A MegaCam Survey of Outer Halo Satellites. VI: The Spatially Resolved Star Formation History of the Carina Dwarf Spheroidal Galaxy

We present the spatially resolved star formation history (SFH) of the Carina dwarf spheroidal galaxy, obtained from deep, wide-field g,r imaging and a metallicity distribution from the literature. Our photometry covers $\sim2$ deg$^2$, reaching up to $\sim10$ times the half-light radius of Carina with a completeness higher than $50\%$ at $g\sim24.5$, more than one magnitude fainter than the oldest turnoff. This is the first time a combination of depth and coverage of this quality has been used to derive the SFH of Carina, enabling us to trace its different populations with unprecedented accuracy. We find that Carina's SFH consists of two episodes well separated by a star formation temporal gap. These episodes occurred at old ($>10$ Gyr) and intermediate ($2$-$8$ Gyr) ages. Our measurements show that the old episode comprises the majority of the population, accounting for $54\pm5\%$ of the stellar mass within $1.3$ times the King tidal radius, while the total stellar mass derived for Carina is $1.60\pm0.09\times 10^{6} M_{\rm{\odot}}$, and the stellar mass-to-light ratio $1.8\pm0.2$. The SFH derived is consistent with no recent star formation which hints that the observed blue plume is due to blue stragglers. We conclude that the SFH of Carina evolved independently of the tidal field of the Milky Way, since the frequency and duration of its star formation events do not correlate with its orbital parameters. This result is supported by the age/metallicity relation observed in Carina, and the gradients calculated indicating that outer regions are older and more metal poor.Comment: Accepted in ApJ (22 pages, 13 figures

Application of Tensor Neural Networks to Pricing Bermudan Swaptions

The Cheyette model is a quasi-Gaussian volatility interest rate model widely used to price interest rate derivatives such as European and Bermudan Swaptions for which Monte Carlo simulation has become the industry standard. In low dimensions, these approaches provide accurate and robust prices for European Swaptions but, even in this computationally simple setting, they are known to underestimate the value of Bermudan Swaptions when using the state variables as regressors. This is mainly due to the use of a finite number of predetermined basis functions in the regression. Moreover, in high-dimensional settings, these approaches succumb to the Curse of Dimensionality. To address these issues, Deep-learning techniques have been used to solve the backward Stochastic Differential Equation associated with the value process for European and Bermudan Swaptions; however, these methods are constrained by training time and memory. To overcome these limitations, we propose leveraging Tensor Neural Networks as they can provide significant parameter savings while attaining the same accuracy as classical Dense Neural Networks. In this paper we rigorously benchmark the performance of Tensor Neural Networks and Dense Neural Networks for pricing European and Bermudan Swaptions, and we show that Tensor Neural Networks can be trained faster than Dense Neural Networks and provide more accurate and robust prices than their Dense counterparts.Comment: 15 pages, 9 figures, 2 table

Derivation and external validation of the SIMPLICITY score as a simple immune-based risk score to predict infection in kidney transplant recipients

Existing approaches for infection risk stratification in kidney transplant recipients are suboptimal. Here, we aimed to develop and validate a weighted score integrating non-pathogen-specific immune parameters and clinical variables to predict the occurrence of post-transplant infectious complications. To this end, we retrospectively analyzed a single-center derivation cohort of 410 patients undergoing kidney transplantation in 2008-2013 in Madrid. Peripheral blood lymphocyte subpopulations, serum immunoglobulin and complement levels were measured at one-month post-transplant. The primary and secondary outcomes were overall and bacterial infection through month six. A point score was derived from a logistic regression model and prospectively applied on a validation cohort of 522 patients undergoing kidney transplantation at 16 centers throughout Spain in 2014-2015. The SIMPLICITY score consisted of the following variables measured at month one after transplantation: C3 level, CD4+ T-cell count, CD8+ T-cell count, IgG level, glomerular filtration rate, recipient age, and infection within the first month. The discrimination capacity in the derivation and validation cohorts was good for overall (areas under the receiver operating curve of 0.774 and 0.730) and bacterial infection (0.767 and 0.734, respectively). The cumulative incidence of overall infection significantly increased across risk categories in the derivation (low-risk 13.7%; intermediate-risk, 35.9%; high-risk 77.6%) and validation datasets (10.2%, 28.9% and 50.4%, respectively). Thus, the SIMPLICITY score, based on easily available immune parameters, allows for stratification of kidney transplant recipients at month one according to their expected risk of subsequent infection

A Fungal Versatile GH10 Endoxylanase and Its Glycosynthase Variant: Synthesis of Xylooligosaccharides and Glycosides of Bioactive Phenolic Compounds

The study of endoxylanases as catalysts to valorize hemicellulosic residues and to obtain glycosides with improved properties is a topic of great industrial interest. In this work, a GH10 β-1,4-endoxylanase (XynSOS), from the ascomycetous fungus Talaromyces amestolkiae, has been het- erologously produced in Pichia pastoris, purified, and characterized. rXynSOS is a highly glycosylated monomeric enzyme of 53 kDa that contains a functional CBM1 domain and shows its optimal activity on azurine cross-linked (AZCL)–beechwood xylan at 70 ◦C and pH 5. Substrate specificity and kinetic studies confirmed its versatility and high affinity for beechwood xylan and wheat arabi- noxylan. Moreover, rXynSOS was capable of transglycosylating phenolic compounds, although with low efficiencies. For expanding its synthetic capacity, a glycosynthase variant of rXynSOS was developed by directed mutagenesis, replacing its nucleophile catalytic residue E236 by a glycine (rXynSOS-E236G). This novel glycosynthase was able to synthesize β-1,4-xylooligosaccharides (XOS) of different lengths (four, six, eight, and ten xylose units), which are known to be emerging prebiotics. rXynSOS-E236G was also much more active than the native enzyme in the glycosylation of a broad range of phenolic compounds with antioxidant properties. The interesting capabilities of rXynSOS and its glycosynthase variant make them promising tools for biotechnological application.This research was funded by the MICIU/AEI/FEDER [RTI2018-093683-B-I00, RTI2018- 094751-B-C22, PID2019-107476GB-I00], Comunidad de Madrid [RETOPROSOST-2-CM P2018/EMT- 4459], and CIBERES (an initiative from the Spanish Institute of Health Carlos IPeer reviewe

Ligand-Directed Chemistry on Glycoside Hydrolases – A Proof of Concept Study

Selective covalent labelling of enzymes using small molecule probes has advanced the scopes of protein profiling. The covalent bond formation to a specific target is the key step of activity-based protein profiling (ABPP), a method which has become an indispensable tool for measuring enzyme activity in complex matrices. With respect to carbohydrate processing enzymes, strategies for ABPP so far involve labelling the active site of the enzyme, which results in permanent loss of activity. Here, we report in a proof of concept study the use of ligand-directed chemistry (LDC) for labelling glycoside hydrolases near – but not in – the active site. During the labelling process, the competitive inhibitor is cleaved from the probe, departs the active site and the enzyme maintains its catalytic activity. To this end, we designed a building block synthetic concept for small molecule probes containing iminosugar-based reversible inhibitors for labelling of two model β-glucosidases. The results indicate that the LDC approach can be adaptable for covalent proximity labelling of glycoside hydrolases.T. M. W. thanks the FWF (Wien, Austria) for financial support (project number P30372-B21). Authors from TU Graz acknowledge support from NAWI Graz.Peer reviewe

Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma

RATIONALE: Albuterol, a bronchodilator medication, is the first-line therapy for asthma worldwide. There are significant racial/ethnic differences in albuterol drug response. OBJECTIVES: To identify genetic variants important for bronchodilator drug response (BDR) in racially diverse children. METHODS: We performed the first whole-genome sequencing pharmacogenetics study from 1,441 children with asthma from the tails of the BDR distribution to identify genetic association with BDR. MEASUREMENTS AND MAIN RESULTS: We identified population-specific and shared genetic variants associated with BDR, including genome-wide significant (P \u3c 3.53 × 10 CONCLUSIONS: The lack of minority data, despite a collaboration of eight universities and 13 individual laboratories, highlights the urgent need for a dedicated national effort to prioritize diversity in research. Our study expands the understanding of pharmacogenetic analyses in racially/ethnically diverse populations and advances the foundation for precision medicine in at-risk and understudied minority populations

IGAPS: the merged IPHAS and UVEX optical surveys of the Northern Galactic Plane

The INT Galactic Plane Survey (IGAPS) is the merger of the optical photometric surveys, IPHAS and UVEX, based on data from the Isaac Newton Telescope (INT) obtained between 2003 and 2018. Here, we present the IGAPS point source catalogue. It contains 295.4 million rows providing photometry in the filters, i, r, narrow-band Hα, g, and U_(RGO). The IGAPS footprint fills the Galactic coordinate range, |b| 5σ confidence)