Non-relativistic ten-dimensional minimal supergravity

We construct a non-relativistic limit of ten-dimensional N=1 supergravity from the point of view of the symmetries, the action, and the equations of motion. This limit can only be realized in a supersymmetric way provided we impose by hand a set of geometric constraints, invariant under all the symmetries of the non-relativistic theory, that define a so-called `self-dual' Dilatation-invariant String Newton-Cartan geometry. The non-relativistic action exhibits three emerging symmetries: one local scale symmetry and two local conformal supersymmetries. Due to these emerging symmetries the Poisson equation for the Newton potential and two partner fermionic equations do not follow from a variation of the non-relativistic action but, instead, are obtained by a supersymmetry variation of the other equations of motion that do follow from a variation of the non-relativistic action. We shortly discuss the inclusion of the Yang-Mills sector that would lead to a non-relativistic heterotic supergravity action.Comment: 40 pages, minor change

Generalized Newton-Cartan geometries for particles and strings

We discuss the generalized Newton-Cartan geometries that can serve as gravitational background fields for particles and strings. In order to enable us to define affine connections that are invariant under all the symmetries of the structure group, we describe torsionful geometries with independent torsion tensors. A characteristic feature of the non-Lorentzian geometries we consider is that some of the torsion tensors are so-called ‘intrinsic torsion’ tensors. Setting some components of these intrinsic torsion tensors to zero leads to constraints on the geometry. For both particles and strings, we discuss various such constraints that can be imposed consistently with the structure group symmetries. In this way, we reproduce several results in the literature.</p

Sperm motility in fish: technical applications and perspectives through CASA systems

[EN] Although a relatively high number of sperm quality biomarkers have been reported over the years in several fish species, sperm motility is nowadays considered the best biomarker for fish spermatozoa. The first scientific reports focusing on fish sperm motility date from a century ago, but the objective assessment allowed by computer-aided sperm analysis (CASA-Mot) systems was not applied to fish species until the mid-1980s. Since then, a high number of sperm kinetic parameters from more than 170 fish species have been reported in more than 700 scientific articles, covering a wide range of topics, such as sperm physiology, sperm storage, broodstock management, the phenomenon of sperm competition, ecotoxicology and understanding the life cycle of the species. The sperm kinetic parameters provided by CASA-Mot systems can serve as powerful and useful tools for aquaculture and ecological purposes, and this review provides an overview of the major research areas in which fish sperm motility assessment by a CASA-Mot system has been used successfully.This writing of this manuscript as received funding from the European Union's Horizon 2020 Research and Innovation program under the Marie Sklodowska-Curie Grant Agreement No. 642893 (ETN IMPRESS). V. Gallego has a postdoctoral grant from the Universitat Politecnica de Valencia (PAID-10-16).Gallego Albiach, V.; Asturiano Nemesio, JF. (2018). Sperm motility in fish: technical applications and perspectives through CASA systems. Reproduction Fertility and Development. 30(6):820-832. https://doi.org/10.1071/RD17460S82083230

Viral Coinfections in Hospitalized Coronavirus Disease 2019 Patients Recruited to the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK Study

Background: We conducted this study to assess the prevalence of viral coinfection in a well characterized cohort of hospitalized coronavirus disease 2019 (COVID-19) patients and to investigate the impact of coinfection on disease severity. Methods: Multiplex real-time polymerase chain reaction testing for endemic respiratory viruses was performed on upper respiratory tract samples from 1002 patients with COVID-19, aged <1 year to 102 years old, recruited to the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK study. Comprehensive demographic, clinical, and outcome data were collected prospectively up to 28 days post discharge. Results: A coinfecting virus was detected in 20 (2.0%) participants. Multivariable analysis revealed no significant risk factors for coinfection, although this may be due to rarity of coinfection. Likewise, ordinal logistic regression analysis did not demonstrate a significant association between coinfection and increased disease severity. Conclusions: Viral coinfection was rare among hospitalized COVID-19 patients in the United Kingdom during the first 18 months of the pandemic. With unbiased prospective sampling, we found no evidence of an association between viral coinfection and disease severity. Public health interventions disrupted normal seasonal transmission of respiratory viruses; relaxation of these measures mean it will be important to monitor the prevalence and impact of respiratory viral coinfections going forward

Combining literature-based and data-driven fuzzy models to predict brown trout (salmo trutta l.) spawning habitat degradation induced by climate change

[EN] A fuzzy rule-based system combining empirical data on hydraulic preferences and literature information on temperature requirements was used to foresee the brown trout (Salmo trutta L.) spawning habitat degradation induced by climate change. The climatic scenarios for the Cabriel River (Eastern Iberian Peninsula) corresponded to two Representative Concentration Pathways (4.5 and 8.5) for the short (2011¿2040) and mid (2041¿2070) term horizons. The hydraulic and hydrologic modelling were undertaken with process-based numerical models (i.e., River2D© and HBV-light) while the water temperature was modelled by assembling the predictions of three machine learning techniques (M5, Multi-Adaptive Regression Splines and Support Vector Regression). The predicted rise in the water temperature will not be compensated by the more benign lower flows. Consequently, the suitable spawning habitat will be reduced between 15.4¿48.7%. The entire population shall suffer the effects of climate change and will probably be extirpated from the downstream segments of the river.The study has been partially funded by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) and FEDER funds and by the Confederación Hidrográfica del Júcar (Spanish Ministry of Agriculture, Food and Environment). The authors thank AEMET and UC for the data provided for this work (dataset Spain02). Finally, we are grateful to the colleagues who worked in the field and in preliminary data analyses; especially Marcello Minervini (funded by the EU programme of Erasmus Traineeships, at the Dept. of Hydraulic Engineering and Environment, Universitat Politècnica de València).Muñoz Mas, R.; Marcos-García, P.; Lopez-Nicolas, A.; Martínez-García, F.; Pulido-Velazquez, M.; Martinez-Capel, F. (2018). Combining literature-based and data-driven fuzzy models to predict brown trout (salmo trutta l.) spawning habitat degradation induced by climate change. Ecological Modelling. 386:98-114. https://doi.org/10.1016/j.ecolmodel.2018.08.012S9811438