Universidad Pablo de Olavide

Context. We present the second Gaia data release, Gaia DR2, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21. In addition epoch astrometry and photometry are provided for a modest sample of minor planets in the solar system. Aims. A summary of the contents of Gaia DR2 is presented, accompanied by a discussion on the differences with respect to Gaia DR1 and an overview of the main limitations which are still present in the survey. Recommendations are made on the responsible use of Gaia DR2 results. Methods. The raw data collected with the Gaia instruments during the first 22 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into this second data release, which represents a major advance with respect to Gaia DR1 in terms of completeness, performance, and richness of the data products. Results. Gaia DR2 contains celestial positions and the apparent brightness in G for approximately 1.7 billion sources. For 1.3 billion of those sources, parallaxes and proper motions are in addition available. The sample of sources for which variability information is provided is expanded to 0.5 million stars. This data release contains four new elements: broad-band colour information in the form of the apparent brightness in the G BP (330–680 nm) and G RP (630–1050 nm) bands is available for 1.4 billion sources; median radial velocities for some 7 million sources are presented; for between 77 and 161 million sources estimates are provided of the stellar effective temperature, extinction, reddening, and radius and luminosity; and for a pre-selected list of 14 000 minor planets in the solar system epoch astrometry and photometry are presented. Finally, Gaia DR2 also represents a new materialisation of the celestial reference frame in the optical, the Gaia -CRF2, which is the first optical reference frame based solely on extragalactic sources. There are notable changes in the photometric system and the catalogue source list with respect to Gaia DR1, and we stress the need to consider the two data releases as independent. Conclusions. Gaia DR2 represents a major achievement for the Gaia mission, delivering on the long standing promise to provide parallaxes and proper motions for over 1 billion stars, and representing a first step in the availability of complementary radial velocity and source astrophysical information for a sample of stars in the Gaia survey which covers a very substantial fraction of the volume of our galaxy.

Gaia is a cornerstone mission in the science programme of the EuropeanSpace Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page.

Context. We present the early installment of the third Gaia data release, Gaia EDR3, consisting of astrometry and photometry for 1.8 billion sources brighter than magnitude 21, complemented with the list of radial velocities from Gaia DR2. Aims. A summary of the contents of Gaia EDR3 is presented, accompanied by a discussion on the differences with respect to Gaia DR2 and an overview of the main limitations which are present in the survey. Recommendations are made on the responsible use of Gaia EDR3 results. Methods. The raw data collected with the Gaia instruments during the first 34 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium and turned into this early third data release, which represents a major advance with respect to Gaia DR2 in terms of astrometric and photometric precision, accuracy, and homogeneity. Results. Gaia EDR3 contains celestial positions and the apparent brightness in G for approximately 1.8 billion sources. For 1.5 billion of those sources, parallaxes, proper motions, and the ( G BP − G RP ) colour are also available. The passbands for G , G BP , and G RP are provided as part of the release. For ease of use, the 7 million radial velocities from Gaia DR2 are included in this release, after the removal of a small number of spurious values. New radial velocities will appear as part of Gaia DR3. Finally, Gaia EDR3 represents an updated materialisation of the celestial reference frame (CRF) in the optical, the Gaia -CRF3, which is based solely on extragalactic sources. The creation of the source list for Gaia EDR3 includes enhancements that make it more robust with respect to high proper motion stars, and the disturbing effects of spurious and partially resolved sources. The source list is largely the same as that for Gaia DR2, but it does feature new sources and there are some notable changes. The source list will not change for Gaia DR3. Conclusions. Gaia EDR3 represents a significant advance over Gaia DR2, with parallax precisions increased by 30 per cent, proper motion precisions increased by a factor of 2, and the systematic errors in the astrometry suppressed by 30–40% for the parallaxes and by a factor ~2.5 for the proper motions. The photometry also features increased precision, but above all much better homogeneity across colour, magnitude, and celestial position. A single passband for G , G BP , and G RP is valid over the entire magnitude and colour range, with no systematics above the 1% level

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7.
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\nAims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release.
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\nMethods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue.
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\nResults. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the Hipparcos and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ~3000 Cepheid and RR Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr-1 for the proper motions. A systematic component of ~0.3 mas should be added to the parallax uncertainties. For the subset of ~94 000 Hipparcos stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr-1. For the secondary astrometric data set, the typical uncertainty of the positions is ~10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ~0.03 mag over the magnitude range 5 to 20.7.
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\nConclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data.

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

A new software package, RASPA, for simulating adsorption and diffusion of molecules in flexible nanoporous materials is presented. The code implements the latest state-of-the-art algorithms for molecular dynamics and Monte Carlo (MC) in various ensembles including symplectic/measure-preserving integrators, Ewald summation, configurational-bias MC, continuous fractional component MC, reactive MC and Baker's minimisation. We show example applications of RASPA in computing coexistence properties, adsorption isotherms for single and multiple components, self- and collective diffusivities, reaction systems and visualisation. The software is released under the GNU General Public License.

Experiments suggest that biodiversity enhances the ability of ecosystems to maintain multiple functions, such as carbon storage, productivity, and the buildup of nutrient pools (multifunctionality). However, the relationship between biodiversity and multifunctionality has never been assessed globally in natural ecosystems. We report here on a global empirical study relating plant species richness and abiotic factors to multifunctionality in drylands, which collectively cover 41% of Earth's land surface and support over 38% of the human population. Multifunctionality was positively and significantly related to species richness. The best-fitting models accounted for over 55% of the variation in multifunctionality and always included species richness as a predictor variable. Our results suggest that the preservation of plant biodiversity is crucial to buffer negative effects of climate change and desertification in drylands.

Climate change affects human health; however, there have been no large-scale, systematic efforts to quantify the heat-related human health impacts that have already occurred due to climate change. Here, we use empirical data from 732 locations in 43 countries to estimate the mortality burdens associated with the additional heat exposure that has resulted from recent human-induced warming, during the period 1991–2018. Across all study countries, we find that 37.0% (range 20.5–76.3%) of warm-season heat-related deaths can be attributed to anthropogenic climate change and that increased mortality is evident on every continent. Burdens varied geographically but were of the order of dozens to hundreds of deaths per year in many locations. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to minimize the public health impacts of climate change. Current and future climate change is expected to impact human health, both indirectly and directly, through increasing temperatures. Climate change has already had an impact and is responsible for 37% of warm-season heat-related deaths between 1991 and 2018, with increases in mortality observed globally.

Ustilago maydis is an important fungal pathogen of maize, causing corn smut. It is well adapted to its host and proliferates in living plant tissue without inducing a defence response. The genome sequence of U. maydis has now been determined, the first for a biotrophic plant parasite. Several gene clusters that encode secreted proteins of unknown function were identified: genome-wide expression analysis shows that the clustered genes are upregulated during disease. Mutations in these gene clusters frequently affect virulence, ranging from complete loss of pathogenicity to hypervirulence. Ustilago maydis is a ubiquitous pathogen of maize and a well-established model organism for the study of plant–microbe interactions1. This basidiomycete fungus does not use aggressive virulence strategies to kill its host. U. maydis belongs to the group of biotrophic parasites (the smuts) that depend on living tissue for proliferation and development2. Here we report the genome sequence for a member of this economically important group of biotrophic fungi. The 20.5-million-base U. maydis genome assembly contains 6,902 predicted protein-encoding genes and lacks pathogenicity signatures found in the genomes of aggressive pathogenic fungi, for example a battery of cell-wall-degrading enzymes. However, we detected unexpected genomic features responsible for the pathogenicity of this organism. Specifically, we found 12 clusters of genes encoding small secreted proteins with unknown function. A significant fraction of these genes exists in small gene families. Expression analysis showed that most of the genes contained in these clusters are regulated together and induced in infected tissue. Deletion of individual clusters altered the virulence of U. maydis in five cases, ranging from a complete lack of symptoms to hypervirulence. Despite years of research into the mechanism of pathogenicity in U. maydis, no ‘true’ virulence factors3 had been previously identified. Thus, the discovery of the secreted protein gene clusters and the functional demonstration of their decisive role in the infection process illuminate previously unknown mechanisms of pathogenicity operating in biotrophic fungi. Genomic analysis is, similarly, likely to open up new avenues for the discovery of virulence determinants in other pathogens.

Soil bacteria and fungi play key roles in the functioning of terrestrial ecosystems, yet our understanding of their responses to climate change lags significantly behind that of other organisms. This gap in our understanding is particularly true for drylands, which occupy ∼41% of Earth´s surface, because no global, systematic assessments of the joint diversity of soil bacteria and fungi have been conducted in these environments to date. Here we present results from a study conducted across 80 dryland sites from all continents, except Antarctica, to assess how changes in aridity affect the composition, abundance, and diversity of soil bacteria and fungi. The diversity and abundance of soil bacteria and fungi was reduced as aridity increased. These results were largely driven by the negative impacts of aridity on soil organic carbon content, which positively affected the abundance and diversity of both bacteria and fungi. Aridity promoted shifts in the composition of soil bacteria, with increases in the relative abundance of Chloroflexi and α-Proteobacteria and decreases in Acidobacteria and Verrucomicrobia. Contrary to what has been reported by previous continental and global-scale studies, soil pH was not a major driver of bacterial diversity, and fungal communities were dominated by Ascomycota. Our results fill a critical gap in our understanding of soil microbial communities in terrestrial ecosystems. They suggest that changes in aridity, such as those predicted by climate-change models, may reduce microbial abundance and diversity, a response that will likely impact the provision of key ecosystem services by global drylands.

Aridity, which is increasing worldwide because of climate change, affects the structure and functioning of dryland ecosystems. Whether aridification leads to gradual (versus abrupt) and systemic (versus specific) ecosystem changes is largely unknown. We investigated how 20 structural and functional ecosystem attributes respond to aridity in global drylands. Aridification led to systemic and abrupt changes in multiple ecosystem attributes. These changes occurred sequentially in three phases characterized by abrupt decays in plant productivity, soil fertility, and plant cover and richness at aridity values of 0.54, 0.7, and 0.8, respectively. More than 20% of the terrestrial surface will cross one or several of these thresholds by 2100, which calls for immediate actions to minimize the negative impacts of aridification on essential ecosystem services for the more than 2 billion people living in drylands.

Production of biogas from different organic materials is a most interesting source of renewable energy. The biomethane potential (BMP) of these materials has to be determined to get insight in design parameters for anaerobic digesters. Although several norms and guidelines for BMP tests exist, inter-laboratory tests regularly show high variability of BMPs for the same substrate. A workshop was held in June 2015, in Leysin, Switzerland, with over 40 attendees from 30 laboratories around the world, to agree on common solutions to the conundrum of inconsistent BMP test results. This paper presents the consensus of the intense roundtable discussions and cross-comparison of methodologies used in respective laboratories. Compulsory elements for the validation of BMP results were defined. They include the minimal number of replicates, the request to carry out blank and positive control assays, a criterion for the test duration, details on BMP calculation, and last but not least criteria for rejection of the BMP tests. Finally, recommendations on items that strongly influence the outcome of BMP tests such as inoculum characteristics, substrate preparation, test setup, and data analysis are presented to increase the probability of obtaining validated and reproducible results.

BACKGROUND: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. METHODS: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature-mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature-mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. FINDINGS: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967-5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58-11·07) of all deaths (8·52% [6·19-10·47] were cold-related and 0·91% [0·56-1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60-87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000-03 to 2016-19, the global cold-related excess death ratio changed by -0·51 percentage points (95% eCI -0·61 to -0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13-0·31), leading to a net reduction in the overall ratio. The largest decline in overall excess death ratio occurred in South-eastern Asia, whereas excess death ratio fluctuated in Southern Asia and Europe. INTERPRETATION: Non-optimal temperatures are associated with a substantial mortality burden, which varies spatiotemporally. Our findings will benefit international, national, and local communities in developing preparedness and prevention strategies to reduce weather-related impacts immediately and under climate change scenarios. FUNDING: Australian Research Council and the Australian National Health and Medical Research Council.

Context. Gaia Data Release 2 provides high-precision astrometry and three-band photometry for about 1.3 billion sources over the full sky. The precision, accuracy, and homogeneity of both astrometry and photometry are unprecedented. Aims. We highlight the power of the Gaia DR2 in studying many fine structures of the Hertzsprung-Russell diagram (HRD). Gaia allows us to present many different HRDs, depending in particular on stellar population selections. We do not aim here for completeness in terms of types of stars or stellar evolutionary aspects. Instead, we have chosen several illustrative examples. Methods. We describe some of the selections that can be made in Gaia DR2 to highlight the main structures of the Gaia HRDs. We select both field and cluster (open and globular) stars, compare the observations with previous classifications and with stellar evolutionary tracks, and we present variations of the Gaia HRD with age, metallicity, and kinematics. Late stages of stellar evolution such as hot subdwarfs, post-AGB stars, planetary nebulae, and white dwarfs are also analysed, as well as low-mass brown dwarf objects. Results. The Gaia HRDs are unprecedented in both precision and coverage of the various Milky Way stellar populations and stellar evolutionary phases. Many fine structures of the HRDs are presented. The clear split of the white dwarf sequence into hydrogen and helium white dwarfs is presented for the first time in an HRD. The relation between kinematics and the HRD is nicely illustrated. Two different populations in a classical kinematic selection of the halo are unambiguously identified in the HRD. Membership and mean parameters for a selected list of open clusters are provided. They allow drawing very detailed cluster sequences, highlighting fine structures, and providing extremely precise empirical isochrones that will lead to more insight in stellar physics. Conclusions. Gaia DR2 demonstrates the potential of combining precise astrometry and photometry for large samples for studies in stellar evolution and stellar population and opens an entire new area for HRD-based studies.

PURPOSE: This study aimed to analyze the acute mechanical and metabolic response to resistance exercise protocols (REP) differing in the number of repetitions (R) performed in each set (S) with respect to the maximum predicted number (P). METHODS: Over 21 exercise sessions separated by 48-72 h, 18 strength-trained males (10 in bench press (BP) and 8 in squat (SQ)) performed 1) a progressive test for one-repetition maximum (1RM) and load-velocity profile determination, 2) tests of maximal number of repetitions to failure (12RM, 10RM, 8RM, 6RM, and 4RM), and 3) 15 REP (S × R[P]: 3 × 6[12], 3 × 8[12], 3 × 10[12], 3 × 12[12], 3 × 6[10], 3 × 8[10], 3 × 10[10], 3 × 4[8], 3 × 6[8], 3 × 8[8], 3 × 3[6], 3 × 4[6], 3 × 6[6], 3 × 2[4], 3 × 4[4]), with 5-min interset rests. Kinematic data were registered by a linear velocity transducer. Blood lactate and ammonia were measured before and after exercise. RESULTS: Mean repetition velocity loss after three sets, loss of velocity pre-post exercise against the 1-m·s load, and countermovement jump height loss (SQ group) were significant for all REP and were highly correlated to each other (r = 0.91-0.97). Velocity loss was significantly greater for BP compared with SQ and strongly correlated to peak postexercise lactate (r = 0.93-0.97) for both SQ and BP. Unlike lactate, ammonia showed a curvilinear response to loss of velocity, only increasing above resting levels when R was at least two repetitions higher than 50% of P. CONCLUSIONS: Velocity loss and metabolic stress clearly differs when manipulating the number of repetitions actually performed in each training set. The high correlations found between mechanical (velocity and countermovement jump height losses) and metabolic (lactate, ammonia) measures of fatigue support the validity of using velocity loss to objectively quantify neuromuscular fatigue during resistance training.

In recent decades there has been growing interest in the nature and scale of scientific collaboration. Studies into co‐authorship have taken two different approaches. The first one attempts to analyse the reasons why authors collaborate and the consequences of such decision ( Laband and Tollison, 2000 ). The second approach is based on the idea that co‐authorship creates a social network of researchers ( Barabási et al., 2002 ; Moody, 2004 ; Newman, 2001 ). In this study we have carried out an exploratory analysis of co‐authorships in the field of management from the two aforementioned approaches. The results obtained show a growing tendency of the co‐authored papers in the field of management, similar to what can be observed in other disciplines. Our study analyses some of the underpinning factors, which have been highlighted in the literature, explaining this tendency. Thus, the progressive quantitative character of research and the influence of the collaboration on the articles' impact are enhanced. The network analysis permits the exploration of the peculiarities of the management in comparison with other fields of knowledge, as well as the existing linkages between the most central and prominent authors within this discipline.

Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyper-parameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

This study examined the possibility of using movement velocity as an indicator of relative load in the bench press (BP) exercise. One hundred and twenty strength-trained males performed a test (T1) with increasing loads for the individual determination of the one-repetition maximum (1RM) and full load-velocity profile. Fifty-six subjects performed the test on a second occasion (T2) following 6 weeks of training. A very close relationship between mean propulsive velocity (MPV) and load (%1RM) was observed (R (2)=0.98). Mean velocity attained with 1RM was 0.16+/-0.04 m x s(-1) and was found to influence the MPV attained with each %1RM. Despite a mean increase of 9.3% in 1RM from T1 to T2, MPV for each %1RM remained stable. Stability in the load-velocity relationship was also confirmed regardless of individual relative strength. These results confirm an inextricable relationship between relative load and MPV in the BP that makes it possible to: 1) evaluate maximal strength without the need to perform a 1RM test, or test of maximum number of repetitions to failure (XRM); 2) determine the %1RM that is being used as soon as the first repetition with any given load is performed; 3) prescribe and monitor training load according to velocity, instead of percentages of 1RM or XRM.

Age-related accumulation of cellular damage and death has been linked to oxidative stress. Calorie restriction (CR) is the most robust, nongenetic intervention that increases lifespan and reduces the rate of aging in a variety of species. Mechanisms responsible for the antiaging effects of CR remain uncertain, but reduction of oxidative stress within mitochondria remains a major focus of research. CR is hypothesized to decrease mitochondrial electron flow and proton leaks to attenuate damage caused by reactive oxygen species. We have focused our research on a related, but different, antiaging mechanism of CR. Specifically, using both in vivo and in vitro analyses, we report that CR reduces oxidative stress at the same time that it stimulates the proliferation of mitochondria through a peroxisome proliferation-activated receptor coactivator 1 alpha signaling pathway. Moreover, mitochondria under CR conditions show less oxygen consumption, reduce membrane potential, and generate less reactive oxygen species than controls, but remarkably they are able to maintain their critical ATP production. In effect, CR can induce a peroxisome proliferation-activated receptor coactivator 1 alpha-dependent increase in mitochondria capable of efficient and balanced bioenergetics to reduce oxidative stress and attenuate age-dependent endogenous oxidative damage.