dataesr/scientific-paragraphs-categorization huggingface dataset:
These observations indicate a growing burden of HF in individuals < 50 yo and highlight the need to identify specific and preventable risk factors in this sub-population. In this review, we focus on the etiologies of HF that will manifest in young subjects  (18-50 years). Data were identified by searches of Medline and from the references in relevant articles published after 1970. Different subsets of studies were potentially eligible for inclusion in different parts of this review. We used the search terms HF, young adults, prevalence, incidence, survey, cardiomyopathies, congenital heart disease, arterial hypertension, Fabry disease, diabetes, obesity, cancer, alcohol, drugs, coronary heart disease, myocarditis, arrhythmias, valvular disorders, mitral regurgitation, inflammatory disease, sarcoidosis, eclampsia, and peripartum cardiomyopathy, and we only included papers reporting incidence rates of HF in subjects < 50 yo or in cohort median age under 56 yo.
DCM DCM can be genetic or acquired and is the most prevalent cause of HF in young patients aged < 40  [11]. The CHARM program included patients from United Stated and Canada with NYHA class II-IV heart failure for at least 4 weeks and reported that idiopathic dilated cardiomyopathy (reported as the primary etiology) was present in 62% of patients aged 20-39 years  [5]. A similar proportion (63% of patients aged < 40 years) was found in the MAG-GIC (Meta-Analysis Global Group in chronic heart failure) database. However, in both studies, the proportion of DCM declined sharply > 40 years representing 35-37% of patients aged 40-49 whereas ischemic heart failure was found in 45-46% of patients aged 40-49  [ 5 , 12 ]. A similar pattern was found in two European studies  [ 3 , 13 ]. Its estimated prevalence is ≥1 in 250 individuals  [14]  , and sexrelated differences are less consistent, whereas racial differences exist  [11].
We hypothesized that pembrolizumab plus enzalutamide may have superior antitumor activity to either agent alone in patients with mCRPC  [11]. The phase 1b/2 KEYNOTE-365 study (NCT02861573) investigated the efficacy, safety, and tolerability of pembrolizumab combination therapies in patients with mCRPC. Here, we report results from cohort C of KEYNOTE-365, which included patients with chemotherapy-naive mCRPC whose disease progressed after first-line therapy with abiraterone acetate and were then treated with pembrolizumab plus enzalutamide.
KEYNOTE-365 is an ongoing, nonrandomized, multicenter, multicohort, open-label, phase 1b/2 trial of pembrolizumab-based combination therapies in patients with mCRPC (NCT02861573). Planned enrollment was approximately 100 patients in each cohort. Patients in cohort C were enrolled at sites in Australia, Canada, France, Germany, New Zealand, Spain, the UK, and the USA. This trial was conducted in accordance with good clinical practice and the Declaration of Helsinki. The protocol and its amendments were approved by the appropriate ethics body at each participating institution. All patients provided written informed consent. [26] , unacceptable adverse events (AEs), or noncompliance with trial treatment were required to discontinue treatment but were monitored. Patients who discontinued one study drug in the combination regimen because of treatment-related AEs (TRAEs) could continue the other study drug until discontinuation criteria were met for that drug.
Patient disposition and demographics As of February 14, 2022, 103 patients were assigned to cohort C. Following one screen failure, 102 patients initiated study treatment in this cohort. The median follow-up, defined as the time from the first dose of study treatment to the database cutoff date, was 51 mo (interquartile range [IQR] 37-56). Thirty-two patients (31%) had PD-L1-positive tumors (defined as CPS 1), 17 (17%) had visceral disease, and 38 (37%) had RECIST v1. 1-measurable disease on BICR (Table  1 ). Twenty-four patients (24%) received docetaxel before enrollment in the study. As of the database cutoff date, 100 patients (98%) had discontinued treatment (Supplementary Fig. 1 ). Among these, 81 discontinued because of disease progression (13 with clinical progression; 68 with progressive disease) and ten because of AEs.
The phase 3 KEYNOTE-641 study was launched on the basis of the current analysis, indicating that most patients (51%) experienced some reduction in target lesion size and 24% of patients had at least a 30% reduction. However, data from subsequent clinical studies after initiation of KEYNOTE-365 showed that enzalutamide monotherapy provided similar efficacy to the combination of pembrolizumab plus enzalutamide  [18, 35]. Furthermore, the phase 3 KEYNOTE-991 trial (NCT04191096) recently  failed to show any improvement in OS or rPFS with pembrolizumab plus enzalutamide and androgen deprivation therapy (ADT) over placebo plus enzalutamide and ADT in patients with mHSPC, as did the phase 3 KEYNOTE-641 study (NCT03834493) that evaluated the combination of pembrolizumab plus enzalutamide and ADT versus placebo plus enzalutamide and ADT in patients with mCRPC  [36, 37].
[41]  proposed a new bounded version of the transformed gamma process  [38]  and used it to describe the degradation process of the cylinder liners of a Diesel engine for marine propulsion. Model parameters were estimated from a set of real wear data. The upper bound was treated as an unknown parameter and was estimated directly from the available dataset. The proposed process was then used to estimate the remaining useful life and residual reliability of the liners. Obtained results were compared to those obtained by using a classical (unbounded) transformed gamma process. The study showed that, in the examined case, the unbounded transformed gamma process provides estimates much more pessimistic than those obtained by using the new bounded model. Although, due to risk aversion, the use of a more conservative unbounded model could be generally preferred to a bounded one, the high cost of the liners, the strong interest to extend their operating life, and the fact that the considered wear induced soft failure does not produce catastrophic consequences, rise the doubt that neglecting the presence of the bound, when available data give statistical evidence of its existence, could significantly affect the prognostic ability of degradation models and consequently undermine the effectiveness of implemented CBM policy.
From Eq. (  28 ), the log-likelihood function relative to the whole observed dataset w = (w 1,1 , …, w 1,n1 , …, w m,1 , …, w m,nm ) is: l (w; θ, w lim ) = ∑ m i=1 ∑ ni j=1 ln ( f ΔW(ti,j-1 ,ti,j)|W(ti,j-1 ) ( δ i,j ⃒ ⃒ w i,j-1 ) ) , ( 29 ) where θ denotes the vector of parameters which, together to w lim , index the age and state functions. For example, if the age function is the powerlaw function in Eq. (  12 ) or the exponential function in Eq. (  13 ) and the state function is one of those in Eqs. (  6 )-(  8 ), then θ = (a, b, β, γ). The maximum likelihood (ML) estimates θ and ŵlim of the process parameters can be easily obtained by numerically maximizing the loglikelihood function in Eq. (  29 ) with respect to θ and w lim. Of course, the maximization procedure of the log-likelihood in Eq.
Case study Let now consider the wear measurements of the liners of the 8-cylinder SULZER engine which equips a cargo ship of the Grimaldi Lines, which has operated under homogeneous conditions. The dataset, given in Table  1  and depicted in Fig. 4 , consists of a total of 23 wear measurements made via ad hoc inspections carried out between 1994 and 2004. At each inspection, the wear of the inspected liner is measured by positioning a micrometer inside a predetermined hole located at the top dead center of the liner (the point where maximum mechanical and thermal loads are concentrated). Each datum consists of the age (in hours) of the liner at the inspection epoch and of the corresponding wear measurement (in mm). Hereinafter, for the sake of simplicity, we will use interchangeably the wording "age of the liner at the inspection epoch" and "inspection time".
e. , 100 mm), so that an asymptotical upper bound w lim for the wearing phenomenon exists, although the same physical considerations alone do not allow to determine it exactly. However, previous experiences suggest that the liner wear can approach the value of 4. 3 mm, a wear value which is obviously larger than the maximum value w M = 3. 05 mm of the considered dataset. Thus, the liner wear data were analyzed in  [41]  under the BTGP by treating the upper bound as an unknown parameter subject to the constraint w lim ≥ w L = 4. 3 mm. In particular, the three "bounded" state functions in Eqs. (  3 )-(  5 ) and the power-law age function η(t) = (t/a) b in Eq. (  12 ) were there considered. According to the Akaike information criterion (see Akaike  [42] ), the BTGP that provided the best fit to the wear data was the (4-parameters) BTGP with the state function g 2 (w) in Eq.
Simulation study In order to investigate the consequences caused by a misspecification of a bounded PL-BTGP2 with an unbounded TGP, we have carried out a small Monte Carlo simulation study. The investigation is conducted by generating 100 pseudo-random samples of degradation data from the PL-BTGP2 whose parameters are equal to the ML estimates reported in Hereinafter, this PL-BTGP2 will be referred to as "true wear model". The 100 datasets have the same structure (i. e. , the same number of  units, the same number of measurements, and the same measurement times) of the real wear data reported in Table  1. The considered TGP is the one with power-law state function g(w) = (w/α) β and power-law age function η(t) = (t/a) b , which provided the best fit for the real wear data within the (unbounded) TGP.
For each simulated dataset, we have estimated the parameters of both the PL-BTGP2 and TGP models. As in the case of the real data, the MLE of the parameters of the PL-BTGP2 have been obtained under the constraint w lim ≥ w L = 4. 3 mm. Hence, for each of the two processes, based on the estimated parameters and the maintenance policy presented in Section 6, we have identified the liners that should be immediately replaced at the last inspection time. In the rest of this section, the estimated PL-BTGP2 and TGP models will be indicated as PL-BTGP2 and TGP, by omitting the term "estimated". As in Section 6, we have considered the case where the decision is made under the assumption that the next inspection is planned after τ = 15, 000 h. The cost model is also calibrated as in Section 6. The results of this simulation study are summarized in Table  8.
For comparative purposes, we have selected a dataset for which, as in case of the real data, the use of the TGP leads to immediately replace the liners #1 and 5. Again, as in the case of the real data, we have assumed that τ = 15, 000 h. Obviously, the main difference with respect to the application discussed in Sections 5 and 6 is that here the true wear model is known. These simulated data are reported in Table  12  and depicted in Fig. 14. Table  13  provides the maximum likelihood estimates of the Fig. 14. Simulated wear paths of the liners (measurements that pertain to the same path are linearly connected for graphical convenience).
e. , the expected utility losses evaluated under the "true" PL-BTGP2 used to generate the simulated dataset) together with the corresponding expected utility losses evaluated by using the estimated PL-BTGP2 and TGP. Obtained results show that the expected utility losses evaluated by using the PLBTGP2 are satisfactorily close to the true ones. On the contrary, the comparison between the true expected utility losses and those evaluated under the (unbounded) TGP gives clear evidence that this latter model hugely overestimates the expected utility loss associated to the action (a): deferring the liner replacement, and almost always underestimates the expected utility loss associated to action (b): replacing immediately the liner. These circumstances, in the case of the examined dataset, lead the TGP to produce incorrect decisions for the liners #1 and 5.
Acknowledgements We thank all patients and their representatives for their participation in the trial. We thank the members of the Data and Safety Monitoring Board Professors  Werner Hacke ,  Peter Langhorne  and  Matthias Gondan-Rochon  for their contribution to this trial. This study was funded by the  European Union 's  Horizon 2020 research and innovation programme  ( 634809 ). This text reflects only the author's view and that the Agency is not responsible for any use that may be made of the information it contains.
C. Application to scRNA-seq data A recent study has used the method of maximum likelihood to fit the mRNA counts from allele-sensitive singlecell RNA sequencing to the standard telegraph model thus determining the transcription parameters for endogenous mouse genes transcriptome-wide  [6]. For a large number of genes, the switching on rate was found to be much less than the switching off rate and the synthesis rate thus leading to the conclusion that transcriptional bursting is ubiquitous in mammalian genes. This suggests that the distributions of mRNA counts for many genes in this dataset are well fit by a negative binomial distribution, i. e. the only two parameters that can be determined accurately are the burst frequency (switching on rate) and the burst size (the ratio of the synthesis and switching off rates).
We find that this law is reflected in the sc-RNA seq data (it has also been found in other scRNA-seq datasets  [48, 53] ). In Fig. 6 (a) we plot the coefficient of variation squared as a function of the mean expression for about 10 4 mouse fibroblast genes with mean expression > 1. For both the C57 and the CAST alleles, the law CV 2 i ∝ 1/⟨m i ⟩ manifests for mean expression below 10 (red line in inset of Fig. 6 (a)) followed by a transition to the law CV 2 i equals some constant (independent of the gene index i) for larger mean expression levels. These observations follow Eq. (  14 ) -the first two terms describe the small expression law and the last three terms explain the high expression law. The significant scatter about the red line for low mean expression is expected because each gene will have a different value of the mean burst size and hence the first two terms in Eq.
(  14 ) can only be approximately inversely proportional to the mean expression. On the other hand, the low scatter about the line CV 2 equals some constant (for high-expression genes) indicates that the coefficient of variation of the burst size and frequency are similar across genes; these measures of the size of extrinsic noise are also independent of the allele. These observations about extrinsic noise can be explained as follows. Since burst frequency changes with cell cycle phase  [4, 13, 14] , it is expected that its coefficient of variation is not particularly large for this dataset because the population of cells is mostly in the G1 phase  [6]. Thus we assume that the variation in the parameters is dominated by the burst size since this is often influenced by the cell volume  [14].
The horizontal lines show the medians of the distributions. Based on the foregoing arguments we shall assume that for each mouse fibroblast gene displaying apparent bursting behaviour in the sc-RNA seq dataset, the mean expression and the coefficient of variation squared of the mRNA counts are given by Eqs. (  13 )-(  14 ) with CV 2 k i on = 0, CV 2 bi = 0. 32. Note that the genes selected for inference are the subset of the highly bursty genes in Fig. 8  for which CV 2 bi > 0. 32. Solving the equations for the mean expression and the coefficient of variation squared simultaneously we obtain estimates of the mean burst size and frequency for each gene. The distribution of these two parameters across the transcriptome are shown in blue in Fig. 6(c)  and (d) ; for comparison, we also show (in yellow) the corresponding distributions if we had to ignore extrinsic noise, i.
This method also predicts corrected estimates for the burst size and frequency of the same scRNA-seq dataset we analysed in Fig. 6. The burst size is defined differently than in our case (in our notation for gene i it is defined as the parameter r i ) and hence it is not possible to compare our estimate with those from the NN method. The burst frequencies can however be compared: both the NN method and ours predict that taking into account extrinsic noise considerably widens the burst frequency histogram. Our theory additionally predicts that independent of the source of the extrinsic noise, if transcriptional bursting is evident, the median burst frequency of bursty genes should be larger after correcting for it; this might be consistent with the results of the NN study but it is difficult to ascertain from the published data analysis.
Acknowledgment We thank  Elio Castagnola  and  Cecile Ronckers  for their contribution to the title and abstract and full text screening;  Prof Thorsten Langer  ( Pediatric Oncology and Hematology, University Hospital for Children and Adolescents, Lübeck, Germany ) and  Daniel Mulrooney  ( St Jude Children's Research Hospital, Memphis, TN, USA ) for critically appraising the recommendations and manuscript as external reviewers; and  Anna Apel  ( CEO of Saving Kids with Cancer Foundation, Poland ;  PanCare Board Member ) and  Katie Weyer  as survivor representatives. Lastly, we thank  Kristin Marie Bivens  ( Institute of Social and Preventive Medicine, University of Bern ) for her editorial assistance.
Finally, resiliency refers to the ease with which prices revert to their normal levels ✩ We are grateful to the Editor, Professor Jonathan Batten, and two anonymous referees for their constructive comments and suggestions. We would like to thank Botao Wu, Sebeom Oh, and conference participants at the Annual Financial Market Liquidity Conference 2022 in Budapest, Hungary, and at the First Annual Meeting of the International Society for the Advancement of Financial Economics  (ISAFE-2022)  in Ho Chi Minh City, Vietnam. O'Sullivan would like to acknowledge the support of New York University (NYU) Tandon's Finance and Risk Engineering department for granting access to the Markit database. Papavassiliou would like to acknowledge the support of University College Dublin, Ireland for financial assistance under grant numbers SF1258 and R18254.
All errors remain our own. Third, we focus our empirical analysis on a detailed high-frequency dataset from the MTS markets, in contrast to earlier studies that have used datasets of lower sampling frequencies, such as daily or monthly  (Bhattacharya and Spiegel, 1998). The advantages of using high-frequency data have been highlighted in the related literature and extend from statistical gains to higher predictive accuracy, among other things (see  Gargano et al. , 2019  for a discussion). Moreover, the use of high-frequency data enables the construction of more accurate microstructure-based liquidity measures (as well as more accurate return and volatility measures) that are able to capture multiple liquidity dimensions, and thus of paramount importance for the study of liquidity resiliency.
The OLS-based relative spread resiliency is estimated according to Eq. (  2 ). The pre-crisis period spans the dates from January 2008 to October 2009 whilst the crisis period extends from November 2009 to December 2013. We use benchmark securities across four maturity segments, i. e. 2-, 5-, 10-, and 30-year maturity. Panel B shows the corresponding statistics for the LASSO-based relative spread resiliency which is estimated according to Eqs. (  2 ) and (3) following a two-stage regression approach. The liquidity measures have been winsorized by 95% in order to avoid extreme values in the resiliency estimates. Coppejans et al. (2004)  use a dataset from the Swedish stock index futures market and show that shocks to liquidity dissipate quickly, indicating a high degree of resiliency. Degryse et al.
Data We employ a rich high-frequency dataset provided by MTS (Mercato dei Titoli di Stato), Europe's premier interdealer electronic fixed-income market for euro-denominated government bonds. MTS group counts to over 500 counterparties and 2,000 traders trading an average trading volume exceeding EUR 130 billion across all MTS platforms. Our dataset spans the dates from January 2008 to December 2013 and includes a tranquil period (January 2008 to October 2009) and a crisis period (November 2009 to December 2013). 8 It consists of the following core and periphery countries: Austria, Belgium, Finland, France, Germany, Greece, 8 The U. S. global crisis did not have a significant impact on euro area sovereign bond markets, as discussed in  Dellas and Tavlas (2013)  and O'Sullivan and  Papavassiliou (2020) , justifying our selection to use the period January 2008 until October 2009 as the pre-crisis sample.
Conclusions We offer new insights into liquidity resiliency using a high-frequency dataset from the MTS sovereign bond markets. We measure resiliency using an OLS approach and a combination of an OLS approach and the LASSO machine learning approach and show that these methods can be used interchangeably in the measurement of market resiliency regardless of whether resiliency is estimated in terms of spreads or depths. We find that the information contained in resiliency is unique and independent of other dimensions of liquidity. We find significant intertemporal relationships between resiliency and sovereign credit risk, volatility, and sovereign bond returns, as well as strong commonalities in resiliency which are greater for spread-based than depth-based resiliency proxies. Liquidity resiliency is of paramount importance today given that the speed of the order book replenishment mechanism has increased.
Empirical model We model the demand for schools following the literature on the demand for differentiated products and a recent literature on neighborhood choice. We adapt the procedures proposed in  Berry et al. (1995) ,  Berry et al. (2004), and Bayer and Timmins (2007)  to the particular characteristics of our problem and dataset, defining the village as the relevant education market for each household, and estimating different models for boys and girls. This is consistent with our data, where students do not attend primary schools outside their village of residence. In each village there are several schools with different attributes. A household chooses a single school among those in her market, and derives utility from its attributes. The utility household i obtains from its child (of gender g) attending school j in village t is given by u ijtg = K k=1 x jktg β ikg + γ ig d ijtg + ξ jtg + ε ijtg (1) where j = {0,.
However, since we also have a household level census detailing school choices in each village, it is possible to reliably estimate market shares, and recover δ jtg using the contraction mapping procedure proposed in  Berry et al. (1995). Apart from this detail, the way we implement these procedures is standard in the literature. See online Appendix B. The second step estimates β kg , are obtained by running a regression of the school fixed effect (δ jtg ) on the observed school characteristics, as in equation (6). δ 0tg , which reflects the outside option, is normalized to zero. The household and school variables used to estimate the model are described in online Appendix A -Table  A. 1. At the school level (x jktg ), we use almost every variable available in the dataset, including an indicator variable for whether a school is private.