Antibody RING-Mediated Destruction of Endogenous Proteins

International audienc

Inflation and Dark Energy from spectroscopy at z > 2

The expansion of the Universe is understood to have accelerated during two epochs: in its very first moments during a period of Inflation and much more recently, at z < 1, when Dark Energy is hypothesized to drive cosmic acceleration. The undiscovered mechanisms behind these two epochs represent some of the most important open problems in fundamental physics. The large cosmological volume at 2 < z < 5, together with the ability to efficiently target high-$z$ galaxies with known techniques, enables large gains in the study of Inflation and Dark Energy. A future spectroscopic survey can test the Gaussianity of the initial conditions up to a factor of ~50 better than our current bounds, crossing the crucial theoretical threshold of $\sigma(f_{NL}^{\rm local})$ of order unity that separates single field and multi-field models. Simultaneously, it can measure the fraction of Dark Energy at the percent level up to $z = 5$, thus serving as an unprecedented test of the standard model and opening up a tremendous discovery space

Chromatin and oxygen sensing in the context of JmjC histone demethylases

Responding appropriately to changes in oxygen availability is essential for multicellular organism survival. Molecularly, cells have evolved intricate gene expression programmes to handle this stressful condition. Although it is appreciated that gene expression is co-ordinated by changes in transcription and translation in hypoxia, much less is known about how chromatin changes allow for transcription to take place. The missing link between co-ordinating chromatin structure and the hypoxia-induced transcriptional programme could be in the form of a class of dioxygenases called JmjC (Jumonji C) enzymes, the majority of which are histone demethylases. In the present review, we will focus on the function of JmjC histone demethylases, and how these could act as oxygen sensors for chromatin in hypoxia. The current knowledge concerning the role of JmjC histone demethylases in the process of organism development and human disease will also be reviewed

Information Systems and Health Care IX: Accessing Tacit Knowledge and Linking It to the Peer-Reviewed Literature

Clinical decision-making can be improved if healthcare practitioners are able to leverage both the tacit and explicit modalities of healthcare knowledge, yet at present there do not exist knowledge management systems that support any active and direct mapping between these two knowledge modalities. In this paper, we present a healthcare knowledge-mapping framework that maps (a) the tacit knowledge captured in terms of email-based discussions between pediatric pain practitioners through a Pediatric Pain Mailing List (PPML), to (b) explicit knowledge represented in terms of peer-reviewed healthcare literature available at PubMed. We report our knowledge mapping strategy that involves methods to establish discussion threads, organize the discussion threads in terms of topic-specific taxonomy, formulate an optimal search query based on the content of a discussion thread, submit the search query to PubMed and finally to retrieve and present the search results to the user

Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks

Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys. Despite continual improvements to the quality of density estimation by learned models, applications of such techniques to real data are entirely reliant on the generalization power of neural networks far outside the training distribution, which is mostly unconstrained. Due to the imperfections in scientist-created simulations, and the large computational expense of generating all possible parameter combinations, SBI methods in cosmology are vulnerable to such generalization issues. Here, we discuss the effects of both issues, and show how using a Bayesian neural network framework for training SBI can mitigate biases, and result in more reliable inference outside the training set. We introduce cosmoSWAG, the first application of Stochastic Weight Averaging to cosmology, and apply it to SBI trained for inference on the cosmic microwave background.Comment: 5 pages, 3 figures. Accepted at the ML4Astro Machine Learning for Astrophysics Workshop at the Thirty-ninth International Conference on Machine Learning (ICML 2022

Acute effects of maximal versus submaximal hurdle jump exercises on measures of balance, reactive strength, vertical jump performance and leg stiffness in youth volleyball players

Background: Although previous research in pediatric populations has reported performance enhancements following long-term plyometric training, the acute effects of plyometric exercises on measures of balance, vertical jump, reactive strength, and leg stiffness remain unclear. Knowledge on the acute effects of plyometric exercises (i.e., maximal versus submaximal hurdle jumps) help to better plan and program warm-up sessions before training or competition.Objectives: To determine the acute effects of maximal vs. submaximal hurdle jump exercise protocols executed during one training session on balance, vertical jump, reactive strength, and leg stiffness in young volleyball players.Materials and methods: Thirty male youth volleyball players, aged 12–13 years, performed two plyometric exercise protocols in randomized order. In a within-subject design, the protocols were conducted under maximal (MHJ; 3 sets of 6 repetitions of 30-cm hurdle jumps) and submaximal (SHJ; 3 sets of 6 repetitions of 20-cm hurdle jumps) hurdle jump conditions. Pre- and post-exercise, balance was tested in bipedal stance on stable (firm) and unstable surfaces (foam), using two variables [center of pressure surface area (CoP SA) and velocity (CoP V)]. In addition, the reactive strength index (RSI) was assessed during countermovement maximal jumping and leg stiffness during side-to-side submaximal jumping. Testing comprised maximal countermovement jumps (CMJ).Results: Significant time-by-condition interactions were found for CoP SA firm (p &amp;lt; .0001; d = 0.80), CoP SA foam (p &amp;lt; .0001; d = 0.82), CoP V firm (p &amp;lt; .0001; d = 0.85), and CoP V foam (p &amp;lt; .0001; d = 0.83). Post-hoc analyses showed significant improvements for all balance variables from pretest to posttest for MHJ but not SHJ. All power tests displayed significant time-by-group interactions for countermovement jumps (p &amp;lt; .05; d = 0.42), RSI (p &amp;lt; .0001; d = 1.58), and leg stiffness (p &amp;lt; .001; d = 0.78). Post-hoc analyses showed significant pre-post CMJ (p &amp;lt; .001, d = 1.95) and RSI (p &amp;lt; .001, d = 5.12) improvements for MHJ but not SHJ. SHJ showed larger pre-post improvements compared with MHJ for leg stiffness (p &amp;lt; .001; d = 3.09).Conclusion: While the MHJ protocol is more effective to induce acute performance improvements in balance, reactive strength index, and vertical jump performance, SHJ has a greater effect on leg stiffness. Due to the importance of postural control and muscle strength/power for overall competitive performance in volleyball, these results suggest that young volleyball players should implement dynamic plyometric protocols involving maximal and submaximal hurdle jump exercises during warm-up to improve subsequent balance performance and muscle strength/power.</jats:p

Cosmological Information in the Marked Power Spectrum of the Galaxy Field

Marked power spectra are two-point statistics of a marked field obtained by weighting each location with a function that depends on the local density around that point. We consider marked power spectra of the galaxy field in redshift space that up-weight low density regions, and perform a Fisher matrix analysis to assess the information content of this type of statistics using the Molino mock catalogs built upon the Quijote simulations. We identify four different ways to up-weight the galaxy field, and compare the Fisher information contained in their marked power spectra to the one of the standard galaxy power spectrum, when considering monopole and quadrupole of each statistic. Our results show that each of the four marked power spectra can tighten the standard power spectrum constraints on the cosmological parameters $\Omega_{\rm m}$, $\Omega_{\rm b}$, $h$, $n_s$, $M_\nu$ by $15-25\%$ and on $\sigma_8$ by a factor of 2. The same analysis performed by combining the standard and four marked power spectra shows a substantial improvement compared to the power spectrum constraints that is equal to a factor of 6 for $\sigma_8$ and $2.5-3$ for the other parameters. Our constraints may be conservative, since the galaxy number density in the Molino catalogs is much lower than the ones in future galaxy surveys, which will allow them to probe lower density regions of the large-scale structure.Comment: 19 pages, 12 figure

SIMBIG{\rm S{\scriptsize IM}BIG}: A Forward Modeling Approach To Analyzing Galaxy Clustering

We present the first-ever cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the new ${\rm S{\scriptsize IM}BIG}$ forward modeling framework. ${\rm S{\scriptsize IM}BIG}$ leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small non-linear scales, inaccessible with standard analyses. In this work, we apply ${\rm S{\scriptsize IM}BIG}$ to the BOSS CMASS galaxy sample and analyze the power spectrum, $P_\ell(k)$, to $k_{\rm max}=0.5\,h/{\rm Mpc}$. We construct 20,000 simulated galaxy samples using our forward model, which is based on high-resolution ${\rm Q{\scriptsize UIJOTE}}$ $N$-body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of $\Lambda$CDM cosmological parameters: $\Omega_m, \Omega_b, h, n_s, \sigma_8$. We derive significant constraints on $\Omega_m$ and $\sigma_8$, which are consistent with previous works. Our constraints on $\sigma_8$ are $27\%$ more precise than standard analyses. This improvement is equivalent to the statistical gain expected from analyzing a galaxy sample that is $\sim60\%$ larger than CMASS with standard methods. It results from additional cosmological information on non-linear scales beyond the limit of current analytic models, $k > 0.25\,h/{\rm Mpc}$. While we focus on $P_\ell$ in this work for validation and comparison to the literature, ${\rm S{\scriptsize IM}BIG}$ provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent ${\rm S{\scriptsize IM}BIG}$ analyses of summary statistics beyond $P_\ell$.Comment: 9 pages, 5 figure

SIMBIG{\rm S{\scriptsize IM}BIG}: Mock Challenge for a Forward Modeling Approach to Galaxy Clustering

Simulation-Based Inference of Galaxies (${\rm S{\scriptsize IM}BIG}$) is a forward modeling framework for analyzing galaxy clustering using simulation-based inference. In this work, we present the ${\rm S{\scriptsize IM}BIG}$ forward model, which is designed to match the observed SDSS-III BOSS CMASS galaxy sample. The forward model is based on high-resolution ${\rm Q{\scriptsize UIJOTE}}$ $N$-body simulations and a flexible halo occupation model. It includes full survey realism and models observational systematics such as angular masking and fiber collisions. We present the "mock challenge" for validating the accuracy of posteriors inferred from ${\rm S{\scriptsize IM}BIG}$ using a suite of 1,500 test simulations constructed using forward models with a different $N$-body simulation, halo finder, and halo occupation prescription. As a demonstration of ${\rm S{\scriptsize IM}BIG}$, we analyze the power spectrum multipoles out to $k_{\rm max} = 0.5\,h/{\rm Mpc}$ and infer the posterior of $\Lambda$CDM cosmological and halo occupation parameters. Based on the mock challenge, we find that our constraints on $\Omega_m$ and $\sigma_8$ are unbiased, but conservative. Hence, the mock challenge demonstrates that ${\rm S{\scriptsize IM}BIG}$ provides a robust framework for inferring cosmological parameters from galaxy clustering on non-linear scales and a complete framework for handling observational systematics. In subsequent work, we will use ${\rm S{\scriptsize IM}BIG}$ to analyze summary statistics beyond the power spectrum including the bispectrum, marked power spectrum, skew spectrum, wavelet statistics, and field-level statistics.Comment: 28 pages, 6 figure

Legumain is an independent predictor for invasive recurrence in breast ductal carcinoma in situ

© 2018, United States & Canadian Academy of Pathology. Legumain is a proteolytic enzyme that plays a role in the regulation of cell proliferation in invasive breast cancer. Studies evaluating its role in ductal carcinoma in situ (DCIS) are lacking. Here, we aimed to characterize legumain protein expression in DCIS and evaluate its prognostic significance. Legumain was assessed immunohistochemically in a tissue microarray of a well-characterized cohort of DCIS (n = 776 pure DCIS and n = 239 DCIS associated with invasive breast cancer (DCIS-mixed)). Legumain immunoreactivity was scored in tumor cells and surrounding stroma and related to clinicopathological parameters and patient outcome. High legumain expression was observed in 23% of pure DCIS and was associated with features of high-risk DCIS including higher nuclear grade, comedo necrosis, hormone receptor negativity, HER2 positivity, and higher proliferation index. Legumain expression was higher in DCIS associated with invasive breast cancer than in pure DCIS (p < 0.0001). In the DCIS-mixed cohort, the invasive component showed higher legumain expression than the DCIS component (p < 0.0001). Legumain was an independent predictor of shorter local recurrencefree interval for all recurrences (p = 0.0003) and for invasive recurrences (p = 0.002). When incorporated with other risk factors, legumain provided better patient risk stratification. High legumain expression is associated with poor prognosis in DCIS and could be a potential marker to predict DCIS progression to invasive disease