Preparedness needs research: How fundamental science and international collaboration accelerated the response to COVID-19

The first cluster of patients suffering from coronavirus disease 2019 (COVID-19) was identified on December 21, 2019, and as of July 29, 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have been linked with 664,333 deaths and number at least 16,932,996 worldwide. Unprecedented in global societal impact, the COVID-19 pandemic has tested local, national, and international preparedness for viral outbreaks to the limits. Just as it will be vital to identify missed opportunities and improve contingency planning for future outbreaks, we must also highlight key successes and build on them. Concomitant to the emergence of a novel viral disease, there is a ‘research and development gap’ that poses a threat to the overall pace and quality of outbreak response during its most crucial early phase. Here, we outline key components of an adequate research response to novel viral outbreaks using the example of SARS-CoV-2. We highlight the exceptional recent progress made in fundamental science, resulting in the fastest scientific response to a major infectious disease outbreak or pandemic. We underline the vital role of the international research community, from the implementation of diagnostics and contact tracing procedures to the collective search for vaccines and antiviral therapies, sustained by unique information sharing efforts

Effectiveness of Protein Supplementation Combined with Resistance Training on Muscle Strength and Physical Performance in Elderly: A Systematic Review and Meta-Analysis

The aim of this study was to evaluate the effectiveness of the combination of resistance training (RT) and protein supplementation (PS), compared to RT alone or combined with a placebo (plS), in the improvement of muscle strength and physical performance. The search strategy in PubMed, Cochrane Library, and Web of Sciences databases found a total of 294 studies. Once inclusion and exclusion criteria were applied, 16 studies were included for the qualitative analysis. A total of 657 healthy elderly (>60 years) participants were analysed. Finally, 15 articles were included in the quantitative analysis with one being excluded due to issues with data availability. Upper-limb, lower-limb, and handgrip strength were the primary outcomes of the meta-analysis. The secondary outcomes, related to physical performance, were Short Physical Performance Battery (SPPB), gait speed, and the five-chair-rise test (5CRT). The main results of the meta-analysis show no statistical differences for upper-limb (SMD: 0.56, 95% CI: -0.09, 1.21, p = 0.09, I2 = 68%), lower-limb (SMD: 0.00, 95% CI: -0.18, 0.18, p = 1.0, I2 = 11%), and handgrip strength (SMD: 0.03, 95% CI: -0.26, 0.32, p = 0.84, I2 = 0%) between the RT + PS and the RT alone (or combined with plS). Moreover, no statistical differences were found relating to physical performance. In view of these results, protein supplementation combined with RT does not provide additional benefits compared to RT alone or with plS in healthy elderly adults

The ACS LCID project. VI. The SFH of the Tucana dSph and the relative ages of the isolated dSph galaxies

We present a detailed study of the star formation history (SFH) of the Tucana dwarf spheroidal galaxy. High quality, deep HST/ACS data, allowed us to obtain the deepest color-magnitude diagram to date, reaching the old main sequence turnoff (F814 ~ 29) with good photometric accuracy. Our analysis, based on three different SFH codes, shows that Tucana is an old and metal-poor stellar system, which experienced a strong initial burst of star formation at a very early epoch (~ 13 Gyr ago) which lasted a maximum of 1 Gyr (sigma value). We are not able to unambiguously answer the question of whether most star formation in Tucana occurred before or after the end of the reionization era, and we analyze alternative scenarios that may explain the transformation of Tucana from a gas-rich galaxy into a dSph. Current measurements of its radial velocity do not preclude that Tucana may have crossed the inner regions of the Local Group once, and so gas stripping by ram pressure and tides due to a close interaction cannot be ruled out. On the other hand, the high star formation rate measured at early times may have injected enough energy into the interstellar medium to blow out a significant fraction of the initial gas content. Gas that is heated but not blown out would also be more easily stripped via ram pressure. We compare the SFH inferred for Tucana with that of Cetus, the other isolated LG dSph galaxy in the LCID sample. We show that the formation time of the bulk of star formation in Cetus is clearly delayed with respect to that of Tucana. This reinforces the conclusion of Monelli et al. (2010) that Cetus formed the vast majority of its stars after the end of the reionization era implying, therefore, that small dwarf galaxies are not necessarily strongly affected by reionization, in agreement with many state-of-the-art cosmological models. [abridged]Comment: Accepted for publication on ApJ, 19 pages, 10 figures, 2 tables. A version with full resolution figures is available at http://www.iac.es/project/LCID/?p=publication

Investigating the potential of Sentinel-2 configuration to predict the quality of Mediterranean permanent grasslands in open woodlands

The assessment of pasture quality in permanent grasslands is essential for their conservation and management, as it can contribute to making real-time decisions for livestock management. In this study, we assessed the potential of Sentinel-2 configuration to predict forage quality in high diverse Mediterranean permanent grasslands of open woodlands. We evaluated the performance of Partial Least Squares Regression (PLS) models to predict crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF) and enzyme digestibility of organic matter (EDOM) by using three different reflectance datasets: (i) laboratory measurements of reflectance of dry and ground pasture samples re-sampled to Sentinel-2 configuration (Spec-lab) (ii) field in-situ measurements of grasslands canopy reflectance resampled to Sentinel-2 configuration (Spec-field); (iii) and Bottom Of Atmosphere Sentinel-2 imagery. For the three reflectance datasets, the models to predict CP content showed moderate performance and predictive ability. Mean R2test = 0.68 were obtained using Spec-lab data, mean R2test decreased by 0.11 with Spec-field and by 0.18 when Sentinel-2 reflectance was used. Statistics for NDF showed worse predictions than those obtained for CP: predictions produced with Spec-lab showed mean R2test = 0.64 and mean RPDtest = 1.73. The mean values of R2test = 0.50 and RPDtest = 1.54 using Sentinel-2 BOA reflectance were marginally better than the values obtained with Spec-field (mean R2test = 0.48, mean RPDtest = 1.43). For ADF and EDOM, only predictions made with Spec-lab produced acceptable results. Bands from the red-edge region, especially band 5, and the SWIR regions showed the highest contribution to estimating CP and NDF. Bands 2, blue and 4, red also seem to be important. The implementation of field spectroscopy in combination with Sentinel-2 imagery proved to be feasible to produce forage quality maps and to develop larger datasets. This study contributes to increasing knowledge of the potential and applicability of Sentinel-2 to predict the quality of Mediterranean permanent grasslands in open woodlands

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors. View Full-TextThe APC was funded by IKERDATA, S.L. under grant 3/12/DP/2021/00102—Area 1: Development of innovative business projects, from Provincial Council of Vizcaya (BEAZ for the Creation of Innovative Business Innovative business ventures)

A colorectal cancer susceptibility new variant at 4q26 in the Spanish population identified by genome-wide association analysis

This work was partially supported by the CENIT program from the Centro Tecnológico Industrial (CEN-20091016), grants from the Spanish Institute of Health Carlos III (ADE10/00026, PI09/02444, PI12/00511, Acción Transversal de Cáncer) grants from the Fondo de Investigacion Sanitaria/FEDER (08/1276, 08/0024, PS09/02368, 11/00219, 11/00681), and by COST office through COST action BM1206. SCB is supported by contracts from the Fondo de Investigación Sanitaria (CP 03-0070). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Centro Tecnológico IndustrialInstituto de Salud Carlos IIIFondo de Investigación Sanitaria / FEDE