Plasma total homocysteine and carotid intima-media thickness in type 1 diabetes: A prospective study

Objective: Plasma total homocysteine (tHcy) has been positively associated with carotid intima-media thickness (IMT) in non-diabetic populations and in a few cross-sectional studies of diabetic patients. We investigated cross-sectional and prospective associations of a single measure of tHcy with common and internal carotid IMT over a 6-year period in type 1 diabetes. Research design and methods: tHcy levels were measured once, in plasma obtained in 1997–1999 from patients (n = 599) in the Epidemiology of Diabetes Interventions and Complications (EDIC) study, the observational follow-up of the Diabetes Control and Complications Trial (DCCT). Common and internal carotid IMT were determined twice, in EDIC “Year 6” (1998–2000) and “Year 12” (2004–2006), using B-mode ultra-sonography. Results: After adjustment, plasma tHcy [median (interquartile range): 6.2 (5.1, 7.5) μmol/L] was significantly correlated with age, diastolic blood pressure, renal dysfunction, and smoking (all p < 0.05). In an unadjusted model only, increasing quartiles of tHcy correlated with common and internal carotid IMT, again at both EDIC time-points (p < 0.01). However, multivariate logistic regression revealed no significant associations between increasing quartiles of tHcy and the 6-year change in common and internal carotid IMT (highest vs. lowest quintile) when adjusted for conventional risk factors. Conclusions: In a type 1 diabetes cohort from the EDIC study, plasma tHcy measured in samples drawn in 1997–1999 was associated with measures of common and internal carotid IMT measured both one and seven years later, but not with IMT progression between the two time-points. The data do not support routine measurement of tHcy in people with Type 1 diabetes.

Ecological impacts of invasive alien species along temperature gradients: testing the role of environmental matching

Invasive alien species (IAS) can cause substantive ecological impacts, and the role of temperature in mediating these impacts may become increasingly significant in a changing climate. Habitat conditions and physiological optima offer predictive information for IAS impacts in novel environments. Here, using meta-analysis and laboratory experiments, we tested the hypothesis that the impacts of IAS in the field are inversely correlated with the difference in their ambient and optimal temperatures. A meta-analysis of 29 studies of consumptive impacts of IAS in inland waters revealed that the impacts of fishes and crustaceans are higher at temperatures that more closely match their thermal growth optima. In particular, the maximum impact potential was constrained by increased differences between ambient and optimal temperatures, as indicated by the steeper slope of a quantile regression on the upper 25th percentile of impact data compared to that of a weighted linear regression on all data with measured variances. We complemented this study with an experimental analysis of the functional response - the relationship between predation rate and prey supply - of two invasive predators (freshwater mysid shrimp, Hemimysis anomala and Mysis diluviana) across relevant temperature gradients; both of these species have previously been found to exert strong community-level impacts that are corroborated by their functional responses to different prey items. The functional response experiments showed that maximum feeding rates of H. anomala and M. diluviana have distinct peaks near their respective thermal optima. Although variation in impacts may be caused by numerous abiotic or biotic habitat characteristics, both our analyses point to temperature as a key mediator of IAS impact levels in inland waters and suggest that IAS management should prioritize habitats in the invaded range that more closely match the thermal optima of targeted invaders.

Ferroelectricity in Si-doped HfO2 Revealed: A Binary Lead-free Ferroelectric

Static domain structures and polarization dynamics of silicon doped HfO2 are explored. The evolution of ferroelectricity as a function of Si-doping level driving the transition from paraelectricity via ferroelectricity to antiferroelectricity is investigated. Ferroelectric and antiferroelectric properties can be observed locally on the pristine, poled and electroded surfaces, providing conclusive evidence to intrinsic ferroic behavior.

Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes

Semiconductor fabrication involves several sequential processing steps with the result that critical production variables are often affected by a superposition of affects over multiple steps. In this paper a Virtual Metrology (VM) system for early stage measurement of such variables is presented; the VM system seeks to express the contribution to the output variability that is due to a defined observable part of the production line. The outputs of the processed system may be used for process monitoring and control purposes. A second contribution of this work is the introduction of Elastic Nets, a regularization and variable selection technique for the modelling of highly-correlated datasets, as a technique for the development of VM models. Elastic Nets and the proposed VM system are illustrated using real data from a multi-stage etch process used in the fabrication of disk drive read/write heads. © 2013 IEEE.

A dynamic sampling methodology for plasma etch processes using Gaussian process regression

Plasma etch is a key process in modern semiconductor manufacturing facilities as it offers process simplification and yet greater dimensional tolerances compared to wet chemical etch technology. The main challenge of operating plasma etchers is to maintain a consistent etch rate spatially and temporally for a given wafer and for successive wafers processed in the same etch tool. Etch rate measurements require expensive metrology steps and therefore in general only limited sampling is performed. Furthermore, the results of measurements are not accessible in real-time, limiting the options for run-to-run control. This paper investigates a Virtual Metrology (VM) enabled Dynamic Sampling (DS) methodology as an alternative paradigm for balancing the need to reduce costly metrology with the need to measure more frequently and in a timely fashion to enable wafer-to-wafer control. Using a Gaussian Process Regression (GPR) VM model for etch rate estimation of a plasma etch process, the proposed dynamic sampling methodology is demonstrated and evaluated for a number of different predictive dynamic sampling rules. © 2013 IEEE.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.

CCAAT/enhancer binding protein α predicts poorer prognosis and prevents energy starvation-induced cell death in hepatocellular carcinoma

CCAAT enhancer binding protein α (C/EBPα) plays an essential role in cellular differentiation, growth, and energy metabolism. Here, we investigate the correlation between C/EBPα and hepatocellular carcinoma (HCC) patient outcomes and how C/EBPα protects cells against energy starvation. Expression of C/EBPα protein was increased in the majority of HCCs examined (191 pairs) compared with adjacent nontumor liver tissues in HCC tissue microarrays. Its upregulation was correlated significantly with poorer overall patient survival in both Kaplan-Meier survival (P = 0.017) and multivariate Cox regression (P = 0.028) analyses. Stable C/EBPα-silenced cells failed to establish xenograft tumors in nude mice due to extensive necrosis, consistent with increased necrosis in human C/EBPα-deficient HCC nodules. Expression of C/EBPα protected HCC cells in vitro from glucose and glutamine starvation-induced cell death through autophagy-involved lipid catabolism. Firstly, C/EBPα promoted lipid catabolism during starvation, while inhibition of fatty acid beta-oxidation significantly sensitized cell death. Secondly, autophagy was activated in C/EBPα-expressing cells, and the inhibition of autophagy by ATG7 knockdown or chloroquine treatment attenuated lipid catabolism and subsequently sensitized cell death. Finally, we identified TMEM166 as a key player in C/EBPα-mediated autophagy induction and protection against starvation.

CONCLUSION: The C/EBPα gene is important in that it links HCC carcinogenesis to autophagy-mediated lipid metabolism and resistance to energy starvation; its expression in HCC predicts poorer patient prognosis.

Parent ratings of child cognition and language compared with Bayley-III in preterm 3-year-olds

Background
Parent ratings on questionnaires may provide valid and cost-effective tools for screening cognitive development of children at risk of developmental delay.

Aims
In this study, we examined the convergent validity of combining parent-based reports of non-verbal cognitive abilities (PARCA3) and verbal abilities (CDI-III) in relation to the Bayley-III cognitive scale in 3-year-olds born late pre-term.

Methods
Mothers of 185 late-preterm children were asked to complete the PARCA3 and the CDI-III shortly before children reached age three; children were then assessed using the Bayley-III close to their third birthday.

Results
The two maternal questionnaires were significantly and moderately correlated with the Bayley-III cognitive scores. Together the maternal ratings accounted for 15% of the variance in the Bayley-III cognitive scores, after controlling for other covariates in regression analysis. In particular, the PARCA3 contributed significantly to explain variance in the Bayley-III cognitive scores when controlling for the CDI-III. However, the CDI-III was also independently associated with the Bayley-III cognitive scores.

Conclusions
Parent ratings of child cognition and language together may provide cost-effective screening of development in “at risk” preschoolers.

On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables

Influence diagrams are intuitive and concise representations of structured decision problems. When the problem is non-Markovian, an optimal strategy can be exponentially large in the size of the diagram. We can avoid the inherent intractability by constraining the size of admissible strategies, giving rise to limited memory influence diagrams. A valuable question is then how small do strategies need to be to enable efficient optimal planning. Arguably, the smallest strategies one can conceive simply prescribe an action for each time step, without considering past decisions or observations. Previous work has shown that finding such optimal strategies even for polytree-shaped diagrams with ternary variables and a single value node is NP-hard, but the case of binary variables was left open. In this paper we address such a case, by first noting that optimal strategies can be obtained in polynomial time for polytree-shaped diagrams with binary variables and a single value node. We then show that the same problem is NP-hard if the diagram has multiple value nodes. These two results close the fixed-parameter complexity analysis of optimal strategy selection in influence diagrams parametrized by the shape of the diagram, the number of value nodes and the maximum variable cardinality.

Thermometry precision in strongly correlated ultracold lattice gases

The precise knowledge of the temperature of an ultracold lattice gas simulating a strongly correlated
system is a question of both fundamental and technological importance. Here, we address such
question by combining tools from quantum metrology together with the study of the quantum
correlations embedded in the system at finite temperatures. Within this frame we examine the spin-
1 2 XY chain, first estimating, by means of the quantum Fisher information, the lowest attainable
bound on the temperature precision. We then address the estimation of the temperature of the sample
from the analysis of correlations using a quantum non demolishing Faraday spectroscopy method.
Remarkably, our results show that the collective quantum correlations can become optimal
observables to accurately estimate the temperature of our model in a given range of temperatures.

Thermal Properties of Alkyl-triethylammonium bis{(trifluoromethyl)sulfonyl}imide Ionic Liquids

In this work, we address the thermal properties of selected members of a
homologous series of alkyltriethylammonium bisf(trifluoromethyl)sulfonylgimide ionic
liquids. Their phase and glass transition behavior, as well as their standard isobaric heat
capacities at 298.15 K, were studied using differential scanning calorimetry (DSC),
whereas their decomposition temperature was determined by thermal gravimetry analysis.
DSC was further used to measure standard molar heat capacities of the studied ionic liquids
and standard molar heat capacity as a function of temperature for hexyltriethylammonium,
octyltriethylammonium, and dodecyltriethylammonium bisf(trifluoromethyl)sulfonylgimide
ionic liquids. Based on the data obtained, we discuss the influence of the alkyl chain
length of the cation on the studied ionic liquids on the measured properties. Using viscosity
data obtained in a previous work, the liquid fragility of the ionic liquids is then discussed.
Viscosity data were correlated by the VTF equation using a robust regression along a
gnostic influence function. In this way, more reliable VTF model parameters were obtained than in our previous work and a good estimate of the liquid fragility of the ionic liquids was made.