The Least Luminous Galaxy: Spectroscopy of the Milky Way Satellite Segue 1

We present Keck/DEIMOS spectroscopy of Segue 1, an ultra-low luminosity (M_V = -1.5) Milky Way satellite companion. While the combined size and luminosity of Segue 1 are consistent with either a globular cluster or a dwarf galaxy, we present spectroscopic evidence that this object is a dark matter-dominated dwarf galaxy. We identify 24 stars as members of Segue 1 with a mean heliocentric recession velocity of 206 +/- 1.3 kms. We measure an internal velocity dispersion of 4.3+/-1.2 kms. Under the assumption that these stars are in dynamical equilibrium, we infer a total mass of 4.5^{+4.7}_{-2.5} x 10^5 Msun in the case where mass-follow-light; using a two-component maximum likelihood model, we determine a similar mass within the stellar radius of 50 pc. This implies a mass-to-light ratio of ln(M/L_V) = 7.2^{+1.1}_{-1.2} or M/L_V = 1320^{+2680}_{-940}. The error distribution of the mass-to-light ratio is nearly log-normal, thus Segue 1 is dark matter-dominated at a high significance. Using spectral synthesis modeling, we derive a metallicity for the single red giant branch star in our sample of [Fe/H]=-3.3 +/- 0.2 dex. Finally, we discuss the prospects for detecting gamma-rays from annihilation of dark matter particles and show that Segue 1 is the most promising satellite for indirect dark matter detection. We conclude that Segue 1 is the least luminous of the ultra-faint galaxies recently discovered around the Milky Way, and is thus the least luminous known galaxy.Comment: 12 pages, 6 figures, ApJ accepte

Chemical Cues Influence Pupation Behavior of Drosophila simulans and Drosophila buzzatii in Nature and in the Laboratory

In the wild, larvae of several species of Drosophila develop in heterogeneous and rapidly changing environments sharing resources as food and space. In this scenario, sensory systems contribute to detect, localize and recognize congeners and heterospecifics, and provide information about the availability of food and chemical features of environments where animals live. We investigated the behavior of D. simulans and D. buzzatii larvae to chemicals emitted by conspecific and heterospecific larvae. Our goal was to understand the role of these substances in the selection of pupation sites in the two species that cohabit within decaying prickly pear fruits (Opuntia ficus-indica). In these breeding sites, larvae of D. simulans and D. buzzatii detect larvae of the other species changing their pupation site preferences. Larvae of the two species pupated in the part of the fruit containing no or few heterospecifics, and spent a longer time in/on spots marked by conspecifics rather than heterospecifics. In contrast, larvae of the two species reared in isolation from conspecifics pupated randomly over the substrate and spent a similar amount of time on spots marked by conspecifics and by heterospecifics. Our results indicate that early chemically-based experience with conspecific larvae is critical for the selection of the pupation sites in D. simulans and D. buzzatii, and that pupation site preferences of Drosophila larvae depend on species-specific chemical cues. These preferences can be modulate by the presence of larvae of the same or another species

Phenomenology of Non-Standard Embedding and Five-branes in M-Theory

We study the phenomenology of the strong-coupling limit of $E_8\times E_8$ heterotic string obtained from M-theory, using a Calabi-Yau compactification. After summarizing the standard embedding results, we concentrate on non-standard embedding vacua as well as vacua where non-perturbative objects as five-branes are present. We analyze in detail the different scales of the theory, eleven-dimensional Planck mass, compactification scale, orbifold scale, and how they are related taking into account higher order corrections. To obtain the phenomenologically favored GUT scale is easier than in standard embedding vacua. To lower this scale to intermediate ($\approx 10^{11}$ GeV) or 1 TeV values or to obtain the radius of the orbifold as large as a millimetre is possible. However, we point out that these special limits are unnatural. Finally, we perform a systematic analysis of the soft supersymmetry-breaking terms. We point out that scalar masses larger than gaugino masses can easily be obtained unlike the standard embedding case and the weakly-coupled heterotic string.Comment: Discussions about bounds on $\epsilon_O$ and $e_O$ simplified, minor misprints corrected, more references added. Final results unchange

Reviews and syntheses:Remotely sensed optical time series for monitoring vegetation productivity

International audienceAbstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review

[EN] Purpose: To systematically review evidence regarding the association of multi-parametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. Materials and Methods: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th , 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. Results: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and high-risk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, alpha=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. Conclusion: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.This work was supported by the Spanish Ministry for Investigation, Development and Innovation project with identification number DPI2016-80054-R.Oltra-Sastre, M.; Fuster García, E.; Juan -Albarracín, J.; Sáez Silvestre, C.; Perez-Girbes, A.; Sanz-Requena, R.; Revert-Ventura, A.... (2019). 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Gut microbiota functions: metabolism of nutrients and other food components

The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays

Multitarget-directed ligands combining cholinesterase and monoamine oxidase inhibition with histamine H3R antagonism for neurodegenerative diseases

J.M.C. thanks MINECO (SAF2012-33304 and SAF2015-65586-R). J.M.C., F.L.M., and A.R. thank UCJC for grants 2015-12, 2014-35, and 2015-21, respectively. J.E. thanks the Fondo de Investigaciones Sanitarias (FIS) (ISCIII/FEDER) (Programa Miguel Servet: CP14/00008 and PI16/00735) and Fundación Mutua Madrileña. O.S. and J.J. thank MHCZ-DRO (UHHK 00179906) for support. R.R.R., H.S., and J.M.C. acknowledge the EU COST Actions CM1103 and CM15135. E.P. and H.S. thank the German Research Foundation (DFG; PRO 1405/2-2, PRO 1405/4-1, SFB 1039 A07, and INST208/664-1).The therapy of complex neurodegenerative diseases requires the development of multitarget-directed drugs (MTDs). Novel indole derivatives with inhibitory activity towards acetyl/butyrylcholinesterases and monoamine oxidases A/B as well as the histamine H3 receptor (H3R) were obtained by optimization of the neuroprotectant ASS234 by incorporating generally accepted H3R pharmacophore motifs. These small-molecule hits demonstrated balanced activities at the targets, mostly in the nanomolar concentration range. Additional in vitro studies showed antioxidative neuroprotective effects as well as the ability to penetrate the blood–brain barrier. With this promising in vitro profile, contilisant (at 1 mg kg−1 i.p.) also significantly improved lipopolysaccharide-induced cognitive deficits.PostprintPeer reviewe