Efficiency of the Sieved-NPMLE in CAR-Missing Data Models

We consider nonparametric missing data models for which the censoring mechanism satisfies coarsening at random and which allow complete observations on the variable X of interest. W show that beyond some empirical process conditions the only essential condition for efficiency of an NPMLE of the distribution of X is that the regions associated with incomplete observations on X contain enough complete observations. This is heuristically explained by describing the EM-algorithm. We provide identifiably of the self-consistency equation and efficiency of the NPMLE in order to make this statement rigorous. The usual kind of differentiability conditions in the proof are avoided by using an identity which holds for the NPMLE of linear parameters in convex models. We provide a bivariate censoring application in which the condition and hence the NPMLE fails, but where other estimators, not based on the NPMLE principle, are highly inefficient. It is shown how to slightly reduce the data so that the conditions hold for the reduced data. The conditions are verified for the univariate censoring, double censored, and Ibragimov-Has'minski models.

Proving Efficiency of NPMLE and Identities

A method is given for proving efficiency of NPMLE directly linked to empirical process theory. The conditions in general are appropriate consistency of the NPMLE, differentiability of the model, differentiability of the parameter of interest, local convexity of the parameter space, and a Donsker class condition for the class of efficient influence functions obtained by varying the parameters. For the case that the model is linear in the parameter and the parameter space is convex, as with most nonparametric missing data models, we show that the method leads to an identity for the NPMLE which almost says that the NPMLE is efficient and provides us straightforwardly with a consistency and efficiency proof. This identify is extended to an almost linear class of models which contain biased sampling models. To illustrate, the method is applied to the univariate censoring model, random truncation models, interval censoring case I model, the class of parametric models and to a class of semiparametric models.

Efficiency of arthroscopic lysis and lavage for internal derangement of the temporomandibular joint correlated with Wilkes classification

OBJECTIVES: To compare the outcome of arthroscopic lysis and lavage of TMJ with internal derangement of Wilkes stages II, III, IV, and V. STUDY DESIGN: Arthroscopic lysis and lavage was performed in 45 TMJ of 39 patients with internal derangement. The cases were divided into 4 groups corresponding to Wilkes stages II, III, IV, and V. Two parameters were compared pre- and postoperatively: pain and mouth opening. Statistical significance was determined using the chi(2) test. RESULTS: Overall success rate was 86.7% (Wilkes stage II 90.9%, Wilkes stage III 92.3%, Wilkes stage IV 84.6%, Wilkes stage V 75%). There were no statistically significant differences between the success rates for Wilkes stages II, III, IV, and V. CONCLUSION: Arthroscopic lysis and lavage should be performed as a standard operation for internal derangement of the TMJ after failure of conservative treatment in all Wilkes stages.

Functional brain network efficiency predicts intelligence

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.

Modelling Nd-isotopes with a coarse resolution ocean circulation model: Sensitivities to model parameters and source/sink distributions

The neodymium (Nd) isotopic composition (Nd) of seawater is a quasi-conservative tracer of water mass mixing and is assumed to hold great potential for paleoceanographic studies. Here we present a comprehensive approach for the simulation of the two neodymium isotopes 143Nd, and 144Nd using the Bern3D model, a low resolution ocean model. The high computational efficiency of the Bern3D model in conjunction with our comprehensive approach allows us to systematically and extensively explore the sensitivity of Nd concentrations and Nd to the parametrisation of sources and sinks. Previous studies have been restricted in doing so either by the chosen approach or by computational costs. Our study thus presents the most comprehensive survey of the marine Nd cycle to date. Our model simulates both Nd concentrations as well as Nd in good agreement with observations. Nd covaries with salinity, thus underlining its potential as a water mass proxy. Results confirm that the continental margins are required as a Nd source to simulate Nd concentrations and Nd consistent with observations. We estimate this source to be slightly smaller than reported in previous studies and find that above a certain magnitude its magnitude affects Nd only to a small extent. On the other hand, the parametrisation of the reversible scavenging considerably affects the ability of the model to simulate both, Nd concentrations and Nd. Furthermore, despite their small contribution, we find dust and rivers to be important components of the Nd cycle. In additional experiments, we systematically varied the diapycnal diffusivity as well as the Atlantic-to-Pacific freshwater flux to explore the sensitivity of Nd concentrations and its isotopic signature to the strength and geometry of the overturning circulation. These experiments reveal that Nd concentrations and Nd are comparatively little affected by variations in diapycnal diffusivity and the Atlantic-to-Pacific freshwater flux. In contrast, an adequate representation of Nd sources and sinks is crucial to simulate Nd concentrations and Nd consistent with observations. The good agreement of our results with observations paves the way for the evaluation of the paleoceanographic potential of Nd in further model studies.

Electron reconstruction and identification efficiency measurements with the ATLAS detector using the 2011 LHC proton-proton collision data

Many of the interesting physics processes to be measured at the LHC have a signature involving one or more isolated electrons. The electron reconstruction and identification efficiencies of the ATLAS detector at the LHC have been evaluated using proton–proton collision data collected in 2011 at √s = 7 TeV and corresponding to an integrated luminosity of 4.7 fb−1. Tag-and-probe methods using events with leptonic decays of W and Z bosons and J/ψ mesons are employed to benchmark these performance parameters. The combination of all measurements results in identification efficiencies determined with an accuracy at the few per mil level for electron transverse energy greater than 30 GeV.

A Dynamic Stochastic Frontier Production Model with Time-Varying Efficiency

In this paper we introduce technical efficiency via the intercept that evolve over time as a AR(1) process in a stochastic frontier (SF) framework in a panel data framework. Following are the distinguishing features of the model. First, the model is dynamic in nature. Second, it can separate technical inefficiency from fixed firm-specific effects which are not part of inefficiency. Third, the model allows one to estimate technical change separate from change in technical efficiency. We propose the ML method to estimate the parameters of the model. Finally, we derive expressions to calculate/predict technical inefficiency (efficiency).

Molecular dissolved organic matter composition and environmental parameters from a laboratory incubation study

Dissolved organic matter (DOM) in the oceans constitutes a major carbon pool involved in global biogeochemical cycles. More than 96% of the marine DOM resists microbial degradation for thousands of years. The composition of this refractory DOM (RDOM) exhibits a molecular signature which is ubiquitously detected in the deep oceans. Surprisingly efficient microbial transformation of labile into RDOM was shown experimentally, implying that microorganisms produce far more RDOM than needed to sustain the global pool. By assessing the microbial formation and transformation of DOM in unprecedented molecular detail for 3 years, we show that most of the newly formed RDOM is molecularly different from deep sea RDOM. Only <0.4% of the net community production was channeled into RDOM molecularly undistinguishable from deep sea DOM. Our study provides novel experimentally derived molecular evidence and data for global models on the production, turnover and accumulation of marine DOM.

Global Ocean Particulate Organic Carbon flux merged with satellite parameters

The efficiency of the biological pump of carbon to the deep ocean depends largely on the biologically mediated export of carbon from the surface ocean and its remineralization with depth. Global satellite studies have primarily focused on chlorophyll concentration and net primary production (NPP) to understand the role of phytoplankton in these processes. Recent satellite retrievals of phytoplankton composition now allow for the size of phytoplankton cells to be considered. Here, we improve understanding of phytoplankton size structure impacts on particle export, remineralization and transfer. Particulate organic carbon (POC) flux observations from sediment traps and 234Th are compiled across the global ocean. Annual climatologies of NPP, percent microplankton, and POC flux at four time series locations and within biogeochemical provinces are constructed, and sinking velocities are calculated to align surface variables with POC flux at depth. Parameters that characterize POC flux vs. depth (export flux ratio, labile fraction, remineralization length scale) are then fit to the aligned dataset. Times of the year dominated by different size compositions are identified and fit separately in regions of the ocean where phytoplankton cell size showed enough dynamic range over the annual cycle. Considering all data together, our findings support the paradigm of high export flux but low transfer efficiency in more productive regions and vice versa for oligotrophic regions. However, when parsing by dominant size class, we find periods dominated by small cells to have both greater export flux and lower transfer efficiency than periods when large cells comprise a greater proportion of the phytoplankton community.

Enhancement of photosynthetic carbon assimilation efficiency by phytoplankton in the future coastal ocean

A mesocosm experiment was conducted to evaluate the effects of future climate conditions on photosynthesis and productivity of coastal phytoplankton. Natural phytoplankton assemblages were incubated in field mesocosms under the ambient condition (present condition: ca. 400 ppmv CO2 and ambient temp.), and two future climate conditions (acidification condition: ca. 900 ppmv CO2 and ambient temp.; greenhouse condition: ca. 900 ppmv CO2 and 3 °C warmer than ambient). Photosynthetic parameters of steady-state light responses curves (LCs; measured by PAM fluorometer) and photosynthesis-irradiance curves (P-I curves; estimated by in situ incorporation of 14C) were compared to three conditions during the experiment period. Under acidification, electron transport efficiency (alpha LC) and photosynthetic 14C assimilation efficiency (alpha) were 10% higher than those of the present condition, but maximum rates of relative electron transport (rETRm,LC) and photosynthetic 14C assimilation (PBmax) were lower than the present condition by about 19% and 7%, respectively. In addition, rETRm,LC and alpha LC were not significantly different between and greenhouse conditions, but PBmax and alpha of greenhouse conditions were higher than those of the present condition by about 9% and 30%, respectively. In particular, the greenhouse condition has drastically higher PBmax and alpha than the present condition more than 60% during the post-bloom period. According to these results, two future ocean conditions have major positive effects on the photosynthesis in terms of energy utilization efficiency for organic carbon fixation through the inorganic carbon assimilation. Despite phytoplankton taking an advantage on photosynthesis, primary production of phytoplankton was not stimulated by future conditions. In particular, biomass of phytoplankton was depressed under both acidification and greenhouse conditions after the the pre-bloom period, and more research is required to suggest that some factors such as grazing activity could be important for regulating phytoplankton bloom in the future ocean.