Lack of influence of the environment in the earliest stages of massive galaxy formation

We investigate how the environment affects the assembly history of massive galaxies. For that purpose, we make use of SHARDS and HST spectro-photometric data, whose depth, spectral resolution, and wavelength coverage allow to perform a detailed analysis of the stellar emission as well as obtaining unprecedentedly accurate photometric redshifts. This expedites a sufficiently accurate estimate of the local environment and a robust derivation of the star formation histories of a complete sample of 332 massive galaxies ($\mathrm{>10^{10}M_{\odot}}$) at redshift $1\leq z \leq 1.5$ in the GOODS-N field. We find that massive galaxies in this redshift range avoid the lowest density environments. Moreover, we observed that the oldest galaxies in our sample with with mass-weighted formation redshift $\mathrm{\overline{z}_{M-w} \geq 2.5}$, avoid the highest density regions, preferring intermediate environments. Younger galaxies, including those with active star formation, tend to live in denser environments ($\Sigma = \mathrm{5.0_{1.1}^{24.8}\times 10^{10}M_{\odot}Mpc^{-2}}$). This behavior could be expected if those massive galaxies starting their formation first would merge with neighbors and sweep their environment earlier. On the other hand, galaxies formed more recently ($\overline{z}_{M-w} < 2.5$) are accreted into large scale structures at later times and we are observing them before sweeping their environment or, alternatively, they are less likely to affect their environment. However, given that both number and mass surface densities of neighbor galaxies is relatively low for the oldest galaxies, our results reveal a very weak correlation between environment and the first formation stages of the earliest massive galaxies.Comment: Accepted for publication in MNRA

Probing the Star Formation Main Sequence down to 10810^{8} M⊙_\odot at 1.0<z<3.01.0<z<3.0

We investigate the star formation main sequence (MS) (SFR-M$_{\star}$) down to 10$^{8-9}\mathrm{M}_\odot$ using a sample of 34,061 newly-discovered ultra-faint ($27\lesssim i \lesssim 30$ mag) galaxies at $1<z<3$ detected in the GOODS-N field. Virtually these galaxies are not contained in previous public catalogs, effectively doubling the number of known sources in the field. The sample was constructed by stacking the optical broad-band observations taken by the HST/GOODS-CANDELS surveys as well as the 25 ultra-deep medium-band images gathered by the GTC/SHARDS project. Our sources are faint (average observed magnitudes $\sim28.2$ mag, $\sim27.9$ mag), blue (UV-slope $\sim-1.9$), star-forming (rest-frame colors $\sim0.10$ mag, $\sim0.17$ mag) galaxies. These observational characteristics are identified with young (mass-weighted age $\sim0.014$ Gyr) stellar populations subject to low attenuations ($\sim0.30$ mag). Our sample allows us to probe the MS down to $10^{8.0}\,\mathrm{M}_\odot$ at $z=1$ and $10^{8.5}\,\mathrm{M}_\odot$ at $z=3$, around 0.6 dex deeper than previous analysis. In the low-mass galaxy regime, we find an average value for the slope of 0.97 at $1<z<2$ and 1.12 at $2<z<3$. Nearly $\sim$60% of our sample presents stellar masses in the range $10^{6-8}$ M$_\odot$ between $1<z<3$. If the slope of the MS remained constant in this regime, the sources populating the low-mass tail of our sample would qualify as starburst galaxies.Comment: 34 pages, 16 figures, 9 tables. Accepted for publication to Ap

Probing the earliest phases in the formation of massive galaxies with simulated HST+JWST imaging data from Illustris

We use the Illustris-1 simulation to explore the capabilities of the $\textit{Hubble}$ and $\textit{James Webb Space Telescope}$ data to analyze the stellar populations in high-redshift galaxies, taking advantage of the combined depth, spatial resolution, and wavelength coverage. For that purpose, we use simulated broad-band ACS, WFC3 and NIRCam data and 2-dimensional stellar population synthesis (2D-SPS) to derive the integrated star formation history (SFH) of massive (M$_{\ast}>10^{10}\,$M$_{\odot}$) simulated galaxies at $110^{11}\,$M$_{\odot}$ galaxy. In particular, we explore the potential of HST and JWST datasets reaching a depth similar to those of the CANDELS and ongoing CEERS observations, respectively, and concentrate on determining the capabilities of this dataset for characterizing the first episodes in the SFH of local M$_{\ast}>10^{11}\,$M$_{\odot}$ galaxies by studying their progenitors at $z>1$. The 2D-SPS method presented in this paper has been calibrated to robustly recover the cosmic times when the first star formation episodes occurred in massive galaxies, i.e., the first stages in their integrated SFHs. In particular, we discuss the times when the first 1% to 50% of their total stellar mass formed in the simulation. We demonstrate that we can recover these ages with typical median systematic offset of less than 5% and scatter around 20%-30%. According to our measurements on Illustris data, we are able to recover that local M$_{\ast}>10^{11}\,$M$_{\odot}$ galaxies would have started their formation by $z=16$, forming the first 5% of their stellar mass present at $z \sim 1$ by $z=4.5$, 10% by $z=3.7$, and 25% by $z=2.7$.Comment: 28 pages, 13 figures, 4 tables. ApJ in press. Summary of changes from original submission: the major change is that we now include in Sec. 6 the comparison of the results obtained for our sample of massive 1 < z < 4 progenitors with those obtained by considering all massive galaxies at 1 < z < 4 in the simulated images. Several figures and sections have been update

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification

Unraveling the effect of silent, intronic and missense mutations on VWF splicing : contribution of next generation sequencing in the study of mRNA

Large studies in von Willebrand disease patients, including Spanish and Portuguese registries, led to the identification of >250 different mutations. It is a challenge to determine the pathogenic effect of potential splice site mutations on VWF mRNA. This study aimed to elucidate the true effects of 18 mutations on VWF mRNA processing, investigate the contribution of next-generation sequencing to in vivo mRNA study in von Willebrand disease, and compare the findings with in silico prediction. RNA extracted from patient platelets and leukocytes was amplified by RT-PCR and sequenced using Sanger and next generation sequencing techniques. Eight mutations affected VWF splicing: c.1533+1G>A, c.5664+2T>C and c.546G>A (p.=) prompted exon skipping; c.3223-7_3236dup and c.7082-2A>G resulted in activation of cryptic sites; c.3379+1G>A and c.7437G>A) demonstrated both molecular pathogenic mechanisms simultaneously; and the p.Cys370Tyr missense mutation generated two aberrant transcripts. Of note, the complete effect of three mutations was provided by next generation sequencing alone because of low expression of the aberrant transcripts. In the remaining 10 mutations, no effect was elucidated in the experiments. However, the differential findings obtained in platelets and leukocytes provided substantial evidence that four of these would have an effect on VWF levels. In this first report using next generation sequencing technology to unravel the effects of VWF mutations on splicing, the technique yielded valuable information. Our data bring to light the importance of studying the effect of synonymous and missense mutations on VWF splicing to improve the current knowledge of the molecular mechanisms behind von Willebrand disease. identifier:02869074

Elaboración de videograbaciones de casos prácticos teatralizados como medio para el aprendizaje del derecho

Memoria ID-342. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2013-2014

Puesta en marcha de un curso MOOC denominado: formación práctica en la formación inicial de profesorado de secundaria (máster universitario en profesor de educación secundaria obligatoria, bachillerato, formación profesional y enseñanzas de idiomas

Memoria ID-146. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2018-2019

Análisis, diseño de procesos y definición y validación de rúbrica para la evaluación de los Trabajos de Fin de Máster (TFM) pertenecientes a las 19 especialidades del MUPES (Máster Universitario en Profesor de Educación Secundaria Obligatoria, Bachillerato, Formación Profesional y Enseñanzas de Idiomas)

Memoria ID-0051. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2017-2018

Espacios virtuales practicum MUPES. Utilización de todas las potencialidades de la nueva versión de Studium para mejorar la colaboración entre tutores de educación secundaria y tutores de la Universidad de Salamanca

Memoria ID-104. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2020-2021