimutacions had a high performance at low sample sizes and outperformed methods in ramr. Second, we used two general population children cohorts (INMA and HELIX)  to determine the technical and biological factors that affect epimutations detection, providing guidelines on how designing the experiments or preprocessing the data. In these cohorts, most epimutations did not correlate with detectable regional gene expression changes. Finally, we exemplified how epimutacions can be used in a clinical context. We run epimutacions in a cohort of children with autism disorder and identified novel recurrent epimutations in candidate genes for autism.
Although these methods have been successful to identify epimutations as the causative factor of some genetic diseases  [4, 6, 7] , they are not implemented in any library, so they cannot be easily integrated in bioinformatic pipelines. The ramr Bioconductor package implements other approaches to detect epimutations based on outliers using either the interquartile range or beta distribution  [8]. However, ramr methods have not been used in real data studies and their performance has not been compared with others. We have created a Bioconductor package, called epimutacions, that enables detecting epimutations on DNA methylation microarray data and overcomes existing limitations: (i) contains functions implementing previously reported methods (quantile and manova), and (ii) includes more sophisticated statistical methods to detect outliers, such as beta distribution (beta), multivariate regression (mlm), isolation forest algorithm (iForest) and distance-based approach (mahdist).
In this paper, we first evaluated the methods included in our package and compared them with the methods implemented in the ramr package  [8]  using three public datasets with validated epimutations. Second, we used two general population child's cohorts to determine the technical and biological factors that affect epimutations discovery, providing guidelines on how designing the experiments, preprocessing the data, or choosing the reference samples to include in the analysis. We also used these cohorts to define the epimutations burden in the general population and to explore the association between epimutations and gene expression. Third, we evaluated to what extent epimutations were cell-specific or shared among different blood cell types. Finally, we exemplified how epimutacions can be used in a clinical context, by using the package to detect epimutations in a cohort of children with autism.
Implementation The input of epimutacions package is a GenomicRatioSet object, a standard Bioconductor class to manage preprocessed DNA methylation data. epimutacions package includes six methods to detect epimutations, defined as three or more consecutive CpGs with outlier measurements closer than 1kb, following the definition of Barbosa and colleagues  [7]. epimutacions methods can be grouped into two main approaches: (I) bumphunter + outlier test and (II) outlier test + clustering (Figure  1 ). The bumphunter + outlier test approach, which includes manova, mlm, iForest and mahdist, identifies if a differentially methylated region (DMR), defined using bumphunter  [5] , is an epimutation. Each method uses a different statistical test: (I) manova uses the covariance between the outcome variables to test the statistical significance of the mean difference in the methylation level between the case sample and the reference panel  [9] ; (II) mlm creates a multivariate linear model using the reference panel and identifies as epimutations the methylation values that deviated from predicted values  [10] ; (III) iForest repeats random portioning of observations until all instances are isolated and classifies as outliers the instances isolated in a few splits  [11] ; and (IV) mahdist calculates the robust Mahalanobis distance using the minimum covariance determinant  [12].