Hubble Diagram Dispersion From Large-Scale Structure

We consider the effects of large structures in the Universe on the Hubble diagram. This problem is treated non-linearly by considering a Swiss Cheese model of the Universe in which under-dense voids are represented as negatively curved regions of space-time. Exact expressions for luminosity distances and redshifts are used to investigate the non-linear effects of structure on the magnitudes of astrophysical sources. It is found that the intervening voids we consider, between the observer and source, produce changes in apparent magnitude of less than 0.012. Sources inside voids, however, can be affected considerably at redshifts below z~0.5. By averaging observable quantities over many randomly generated distributions of voids we find that the presence of these structures has the effect of introducing a dispersion around the mean, which itself can be displaced the background value. Observers in an inhomogeneous universe, who take averages of observables along many different lines of sight, may then introduce systematic biases, and under-estimate errors, if these effects are not taken into account. Estimates of the potential size of these effects are made using data from simulated large-scale structure.Comment: 16 pages, 26 figures. Major correction

Copula Eigenfaces with Attributes: Semiparametric Principal Component Analysis for a Combined Color, Shape and Attribute Model

Principal component analysis is a ubiquitous method in parametric appearance modeling for describing dependency and variance in datasets. The method requires the observed data to be Gaussian-distributed. We show that this requirement is not fulfilled in the context of analysis and synthesis of facial appearance. The model mismatch leads to unnatural artifacts which are severe to human perception. As a remedy, we use a semiparametric Gaussian copula model, where dependency and variance are modeled separately. This model enables us to use arbitrary Gaussian and non-Gaussian marginal distributions. Moreover, facial color, shape and continuous or categorical attributes can be analyzed in an unified way. Accounting for the joint dependency between all modalities leads to a more specific face model. In practice, the proposed model can enhance performance of principal component analysis in existing pipelines: The steps for analysis and synthesis can be implemented as convenient pre- and post-processing steps

Safety and Reactogenicity of an MSP-1 Malaria Vaccine Candidate: A Randomized Phase Ib Dose-Escalation Trial in Kenyan Children

OBJECTIVE: Our aim was to evaluate the safety, reactogenicity, and immunogenicity of an investigational malaria vaccine. DESIGN: This was an age-stratified phase Ib, double-blind, randomized, controlled, dose-escalation trial. Children were recruited into one of three cohorts (dosage groups) and randomized in 2:1 fashion to receive either the test product or a comparator. SETTING: The study was conducted in a rural population in Kombewa Division, western Kenya. PARTICIPANTS: Subjects were 135 children, aged 12–47 mo. INTERVENTIONS: Subjects received 10, 25, or 50 μg of falciparum malaria protein 1 (FMP1) formulated in 100, 250, and 500 μL, respectively, of AS02A, or they received a comparator (Imovax® rabies vaccine). OUTCOME MEASURES: We performed safety and reactogenicity parameters and assessment of adverse events during solicited (7 d) and unsolicited (30 d) periods after each vaccination. Serious adverse events were monitored for 6 mo after the last vaccination. RESULTS: Both vaccines were safe and well tolerated. FMP1/AS02A recipients experienced significantly more pain and injection-site swelling with a dose-effect relationship. Systemic reactogenicity was low at all dose levels. Hemoglobin levels remained stable and similar across arms. Baseline geometric mean titers were comparable in all groups. Anti-FMP1 antibody titers increased in a dose-dependent manner in subjects receiving FMP1/AS02A; no increase in anti-FMP1 titers occurred in subjects who received the comparator. By study end, subjects who received either 25 or 50 μg of FMP1 had similar antibody levels, which remained significantly higher than that of those who received the comparator or 10 μg of FMP1. A longitudinal mixed effects model showed a statistically significant effect of dosage level on immune response (F(3,1047) = 10.78, or F(3, 995) = 11.22, p < 0.001); however, the comparison of 25 μg and 50 μg recipients indicated no significant difference (F(1,1047) = 0.05; p = 0.82). CONCLUSIONS: The FMP1/AS02A vaccine was safe and immunogenic in malaria-exposed 12- to 47-mo-old children and the magnitude of immune response of the 25 and 50 μg doses was superior to that of the 10 μg dose

Randomized Controlled Trial of RTS,S/AS02D and RTS,S/AS01E Malaria Candidate Vaccines Given According to Different Schedules in Ghanaian Children

Background:The target delivery channel of RTS,S candidate malaria vaccines in malaria-endemic countries in Africa is the World Health Organisation Expanded Program on Immunization. As an Adjuvant System, age de-escalation and schedule selection step, this study assessed 3 schedules of RTS,S/AS01E and RTS,S/AS02D in infants and young children 5&ndash;17 months of age in Ghana.Methodology:A Phase II, partially-blind randomized controlled study (blind to vaccine, not to schedule), of 19 months duration was conducted in two (2) centres in Ghana between August 2006 and May 2008. Subjects were allocated randomly (1:1:1:1:1:1) to one of six study groups at each study site, each defining which vaccine should be given and by which schedule (0,1-, 0,1,2- or 0,1,7-months). For the 0,1,2-month schedule participants received RTS,S/AS01E or rabies vaccine at one center and RTS,S/AS01E or RTS,S/AS02D at the other. For the other schedules at both study sites, they received RTS,S/AS01E or RTS,S/AS02D. The primary outcome measure was the occurrence of serious adverse events until 10 months post dose 1.Results:The number of serious adverse events reported across groups was balanced. One child had a simple febrile convulsion, which evolved favourably without sequelae, considered to be related to RTS,S/AS01E vaccination. Low grade reactions occurred slightly more frequently in recipients of RTS,S/AS than rabies vaccines; grade 3 reactions were infrequent. Less local reactogenicity occurred with RTS,S/AS01E than RTS,S/AS02D. Both candidate vaccines were highly immunogenic for anti-circumsporozoite and anti-Hepatitis B Virus surface antigen antibodies. Recipients of RTS,S/AS01E compared to RTS,S/AS02D had higher peak anti-circumsporozoite antibody responses for all 3 schedules. Three dose schedules were more immunogenic than 2 dose schedules. Area under the curve analyses for anti-circumsporozoite antibodies were comparable between the 0,1,2- and 0,1,7-month RTS,S/AS01E schedules.Conclusions:Both candidate malaria vaccines were well tolerated. Anti-circumsporozoite responses were greater with RTS,S/AS01E than RTS,S/AS02D and when 3 rather than 2 doses were given. This study supports the selection of RTS,S/AS01E and a 3 dose schedule for further development in children and infants

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by similar to 17 +/- 11 days, and lagged air and soil temperature by median values of 8 +/- 16 and 5 +/- 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.Peer reviewe

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0):Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).</p

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%-31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency similar to 0.52-0.63 and 0.53). UpCH(4) estimated annual global wetland CH4 emissions of 146 +/- 43 TgCH4 y(-1) for 2001-2018 which agrees closely with current bottom-up land surface models (102-181 TgCH4 y(-1)) and overlaps with top-down atmospheric inversion models (155-200 TgCH4 y -1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25 degrees from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ ORNLDAAC/2253).Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (similar to 30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001-2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state-of-the-art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground-based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense