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Background Since the release of the first global hepatitis elimination targets in 2016, and until the COVID-19 pandemic started in early 2020, many countries and territories were making progress toward hepatitis C virus (HCV) elimination. This study aims to evaluate HCV burden in 2020, and forecast HCV burden by 2030 given current trends. Methods This analysis includes a literature review, Delphi process, and mathematical modelling to estimate HCV prevalence (viraemic infection, defined as HCV RNA-positive cases) and the cascade of care among people of all ages (age =0 years from birth) for the period between Jan 1, 2015, and Dec 31, 2030. Epidemiological data were collected from published sources and grey literature (including government reports and personal communications) and were validated among country and territory experts. A Markov model was used to forecast disease burden and cascade of care from 1950 to 2050 for countries and territories with data. Model outcomes were extracted from 2015 to 2030 to calculate population-weighted regional averages, which were used for countries or territories without data. Regional and global estimates of HCV prevalence, cascade of care, and disease burden were calculated based on 235 countries and territories. Findings Models were built for 110 countries or territories: 83 were approved by local experts and 27 were based on published data alone. Using data from these models, plus population-weighted regional averages for countries and territories without models (n=125), we estimated a global prevalence of viraemic HCV infection of 0.7% (95% UI 0.7-0.9), corresponding to 56.8 million (95% UI 55.2-67.8) infections, on Jan 1, 2020. This number represents a decrease of 6.8 million viraemic infections from a 2015 (beginning of year) prevalence estimate of 63.6 million (61.8-75.8) infections (0.9% [0.8-1.0] prevalence). By the end of 2020, an estimated 12.9 million (12.5-15.4) people were living with a diagnosed viraemic infection. In 2020, an estimated 641 000 (623 000-765 000) patients initiated treatment. Interpretation At the beginning of 2020, there were an estimated 56.8 million viraemic HCV infections globally. Although this number represents a decrease from 2015, our forecasts suggest we are not currently on track to achieve global elimination targets by 2030. As countries recover from COVID-19, these findings can help refocus efforts aimed at HCV elimination. Copyright (C) 2022 Elsevier Ltd. All rights reserved., John C Martin Foundation [2019-G024]; Gilead Sciences [IN-US-987-5808]; AbbVie [4200907861]; ZeShan Foundation [2021-0101-1-CDA-HEP-10]; The Hepatitis Fund; Ministry of Health, Labour and Welfare of Japan [19HC1001], This analysis was funded by a grant from the John C Martin Foundation (2019-G024) through the Polaris Observatory for low-income and middleincome countries. Grants for analyses in high-income countries and territories were provided by Gilead Sciences (IN-US-987-5808) and AbbVie (4200907861). ZeShan Foundation (2021-0101-1-CDA-HEP-10) supported country and regional analyses in Asia and The Hepatitis Fund supported country and regional analyses in Africa. We thank the Epidemiological Research Group on the Burden of Viral Hepatitis and Measures for its Elimination (grant number 19HC1001; led by JT) funded by the Ministry of Health, Labour and Welfare of Japan. We thank the contributors included in the appendix (pp 2-3), who contributed to the country or territory analyses but did not meet authorship requirements.

Plastic pollution is distributed patchily around the world’s oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species.

Seabirds often spend time on the water in the vicinity of their breeding colonies at the start or end of foraging trips, which may be for bathing, social interaction, information transfer, or to reduce predation risk for small petrels that prefer to return to land in darkness. Although such behaviour (hereafter rafting) is common, there are few data on variation in its incidence or timing across species, or analyses of relationships with intrinsic or extrinsic factors such as breeding stage (reflecting central-place foraging constraints) or weather. Here, we use GPS and immersion data collected over multiple years at Bird Island, South Georgia, to investigate rafting behaviour of four albatross and one burrow-nesting petrel species. Nearly all tracked birds (89%) landed within 10 km of the colony at the start of foraging trips for ~ 30 min, whereas only 17% did so at the end, suggesting they likely use rafting mainly for plumage maintenance after extended breeding shifts on land. Rafting duration, distance and bearing from the colony varied markedly according to species, wind speeds and period of the day (daylight vs. darkness), which may reflect differences in foraging direction, time constraints, degree of plumage soiling, diel activity patterns, or the requirement for high wind speeds for efficient flight. Given that all the study populations are decreasing, and most individuals make extensive use of nearshore waters during the breeding season, effective marine spatial planning is required that eliminates or mitigates human risks around their colonies.

Plastic pollution is distributed patchily around the world’s oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and nonbreeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species

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SPIRIT checklist for Buruli-RifDACC: Evaluation of the efficacy and cost-effectiveness of high-dose versus standard-dose Rifampicin on outcomes in Mycobacterium ulcerans disease, a protocol for a randomised controlled trial in Ghana, This project is funded by the National Institute for Health and Care Research (NIHR) under its Research and Innovation for Global Health Transformation (RIGHT) Programme [Grant Reference Number NIHR200125]. The views expressed are those of the author(s) and not necessarily those of the NIHR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Animal tracking has afforded insights into patterns of space use in numerous species and thereby informed areabased conservation planning. A crucial consideration when estimating spatial distributions from tracking data is whether the sample of tracked animals is representative of the wider population. However, it may also be important to track animals in multiple years to capture changes in distribution in response to varying environmental conditions. Using GPS-tracking data from 23 seabird species, we assessed the importance of multi-year sampling for identifying important sites for conservation during the chick-rearing period, when seabirds are most spatially constrained. We found a high degree of spatial overlap among distributions from different years in most species. Multi-year sampling often captured a significantly higher portion of reference distributions (based on all data for a population) than sampling in a single year. However, we estimated that data from a single year would on average miss only 5 % less of the full distribution of a population compared to equal-sized samples collected across three years (min: − 0.3 %, max: 17.7 %, n = 23). Our results suggest a key consideration for identifying important sites from tracking data is whether enough individuals were tracked to provide a representative estimate of the population distribution during the sampling period, rather than that tracking necessarily take place in multiple years. By providing an unprecedented multi-species perspective on annual spatial consistency, this work has relevance for the application of tracking data to informing the conservation of seabirds. info:eu-repo/semantics/publishedVersion

Funder: British Antarctic Survey; Id: http://dx.doi.org/10.13039/501100007849, Funder: Foreign and Commonwealth Office; Id: http://dx.doi.org/10.13039/501100000617, Funder: Gates Cambridge Trust; Id: http://dx.doi.org/10.13039/501100005370, Funder: Government of South Georgia and the South Sandwich Islands, Funder: South African National Antarctic Program, Foraging niche specialization is thought to occur when different members of speciose communities divide resources in either time or space. Here we compared habitat preferences of the congeneric Grey Petrel Procellaria cinerea and White‐chinned Petrel Procellaria aequinoctialis, tracked in the same calendar year using GPS loggers from Gough Island and Bird Island (South Georgia), respectively. We identified periods of active foraging and determined habitat characteristics using remote‐sensing data. Although these highly pelagic species could potentially overlap at sea across large areas, they showed markedly different foraging preferences during their incubation periods, which are temporally offset because Grey Petrels breed during the austral winter. Grey Petrels foraged mostly in pelagic cold‐water areas to the north‐west of South Georgia, whereas White‐chinned Petrels foraged almost exclusively in the warm, shallow waters of the Patagonian Shelf. Within each species, foraging habitat characteristics were highly consistent. Our results demonstrate the diversity of habitat preferences within genera, and provide further evidence that colony‐specific information on habitat preference is crucial to identify important feeding areas for pelagic predators.

PURPOSE: Degenerative cervical myelopathy (DCM) is the most common cause of adult spinal cord dysfunction worldwide. However, the current incidence of DCM is poorly understood. The Hospital Episode Statistics (HES) database contains details of all secondary care admissions across NHS hospitals in England. This study aimed to use HES data to characterise surgical activity for DCM in England. METHODS: The HES database was interrogated for all cases of DCM between 2012 and 2019. DCM cases were identified from 5 ICD-10 codes. Age-stratified values were collected for 'Finished Consultant Episodes' (FCEs), which correspond to a patient's hospital admission under a lead clinician. Data was analysed to explore current annual activity and longitudinal change. RESULTS: 34,903 FCEs with one or more of the five ICD-10 codes were identified, of which 18,733 (53.6%) were of working age (18-64 years). Mean incidence of DCM was 7.44 per 100,000 (SD ± 0.32). Overall incidence of DCM rose from 6.94 per 100,000 in 2012-2013 to 7.54 per 100,000 in 2018-2019. The highest incidence was seen in 2016-2017 (7.94 per 100,000). The median male number of FCEs per year (2919, IQR: 228) was consistently higher than the median female number of FCEs per year (2216, IQR: 326). The rates of both emergency admissions and planned admissions are rising. CONCLUSIONS: The incidence of hospitalisation for DCM in England is rising. Health care policymakers and providers must recognise the increasing burden of DCM and act to address both early diagnoses and access to treatment in future service provision plans.

Modern corporations have increasingly been adopting a decentred, layered, and multi-jurisdictional form as a strategy of boundary manipulation known amongst tax lawyers and accountants as ‘regulatory arbitrage’. The argument we put forward in this article is that the scholarly work that treats these strategies as mere tax avoidance practices has contributed to an underestimation and misrecognition of the way contemporary multinationals operate in markets. These strategies, which we explain in terms of arbitrage power, exploit the difference between exchanges in an imaginary ‘smooth’ market of the economic textbook and a global market that is divided among legal authorities, each imposing their own rules, regulations, and taxations. Arbitrage power exploits differences between the location of market exchange and the location of the registration of property title transfers, combining this with a manipulation of formal systems for recognizing business enterprises in order to escape some or all the rules and regulations of society. The result is a marked difference between the ‘brochure multinational’, the way multinationals are seen and presented in their glossy brochures, and the way multinationals are legally and practically organized nowadays.

Additional file 1. Table S1. FASTA sequences of HIV-1 fragments used in phylogenetic analysis.

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.

The authors also thank funding sources: the Natural Environment Research Council (NERC; UK National Capability award number NE/R016429/1 as part of the UKSCaPE programme); Joint Nature Conservatio Committee (JNCC); Environment and Climate Change Canada; Natural Resources Canada; New Bedford Harbor Trustee Council; The Norwegian Environment Agency (and its predecessors), the SEAPOP programme (www.seapop.no) and its key institutions: The Norwegian Institute for Nature Research, The Norwegian Polar Institute and Tromsø University Museum and the French Polar Institute. 1. Timing of breeding, an important driver of fitness in many populations, is widely studied in the context of global change, yet despite considerable efforts to identify environmental drivers of seabird nesting phenology, for most populations we lack evidence of strong drivers. Here we adopt an alternative approach, examining the degree to which different populations positively covary in their annual phenology to infer whether phenological responses to environmental drivers are likely to be (i) shared across species at a range of spatial scales, (ii) shared across populations of a species, or (iii) idiosyncratic to populations. 2. We combined 51 long-term datasets on breeding phenology spanning 50 years from nine seabird species across 29 North Atlantic sites and examined the extent to which different populations share early versus late breeding seasons depending on a hierarchy of spatial scales comprising breeding site, small-scale region, large-scale region and the whole North Atlantic. 3. In about a third of cases we found laying dates of populations of different species sharing the same breeding site or small-scale breeding region were positively correlated, which is consistent with the hypothesis that they share phenological responses to the same environmental conditions. In comparison we found no evidence for positive phenological covariation among populations across species aggregated at larger spatial scales. 4. In general we found little evidence for positive phenological covariation between populations of a single species, and in many instances the inter-year variation specific to a population was substantial, consistent with each population responding idiosyncratically to local environmental conditions. Black-legged kittiwake (Rissa tridactyla) was the exception, with populations exhibiting positive covariation in laying dates that decayed with the distance between breeding sites, suggesting that populations may be responding to a similar driver. 5. Our approach sheds light on the potential factors that may drive phenology in our study species, thus furthering our understanding of the scales at which different seabirds interact with interannual variation in their environment. We also identify additional systems and phenological questions to which our inferential approach could be applied. Postprint

Background: Recent studies demonstrated higher prevalence rates of Tropheryma whipplei (T. whipplei) in HIV positive than in HIV negative subjects. However, associations with the immune status in HIV positive participants were conflicting. Methods: For this cross-sectional study, stool samples of 906 HIV positive and 98 HIV negative individuals in Ghana were tested for T. whipplei. Additionally, sociodemographic parameters, clinical symptoms, medical drug intake, and laboratory parameters were assessed. Results: The prevalence of T. whipplei was 5.85% in HIV positive and 2.04% in HIV negative participants. Within the group of HIV positive participants, the prevalence reached 7.18% in patients without co-trimoxazole prophylaxis, 10.26% in subjects with ART intake, and 12.31% in obese participants. Frequencies of clinical symptoms were not found to be higher in HIV positive T. whipplei carriers compared to T. whipplei negative participants. Markers of immune activation were lower in patients colonized with T. whipplei. Multivariate regression models demonstrated an independent relationship of a high CD4+ T cell count, a low HIV-1 viral load, and an obese body weight with the presence of T. whipplei. Conclusions: Among HIV positive individuals, T. whipplei colonization was associated with a better immune status but not with clinical consequences. Our data suggest that the withdrawal of co-trimoxazole chemoprophylaxis among people living with HIV on stable cART regimen may inadvertently increase the propensity towards colonization with T. whipplei.

Distribution shifts—where the training distribution differs from the test distribution—can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets widely used in the ML community today. To address this gap, we present WILDS, a curated benchmark of 10 datasets reflecting a diverse range of distribution shifts that naturally arise in real-world applications, such as shifts across hospitals for tumor identification; across camera traps for wildlife monitoring; and across time and location in satellite imaging and poverty mapping. On each dataset, we show that standard training yields substantially lower out-of-distribution than in-distribution performance. This gap remains even with models trained by existing methods for tackling distribution shifts, underscoring the need for new methods for training models that are more robust to the types of distribution shifts that arise in practice. To facilitate method development, we provide an open-source package that automates dataset loading, contains default model architectures and hyperparameters, and standardizes evaluations. The full paper, code, and leaderboards are available at https://wilds.stanford.edu.

In recent years, federated learning has been embraced as an approach for bringing about collaboration across large populations of learning agents. However, little is known about how collaboration protocols should take agents' incentives into account when allocating individual resources for communal learning in order to maintain such collaborations. Inspired by game theoretic notions, this paper introduces a framework for incentive-aware learning and data sharing in federated learning. Our stable and envy-free equilibria capture notions of collaboration in the presence of agents interested in meeting their learning objectives while keeping their own sample collection burden low. For example, in an envy-free equilibrium, no agent would wish to swap their sampling burden with any other agent and in a stable equilibrium, no agent would wish to unilaterally reduce their sampling burden. In addition to formalizing this framework, our contributions include characterizing the structural properties of such equilibria, proving when they exist, and showing how they can be computed. Furthermore, we compare the sample complexity of incentive-aware collaboration with that of optimal collaboration when one ignores agents' incentives.

As temperatures rise, timing of reproduction is changing at different rates across trophic levels, potentially resulting in asynchrony between consumers and their resources. The match-mismatch hypothesis (MMH) suggests that trophic asynchrony will have negative impacts on average productivity of consumers. It is also thought to lead to selection on timing of breeding, as the most asynchronous individuals will show the greatest reductions in fitness.Using a 30-year individual-level dataset of breeding phenology and success from a population of European shags on the Isle of May, Scotland, we tested a series of predictions consistent with the hypothesis that fitness impacts of trophic asynchrony are increasing.These predictions quantified changes in average annual breeding success and strength of selection on timing of breeding, over time and in relation to rising sea surface temperature (SST) and diet composition.Annual average (population) breeding success was negatively correlated with average lay date yet showed no trend over time, or in relation to increasing SST or the proportion of principal prey in the diet, as would be expected if trophic mismatch was increasing. At the individual level, we found evidence for stabilising selection and directional selection for earlier breeding, although the earliest birds were not the most productive. However, selection for earlier laying did not strengthen over time, or in relation to SST or slope of the seasonal shift in diet from principal to secondary prey. We found that the optimum lay date advanced by almost 4 weeks during the study, and that the population mean lay date tracked this shift.Our results indicate that average performance correlates with absolute timing of breeding of the population, and there is selection for earlier laying at the individual level. However, we found no fitness signatures of a change in the impact of climate-induced trophic mismatch, and evidence that shags are tracking long-term shifts in optimum timing. This suggests that if asynchrony is present in this system, breeding success is not impacted. Our approach highlights the advantages of examining variation at both population and individual levels when assessing evidence for fitness impacts of trophic asynchrony.

Most tree species predominantly associate with a single type of mycorrhizal fungi, which can differentially affect plant nutrient acquisition and biogeochemical cycling. Uncertainties in mycorrhizal distributions are non-trivial, and current estimates disagree in up to 50% over 40% of the land area, including tropical forests. Remote sensing capabilities for mycorrhizal detection show promise for refining these estimates further. Here, we address for the first time the impact of mycorrhizal distributions on global carbon and nutrient cycling. Using the state-of-the-art carbon-nitrogen economics within the Community Land Model version 5, we found Net Primary Productivity (NPP) increased throughout the 21st century by 20%; however, as soil nitrogen has progressively become limiting, the costs to NPP for nitrogen acquisition—that is, to mycorrhizae—have increased at a faster rate by 60%. This suggests that nutrient acquisition will increasingly demand a higher portion of assimilated carbon to support the same productivity., Geophysical Research Letters, 48 (19), ISSN:0094-8276, ISSN:1944-8007

Additional file 3: Supplemental Figure S2. Feature distributions across species.

Additional file 1: Supplemental Information and Figure S1 and Table S1. Details of magnetometer calibration.

Additional file 4: This contains comparison tables of assessment of quality of life; association of mental disorder and functional limitation between participants with active and past BU infection and comparison of mental disorders between individuals with active BU infection and caregivers.

Additional file 2: Supplemental Table S2. Description of candidation features for the hidden Markov models.

Contents Summary 507 I. Introduction 507 II. The return on investment approach 508 III. CO₂ response spectrum 510 IV. Discussion 516 Acknowledgements 518 References 518 SUMMARY: Land ecosystems sequester on average about a quarter of anthropogenic CO₂ emissions. It has been proposed that nitrogen (N) availability will exert an increasingly limiting effect on plants' ability to store additional carbon (C) under rising CO2 , but these mechanisms are not well understood. Here, we review findings from elevated CO₂ experiments using a plant economics framework, highlighting how ecosystem responses to elevated CO₂ may depend on the costs and benefits of plant interactions with mycorrhizal fungi and symbiotic N-fixing microbes. We found that N-acquisition efficiency is positively correlated with leaf-level photosynthetic capacity and plant growth, and negatively with soil C storage. Plants that associate with ectomycorrhizal fungi and N-fixers may acquire N at a lower cost than plants associated with arbuscular mycorrhizal fungi. However, the additional growth in ectomycorrhizal plants is partly offset by decreases in soil C pools via priming. Collectively, our results indicate that predictive models aimed at quantifying C cycle feedbacks to global change may be improved by treating N as a resource that can be acquired by plants in exchange for energy, with different costs depending on plant interactions with microbial symbionts.

Additional file 1: Data collection tools.

The 'corporation' (or 'company' in English jurisprudence or 'societé' in French law) is a legal person that owns assets and partakes in contracts in markets. The 'multinational corporation,' otherwise known as 'transnational corporation' or the 'multinational enterprise' (MNE) is a group, an organization engaged in coordinating business activities by a variety of other entities, usually corporations. The MNE is not a legal person, and hence it does not own things or partake in contracts. Towards the end of the 20th century, the typical MNE, including the majority of the important global players in the energy trading sector, has evolved into highly complex, multisubsidiary, multi-jurisdictional organization, often spanning hundreds, if not thousands, of independent corporate entities, linked together by a complex web of ownership arrangements. Managers control the corporation on behalf of the firm. Despite common assumptions of this hierarchical relationship, they are not, in fact, the agents of shareholders but officers of the corporation. Considering that managers are officers of the corporations and not the agents of shareholders, throughout recent history of the rise of MNEs, suppliers of finance to the corporation had to make sure that managers do not steal the capital they supply, do not waste it on bad projects and more broadly, get managers to return some of the profits to them. Gradually, countries began to introduce rules, laws and regulations that were aimed to safeguard the rights of investors in these entities. A complex and sophisticated system of corporate governance, including reporting of financial activities by the group as a whole and by each of the corporate entity under its control has emerged as ways by which suppliers of finance to corporations assure themselves of getting a return on their investment. (...)

Additional file 3: STROBE checklist.

Additional file 2: Translated data collection tools.

Additional file 4: Supplemental Table S3. HMM-converged parameters for the Weibull distribution of each feature and state.

Mercury (Hg) is an environmental contaminant which, at high concentrations, can negatively influence avian physiology and demography. Albatrosses (Diomedeidae) have higher Hg burdens than all other avian families. Here, we measure total Hg (THg) concentrations of body feathers from adult grey-headed albatrosses (Thalassarche chrysostoma) at South Georgia. Specifically, we (i) analyse temporal trends at South Georgia (1989–2013) and make comparisons with other breeding populations; (ii) identify factors driving variation in THg concentrations and (iii) examine relationships with breeding success. Mean ± s.d. feather THg concentrations were 13.0 ± 8.0 µg g−1 dw, which represents a threefold increase over the past 25 years at South Georgia and is the highest recorded in the Thalassarche genus. Foraging habitat, inferred from stable isotope ratios of carbon (δ13C), significantly influenced THg concentrations—feathers moulted in Antarctic waters had far lower THg concentrations than those moulted in subantarctic or subtropical waters. THg concentrations also increased with trophic level (δ15N), reflecting the biomagnification process. There was limited support for the influence of sex, age and previous breeding outcome on feather THg concentrations. However, in males, Hg exposure was correlated with breeding outcome—failed birds had significantly higher feather THg concentrations than successful birds. These results provide key insights into the drivers and consequences of Hg exposure in this globally important albatross population.

The massive number of seabirds (penguins and procellariiformes) and marine mammals (cetaceans and pinnipeds) – referred to here as top predators – is one of the most iconic components of the Antarctic and Southern Ocean. They play an important role as highly mobile consumers, structuring and connecting pelagic marine food webs and are widely studied relative to other taxa. Many birds and mammals establish dense breeding colonies or use haul-out sites, making them relatively easy to study. Cetaceans, however, spend their lives at sea and thus aspects of their life cycle are more complicated to monitor and study. Nevertheless, they all feed at sea and their reproductive success depends on the food availability in the marine environment, hence they are considered useful indicators of the state of the marine resources. In general, top predators have large body sizes that allow for instrumentation with miniature data-recording or transmitting devices to monitor their activities at sea. Development of scientific techniques to study reproduction and foraging of top predators has led to substantial scientific literature on their population trends, key biological parameters, migratory patterns, foraging and feeding ecology, and linkages with atmospheric or oceanographic dynamics, for a number of species and regions. We briefly summarize the vast literature on Southern Ocean top predators, focusing on the most recent syntheses. We also provide an overview on the key current and emerging pressures faced by these animals as a result of both natural and human causes. We recognize the overarching impact that environmental changes driven by climate change have on the ecology of these species. We also evaluate direct and indirect interactions between marine predators and other factors such as disease, pollution, land disturbance and the increasing pressure from global fisheries in the Southern Ocean. Where possible we consider the data availability for assessing the status and trends for each of these components, their capacity for resilience or recovery, effectiveness of management responses, risk likelihood of key impacts and future outlook.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible. Fil: Pastorello, Gilberto. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Trotta, Carlo. University of Tuscia. DiBAf; Italia Fil: Canfora, Eleonora. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Chu, Housen. Lawrence Berkeley national Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Christianson, Danielle. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Cheah, You - Wei. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Poindexter, Cristina. California State University. Department of Civil Engineering; Estados Unidos Fil: Chen, Jiquan. Michigan State University. Department of Geography, Environment, and Spatial Sciences; Estados Unidos Fil: Elbashandy, Abdelrah man. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Humphrey, Marty. University of Virginia. Department of Computer Science; Estados Unidos Fil: Isaac, Peter. TeRn Ecosystrem Processes; Australia Fil: Polidori, Diego. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Riveca, Alessio. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: van Ingen, Catharine. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Zhang, Leiming. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China Fil: Amiro, Brian. University of Manitoba. Department of Soil Science; Canadá Fil: Ammann, Christof. Agroscope Research Institute. Department of Agroecology and Environment; Suiza Fil: Altaf Arain, M. McMaster University. School of Geography and Earth Sciences; Canadá. Fil: Ardo, Jonas. Lund University. Department of Physical Geography and Ecosystem Science; Suecia Fil: Arkebauer, Timothy. University of Nebraska-Lincoln. Department of Agronomy and Horticulture; Estados Unidos Fil: Arndt, Stefan K. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia Fil: Arriga, Nicola. University of Antwerp. Department of Biology, Research Group PLECO; Bélgica. European Commission. Joint Research Centre; Italia Fil: Aubinet, Marc. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Aurela, Mika. Finnish Meteorological Institute; Finlandia Fil: Baldocchi, Dennis. University of California Berkeley. ESPM; Estados Unidos Fil: Barr, Alan. University of Saskatchewan. Global Institute for Water Security; Canadá. Environment and Climate Change Canada. Climate Research Division; Canadá. Fil: Beamesderfer, Eric. McMaster University. School of Geography and Earth Sciences; Canadá. Fil: Belelli Marchesini, Luca. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia. RUDN University. Agrarian-Technological Institute. Department of Landscape Design and Sustainable Ecosystems; Rusia Fil: Bergeron, Onil. Ministère du Développement durable de l’Environnement et de la Lutte contre les Changements Climatiques. Direction du Marché du Carbone; Canadá. Fil: Beringer, Jason. University of Western Australia. School of Agriculture and Environment; Australia. Fil: Bernhofer, Christia. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Berveiller, Daniel. Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution; Francia Fil: Billesbach, Dave. University of Nebraska-Lincoln. Biological Systems Engineering; Estados Unidos Fil: Black, Tomas Andrew. University of British Columbia. Faculty of Land and Food Systems; Canadá. Fil: Blanken, Peter D. University of Colorado. Department of Geography; Estados Unidos Fil: Bohrer, Gil. Ohio State University. Department of Civil, Environmental & Geodetic Engineering; Estados Unidos Fil: Boike, Julia Alfred. Wegener Institute. Helmholtz Centre for Polar and Marine Research; Alemania. Humboldt-Universität zu Berlin. Geography Department; Alemania Fil: Bolstad, Paul V. University of Minnesota. Forest Resources; Estados Unidos Fil: Bonal, Damien. Université de Lorraine, AgroParisTech, INRAE, UMR Silva; Francia Fil: Bonnefond, Jean - Marc. ISPA, Bordeaux Sciences Agro, INRAE; Francia Fil: Bowling, David R. University of Utah. School of Biological Sciences; Estados Unidos Fil: Bracho, Rosuel. University of Florida.School of Forest Resources and Conservation; Estados Unidos Fil: Brodeur, Jason. McMaster University. McMaster University Library; Estados Unidos Fil: Brummer, Christian. Federal Research Institute of Rural Areas, Forestry and Fisheries. Thünen Institute of Climate-Smart Agriculture; Alemania Fil: Buchmann, Nina. ETH. Department of Environmental Systems Science; Suiza Fil: Burban, Benoit. INRAE UMR ECOFOG; Guyana Francesa Fil: Burns, Sean P. University of Colorado. Department of Geography; Estados Unidos. National Center for Atmospheric Research. Mesoscale and Microscale Meteorology Laboratory; Estados Unidos Fil: Buysse, Pauline. Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Cale, Peter. Australian Landscape Trust; Australia Fil: Cavagna, Mauro. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Cellier, Pierre. Université Paris-Saclay. INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Che, Shiping. Chinese Academy of Sciences. Institute of Botany. State Key Laboratory of Vegetation and Environmental Change; China Fil: Chini, Issac. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Christensen, Storben. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Cleverly, James. University of Technology. School of Life Sciences; Australia. University of Technology. Terrestrial Ecosystem Research Network; Australia. Fil: Collalti, Alessio. University of Tuscia. DiBAf; Italia. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Consalvo, Claudia. University of Tuscia. DiBAf; Italia. National Research Council of Italy. Research Institute on Terrestrial Ecosystems; Italia Fil: Cook, Bruce. NASA Goddard Space Flight Center. Biospheric Sciences Laboratory; Estados Unidos Fil: Cook, David. Argonne National Laboratory. Environmental Science Division; Estados Unidos Fil: Coursolle, Carole. Natural Resources Canada. Canadian Forest Service; Canadá. Université Laval. Faculté de Foresterie, de Géographie et de Géomatique. Centre d’étude de la Forêt; Canadá Fil: Cremonose, Edoardo. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Curtis, Peter. Ohio State University. Department of Evolution, Ecology, and Organismal Biology; Estados Unidos Fil: D'Andrea, Ettore. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: da Rocha, Humberto. Universidade de São Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil Fil: Dai, Xiaoqin. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. Fil: Davis, Kenneth. The Pennsylvania State University. Department of Meteorology and Atmospheric Science; Estados Unidos Fil: De Cinti, Bruno. National Research Council of Italy. Institute of Research on Terrestrial Ecosystems; Italia Fil: de Grandcourt, Agnes. UMR Eco&Sols, CIRAD; Francia Fil: De Ligne, Anne. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: De Oliveira, Raimundo C. Pedology, Embrapa Amazonia Oriental; Brasil. Fil: Delpierre, Nicolas. Université Paris-Saclay. CNRS, AgroParisTech, Ecologie Systématique et Evolution; Francia Fil: Desai, Ankur R. University of Wisconsin-Madison. Atmospheric and Oceanic Sciences; Estados Unidos Fil: Di Bella, Carlos Marcelo. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina Fil: di Tommasi, Paul. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Dolman, Han. Vrije Universiteit Amsterdam. Department of Earth Sciences; Holanda Fil: Domingo, Francisco. CSIC. Experimental Station of Arid Zones. Desertification and Geoecology Department; España Fil: Dong, Gang. Shanxi University. School of Life Science; China. Fil: Dore, Sabina. HydroFocus; Estados Unidos Fil: Duce, Pierpaolo. National Research Council of Italy. Institute of Bioeconomy; Italia Fil: Dufrêne, Eric. Université Paris-Saclay, CNRS., AgroParisTech. Ecologie Systématique et Evolution; Francia Fil: Dunn, Allison. Worcester State University. Department of Earth, Environment, and Physics; Estados Unidos Fil: Dušek, Jiri. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Eamus Derek. University of Technology. School of Life Sciences; Australia. Fil: Eichelmann, Uwe.Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: ElKhidir, Hatim abdalla M. elObeid. Agricultural Research Corporation. Research Station; Sudán Fil: Eugster, Wener. ETH. Department of Environmental Systems Science; Suiza Fil: Ewenz, Cacilia M. Airborne Research Australia. TERN Ecosystem Processes Central Node; Australia Fil: Ewers, Brent. University of Wyoming. Department of Botany. Program in Ecology; Estados Unidos Fil: Famulari, Daniela. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Fares, Silvano. National Research Council of Italy. Institute of BioEconomy; Italia Fil: Feigenwinter, Iris. ETH. Department of Environmental Systems Science; Suiza Fil: Feitz, Andrew. Geoscience Australia; Australia Fil: Fensholt, Rasmus. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Filippa, Gianluca. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Fischer, Marc. Lawrence Berkeley National Laboratory. Energy Analysis & Environmental Impacts Division; Estados Unidos Fil: Frank, John. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Galvagno, Marta. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Gharun, Mana. ETH. Department of Environmental Systems Science; Suiza Fil: Gianelle, Damiano. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Gielen, Bert. University of Antwerp. Department of Biology. Research Group PLECO; Bélgica Fil: Gioli, Beniamino. National Research Council of Italy. Institute of BioEconomy; Italia Fil: Gitelson, Anatoly. University of Nebraska-Lincoln. School of natural Resources; Estados Unidos Fil: Goded, Ignacio. Joint Research Centre, European Commission; Italia Fil: Goeckede, Mathias. Max Planck Institute for Biogeochemistry; Alemania Fil: Goldstein. Allen H. University of California Berkeley. ESPM; Estados Unidos Fil: Gough, Christopher M. Virginia Commonwealth University. Department of Biology; Estados Unidos Fil: Goulden, Michael L. University of California Irvine. Department of Earth System Science; Estados Unidos Fil: Graf, Alexander. Agrosphere. (IBG3), Forschungszentrum Jülich; Alemania Fil: Griebel, Anne. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia Fil: Gruening, Carsten. Joint Research Centre, European Commission; Italia Fil: Grünwald, Thomas. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Hammerle, Albin. University of Innsbruck. Department of Ecology; Austria. Fil: Han, Shijie. Henan University. School of Life Sciences. International Joint Research Laboratory for Global Change Ecology; China. Chinese Academy of Sciences. Institute of Applied Ecology; China. Fil: Han, Xingguo. Chinese Academy of Sciences.Institute of Botany. State Key Laboratory of Vegetation and Environmental Change; China Fil: Hansen, Birger Ulf. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Hanson, Chad. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: , Juha Hatakka, Juha. Finnish Meteorological Institute; Finlandia Fil: He, Yongtao. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. University of Chinese Academy of Sciences. College of Resources and Environment; China Fil: Hehn, Markus. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Heinesch, Bernard. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Hinko-Najera, Nina. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia. Fil: Hörtnagl, Lukas. ETH. Department of Environmental Systems Science; Suiza. Fil: Hutley, Lindsay. Charles Darwin University. Research Institute for the Environment and Livelihoods; Australia Fil: Ibrom, Andreas. Technical University of Denmark. Department of Environmental Engineering; Dinamarca Fil: Ikawa, Hiroki. National Agriculture and Food Research Organization. Institute for Agro-Environmental Sciences; Japón Fil: Jackowicz-Korczynski, Marcin. Lund University. Department of Physical Geography and Ecosystem Science; Suecia. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Janouš, Dalibor. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Jans, Wilma. Wageningen University and Research. Wageningen Environmental Research; Holanda Fil: Jassal, Rachhpal. University of British Columbia. Faculty of Land and Food Systems; Canadá. Fil: Jiang, Shicheng. Ministry of Education, Northeast Normal University. Key Laboratory of Vegetation Ecology; China Fil: Kato, Tomomichi. Hokkaido University. Research Faculty of Agriculture; Japón. Hokkaido University. GICore; Japón Fil: Khomik, Myroslava. Geography and Environmental Management; Canadá. Fil: Klatt, Janina. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Knohl, Alexander. University of Goettingen. Bioclimatology; Alelmania. University of Goettingen. Centre of Biodiversity and Sustainable Land Use; Alemania Fil: Knox, Sara. The University of British Columbia. Department of Geography; Canadá Fil: Kobayashi, Hideki. Japan Agency for Marine-Earth Science and Technology. Research Institute for Global change, Institute of Arctic Climate and Environment Research; Japón Fil: Koerber, Georgia. University of Adelaide. Biological Sciences; Australia. Fil: Kolle, Olaf. Max Planck Institute for Biogeochemistry; Alemania Fil: Kosugi, Yoshiko. Kyoto University. Graduate School of Agriculture; Japón Fil: Kotani, Ayumi. Nagoya University. Graduate School of Bioagricultural Sciences; Japón Fil: Kowalski, Andrew. University of Granada. Department of Applied Physics; España Fil: Kruijt, Bart. Wageningen University, Wageningen. Water systems and Global Change group; Holanda Fil: Kurbatova; Julia A. Russian Academy of Sciences. Severtsov institute of Ecology and Evolution; Rusia Fil: Kutsch, Werner L. Integrated Carbon Observation System (ICOS ERIC). Head Office; Finlandia Fil: Kwon, Hyojung. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: Launiainen, Samuli. Natural Resources Institute Finland; Finlandia Fil: Laurila, Tuomas. Finnish Meteorological Institute; Finlandia Fil: Law, Bev. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: Leuning. Ray. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Li, Yingnian. Chinese Academy of Sciences. Northwest institute of Plateau Biology. Key Laboratory of Adaptation and Evolution of Plateau Biota; China Fil: Liddell, Michael. James Cook University. Centre for Tropical Environmental Sustainability Studies; Australia. Fil: Limousin, Jean-Marc. CEFE, CNRS, Univ Montpellier: Francia Fil: Lion, Marryanna. Forest Research Institute Malaysia. Forestry and Environment Division; Malasia Fil: Lohila, Annalea. University of Helsinki. Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: López-Ballesteros, Ana. Trinity College Dublin. School of Natural Sciences. Department of Botany; Irlanda Fil: López-Blanco, Efren. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Loubet, Benjamin. Université Paris-Saclay. INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Lucas-Moffat, Antje. Centre for Agrometeorological Research. German Meteorological Service; Alemania Fil: Lüers, Johannes. University of Bayreuth. Micrometeorology; Alemania. Bayreuth Center of Ecology and Environmental Research; Alemania Fil: Ma, Siyan. University of California Berkeley. ESPM; Estados Unidos Fil: Macfarlane, Craig. CSIRO Land and Water; Australia Fi: Magliulo, Vincenzo. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Mammarella, Ivan. University of Helsinki . Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: Manca, Giovanni. Joint Research Centre, European Commission; Italia Fil: Marras, Serena. University of Sassari. Department of Agriculture; Italia Fil: Massman, William. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Mastepanov, Mikhail. University of Oulu. Oulanka Research Station; Finlandia Fil: Matamala, Roser. Argonne National Laboratory. Environmental Science Division; Estados Unidos Fil: Matthes, Jaclyn Hatala. Wellesley College. Department of Biological Sciences; Estados Unidos Fil: , Mazzenga, Francesco. National Research Council of Italy. Research Institute on Terrestrial Ecosystems; Italia Fil: McCaughey, Harry. Queen’s University. Department of Geography and Planning; Canadá. Fil: McHugh, Ian. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia. Fil: McMillan, Andrew M.S. Environmental Analytics nZ, Ltd.; Nueva Zelanda Fil: Merbold, Lutz. International Livestock Research Institute. Mazingira Centre; Kenia. Fil: Meyer, Wayne. University of Adelaide. Biological Sciences; Australia. Fil: Meyers, Tilden. NOAA/OAR/Air Resources Laboratory; Estados Unidos Fil: Miller, Scott D. State University of New York at Albany. Atmospheric Sciences Research Center; Estados Unidos Fil: Minerbi, Stefano. Forest Department of South Tyrol; Italia Fil: Monson, Russell K. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Montagnani, Leonardo. Forest Department of South Tyrol; Italia. Free University of Bolzano. Faculty of Science and Technology; Italia Fil: Moore, Caitlin E. University of Illinois at Urbana-Champaign. Department of Plant Biology; Estados Unidos Fil: Moors, Eddy. IHE Delft: Holanda. VU Amsterdam. Faculty of Science; Holanda Fil: Moreaux, Virginie. University Grenoble Alpes; Francia Fil: Moureaux, Christine. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Munger, J. William. Harvard University. School of engineering and Applied Sciences; Estados Unidos. Harvard University. Department of Earth and Planetary Sciences; Estados Unidos Fil: Nakai, Taro. National Taiwan University. School of forestry and Resource conservation; Taiwan. University of Alaska Fairbanks. International Arctic Research Center; Estados Unidos Fil: Neirynck, Johan. Research Institute for Nature and Forest. Environment and Climate; Bélgica Fil: Nesic, Zoran. University of British Columbia. Faculty of Land and Food Systems; Canadá Fil: Nicolini, Giacomo. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Noormets, Asko. Texas A&M University. College Station. Department of Ecosystem Science and Management; Estados Unidos Fil: Northwood, Matthew. Charles Darwin University. Research Institute for the Environment and Livelihoods; Australia Fil: Nosetto, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática Aplicada San Luis. Grupo de Estudios Ambientales. Universidad Nacional de San Luis; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina Fil: Nouvellon, Yann. Université Montpellier-CIRAD-INRA-IRD-Montpellier SupAgro. Eco&Sols; Francia Fil: Novick, Kimberly O’Neill. Indiana University Bloomington. School of Public and Environmental Affairs; Estados Unidos Fil: Oechel, Walter. San Diego State University. Department of Biology. Global Change Research Group; Estados Unidos. University of Exeter. College of Life and Environmental Sciences. Department of Geography; Reino Unido Fil: Olesen, Jørgen Eivind. Aarhus University. Department of Agroecology; Dinamarca. Aarhus University. iCLIMATE; Dinamarca Fil: Ourcival, Jean-Marc. CEFE, CNRS, Université Montpellier; Francia Fil: Papuga, Shirley A. Wayne State University. Department of Geology; Estados Unidos Fil: Parmentier, Frans-Jan. University of Oslo. Department of Geosciences; Noruega Fil: Paul-Limoges, Eugenie. University of Zurich. Department of Geography; Suiza Fil: Pavelka, Marian. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Peichl, Matthias. Swedish University of Agricultural Sciences. Department of Forest Ecology and Management; Suecia Fil: Pendall, Elise. Western Sydney University. Hawkesbury Institute for the Environment; Australia Fil: Phillips, Richard P. Indiana University Bloomington. Department of Biology; Estados Unidos Fil: Pilegaard, Kim. Technical University of Denmark. Department of Environmental Engineering; Dinamarca Fil: Pirk, Norbert. CSiRO Land and Water; Australia. Fil: Posse Beaulieu, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Powell, Thomas. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Prasse, Heiko. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Reed, David. Michigan State University. Center for Global Change & Earth Observations: Estados Unidos. Michigan State University. Center for Global Change & Earth Observations; Estados Unidos Fil: Resco de Dios, Víctor. Western Sydney University. Hawkesbury Institute for the Environment; Australia. Southwest University of Science and Technology. School of Life Science and Engineering; China Fil: Restrepo-Coupe, Natalia. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Reverter, Borja R. Universidade Federal da Paraiba. Departamento de Química e Física; Brasil Fil: Roland, Marilyn. University of Antwerp. Department of Biology. Research Group PLECO; Bélgica Fil: Sabbatini, Simone. University of Tuscia. DiBAf; Italia. Fil: Sachs, Torsten. GfZ German Research Centre for Geosciences. Remote Sensing and Geoinformatics; Alemania Fil: Saleska, Scott R. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Sánchez-Cañete, Enrique P. University of Granada. Department of Applied Physics; España. Andalusian Institute for Earth System Research (CEAMA-IISTA); España Fil: Sanchez-Mejia, Zulia M. Instituto Tecnológico de Sonora. Ciencias del Agua y Medioambiente; México Fil: Schmid, Hans Peter. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Schmidt, Marius. Agrosphere (IBG3), Forschungszentrum Jülich; Alemania Fil: Schneider, Karl. University of Cologne. Geographical Institute; Alemania Fil: Schrader, Frederik Thünen. Federal Research Institute of Rural Areas, Forestry and Fisheries. Institute of Climate-Smart Agriculture; Alemania Fil: Schroder, Ivan. Geoscience Australia. Department of industry, Innovation and Science; Australia. Fil: Scott, Russell L. USDA-ARS. Southwest Watershed Research Center; Estados Unidos Fil: Sedlák, Pavel. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa. Institute of Atmospheric Physics of the Czech Academy of Sciences; República Checa Fil: Serrano-Ortíz, Penélope. Andalusian Institute for Earth System Research (CEAMA-IISTA); España. University of Granada. Department of Ecology; España Fil: Shao, Changliang. Chinese Academy of Agricultural Sciences. National Hulunber Grassland Ecosystem Observation and Research Station & Institute of Agricultural Resources and Regional Planning; China. Fil: Shi, Peili. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. Fil: Shironya, Ivan A.n. Russian Academy of Sciences. Severtsov Institute of Ecology and Evolution; Rusia Fil: Siebicke, Lukas. University of Goettingen. Bioclimatology; Alemania Fil: Šigut, Ladislav. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Silberstein, Richard. University of Western Australia. School of Agriculture and Environment; Australia. Edith Cowan University. School of Science; Australia. Fil: Sirca, Costantino. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia. University of Sassari. Department of Agriculture; Italia Fil: Spano, Donatella. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia. University of Sassari. Department of Agriculture; Italia Fil: Steinbrecher, Rainer. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Stevens, Robert M. Sentek Pty Ltd.; Australia Fil: Sturtevant, Cove. National ecological Observatory Network Program; Estados Unidos Fil: Suyker, Andy. University of Nebraska-Lincoln. School of Natural Resources; Estados Unidos Fil: Tagesson, Torbem. Lund University. Department of Physical Geography and Ecosystem Science; Suecia. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Takanashi, Satoru. Forestry and Forest Products Research Institute. Kansai Research Center; Japón Fil: Tang, Yanhong. Peking University. College of Urban and Environmental Sciences; China. Fil: Tapper, Nigel. Monash University. School of Earth, Atmosphere and Environment; Australia Fil: Thom, Jonathan. University of Wisconsin-Madison. Space Science and Engineering Center; Estados Unidos Fil: Tiedemann, Frank. University of Goettingen. Bioclimatology; Alemania Fil: Tomassucci, Michele. University of Tuscia. DiBAf; Italia. Terrasystem srl; Italia Fil: Tuovinen, Juha-Pekka. Finnish Meteorological Institute; Finlandia Fil: Urbanski, Shawn. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Valentini, Riccardo. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: van der Molen, Michiel. Wageningen University. Meteorology and Air Quality Group; Holanda Fil: van Gorsel, Eva. Australian National University Canberra. Fenner School of Environment and Society; Australia. Fil: van Huissteden, Ko. Vrije Universiteit Amsterdam. Department of Earth Sciences; Holanda Fil: Varlagin, Andrej. Agroscope Research Institute. Department of Agroecology and Environment; Suiza Fil: Verfaillie, Joseph. University of California Berkeley. ESPM; Estados Unidos Fil: Vesala, Timo. University of Helsinki. Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: Vincke, Caroline. Chinese Academy of Sciences. South China Botanical Garden; China. Fil: Vitale, Domenico. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Vygodskaya, Natalia. University of Bayreuth. Micrometeorology; Alemania Fil: Walker, Jeffrey P. Monash University. Department of Civil Engineering; Australia Fil: Walter-Shea, Elizabeth. University of Nebraska-Lincoln. School of natural Resources; Estados Unidos Fil: Wang, Huimin. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China Fil: Weber, Robin. University of California Berkeley. ESPM; Estados Unidos Fil: Westermann, Sebastian. Instituto Nacional de Tecnologia Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Fil: Wille, Christian. GfZ German Research centre for Geosciences. Remote Sensing and Geoinformatics; Alemania Fil: Wofsy, Steven. Harvard University. School of engineering and Applied Sciences; Estados Unidos. Harvard University. Department of Earth and Planetary Sciences; Estados Unidos Fil: Wohlfahrt, Georg. University of Innsbruck. Department of Ecology; Austria. Fil: Woodgate, William. CSIRO Land and Water; Australia. Fil: Li, Yuelin. Chinese Academy of Sciences. South China Botanical Garden; China. Fil: Zampedri, Roberto. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Zhang, Junhui. Chinese Academy of Sciences. Institute of Applied Ecology; China. Fil: Zhou, Guoyi. Nanjing University of Information Science & Technology. College of Applied Meteorology; China. Fil: Zona, Donatella. San Diego State University. Department of Biology. Global Change Research Group; Estados Unidos. University of Sheffield. Department of Animal and Plant Sciences; Reino Unido Fil: Agarwal, Deb. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Biraud, Sebastien. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Torn, Margaret. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Papale, Dario. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia

The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. RAATD consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets and accompanying syntheses provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, support modelling of predator distributions under future climate scenarios and create inputs that can be incorporated into decision making processes by management authorities. In this data paper, we present the compiled tracking data from research groups that have worked in the Antarctic since the 1990s. The data are publicly available through biodiversity.aq and the Ocean Biogeographic Information System. The archive includes tracking data from over 70 contributors across 12 national Antarctic programs, and includes data from 17 predator species, 4060 individual animals, and over 2.9 million observed locations. Publisher PDF

(2020). doi:10.1016/j.etran.2020.100060

Additional file 1. Supplementary information on datasets (sample sizes) and methods (labelling foraging bouts, determining number of pseudo-absences, model selection tables and parameter estimates).

BackgroundSub-Saharan Africa is endemic for intestinal parasites and distinguished for the largest burden of HIV cases. Blastocystis sp. is one of the most common protists infecting humans but its role in human disease is still controversial. Aim of this study was to investigate the prevalence of Blastocystis sp. in HIV positive and negative adults in Ghana and its association with immune status and other risk factors.Methods122 HIV positive outpatients and 70 HIV negative blood donors from the Komfo Anokye Teaching Hospital in Kumasi, Ghana, were included in the present study. Demographic, clinical and laboratory data were collected and HIV positive patients distinguished for CD4+ T cell count 200 cells/μl (n = 68). A Blastocystis's phylogenetic analysis was performed to determine sample subtype (ST).ResultsThe prevalence of Blastocystis sp. in adult HIV positive individuals was lower than in HIV negative persons (6.6% vs. 20.0%, p = 0.008) and Blastocystis sp. ST1 was the most prevalent strain. Within HIV positive participants, the prevalence of Blastocystis sp. was lower in those individuals with CD4+ T cell count ConclusionIt is largely reported that people living with HIV (PLHIV) in Africa are affected from parasite infections and that co-infections may adversely impact on their immune status, accelerating progress to AIDS and worsening gastrointestinal manifestations. Differently, in this study Blastocystis sp. was associated with a better immune status jointly with a healthy body weight while it seems to be reduced with the progression of HIV infection. This data agree with recent suggestions that Blastocystis sp. can represent a component of the healthy gut microbiota.

We begin this article with a close look at some contemporary pictures of sexual life in the Muslim world that have been painted in certain sections of the Western media, asking how and why these pictures matter. Across a range of mainstream print media from the New York Times to the Daily Mail, and across reported events from several countries, can be found pictures of ‘sexual misery’ (Daoud, 2016: np.). These ‘frame’ Muslim men as tyrannical, Muslim women as downtrodden or exploited, and the wider world of Islam as culpable (Morey and Yaqin, 2012). Crucially, this is not the whole story. We then consider how these negative representations are being challenged and how they can be challenged further. In doing so, we will not simply set pictures of sexual misery against their binary opposites, namely pictures replete with the promise of sexual happiness (Ahmed, 2010). Instead, we search for a more complex picture, one that unsettles stereotypes about the sexual lives of Muslims without simply idealizing its subjects. This takes us to the journalism, life writing and creative non-fiction of Shelina Zahra Janmohamed and the fiction of Ayisha Malik and Amjeed Kabil. We read this long-form work critically, attending to manifest advances in depictions of the relationships of Muslim-identified individuals over the last decade or so, while also remaining alert to lacunae and limitations in the individual representations. More broadly, we hope to signal our intention to avoid both Islamophobia and Islamophilia (Shryock, 2010) in scrutinizing literary texts.

Single-photon sources are essential building blocks in quantum photonic networks, where quantum-mechanical properties of photons are utilised to achieve quantum technologies such as quantum cryptography and quantum computing. Most conventional solid-state single-photon sources are based on single emitters such as self-assembled quantum dots, which are created at random locations and require spectral filtering. These issues hinder the integration of a single-photon source into a scaleable photonic quantum network for applications such as on-chip photonic quantum processors. In this work, using only regular lithography techniques on a conventional GaAs quantum well, we realise an electrically triggered single-photon source with a GHz repetition rate and without the need for spectral filtering. In this device, a single electron is carried in the potential minimum of a surface acoustic wave (SAW) and is transported to a region of holes to form an exciton. The exciton then decays and creates a single photon in a lifetime of ~ 100ps. This SAW-driven electroluminescence (EL) yields photon antibunching with $g^{(2)}(0) = 0.39 \pm 0.05$, which satisfies the common criterion for a single-photon source $g^{(2)}(0) < 0.5$. Furthermore, we estimate that if a photon detector receives a SAW-driven EL signal within one SAW period, this signal has a 79%-90% chance of being a single photon. This work shows that a single-photon source can be made by combining single-electron transport and a lateral n-i-p junction. This approach makes it possible to create multiple synchronised single-photon sources at chosen positions with photon energy determined by quantum-well thickness. Compared with conventional quantum-dot-based single-photon sources, this device may be more suitable for an on-chip integrated photonic quantum network.

Motivated by the need to audit complex and black box models, there has been extensive research on quantifying how data features influence model predictions. Feature influence can be direct (a direct influence on model outcomes) and indirect (model outcomes are influenced via proxy features). Feature influence can also be expressed in aggregate over the training or test data or locally with respect to a single point. Current research has typically focused on one of each of these dimensions. In this paper, we develop disentangled influence audits, a procedure to audit the indirect influence of features. Specifically, we show that disentangled representations provide a mechanism to identify proxy features in the dataset, while allowing an explicit computation of feature influence on either individual outcomes or aggregate-level outcomes. We show through both theory and experiments that disentangled influence audits can both detect proxy features and show, for each individual or in aggregate, which of these proxy features affects the classifier being audited the most. In this respect, our method is more powerful than existing methods for ascertaining feature influence.

The postsecular has been conceptualized, both in general terms and also in relation to particular countries and religious groups, as a historical shift in which religion is returning from the margins and the shadows to the centre ground of public life (Habermas 2008). From the perspective of religious groups, minorities in particular, narratives of the return of religion are problematic. We develop this argument with reference to British Muslims of Pakistani heritage, and Polish Muslims with mixed heritage.

Objectives: The resistance profiles of patients receiving long-term ART in sub-Saharan Africa have been poorly described. This study obtained a sensitive assessment of the resistance patterns associated with long-term tenofovir-based ART in a programmatic setting where virological monitoring is yet to become part of routine care.\ud \ud Methods: We studied subjects who, after a median of 4.2 years of ART, replaced zidovudine or stavudine with tenofovir disoproxil fumarate while continuing lamivudine and an NNRTI. Using deep sequencing, resistance-associated mutations (RAMs) were detected in stored samples collected at tenofovir introduction (T0) and after a median of 4.0 years (T1).\ud \ud Results: At T0, 19/87 (21.8%) subjects showed a detectable viral load and 8/87 (9.2%) had one or more major NNRTI RAMs, whereas 82/87 (94.3%) retained full tenofovir susceptibility. At T1, 79/87 (90.8%) subjects remained on NNRTI-based ART, 5/87 (5.7%) had introduced lopinavir/ritonavir due to immunological failure, and 3/87 (3.4%) had interrupted ART. Whilst 68/87 (78.2%) subjects maintained or achieved virological suppression between T0 and T1, a detectable viral load with NNRTI RAMs at T0 predicted lack of virological suppression at T1. Each treatment interruption, usually reflecting unavailability of the dispensary, doubled the risk of T1 viraemia. Tenofovir, lamivudine and efavirenz selected for K65R, K70E/T, L74I/V and Y115F, alongside M184V and multiple NNRTI RAMs; this resistance profile was accompanied by high viral loads and low CD4 cell counts.\ud \ud Conclusions: Viraemia on tenofovir, lamivudine and efavirenz led to complex resistance patterns with implications for continued drug activity and risk of onward transmission.

Ultrasonic testing is routinely used in the nuclear power generation industry to assess the structural integrity of plant components. The regulatory nature of the industry means that ultrasonic inspection procedures require substantiation through experimental trials and semi-analytical simulation, which can be costly and time-consuming. Furthermore, due to the complex defect and component geometries that are commonly encountered in the industry, the generation of evidence that accurately represents real inspections can be challenging. Numerical modelling techniques offer an effective alternative for substantiating inspections as they can accurately simulate the ultrasonic scattering from complex geometries, yet such techniques lead to increased computational cost. Hybrid techniques, which combine both semi-analytical and numerical methods, offer an approach to rapidly simulate an entire ultrasonic inspection whilst maintaining the ability to simulate the scattering from complex defects. However, there is a lack of experimental validation for extant hybrid techniques, particularly for three-dimensional simulations, and they have not been widely applied to the simulation of surface-breaking defect inspections, which are commonly encountered in both manufacturing and in-service inspections. This thesis has extended the functionality of a three dimensional hybrid technique to simulate the inspection of surface-breaking defects. Validation evidence demonstrates that the hybrid model accurately predicts the ultrasonic scattering from a well-characterised reflector. An arbitrary transduction system has been incorporated to the model, enabling the model to be more broadly applied to the simulation of ultrasonic inspections of plant components. Furthermore, validation on a real defect provides a high level of confidence that the model can be used to assess and quantify the performance of inspections where qualification becomes challenging. This work has made a clear step forward in the use of simulation for inspection qualification and further work is required to fully optimise the simulation methodology. Open Access

Background\ud Buruli ulcer can cause disfigurement and long-term loss of function. It is underdiagnosed and under-reported, and its current distribution is unclear. We aimed to synthesise and evaluate data on Buruli ulcer prevalence and distribution.\ud \ud Methods\ud We did a systematic review of Buruli ulcer prevalence and used an evidence consensus framework to describe and evaluate evidence for Buruli ulcer distribution worldwide. We searched PubMed and Web of Science databases from inception to Aug 6, 2018, for records of Buruli ulcer and Mycobacterium ulcerans detection, with no limits on study type, publication date, participant population, or location. English, French, and Spanish language publications were included. We included population-based surveys presenting Buruli ulcer prevalence estimates, or data that allowed prevalence to be estimated, in the systematic review. We extracted geographical data on the occurrence of Buruli ulcer cases and M ulcerans detection from studies of any type for the evidence consensus framework; articles that did not report original data were excluded. For the main analysis, we extracted prevalence estimates from included surveys and calculated 95% CIs using Byar's method. We included occurrence records, reports to WHO and the Global Infectious Diseases and Epidemiology Network, and surveillance data from Buruli ulcer control programmes in the evidence consensus framework to grade the strength of evidence for Buruli ulcer endemicity. This study is registered with PROSPERO, number CRD42018116260.\ud \ud Findings\ud 2763 titles met the search criteria. We extracted prevalence estimates from ten studies and occurrence data from 208 studies and five unpublished surveillance datasets. Prevalence estimates within study areas ranged from 3·2 (95% CI 3·1–3·3) cases per 10 000 population in Côte d'Ivoire to 26·9 (23·5–30·7) cases per 10 000 population in Benin. There was evidence of Buruli ulcer in 32 countries and consensus on presence in 12.\ud \ud Interpretation\ud The global distribution of Buruli ulcer is uncertain and potentially wider than currently recognised. Our findings represent the strongest available evidence on Buruli ulcer distribution so far and have many potential applications, from directing surveillance activities to informing burden estimates.